Defining Planning and Operation Guidelines for European Smart … · 3.3.3 Demand response ......

ERA-Net Smart Grids Plus | From local trials towards a European Knowledge Community

This project has received funding in the framework of the joint programming initiative ERA-Net Smart Grids Plus, with support from the European Union’s Horizon 2020 research and innovation programme.

Defining Planning and Operation

Guidelines for European Smart

Distribution Systems (SmartGuide)

Deliverable D1

SG Solutions and Technologies

Partners:

Bergische Universität Wuppertal, Germany

INESC TEC, Portugal

SINTEF Energi AS, Norway

Skagerak Nett AS, Norway

Smarter Grid Solutions, United Kingdom

03 March 2017

Deliverable No. 1 | SG solutions and technologies 2

INTERNAL REFERENCE

Deliverable No.: D1

Deliverable Name: SG Solutions and Technologies

Lead Partner: INESC TEC

Work Package No.: WP1

Task No. & Name: T1.1, T1.2, T1.3, T1.4 and T1.5

Document (File): SmartGuide WP1 - SG Solutions and Technologies

Issue (Save) Date: 2017-03-03

DOCUMENT SENSITIVITY

☒ Not Sensitive Contains only factual or background information;

contains no new or additional analysis,

recommendations or policy-relevant statements

☐ Moderately Sensitive Contains some analysis or interpretation of results;

contains no recommendations or policy-relevant

statements

☐ Sensitive Contains analysis or interpretation of results with

policy-relevance and/or recommendations or policy-

relevant statements

☐ Highly Sensitive

Confidential

Contains significant analysis or interpretation of results

with major policy-relevance or implications, contains

extensive recommendations or policy-relevant

statements, and/or contain policy-prescriptive

statements. This sensitivity requires SB decision.


DOCUMENT STATUS

Date Person(s) Organisation

Author(s)

Nuno Fonseca INESC TEC

André Madureira INESC TEC

Filipe Soares INESC TEC

Ricardo Ferreira INESC TEC

Julian Wruk Bergische Universität Wuppertal

2016-12-20 Kevin Cibis Bergische Universität Wuppertal

Hanne Sæle SINTEF Energi AS

Lovinda Ødegården SINTEF Energi AS

Robert Macdonald Smarter Grid Solutions

Ross Methven Smarter Grid Solutions

Rachael Taljaaard Smarter Grid Solutions

Verification by 2017-01-25 Graham Ault Smarter Grid Solutions

Approval by 2017-02-14 Markus Zdrallek Bergische Universität Wuppertal


CONTENTS

ABBREVIATIONS .............................................................................................. 10

1 INTRODUCTION ........................................................................................ 15

1.1 INTRODUCTION AND MAIN CHALLENGES OF SMARTGUIDE ...................... 15

1.1.1 Historic conditions and process of change ........................................................15

1.1.2 Overview of SmartGuide project .....................................................................15

1.2 OBJECTIVES AND GOALS OF WORK PACKAGE 1 ........................................ 16

2 COUNTRY SPECIFIC BACKGROUND ........................................................... 17

2.1 PORTUGAL ................................................................................................ 17

2.1.1 MV level characterisation ...............................................................................17

2.1.2 LV level characterisation ................................................................................22

2.1.3 Challenges for DSO .......................................................................................24

2.1.4 Planning principles and standards ...................................................................24

2.1.5 Planning methodologies .................................................................................25

2.2 NORWAY ................................................................................................... 27




2.2.4 Planning premises.........................................................................................32


2.3 UNITED KINGDOM .................................................................................... 34






2.4 GERMANY ................................................................................................. 40






3 STATE OF THE ART OF SG TECHNOLOGIES AND SOLUTIONS ..................... 50


3.1 VOLTAGE CONTROL ................................................................................... 50

3.1.1 On-load Voltage Regulated Distribution Transformers ........................................50

3.1.2 Line Voltage Regulators .................................................................................53

3.1.3 Reactive Power Control ..................................................................................54

3.2 METERING AND COMMUNICATIONS .......................................................... 55

3.2.1 Smart meters ...............................................................................................55

3.2.2 Other sensors ..............................................................................................57

3.2.3 Information Communication Technology (ICT) ..................................................58

3.3 DISTRIBUTED ENERGY RESOURCES MANAGEMENT ................................... 60

3.3.1 Microgeneration, Microgrids, Nanogrids ...........................................................60

3.3.2 Storage .......................................................................................................62

3.3.3 Demand response .........................................................................................63

3.3.4 Electric Vehicles ...........................................................................................64

3.3.5 Asset management .......................................................................................66

3.3.6 Forecasting for DG/Load ................................................................................67

3.4 MANAGEMENT AND CONTROL ................................................................... 68

3.4.1 SCADA/DMS.................................................................................................68

3.4.2 Automation strategies on substations (HV/MV and MV/LV) .................................70

3.4.3 Control and monitoring ..................................................................................70

4 SG PROJECTS/INITIATIVES ...................................................................... 73

4.1 EU RESEARCH PROJECTS .......................................................................... 73

4.1.1 PlanGridEV ...................................................................................................73

4.1.2 Grid4EU .......................................................................................................73

4.1.3 Grid+ ..........................................................................................................74

4.1.4 DISCERN .....................................................................................................75

4.1.5 IDE4L ..........................................................................................................75

4.1.6 NEMO ..........................................................................................................76

4.1.7 SuSTAINABLE ..............................................................................................77

4.1.8 CitInES ........................................................................................................77

4.2 ROLLOUTS OF SG DEMOS .......................................................................... 78

4.2.1 Portugal ......................................................................................................78

4.2.2 Norway .......................................................................................................80

4.2.3 United Kingdom ............................................................................................81

4.2.4 Germany .....................................................................................................82

4.3 INTEROPERABILITY OF SG SYSTEMS ........................................................ 84


4.3.1 Consistent terminology ..................................................................................84

4.3.2 Agreements .................................................................................................85

5 TECHNICAL OVERVIEW OF SG SOLUTIONS PER COUNTRY ........................ 88

5.1 PORTUGAL ................................................................................................ 88

5.1.1 Voltage control .............................................................................................88

5.1.2 Metering and communications ........................................................................89

5.1.3 Distributed Energy Resources Management ......................................................90

5.1.4 Management and Control ...............................................................................91

5.2 NORWAY ................................................................................................... 93

5.2.1 Voltage control .............................................................................................93

5.2.2 Metering and communications ........................................................................93

5.2.3 Distributed Energy Resources Management ......................................................96

5.2.4 Management and Control ...............................................................................99

5.3 UNITED KINGDOM .................................................................................. 101

5.3.1 Voltage control ........................................................................................... 101

5.3.2 Metering and communications ...................................................................... 102

5.3.3 Distributed Energy Resources Management .................................................... 102

5.3.4 Management and Control ............................................................................. 103

5.4 GERMANY ............................................................................................... 104

5.4.1 Voltage control ........................................................................................... 104

5.4.2 Metering and communications. ..................................................................... 105

5.4.3 Distributed Energy Resources Management .................................................... 106

5.4.4 Management and Control ............................................................................. 107

6 CONCLUSIONS ........................................................................................ 109

7 REFERENCES ........................................................................................... 112


FIGURES

Figure 1: Consumption and peak demand in Portugal from 2004 to 2014 [2]. ..............19

Figure 2: Relation Total and residential demand of winter typical day [2]. ...................19

Figure 3: Distribution of demand per sector (2013 data) in Portugal. ..........................20

Figure 4: Progress of power capacity connected to the distribution network [1] in

Portugal. .............................................................................................................21

Figure 5: Structure of the Norwegian power grid (Inspired by [10]). ...........................28

Figure 6: Yearly electricity consumption in Norway, 2014 [15]. ..................................29

Figure 7: DG in Norway, by energy source (per 2014) [17]. .......................................30

Figure 8: Flow chart for planning, as described in the planning guide for power systems

[30], [32]. ...........................................................................................................33

Figure 9: UK Licenses areas and DSOs [36]. ............................................................35

Figure 10: UK Electricity Demand 2015 by Sector [37]. .............................................35

Figure 11: Installed Capacity of Embedded Generation, 2015 [38]. .............................36

Figure 12: Schematic view of UK Power Networks’ network expansion methodology. ....39

Figure 13: Flow Network length sorted by voltage level [42]. .....................................41

Figure 14: Flow Number of DSOs according to their share in the total network length

[43]. ...................................................................................................................41

Figure 15: German Standard Load Profiles from households, small and big industrial

consumers [46]. ...................................................................................................43

Figure 16: Installed capacity of RES including prognosis [52]. ....................................43

Figure 17: Feed-In profiles from PV and WT [49]. .....................................................44

Figure 18: Annual full-load ours of RES in Germany [50]. ..........................................44

Figure 19: Scheme of the dual planning method [53] [54]. ........................................48

Figure 20: DER feed-in surpassing voltage threshold of exemplary allocation of the

voltage variation [64]. ..........................................................................................50

Figure 21: Operation with focus on the low voltage network. Cf [64]. .........................51

Figure 22: Operation with focus on the medium voltage network. Cf [38] ....................52

Figure 23: Operation with a combined operational focus (assuming the regulated

transformers are equipped with nine steps with 2.5 % voltage variation each) cf [64]. .53

Figure 24: Principle of a line voltage transformer, cd [65]. ........................................53

Figure: 25 Possible allocation of the tolerated voltage variation (assuming a control range

of 6 %), cf [64]. ...................................................................................................54

Figure 26: Equivalent circuit diagram and phasor diagram for the basic principles of

reactive power control, cf [64]. ..............................................................................55

Figure 27: Smart meter technology evolution (Adapted from [66]). ............................56

Figure 28: Advanced metering infrastructure – AMI. (From [68]) ................................57

Figure 29: Nanogrid block diagram [80]. .................................................................61

Figure 30: Applicability of Electrical Energy Store Systems [87]. ................................62

Figure 31: Possible scheme of V2G functionality [102]. .............................................65

Figure 32: Asset lifecycle [91]. ...............................................................................66


Figure 33: Diagram of SCADA system [120] ............................................................68

Figure 34: Centralised ANM Architecture [121]. ........................................................71

Figure 35: A map showing the demonstration sites of Demo Norway.([130]) ...............80

Figure 36: LCNF Project Area Activity [132]. ............................................................82

Figure 37: IEEE 2030 smart grid interoperability reference model for the power systems

..........................................................................................................................86

Figure 38: SGAM Reference Architecture [136] ........................................................87

Figure 39: Framework of the voltage control system in SuSTAINABLE project [125]. ....88

Figure 40: Modules for controlling the voltage - SuSTAINABLE project [125]. ...............89

Figure 41: Architecture of communicational infrastructure – InovGrid [138]. ................90

Figure 42: Architecture of InovGrid [91]. .................................................................91

Figure 43: A simple illustration of the main functions of Elhub [146]. ..........................95

Figure 44: Number of registered EV/PHEV in Norway per year (updated September

2015). ................................................................................................................98

Figure 45: Results of the planning process with incurred costs as net present value

(2015) [164]. .................................................................................................... 105


TABLES

Table 1: Summary of MV network characteristics [1]. ...............................................17

Table 2: Standard overhead and underground cables used in Portuguese MV networks. 18

Table 3: HV/MV transformers characteristics in Portugal. ...........................................18

Table 4: Expected demand in the distribution network in Portugal. .............................20

Table 5: Quality of service indicators for transmission and distribution networks at MV

level in Portugal. ..................................................................................................22

Table 6: Summary of LV network characteristics in Portugal. .....................................22

Table 7: Expected demand in the distribution network typical cables used in LV networks

in Portugal. ..........................................................................................................23

Table 8: Characteristics of the fuses used in protective devices at LV network in Portugal.

..........................................................................................................................23

Table 9: Quality of service indicators for transmission and distribution network at LV level

in Portugal referred to 2015. ..................................................................................24

Table 10: Line and cable lengths, by voltage levels. Updated 2015 ([11],[12]).............29

Table 11: Yearly electricity production in Norway, 2010–2015 [15]. ............................30

Table 12: Overview of typical voltage levels in Germany [47]. ...................................42

Table 13: SAIDI values for low and medium voltage in Germany [58]. ........................45

Table 14: Microgrid architecture [79]. .....................................................................61

Table 15: Projects currently operating or executed in the past: ..................................83


ABBREVIATIONS

AC Alternating Current

ADMD After Diversity Maximum Demand

ANM Active Network Management

ANN Artificial Neural Networks

AMR Automated Meter Reading Systems

ARIMA Autoregressive Integrated Moving Average

AVC Automatic Voltage Control

BAN Building Area Network

BB Broadband

BDEW German Association and Water

BMP Biomass Plants

CAIDI Customer Average Interruption Duration Index

CBM Condition-based Maintenance

CEN Comité Européen de Normalisation

CENS Cost of Energy Not Supplied

CERTS Consortium for Electric Reliability Technology Solutions

CENELEC European Committee for Electrotechnical Satandardization

CI Costumer Interruption

CML Costumer Minutes Lost

CNE Combine Neutral-Earth

DC Direct Current

DER Distributed Energy Resources

DERM Distributed Energy Resources Management

DETC De-energised tap changers

DRES Distributed Renewable Energy Sources

DG Distributed Generation

DNO Distribution Network Operator

DLC Direct Load Control

DLMS-COSEM Device Language Message Specification-

- Companion Specification for Energy Metering

DR Demand Response

DSL Digital Subscriber Line

DSM Demand Side Management

DSO Distribution System Operator

DTC Distribution Transformer Controller

EB Energy Box

EDP Energias de Portugal (Portuguese DSO)


EEGI European Electricity Grid Initiative

EHV Extra High Voltage

EMS Energy Management System

ENA Electricity Networks Association

ENS Energy Not Supplied

ERSE Energy Services Regulatory Authority of Portugal

ESS Energy Storage Systems

ETSI European Telecommunications Standards Institute

EU European Union

EV Electric Vehicle

FP7 7th Framework Programme for Research and Technological Development

GPRS General Packet Radio Service

HAN Home Area Networks

HES Head End System

HPP Hydro Power Plants

HV High Voltage

HVDC High Voltage Direct Current

HMI Human-Machine Interface

ICT Information and Communications Technology

IEC International Electrotechnical Comission

IED Intelligent Electronic Device

IT Information Technology

LAN Local Area Network

LCNF Low Carbon Networks Fund

LTDS Long Term Development Statement

LV Low Voltage

LVR Line Voltage Regulators

NAN Neighbourhood Area Network

NB Narrowband

NIA Network Innovation Allowance

NIC Network Innovation Competition

NEK Norwegian Electrotechnical Committee

NIS Network Information System

NIST National Institute of Standards and Technology

Nkom National Communications Authority

NSGC Norwegian Smartgrid Centre

NTNU Norwegian University of Science and Technology

NVE Norwegian water resources and energy directorate

MAOTE Ministério do Ambiente, Ordenamento do Território e Energia


MIBEL Iberian Electricity Market

MTBF Mean Time Between Failures

MTTR Mean Time to Repair

MV Medium Voltage

OLTC On-Load Tap changer

PHEV Plug-in Hybrid Electric Vehicle

PMU Phasor Measurement Unit

PL Public Lighting

PLC Power Line Communication / Programmable Logic Controller

PSH Pumped Storage Hydropower

PV Photovoltaic

QoS Quality of Service

RBM Risk-based Maintenance

RCM Reliability Centred Maintenance

RDT Regulated Distribution Transformers

RES Renewable Energy Sources

REN Rasjonell Elektrisk Nettvirksomhet – Rational electrical grid operations

RLC Remote Load Control

RMS Root Mean Square

RPM Registered Power Measurements

RTU Remote Terminal Unit

RWE Rheinisch-Westfälisches Elektrizitätswerk (German DSO)

SAIDI System Average Interruption Duration Index

SAIFI System Average Interruption Frequency Index

SG Smart Grid

SGAM Smart Grid Architecture Model

SG-CG Smart Grid Coordination Group

SGIRM Smart Grid Interoperability Reference Model

SLP Standard Load Profiles

SME Small and Medium-sizes Enterprises

SN Standards Norway

SNE Separate Neutral-Earth

SS Secondary Substations

SVM Support Vector Machines

TDI Transmission Distribution Interface

TIEPI Equivalent interruption time related to the installed capacity

TMB Time-based Maintenance

ToU Time of Use tariffs

TSO Transmission System Operator


UC Unit Commitment

UK United Kingdom

UPAC Self-consumption Generation Units

UPP Small Generation Units

V2G Vehicle-to-Grid

VDE Association of Electrical, Electronic and Information Technologies

WAN Wide Area Networks

WiMAX Worldwide Interoperability for Microwave Access

WP Work Package

WPP Wind Power Plants

WT Wind Turbines


Disclaimer

The content and views expressed in this material are those of the authors and do not

necessarily reflect the views or opinion of the ERA-Net SG+ initiative. Any reference

given does not necessarily imply the endorsement by ERA-Net SG+.

About ERA-Net Smart Grids Plus

ERA-Net Smart Grids Plus is an initiative of 21 European countries and regions. The

vision for Smart Grids in Europe is to create an electric power system that integrates

renewable energies and enables flexible consumer and production technologies. This

can help to shape an electricity grid with a high security of supply, coupled with low

greenhouse gas emissions, at an affordable price. Our aim is to support the

development of the technologies, market designs and customer adoptions that are

necessary to reach this goal. The initiative is providing a hub for the collaboration of

European member-states. It supports the coordination of funding partners, enabling

joint funding of RDD projects. Beyond that ERA-Net SG+ builds up a knowledge

community, involving key demo projects and experts from all over Europe, to organise

the learning between projects and programs from the local level up to the European

level.

www.eranet-smartgridsplus.eu

http://www.eranet-smartgridsplus.eu/


1 Introduction

This deliverable “Smart Grids Solutions and Technologies” has been produced within the

scope of the first work package (WP1) of SmartGuide, an ERA-NET Smart Grid Plus

project. It is one single document synthesising the results of all project partners during

WP1, which will be the basis for further development in the following work packages.

1.1 Introduction and main challenges of SmartGuide

1.1.1 Historic conditions and process of change

Future electrical distribution systems will come across many modifications mostly due to

new paradigms both at conceptual and technical levels. During the last decades, the

planning and operation procedures of power distribution grids have been changing, one

of the main reasons being the high penetration of Distributed Energy Resources (DER), in

particular Distributed Generation (DG) units based on Renewable Energy Sources (RES).

The intention of developed countries, mainly in Europe, to decrease fossil fuels

dependency and the policies imposing the reduction of Greenhouse Gases (GHG)

emissions have contributed to the development of this paradigm. In fact, environmental

concerns are behind the increase of renewable-based DG, as well as the promotion of

electric mobility and integration of storage units in the distribution network. However,

distribution grids, which have been designed to supply customers through unidirectional

power flows coming from the transmission network, may not be able to handle technical

issues brought by the inclusion of these DER.

The integration of RES in existing energy distribution systems all over Europe is being

promoted following the climate and energy 20/20/20 targets of the European Union (EU).

Due to the variability of these RES, in particular wind turbines and photovoltaic (PV)

systems, the uncertainty associated to the balancing of generation and demand

escalates. Smart Grid (SG) technologies are important to ensure cost-effective expansion

of distribution systems. Naturally, the SG use cases may vary from country to country

depending on specific regulatory and legal parameters as well as historical and

geographical conditions, which have led to different grid topologies and operation

principles. It must be accepted that there is no ‘one size fits all’ approach when it comes

to smart grid implementation – each country or organisation has to first identify what

they really want from their smart grid solution and develop an appropriate strategy and

execution plan accordingly.

1.1.2 Overview of SmartGuide project

SmartGuide is a research project with five project partners from four associated partner

countries: Norway, Portugal, the United Kingdom and Germany. The main objective of

the project SmartGuide is the development of improved and generalised planning and

operating guidelines for European smart distribution systems, considering RES and the

demand-side that arise from smart market applications (e.g. demand response, ancillary

services such as frequency control). The associated Distribution System Operators

(DSOs) will provide network data in order to analyse SG technologies used in current

distribution networks and provide expertise of operational network planning. On this

basis, country specific planning and operation principles will be derived. In a further step,

these principles will be abstracted to form a European planning guideline for using smart

grid technologies in distribution networks. The guideline is supposed to assist DSOs


noncommittally when assessing the deployment of smart grid technologies in their

network. During the project, all partners will develop different software frameworks to

identify increased requirements for network reinforcement and possible SG solutions in

order to upgrade existing distribution networks in a cost-efficient manner.

1.2 Objectives and goals of Work Package 1

The overall purpose of WP1 is to review the current state-of-the-art on distribution

network planning and existing practices in the countries involved in the project, by

benefiting from the experience of local distribution network and system operators.

This report aims at exploring the issues that come from the predefined tasks assigned in

the start of project. One of them is to analyse and compare historic and geographical

circumstances of SG solutions and technologies of the participating countries and the

resulting grid topologies as well as the standard equipment. It is expectable that each

country presents different adapted SG concepts as well as rollouts of deployed

technologies and solutions. Another objective is to describe the most relevant

technologies in detail examining aspects such as abstracted functionalities and

interoperability. Finally, this document intends to review existing DSO specific planning

strategies, as a basis for developing new planning principles in order to understand the

individual approaches of different DSOs in different countries. This review will build upon

existing knowledge coming from previous EU-wide projects public databases and enhance

it with coverage of further countries: Norway, Portugal, United Kingdom and Germany.

This document is divided into five main sections comprising the specified tasks. The first

chapter contains the introduction. In the second chapter, the main characteristics of each

technical specific background such as general equipment, typical demand profiles,

progress of DG connected to the grid and quality of service indicators are detailed. For

each country, the foreseen challenges for DSO in the distribution planning area are also

highlighted as well as the current planning premises and methodologies used to operate

and plan each distribution system. The third chapter brings together the state-of-the-art

of main SG technologies and solutions separated in four categories: voltage control,

metering and communications, DER management, and management and control. In

chapter four, a list of recently completed and ongoing European projects in the area of

planning smart grids is provided. Moreover, in this section, for each country of the

SmartGuide partners, current rollouts of smart grids demonstrations underway are

examined. Additionally, the fourth chapter also addresses the ability of system data

exchange i.e. the interoperability of smart grids systems. The last chapter refers to the

availability, potential and expected impacts of SG technologies previously mentioned of

each partner country.


2 Country specific background

In this section, the main characteristics of the Low Voltage (LV) and Medium Voltage

(MV) networks are detailed for each country.

2.1 Portugal

The next subsections contain a characterisation of the Portuguese distribution network

divided by voltage level. The description englobes the type of equipment used, the

demand and DG circumstances, indicators of quality of service, the main challenges that

the DSO is facing and the planning methodologies and premises that are taken into

account.

2.1.1 MV level characterisation

EDP Distribuição - Energia, S.A. (EDP) is the main DSO operating in the regulated

distribution and supply businesses in Portugal. It owns around 99% of the electricity

distribution network in continental territory of Portugal. EDP’s distribution activity is

regulated by a national regulatory authority for energy services – ERSE which defines the

network tariffs, monitoring and assuring the levels of quality of service required by DGGE

(Direcção Geral de Geologia e Energia).

General characteristics and equipment

The Portuguese MV distribution network is a three-phase system with three main

different voltage levels: 10 kV, 15 kV and 30 kV although a 6 kV short length network is

still in operation. The total length of the distribution lines is about 73,000 km. The total

power rating associated to HV/MV and MV/MV is around 17,000 MVA distributed by 414

substations. In the document of the characterisation of the distribution network [1] at

the end of 2014 we can find the summary of some global numbers regarding the MV

network as Table 1 shows.

Table 1: Summary of MV network characteristics [1].

Number of substations 419

Number of transformers 731

Total power rating (MVA) 17,608

Overhead lines total length (km) 58,433

Underground lines total length (km) 14,316

Urban networks are mostly underground with a meshed structure and they can grow to

open loops with radial arrangements to normally open points. The suburban networks are

a mix between underground and overhead cables in contrast with rural networks, which

have a radial structure and use mostly overhead cables. Table 2 shows the typical

overhead and underground cables used by EDP Distribuição in Portugal at MV level.


Table 2: Standard overhead and underground cables used in Portuguese MV networks.

Overhead

o 55-AL4 (AAAC 3x1x55 mm2 – used to connect load points no network)

o 117-AL4 (AAAC 3x1x117 mm2 – used in main feeders)

o 148-AL4 (AAAC 3x1x148 mm2 – used in main feeders and backup)

o Partridge (ACSR 3x1x160 mm2 – used in main feeders and backup)

Underground

o NA2XSY 3x1x120 mm2 (used in 15 kV and 30 kV main feeders)

o NA2XSY 3x1x240 mm2 (used in main feeders and backup)

Although there are some installations with insulated neutral, most connections are

provided with neutral impedance through resistors or reactors.

The connections of MV network to higher level are made through HV/MV transformers.

Typically, the nominal power of these transformers varies between 10 and 40 MVA. Their

main characteristics are summarised in the Table 3, including the nominal primary and

secondary voltages and the type of winding connections.

Table 3: HV/MV transformers characteristics in Portugal.

• HV/MV nominal power

o 10 MVA, 20 MVA, 31,5 MVA and 40 MVA

• Winding connections

o 60/10,5 kV

YN,d11

o 60/15,75 kV

YN,d11

YN,d5

o 60/31,5 kV

YN,yn0,d(*) (*Stabilization winding)

o 60/31,5/10,5 kV

YN,yn0,d11

o 60/31,5/15,75 kV

YN,yn0,d11

YN,yn0,d5

o 60/31,5-15,75 kV

Yn,d11

YN,d5

Demand

Figure 1 confirms that the economic crisis and the implementation of energy policy

measures have caused a stagnation of the energy consumption as well as a decrease of

the peak load (power) during the last years. Usually the peak demand occurs in the

winter at the end of the afternoon even though there has been a drop in domestic and

public illumination consumption during these last years.


Figure 1: Consumption and peak demand in Portugal from 2004 to 2014 [2].

Residential consumption has a relevant contribution to total demand in Portugal,

especially during peak hours. Figure 2 allows comparing the total demand curve in the

January’s day of 2013 where the load achieved higher values with the residential curve

for the same day. It is possible to observe that residential consumers constitute a

significant part of the total demand.

Figure 2: Relation Total and residential demand of winter typical day [2].

Figure 3 illustrates the distribution of demand per sector.


Figure 3: Distribution of demand per sector (2013 data) in Portugal.

Consumption at the MV and LV levels has grown during recent years in Portugal.

According to the Development Plan Networks for 2015-2019 the forecasts of the global

Portuguese demand of MV and LV networks as well as the public lighting (PL) are as

presented in the Table 4.

Table 4: Expected demand in the distribution network in Portugal.

Distributed energy

Real Forecast

2013 2014 2015 2016 2017 2018 2019

MV + LV + PL (GWh) 35,115 35,606 36,037 36,636 37,343 38,175 39,107

Annual variation (%) -3.2% 1.4% 1.2% 1.7% 1.9% 2.2% 2.4%

Distributed Generation (DG)

In order to face the growth of consumption also DG capacity has increased. PV and wind

technologies are the ones, which exhibit greater growth. In Figure 4, the power capacity

situation of each technology connected to the distribution network since 2007 until the

end of 2013 is presented.

The last columns refer to ongoing projects or already committed power, which elevates

the total amount of distributed energy available soon.


Figure 4: Progress of power capacity connected to the distribution network [1] in Portugal.

In terms of storage technologies, currently this type of technology is not widespread in

the distribution network. So far, there is a demonstration project going on located in

Évora with Lithium-Ion batteries (flexible and removal). The power and energy ratings

are 493 kW and 196 kWh respectively and they are connected to 15 kV and 30 kV levels

in the MV grid.

EDP Distribuição is keen on taking this opportunity to explore new possibilities such as

business models to use storage in flexibility and ancillary services, energy efficiency,

assessment of renewable sources integration benefits, further exploring legal and

regulatory issues and understanding the storage impact for network planning.

Quality of service

Table 5 summarizes the following quality service indicators for the distribution network

referred to 2015 according to PDIRD 2015-2019 annual document [1]:

TIEPI - Equivalent interruption time related to the installed capacity

SAIFI – System Average Interruption Frequency Index

SAIDI – System Average Interruption Duration Index

These indicators are divided by different geographical zones with different quality of

service demands and are compared with the same indicators for the transmission

network. These zones are defined in the Portuguese regulation of quality of service [3]

where the zones classified as zone A (associated to big cities or localities with more than

25 thousand clients) are zones with the quality of service level more demanding. The B

zones are the zones with quality of service requirements and correspond to localities with

a number of clients between 2,500 and 25,000 while the C zones correspond to the other

locations.


Table 5: Quality of service indicators for transmission and distribution networks at MV level in

Portugal.

Indicators Zones Transmission

network

Distribution

network Total

TIEPI MV

(minutes)

Zone A 0.00 24.30 24.30

Zone B 0.42 48.91 49.33

Zone C 0.43 69.55 69.98

SAIFI MV

(no.)

Zone A 0.00 0.70 0.7

Zone B 0.05 1.23 1.27

Zone C 0.03 1.91 1.94

SAIDI MV

(minutes)

Zone A 0.00 34.42 34.42

Zone B 0.44 58.72 59.16

Zone C 0.40 87.84 88.25

Regarding other indicators, in the year of 2013 the Energy Not Supplied (ENS) in the

Portuguese distribution network reached the total amount of 4,744 MWh according to

PDIRD document [1]. Although exceptional metrological events occurred in this year, this

value is within the range verified in the recent years.

2.1.2 LV level characterisation

General characteristics and equipment

The Portuguese LV distribution network is a three-phase plus neutral system with 230 V

phase-to-neutral voltage. The LV system has around 141,000 km and is typically

explored in a radial way. In Table 6 the global dimension of LV network is summarised.

Table 6: Summary of LV network characteristics in Portugal.

Number of substations 65,151

Total power rating (MVA) 19,610

Overhead lines total length (km) 107,516

Underground lines total length (km) 3,899

In urban areas the system is mostly underground while in the rural zones it is commonly

overhead (bundled). The standard lines and cables used by EDP Distribuição are

presented in Table 7.


Table 7: Expected demand in the distribution network typical cables used in LV networks in

Portugal.

o Overhead

o NFA2X 4x25+16 mm2

o NFA2X 4x50+16 mm2

o NFA2X 4x70+16 mm2

o NFA2X 4x95+16 mm2

o Underground

o NAYBY 4x35 mm2

o NAYBY 4x50 mm2

o NAYBY 4x95 mm2

o NAYBY 3x185+95 mm2

The LV system uses pad-mounted MV/LV substations in urban and suburban areas while

in the rural zones pole-mounted MV/LV substations are used. The pad-mounted MV/LV

substations have a range of rated power between 250 kVA and 630 kVA using step-down

distribution transformers with delta-star connections (grounded neutral). The pole-

mounted MV/LV substations power rate varies between 50 kVA and 250 kVA using also

step-down distribution transformers with delta-zigzag or delta-star connections

(grounded neutral). Both overhead and underground networks have the neutral directly

connected to earth and exposed conductive parts connected to the neutral (TN).

Regarding the LV protective devices fuses are used in both pad-mounted and pole-

mounted substations. Its use follows the International Electrotechnical Comission - IEC

60269 standard taking into account also the technical specifications of EDP code of

practice for substations. The nominal ratings of the fuses per type of substation and for

intermediate protective devices are detailed in Table 8.

Table 8: Characteristics of the fuses used in protective devices at LV network in Portugal.

Device Nominal power (KVA) Fuse rating current (A)

Pad-mounted substation 250/400/630 100, 125, 160, 200, 250, 315

Pole-mounted substation

50 63

100 100, 125

160/250 100, 125, 160

Urban intermediate

protective devices - 63, 80, 100, 125, 160, 200, 250

Rural intermediate

protective devices - 63, 80, 100, 125, 160

Quality of service

Similar to the values presented for MV network, Table 9 summarizes the following quality

service indicators for the LV level referred to 2015 divided by the same geographical

zones explained above according to PDIRD 2015-2019 annual document [1]:


Table 9: Quality of service indicators for transmission and distribution network at LV level in

Portugal referred to 2015.

Indicators Zones Transmission network Distribution network Total

SAIFI LV

(nº)

Zone A 0.00 0.78 0.78

Zone B 0.04 1.12 1.16

Zone C 0.03 1.92 1.95

SAIDI LV

(minutes)

Zone A 0.00 35.69 35.69

Zone B 0.44 51.44 51.88

Zone C 0.38 91.02 91.40

2.1.3 Challenges for DSO

In Portugal, like in other European countries, the DSO has to deal with fast growing

Distributed Renewable Energy Sources (DRES). The increasing of available DRES may

cause congestion problems, reverse power flows, reducing the components life, and

distress system stability. Managing a distribution system is becoming more complex due

to the development of intermittent decentralized production capacities but also due to a

growth of the peak load and the foreseen new uses of electricity (demand response,

electric vehicles).

This will imply an improved cooperation between the main DSO and Transmission System

Operator (TSO). At this moment, the centre of attention of the cooperation between DSO

and TSO is changing from long term activities (e.g. planning) to short-term operational

activities such as exchanging of real time information. The TSO and the main DSO are

planning to join both dispatch activities via ICCP (Inter Control Centre Protocol) to in-

crease the real-time information exchange of grid topology, production and power flows

[4]. Additionally, the DSO is cooperating in other services, such as compensation for re-

active power, and is in the process of establishing agreements to manage static compen-

sation devices in conjunction with TSO.

The congestion management is also a concern since, at this moment, the DSO does not

contract services to handle network constraints. In case of imminent emergency

operation though, they have interruptible contracts with big clients, which can be

curtailed if necessary.

There are expected progresses in achieving the requirements to implement an active

distribution system management approach provided by the DSO in the next years. DSOs

may be able to use services that help to optimise the grid operation and planning,

promote the participation of DRES, Demand Response (DR) and storage to the electricity

markets and support seamless information exchange between stakeholders.

2.1.4 Planning principles and standards

NP EN 50160 is a Portuguese standard based on the European standard EN 50160 since

1994. It aims to define and describe the values, which characterise the voltage supply

such as frequency, amplitude, wave shape and symmetry of three-phase voltages.

Besides the standard NP EN 50160, EDP also practices activity following other regulatory

directives [5] such as:

Portuguese regulation about security in substations and switching stations

(Decreto-Lei n.º 42895, de 31/03/60, alterado pelo Dec. Regulamentar n.º 14/77,

de 18 de Fevereiro).


Portuguese regulation about security when operating HV lines (Decreto

Regulamentar n.º 1/92, de 18/02).

Portuguese regulation about security when operating distribution networks at LV

level (Decreto Regulamentar n.º 90/84, de 26/12).

Portuguese regulation about security on usage electrical energy facilities (Decreto-

Lei n.º 740/74, de 26/12).

Portuguese regulation about security on collective facilities and buildings

(Decreto-Lei n.º 740/74, de 26/12).

Portuguese regulation about usage of electrical equipment in explosive

environment (Decreto-Lei n.º 202/90, de 14/12).

Standard IEC 479-1 e 479-2: 1994 - Effects of currents passing through human

body.

Standard IEC 529, 1989 - 1 - Degrees of Protection which enclosures of a product

are designed to provide when properly installed.

Standard IEC 536, 1976 - Classification of electrical and electronic equipment with

regard to protection against electric shock.

Standard EN 50110-1, 1996 – Operation of electrical installations.

2.1.5 Planning methodologies

The Portuguese DSO have some current methodologies in order to manage planning

issues such as power quality, reliability, energy losses, ENS or remote switching,

integration of DER criteria. EDP Distribuição uses also a few tools and several studies

towards making decisions to meet the planning goals. In the future, it is expected that

DSO will promote new trends related with optimisation aiming to tackle the new

challenges and concerns related to the progress of grid management.

Current methodologies

Regarding the power quality criteria, EDP Distribuição takes into account the winter and

summer maximum synchronous load data to find load simultaneity factors, load growth

rates, thermal overloading of lines and cables and historical reliability data (rate failure

and failure duration). Concerning load shift criteria, the DSO distinguishes the

substations HV/MV with two transformers and with only one transformer. In substations

with one transformer, the transformer load needs to stay under 50% of power load rating

in order to make load shift possible. In the case of substations with two transformers,

this value rises to 75%. The criteria to voltage drop is maintaining the nominal voltage

within the values [0.94; 1.10] p.u.

With respect to reliability and security, the DSO seeks to ensure the regulatory voltage

profile for all areas. However, it has different reliability plans for three different areas (A,

B and C) which differ on quality service demands. Area “A” demands DSO to provide a N-

1 backup for HV/MV substations load and N-1 backup for MV feeder load by adjacent MV

feeder while areas “B” and “C” do not demand backup in the HV/MV substations. Besides

these planning security standards, the results derived from risk analysis are being

considered.

The reduction of energy losses and ENS are valued at 0.0752 and 1.5 €/kWh

respectively. The capitalised losses and ENS are used in all investments studies over a

30-year period. In the case of LV loss, the value reaches 0.0911 €/kWh.

The rules for remote switching vary between urban or suburban zones and rural zones.

For urban/suburban zones, there should be at least one switching point for each 3 MVA of

rated power. For rural zones, the rule is related to the distance between switching points,

where the maximum distance should be 25 km.

Currently new RES plants can only be remunerated through the open energy market.

With the publishing of Decree-Law 153/2014 on October of 2014 a unique remuneration

regime for electricity produced from small production (UPP) and self-consumption (UPAC)

units based has come, which is based on a bidding model in which producers offer


discounts to a reference tariff [6]. According to Articles 1 and 2 of this Decree-Law, the

new regime covers the generation of electricity:

UPAC: For self-consumption in a usage installation connected to the respective

generation unit, with or without a connection to the public energy grid, based on

renewable and non-renewable generation technologies whose surplus energy can

be injected into the public energy grid;

UPP: Through small generation units from renewable energy sources whose power

output is no more than 250 kilowatts (kW) and exclusively intended for sale to the

public electric grid.

The installation of UPACs and UPPs is generally subject to prior registration and

certification for operation. The following rules apply to UPACs:

UPACs with an installed capacity exceeding 1 megawatt (MW) require the relevant

licences for installation and operation.

UPACs with an installed capacity above 200 watts (W) but no more than 1.5kW, or

whose electrical usage installation is not connected to the public energy grid,

require only prior notification before beginning operation.

UPACs with an installed capacity of no more than 1.5 kW and whose owner

intends to supply the electricity, which is not consumed in the electrical usage

installation, are subject to prior registration and operational certification.

UPACs with an installed capacity of no more than 200W are excused from any

form of prior control.

Owners of UPACs may enter into a power purchase agreement with the last recourse

supplier to sell their surplus electricity if they use renewable energy sources and have an

installed capacity of up to 11 MW, and if their electrical usage installation is connected to

the public energy grid. If the UPACs have an installed capacity of more than 1.5 kW and

are connected to the public energy grid, the owners are subject to pay fixed monthly

compensation intended to recover part of the costs arising from measures relating to

energy policy, sustainability and general economic interests. The connection capacity that

may be attributed each year to UPPs cannot exceed 20 MW, in accordance with the

programme established annually by the director general for energy and geology. Further,

UPP owners may enter into power purchase agreements with the last recourse supplier to

sell the electricity that they generate.

Remuneration of electricity generated by UPPs is calculated through a bidding system.

Producers bid by offering discounts of a benchmark tariff, which is set annually by the

government. The applicable tariff for each UPP will be the highest amount resulting from

the highest discount offered. The remuneration tariff will vary according to the primary

energy used and will be determined by applying different percentages contained in Article

3 of Ministerial Order 15/2015 (January 23 2015) to the benchmark tariff, which for 2015

is 95 €/MWh. The application of the remuneration tariff is limited to 2.6 MWh per year for

all generation technologies except hydropower (the limit for which is 5 MWh per year),

and this tariff will remain in force for 15 years following the date on which the producer

started to supply electricity to the public energy grid.

Current used tools/software

Currently, EDP uses two main tools within the planning dominion. DPlan is an instrument

that is used to determine an optimal network configuration minimising the ENS, energy

losses and investment proposals. Through INVESTE tool it is possible to make an

economic evaluation performed in an excel sheet. The input values such as ENS and

energy losses come from previous DPlan analysis. This tool provides to the management

level the economic indices in order to help on the decision process. Moreover, it can

incorporate an economical evaluation also of other technical benefits like maintenance

cost reduction and operational cost reduction.


Besides, there are other studies (published in the PDIRD annual report) by which EDP

take decisions in the planning ambit. Some of them are briefly described in the next

paragraphs [1]:

Forecast of required investments – The objective of this model is to implement

procedures to estimate the value of the required investment to perform in the

distribution network, with a horizon of 5 years, giving special relevance to the

estimations obtained for the first two years, and considering levels of

disintegration of the results.

Evaluation of power losses at distribution grid – This study aims to characterize

the distribution network with the purpose of evaluating the potential of losses

reduction as well as establish reasonable goals to accomplish that reduction in the

current regulatory framework.

Evaluation model about the investment necessities on quality of service and

definition of quality of service ranges – Methodology that estimates the financial

resources in order to achieve a determined level of continuity of service. The

resulting forecast model establishes levels of confidence based on the available

historic, even if not being a strictly deterministic model.

Transformers HV/MV reserve – Dimensioning the technical reserve of a substation

(or a set of substations) is determined by the level of required reliability and by

the costs aggregated to the system operation, such as investment in acquisition,

storing and maintenance of reserve equipment, interruption of energy supply,

summing to penalizations and compensations established in sector regulation.

Identification of alternative constructive solutions – It aims to identify better

constructive solutions, which minimize consequences of extreme atmospheric

phenomenon in the sensitive zones. Besides, another objective is to analyse costs

and benefits of different solutions.

Methodologies of risk analysis by projects of distribution networks investments –

This study has the objective to define a methodology about risk analysis, which

allows substantiate the decision regarding the investment proposals on

distribution network.

Future trends

The future distribution networks are changing rapidly which affect planning trends.

However, it is expected that DSO follow the trends in the rest of Europe towards facing

the new challenges of grid management. In the short-term horizon, it is expected also

that the suites of forecasting load and generation could be integrated into operational

procedures in order to contribute to a realistic decision making. With the foreseen smart

meters rollout, it would be possible to evaluate the network state of operation in real

time, and allow predicting of its behaviour and adjusting to new circumstances and

environments. Another current research direction is about the customer-based storage

capabilities, the stochastic nature and intermittency of utilization patterns of Electric

Vehicles (EVs), residential thermal storage and cooling [7].

2.2 Norway


In 2013, there were 148 grid companies in Norway, plus the TSO1, Statnett. 131 of these

were DSOs, 85 were operating in the regional grid and 15 in the transmission grid. Today


(2016) there are 130-140 different DSOs in Norway, supplying about 2.9 million

customers. There is a large variation in the sizes of DSOs, where the seven largest DSOs

each have more than 100,000 customers and supplying almost 1.7 million of the total

number of customers (per 2014). The different DSOs operate in different geographical

areas, and may have different practices regarding planning and operation of the grid [8],

[9].

Network configurations

The power grid in Norway can be divided into three segments. The transmission grid

represents the largest part of the total grid, and almost all of it is owned by Statnett

(TSO). Typical voltage levels are 420 and 300 kV, with some parts at 132 kV. The

transmission grid transports power across the regions of Norway and across the borders

to the neighbouring countries. The regional grid ties the transmission grid and

distribution grid together and has typical voltage levels of 132, 66, 47 and 33 kV. The MV

part of the distribution grid has typical voltages of 11 and 22 kV. All transmission and

distribution of power is in three-phase alternating current (AC), while single-phase is

most common in households [10]. Figure 5 shows the power grid in Norway divided into

the different segments.

Figure 5: Structure of the Norwegian power grid (Inspired by [10]).

Most of the transmission grid and parts of the regional grid has a meshed structure, while

the rest is mainly radial. There is also some meshed structure in parts of the MV

distribution grid (1–22 kV), mainly around urban areas. The meshed grid increases the

security of supply in case of e.g. a fault in a power line or transformer, when the

electricity may be redirected through an alternative path through switches [9]. Table 10

shows an overview of the approximated lengths of overhead lines and cables for the

different voltage levels of the power grid in Norway per 2015. The remaining LV

distribution grid constitutes approximately 195,000 km, making the total length of the

Norwegian power grid close to 330,000 km. according to NVE and Statnett.


Table 10: Line and cable lengths, by voltage levels. Updated 2015 ([11],[12])

Voltage level Overhead lines Cables (underground, sea)

400 kV 2 941 km 20 km

220–300 kV 5 300 km 60 km

132 kV 10 600 km 410 km

33–110 kV 11 600 km 1 030 km

1–22 kV 60 000 km 42 000 km

TOTAL 90 500 km 43 520 km

Consumption and peak load

The peak load in Norway occurs during the coldest winter days. The time it occurs

depends on the outdoors temperature through the winter, and the last three national

records for power consumption have all been set in January. The 21st of January 2016

between 8:00 and 9:00 AM, a new record was set for peak load, where the consumption

of electricity was 24 485 MWh/h. The previous record for peak load of 24,180 MWh/h

occurred on January 23rd 2013, also between 8:00 and 9:00 AM. Before 2013, the record

was 23,994 MWh/h from January 6th 2010. This large consumption has not affected the

power system stability, as the production capacity in Norway during the winter is

estimated to be 27,400 MW [13], [14].

The yearly electricity consumption in Norway divided between different types of loads

from 2014 is presented in Figure 6, and this division of percentages has been similar for

the previous years. The total consumption of electricity per year was 117.1 TWh in 2014,

119.5 TWh in 2013 and 118.7 TWh in 2012.

Figure 6: Yearly electricity consumption in Norway, 2014 [15].

Distributed generation

Large production facilities and power-consuming industries are usually connected to the

transmission or regional grid, while smaller production facilities (DG) are connected to

the distribution grid. The electricity in Norway is mainly generated by hydropower, which

accounts for around 95 % of the total power production through the year – see Table 11.


Table 11: Yearly electricity production in Norway, 2010–2015 [15].

2010 2011 2012 2013 2014 2015

Hydro power [TWh] 117,94 122,08 142,90 129,02 136,64 139,01

Thermal power [TWh] 5,61 4,77 3,39 3,32 3,47 3,49

Wind power [TWh] 0,89 1,29 1,56 1,89 2,22 2,52

Total el. production [TWh] 124,45 128,14 147,85 134,24 142,33 145,02

At January 1st 2016, the total number of hydropower plants was 1,543 with a yearly

production of approximately 132.3 TWh. DG includes generation units that are below 10

MW and decentralized, located near end users. The number of small hydropower plants

(<10 MW) was 1,207, contributing with 9.56 TWh to the yearly production [16]. As

shown in Figure 6 , hydropower represents the largest share of DG in Norway, in addition

to some wind and bio, and small amounts of DG from gas and sun [17]. The smaller

power plants that are connected to the grid are often connected to weak grids, in areas

with little population [18].

In Norway, there are no dedicated solar power plants; the installed PV capacity is mainly

roof-mounted, located at private and commercial buildings. By the end of 2015, the

registered total capacity for solar power was about 15 MWp, where one third of this

capacity was installed during 2014 and 2015 alone. Before 2014, the largest share of

installed PV power were residential stand-alone systems (not connected to the grid), but

in 2014 and 2015 more than half of the new installed PV power was grid-tied [19].

Figure 7: DG in Norway, by energy source (per 2014) [17].

So far in 2016, much of the installed PV systems has been mounted on commercial

buildings and grid-tied, which has given a significant increase in Norway's accumulated

PV capacity. However, the solar power capacity is rather insignificant compared to the

other DG resources [20]. At June 2016, there were just below 300 registered prosumers

in Norway, most of which have their power production from rooftop PV systems [21].

Quality of Service (QoS) standards

In 2005, the Norwegian Regulator (NVE) introduced the Quality of supply (QoS) directive

as a part of the Energy Act from 1991. This directive is based on the European Standard

EN 50160, but is somewhat more "strict" than EN 50160 in some points. For instance,

where EN 50160 uses 95 % of an evaluation period, the Norwegian QoS directive uses

100 %, and for RMS variation averaging period, EN 50160 uses 10 minutes, while the

Norwegian QoS directive uses 1 minute [22].

Since 1995, it has been mandatory for network companies to report interruptions above

1 kV, and since 2014, interruptions on all voltages has required reporting. All disturb-

ances and interruptions above 1 kV are analysed and reported to the TSO. For reporting


interruptions, the FASIT system was introduced to the network companies in 1995 and

has been used since then. FASIT is a standardised system for reporting of faults and in-

terruptions, including common terminology, calculation methods (for e.g. ENS), structure

and classification of data. The Norwegian Regulator, NVE, publishes an annual report of

statistics from these reported interruptions, where key numbers such as ENS (divided

into short (≤ 3 minutes) and long (> 3 minutes) interruptions), SAIDI, SAIFI, Customer

Average Interruption Duration Index (CAIDI), etc. are included. In addition, Statnett

publishes a report regarding fault statistics [23], [24].

Standard network equipment

As there are over 130 different DSOs in Norway, the "typical" equipment is not the same

for the whole distribution system, and there are no common standards. Some DSOs

establish their own internal standards for equipment, like Skagerak Energi Nett, one of

the largest DSOs in Norway. These includes standards for protection, network stations,

lines and cables, etc. and can be based on Norwegian standards and recommendations

from the Planning Book or REN (Rasjonell Elektrisk Nettvirksomhet – Rational electrical

grid operations) leaflets (read more in section 2.2.4).

As mentioned, there are many DSOs in Norway, and thus many different practices.

Several power transformers (transformers connecting the regional and the MV

distribution grid) have tap changers installed, which is commonly configured to keep the

voltage at the MV distribution grid side at a fixed level (e.g. 22 kV) [25].

Energy storage and DG

Norway is in the special situation of having more than 95 % of the produced energy as

hydro power (reservoirs and run-of-the-river plants). Hydro power is a very flexible

energy source with fast and simple regulation. By using the reservoirs to store water

during the summer and autumn, the energy can be saved for the cold and highly energy-

consuming winters. A few of the hydro power plants in Norway are pumped storage

hydropower (PSH) plants, where electricity is used to pump water from a lower reservoir

up to reservoir of higher altitude (in low price periods), thus increasing the potential

energy to be used for production of electricity in high-price periods. PSH systems may

have an efficiency up to 80 % [26].


LV levels denotes voltage levels of 1 kV and below, and the typical voltage levels are

230 V and 400 V. About 70 % of the LV part of the distribution network in Norway is of

the type 230 Volt (line voltage) isolated terra (IT) system, where the transformer's

neutral point is isolated from earth through a surge protector. TT networks are also used

in some parts of southwestern Norway, and TN systems are implemented in areas with

new constructions. As most of Europe uses 400 Volt (line voltage) TN systems, a three-

phase load connected to a 230 V network requires a current that is √3 times larger to

achieve the same power. The LV network is mostly radial [27].

At approx. 40–50 % of the customer connection points, the 230 V IT grids have a higher

impedance compared to the standardized EMC reference impedance (for 230/400 V TN

networks; 230 V IT networks do not have a standardized reference impedance), and

some places the impedance is significantly higher than the reference [18]. In other

words, there are large parts of the grid that are weak, which implies more significant

impacts regarding voltage quality due to connection of DG, EVs, etc. [27].

Similar to the MV distribution grid, the LV grid is dominated by overhead power lines. The

end-user density is low, i.e. there is a large amount of power grid per user. Norway is

also characterised by relatively large electricity consumption per customer, and a

significant amount of holiday houses [18].



The electricity consumption is changing towards increased peak load, and reduced use of

energy, resulting in reduced utilization time of the distribution grid. This change in the

electricity consumption is due to more energy efficient electrical appliances, with a higher

peak load (instant water heaters, induction cookers etc.), improved building standards

(passive houses) and new electrical appliances (electrical vehicles). This trend in

consumption also results in an increased peak load occurring in a limited amount of

hours during a year. (For a typical household meter data [kWh/h] show that only approx.

200 hours during a year have a load higher than 70 % of peak load.)

The amount of DG is increasing in the distribution grid in Norway. Smaller hydro plants

(run of river plants) installed in rural areas and in weak distribution grid were previously

the typical type of distributed generation, but today distributed generation in case of top-

roof PV-panels are accounted for as distributed generation, and the penetration is

increasing. These customers with both production and consumption of electricity

("prosumers") are a new type of customer that the DSOs have to handle. The connection

requirements specified for the first prosumers should be robust enough to also be valid if

the penetration of the prosumers are further increasing.

Plans for the period 2012-2021 [28] show an investments need of 100-140 billion NOK,

where 51-68 billion NOK is in the distribution and regional grid. The investment need is

among others related to an ageing infrastructure and increased peak load. Traditionally

the DSOs have invested in the grid when bottleneck occurs, but with the trend of

continuously increased peak load, grid investment is not always the best solution, and

other alternatives should be evaluated.

Full-scale deployment of smart meters will be performed within January 1st, 2019. This

will give the DSOs updated information about the power flow in their grid and the status

for different grid components, enabling more target-oriented investments.

2.2.4 Planning premises

An increasing amount of the framework for the power sector is decided internationally. In

Norway, the Norwegian Electrotechnical Committee (NEK), Standards Norway (SN) and

National Communications Authority (Nkom) are the relevant organisations for

standardisation. Norway's membership in European Committee for Electrotechnical

Satandardization (CENELEC) requires that Norway be obligated to implement European

norms in the Norwegian regulations through electrotechnical norms (NEK EN). NEK may

publish their own standards (NEK), and also standards from IEC as a Norwegian

electrotechnical norm (NEK IEC). Similarly, Standards Norway publishing Norwegian

standards (NS), are obligated to publish CEN standards (NSEN), and may also publish

standards form ISO, IEC, etc. [29].

By the Norwegian Energy Act, grid companies are obligated to offer access to the grid for

all power producers that wish to be connected. There are no common standards for the

planning methodology of power grids, but there has been created a "Network planning

guide for power systems" for the Norwegian power grid ([30]), which is used by a large

number of the Norwegian grid companies. The intention of this guide is to provide

guidelines for grid- and operation planning divisions within DSOs, industry companies,

suppliers, consultants and educational facilities. The planning guide was developed by

SINTEF, in cooperation with the company REN, and is updated yearly based on open R&D

results from ongoing projects. Per March 2016, the planning book contains [31]:

Objectives and framework conditions for technical/economic planning in the

power grid.

Systematics for: planning, expansion and renewal of the grid, integration of

distributed generation, planning with several energy carriers, procedures.

Analyses of load and distributed generation.


Analyses of actions/measures: grid configurations, cables versus overhead

lines, grid compensation, grid planning and end-user measures.

Technical analyses regarding security of supply and voltage quality.

Technical data and fault statistics for current-carrying capacity for power lines.

Economic analyses.

REN also publishes leaflets, which are guidelines within project management,

installation, operation and maintenance. The different REN leaflets are dedicated to a

specific topic, e.g. technical requirements for connection of PV systems, the process of

connecting DG units, etc. The guidelines are intended to describe the best practice

available for the industry [29].


The planning process described by this guide consists of evaluating different

alternatives for investment, considering both economy and (technical) restrictions.

The optimal alternative(s) are determined by how well the solutions fulfil the

customers' needs, regarding geographical location, consumption (and production)

profiles, and requirements related to quality (frequency, voltage level) and security of

supply. The general planning process for power grids are described in the Planning

Book as shown in Figure 8, and may be used for grid planning, operation and

maintenance planning, and reinvestment planning [29], [32].

Figure 8: Flow chart for planning, as described in the planning guide for power systems [30], [32].

There may be large differences within grid conditions and where generation units are

located. This is one of the reasons for not having standardised planning procedures.


For connecting small-scale production plants (DG), there is whole series of frequently

used REN leaflets. It is desirable for the grid companies to establish an early dialogue

with the constructors of the planned power plant, to gain an overview of planned DG

in their area and thus choose the best alternatives for grid investments. Today, most

capacity limitations are solved by grid expansion or investments (versus investing in

voltage regulation, active/reactive power control, etc.) [33].

Future trends

Traditionally, planning within the power system has been relatively simple and

predictable, with small changes in consumption patterns, few generation units connected

to the distribution system, and mainly a unidirectional flow of energy. Now, end-user

behaviour is changing, and there are many uncertainties regarding future trends.

Building regulations, technology and initiatives for energy efficiency may lead to lower

energy consumption in households. An increasing number of households and public

buildings are also integrating power generation, typically roof-mounted PV. At the same

time, there will be an increasing amount of electric vehicles, induction cooking,

instantaneous water heaters and other equipment that use less energy but more power.

This will contribute to increased demand for power in the power system, requiring rapid

response to keep a stable power system.

The upcoming trends that will be important for establishing planning methodologies are

the increasing amount of DG integration, the roll-out of smart meters, new types of loads

(EV charging, flexible loads, etc.) and integration of smart grid/ICT (Information and

Communications Technology) solutions. The technological developments are creating new

possibilities for a better overview of real-time power consumption and increased control

of system components. This leads to opportunities for financial savings and more rational

grid planning.

The smart meters will provide data of a better resolution than the calculated values that

are mainly being used today, leading to a better foundation for e.g. load flow analysis

and planning. By using hourly pricing, and providing end-users with consumption data

and price signals, consumer patterns may also be influenced to reduce peak loads.

Due to the uncertainties of the developments in production and consumption trends, it

may be wise to use planning methodologies that are more based on scenarios and risk

assessments. Improved planning strategies and tools, in combination with more

surveillance, information and improved data quality will be beneficial for both utilities and

customers: less over-investing, optimal utilisation of the grid's capacity and increased

quality of supply [34], [35].

2.3 United Kingdom

Seven DSOs operate in 15 different license areas across the UK (locally referred to as

Distribution Network Operators). Each DSO is responsible for maintaining the network

assets; managing the network; operating the system within safe limits; and ensuring

supply of electricity to customers.

In addition to the principal DSOs, Independent Distribution Network Operators (IDNOs)

develop, operate, and maintain local electricity distribution networks. IDNO networks are

connected to a DSO network, either directly or indirectly via another IDNO network.

UK DSOs own and operate networks up to 132 kV, limited to 33 kV in Scotland.


Figure 9: UK Licenses areas and DSOs [36].

UK peak system load in 2015/2016 was 51 GW, occurring in the month of January. This

has decreased from a historical peak of 61.5 GW in 2007. The split of demand by sector

for 2015 is presented in Figure 10.

Figure 10: UK Electricity Demand 2015 by Sector [37].

UK distribution networks have experienced a significant growth in the connection of gen-

eration, in recent years defined by an acceleration in solar photovoltaic connections

across all distribution voltage levels. Figure 11 presents the installed capacity of embed-

ded generation, as recognised by the UK-wide transmission system operator, National

Grid.


Figure 11: Installed Capacity of Embedded Generation, 2015 [38].


Above 1 kV, distribution networks are design at voltage levels of 6.6/11 kV (in the UK

described as HV) and 33/66/132 kV (in the UK described as Extra High Voltage - EHV).

The 132 kV networks in Scotland are considered Transmission infrastructure. Throughout

this section, the terms HV and EHV will be applied in place of MV.

A series of generic UK distribution network configurations were developed in the Electrici-

ty Networks Association (ENA) Smart Grid Forum DS2030 project [39]; this presents a

useful reference for further information. Network information is published on an annual

basis by all DSOs in a publicly available document: the ‘Long Term Development State-

ment’ (LTDS). Each LTDS publishes details of network design and protection standards

alongside single-line diagrams of EHV networks. The LTDS also provides data of EHV as-

sets: impedance parameters, circuit length, asset seasonal capacities, transformer pa-

rameters, and substation peak/minimum demand levels.

In the majority of cases, EHV networks are typically radial in design, with meshing and

parallel topologies at higher voltage levels to enhance redundancy in supply. 33 kV net-

works operate as radial networks in open-loop configuration. In most cases, neighbouring

33 kV networks are linked by interconnecting circuits consisting of a normally-open point;

under emergency outage conditions this circuit is used to feed supply to the neighbouring

33 kV network.

The HV (6.6/11 kV) networks are mostly designed in a loop-tee-loop topology, though

under normal conditions operated in a radial topology, with normally-open points. In a

small number of cases, rural HV networks are operated in an interconnected meshed to-

pology, enhancing hosting capacity though increased complexity in system planning

tasks.

EHV/HV ‘Primary’ substations typically consist of two transformers operating in parallel,

connected to a two-section bus bar. In very rural cases where a low 11 kV demand is

met, a single transformer may be employed.


Rural networks consists of overhead lines, with three-phase wood pole construction at

33 kV and 11 kV. Urban networks mainly consist of underground cable circuits. In some

cases, legacy design process has resulted in tapering of 11 kV radial circuits, where con-

ductor size decreases further down the circuit as circuit loading typically reduces. Recent-

ly this design has exacerbated voltage rise issues as generation connects to the 11 kV

networks. Overhead line and cable types vary across DSO license areas and reflects di-

versity in legacy design policy. Engineering Recommendations provide guides for the ca-

pacity rating of transformers, cables and overhead lines (ER-P15, ER-P17, and ER-P27

respectively).


The low voltage network in the UK operates at 230 V (1ϕ) and 400 V (3ϕ). Typically, do-

mestic properties are provided with a single-phase supply, while larger domestic or

commercial properties are provided with a three-phase supply.

The majority of LV networks operate under a radial topology; high-demand urban LV

networks, such as London, run an interconnected topology to increase security of supply.

Rural LV networks consist of overhead lines, with underground cables used in urban are-

as. Although a variety of cable types exists due to legacy design principles, most DSOs

have introduced standard sizing of cables, overhead conductors and transformers; the

standard equipment types vary across each DSO.

Standard cable types include:

3 Core Waveform Combined Neutral-Earth (CNE) Cable, 95, 185, and 300

mm2 is the standard low voltage cable used to construct all new extensions to

the network.

4 Core Waveform Separate Neutral Earth (SNE) Cable, 95, 185, and 240 mm2

is used for repairs and deviations on existing LV cable.

Small-scale generation with a rated export capacity of less than 3.68 kW per-phase is

able to connect to the LV network through little engagement with the DSO. Those with a

higher rated capacity must apply for a connection directly with the DSO.

The volume of energy storage connected at domestic level has increased in the last 5

years, but it is still relatively expensive and the technology has not yet proven itself cost

effective enough for wider uptake.


Under regulation, DSOs are obliged to provide customers with a least-cost, technically

feasible offer of connection for every DER connection application received. The connec-

tion offer must be processed and issued to the customer within 65 working days once

received by the DSO. There is no charge for the connection application process, therefore

DSOs receive a large number of speculative offers for connection. In areas of high DER

connection activity, large volumes of speculative connection applications are straining

DSO planning and design teams.

In many areas, the growth in DER connection has resulted in the saturation of network

hosting capacity, triggering expensive reinforcement. This has acted as a barrier to DER

growth in many areas, resulting in customer, regulatory and political pressure to identify

novel ways of accommodating generation. DSOs are currently transitioning to a state

where such smart grid technologies solutions are considered Business-as-Usual, address-

ing challenges of up-skilling engineering staff and introducing new procedures and stand-

ards. The increasing need for visibility of network operation and parameters, across the

voltage levels, has highlighted shortcomings in monitoring and measurement infrastruc-

ture. This is exacerbated by DER growth as power flows and demand as seen at HV and

EHV substations becomes increasingly stochastic.

Another challenge for DSOs is understanding the implication of developments on the LV

network. Smart meters are being phased in by supply companies with the aim of UK-


wide coverage by 2020. Once there is enhanced visibility of demand and collection of

trend data, DSOs must understand how to best serve the demand more efficiently. It is

widely recognised that demand will continue to change with a growing uptake of EVs.


Distinct network planning processes are separately defined by each DSO, although all are

governed and informed by the RIIO regulatory framework (Revenue=Incentives+ Inno-

vation+Outputs), UK-wide Technical Specifications, Engineering Recommendations, and

EU Network Codes that apply to the UK. ENA, a trade body for UK electricity and gas

network operators, works with industry members including the DSOs to develop and re-

vise Technical Specifications and Engineering Recommendations.

Engineering Recommendations set out standards and guidance on technical requirements

for network planning and cover, for example:

Guidelines for the connection of small-scale generators (up to 16A per-phase) –

ER G83/2;

Guidelines for the connection of generation (greater than 16A per phase) –

ER G59/3-2

Methodology for assessing and meeting IEC909 short-circuit current requirements

for three-phase AC systems – ER G74;

Planning standards for security of supply – ER P2/6;

Guidelines for innovation in electrical distribution network systems – ER G85; and

Framework for planning and design, materials specification, and installation of

HV/LV distribution substations and underground-connected industrial and com-

mercial loads up to and including 11 kV – ER G81.

ER P2/6 is the security of supply standard, which has significant influence on network

planning activities. The standard sets timescales for restoring supply to load groups fol-

lowing network outages or faults, with escalating requirements as the magnitude of load

supplied increases. The need to maintain supply during asset outage conditions defines

the degree of redundancy required from network design. Regulatory incentives for reduc-

tion of Customer Interruptions (CIs) and Customer Minutes Lost (CMLs) also influences

network design to ensure high availability of supply.

DSOs are obliged under regulation to manage electrical losses, with additional commer-

cial incentive available for cases where the DSO takes pro-active steps to quantify and

minimise losses.

Integrating DER has become a major challenge for DSO’s in the UK. As addressed previ-

ously, there are many connection challenges including managing constraints and limited

capacity. Despite these challenges, there are currently methodologies in place to facili-

tate good practice in connecting DER to today’s grid. The ENA’s “Active Network Man-

agement (ANM) Good Practice guide” gives an overview of the current state of ANM and

how it is being implemented in the UK. ANM is a crucial component of modern electricity

networks that have a high penetration of Distributed Energy Resources (DER). ANM inte-

grates with DMS and other network management systems, becoming the modular and

scalable automation layer within the array of systems being used to manage the distribu-

tion grid. But crucially, it accesses key operational data faster and acts on it quicker than

these existing systems are able to do. Many DSO’s have integrated ANM systems to max-

imise utilisation of networks and allow for greater volumes of DER connection. ANM has

provided DER connections much more quickly and cheaply than reinforcing grid connec-

tions or increasing network capacity. This is deferring long term investment while facili-

tating regulator expectations of DER connections.

Additional guidelines are being drafted, or have been recently published, reflecting con-

temporary developments for the distribution network:


Energy storage connection guidelines, currently being developed in response to

the recent peak in energy storage applications for provision of network balancing

services;

Active Network Management (ANM) Good Practice Guide, published by ENA with

the ambition of establishing a common ground for implementing ANM solutions

[201].

The ENA and its members have established the Transmission Distribution Interface (TDI)

Steering Group. Network companies recognise the need for distribution and transmission

companies to work together more closely in order to consider how they could tackle the

whole system impact of DER, such as generation and energy storage devices.



The tools and models that are used by DSOs to support network expansion planning ac-

tivities vary between DSOs. As an example, Figure 12 provides a schematic view of UK

Power Networks’ network expansion methodology. The methodology consists of two key

phases, load growth forecasting and network investment planning. The following models

and techniques are implemented in the methodology [40]:

Load Growth Forecasting Model (developed by consultants Element

Energy).

Planning Load Estimates Model, using load growth forecasts to pro-

vide annual peak demand at 33 kV and 132 kV for the next 11

years.

Load Related Expenditure Model, estimating costs associated with

load growth across scenarios up to 2050.

Transform Model, a techno-economic modelling tool that supports

network planning and considers smart solutions to minimise invest-

ment. This is used to complement the other investment planning

models.

Figure 12: Schematic view of UK Power Networks’ network expansion methodology.


Network planning is supported through the modelling and simulation of network behav-

iour, achieved by the use of power system analysis software packages. Modelling and

simulation of EHV network operation (33/66/132 kV) applies analysis packages that are

capable of load flow, fault level, transient and harmonic studies. The following analysis

packages are used for EHV simulation: PSS/E; DIgSILENT PowerFactory; DINIS; and

IPSA. For HV network (6.6/11 kV) modelling and simulation, simplified modelling is also

performed using tools such as Sincal and PSS Adept. Low levels of monitoring, on voltage

levels at 11 kV and below, and in some cases limited understanding of asset parameters,

results in overly-conservative network planning.

Future trends

In recent years, UK DSOs have begun roll-out of increased automation, through actively

managed network connections for generators and novel commercial arrangements for

demand customers. The emergence of these ‘non-wires‘ alternatives is effecting a change

in the analysis methodologies applied in network planning activities. The application of

time-series analysis to understand effect of varying network conditions is a relevant ex-

ample of increasing complexity in the planning task.

DSOs are looking to operational alternatives to load-driven reinforcement, establishing

commercial contracts with demand-side resources and DER to provide demand turndown

services during periods of high loading or network outage. The DSO procurement of such

services starts to mirror the business processes of the UK-wide system operator. UK

DSOs are increasing looking towards the establishment of commercial agreements with

active network participants to resolve network issues and defer infrastructure reinforce-

ment.

The UK regulator has introduced funding mechanism for the demonstration of novel solu-

tions and commercial agreements: the Network Innovation Competition (NIC) and the

Network Innovation Allowance (NIA). The NIC is an annual competition for both electrici-

ty and gas network operators to fund research and demonstrate new technologies. The

NIA is a smaller allowance for projects that will provide benefits and value-for-money for

customers. It is expected that this funding mechanism will continue to drive innovation in

the electricity industry [41].

2.4 Germany

In the next section the country specific background for Germany, containing different

feed-in and load situations, voltage levels, technologies and a detailed characterisation of

German distribution networks will be described.

General characteristics

According to data from 2014, the German electricity network has a length of 1.807

million km, of which 1.676 million km are attributable to the LV and MV network (Figure

13). During 2015, the peak load in Germany was 83.2 GW, which occurred on the 15th of

April at 1 pm. The low load was 36 GW on 26th of December.

There are currently around 900 DSOs. Most of local regions, cities and towns have their

own utility companies that own and/or operate their own electricity networks. Most of

them are run as municipal utilities. For instance, in 2014 36 % of the DSOs operated a

network with a line length of merely up to 250 km (Figure 14).


Figure 13: Flow Network length sorted by voltage level [42].

The regulatory authority is the Federal Network Agency (Bundesnetzagentur, BNetzA)

which is responsible for ensuring a non-discriminatory access the German electricity

network and for regulating the network operators’ revenues.

Figure 14: Flow Number of DSOs according to their share in the total network length [43].



In Germany, all networks with a voltage between 1 kV and 60 kV are commonly referred

to as medium voltage (MV). Typically used voltage levels are 10 and 20 kV. However,

there are also networks and equipment with nominal voltage levels of 1 kV, 6kV, 15 kV

and 30 kV [44] [47]. In rural distribution networks used for public supply with a low load

density and high distances, 20 kV networks are usually used. In urban distribution

networks with a great load density, short distances between loads and a high share of

cables 10 kV networks are common. Most industrial power consumers are connected to

the German MV networks [48] . Households and smaller companies or office buildings are

supplied through low voltage (LV) networks, which will be described in the following

0

200

400

600

800

1.000

1.200

1.400

1.600

1.800

2.000

LV MV HV Total

Net

wo

rk le

ngt

h in

th

ou

san

d k

m

2010 2014

0%

5%

10%

15%

20%

25%

30%

35%

40%

0 to 250 251 to 500 501 to 1,000 1,001 to 4,000 4,001 to 8,000 Above 8,000

Shar

e in

to

tal n

etw

ork

len

gth

Network length [km]


section. Industrial consumers with a high demand of electrical energy and power, such as

aluminium mills, are connected to the High Voltage (HV) networks to fulfil their needs.

Table 12: Overview of typical voltage levels in Germany [47].

Designation Nominal voltage

High voltage 380 kV

110 kV

Medium voltage 20 kV

10 kV

Low voltage 230 V/ 400 V

The MV networks in Germany usually consist of three-phase AC lines. Only the high

voltage network used by the main German railway network operator Deutsche Bahn AG

and parts of LV networks are operated as single-phase AC. Direct Current (DC) is only

used for long distance routes as extra-high voltage grid (High Voltage Direct Current -

HVDC). Those lines are exclusively overhead-lines with a length of at least 500 km. For

example, the German wind farms in the north see will be connected by HVDC. Another

example is the HVDC called “NordLink” which will connect Germany with Norway and will

be finished in 2019.

Another area of application is the connection of two AC lines with different frequencies.

Therefore, only three-phase AC lines exist in MV networks used in our project.

The MV lines are planned and now operated in different topologies. The most common

topology in MV networks in Germany is the open loop topology. The MV networks are

built as radial operated ring networks with open cut-off points to allow a changing of the

topologies in case of network failures. Secondary MV/LV network stations are connected

to the ring networks. Different forms of meshed networks are also used but are rather

uncommon and will be replaced in the upcoming years. Furthermore, the types of lines

distinguish between the different voltage levels. In German MV networks, the share of

cable is 78% [45]. Most of all MV networks, especially in urban areas, consist of

underground cables. Due to the great number of DSOs, there are no details publicly

available about the cable types used.

Because most of the large loads are connected to the MV networks, the changing load

situation has a huge impact on the distribution network (DN). During the year, the load

varies between summer, winter and the transition seasons.

Load Profiles

Figure 15 shows the standard load profiles (SLP) for the German federal state North-

Rhine Westphalia. They can be used as an example of German SLP and show different

load curves separated in seasons and daytimes. The registered power measurement

(RPM) profiles, displayed in the right figure, are provided by a German DSO and used as

example for Germany’s industrial consumers with high demand for electrical energy.

During winter, the load is usually higher than during summer with the load during spring

and autumn situated in between the load situation in summer and winter. Beside SLP the

high load demanding industries has to participate at the RPM.


Figure 15: German Standard Load Profiles from households, small and big industrial consumers [46]

The RPM profile for consumers with a load of 100 MWh or more shows a huge reduction

during the German summer holidays. During the rest of the year, the load profile is

similar to those of small industries. The daily load from industry consumers rises around

8 am and drops at 5 pm, which can be ascribed to the typical working hours during the

week. The big companies with a demand higher than 100 MWh have a different load

profile [55] [56]. During all seasons and all daytimes, the load tends to stagnate on the

same level. The reason can be huge machines like furnaces, which runs at all day and

night and do not change their load demand during the day.

Due to large-scale incentives through legislation, the installed capacity of RES has

steadily increased since 2009. Figure 16 shows the installed capacity of RES in Germany.

Figure 16: Installed capacity of RES including prognosis [52].

Biomassplants

Hydro Gas GeothermalWind

offshoreWind

onshorePV

After 2015 forecasts by DSOs

Pow

er

capacity

(MW

)

0

20,000

40,000

60,000

80,000

100,000

51,068

120,000

140,000

41,447

60,077

70,561

77,645

83,922

92,11197,060

107,231112,246

116,757

102,326

Industry (<100MWh) Load Profile (Year/Day)

HouseholdsLoad Profile (Year/Day)

Industrial ConsumersLoad Profile (Year/Day)

Daytime

Active P

ow

er

[W]

0

50

100

150

200

250

300

0:00 4:00 8:00 12:00 16:00 20:00 0:00

Winter Summer Spring/AutumnActive P

ow

er

[W]

Daytime

0

500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

0:00 4:00 8:00 12:00 16:00 20:00 0:00

Winter Summer Spring/Autumn

Active P

ow

er

[W]

Daytime

0

50

100

150

200

250

300

Active Power [W]

0

50

100

150

200

250

300

Active Power [W]

0

500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

4.500

Active Power [W]

Active P

ow

er

[W]

Active P

ow

er

[W]

Active P

ow

er

[W]

0

50

100

150

200

250

0:00 4:00 8:00 12:00 16:00 20:00 0:00

Winter Summer Spring/Autumn

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4


The feed-in profile especially from RES fluctuates. Figure 17 shows a typical feed-in

profile of photovoltaics and wind turbines in Germany during the year.

The PV power is generally low during the winter and high in the summer months, while

wind turbines generate more power during the winter. Nevertheless, Wind Turbines (WT)

show also feed-in peaks during the summer and therefore can lead to voltage increases

or thermal equipment overloads. Hydropower show very little variation and fluctuation

during the year. Therefore, seasonal fluctuation exists, the influence regarding the need

for network reinforcement measures is minimal. Biomass plants (BMP) shows a constant

feed-in profile and are therefore not displayed.

Figure 17: Feed-In profiles from PV and WT [49].

Figure 18 displays the full-load hours of DER in Germany. Especially Biomass power

plants (BPP) have a high amount of full-load hours. They, similar to fossil power plants

and nuclear power plants, can provide a high infeed, independently from weather

conditions. Wind turbines and photovoltaics have fewer full-load hours due to their

fluctuating and weather dependent primary energy sources.

Figure 18: Annual full-load ours of RES in Germany [50].

6.000

3.420 3.380

1.980

990

0

1.000

2.000

3.000

4.000

5.000

6.000

7.000

Biomass power

plants

Wind offshore Hydropower Wind onshore Photovolatics

Annual fu

ll-l

oad h

ours

Annual full-load hours

0

2

4

6

8

10

12

14

16

0:00 4:00 8:00 12:00 16:00 20:00 0:00

Active p

ow

er

[W]

Daytime

Winter Spring/Autumn Summer

0

200

400

600

800

1000

0:00 4:00 8:00 12:0016:0020:00 0:00

Active p

ow

er

[W]

Daytime

Winter Spring/Autumn Summer

WT Feed-In ProfilePV Feed-In Profile

0

5

10

15

20

Active p

ow

er

[W]

PV Feed-In [W]

0

200

400

600

800

1.000

1.200

1.400

1.600

Active p

ow

er

[W]

WT Feed-In [W]

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4


Quality of service

To guarantee a high quality of power supply, the MV networks are monitored by the

regulatory authority. The DSOs have to disclose network parameters, which determine

certain indicators for the quality of service. These indicators are taken into account in the

regulation and hence influence the DSOs allowed revenues. In MV networks, the used

quality standards are ASIDI and SAIDI.

ENS is used as an indicator to define the connection quality and supply guarantee [57].

Table 13 lists the historic SAIDI values for the LV and MV networks.

Table 13: SAIDI values for low and medium voltage in Germany [58].

General Data Low Voltage Medium Voltage

Year No. of DSOs/

networks

No. of consumers

(in million)

Interrup-tions

(in

thousands)

SAIDI (min.)

Interrup-tions

(in

thousands)

SAIDI (min.)

SAIDI total

(min.)

2014 874 / 884 49.6 147.8 2.19 26.0 10.09 12.28

2013 868 / 878 49.5 151.4 2.47 27.8 12.85 15.32

2012 866 / 883 49.3 159.0 2.57 32.0 13.35 15.91

2011 864 / 928 48.9 172.0 2.63 34.7 12.68 15.31

2010 890 / 963 49.0 169.2 2.80 37.1 12.10 14.90

2009 821 / 842 48.4 163.9 2.63 35.1 12.00 14.63

2008 814 / 835 48.4 171.5 2.57 36.6 14.32 16.89

2007 825 48.5 196.3 2.75 39.5 16.50 19.25

2006 781 48.5 193.6 2.86 34.4 18.67 21.53

To ensure safe network service and quick and easy maintenance, some equipment is

used as standard. The network equipment used in MV networks are cables, overhead

lines, substations, secondary substations and different kind of switches to provide

security and failure protection. The technical specifications vary between different

regions. Most of the networks are historically grown and consist of different technologies

with huge varying ages. During the last years more and more of old overhead lines have

been replaced by underground cables and therefore increased the share of cables

especially in urban areas [59]. The old network structures now have to be reinforced to

be able to replace MV overhead lines and reduce losses.

Distributed generation consist of WT, PV, BMP and Hydropower plants (HPP). WT and PV

show a very fluctuating feed-in profile. As seen in Figure 17 the feed-in situation depends

on the time during the day and even the different seasons shows differences. Those are

the main problems caused by decentralised generations and the origin of need for

network reinforcement in MV networks.




The low voltage networks (LV) in Germany supplies most of the households and other

business consumers. In Germany LV is defined as voltages under 1kV (AC) and 1,5kV

(DC). German LV networks are operated as three phased 230V or 400V AC networks,

which normally depends whether the network has urban or rural characteristics. The

classic radial networks in urban areas are sometimes growing to open-loop ring networks

or less frequently meshed networks The mostly used cables are 120-240mm2 NAYY for

the main lines.

The typical used secondary distribution MV/LV transformers are operated as ground

stations. Only in sparsely populated areas with huge distances between the loads, pole

mounted transformers are used to be able to use MV overhead lines most of the time and

reduce losses.

Especially in urban areas, most of the LV networks are constructed with underground

cables. The share of cables is 89% and overhead lines are rarely used, even in rural

areas. Beside cables, German LV networks consist of secondary substations (typical

250kVA, 400kVA, 630kVA or 800kVA) and isolating switches are used in open-loop

topologies. Innovative equipment like the regulated voltage transformers or line voltage

regulators (LVR) are rarely used, most of the time only at demo sites.

Beside households and small business consumers, small RES are connected to the LV

networks. These installations are normally private operated PV systems mounted on

roofs or at small open areas, producing fluctuating electrical power causing thermal

problems and exceed voltage increases [51].


During the German energy transition (“Energiewende”) the necessity for network

reinforcements is very high. Due to support incentives, such as fixed feed-in payments

and investment promotion, the share of fluctuating RES has grown, while static

generators, especially nuclear power plants, reduced their power generation. Starting

with the transmission network, changing structures and laying cables is mandatory.

Building new power lines, transformers and reorganising the transmission network is a

huge challenge for German TSOs. They need to identify solutions, which provide

necessary functionality, while being cost efficient. Furthermore, distribution networks are

challenging because most of the fluctuating RES feed into the LV to HV networks.

In this context, the voltage stability in MV and LV networks is a big challenge for the

DSOs because secondary substations are usually not voltage controlled. However, some

DSOs have competencies with on-load voltage regulation.

Additionally, the distribution networks contain most of German power cable and line

routes and need to be reinforced Therefore, although only a fraction of all distribution

networks has to be enforced, the problem and challenge is still significant. PV and WPP in

small distribution networks require to be focused on, to maintain a secure power supply.

Additionally new flexible technologies and network components need to be integrated:

Integration of DER

The integration of DER is the main challenge of German DSOs. Old historically grown

networks with old cables or overhead lines need to be reviewed and new planning

methods needs to be developed, to allow the future installation of DER in LV and MV

networks.

Integration of new loads like Electric vehicles and Heat Pumps

The integration of EVs will be a huge challenge especially in urban networks. A high

amount of new loads, which will be connected to the distribution network at the same


time, needs to be managed. Using decentralised network automation (DNA) or another

intelligent method to distribute the load and reduce peak loads will be the challenge for

DSOs running urban networks. Additionally heat pumps will be integrated into the

networks on a larger scale, if more and more households start to address energy efficacy

and the growing heating costs.

Integration of Storages

The third main challenge will be the integration of storages, although at the moment the

costs for storages are too high to allow economically feasible usage. Beside EVs, which

may in future be used as mobile storages if connected to an intelligent DNA system,

other storages probably will help to balance load and production on different time scales

(i.e. short term to long term balancing). Cross-energy carrier systems like power-to-gas

systems might gain importance as well.

Therefore, the challenges for DSOs in Germany are growing while new technologies are

being implemented and the historical and traditional function of DSOs will change in the

upcoming years.


The operation of transmission and distribution networks is highly regulated in Germany.

Different standards define which voltage fluctuation or frequency deviations are

tolerated.

The European standard EN 50160 is adopted to the German regulation system. According

to the standard, all operators of public electrical power networks have to maintain their

networks to follow the specifications defined in the European standard. The DIN EN

50160 standard describes and sets characteristics of the voltage quality: frequency,

voltage fluctuation and symmetry of phase voltage. Most important for future network

reinforcement measurements is an analysis of voltage increases caused by decentralised

RES.

The Association of Electrical, Electronic and Information Technologies (VDE) as well as

the German Association and Water (BDEW) compile and constantly update technical rules

and agreements of network operators and manufacturers which DSOs are usually bound

by when planning and operating their networks as they pose so called best available

techniques.

The Distribution Code generally regulates the access to distribution networks. It is

complemented by regulations such as VDE-AR-N 4105, which contains rules for

connecting DER to the low voltage network. For the medium voltage network, the BDEW

published the connection requirements (“Technische Anschlussbedingungen”, TAB) that

need to be met when someone wants to have a load or DER connected to the MV

network.

Concerning the influence of RES, the German industry standard guideline (by BDEW) e.g.

defines the allowed voltage increase in MV grids. The limit is set to +2% voltage increase

in MV grids [60]. For LV grids, the VDE 4105 directive sets the allowed voltage increase

to 3% [61]. Those are only examples for parameters, but they are the most important

for analysing distribution networks.


Considering the high number of network operators in Germany, various planning

approaches and methodologies that are not publically available. They cover, for instance,

standard equipment used, the configuration of MV/LV transformer stations, the

management of voltage drops and peaks and dealing with (n-1)-safety [53].


However, two different planning methodologies are commonly followed in Germany

characterised by a different time horizon. The first one is the operational planning

method. It mainly focuses on situational and short-term construction measures. The

DSOs aim to reinforce their networks as cost effectively and fast as possible. The actual

integration of RES and connection of new loads into the distribution network is typically

planned within the operational planning. In many cases the current networks are

optimised for the high load scenarios and now are changed to include more RES [62].

The second planning level is denoted as “strategic” and aims at providing long-term

supply security while using historically grown network topologies. This method is carried

out by coordinating operational measures to fit into the long-term perspective.

The combination of used network planning methodologies is a method called “dual

network planning”. A scheme of the planning process is displayed in Figure 19. This is

now necessary caused by the large amount of RES feed-in. After the collection of

necessary information, the second step is the network planning without the integration of

RES. Necessary measures have to be identified and considered in the following network

planning. The third step is the integration of RES into existing distribution network.

Figure 19: Scheme of the dual planning method [53] [54].

Additionally, the feed-in situations have to be simulated and added to existing high load

situations. Afterwards the results have to be merged to avoid critical situations with

impermissible voltage increases or equipment overload.

The conclusion of RES integration and old distribution network planning leads to the

necessary network optimisation. The results have to be verified by several load flow

calculations. Afterwards the results are documented to be used in future network

extensions or enforcements.


Lots of tools and models are used by DSOs to support and improve their network expan-

sion planning. However, they are not consistently used and may vary between different

DSOs. Some typically used tools and software applications are:

• Accounting Software

German DSOs needs to use various systems to allow proper accountancy and monitoring

of network charges.

Main Principle

Fundamental Principle

Project-Planning

Executed-Planning

Strategic-Planning Operation-Planning

AC/DC Voltage QoS Network

structure

Aimed at network structure

Standard network equipment

Network planning

Dealing with problems

Connect new consumers

Restructuring of networks

Construction works

Planned shut-downs

Commissioning of network equipment

Up to 50 years 10 – 50 years 1 – 10 years Days to 1 year


• Supporting tools for the Asset Management

Many of currently used accounting software can be used as tools for the company’s asset

management too. Tools like SAP are often used to combine both tasks in one software

and allow easy management of financial and asset belongings.

• Geographical Information System (GIS)

GIS are used to document and manage a geographical reflection of the network assets.

This system may be linked to the accounting software and network calculation tools.

• Network Calculation Tools

One of the most important tools are the network calculation tools. Using Software like

SINCAL, NEPLAN, DigSilent or INTEGRAL, allows DSOs to perform power flow calcula-

tions, short circuit analysis, reliability calculations etc. to identify problems and the cur-

rent condition of their network.

• Excel Tools

Beside network calculation tools and management software like SAP, many DSOs are still

using Excel for different tasks like simplified network calculations (mostly LV), document-

ing or economic evaluations.

Due to the high number of DSOs in Germany, no consistent use of specific tools or soft-

ware can be derived. Varying combinations of the described tools and other individual

solutions may be in use and can fit the needs of a DSO.

Future Trends

Currently more and more DSOs start to develop new planning and simulation tools and

guidelines to improve their planning process. Multi-objective analysis to evaluate cable or

overhead line routes will be used more often are currently developed by different DSOs

and research organisations [208].

Some additional future trends are listed below:

• Time-variant network equipment

In the near future, more and more DSOs will start to analyse their networks in more de-

tails. They want to gain more information regarding the network status, needed reserves,

as well as suppliers of flexible network loads and optimise their network capacity accord-

ingly.

• Time-Series Analysis

DSOs will start to analyse the networks as described above. Additionally they will start to

consider time-series analysis beside conventional, worst case scenarios. In the past the

lack of reliable time-series data limited the range of application. Smart meter data may

help to improve the data quality.

• Automated Network Planning

Automated Network planning tools will be developed to reduce the effort required to plan

and optimise distribution and transmission networks. Especially the optimised routes for

cables or overhead lines could be useful and reduce expansion.


3 State of the art of SG technologies and solutions

3.1 Voltage Control

Since the electric power networks were not designed for the occasionally immense feed-

in of DER, particularly MV and LV networks face systematic challenges. One commonly is

surpassing the upper voltage value that is fixed in the standard EN 50160. According to

that, under normal operating conditions the supply voltage shall be within the range of

±10 % of its nominal value.

As long as the medium and low voltage networks are coupled with each other using

transformers with a fixed transmission ratio, which is usually the case in current network

topologies, it is necessary to allocate the voltage variation bandwidth to the different

voltage levels. It is common operational practice to set the voltage at the secondary bus

at the HV/MV transformer to a value above 100 % of the nominal value. This is done to

keep the voltage value above the minimum of 90 % over the entire line, taking into

account the line’s impedance and consequent voltage drop along the line. A feasible

configuration to allocate the voltage bandwidth then is to allow +2% voltage increase in

the MV network and +3 % in the LV network and a decrease of -5 % on each voltage

level. The mentioned characteristics are shown in Figure 20.

Figure 20: DER feed-in surpassing voltage threshold of exemplary allocation of the voltage variation [64].

3.1.1 On-load Voltage Regulated Distribution Transformers

In conventional distribution networks, primary substation transformers are configured

with a transformation ratio that is optimal to hold the voltage within the tolerable

bandwidth according to the original network dimensioning. The transformers are

commonly equipped with de-energised tap changers (DETC) that enable different

transformation ratios by manual off-load tap changing. Nowadays, on-load voltage

regulated distribution transformers are available on the market, which allow the

transmission ratio to be changed on-load using an on-load tap changer (OLTC).

U/Un

HV/MV

transformer

substation

transformer

LV networkMV network

110%

90%

EN

50160(102,5 1,5) %

-1,0%

>2,0%

-5,0%

-5,0%

3,0%


Regardless of the manufacturer’s specific design an on-load voltage regulated

transformer consists of the active transformation unit, the actuator and a control unit

with feedback control. Compared to a conventional secondary substation transformer, an

on-load actuator is added to the DETC or it is completely replaced by an OLTC. Due to

changes to the design of the transformer casing including insulation and cooling it is not

feasible to upgrade a conventional transformer [63]. However, depending on the

distinctive design, different types of electromagnetic or electrical units are used for the

actual tap changing [64].

The control range determines the actuating range of the on-load transformer and is

usually expressed as the nominal transmission ratio and the number of steps (discrete

tap changes), e.g. 20.0/0.4 kV ±4 x 2.5 % [63]. Depending on the number of steps of

the tap changer and the consequent voltage, typical configurations feature a combined

control range between ±4 % and ±10 % of the dimensioning voltage at the low-voltage

bus. A control unit uses a three-point-regulation based on measured values for the

voltage [64].

There are three operational strategies that can be distinguished: focus on the low voltage

network, focus on the medium voltage grid or a combination of the two.

Focus on the low voltage network:

When an on-load voltage regulated distribution transformer is operated with focus on the

low voltage network, the voltage variation that was defined for the medium voltage

network is retained. If the innovative transformer is capable of regulating the voltage

within a range ±4 %, a possible voltage rise of usually 7 %, caused by the feed-in of

DER for instance, can be tolerated. Figure 21 shows the possible tolerance of the voltage

variation in the medium and low voltage network.

Figure 21: Operation with focus on the low voltage network [64].

Focus on the medium voltage network:

The proportion of the tolerated voltage variation that was allocated to the low voltage

network stays in place. The control range of the regulated transformer is used for the

medium voltage network. However, this requires the medium voltage network to be

U/Un

HV/MV

transformer

on-load reg.

distribution

transformer


110%

90%

EN

50160(102,5 1,5) %

+1,0%

-1,0%

+2,0%

-5,0%

+7,0%

-9,0%


entirely equipped with regulated transformers (at least towards the end of the lines since

the voltage increase close to the substation might still be within a tolerable range). This

operational strategy allows the tolerated voltage rise in the medium voltage network to

be increased. Figure 22 shows a possible division of the tolerated voltage variation

bandwidth on the medium and low voltage network.

Figure 22: Operation with focus on the medium voltage network [38].

Combined operational strategy:

The tolerated voltage variation bandwidth is allocated both on the medium and on the

low voltage network. In both cases higher voltage drops and peaks than in conventional

network planning can be tolerated. However, according to the targeted voltage variation

one regulated transformer per network level is necessary. The transformer needs to be

equipped with numerous numbers of tap change positions in order to provide a wide

control range [56], [64].

U/Un

HV/MV

transformer

on-load reg.

distribution

transformer


110%

90%

EN

50

16

0(102,5 1,5) %

+1,0%

-1,0%

+6,0%

-9,0%

+3,0%

-5,0%


Figure 23: Operation with a combined operational focus (assuming the regulated transformers are equipped with nine steps with 2.5 % voltage variation each) [64].

Instead of replacing a regular transformer with an on-load voltage regulation

transformer, a line voltage regulator can be installed behind the transformer. Although

this follows a different technical approach, the basic functionality is consistent.

3.1.2 Line Voltage Regulators

A line voltage regulator is a device within one voltage level that is capable of regulating

the voltage of one single line of the network behind it in order to decouple the voltage in

the section before and behind the line voltage regulator. It uses the principle of

superposition: The non-regulated voltage UL over the line is superimposed by a regulated

voltage UB. The feeder transformer, which is feed by the line itself, is used as a variable

voltage source. The variable voltage value is adjusted using an OLTC. To generate a

regulating voltage that decreases the voltage over the line, the direction of the current

flow in the regulation circuit is inverted [57], [65]. Figure 24 shoes the circuit in

principle.

Figure 24: Principle of a line voltage transformer, cd [65].

In low and medium voltage networks, line voltage regulators can be used for individually

regulating the voltage of separate lines. Mentioned regulators act like regulated medium

or low voltage transformers with a transformation ratio close to one. Placing the

regulator directly in the line is a trade-off between the possible voltage

U/Un

HV/MV

transformer

on-load reg.

distribution

transformer


110%

90%

EN

50160(102,5 1,5) %

+1,0%

-1,0%

+6,0%

-11,0%

+8,0%

-8,0%


change/adjustment alongside the line and the designated/planned power transmission.

The use of a line voltage regulator in the medium voltage network allows higher voltage

peaks in the low voltage sub-network and hence influences the dimensioning of the low

voltage sub-network [64]. Figure: 25 depicts the positive influence on the allocation of

the voltage bandwidth allocated to the medium or low voltage network.

Line voltage regulators use different methods for regulating the voltage. They can be

distinguished between determining the input parameter (static or characteristic value) or

the output parameter (number and position if the nodes with voltage measurement)

[64].

Figure: 25 Possible allocation of the tolerated voltage variation (assuming a control range of 6 %) [64].

To implement a line voltage regulator in a network planning tool, it can be represented

by a transformer with an OLTC. The already set up regulation of the transformer can

usually be used to imitate the variable transmission ratio of the line voltage regulator.

The necessary parameters are the control range and the tolerance range of the actuator.

3.1.3 Reactive Power Control

Reactive power control describes the controlled exchange of reactive power between DER

(e.g. the inverters) and the network they feed into. This is generally expressed by the

power factor cos(φ) which is defined as the ratio of active and the absolute value of the

apparent power. For sinusoidal voltages and currents it is equal to the absolute value of

the cosine of the phase angle of the voltage relative to the current [64].

The static reactive power control uses predefined nominal or characteristic values either

for the power factor cos(φ) or for the reactive power Q. It is also possible to use dynamic

methods. In low and medium voltage networks, this however requires the application of

a decentralised network automation system [64].

There are different, partly competing applications for the use of reactive power control.

Considering the network operators current challenges, the objective usually is to reduce

the absolute node voltage at the DER by varying the phase of current and voltage. In

U/Un

HV/MV

transformer

LV network

LVR

110%

90%

EN

50160(102,5 1,5) %

+1,0%

-1,0%

+2,0%

-5,0%

+3,0%

-5,0%

+6,0%

+1,0%

MV network substation

transformer


most cases, the voltage rise in the network caused by the feed-in of active power by the

DER can so be curtailed. Figure 26 shows the principle of the reactive power control in a

phasor diagram. It should be noted that with a constant feed-in of active power the

apparent power and so the current on the line rises when using reactive power control.

This means that especially the thermal load capacity of the cables need to be considered

[63], [64].

Figure 26: Equivalent circuit diagram and phasor diagram for the basic principles of reactive power control [64].

It should be noted that that any combination of DER and transformer type voltage control

methodology may require wider area control and communication infrastructure and so-

phisticated control methods.

3.2 Metering and Communications

3.2.1 Smart meters

In short, a smart meter is a digital electric meter for measuring, logging and transmitting

data at the metering point every specified time period. The smart meter enables two-way

communication between the utility company and the electricity customer through an AMI

𝑈𝐾1 = 𝑈𝐷𝐸𝐴 − 𝑈𝑅𝐿 − 𝑈𝑋𝐿

cos 𝜑 = 1

cos 𝜑 < 1, untererregt

(Verhalten wie eine Induktivität)

cos 𝜑 < 1, übererregt

(Verhalten wie eine Kapazität)

Spannungsanhebung über der

Leitung durch

Wirkleistungseinspeisung

Spannungssenkende Wirkung

der induktiven

Blindleistungsaufnahme

Spannungshebende Wirkung der

kapazitiven

Blindleistungsaufnahme

DEAU

LX

1KU

DEAI LR 1K

Voltage increase over the

line due to active power

feed-in

Voltage decrease due to

reactive power feed-in

(inductive)

Voltage decrease due to

reactive power feed-in

(capactive)

(under-excited) (over-excited)

(analogue to an inductance) (analogue to a capacitor)


– advanced metering infrastructure, which is one of the key differences between

conventional electricity meters with one-way communication for transmitting meter data

(AMR – automated meter reading systems).

Figure 27: Smart meter technology evolution (Adapted from [66]).

The principle of how a smart meter operates is generally the same for all types of smart

meters, though there are different technologies used to perform these operations. The

smart meter installed at a metering point can collect and log information about customer

consumption and/or generation data (active and reactive power), measure power/voltage

quality, detect faults or outages, etc. Smart meters use the local-area network (LAN) or

neighbourhood area network (NAN) to transmit this information to a data collector/

concentrator at a specified time interval (typically every 15 to 60 minutes). The collector

communicates with the utility's central collection points – the head-end system (HES) –

through the wide-area network (WAN) for further processing. The path of communication

between these three devices is two-way, thus commands or other signals may be sent to

the meters from a central point. Smart appliances with communication possibilities may

be connected through the home area network (HAN) to an energy management system

(EMS) [66], [67]. See Figure 28 for an illustration of an AMI. More details regarding the

AMI information communication technologies are presented in section 3.2.3.

Smart meters and the AMI offer several benefits for both the customer and the system

operator. For example, the customers achieves more insight and control of their own

power consumption and thus their electricity bill. By integrating real-time price signals,

the customers may be encouraged to reduce the power demand during peak load hours.

Smart meters that offer remote management may also allow the system operator to

adjust loads for optimising power flow. The smart meters with their communication

system will be essential for a more efficient operation of the power system [67].


Figure 28: Advanced metering infrastructure – AMI. (From [68])

3.2.2 Other sensors

PMU – Phasor Measurement Unit

PMUs are mostly installed in the transmission grid, and play an important role in a smart

grid. PMUs are electronic devices that measure voltage and current as synchrophasors,

which are time-synchronised measurements of a quantity that is described by a phasor

(magnitude and phase angle). The PMU uses these measurements to calculate

parameters such as active power, reactive power, frequency and phase angle. These

sensors typically report at a rate of 30 to 60 times per second, and this rate may be

higher. Since the measurements are time-synchronised, measurements done by other

PMUs located elsewhere in the grid may be aligned in time, enabling the possibility to

calculate relative phase angles between different points in the grid, and these

calculations may be time-determined as directly measured values [69].

The data obtained from a system of PMUs gives an overview of the whole system's

conditions. Advanced analytical applications may analyse and utilize these data to

improve grid reliability, efficiency and operating costs. The data may be used for real-

time applications within monitoring, state analysis and control, as well as for system

planning, state estimation, validating models and post-event analysis [69].

RTU – Remote Terminal Unit

A remote terminal unit (RTU) – not to be confused with remote telemetry unit – is in

general a microprocessor-controlled device used for remote monitoring and control, and

can be used for applications in several fields. RTUs communicate with a master system

(distributed control system, distribution automation system, SCADA system; read more

in section 3.4.1), where the RTU can send metered data or notifications to the main

control system and receive commands for operating other devices connected to the RTU.

These connections can be both wired and wireless.

Within power system applications, RTUs can contribute to increased automation,

efficiency and reliability, as well as to reduced loss of power and risk of damaging

equipment. RTUs provide the system operator with real-time information about the

system's local state and notifications about situations or anomalies that require attention.


An RTU may be configured such that it will report if a given condition is present or if

parameter exceeds a specified value (e.g. temperature or oil pressure in a transformer),

thus facilitating administration and maintenance. Remote control mechanisms may also

be integrated in RTUs, which can be used for operating equipment through the RTU [70],

[71].

Intelligent Electronic Device

IEDs are microprocessor-based devices for communication and control, and can serve

several different purposes in the power system. Typical functionalities for an IED are

protection, control functions and control logics, (self) monitoring and event recording,

metering, and serial communication. The serial communication capability for

interoperation with other devices is one of the key components of an IED. Based on

interpretation of received data from sensors and equipment, the IED can send control

commands to other devices in the system. Examples of common types of IEDs are

protective relay devices, circuit breaker controllers, capacitor bank switches, on-load tap

changer controllers, voltage regulators, etc.

IEDs feature two-way communication amongst devices and with the main control, system

and most IEDs are equipped with an interface for human interaction. Newer IEDs are also

designed according to standards for substation automation [72], [73].

3.2.3 Information Communication Technology (ICT)

Several wireline and wireless communication technologies are suitable for use within

smart grid application. The most common ICT solutions are presented in this section

[74], [75].

Wireline technologies

a) Power Line Communication (PLC) utilises the already built power lines to

communicate, and is the most widely used wireline ICT solution.

Network types: NB (narrowband)-PLC: NAN, FAN, WAN, large scale AMI. BB

(broadband) PLC: HAN, Building Area Network (BAN), IAN, small scale AMI

Advantages:

Already constructed wide communication infrastructure

Physical disconnection opportunity

according to other networks

Lower operation and maintenance

costs

Disadvantages:

Higher signal losses and channel interference

Disruptive effects caused by appliances

and other electromagnetic interferences

Hard to transmit higher bit rates

Complex routing

b) Fiber optics works by transmitting information through pulses of light sent through

thin, transparent fibres made of glass/silica or plastic.

Network type: WAN

Advantages:

Long-distance communications

Ultra-high bandwidth

Robustness against electromagnetic

and radio interference

Disadvantages:

Higher installing costs

High cost of terminal equipment

Not suitable for upgrading and

metering applications


c) Digital Subscriber Line (DSL) uses the telephone lines to transmit data.

Network type: NAN, FAN, AMI

Advantages:

Already constructed wide

communication infrastructure

Most widely distributed broadband

Disadvantages:

Communication operators can charge

high prices to use their networks

Not suitable for network backhaul

Wireless technologies

a) Wireless Personal Area Network (WPAN) / ZigBee is a low-range wireless

network, which has a typical range of some tens of meters. ZigBee is simpler and less

expensive than other solutions like Wi-Fi and Bluetooth.

Network types: HAN, BAN, IAN, NAN, FAN, AMI

Advantages:

Very low power consumption, low

cost deployment

Fully compatible with IPv6-based networks

Disadvantages:

Low bandwidth

Limitations to build large networks

b) Wi-Fi connects devices through a wireless local area network (WLAN), using radio waves for Internet or network connections.


Advantages:

Low-cost network deployments

Cheaper equipment

High flexibility, suitable for different use cases

Disadvantages:

High interference spectrum

Too high power consumption for

many smart grid devices

Simple QoS support

c) Worldwide Interoperability for Microwave Access (WiMAX) follows the IEEE

802.16 standard, provides high-speed connections via broadband over long distances,

and can be used in a wide set of applications.

Network types: NAN, FAN, WAN, AMI

Advantages:

Supports huge groups of simultaneous users, longer distances than Wi-Fi

A connection-oriented control of the

channel bandwidth

More sophisticated QoS than the IEEE standard for Wi-Fi

Disadvantages:

Complex network management


Licensed spectrum requirement

d) Cellular networks are well established in most countries, and include GSM, General

Packet Radio Service (GPRS), 2G, 3G, 4G (and WiMAX).



Advantages:

Existing infrastructure, supports millions of devices

Low power consumption of terminal

equipment

High flexibility, suitable for different use cases

Open industry standards

Disadvantages:

High prices to use service provider networks

Increased costs since the licensed

spectrum

Shared with many other users

e) (Communication) Satellites orbit around the Earth and use a wide range of radio

and microwave frequencies, allowing transmission of information across long distances.

Network types: WAN, AMI

Advantages:

Long distance

Highly reliable

Disadvantages:


High latency

3.3 Distributed Energy Resources Management

3.3.1 Microgeneration, Microgrids, Nanogrids

The microgrid concept was originally endorsed within the scope of Consortium for Electric

Reliability Technology Solutions (CERTS) [76] from the United States. It was also

developed in a European microgrid concept within the framework of the European project

MICROGRIDS-Large Scale Integration of Microgeneration to Low Voltage Grids [77]. The

first approach consider that the majority of the bulk of microsources must be power

electric based to provide the necessary flexibility to guarantee operation as a single

aggregated system. This flexibility of control makes the microgrid the single controlled

element that meets local needs for reliability and security. The concept from CERTS does

not consider a traditional principle, which shut down the DG automatically when

complications arise in the grid.

Lopes et al. define microgrid as an LV distribution system to which small modular

systems are to be connected [78]. Thus, a microgrid is an association of electrical loads

and small generation systems through an LV distribution network. This means that a

microgrid can correspond, for instance, to the network of a small urban area, to an

industry or to a large shopping centre, since the loads and sources are physically close.

Moreover, a microgrid may also contain storage equipment, network control and

management systems, and heat recovery systems [combined heat and power

applications (CHP)] besides the microgeneration devices and controllable electrical loads.

Through reorganization of the electricity system, based on microgrids architectures, it will

be possible to obtain a large-scale DER integration in distribution networks [79]. A

microgrid design also offers a logical approach for planning and independent control of

DER and particularly RES. Additionally, microgrids uses DER controls, DR and control of

power exchange to energy management and power balancing. Table 14 presents possible

microgrid architectures taking into account the applications, owners and nature of loads

served by the microgrid [79].


Table 14: Microgrid architecture [79].

Microgrid structures are usually sufficiently large to supply facilities such as hospitals and

other public establishments. If this structure is scaled down to meet the necessities of a

residential home or small building, we can call it a nanogrid in spite of the literature

having different definitions and interpretations. Authors of [80] presented a nanogrid

structure (see Figure 29), in this case formed by a hybrid system with wind and solar

generation.

Figure 29: Nanogrid block diagram [80].

The research in the field of nanogrids reveals a major preference for DC due to higher

efficiency. It is expected that Nanogrid Controller (Figure 29) may implement some kind

of Demand Side Management (DSM) or other intelligent supply form. Such

implementation may allow maximising the overall efficiency of the system.

Some research in this area has studied the microgeneration within power distribution

planning context. For instance, in [81] the authors conducted an optimization for

distribution system planning over a 20-year horizon aiming to minimise the cost. As the

optimal solution contains DG, to maximise reliability it is foreseen that DG must be able

to operate in islanded mode, which falls within the microgrid concept. Another paper [82]

highlights the consequences of the presence of several microgrids into a distribution


network. It concludes that even with a high penetration of microgrids, the networks

would seem as expensive to build and maintain. It also shows that the construction of

microgrids in the networks will be on benefit of lower losses, lower interruption costs and

deferral of network upgrade. Actually, the ability of operating in islanded mode has a

large impact on the power distribution system planning since it contributes to improve

the reliability of the grid.

3.3.2 Storage

Energy storage systems (ESS) are used to balance the fluctuations between the

electricity supply and demand [83], helping to manage energy efficiently since they can

be applied as source of production, support for transmission, distribution and the end

user. Their main advantages are the improvement of grid stability, increase of power

quality and rise of penetration of RES [84]. The growing development of new storage

technologies influences the customers and electrical utilities to adopt smart grids [85].

There are several storage technologies available, each one with its own performance

characteristics that make it optimal for certain network services and less for other

network applications. The various approaches being deployed around the world could be

divided in different categories. In [86] the following division is proposed:

Solid State Batteries - electrochemical storage solutions, including advanced

chemistry batteries and capacitors;

Flow Batteries - batteries where the energy is stored directly in the electrolyte

solution for longer cycle life, and quick response times;

Flywheels - mechanical devices that harness rotational energy to deliver

instantaneous electricity;

Compressed Air Energy Storage - utilizing compressed air to create a potent

energy reserve;

Thermal - capturing heat and cold to create energy on demand;

Pumped Hydro-Power - creating large-scale reservoirs of energy with water.

The different solutions can be distinguished by their applicability. One way to do that is

compare the range of operation through the power rate and discharge time as illustrated

in Figure 30.

Figure 30: Applicability of Electrical Energy Store Systems [87].


Some research studies addressed the optimal operation of distribution grids using

storage systems such as the ones approached in [88]. The proposed model considers

dispatch able DG, capacitor banks, Voltage Regulators, transformers with OLTCs, RES,

and storage devices simultaneously. In addition, the proposed formulation for storage

devices permits flexible operation and modification of the number of charge cycles in the

period of analysis. The objective considered was to minimise the total cost of energy

purchased from the distribution substations and the dispatch able DG.

Several projects in Europe expect to launch a large-scale demonstration for transmission

operators. For distribution networks more development and research is still underway

such is the case of SENSIBLE [89] and HYPERBOLE [90]. At this level, some significant

questions remain, such as choosing between using centralised or distributed storage

systems and the relation between the business model and the services that storage can

provide, namely ancillary services [91].

Recent works have proposed to solve the issues of planning and optimizing using

distributed storage devices within distribution networks. In [92] a model for optimal

integration of energy storage in distribution networks was created aiming to find out the

position and the size of the storage device. From this point, they calculate the optimal

network expansion plan for a given consumption and DG data. The main conclusions of

this work highlight that, using this approach in real networks, would allow deferring

investments in power distribution lines and increasing the amount of DG. The authors of

[93] describe a modelling storage for different periods typically involving simulations with

step sizes of 1 minute or less. One of objectives of this study is to identify some planning

issues introduced by energy storage such as overvoltages while discharging, low voltages

while charging, voltage regulation while compensating for transmission grid support,

interference with overcurrent protection practices and sufficient short circuit capacity to

operate overcurrent protective devices when operating as microgrid. It is predictable that

storage systems may play a significant role in future electricity distribution systems,

because they facilitate the transfer of RES production from one hour to another. Such

capability has an impact on power distribution system planning since it will allow

integrating more RES without the necessity of additional investments [94].

3.3.3 Demand response

DR is described by Energy Efficiency Directive of 2012 [95] as an important instrument to

take action on consumption providing a mechanism to reduce or shift consumption.

These actions may result in energy savings in both final consumption and in energy

generation, through the more optimal use of networks and generation assets. The

Agency for the Cooperation of Energy Regulators (ACER) [96] defines DR as the

behaviour of the end-user consumers to changes in electricity prices and /or incentive

payments designed to adjust electricity usage or the acceptance of the consumers bid.

Such response is made by changing their usual load profiles and can be reckoned

through aggregation.

Different types of DR programs are implemented, either at test level or at concept level.

These programs can have different models depending on whether the option is to use

direct load control or price response control and if the programs are incentive or price

based [97]. With incentive-based programs, the costumers are paid for an agreed

reduction of their consumption. This payment can be a discount for participating

(classical) or can be based on their performance (market). For instance, using Direct

Load Control (DLC) programs, the customer agrees on giving control to a limited number

of actions namely switching some devices during critical events. Market programs are

performance based where customers reduce their payment according the amount of load

they can reduce or shift. In one of these demand-bidding programs, the customers bid

on how much load decrease can be done in the next period and the bid is accepted if it is

lower than the market price. In spite of the customers gaining money according to their

performance, they will be penalised if the reduction is not accomplished. Besides demand

bidding there are other market programs such as emergency, capacity market and


ancillary services programs where customers are bidding on the spot market as an

operating reserve [97].

The programs listed before are based on incentives. However, it is possible to design

price based programs also which are linked to dynamic pricing where the price is

established taking into account the real time electricity price. The main objective is to

incentivise reducing peak demand by having high prices in the peak hours towards to

peak load shaving. An important issue to consider is when the customer is informed

about the prices. If the period until the actual time of use is too short, it could be difficult

to act in time. Instead of giving the information about real time pricing, operator could

give only the information about predetermined peak pricing or establish time of use

tariffs [97].

The adoption of DR programs may bring a relevant contribution to the network reliability

performance as mentioned and tested in [96]. In this paper, the contribution of DR

services to the reliability is evaluated through the following reliability indices: CI and

CML. For the scenarios where the DR scheme was incorporated, the reliability levels from

the customer point of view were improved (45.5% in the case of CI and 26.8% in the

case of CML).

Until now, European residential and small commercial users have seen their participation

limited due the lack of a real-time metering infrastructure and smarter electricity grids

[91]. DR could aim to be more effective in the future when the energy prices become

more volatile, the capacity of renewable rises and the combination of smart meters with

market information becomes operational [91].

The literature has several examples of studies about planning power distributions

systems with DR programs. One of them, in [98], explores the economic impact of DR

programs using a tool to estimate the MV network reinforcements needed to meet the

demand growth in a ten year horizon. This work considers not only real-time energy

prices but also a demand charge based on the peak demand, which further incentivizes

consumers to reduce their peak demand with positive impacts for network costs. As

conclusions, this paper states that there are various potential economic benefits of DR on

power systems including operational savings, investment deferral in both generation and

networks and emission reductions. It highlights the importance of a demand charge

signal on the costs reduction. Furthermore, the authors conclude that even with a small

adoption of DR by the consumers, the cost reductions would not be insignificant.

3.3.4 Electric Vehicles

EVs have unique characteristics since they can be viewed as storage purposes or having

a role in DSM depending on how much control, the system operator has on the EVs [99].

They can be considered as the active loads when in the charging process increasing the

demand on the distribution network, and generators when operating in discharging

mode. This approach is based in the Vehicle-to-Grid (V2G) concept, which states that EV,

when parked and plugged-in into the network, can either absorb energy and store it or

inject electricity in the grid [100].

Currently, the study about the EVs connection to the grid is focused on three main

aspects. Primarily it is essential to examine the charging load modelling taking into

account the power battery charging characteristics. It is also important to explore the

effects of EV integration on the distribution network. Authors of [101] state that EV

deployment upsurges the concerns about the effect on the power grid. Several other

studies track these concerns, which are the following:

Impact on load profile – The growth of EV charging leads to changes in power load

profile as additional loads especially in the residential peak load hours. To solve

the problem many solutions are being implemented such as time-of-use (TOU)

tariffs, which shift the EV loads from off-peak hours.


Impact on system components – The additional demand of EVs will increase the

loading of the system components such as the distribution transformers and

cables. Consequently, adequate network planning and load management are

being studied.

Impact on system losses – The increase of power flowing to larger loads causes

more system losses. The coordination of EV charging and the nearby DG could

help avoiding this issue.

Impact on voltage profile and phase unbalance – EV charging may cause voltage

drop and voltage deviation on the EV interconnection point. Voltage regulation

equipment and voltage support strategies should anticipate the adoption rate of

EVs.

Harmonic impact – The power electronics used for charging operation may cause

problems to the power quality of the power grid. Nevertheless, many solutions are

available to tackle this issue such as filtering devices.

Stability impact – Some studies state that EV charging reduces considerably the

system stability being more sensitive to the disturbance. These studies propose a

usage of wide-area controller to provide auxiliary control signals to the power grid

components for power system stability improvement during EV charging and V2G

operation.

The other aspect that is being studied more lately is related to the charging and

discharging control. The current research work embraces harmonic control, coordinated

charging, V2G and distribution network planning [102]. Due to the expected massive

integration of EV, several services to the grid can achieve its potential such as load

shaping, ancillary services provision and market mechanisms for V2G, incentivizing an

optimized EV charging. Figure 31 shows a scheme of possible connections between EVs

and the power grid. The concept of V2G is in constant change and under different

interpretations. According to some authors, the basic concept of V2G is providing power

to the grid while parked, only when power flows from the batteries to the grid [103].

Figure 31: Possible scheme of V2G functionality [102].

Some other research has been conducted towards the improvement of EV demand

forecasting through the analysis of driving patterns. The authors of [104] propose a

charging classification based on four categories: residential slow charging, workplace

slow charging, fast charging and ultrafast charging.

The concept of V2G applied in the operation of distribution grids will cause an impact on

their planning since the EVs could be, this way, additional sources of upward power

reserve for ancillary services. Besides, EVs as controllable loads with other devices could

be controlled in order to reduce congestion branches and improve nodal voltage profiles

in a local and dynamic manner [94]. Such changes in power distribution operation may

influence the power distribution reinforcement planning by reducing generation and load

uncertainty while expanding the control possibilities in operation, it may be possible to

defer reinforcement investments while maintaining or even improving the standards for

quality of service and reliability [94].


3.3.5 Asset management

Asset management is a process of reducing costs without compromising on risks involved

by operating, exploitation, rehabilitating (preventive or corrective maintenance) or

transferring network assets. Besides the reduction of long run costs, the management of

network assets allows also the improvement of reliability. The investments are

attenuated with maintenance strategy programs aiming to increase the assets lifecycle

[91]. Short-term asset management is represented with operational concerns of the

network, while mid-term asset management are connected to the maintenance of

systems assets and long-term asset management corresponds to strategic planning of

distribution system [105]. One way to represent the steps of asset management during

the lifecycle of each asset is shown in Figure 32.

Figure 32: Asset lifecycle [91].

The assets, which the Distribution System Operator is responsible for, can be divided in

different categories. At the HV level, usually there are overhead lines, underground

cables, substations and switching stations. Besides that, at the MV level it is normal to

see also MV metering systems, while at LV level there are additionally power

transformers and public lighting [106].

An Asset Management model should balance the performance (continuity), the cost

(maintenance, revision, replacements) and business risk (external damages, obligations).

In order to add value to the different stakeholders, asset management should promote

maintaining the assets. The presence of Information Technology (IT) tools in the system

may help to manage all the information regarding life cycle of the assets making it easier

to make decisions towards to saving money and extending the life of the assets.

In order to assure that each technical asset performs properly, the maintenance activities

should be: Time-based Maintenance (TMB), Condition-based Maintenance (CBM), Risk-

based Maintenance (RBM) and Reliability Centred maintenance (RCM). These activities

follow the information that some indicators provide such as:

Reliability - probability that the device will function correctly, during specific time

intervals and operation conditions

MTBF - Mean Time between Failures

MTTR - Mean Time to Repair

TIEPI - Interruption Time Equivalent to Installed Capacity

MAIFI - Momentary Average Interruption Frequency Index

SAIFI - System Average Interruption Frequency Index


SAIDI - System Average Interruption Duration Index

Asset management will perform an increasing role in the planning of the MV and LV

distribution grids mainly at the LV level where the number of malfunctions is higher. The

planning and development of asset management will pursue some objectives such as

[107] [108]:

Forecasting distribution network load;

Identifying constraints and eliminating them;

Minimising power losses;

Improving network reliability with minimisation of unsupplied load

and reduced customer loss minutes;

Maintaining appropriate quality of supply and levels of reliability of

the distribution network while minimising investment and operation

and maintenance costs;

Developing an effective capital investment program based on

project priorities and risk assessments;

Integrating distribution augmentation plans with other capital

works, etc.;

Facilitating customer level outcomes such as DG.

The global goals to the future are focus on have an organising and coordinated

predictive-preventive-corrective maintenance always balancing the three vectors: risk,

condition and reliability.

The asset management is a crucial element to consider in distribution network planning,

since it is an instrument used to identify the best way to achieve the balance between

cost, performance and risk assuming a decisive role in power system design.

3.3.6 Forecasting for DG/Load

The increasing penetration of RES in distribution networks at the MV and LV level is the

reason for bigger operational uncertainty, which is also reflected during short-term

operational planning. A way to counteract this uncertainty is using forecast methods for

both load and DER [109]. It is possible to find several research solutions regarding the

short-term forecasts. Usually, short-term forecasting methods can be divided in two

categories: statistical methods and artificial intelligence-based forecasting approaches

[104]. The first ones embrace methods such Autoregressive Integrated Moving Average

(ARIMA) [110], state-space models [111] support-vector-regression [112] or linear-

regression-methods [113]. The second category includes methods, which are capable of

finding nonlinear connection between response variables and its impact factors. These

methods range from Artificial Neural Networks (ANN) [114] to expert system methods

and fuzzy inference. Their disadvantages are possible over-fitting and the difficulty to

express intuitive knowledge. Moreover, there is a growing amount of literature

suggesting the combination of both methods, for instance in [115], [116].

Since the generation of distributed power is significantly influenced by meteorological

factors, using the traditional methods to predict power load with distributed power

generation is not advisable. DER are mostly affected by the weather, which makes it

difficult to predict accurately such values with characteristics of strong randomness.

The existing forecasting algorithms present acceptable forecast errors and provide

diverse approaches for modelling uncertainty. The uncertainty forecasts are an essential

input for stochastic management tools planned for numerous problems, such as setting


the reserve necessities or the Unit Commitment, which permit an accurate

characterisation of the risk linked to different decisions [117].

Recent research studies tend to use back-propagation neural networks [118] [119] or

support vector machines (SVM) methods. SVM methods, besides other advantages

related with avoiding local optima and high dimension dealing, has very strong

generalization ability [117].

Proper models of forecasting load and DG penetration will permit a better planning of

power distribution systems. Such information may be used for creation of long-term

scenarios towards to evaluate the necessary investments in the network. With accurate

forecasts models, these investments may be postponed bringing more money savings to

the operator. It is expected that such models will help energy planners to precisely plan

for the future and apply the sustainable and renewable energy resources to a larger

extent.

3.4 Management and Control

3.4.1 SCADA/DMS

Supervisory Control and Data Acquisition (SCADA) systems provide monitoring and con-

trol functionality on distribution networks. A SCADA system makes use of communica-

tions infrastructure to collect data from field devices, providing visibility of network oper-

ation and allowing the control of devices remotely from a central control centre. Figure

33 provides an example of a SCADA system architecture. SCADA infrastructure is becom-

ing a single element of feature-rich Distribution Management Systems (DMS).

Figure 33: Diagram of SCADA system [120]

SCADA systems are large-scale processes that can include multiple sites over large dis-

tances. Key components of a SCADA system are:

RTU

o RTUs process measurements and signals and converts them to digital data.

They are capable of sending signals and receiving control signals from the

wider SCADA system.


Programmable Logic Controller (PLC)

o An industrial computer control system that monitors input devices, and

processes automated control actions for controlling output devices.

Data acquisition server

o Collects data from monitoring points across the system, supporting DNO

data transfer protocols.

Human-Machine Interface (HMI)

o User interface that allows control engineers to observe network measure-

ments, interact with and control network devices.

Data historian

o Stores network measurement data, allowing the extraction of historical op-

erational data to support network-planning activities.

Supervisory system

o Reviews the acquired data and determines control actions that should be

taken

Communications infrastructure

o Facilitates the transfer of data and control signals between the Distribution

Control Centre and devices in the field.

SCADA systems typically provide visibility and telecontrol capabilities for MV networks,

with limited control and visibility of operation on the LV network or lower voltage levels

at MV. The large number of network assets and geographical distribution of sites limits

expansion of SCADA infrastructure at LV, resulting in excessive costs for providing moni-

toring and communications equipment across all LV networks. Even at MV voltage levels,

extending visibility and control to remote rural sections of network can present challeng-

es where communications infrastructure cannot meet the bandwidth requirements for

data transfer. DSOs must employ a variety of communications methods to communicate

with devices on the outer reaches of the network.

The all-encompassing nature of the SCADA system supports DSOs in operating networks

in a more efficient and reliable manner. Monitoring of network operating characteristics

provides the control centre with alarms that inform operational remedial actions. Tele-

control facilitates the remote issue of control signals to network devices, reducing the

requirement for field engineers to support network reconfiguration activities.

As technological capability advances, SCADA and DMS functions are becoming increas-

ingly intelligent with new features being added to support wider network operation. Con-

temporary DMS/SCADA systems present advanced functionality to improve wide-area

network automation and extend support to operational activities such as outage man-

agement.

Advanced features of DSO SCADA/DMS systems bring together various functions and

include the following:

Emergency Control Switching

Fault Detection, Analysis and Recovery

Load Forecasting

Supporting Demand-Side Response

DER Control and Scheduling

LV Network Management

State Estimation

Network Security Analysis

The logging of measured network data provides a valuable source of information to in-

form planning and design activities. As new functions such as demand-side response, and

active network management received greater visibility of network operation, the logging

of field measurements presents an understanding of network behaviour in increasingly

stochastic systems.


3.4.2 Automation strategies on substations (HV/MV and MV/LV)

Substation automation solutions offer voltage management capability, delivered through

the OLTC functionality of HV/MV transformers. The OLTC feature introduces the capability

to step voltage at the LV terminals of transformers whilst under operation. OLTC trans-

former features allow the management of network voltages in response to changing net-

work conditions, increasing voltage headroom and deferring reinforcement on weak net-

works.

At MV/LV substations, the management of voltage remains primarily passive, with trans-

formers operating in a fixed-tap manner. MV/LV transformers are available with OLTC

capability; however have yet to be taken on board by DSOs as a design standard.

Automatic Voltage Control (AVC) relays provide advanced voltage control functionality,

automating the operation of tap changers in response to changing network operating

conditions. Utilising input measurements of feeder voltage, AVC updates OLTC steps to

maintain the network voltage profile within planning limits. The autonomous manage-

ment of network voltage profile reduces requirement for operator intervention, maximis-

ing the hosting capacity of the network for increased demand or generation connections.

The functionality of AVC relays continues to develop, with automation algorithms de-

ployed to consider influencing factors such as:

Transformer parallelism;

Distributed generation;

Network configuration change;

Voltage targets; and

Load power factor shift.

The emergence of interoperability standards, such as IEC61850, has facilitated co-

ordination between technology devices within the substation environment. This supports

the use of increasingly fibre-based communications infrastructure.

3.4.3 Control and monitoring

DER management

As described in section 3.4.1, a degree of control and monitoring functionality is provided

through SCADA/DMS infrastructure. It is widely understood that the previously applied

passive means of both planning and operating distribution networks is unsuitable for am-

bitions of highly reliable grids hosting large volumes of DER. The growing number of con-

trollable devices on the network, together with the need to better utilise existing hosting

capacity, is driving the establishment of wide-area control schemes to manage network

devices autonomously, and in a secure manner.

Challenges associated with accommodating DER on congested networks have led to the

deployment of Active Network Management (ANM): technology that facilitates the real-

time management of the network constraints inhibiting the connection of further DER. In

the UK, ENA defines ANM as “Using flexible network customers autonomously and in real-

time to increase the utilisation of network assets without breaching operational limits,

thereby reducing the need for reinforcement, speeding up connections and reducing

costs“ [121]. The real-time control of DER increases network-hosting capacity, moving

away from reliance on a deterministic, worst-case planning premise. Instead, DER is able

to utilise the additional hosting capacity offered by the range and demand in diversity

between DER export profiles.

ANM has facilitated the connection of hundreds of MW of generation while avoiding or

deferring the need for network upgrades and reinforcements. ANM monitors critical con-

straint locations in the network in real time, observing power flows or voltage levels

against a set of pre-defined thresholds that sit a safe margin below network planning


limits. In the event of an operating parameter exceeding a constraint threshold, the

scheme sends control set-points to participating DER (typically generators) to regulate

export, avoiding overload or overvoltage conditions. Network measurements are taken

directly from field devices to feed into a sub-second refresh of a deterministic algorithm

hosted on a central controller.

The planning of DER connections beyond traditional planning limits introduces possibility

of overload, therefore ANM must ensure suitable fail-safe actions are taken if necessary,

for example during periods of communication outages between elements of ANM infra-

structure. The architecture of a centralised ANM system is shown in Figure 34.

Figure 34: Centralised ANM Architecture [121].

State estimation

Power System State Estimation is the process of estimating network parameters from a

limited set of real network measurement data. Where confidence can be assigned to

state estimation outcomes, it can provide valuable visibility of wide-area network opera-

tion whilst minimising need for investment in monitoring and communications infrastruc-

ture. Benefits of state estimate also include:

Identification of faulty measurements;

Improved accuracy of measurements;

Enhanced real-time distribution network operation activities; and

Assessment of meter performance.

Network measurement data used for state estimation typically includes conventional

power and voltage measurements as wells as current magnitude and voltage phasor

measurements. State estimation can be vulnerable to measurement errors or telemetry

failure, therefore redundant measurements are used in order to minimise error. Where

low measurement refresh rates exist, tolerance must be built into state estimation to

account for mismatch in measurement synchronisation.


The application of state estimation on distribution networks is viewed as an innovative

activity, with the relatively low levels of network monitoring equipment enhancing the

challenge of accurate estimation. Trial projects [122] have generated learning from ap-

plications of distribution state estimation, with challenges identified around:

Availability of measurements at key network locations;

Resolution of measurement updates due to SCADA communications scanning rate;

Accuracy of asset parameter data for state estimation modelling compared to as-

sets on network;

Procedure for maintaining network model accuracy for state estimation.

The ongoing rollout of Smart Metering can address measurement challenges for bottom-

up state estimation on the LV network, although significant challenges still exist in the

volume of data that must be processed for this application.

Self-healing actions

In complement to accommodating DER, an enhanced degree of automation is being har-

nessed on distribution networks to increase reliability and reduce the duration or custom-

er interruptions or outages. The application of automation for self-healing functions is

growing in complexity due to integration of distributed generation, energy storage tech-

nologies and responsive demands, which are increasingly under autonomous control.

Self-healing automation solutions operate in the period following a fault and protection

operation, using sequential switching to locate and isolate faulted assets whilst restoring

connection to customers elsewhere on the network. Self-healing network solutions com-

prise of conventional protection equipment, automation controllers with pre-programmed

software and switchgear devices. This automation is increasingly based upon distributed

control algorithms, with network devices transmitting data in a peer-to-peer manner,

rather than reliance on centralised control.

Although operating in an autonomous and distributed manner, self-healing automation

solutions can complement SCADA/DMS, providing visibility of network operation to the

control centre and allowing control engineers to remotely enable/disable automation.


4 SG projects/initiatives

4.1 EU research projects

4.1.1 PlanGridEV

The 3-year PlanGridEV (Distribution grid planning and operational principles for EV mass

roll-out while enabling DER integration, 2013/06/01-2016/02/29) project focused on de-

veloping the network planning principles that will facilitate the successful integration of

EV adoption across Europe, completing in February 2016. In complement to addressing

EV integration grid challenges, the project investigated areas where integration can sup-

port network operation.

One key deliverable from the project was the development of a prototype tool to provide

decision-support for network planning in areas with high EV penetrations. Reflecting the

need for network planning to move beyond deterministic worst-case conditions, the tool

understands the stochastic nature of network operation, using Optimal Power Flow (OPF)

to optimise operational choices including the smart charging of EVs. The development of

the tool was supported by deep development of synthetic models to understand the sto-

chastic nature of EV demand and correlation with renewable exports. The creation of

these data sets informed the definition of use-cases and scenarios for investigation, the

outcomes of which has informed the development of a white paper presenting novel

planning rules for EV in Smart Grids.

Project contribution to SmartGuide

The findings from this project, in particular the work towards establishing planning rules

for EV in Smart Grids will support the SmartGuide project, particularly in the WP3 ad-

vancement of Smart Grid planning methods and tools. The stochastic models of EV

charging and co-ordination with renewable generation export will provide a valuable set

of reference data to inform the simulation and analysis of Smart Grid control algorithms,

optimal planning models, curtailment analysis and prosumer behaviour estimation. The

rationale that informed development of study scenarios and use cases can be used as a

starting point for the specification of study cases across both WP3 and WP4.

4.1.2 Grid4EU

GRID4EU (2011/11/01-2016/01/31) brought together 27 organisations from 15 coun-

tries, including 6 DSOs and covering over 50% of metered connections across Europe,

with the aim of creating a ‘Large-Scale Demonstration of Advanced Smart Grid Solutions

with wide Replication and Scalability Potential for Europe’. The project was demonstra-

tion-focused, deploying innovative new technologies onto live networks and subsequently

evaluating feasibility and addressing barriers to wide-scale deployment. The project fo-

cused on the demonstration of technologies and solutions to achieve the following objec-

tives:

Reduction of energy losses Reduction of fault identification and isolation

times

Improvement of voltage con-

trol

Islanded network operation

Increase network hosting ca-

pacity

Enabling active customer participation

The objectives were achieved through application of technologies and solutions such as:

Grid-scale storage at MV Advanced network automation solutions


Customer behaviour incentive Co-ordinated Volt-Var control of DER

Demand-side response Advanced Smart Meter applications


Although the GRID4EU project primarily focused on the demonstration of Smart Grid

Technology, rather than planning, the learning from each trial can inform SmartGuide of

those solutions that are most likely to see transition to Business-as-Usual (BaU). This

confirmation of the success of solutions as a BaU option will define those that will be con-

sidered in planning methods and tools.

The detailed description of solution operating characteristics that has been extracted

from GRID4EU will support SmartGuide modelling by informing the characterisation of

solutions such as advanced automation, active demand, and voltage control. Learning

from GRID4EU can provide an indication to the scale of solution rollout and likely network

impact.

4.1.3 Grid+

Summary of the project

The GRID+ (Supporting the Development of the European Electricity Grids Initiative

(EEGI), 2011/10/01-2014/09/30) project is a coordination and support action with the

purpose of providing operational support for the development of the EEGI [123]. The

main mission of GRID+ has been organising the networking among European

demonstration projects within smart grids, with the goal of implementing and supporting

the management, planning and networking process of the EEGI through 2012 to 2014.

GRID+ has conducted activities such as surveys and mappings, workshops and have

developed and launched an online knowledge-sharing platform, called Grid Innovation

Online [91].


The online platform for knowledge sharing allows users to submit and access articles,

divided into TSO activities, DSO activities and joint TSO-DSO activities. The website also

has a technology database for transmission technologies, and publications and links to

new research and innovation results.

Conducted surveys within the GRID+ project showed that it is essential that the

governments support and invest in smart grid application developments and start-ups,

mainly within demonstration projects. A key to a successful smart grid is winning

consumer support, which is the most difficult challenge to overcome. A radical change in

the utilities' view on consumers is needed, as well as in consumers' relation to electricity;

end-users will no longer play a passive role in the power system. Another challenge is

that in several developed countries large parts of the electrical grid were built in the

period after World War II. These aged infrastructures are especially vulnerable to issues

related to the increasing electricity demand and changing load profiles.

A survey among ongoing smart grid activities was conducted at a global level and

showed that the different parts of the world have different drivers for smart grids. In

America, there is a focus on DG for reducing peak load and dynamic tariffs; in China, a

main driver is modernisation and reliability improvements in the grid; in Australia, there

is an interest in techniques for load management; in Europe, there is an emphasis on

energy efficiency and reducing emissions through more DG.

A mapping of 331 R&D projects regarding grid-connected energy storage was conducted

in the project. The results showed that in the years to come, one could expect many

demo and pilot projects including energy storage, related to distributed, electrochemical,

chemical and thermal storage technologies. Europe as a whole is betting heavily on

batteries, power-to-gas storage and thermal storage.


A performed research and innovation cluster analysis for DSOs reveals that the following

functional areas require more effort: asset management, integration of storage in net-

work management, integration of infrastructure to host EVs, and integration of DER at LV

level.

4.1.4 DISCERN


DISCERN, Distributed Intelligence for Cost-Effective and Reliable Distribution Network

Operation (2013/02/01–2016/04/30), handles multi-dimensional, common challenges

faced by DSOs as consequences of the shift towards a more complex power system and a

change in the role of DSOs. An important, yet difficult task for DSOs is to find the optimal

balance between minimising grid costs, while ensuring safety, security and reliability of

supply. The purpose of DISCERN is to provide DSOs and other industrial partners with

tools and knowledge for better decision-making, planning, design and operation of the

future power systems.


The activities of DISCERN have helped to develop a framework for evaluating solutions

that may be reproduced in different regions while including local regulations,

environmental conditions and network restrictions.

DISCERN presents recommendations for DSOs [124], which includes utilising the

structural approach of Use Case & Smart Grid Architecture Models (SGAM). Such tools

have been developed in the project, and the specific tools may be used separately, but

are also able to exchange data with each other and external applications. DISCERN

recommends using SGAM as a part of grid planning, as this framework enables a

structured overview and connection between all the relevant aspects of a specific

solution. The project's key recommendations based on experiences from the DISCERN

demonstration sites include: deployment of fault passage indicators, using

sensors/monitoring for observability in the LV network, considering different

methods/systems for data gathering when designing communication infrastructures, and

exploiting the LV network's AMI to separate technical and non-technical losses (e.g. theft

of power).

4.1.5 IDE4L


The main objective of the project IDE4L (Ideal Grid for all, 2013/09/01–2016/10/31) is

to develop an automation concept for distribution networks based on existing technology

and solutions, while meeting future requirements. The concept includes the development

of advanced applications that enable monitoring and control of the entire network and

connected DER, including utilisation of ancillary services of distributed energy resource

and aggregation. IDE4L develops the whole system of distribution network automation,

IT systems and functions for active network management. Automation systems and

management solutions will be tested in laboratories to ensure the functioning of the

complete system. Field tests are conducted with actual customers Italy, Spain and

Denmark in order to verify functionalities of the developed functions.


By the time, this deliverable was written the final report of IDE4L was not yet published.

As to the project’s concept, there is a thematic overlap concerning network-planning


methods. The methods developed and used in IDE4L may be a valuable input for

SmartGuide. Depending on the efficacy of the developed automation strategies, their

underlying concepts will be regarded when possible smart grid technologies and methods

will be chosen for the simulations in WP3.

4.1.6 NEMO


The objective of the project NeMo (Hyper-Network for electroMobility, 2016/10/01-

2019/09/30) is to reduce the obstacles electromobility faces by developing a full open

eco-system that allows continuous and uninterrupted provision of services.

Although electromobility is a major factor towards transport decarbonisation, it faces a

number of challenges, such as limited charging options and lack of a universal payment

process. The initiators of the project identified the lack of standardisation in

electromobility data, services and load architectures as the main reasons for the

obstacles e-mobility faces. NeMo strives to face these challenges by setting up a pan-

European network allowing seamless and interoperable use of electromobility services in

Europe. This hyper-network is a distributed environment with open architecture based on

standardised interfaces. It can be used by all actors and stakeholders in electromobility

(charge points, power networks, electric vehicles, charge point operators, DSOs, vehicle

owners, etc.) to interact and exchange data. Providing more elaborate electromobility

ICT services in a B2B and B2C relationship will be possible. The connection will be based

on dynamic translation of data and services interfaces according to the needs of the

specific scenarios and involved stakeholders.

NeMo will raise awareness, interwork with standardisation bodies and contribute to the

evolution of protocols and standards by developing public Common Information Models.

Those models will incorporate all existing electromobility related standards and

constantly update them to reflect standards evolution. NeMo will also propose sustainable

business models for all electromobility actors opening new opportunities for SMEs and EU

Industry.

NeMo is coordinated by the Institute of Communication and Computer Systems of the

National Technical University of Athens, Greece. Another academic research partner is

the TU Berlin. Industry partners are, e.g. IBM, FIAT, Renault and TomTom. As the project

has only started recently, first results will be available in due course.

Whilst DER in form of RES pose the root cause for challenges in the distribution networks

concerning generation feed-in issues, it could be EVs concerning load

consumption/import issues in the future. As many countries (such as Norway and

Germany) promote the purchase of EVs through monetary incentives, the number of EVs

connected to the distribution network will increase.


Since the project has only started recently, an actual contribution to SmartGuide will

have to be evaluated when first results are published. The information NeMo is gathering

at the moment about electro-mobility actors and their needs, use cases and

requirements, however, might be valuable for the simulations in WPs 3 and 4 of

SmartGuide. This is because electro-mobility will have to be taken into account in

network planning in the future as laid out in section 3.3.4.


4.1.7 SuSTAINABLE


Smart distribUtion System operaTion for mAximizing the INtegration of renewABLE

generation (SuSTAINABLE, 2013/01/01-2016/03/31) was an 7th Framework Programme

for Research and Technological Development (FP7) European project from January of

2013 until March 2016 developed by 8 partners from Germany, Greece, Spain, Portugal

and the United Kingdom. The objectives of the project were to develop and demonstrate

a new operation paradigm, leveraging information from smart meters and short-term

localized predictions to manage distribution systems in a more efficient and cost-effective

way, enabling a large-scale deployment of a variable distributed resources [125]. The

SuSTAINABLE concept was based on the cloud principle, where the DSO:

i) Collects information from smart metering infrastructure and other distributed

sensors, and communications from external partners, market operators, and

maintenance staff;

ii) Processes the information using tools such as distribution state-estimation,

prediction tools, data mining, risk management and decision-making

applications;

iii) Communicates settings to power quality mitigation devices, protection relays and

actuators, distribution components and distributed flexible resources;

iv) Assesses its market strategy as a provider of ancillary and balancing services.


SuSTAINABLE project can provide an important input to SmartGuide since in its scope

covers concepts and methodologies for DER management and power quality planning.

Under the SuSTAINABLE concept, managing DER within power distribution may enable

investment deferral by avoiding technical violations. For this purpose two planning

models were developed. One of them determines the location, the size and the time

period of new network components in order to meet the load growth demand and to host

large shares of RES over the planning period. This model was tested using the real power

distribution network of the Greek island of Rhodes. The other model that was developed

uses a multi-temporal OPF for simulating the daily operation of the future power

distribution system in order to obtain the corresponding schedules of investment and

decision parameters. This model was tested using the distribution network of the

Portuguese city of Évora and its suburban area [125].

Regarding the power quality planning, a new modelling methodology for tracking losses

and other network impacts was developed proposing an incentive scheme to promote

harmonic compensation by DER. Another methodology was developed based on an

algorithm to search the optimal mitigation scheme in order to enable the provision of

differentiated power quality levels [125].

4.1.8 CitInES


Design of a decision support tool for sustainable, reliable and cost-effective energy

strategies in cities and industrial complexes (CitInES, City and Industry Energy Strategy,

2011/01/01–2013/12/31) was an FP7 European project started in 2011 with duration of

36 months. The overall objective of CitInES was to design and demonstrate a multi-scale

multi-energy decision-making tool to optimize the energy efficiency of cities or large

industrial complexes by enabling them to define sustainable, reliable and cost-effective

long-term energy strategies. Demonstrations have taken place in two cities in Italy,

Cesena and Bologna, and in one oil refinery in Turkey, Tupras. All energy vectors


(electricity, gas, heat...), usages (heating, air conditioning, lighting, transportation...)

and sectors (residential, industrial, tertiary, urban infrastructure) are considered to draw

a holistic map of the city/industry energy behaviour. Two software tools were developed

and experimented during the project. The first one, Crystal City, is dedicated to the

design and monitoring of local energy strategy. The second tool, Crystal Industry is

dedicated to the design of reliable, practical, sustainable and cost-effective management

policies for energy-intensive industrial plants [127].


Considering that CitInES concerns energy strategies planning, further test studies were

developed to analyse the combination of smart grid impact with long-term network

reinforcement allowing to compare the impact of alternative energy strategies,

integrating at the same time the costs of network reinforcements. The approaches used

to obtain the results in these studies can be helpful to SmartGuide project, especially in

Work Package 3. The studies comprised the impact analysis of EV integration, DSM and

energy storage, with and without smart grids. One of these studies analysed the impact

of DSM actions and the effect of DSM controllable load in the long-term grid upgrade

investment costs showing how the amount of DSM controllable load influences the shape

of the load diagram and how it affects the long-term costs in grid reinforcement. Another

approach concerned the test of a multi-level approach using multi-level modelling of a

typical MV network in order to optimize the EV charging policy. Another study involved

the simulation of combined effects of EV charging type, DSM and solar PV

microgeneration, and, at the same time, its impact on investments grid upgrade costs.

This test was developed for long-term analysis and the system performance was

characterized by both technical and economic indexes. Besides, an original development

was created, which is a procedure for the estimation of network expansion based on

common city data (inhabitant densities, dwelling size and specific heat intensity) and

public data on network infrastructure costs (e.g. cable costs, transformer costs, etc.)

[128].

4.2 Rollouts of SG demos

In this section, the current rollouts of smart grids demonstrations underway in each

country of the project partners will be mentioned.

4.2.1 Portugal

InovGrid is EDP’s umbrella project for smart grids. It presents an answer to several

challenges including: the need for increased energy efficiency; the pressure to reduce

costs and increase operational efficiency; the integration of a large share of dispersed

generation; the integration of electric vehicles and the desire to empower customers and

support the development of new energy services. InovGrid is a distinctive project in the

European landscape because it combines a reasonable size, in terms of the number of

customers reached, with a strong focus on the Smart Grid vision (as opposed to other

projects, which are purely smart meter oriented) [91].

At the moment, in terms of rollout of smart grid demonstrations, only InovGrid is going-

on and is positioned in Évora with about 54,000 inhabitants and an area of 1307 km².

The distribution network in Évora municipality is supplied by two 60 kV substations with

15 kV and 30 kV feeders with MV switching breakers. These 25 feeders supply a total of

655 secondary substations (SS) which corresponds to supplying customers with total

installed power of 163 MVA, counting 2600 MV feeders. The customer demand totals

nearly 215 MVA of contracted power. In addition, there are also some MV and large LV

clients. In distributed generation, there are 223 microgenerators, mainly solar PV

equipment, with an installed capacity of 701 kW, and a growth rate of 48% year on year,


which increases the challenge in this area to integrate all this new Distributed Generation

[129].

The LV distribution network in Évora has no restoration possibilities in the event of a

fault. Thus, the possibility of installing storage devices and the implementation of load

shedding and DSM becomes very important for network operation. In section 2.1.1 the

presence of storage technology was already mentioned.

The EDP Box (EB) and the Distribution Transformer Controller (DTC) are the main

components of the InovGrid infrastructure. During 2 years, more than 30,000 EDP Boxes

and 300 DTCs were installed in Évora, including all customers and substations, in order

to have the entire municipality covered. The EB, with its smart metering functions, is the

energy management device located at every delivery point, allowing the replacement of

the conventional meters. It has the capacity of local interaction with other devices

through a HAN interface, such as local displays. The DTC is a local control device installed

in MV/LV secondary substations comprising a measurement module, control module and

communications module. It collects data from EBs and MV/LV substation and performs

data analysis functions, monitors the grid and provides an interface with commercial and

technical central systems. It is a vital component of network intelligent control providing

a set of functions core to a true Smart Grid system. It communicates upwards with the

SCADA/DMS and the Metering and Energy Data Management Systems via a Wide Area

Network (WAN) based on GPRS communications and downwards with the EBs by GPRS or

PLC (Power Line Communications). This new infrastructure originated the emergence of a

new system called Sysgrid, which not only allowed to deal with a large volume of the

available information but also to act on the several equipment while at the same time

ensuring the corresponding interaction between existing players systems in the

distribution network. This new system provides an overview of all existing devices,

allowing the operation of a truly active network, acting as a middleware between the

infrastructure and the many other systems, such as the Active Management Systems,

Commercial Systems, SCADA and the geographic information system. Huge changes

have been made in the IT systems in order to adapt and create several interfaces and

business processes to allow an integrated approach leveraging on the emerging

synergies. The Sysgrid design was made modular and expandable in order to include new

features in each equipment (EDP BOX and DTC), and other related with new services

derived from energy data management, without affecting features already covered [129].

Before the InovGrid project, the information that the utilities had about the low voltage

(LV) network was very limited and dispatch centres were able to work in reactive mode

only. Today, the remote access to the EBs and their integration in the system allows to

receive an alarm in case of a failure in the LV network and to verify a fault reported by a

client without sending a team to the location. The tests that were made also show that

consumer empowerment by added information can bring significant benefits to the

overall consumption reduction. In what regards technical and commercial operations,

several operations have become automated (e.g. contracted power changes, connection,

reconnection, disconnection), all can be done centrally saving costs. Regarding the real

consumption, before invoicing was mostly done with estimations, consumers’ behaviour

changes would not be reflected until the next real reading, which could take, in the worst

cases, about 3 months. Today, most invoicing is done based on real consumption [129].

After the first installation in Évora, the project is expanding to six new locations across

Portugal - Marinha Grande, São João da Madeira, Faro’s Islands, Alcochete, Lamego and

Guimarães. It is an opportunity to keep testing and monitoring the adopted technological

solutions in different network topologies and environments. The approach is still to

maintain the promotion of customer's’ engagement and empowerment, to monitor and

control the LV network and to implement strong system integration and end-to-end

processes [129].


4.2.2 Norway

The Norwegian Smartgrid Centre (NSGC) [130] is Norway's centre for competence within

smart grids. In addition to promoting research and development, education,

demonstration projects and commercialisation, the centre is running a national

coordination committee for smart grid related demonstration activities, named "Demo

Norway". The main purpose of this committee is to encourage cooperation and

exchanging experiences from the "living lab" demonstration sites hosted by power

companies, where more than 20,000 customers are involved. Demo Norway also includes

a national smart grid lab, led by the Norwegian University of Science and Technology

(NTNU) and SINTEF [27], [131].

Figure 35: A map showing the demonstration sites of Demo Norway [130].

The following are selected ongoing demo projects in Norway, addressing various topics

within smart grids and coordinated by the Norwegian Smartgrid Centre [27], [131]:

1. Demo BKK (FlexNett): Flexible Grid Operation

The goal of FlexNett is to contribute to flexible and automated grid operation, focusing

on cost-effectiveness, reliability and the environment. This will be achieved by

demonstration and verification of technical and market-based solutions for increasing

flexibility in different levels of the grid. The project involves three demo sites: Demo

Bergen (BKK), Demo Steinkjer and Demo Hvaler.

2. Demo Hafslund: Grid Faults and Interruptions Handling

This project investigates how new smart grid technology can be utilized in the

distribution grid to reduce the interruption time of electricity supply and socioeconomic

costs of interruption. New sensors are being installed for detecting grid faults, sending


information to a control system, and the control system combines this with various grid

measurements for calculating the fault location.

3. Demo Lyse: Customer Services & Demo Smart City Grid

Demo Lyse focuses on ICT infrastructures, and involves testing of new energy services

for residential customers with smart meters. An easy-to-use user portal is provided for

Lyse's customers, which will act as a communication channel between network

operator and customer. Supplementary services – focusing on welfare technology – are

also integrated. The Smart City Grid demo includes testing of 30 smart, automatic

secondary substations.

4. Demo Smart Energy Hvaler

This project involves 6700 smart meters located at customers in an island community.

The connected customers are about 4000 cabins, 2700 residents and some commercial

properties. The demo focuses on enhanced network utilisations, end-user flexibility,

residential PV and energy storage, prosumers and local energy market solutions.

5. Demo Steinkjer

A demo site where about 800 consumers are available for testing solutions for smart

meters, tariff schemes, communication solutions, system services and other products.

The group of consumers consists of ordinary households, commercial and industrial

customers. The main focus of Demo Steinkjer is the development of commercial

products and services for smart grids and study how these can enhance efficiency in

the power system.

6. TSO (Statnett) Pilot North Norway

This is the only demo project executed by the TSO in Norway, Statnett, in cooperation

with local DSOs. In this demo, new modes and tools for planning and operation are

implemented and tested in the regional control centre located in Alta. The challenges

for the future that are being addressed are reliability, quality of supply and reducing

system costs. The project involves monitoring and control of 200 load objects,

prognosis and control of customer energy usage, and TSO-DSO-customer interaction.

7. The National Smart Grid Laboratory

The National Smart Grid Laboratory and Demonstration Platform is a joint laboratory of

NTNU and SINTEF, hosted by NTNU. The lab represents a complete physical model of a

power system, including generators, transmission and distribution systems,

loads/smart homes/prosumers, and an ICT infrastructure and control structure.

Remote access to the laboratory can be made available for research and industrial

partners, so that regional facilities and demonstration sites may be linked to the central

laboratory.

4.2.3 United Kingdom

Smart Grid Technology demonstrations in the United Kingdom have received significant

support from regulator-funded mechanisms, starting with the Low Carbon Networks Fund

(LCNF), a £500m pool of funding for innovation projects made available to DSOs between

2010 and 2015. The LCNF funded smaller ‘Tier 1’ projects, which focused on new tech-

nology and novel solutions, and larger ‘Tier 2’ projects which following a competitive pro-

cess demonstrated the deployment of solutions across large-scale trials. The open dis-

semination of project outcomes was a key condition of LCNF funding, with DSOs publish-

ing detailed learning reports and project closedown reports to share lessons learned and

evaluate technical feasibility and likelihood of business-as-usual adoption.

The LCNF mechanism supported 41 Tier 1 projects, with a total funding of £29.5m. 23

Tier 2 projects demonstrated solutions on networks cases at larger scales, with a total

external funding value of £220.3m.


Figure 36: LCNF Project Area Activity [132].

Figure 36 presents a heat-map of LCNF project areas, where areas with a higher heat

scoring reflect the most frequent learning topics and contexts from LCNF projects. The

specific focus of LCNF Tier 2 projects on live network demonstrations has resulted in sin-

gle projects not only proving the operational functionality of technology, but also ad-

dressing challenges related with customer interactions, integration with existing systems,

development of business processes and technical standards, and the transition beyond

the innovation context to business-as-usual.

Following 2015, the LCNF was replaced by the Network Innovation Allowance, which re-

placed Tier 1 projects; and the Network Innovation Competition, which replaced Tier 2

projects. This innovation stimulus aims to continue driving smart grid innovation on elec-

tricity networks and ultimately bring value to all electricity users.

4.2.4 Germany

The Federal Ministry of Economic Affairs and Energy (BMWi) sponsor most of Germany

SG demo sites. They are used to promote SG solutions and test their practical

capabilities. Some demo sites are presented and explained below.

SECVER

In the project SECVER (Security and reliability of distribution networks on their way to a

future energy supply system) researchers are developing new solutions and

measurements and are simultaneously testing their capabilities in demo sites. The

development and testing during SECVER focuses on new algorithms to integrate

monitoring, flexible loads and storages into existing distribution grids. The model region

Harz (RegModHarz) is used to test the theoretical results and is the source of input data

[133].


Grid4EU

Grid4EU are demonstration projects in Europe, including six demo sites in different

countries. Demo Site 1 is located in Germany, North-Rhine Westphalia, in the area of the

municipality of Reken. There a handbook for implementing multi-agent systems in MV

networks is developed. The demo site is operated by RWE, ABB and TU Dortmund

University.

Smart Operator

The project “Smart Operator” is managed by RWE and operates three demo sites to

analyse the capabilities of Smart Meter. Beside metering loads and feed-in, the DSO try

to integrate heat pumps, storages and smart homes into the distribution network. Their

communication system is using fibreglass cables to connect all components and to allow

of the analysation of situations in order to provide future forecasts of voltage increases

and stabilisation of distribution networks [134].

Smart Country

The project Smart Country is also managed by RWE and is operated with ABB, TU

Dortmund University and Consentec. Different technologies, such as regulated

distribution transformers, medium voltage regulation and conventional planning methods

are tested. The model region Rhineland-Palatinate is analysed and ICT is used to

implement new components, such as biogas plants.

RiesLing

The project region “Nördlinger Ries”, located in southern Germany, hosts the demo site

of the project RiesLing, which analyses the use of intelligent secondary substations.

Voltage regulators provided by the “Maschinenbfabrik Reinhausen” was used to gain the

full voltage range of ±10 %.

Table 15: Projects currently operating or executed in the past

Project Technologies Region

DESIGNETZ ICT Ruhr region – North-

Rhine Westphalia

Smart Country RDT/OVC/Biogas Storag-

es/MV Regula-

tion/innovative cable de-

sign

Trier

Smart Operator Smart Meter Kisselbach, Wincher-

ingen, Schwabmünchen

SECVER ICT, Monitoring System Harz

RiesLing RDT Nördliner Ries

Grid4EU ICT/DNA Reken (German site)


4.3 Interoperability of SG systems

Interoperability is the ability of two or more networks, systems, devices, applications, or

components to interwork, to externally exchange and use information readily, securely

and effectively in order to perform the required functions [135] [136].

Despite using different information systems and infrastructures, interoperability of smart

grids may enable organisations to communicate effectively and transfer data securely.

When manufacturers follow one interoperability model, hardware and software that

utilities, companies and other stakeholders install in the power networks will interwork

properly with other components [137].

Smart grid technologies are evolving across the whole value chain of the energy system.

This of course includes technology, which network operators will use in the distribution

network. That is why the scope of the research project makes it necessary to look at

interoperability.

Smart grid interoperability models were defined by the standards organisations IEEE as

well as by the Comité Européen de Normalisation (CEN), CENELEC and the European

Telecommunications Standards Institute (ETSI). They are briefly introduced in the

following sections.

4.3.1 Consistent terminology

In order to reach a common understanding on the interoperability of smart grid systems,

key terms and definitions need to be clarified. In the following, definitions according to

CEN, CENELEC and ETSI [137] are given:

Compliance

Accordance of the whole implementation with specified requirements or standards.

However, some requirements in the specified standards may not be implemented.

Conformance

Accordance of the implementation of a product, process or service with all specified

requirements or standards. Additional features to those in the requirements / standards

may be included.

All the features of the standard/specification are implemented and in accordance, but

some additional features are not covered by the standard/specification.

Conformance testing

The act of determining to what extent a single implementation conforms to the individual

requirements of its base standard. One important condition in achieving interoperability

is the correct implementation of standards. This can be verified by conformance testing.

Conformance testing determines whether an implementation conforms to a profile as

written in the Protocol Implementation Conformance Statement (PICS). Related testing

can be interoperability testing if the profile covers the interoperability requirements

additional to the conformance testing requirements of the standards applied.

Conformance testing is a prerequisite for interoperability testing.

Interoperability Profile (IOP)

An IOP profile is a document that describes how standards or specifications are deployed

to support the requirements of a particular application, function, community, or context.


Interoperability Testing

Interoperability testing is performed to verify that communicating entities within a

system are interoperable, i.e. they are able to exchange information in a way that is

semantically and syntactically correct. During interoperability testing, entities are tested

against defined profiles.

Reference Model

A collection of concepts and their relationships that cover a subject facilitate the

partitioning of the relationships into topics relevant to the overall subject and can be

expressed by a common means of description [135].

4.3.2 Agreements

There are at least two similar interoperability models for smart grids: the IEEE 2030

smart grid interoperability reference model (SGIRM) [135] and SGAM [136] that was

jointly developed by the European standards organisations CEN, CENELEC and ETSI.

The IEEE 2030 SGIRM describes three interoperability architectural perspective concepts

(IAP) including the perspectives of power systems, communications and information

technology. The aim is to achieve interoperability by considering these perspectives. All

domains, entities and interfaces or data flows that comprise each perspective are defined

in the SGIRM. Common domains are bulk generation, the transmission and distribution

system, service providers and end-use applications. Entities means devices, such as

computer systems, that are located inside a domain and are interconnected. Interfaces

are logical connections between the entities, supporting data flows implemented with

data links [135].

The power system perspective (Figure 37) is the most relevant for the Smart Guide

project, as it focuses on the production, delivery and consumption of electrical energy

including apparatus and applications.


Figure 37: IEEE 2030 smart grid interoperability reference model for the power systems


A similar model was defined by CEN, CENELEC and ETSI [136], accounting for

characteristics of the European electricity networks. The Smart Grid Coordination Group

(SG-CG) Reference Architecture Working Group (SG-CG/RA) developed a technical

reference architecture for smart grids. Technical architecture model means a set of

models allowing description and prescription including the new stakeholders, applications

and networks that need to operate in network that is evolving toward a smart grid.

Considering the extent of the architecture, the group focused on particular aspects of the

architecture. These are, for instance, the means of communication on a common view

and language e.g. with the industry, customers and regulators. They also include the

support for planning – transition from an existing legacy architecture to a new smart-

grid-driven architecture. Focussing on interoperability itself the group strived to develop

criteria to properly asses the conformance with identified standards and given

interoperability requirements. Incorporating these aspects, it was their objective to

define an architectural framework supporting a variety of different approaches and a

methodology that can be applied to a variety of cases. They did this by integrating

various existing approaches into one model with additional European aspects. Integrating

existing implementations of an architecture, they build upon the National Institute of

Standards and Technology (NIST) Conceptual Model.

The main outcomes of the work are a European conceptual model, architecture

viewpoints as well as SGAM Framework. The model is an evolution of the NIST model,

which now includes DER. The stakeholders’ viewpoints represented in the model are

those of business, function, information and communication. The SGAM framework (cf.

Figure 38) is composed of the three dimensions Domain, Interoperability (Layer) and

Zone. The five interoperability layers allow the representation of entities and their

relationships. The layers include a view on components, communication, information,

function and business.

Figure 38: SGAM Reference Architecture [136]

In the third Work Package of SmartGuide project, several distribution networks will be

studied and their operation and planning will be optimised considering a smart grid


environment. For the purpose of this work, it will be assumed that all network

equipment, including the distributed energy resources considered to be deployed in each

scenario, are interoperable and thus capable of exchanging information in a

straightforward and accurate manner.

5 Technical overview of SG solutions per country

In this chapter, the technologies and solutions available or expected in each country are

addressed.

5.1 Portugal

Most of the technologies are not widespread at this time in Portugal, being exploited by

small-scale demonstrations or at least by concepts form. Its availability, potential and

expected impacts are presented in the next sections considering mainly project demos

and based on foreseeable scenarios.

5.1.1 Voltage control

Currently, in Portugal the voltage control at MV level is implemented using transformers

with OLTC mechanism and capacitor banks. These devices can change their taps

automatically but, at this time, there is no coordination in these actions with the

management of DER.

Currently, there is no procedure of voltage control at LV level except the protection

systems used in the microgeneration units, which are activated when voltage at the

connection point of these units is outside admissible limits.

In the SuSTAINABLE project (see more in section 4.1.7) a concept for advanced voltage

control was proposed that exploited two different levels of control – MV and LV levels –

as shown in Figure 39.

Figure 39: Framework of the voltage control system in SuSTAINABLE project [125].

Within the proposed methodology, an advanced voltage control implicates a coordinated

management of the several DER connected at the MV and LV levels in order to guarantee


a smooth and efficient operation of the distribution system as a whole. The coordinated

control concept defines an optimal day-ahead scheduling in a distribution network,

aiming to minimize an objective function that may include several objectives beyond

voltage regulation (losses, tap wear, overall power factor etc.). The algorithm considers

all network devices and systems that contribute to voltage regulation [125]. A multi-

temporal OPF operation at the level of the HV/MV primary substation level is responsible

for controlling MV network operation.

In the field demonstration, various functionalities were tested within the SuSTAINABLE

context. One of them has the purpose to assure voltage values remain within regulatory

limits, by making use of several DER spread throughout the network. Following the

SuSTANAIBLE architecture, each DER contains a prototype inverter that is controlled by a

smart meter, which communicates with the DTC installed in secondary substation where

the voltage control is embedded. The smart meters are connected to the inverters via

local interface - HAN interface allowing the smart meter to serve as a gateway to the

costumer’s house. Two different prototype inverters have been developed within the

demonstration, Smart solar inverter for interfacing PV generation and Smart battery

charger for interfacing battery storage units. The inverters have active power/voltage

drop control embedded in their hardware for local voltage control purposes.

Figure 40: Modules for controlling the voltage - SuSTAINABLE project [125].

The user can interact with LV controllable resources such as batteries and solar inverters

by using available modules for visualization and control the smart meters data as shown

in Figure 40 [125].

5.1.2 Metering and communications

A growing number of DER installations, mainly those associated to micro-generation,

have impact in the network operational conditions creating new challenges. That will

necessitate the implementation of improvement mechanisms to assure the quality and

continuity of supply by changing the configuration of LV network and controlling

connected generation devices. Eventually this could lead to different modes of operation,

such as self-healing features and islanding operational modes. These new requirements

of a truly smart grid concept encompass also metering features and demand for energy


efficiency. In order to tackle them, an innovative and transformational approach is

needed towards the development of a new technological and communicational

infrastructure for the short-term future distribution network. Figure 41 shows that a new

set of devices – EDP Boxes (EB’s) – will physically substitute the actual meters, providing

various functions that, besides the metering and remote management of contractual life-

cycles operations, will interface and control the micro-generation installations and help to

promote end-use energy efficiency, load control and demand response [138].

Figure 41: Architecture of communicational infrastructure – InovGrid [138].

Those EB’s will provide the energy supplying and monitoring information and receive the

essential commands to control micro-generation injection, connected through a LV

network to a DTC. These commands go from simple security switch states operation to

next step advanced functions for inverters set point regulation [138].

5.1.3 Distributed Energy Resources Management

The management of microgeneration, storage and EV charging is directly related with the

voltage control functionality. Thus, the foreseen developments in this area could be also

described taking into account the demonstrations produced under the scope of SuSTAIN-

ABLE project.

As referred to earlier in the section 5.1.1, each DER contains a prototype inverter that is

controlled by a smart meter which communicates with DTC installed in secondary substa-

tion where the voltage control is embedded assuring an advanced voltage control with a

coordinated management of the several DER connected at the MV and LV levels.

Also within the SuSTAINABLE project, a solar power forecasting system was developed

and is running operationally for several MV/LV secondary substations in Évora, Portugal.

It uses different types of statistical models that combine information from weather pre-

dictions and time-series data collected by smart meters / data loggers geographically

distributed. Regarding the load forecast, an innovative method using artificial neural


network was developed to estimate percentage of different load categories and distin-

guish controllable load from the total demand. This module could be integrated with the

total demand-forecasting tool, which will provide prediction of load compositions and

their controllability and eventually facilitate effective DSM.

5.1.4 Management and Control

Under the scope of the demonstration, going-on on the InovGrid project it is possible to

analyse the architecture proposed to management and control in a smart grid

environment. Figure 42 shows a global summary of the InovGrid’s architecture. In this

context, EDP has used a simulation and analysis software (DPlan) that optimizes

operations and investment planning.

Figure 42: Architecture of InovGrid [91].

Architecture of InovGrid

Through interfaces for consumers with information on energy consumption, generated

energy and management tools to react to externals signals, active demand response is

encouraged. The use of real-time gateway for stakeholders to control the local

consumption of electricity using demand-side management of large consuming devices

such as heat pumps and electric vehicles is also investigated in the InovGrid project [91].

Integration with Smart Homes is achieved by providing energy management functions of

home automation devices and smart appliances that stimulate energy efficiency. Smart

Metering Infrastructure includes the EDP Box to substitute the conventional meters at the

consumer/producer premises and the DTC at the MV/LV substations. This equipment

enables grid monitoring through data gathering at the consumer and substations level,

data analysis functions and interface with commercial and technical central systems,

enhancing grid automation and new market solutions [91].


Smart Grid Asset Data Management System (Sysgrid) has the functionality of Smart

Metering Data Processing where commands are executed and the data is gathered of the

InovGrid infrastructure [91].

The EBs and DTCs (see section 4.2.1) are essential to monitor and control LV networks

providing real-time information on the grid that will be used also to evaluate the impact

of micro-generation on system voltages, currents, reliability and losses. To automate and

control MV networks the control intelligence integrated in the DTC and automation

mechanism can be used. Remote controls of MV devices and monitoring at the substation

level are essential to anticipate complications and also reduce the need of intervention in

the work field and guarantee short time failures [91].

Control and automation functionalities distributed over a hierarchical control structure

enable the synchronized and synergistic management of DER, including DG, responsive

loads and storage. In the case of integration of EVs, due to the foreseen development,

the already existing Portuguese EV charging infrastructure energy flow is monitored and

controlled by the InovGrid platform, exploiting the potential flexibility of actively

managing electric vehicles battery charging [91].

The Device Language Message Specification- Companion Specification for Energy

Metering (DLMS-COSEM) is used in InovGrid to enable a structured way of transmission

of data currently from the Smart Meter EDP Box to the Systems. A SCADA application is

used to collect data from the DTC and support remote control on the medium voltage.

For communications, Prime PLC technology is being applied [91].

DPlan

DPlan evaluates immediately the impact of a grid change simulation on system voltages,

currents, reliability and losses. Due to the new smartgrids paradigm associated with the

InovGrid project, new simulation requirements are necessary in order to allow DPlan to

use the available data. DPlan had a LV module based on probabilistic assumptions related

with typical LV loads behaviour. On top of that, probabilistic module a new deterministic

module was created that allows simulating chronologically the behaviour of the low

voltage grid using the data made available by the InovGrid infrastructure. Moreover,

three-phase unbalanced power flow was extended to include microgeneration (solar PV,

wind, or other) as well as capacitor banks installation. DPlan’s LV analysis thus comprises

probabilistic and chronological three-phase unbalanced power flow analysis, as well as

protection analysis against short circuits, overloads and people’s safety. The new

functionalities of DPlan allow inserting smart metering data, visualizing and editing load

diagrams, and performing daily analysis of the grid behaviour. Graphics and reports

compare power flow and losses results with metering data. The comparisons between

synchronous power consumed at different voltage levels of the grid (Energy Boxes at LV

and DTCs at MV) together with synchronous power flow results are synthesised to

provide estimates on technical losses and to validate commercial losses [139].

The impact of microgeneration on real grids can be estimated using the probabilistic and

the chronological analysis tools. The impacts on losses and voltages are a direct result of

both analyses. The probabilistic analysis is more appropriate to assess the risk of voltage

unbalanced and possible overvoltages, as it deals with limiting values with 95%

guarantee. The chronological analysis is more appropriate to assess the impact of

microgeneration on losses reduction, as it deals with energy load diagrams of 15 by 15-

min resolution [139].


5.2 Norway


Large investments in the distribution grid in Norway are planned the next 20 years due

to the need for upgrades in the power system. The estimated cost are at least 60 billion

NOK (excluding the costs of the smart meter roll-out), but in a report from SINTEF

Energy Research, these investment costs may be reduced by 40–50 % in many cases by

considering voltage regulation as an option to grid reinforcements. Much of this potential

lies within connecting new loads or generation units, where the limit for drop/rise in

voltage often becomes the restricting factor. By including voltage regulation in new

power plants instead of upgrading the power lines, this may lead to significantly smaller

costs and potentially that more of these plants will be completed. Voltage regulation can

eliminate the need for reinforcements, or postpone the decision regarding reinforcements

until the expected load and production becomes more certain [140].

On-load voltage regulated distribution transformers

No on-load (automatic) voltage regulated distribution transformers have been installed in

Norway so far. Such transformers are most relevant for lines with few substations and

large voltage variations [141].

Line voltage regulators

Line voltage regulators have been used for a long time in the LV distribution grid, mainly

for ensuring a stable voltage for loads that are sensitive to voltage variations. It has also

been used for compensating voltage drops in the LV distribution grid due to high loads. A

few line voltage regulators have also been installed in the MV distribution grid through

pilot projects, and some of these are still running. The voltage regulators being used

today can typically regulate the voltage by ± 10 %, and some can regulate by ± 20 %

[142].

Reactive power control

Most small-scale power plants in Norway are equipped with synchronous generators, and

can therefore control reactive power without affecting the active power. In most such

cases, it will be cheaper to choose a generator with low synchronous reactance or with

sufficient capacity for drawing reactive power, than installing shunt reactors. The use of

shunt reactors is most relevant for cases where the generator is already chosen and does

not have the sufficient capacity to draw reactive power [142].


National roll-out of smart meters

In 2011, the Norwegian Regulator (NVE) decided that by January 1st 2017, all metering

points must have a smart meter installed. In 2013, this deadline was postponed to

January 1st 2019. The DSOs are responsible for the smart meters. Since January 1st

2015, it has been mandatory for the DSOs to present their progress in the installation of

smart meters, by reporting periodically to the Norwegian Regulator (NVE). The statutory

requirements as of January 2012 are that the smart meter must [143]:

1) register measurements with a maximum logging interval of 60 minutes, and have

the possibility to set the logging interval to minimum 15 minutes,

2) have a standardised interface that facilitates communication with external

equipment based on open standards,

3) allow connection and communication between other types of smart meters (water,

gas, ...),


4) ensure that saved data are not lost in case of voltage interruptions/outages,

5) be able to disconnect or limit the power output at the metering point (except for

installations metered via a transformer),

6) be able to send and receive information about electricity prices and tariffs, and be

able to transmit control signals and earth fault signals,

7) provide security against abuse of data and unauthorised access to control functions,

and

8) register flow of active and reactive power in both directions (four-quadrant

measurements).

When the directive regarding smart meters was introduced in 2011, 34 DSOs had already

installed automatic meter readings at approximately 200 000 customers. In total, smart

meters will be installed at approximately 2.9 million metering points, and 76 % of the

meters are planned to be installed between 2017-2018. The total cost for the national

smart meter project has been estimated to be 10 billion NOK [144]. The most popular

solution for communication between meter and concentrator is radio/radio mesh, while

mobile/cellular and fibre networks are the most chosen solutions for concentrator – head

end communication. [1] The market for delivering smart meters and communication

solutions for AMI is shared between Nuri Telecom LtD (25.5 %), Aidon (52.0 %) and

Kamstrup (22.5 %) [145].

Elhub

Along with the national rollout of smart meters, a nation-wide data hub named "Elhub" is

under construction, and will include functions for handling these enormous amounts of

data. The main goal of Elhub is establishing an economically efficient IT infrastructure for

the retail market for electricity. The Elhub project was assigned to Statnett by NVE in

2013, and the planned release date for Elhub is now October 23rd 2017. Electricity

consumption at an end user is registered by their smart meter, which transmits the data

to the DSO and further to Elhub until 7AM the following day. The data are then available

for electricity suppliers/balance suppliers and the end user by 9 AM. Elhub will contribute

to the efficient distribution of high-quality metering values, performing certain tasks that

will save the DSOs both time and money, and simplifying business and market processes

[146]. A simple illustration of how Elhub will work is shown in Figure 43.


Figure 43: A simple illustration of the main functions of Elhub [146].

Sensors, devices

In the Norwegian transmission grid there are approximately 20 installed and 12

immediately planned PMUs. The deployment and use of PMUs in Norway has been mainly

within R&D activities, but the TSO (Statnett) has started to put this technology into use2.

Thus, the PMUs are becoming a part of the grid information infrastructure, and are being

increasingly used for disturbance and fault analysis in addition to model validation. An

example of R&D involving PMUs is the Nordic R&D project STRONgrid, which includes

development and testing of synchrophasor applications [69].

A large portion of the substations in Norway were built in the 1980's. With the changes in

the power system, there is a need for modernising the substations using smart grid

technologies for monitoring and control [147]. Smart meters can be installed in

substations to register the flow of power, but they have a more limited application than

RTUs. The power metered in the substation may be compared to the power consumption

registered by the smart meters of the connected customers. This may uncover abnormal

power losses or illegal tapping of power. The use of RTUs in substations is not

widespread, but some DSOs are gradually investing in smart substations. One example is

Lyse Elnett (DSO), who will make 25–30 substations fully automatic, testing smart

substations in combination with the new smart meters installed in customers' homes.

This can provide remote monitoring and control, and automatic switching in case of

faults. This large-scale project is being deployed in the Stavanger area, in cooperation

with ABB [148].

2 Read more about Statnett's project SPANDEx (Synchrophasor/PMU Application Integra-

tion Data Exchange): http://www.statnett.no/en/Sustainability/Research-and-

Development-/Our-priority-areas/Smart-Grid/SPANDEx/

http://www.statnett.no/en/Sustainability/Research-and-Development-/Our-priority-areas/Smart-Grid/SPANDEx/

http://www.statnett.no/en/Sustainability/Research-and-Development-/Our-priority-areas/Smart-Grid/SPANDEx/



Microgeneration, microgrids, nanogrids

Microgrids in Norway have been considered in relation to supplying islands, as an

alternative to investing in expensive sea cables. Microgrids have not come closer to

realisation than demonstration projects. Nanogrids are not relevant for Norway.

The development of microgeneration is trending in Norway, where both private and

professional consumers are becoming increasingly interested in installing grid-connected

rooftop PV systems, becoming prosumers. Financial incentives (mainly support for

investment costs) and the decreasing costs of PV systems are important drivers behind

this trend. Studies have shown that consumers are also motivated by producing their

own electricity, contributing to the environment, and the interest in new technology. New

market actors are emerging, offering complete solutions (solar panels, inverters,

installation, grid connection, etc.) for grid-tied PV systems. An example of such a

company is Otovo. These complete solutions simplifying the process of becoming a

prosumer, for customers as well as grid companies. This contributes to an increasing

number of Norwegian prosumers, despite the long payback period of the PV system

investment [149].

Storage

As described in section 2.2.1, energy storage in Norway is dominated by hydropower

storage in reservoirs. Other types of energy storage are not widespread, and are mostly

parts of R&D activities. A form of distributed energy storage that has been part of a pilot

study regarding demand response in households, is exploiting the thermal storage

capacity of electrical water heaters. Since a large share of heating systems in Norway are

electric, disconnecting these loads for a few hours (without causing discomfort for the

customer) represents a significant potential capacity [150]. The many plug-in EVs in

Norway may also be utilised as energy storage (V2G) but this requires smart control

systems and is still quite far into the future [27].

Demand response

Some activities regarding demand response are already implemented by some Norwegian

DSOs, but most of the activity is still in the stage of demonstration and research

projects. Energy services and technologies that are available today are mainly aimed at

efficient energy use for large consumers such as commercial buildings, which already

have hourly metering. Available services for the customer include consumption-

monitoring, control of lighting and heating systems, and optimising energy consumption

(typically consulting). Large customers, as well as households, may also be offered tariffs

based on peak load (kW), instead of the more commonly used energy tariff (based on

kWh). By using price signals, the consumers are encouraged to reduce their power

demand, typically apparatus with high power consumption such as electrical boilers.

Today, very few DSOs have introduced peak-load tariffs for all customers, including

households, cabins, etc. [151].

Several R&D projects studied by [151] are focused on reducing peak loads for

households and cabins, for contributing to a reduced need for investments in the

distribution grid. The different project investigate different aspects of demand response,

such as demand response potential, consumers' reactions to pricing signals and

information about their consumption, and which technologies for control are preferred by

end-users.


In May 2015, Enova3 announced a contest for new technologies, entitled "smart meters –

smarter consumption” [152]. The purpose is to deploy different technologies for

applications and interfaces to engage the end-users with smart meters, to study the

effects on user behaviour and energy consumption. Almost 60 million NOK were

distributed among seven new projects, each with their own solution for engaging the

end-user [152].

Electric vehicles

In 2015, 17 % of all sold (personal) cars in Norway were electric [153]. The main reason

why Norway has become the leading market for EVs, is the Norwegian EV policy. EV

owners have several benefits: There are no value-added tax (guaranteed by the

government until 2018) or one-off registration tax (until 2020) when purchasing and

leasing EVs, which makes the price of EVs equal to or lower than an equivalent cars

running on gasoline or diesel; EV drivers do not have to pay fees for driving on toll

roads; EVs have free parking at municipal parking lots; EVs may drive in the public

transport lanes; on some ferries, passage for the EV is free (but the driver and

passengers must be paid for) [154]. Figure 44 shows an accumulative overview of the

number of registered electric vehicles and plug-in hybrid electric vehicles (PHEV) in

Norway.

In areas with weak distribution grids, the voltage quality may be significantly lowered

when many EVs are charging simultaneously, and single-phase charging may cause

challenges related to voltage imbalances. Systems for smart charging and load shifting

may reduce these challenges, and the smart meters will play an important role in this.

Implementing peak load tariffs may also be important for motivating end-users to reduce

demand peaks, by for example charging the EV at night [155]. Large amounts of

reinvestments are planned towards 2030, and when planning these upgrades, the

electrification of the transport sector should be kept in mind. In a scenario where all

vehicles must be emission free from 2025, it is estimated 1.5 million EVs by 2030, which

may increase the yearly energy consumption by approximately 3 % (4 TWh) [155].

3 Enova is public enterprise that is owned by the Norwegian Ministry of Petroleum and

Energy, established with the purpose of promoting more environmentally friendly con-

sumption and generation.


Figure 44: Number of registered EV/PHEV in Norway per year (updated September 2015).

Asset management

In 2012, the TSO Statnett was certified according to the British asset management

standard PAS 55 (the "prequel" to ISO 55000), as the first Norwegian company to

receive this certification [156]. There are no common methods for asset management

among the numerous Norwegian DSOs.

Risk and vulnerability analyses are required by the regulator (NVE), which also provides

guidelines for risk and vulnerability analysis. The suggested method consists of

identifying hazards/threats/undesirable events, risk assessment (probability vs.

consequences), identifying measures for reducing the risks, presenting the results

(typically in risk matrices), and finally planning measures for reducing risks and being

prepared for emergencies. There is also the Norwegian Directorate for Civil Protection

(DSB) that decides maintenance requirements for the grid companies, for example

frequencies for inspecting overhead lines and replacing pole-mounted transformers

[157].

The CENS (cost of energy not supplied; Norwegian: "KILE" [158]) regulation scheme is

used for adjusting the grid companies' revenue caps, as an encouragement to improving

their reliability. As mentioned in chapter 2.2, reporting faults and outages through the

FASIT system is mandatory. This collection of reports create useful statistics for (roughly)

estimating failure rates, which may be used for e.g. estimations of CENS. CENS is an

important input parameter in risk and technical-economic analyses for many Norwegian

network operators [157].

There is a shift in the general strategy, from maintenance at predetermined intervals,

towards predictive, risk-based maintenance and reinvestment management strategies.

The incoming implementation of new ICT and smart meters will provide value within

asset management, in the form of new data enabling better decisions within planning,

operation and maintenance.

Forecasting for DG/load

Today, grid planning is performed with limited knowledge of the load conditions in the

grid, using standard load profiles and yearly consumption data for estimating peak loads

(Velander's formula is often used). For load flow and peak load calculations in relation to

grid planning, network information systems (NIS) such as NetBas and GeoNIS are

frequently used [159]. The increasing amount of unpredictable loads and DG units, such

as EVs and grid-connected wind and PV generation units (prosumers), causes greater

uncertainties for forecasting, depending highly on weather conditions and consumer

behaviour. Smart meter data may be useful for constructing dynamic load profiles,


continuously updated by smart meter data. This topic is integrated in research project

such as the Norwegian project DeVID [160].


SCADA/DMS

The grid companies' control and communication systems are separated at the voltage

level of 11/22 kV. From voltage levels of 11/22 kV and up (regional and transmission

grid), SCADA systems are used, which includes the transformers where the LV

(secondary) side is at 11 kV, but generally not transformers with 11 kV at the HV

(primary) side. From 11/22 kV and down to the consumer level (LV distribution grid),

DMS can be used (not all DSOs have implemented DMS). The main reasons that

components at lower voltage levels are not included in the SCADA system, are safety

regulations in addition to costs that are too high compared to the potential gains [161].

DMS has become more relevant in the later years, much due to the Norwegian rollout of

smart meters, but is still quite new. Some DSOs have already implemented DMS, and

there are several demonstration projects that include DMS, such as Demo Steinkjer and

Smart Energy Hvaler, both part of the DeVID project [161]. The DeVID project has

identified potential functionalities for DMS such as: automatic analysis of earth fault,

locating and presenting faults in the MV distribution grid and end-user outages, warning

the DSO in case of too high or too low voltages over time, disconnecting customers

through their smart meters in cases of faults in the grid that may harm customers'

equipment, locating LV grid faults and deciding whether it is in the grid or in a customer

installation [162].

Automation strategies

The degree of automation in today's Norwegian distribution system is generally low, but

not non-existent. As mentioned in section 2.2.1, 25–30 fully automated substations are

being built by the DSO Lyse Elnett. These substations will be controlled via the control

centre and DMS, and the goal is to study the advantages of smart substations combined

with the new automatic smart meters located at approximately 1300 customers. Some of

the most important gains of grid automation are: optimisation of switch positions,

voltage regulation of transformers, customer satisfaction through quicker notifications,

and more efficient fault management and registration in FASIT [148].

As a consequence of the launching of the nation-wide Elhub, there will be more

requirements regarding automation and standardization of components in the power

system. One of Elhub's main functions is automatic processing and distribution of meter

data, which simplifies operation for DSOs as well as electricity suppliers/balance

suppliers.

Control and monitoring

A survey among Norwegian DSOs conducted in 2014 by SINTEF Energy Research and EC

Group, shows that there are large variations in how DSOs operate. Most large DSOs

(> 50 000 customers) own and operate their own control centres, while some of the

smallest DSOs (< 10 000 customers) do not have control centre functionalities. Today,

the control centres mainly perform the following tasks [163]:

Fault management

Revision planning

Optimising grid topology i.e. optimal configuration

Generation management to some extent

Management of switching operations


Operation statistics including fault and interruption statistics (FASIT)

Change power system variables and set points (e.g. set points of regulators,

voltage control, etc.)

Management of customer requests and providing information for the costumer

(this responsibility is typically shared with the customer call centre)

There is almost no monitoring of components in the LV distribution grid (<1 kV).

Monitoring of the LV system is mostly done manually, through telephone calls, e-mails,

Internet pages, and not by on-line, real-time measurements. Monitoring of the MV

distribution grid is limited to a few circuit breakers and short-circuit indicators in MV

feeders, along with feeder bays in secondary substations (where circuit breakers, relay

protection and monitoring equipment is placed). There are circuit breakers and fault

indicators in a few distribution transformers that are monitored, and some installations

have relays that can estimate the distance to faults.

In general, the DSOs have little overview of the status (current, voltage) at customer

connection points, and DG is normally not monitored from the control centres in the

distribution systems. Hence, DG power plants are not available in the SCADA systems,

and the control centre operator does not have real-time information regarding these

plants' active or reactive power flows. Telephone calls and other channels are used for

obtaining information about DG units [163].

Load management, in the form of disconnection of customers and limiting consumption,

has been implemented by several DSOs, but only for a few customers each. These

customers are mainly within the industry or power producers, often connected to the

regional grid. For household customers, such functionalities are basically non-existent in

practice, but it has been an element within R&D [161]. A pilot study was conducted in

2006–2007, hosted by the small DSO Malvik Everk, which at that time was one of the

few DSOs with full rollout of meters for automatic reading of the electricity consumption

(AMR). The 40 participating household customers were offered remote load control (RLC)

of their electrical water heaters through the AMR. The load was reduced in predefined

peak load periods with high electricity prices, and shifted to off-peak periods, thus

creating economic savings for the customer [150].


5.3 United Kingdom

The following sections present examples of Smart Grid Technologies that have been

trialled in the UK and either under business-as-usual roll out, or further demonstration to

enhance operational learning.


OLTC Technology at Distribution Substations.

OLTC technology, as described in Section 3.1.1, is commonly found at primary substa-

tions in the UK (33/11 kV or 66/11 kV). At distribution substations (11/0.4 kV), trans-

formers conventionally have fixed-tap operation which limits the capability to manage

voltages on the LV network. Recent innovation projects have demonstrated the deploy-

ment of OLTC transformers at distribution substations (11/0.4 kV), providing the capabil-

ity to dynamically control the LV network voltage profile with the support of AVC relays.

Supporting desktop analysis has identified increase in LV hosting capacity, improving

both voltage-rise “headroom” for 50% increased DER connections and voltage-drop “leg-

room” supporting an additional 87% demand growth [165].

The benefits of this solution will depend on the network conditions, such as feeder im-

pedance and DER location, where a heavily loaded feeder may benefit from specific local

voltage management solutions, such as energy storage or reactive power support. The

inclusion of further voltage monitoring on specific feeders increases network headroom

as observability is enhanced. Although only demonstrated within the context of innova-

tion projects, at least one UK DSO has included distribution transformer OLTC technology

for future deployment, although the additional cost must be justified at the planning

stage using look-ahead forecasts of voltage fluctuation and DER uptake [166].

Enhanced AVC

The management of voltage profile across 33 kV and 11 kV networks is conventionally

delivered via the AVC relay at OLTC transformers, described in Section 3.1.1. Varying

degrees of automation and controllability are found across networks, as a minimum of-

fering local tap-control based upon local voltage measurement at the transformer LV bus.

Modern AVC capability includes the option of remote control, with input from an engineer

within the network control centre or from a distributed autonomous controller.

Wider co-ordinated management of system voltage has been demonstrated in innovation

projects, with the trial of Enhanced Automatic Voltage Control. Such projects are moti-

vated by further increasing hosting capacity for demand growth and DER connections,

taking an operational active approach to increasing connection capacity. This control has

seen the development of voltage control algorithms that utilise greater visibility of net-

work operation to inform AVC set-points through remote measurements of voltage, DER

operation and demand [167][168].

Further enhancement of voltage control has demonstrated the control of other technolo-

gies such as such as energy storage, capacitor banks and demand response to manage

the voltage profile on at-risk feeders [169]. Learning from these projects has identified

that any solution must be able to secure the network if smart devices or technology is

not available, thus without sufficient fail-safes, the additional network hosting capacity

that can be released is limited. Ongoing demonstration projects are continuing to study

wide-area voltage control, including the integration of forecasting to inform AVC settings

and minimise the number of tap-changing events [170].

Reactive Power Control

Several trials have investigated the dynamic control of reactive compensation devices to

manage voltage profile on distribution networks, providing localised control to smooth

the voltage profile and mitigate voltage drop and peak events [171]. Although found to

be an expensive solution when compared to the MVA capacity released, the technology

has successfully managed voltage profile. Experience from demonstration has highlighted


the importance of integrating such solutions into the network planning process with suit-

able Cost-Benefit Analysis to inform deployment.


Advanced Metering Infrastructure

The UK Government aims to install Smart Meters in 53 million homes and small busi-

nesses by 2020. In response to this, UK DSOs have sought to leverage this growing vol-

ume of Advanced Metering Infrastructure to improve demand response capability and

better inform planning assumptions.

Disaggregated electricity usage data from large numbers of Smart Meters has been uti-

lised within trial projects to provide clearer visibility of LV network load profiles. This en-

hanced monitoring has informed the revision of planning assumptions, allowing the re-

placement of conservative demand forecasts with empirical demand levels, updating fac-

tors such as After Diversity Maximum Demand (ADMD) levels [173]. These revised as-

sumptions have been applied to standard planning procedures to increase the network

capacity in demand-saturated networks [172], [174]. AMI projects have identified the

need for new and improved novel tools for future network planning and forecasting to

better utilise the live data from Smart Meters. For Smart Meters to provide operational

network support, regulatory barriers currently limit the capability of a DSO to directly

access customer demand data from individual Smart Meters, therefore DSO-specific in-

frastructure must be added to provide such benefits.

DSOs have trialled the use of Time of Use tariffs to incentivise demand shifting to support

demand; however the regulatory basis requires this to be implemented via the energy

supplier, therefore limits the capability for real-time dynamic control, increasing costs of

implementing solutions [175].

Communications Technologies

The UK roll-out of Smart Grid Technologies has triggered the trial of novel communica-

tions technologies, used to provide the enhanced transfer of data required for increased

controllability of network assets.

Power Line Communications

PLC has been applied under trial conditions to LV network applications, such as commu-

nications between distribution substations and EV chargers to limit EV charging when

required by the LV network. Project learning has reported that PLC communications was

not a highly reliable method of communication, with effective availability of 65 % [176].

Fibre Optic communications provides a high-speed, high capacity medium largely used

in transmission networks. This is acknowledged as a high-availability and high-bandwidth

communications medium that can support the need for fast-acting response. High instal-

lation costs and limited distribution to rural areas, often the location of DER’s has pre-

vented larger utilisation across distribution networks. In some area, DSOs are rolling out

fibre-wrapped conductors to enhance field communications [177][168].

GPRS, GSM and LTE Wireless

The UK cellular networks (GPRS, GSM and LTE Wireless) present a viable option for

secure and stable channel communications due to the mature infrastructure. Trial use of

cellular networks for SGT communications has identified reliability challenges, due to var-

iations in coverage and interference [178]. The implications of intermittent-availability

communications for automation and monitoring will restrict the applications for this low-

cost communications medium.


Demand Response


UK DSOs are increasingly utilising flexible demand provided by Industrial and Control

(I&C) customers, with flexible contracts used to unlock network capacity and defer rein-

forcement. One example takes the form of non-firm demand connection contracts with

individual customers, where an interruptible connection is taken that does not guarantee

supply following an outage or fault elsewhere on the network, where redundancy is con-

ventionally provided [178]. In one example, a new I&C connection charge was reduced

from over £7m to £0.37m through deferral of reinforcement; such an approach would

save approximately £50-70m per DSO in investment towards 2050 [181].

In other cases, DSOs have procured call-off contracts with customers through aggrega-

tors to provide network support services to avoid costly demand-driven reinforcement

[182]. Dispatch systems have used Active Network Management technology to monitor

loading on congested assets, triggering demand response actions from aggregators once

thresholds are tripped. Such operational solutions to planning challenges has required

the development of new cost-benefit analysis methodologies to support the cost-efficient

planning of network expansion [182]. Customer participation factors must be applied to

determine the number of customers and available demand that is required to rely on DSR

for security of supply factors.

Grid-Scale Energy Storage has been deployed in the investigation of revenue streams

that will facilitate deployment and ultimately allow storage operators to provide services

to DSOs. Technical trials have demonstrated the use of energy storage to off-set genera-

tor curtailment in response to network constraints, where storage device is used to im-

port energy, raise local demand levels, and avoid the need for generator export curtail-

ment [198][199].


With increasing volumes of DER seeking access to networks, accommodating new DER

connections whilst observing traditional capacity limits has been problematic for UK

DSOs. High levels of DER penetration have left many areas of network with no traditional

capacity for new connections, driving the deployment of ANM solutions. ANM has enabled

the connection of DER beyond traditional planning limits, managing DER export or import

in real-time against network thermal or voltage constraints [201]. Following successful

demonstration, ANM is a business-as-usual solution for the release of DER connection

capacity.

In the UK, DER is actively managed in accordance with commercial Principles of Access

[199] that define the order in which export is curtailed. DSOs do not issue compensation

for lost energy, however the DER customer benefits for significantly reduced connection

costs under ANM, avoiding reinforcement costs. Two approaches to Principles of Access

have been applied to ANM schemes in the UK: Last-In First Off (LIFO), which manages

DER devices based upon their date of connection; and Pro-Rata, which shares the cur-

tailment across a pre-defined quota of managed DER customers [202].

The deployment of ANM has saved DER developers and DSOs significant reinforcement

costs, with the first demonstration site on Orkney avoiding £30m reinforcement at a cost

of £0.5m [203]; other sites have seen reduction in customer connection costs with one

particular case saving £44m in reinforcement costs [204]. The deferral of reinforcement

has allowed the acceleration of network connection, with DER developers achieving an

interruptible connection ahead of reinforcement brining forward energisation by 6 years

[205].

The rollout of autonomous DER control has required the derivation of new methodologies

for the simulation of network operation to support the planning process. The introduction

of generator curtailment has raised the need for a time-series evaluation of network ca-

pacity, moving beyond deterministic analysis of worst-case network conditions [206].

Online tools have been developed to provide DER developers with a high-level estimate

of curtailment, based upon off-line time-series analysis of network capacity and the latest

list of contracted generation connections [205]. This aids DSO planning teams by mini-


mise the need to process speculative connection applications that will not proceed to de-

velopment.

Active Network Management has been deemed an extremely successful innovation that is

reflected in its transition to a business-as-usual solution. DSOs have collectively collabo-

rated with the Energy Networks Association to create an “ANM Good Practice Guide”

[201]. Future development of ANM looks to move beyond DER control for constraint

management and towards the support of local balancing and scheduling, providing co-

ordination of operational actions.

5.4 Germany

In Germany, most of the DSOs still use conventional solutions to run and maintain their

distribution networks. Nevertheless, at present many SG solutions are in development

and being tested by DSOs and different research organisations.


Voltage control is used mainly in substations between HV and MV networks. Those sub-

stations are able to regulate the voltage in MV network. New and innovative

technologies, such as described Regulated Distribution Transformers (RDTs), or LVRs are

mainly used in pilot projects and are not common in German distribution networks.

Currently used methods to regulate the voltage in MV and LV networks are adjustments

to substations and secondary substations transformers (e.g. adjusting the output voltage

at the secondary bus). At least substation transformers between HV and MV networks are

able to change their transmission ratio during load and can be used to regulate the MV

voltage. Although secondary substations between MV and LV are equipped with tap

changers to adjust the transmission ratio they can neither execute the change during

load nor automatically. The ratio has to be switched manually. This is why DSOs can only

constantly lower the voltage level at the secondary bus to allow voltage increases caused

by RES feed-in over the line without surpassing the voltage threshold.

Reactive Power Control

The needed capabilities of installed network equipment concerning reactive power is

defined in several grid codes, which are specified by DSO’s and differ between each

company. Nowadays reactive power control is used statically (static VAR compensator) or

while considering characteristic curves. The increase of dynamisation of providing

reactive power in the future is also expected and is planned to be used in different

voltage levels. Especially higher voltage levels have to be considered.

Regulated Distribution Transformer and Line Voltage Regulator

RDTs are used to optimise voltage levels in distribution networks. Currently they are

available from different manufacturers in Germany. Typical examples are the Schneider

Electric model Minera SGrid or a Siemens RDT called FITformer REG. They are used to

replace conventional secondary substations and implement an on-load voltage regulation

between MV and LV networks.

LVRs are also available and are manufactured by different companies. For example, AEG

Power Solutions produces a regulator called Thyrobox VR. It can be used as an

alternative to centralised voltage regulation, for instance, when only a small part (e.g. a

single line) of the electrical network has to deal with the integration of RES.

Wide Range Power Regulation

The Wide Range Power Regulation can be used easily by optimising the regulation at the

substations between HV and MV networks. There the voltage increases and decreases in

MV and subordinated LV networks have to be analysed and considered to determine the

most suitable voltage level at the substation.


Impact and potential of voltage control solutions in Germany

Using voltage control can reduce the necessity for conventional network reinforcement

significantly. Reworking and reinforcing existing cables and overhead lines can be

avoided by reducing the voltage level at substations or even in single line of the network

by using LVR. The project “PuB-Verteilung”, which was successfully finished by the

University of Wuppertal in early 2016, verified the potentials of innovative SG solutions

using voltage control as a possible measure.

Figure 45: Results of the planning process with incurred costs as net present value (2015) [164].

Figure 45 shows the results of the planning process for one exemplary MV network. The

three most cost-efficient solutions all contain optimised voltage control at the substation

between the HV and MV network. The acceptance was that a reduction by 1 % of the

nominal voltage was possible without the voltage dropping below 90% of the nominal

voltage. Even only using LVR or RDT is cheaper than focusing on conventional planning

methods.

As proven above, innovative SG solutions are available and can be cost efficiently

implemented into existing distribution networks. Therefore, voltage control, especially

with innovative technologies, will become an important part of network reinforcement

measures in future network-planning processes.

Another survey was commissioned by the German Federal Ministry for Economic Affairs

and Energy and was publicised in 2014. The “Moderne Verteilnetze für Deutschland”

survey (Modern Distribution Networks in Germany) also analysed future requirements for

network reinforcement measurements. The result of the survey was that intelligent

network equipment, such as RDT and LVR, are able to reduce the costs for network

reinforcements by 10% annually [183].

In addition, P3 Energy published a survey in 2013. Different synthetic networks were

analysed and problems were solved using innovative technologies. According to the

study, RDT and EVR are an effective option to avoid impermissible voltage increases and

secure a stable power supply. Nevertheless, the results varied according to the network

structure and other local characteristics [184].

5.4.2 Metering and communications.

In July 2016, the new “Digitisation of the Energy Turnaround Act” was finally passed by

German legislation. Its motivation is to incorporate EU regulations into German federal

0,0

1,0

2,0

3,0

4,0

5,0

Mio. EUR

Compensation expenses

Innovative Equipment

Cable

Net

pre

sent

valu

e 2

015

Investments and operational additional expenditures 2015-2050

SFM: Static Feed-in ManagementLVR: Line Voltage RegulatorRDT: Regulated Distribution Transformer

DFM: Dynamic Feed-in ManagementOVC: Optimized HV/MV Voltage Control


law while ensuring opening up the German energy market to digitisation, ensuring high

data protection and ICT security standards. The law defines the rollout of smart meters

and future roles and tasks for all market participants. It includes requirements for the

design of smart meters, associated equipment and their data transmission.

The DSO is originally responsible for installing, maintaining and reading the meters.

However, according to the new law this task can also be given to a third party. The

rollout of smart meters is to be conducted between 2017 and 2032, while specific

deadlines depend on the type of network customer.

The new law mandates that consumers with an energy consumption of at least 6,000

kWh pear year (this covers about 10 % of all households) starting 2020 and RES with an

installed capacity of at least 7 kW (starting 2017) be equipped with a smart metering

system. In addition, RES with an installed capacity of at least 7 kW have to be equipped

with such a system. Below that limit, a rollout is voluntary. Larger consumers like

storage heating or heat pumps will have to be equipped, too, when taking part in a

flexibility market (which is not yet designed). To reimburse the network operator but

regulate the customers’ possible contribution, a strict price cap is implemented.

According to the law, a smart metering system consists of the smart meter itself and the

smart meter gateway, the communication unit. The smart meter gateway transmits the

meter data to the network operator in 15-minutes-intervalls.

In 2016, there are about 44.4 million electricity meters in Germany [164], most of them

standard electromechanical meters. Since 2010 in new constructions and buildings that

underwent extensive refurbishments, meters reflecting the actual energy consumption

and the duration of energy consumption had to be installed. On the customers’ request

these meters could be upgraded with communication modules (multi utility controller,

MUC) to transmit the measured values to the utility company. This is usually used for

billing monthly or quarterly by the utility company when having RES installed [133].


Microgrids

Microgrids only exist in Germany in several demonstration projects. For example,

“Stromnetz Berlin”, the DSO of the German capital, is currently operating 120

microgrids[185]. They try to equalise the demand and feed-in of power in those micro

grids, but still connect them to the distribution network of Berlin, to be able to

compensate unbalanced situations.

Furthermore, other microgrids are still being researched. Demonstration projects exist,

but are not common and used for very specific applications. The German DSO E.ON is

trying to integrate a microgrid to the power supply system of Pellworm, which is an

island in the North Sea. The remote situation of the island makes it a suitable place for

testing self-sufficient microgrid.

Storage

In Germany most of the energy storage is realised through hydropower storage in

reservoirs. Currently storages in Germany are able to store a capacity of 40 GWh.

Pumped storage power plants provide an installed capacity of 9,200 MW and air-pressure

storage feature additional 300 MW of installed capacity (as of 2014) [186]. Other types

of storages are only used in demonstration projects and are not common, especially

considering network stability.

Other storage technologies, such as batteries are being developed regarding their

usability in distribution networks. Power-to-gas facilities, which feed in into the gas

network are also considered as a long-term solution of storing surplus electrical energy.


Demand Response

According to the German regulation on the disconnection of loads, (AblaV) TSOs are

allowed to disconnect certain large loads of at least 50 MW from their networks in order

to stabilise it. It is a voluntary process, which is fixed in a bilateral contract. The

consumers, which are usually industrial loads, are reimbursed and the costs are spread

over all customers. Each month the TSO have a joint competitive bidding in order to find

the cheapest provider of the flexibility of 3 GW [187].

Furthermore, consumers with a high and so-called atypical use of the network pay less

network tariffs. It refers to those customers that have a severely smaller load at the time

of the general maximum load of the network or those that at least consume a total of

10 GWh at 7000 hours per year [188].

In various projects, utilities are developing variable electricity prices for households with

the objecting of matching of RES feed-in and consumption.

Electric Vehicles

As of the beginning of 2016, there are approximately 25,000 electric vehicles and

130,000 hybrid vehicles licensed in Germany (number of passenger cars totalling approx.

46 million) [189][190][191].The original objective of the German federal government is

to have one million electric vehicles in German streets by 2020 [192].

As an incentive, buyers receive 4,000 € for a full electric vehicle and 3,000 € when

buying a hybrid vehicle until 2019. Furthermore, they profit from reduced motor-vehicle

taxation for 10 years [193].Concerning the charging infrastructure, the government is

also aiming at sponsoring the installation of charging points and quick chargers with at

least 300 million € during the coming years [194].

However, until the end of 2016, only approximately 9,000 applications for the purchase

premium were filed, which is behind the government’s expectations [195].For the

governments’ aimed number of EVs, 70,000 public charging points and 7,100 quick

chargers will be necessary [196].

As of mid-2015, there were approximately 5,600 charging points and 2,500 publicly

accessible quick chargers, with the growth rate of licensing of EVs exceeding that of

charging points [197].


The curtailment of power from WT and PV has to be compensated by the DSOs. Lost

profits must be refunded by the DSOs and can cause high costs, but are necessary for a

secure and reliable power supply in Germany.

But not all DER have to take part in that system. For example, PV systems have to meet

different obligations to take part. PV systems with a power rating below 30 kWp can

choose if they want to constantly reduce their feed-in to a maximum of 70 % of their

installed capacity or if they want to take part in the curtailment system. Those PV sys-

tems then have to be equipped with a remote control to allow DSOs to reduce the feed-in

if needed. For PV systems with a power between 30 kWp up to 100 kWp it is compulsory

to be equipped with a remote control; static feed-in reduction is not an option. PV sys-

tems with power feed-in of 100 kWp or more have to be equipped with a remote control

to reduce feed-in and equipment to allow data transfer to the DSO.

With the objective to secure power supply, WT must also participate in curtailment and

need to be equipped with the same metering and control mechanisms as large PV plants.

Those have to be able to be turned from the wind. This ensures the power reduction and

is part of a complex system to avoid voltage increases and equipment overload. The

German curtailment for RES is defined in the Renewable Energies Act (EEG) in para-

graphs §6, §11 and §12. Additionally it is mentioned in §13 of Energy Economy Act

(EnWG). All those solutions are not automated and must be monitored and performed by

the DSO’s control centres. Apart from TSOs, only large DSOs use SCADA systems.


Automated network management is developed and partly used at demo sites in Germa-

ny. One technology is called iNES (Intelligent Distribution Network Management) and was

developed by the SAG GmbH and the University of Wuppertal. It allows metering and

control of the feed-in power of several connected RES without the need to be monitored

by a control centre. This decentralised network automation (DNA) allows a secure power

supply without monitoring the network for possible voltage problems or equipment over-

loads. As seen in Figure 45 the solution is cost efficient as well, but is mainly used in

demo sites now.

Due to the simpler tasks and challenges compared to TSOs when managing the network,

DSOs mainly focus on surveillance, control and fault detection and restoration as well as

switching strategies for maintenance work. In some cases only simplified power flow cal-

culations are executed, e.g. when planning repair work. However, because German dis-

tribution networks are not often operated in solidly earthed mode, a recurrent task is the

detection of high-resistance short circuits or ground faults [207].

In LV networks, for the most part NH fuses ensure protection, whereas in MV networks

typically instantaneous overcurrent, time overcurrent, differential protection or distance

protection is applied. However, the precise execution of line protection depends on the

network topology. In simple radial networks time delayed overcurrent protection is used.

In open-loop and meshed networks additional relays have to be set up or distance pro-

tection has to be used. [207].


6 Conclusions

This document presented, in compact form, the study of WP1 baselining studies in the

SmartGuide project. This deliverable gathered all available data regarding the state-of-

art of the current SG solutions and technologies in each country of the participating

partners. It also summarized the main SG solutions and technologies explored by the

literature in the last years.

The current deliverable showed the differences and similarities between specific

technologies and approaches of each participating country as well as the differences

between the circumstances that could favour or delay the development of the planning

and operation of smart grids in the future.

For instance, in Portugal one single DSO owns almost all the distribution network, while

in Norway there are 130-140 different DSOs, seven DNOs operate across the UK and

around 900 DSOs in Germany. The different DSOs operate in different geographical

areas, and may have different practices regarding planning and operation of the grid.

Concerning the foreseen challenges for DSO/DNO, all the countries share the major

concerns related to the increasing DER connected to the grid and the appearance of

prosumers entities, which could aggravate the inversion of power flows issue. This

problem is the main issue in most European countries since it can significantly affect the

planning and operation of distribution grids. The DSOs are reaching the conclusion that

the investment in the grid may not be always the best choice when congestion occurs

and alternatives should be taken into account. Another common challenge for DSOs is

related to the consequences of changes in the LV grid where smart meters are being

installed, which increase the available information of the grid status in real-time and

enable more target-oriented investments.

On the subject of the planning guidelines and standards, all the countries of the partners

involved in the project have been updating them in order to address the developments in

the SG area. There has been an increasing dependency on international decisions

towards a more relevant standardisation in Europe with the creation and update of more

standards from the IEC. The planning methodologies used by the DSOs vary from

country to country and sometimes even between DSOs in the same country. In Portugal,

some methodologies are being used to support planning such as power quality, reliability,

energy losses, ENS, remote switching and integration of DER. This last topic stands out

due the unique remuneration regime for electricity produced from small production and

self-consumption units published in the Decree-Law 153/2014. In Norway, a large

number of DSOs use a planning book/guidebook with the general planning process to

plan, to operate, to maintain and for reinvestment, but the differences of the grid

conditions cause difficulties for standardizing planning procedures. In Germany, there are

two main approaches to network planning characterised by the considered time horizon.

One is the operational planning method and is used for short-term of operation, while the

other methodology assures long-term supply using historically grown network topologies.

A combination of two methodologies (dual network planning) is usually used. Besides the

planning methodologies, DSOs use tools to help in the planning domain. EDP (Portuguese

DSO) treats the planning issues through two main applications, DPlan and INVESTE,

finding optimal network configuration and obtaining an economic evaluation. In the UK,

there are tools that allow predicting load growth, which helps decision making in the

investment planning process although these activities vary between DSOs.

Several European projects that undertake research in the topic of distribution grid

planning were also explored in this deliverable. Their information helped to identify

trends and produce a more complete state-of-the-art within the context of SG. The

current rollouts of the SG demonstration projects in each country of the participating

partners were reviewed. In Portugal, the InovGrid project initiative is underway aiming to

respond to challenges such as the integration of a large share of DG and EVs into the

grid. Demo Norway is the programme of demonstration activities going on in Norway and

it includes a national SG lab addressing several topics such as automation of grid


operation, interruption of supply, integration of smart meters, communication solutions

and cooperation with TSO. In the UK several network demonstrations has been trying to

tackle challenges related with customer interactions, integration with existing systems,

transition beyond innovation to business-as-usual, among others. The situation in

Germany allows promoting SG solutions by testing their capabilities in demo sites. The

main solutions tested are flexible loads and energy storage, ICT, RDTs and smart meters.

Currently, in some countries most of the solutions and technologies are still not

widespread. That is the case of Portugal, where small-scale demos, architectures and

concepts allow developing strategies and studying the near future. The SuSTAINABLE

project is one of those examples, which proposes an advanced voltage control concept. It

supports improving the coordination between OLTC and management of DER. Regarding

the communication infrastructure, the main development comes from InovGrid

demonstration, namely its smart metering architecture where smart meters play the

main role by providing energy supply and monitoring information through to essential

commands to control micro-generation injection, connected through a LV network.

InovGrid has also an architecture proposed for management and control in a SG

environment, which together with the DPlan software are the main approaches to

optimize the operation and investment planning.

In Norway, it is expected that the future developments in voltage regulation may lead to

reduced costs because it permits deferring grid reinforcements. At this time, the main

solutions used in Norway for voltage regulation are line voltage regulators and the

capacity of synchronous generators to control reactive power. The Norwegian regulator

imposed a deadline to all the metering points to have a smart meter installed in a recent

directive although the smart meters already installed cover around 200,000 costumers.

To treat the large amount of data a nation-wide data hub is under construction, which

will also require more automation and standardisation of components in the power

system. The subsequent developments in smart meters area will also play an important

role in the EV charging management in Norway, currently the leading market for EVs due

mainly to their incentives policy.

In the UK, a modern Automatic Voltage Control is underway with the deployment of OLTC

but a wider coordination in the management of system voltage has been demonstrated

only in innovation projects. The installation of smart meters in millions of homes and

industry is expected in the next years contributing to the development of better

operational network support. Flexible demand provided by industrial customers has

resulted in savings by deferring investment in reinforcement and in other cases such as

usage of aggregators providing network support services. Regarding the development of

managing and control, the deployment of Active Network Management solutions has

saved DSOs substantial reinforcement costs and it is now in the transition to a business-

as-usual solution and can be enhanced in support of local balancing and scheduling

capability.

Also in Germany, most SG solutions are under development and being tested. Besides

the regulated distribution transformer and line voltage regulator, other potential

innovative solutions are being studied using voltage control as a possible measure. With

the recent legislation it is expected that rollout of smart meters start to increase in the

following years in Germany. The new law imposes that consumers with at least 6000

kWh energy consumption per year and RES with installed capacity of 7 kW be equipped

with a smart metering system. Automated network management is developed and partly

used at demo sites. One of them, iNES, allows metering and control of the feed-in power

of several connected RES without the need to be monitored by a control centre.

The main merit of this deliverable is to have gathered the information in a detailed

overview of SG state-of-the-art in several countries, namely Portugal, Norway, UK and

Germany. By identifying the current solutions in use and the emerging trends, it allows to

tackle the possible gaps that persist in the operation/planning of distribution systems.

Through the analysis of the subjects addressed in this report, it is possible to conclude

that European distribution systems are facing new challenges with the increased amount

of RES and subsequent development of SG solutions to be better prepared to tackle all


these changes. In spite of the different states of integration by the SG

solutions/technologies in each country, there is a global common transition to a smarter

and more interoperable distribution power system throughout Europe. The contents of

this deliverable will serve as the basis for the future work to be developed in the

remaining SmartGuide work packages.


7 References

[1] EDP Distribuição, “Plano de Desenvolvimento e Investimento da Rede de

Distribuição (PDIRD 2015-2019),” 2014. [Online]. Available:

http://www.erse.pt/pt/consultaspublicas/consultas/Documents/49_1/PDIRD%2020

15-2019%20-%20Anexos.pdf. [Accessed October 2016].

[2] REN, “Plano de Desenvolvimento e Investimento da Rede Nacional de Transporte

(PDIRT 2016-2025),” 2015. [Online]. Available:

http://www.erse.pt/pt/consultaspublicas/consultas/Documents/53_Proposta%20PD

IRT-E_2015/PDIRT%202016-2025%20-%20Junho%202015%20-

%20Relat%C3%B3rio.pdf. [Accessed October 2016].

[3] Diário da República, 2ª série - Nº232, “Regulamento nº 455/2013, Regulamento de

Qualidade de Serviço do Sector Elétrico,” [Online]. Available:

http://www.erse.pt/pt/electricidade/regulamentos/qualidadedeservico/Documents/

DR_Regulamento%20455-2013-RQS.pdf.

[4] evolvDSO Project, [Online]. Available: http://www.evolvdso.eu. [Accessed October

2016].

[5] EDP, [Online]. Available: http://www.edp.pt. [Accessed October 2016].

[6] [Online]. Available: http://www.res-legal.eu/. [Accessed 20 October 2016].

[7] “The Smart Grid—State-of-the-art and Future Trends,” Electric Power Components

and Systems, vol. 42, no. 3–4, pp. 239–250, Mar. 2014..

[8] M. Lagergren, S. C. Syvertsen and K. Vøllestad,

“Utvikling i nøkkeltal for nettselskap,” NVE, Oslo, 2015.

[9] M. Gleditsch, C. Aabakken, A. Gillund and S. L. Paulen, “Det høyspente

distribusjonsnettet,” NVE, Oslo, 2014.

[10] Energi Norge (Energy Norway), “Nettstruktur og organisering,” Energi Norge, 2

March 2016. [Online]. Available:

https://www.energinorge.no/fagomrader/stromnett/kraftsystemet/nettstruktur-og-

organisering/. [Accessed 25 August 2016].

[11] NVE, “Statistikk for ledninger,” June 1 2014. [Online]. Available:

https://www.nve.no/energiforsyning-og-konsesjon/nett/statistikk-for-ledninger/.

[Accessed 30 August 2016].

[12] A. Ånestad, C. Sepúlveda, S. Kavli , H. Ulsberg, J.-M. Pettersen, A. Gillund, E.

Eggum, H. Hansen and A. Oftebro, “Driften av kraftsystemet 2015 – rapport 56

2016,” NVE, Oslo, 2016.

[13] NVE, “Kraftmarkedsrapporten 1. kvartal 2016,” NVE, 2016.

[14] Statnett, “Ny rekord i strømforbruket,” Statnett, 12 December 2013. [Online].

Available: http://www.statnett.no/Media/Nyheter/Nyhetsarkiv-2013/Ny-rekord-i-

stromforbruket/. [Accessed 29 August 2016].

[15] Statistics Norway, SSB

[16] NVE, “Vannkraftpotensialet (hydro power potential),” 22 August 2016. [Online].

Available: https://www.nve.no/energiforsyning-og-

konsesjon/vannkraft/vannkraftpotensialet/. [Accessed 29 August 2016].


[17] K. Sand, Regulations, requirements and standards for integration of DG/DER in

Norway, CIRED 2014, Kyoto, 2014.

[18] K. Sand, “Planlegging av plusshus og mikronett i lys av krav [presentation],” 15

January 2014. [Online]. [Accessed 19 September 2016].

[19] Ø. Holm, “Pressemelding - Vekst i solkraftmarkedet 2015,” Forskningsrådet,

Multiconsult.

[20] J. Nilsen, “Norsk sol-eksplosjon: – Vi har ristet på ketchup-flasken i tre år. Nå

løsner det,” Teknisk Ukeblad (nettavis), 11. august 2016.

[21] M. L. Hirth, “Slik går du frem om du vil selge egenprodusert strøm,” Sysla Fornybar

Energi, 10 June 2016.

[22] H. Seljeseth, K. Sand and K. Samdal, “Quality of supply regulation in Norway going

beyond EN 50160,” SINTEF Energy Research, 2005.

[23] NVE, “Quality of electricity supply,” NVE, 23 August 2016. [Online]. Available:

https://www.nve.no/energy-market-and-regulation/network-regulation/quality-of-

electricity-supply/. [Accessed 31 August 2016].

[24] G. Kjølle, “KILE-satsene og hva de dekker,” SINTEF Energy Research, Trondheim,

2011.

[25] M. L. Kolstad, “Alternative tiltak for å øke tilknytningskapasitet i distribusjonsnett,”

SINTEF Energy Research, 2016.

[26] NVE, Enova, Norges forskningsråd, Innovasjon Norge, “Teknologier for lagring av

energi,” Fornybar.no, (last updated) May 2016. [Online]. Available:

http://www.fornybar.no/overforing-og-lagring-av-energi/lagring-av-

energi/teknologier-for-lagring-av-energi. [Accessed 23 September 2016].

[27] The Norwegian Smartgrid Centre's Scientific Committee, “Norwegian Smart Grid

Research Strategy,” 2015.

[28] "Utvikling i nøkkeltall for strømnettselskapene", NVE-rapport 18-2013

[29] H. Kirkeby, K. Sand and H. Sæle, “TR A7517 - Rammevilkår for plusskunder,”

SINTEF Energy Research, Trondheim, 2015.

[30] SINTEF Energy Research, “Planleggingsbok for kraftnett (Planboka),” Available from

www.planbok.no.

[31] REN, “Planbok: Oppdatert og systematisert planleggingsbok for kraftnettet,” 2016

March 16. [Online]. Available: http://www.ren.no/produkter/planbok. [Accessed 19

September 2016].

[32] K. Sand and H. Seljeseth, “TR A7290 - A guide to voltage quality planning,” SINTEF

Energy Research, 2012.

[33] M. L. Kolstad, “Dagens praksisk for planlegging og prosjektering av nett med DG,”



[34] E. Tønne, . J. A. Foosnæs and K. Sand, “Planlegging av fremtidens smarte

distribusjonsnett,” NEF Teknisk Møte 2014, 2014.

[35] A. Lont, “En kraftfull fremtid,” Statnett, 4 November 2014. [Online]. Available:

http://www.statnett.no/Samfunnsoppdrag/Neste-generasjon-kraftsystem-

magasin/En-kraftfull-fremtid/. [Accessed 26 September 2016].

[36] Energy Network Association.

[37] [Online]. Available:

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/55

2059/Chapter_5_web.pdf.

[38] National Grid, “System Operability Framework,” 2015.

[39] ENA, “DS2030 - Study into the Distribution System, Stage 2 Report,” 2015

[40] UK Power Networks, “Distributed Generation Addressing Security of Supply and

Network Reinforcement,” 2014.

[41] [Online]. Available: https://www.ofgem.gov.uk/network-regulation-riio-

model/network-innovation .

[42] “Statista,” [Online]. Available:

https://de.statista.com/statistik/daten/studie/37962/umfrage/laenge-der-

stromnetze-in-deutschland-in-1998-und-2008/ . [Accessed 20 October 2016].

[43] “Statista,” [Online]. Available:

https://de.statista.com/statistik/daten/studie/617995/umfrage/verteilnetzbetreiber

-in-deutschland-nach-stromkreislaenge/ . [Accessed 20 October 2016].

[44] L. Müller, Handbuch der Elektrizitätswirtschaft Technische, wirtschaftliche und

rechtliche Grundlagen, Zweite., Springer-Verlag Berlin Heidelberg, 2001.

[45] BDEW, „Verkabelungsgrad des Stromnetzes in Deutschland nach Spanningsbenen

im Jahresvergleich 1993 und 2013,“ [Online]. Available:

https://de.statista.com/statistik/daten/studie/316233/umfrage/verkabelungsgrad-

des-stromnetzes-in-deutschland-nach-spannungsebene/. [Zugriff am 21 12 2016].

[46] Energienetz Mitte, ‘Normierte Standardlastprofile für Nordrhein-

Westfalen/Rheinland-Pfalz’.

[47] K. Heuck, K.-D. Dettmann, and D. Schulz, Elektrische Energieversorgung:

Erzeugung, Übertragung und Verteilung elektrischer Energie für Studium und

Praxis, 8., Überarb. und aktualisierte Aufl. Wiesbaden: Vieweg + Teubner, 2010.

[48] Reinhard Grünwald, ‘Moderne Stromnetze als Schlüsselelement einer nachhaltigen

Energieversorgung’, 162, Dezember 2014.

[49] 50hertz, „Photovoltaik - Erzeugung,“ 2014. [Online]. Available:

http://www.50hertz.com/de/Kennzahlen/Erzeugung. [Zugriff am 16 12 2016].

[50] BDEW, „Jahresvollaststunden der Kraftwerke in Deutschland nach Energieträger im

Jahr 2015,“ 1 05 2016. [Online]. Available:


https://de.statista.com/statistik/daten/studie/37610/umfrage/jahresvolllaststunden

-deutscher-kraftwerke-im-jahr-2009/. [Zugriff am 21 12 2016].

[51] V. Crastan, Elektrische Energieversorgung 1, Berlin, Heidelberg: Springer Berlin

Heidelberg, 2015.

[52] Bundesministerium für Wirtschaft und Energie, „Entwicklung der erneuerbaren

Energien in Deutschland im Jahr 2050,“ 2016.

[53] Johann Jäger, Christian Romeis, and Edmond Petrossian, Duale Netzplanung -

Leitfaden zum netzkompatiblen Anschluss von dezentralen

Energieeinspeiseanlagen., Springer Vieweg, 2016.

[54] P. M. Zdrallek, „Planung und Betrieb elektrischer Netze,“ Wuppertal, 2017.

[55] Bundesministerium der Justiz und für Verbraucherschutz, Verordnung über den

Zugang zu Elektrizitätsversorgungsnetzen, 2005.

[56] Energienetz Mitte, ‘Normierte Standardlastprofile für Nordrhein-

Westfalen/Rheinland-Pfalz’.

[57] ‘IEEE Guide for Electric Power Distribution Reliability Indices’, IEEE Std 1366-2012

Revis. IEEE Std 1366-2003, pp. 1–43,, May 2012.

[58] Bundesnetzagentur, “Bundesnetzagentur,” [Online]. Available:

http://www.bundesnetzagentur.de/DE/Sachgebiete/ElektrizitaetundGas/Unternehm

en_Institutionen/Versorgungssicherheit/Stromnetze/Versorgungsqualitaet/Versorgu

ngsqualitaet-node.html . [Accessed 20 October 2016].

[59] V. Crastan, Elektrische Energieversorgung 1, Berlin, Heidelberg: Springer Berlin

Heidelberg, 2015.

[60] Bundesverband der Energie- und Wasserwirtschaft e.V. (BDEW), Technische

Richtlinie Erzeugungsanlagen am Mittelspannungsnetz, 2008.

[61] VDE Verband der Elektrotechnik Elektronik Informationstechnik e.V., VDE-AR-N

4105:2011-08 -, Technische Anschlussbedingungen, 2011.

[62] Johann Jäger, Christian Romeis, and Edmond Petrossian, Duale Netzplanung -

Leitfaden zum netzkompatiblen Anschluss von dezentralen

Energieeinspeiseanlagen., Springer Vieweg, 2016.

[63] VDE FNN, ‘FNN-Hinweis: rONT - Einsatz in Netzplanung und Netzbetrieb’. VDE.

[64] M. Zdrallek and Lehrstuhl für Elektrische Energieversorgungstechnik der Bergischen

Universität Wuppertal, Eds., Planungs- und Betriebsgrundsätze für ländliche

Verteilungsnetze, vol. 8. Wuppertal, 2016.

[65] M. Carlen, F. Cornelius, J. Tepper, R. Jakobs, M. Schneider, H. Wiesler, A. Slupinski,

and I. Buschmann, ‘Line Voltage Regulator for Voltage Adjustment in MV-Grids’,

presented at the 23rd International Conference on Electricity Distribution, Lyon ,

2015.

[66] E. E. Queen, “Smart Meter and Smart Meter Systems: A Metering industry

perspective,” Edison Electric Institute, Washington, 2011.


[67] J. Zheng, D. W. Gao and L. Lin, “Smart meters in smart grid: An overview,” Green

Technologies Conference, IEEE, 2013.

[68] A. Mahmood, N. Javaid and S. Razzaq, “A Review of Wireless Communications for

Smart Grid,” Renewable and Sustainable Energy Reviews, 2013.

[69] P. Overholt, K. Uhlen, B. Marchionini and O. Valentine, “Synchrophasor Applications

for Wide Area Monitoring and Control,” International Smart Grid Action Network

(ISGAN), 2016.

[70] DPS Telecom, “An Introduction To Remote Terminal Units,” n.d.. [Online].

Available: http://www.dpstele.com/network-monitoring/remote/terminal-unit.php.

[Accessed 8 September 2016].

[71] Technavio, “Rremote terminal unit (RTU) in smart grid,” n.d.. [Online]. Available:

http://www.technavio.com/glossary/remote-terminal-unit-rtu-smart-grid.


[72] Wikipedia, “Intelligent electronic device,” Last modified April 2016. [Online].

Available: https://en.wikipedia.org/wiki/Intelligent_electronic_device. [Accessed 9

September 2016].

[73] H. Stutvoet, “Intelligent Electronic Device (IED),” SVRI bv, 30 September 2014.

[Online]. Available: http://www.svri.nl/en/intelligent-electronic-device-ied/.


[74] Y. Kabalci, “A survey on smart metering and smart grid communication,”

Renewable and Sustainable Energy Reviews, 2016.

[75] D. Baimel, S. Tapuchi and N. Baimel, “Smart Grid Communication Technologies,”

Journal of Power and Energy Engineering, 4, 1-8., 2016.

[76] “ Consortium for Electric Reliability Technology Solutions, (CERTS),” [Online].

Available: https://certs.lbl.gov/. [Accessed October 2016].

[77] “MICROGRIDS - Large Scale Integration of Microgeneration to Voltage Grids,”

[Online]. Available: http://www.microgrids.eu/micro2000/index.php. [Accessed

October 2016].

[78] Lopes, J. A. P., Madureira, A. G. and Moreira, C. C. L. M. (2013), A view of

microgrids. WENE, 2: 86–103. doi:10.1002/wene.34

[79] J. Driesen and F. Katiraei, "Design for distributed energy resources," in IEEE Power

and Energy Magazine, vol. 6, no. 3, pp. 30-40, May-June 2008..

[80] Daniel Burmester; Ramesh Rayudu; Winston K. G. Seah;Distributed generation

nanogrid load control system;Power and Energy Engineering Conference (APPEEC),

2015 IEEE PES Asia-Pacific.

[81] M. T. Wishart, M. Dewadasa, I. Ziari, G. Ledwich and A. Ghosh, "Intelligent

distribution planning and control incorporating microgrids," 2011 IEEE Power and

Energy Society General Meeting, San Diego, CA, 2011, pp. 1-8..


[82] R. J. Millar, S. Kazemi, M. Lehtonen and E. Saarijarvi, "Impact of MV Connected

Microgrids on MV Distribution Planning," in IEEE Transactions on Smart Grid, vol. 3,

no. 4, pp. 2100-2108, Dec. 2012.

[83] N. S. Wade et al, “Evaluating the benefits of an energy storage system in a future

smart grid”, Energy Policy 38, 11, 7180-7188, 2010.

[84] R. B. Schainker, “Executive overview: energy storage option for a sustainable

energy future”, IEEE, Power Engineering Society General Meeting, Denver, 10 June

2004.

[85] Power quality issues produced by embedded storage technologies in smart grid

environment;Ramona Vatu; Oana Ceaki; Monica Mancasi; Radu Porumb; George

Seritan; Power Engineering Conference (UPEC), 2015 50th International

Universities; Year: 2015.

[86] “Energy Storage,” [Online]. Available: www.energystorage.org. [Accessed October

2016].

[87] Suitability analysis of Fuzzy Logic as an evaluation method for the selection of

energy storage technologies in Smart Grid applications ;Cong-Toan Pham; Daniel

M#x00E5;nsson ;Smart Electric Distribution Systems and Technologies (EDST),

2015 Internati.

[88] L. H. Macedo, J. F. Franco, M. J. Rider and R. Romero, "Optimal Operation of

Distribution Networks Considering Energy Storage Devices," in IEEE Transactions

on Smart Grid, vol. 6, no. 6, pp. 2825-2836, Nov. 2015.

[89] “SENSIBLE - Storage-Enabled Sustainable Energy for Buildings and Communities,”

[Online]. Available: http://www.h2020-project-sensible.eu/sensible/index.aspx.

[Accessed October 2016].

[90] “HYPERBOLE - Hydropower plants Performance and flexible Operation towards lean

integration of new renewable Energies,” [Online]. Available:

https://hyperbole.epfl.ch/SitePages/Home.aspx. [Accessed October 2016].

[91] http://www.gridinnovation-on-line.eu/, “Grid innovation Online,” [Online].

Available: http://www.gridinnovation-on-line.eu/. [Accessed October 2016].

[92] G. Celli, S. Mocci, F. Pilo, M. Loddo, “Optimal integration of energy storage in

distribution networks” IEEE Power Tech Conference, June 28th – July 2nd,

Bucharest, Romania, 2009.

[93] R. C. Dugan, J. A. Taylor and D. Montenegro, "Energy Storage Modeling for

Distribution Planning," 2016 IEEE Rural Electric Power Conference (REPC),

Westminster, CO, 2016, pp. 12-20..

[94] R.Ferreira,M. Matos, J. Lopes ,”Models and algorithms for network reinforcement

planning in a smart grid environment”, [Online].

[95] Energy efficiency directive, European Comission.

[96] ACER Framework guidelines on electricity balancing. September 2012.


[97] Evaluation of Demand Response Systems for Smart Grids: state of the art, value

potential and the Hyllie case Master’s Thesis within the Sustainable Energy Systems

programme PANAGIOTIS MAVRIKAS ULRIKA HOLMGREN Department of Computer

Science and Engine.

[98] J. Domínguez, J. P. Chaves-Ávila, T. G. S. Román and C. Mateo, "The economic

impact of demand response on distribution network planning," 2016 Power Systems

Computation Conference (PSCC), Genoa, 2016, pp. 1-7., [Online].

[99] W. Kempton, and S. E. Letendre, “Electric vehicles as a new power source for

electric utilities”, , Transportation Research Part D: Transport and Environment, vol.

2, no. 3, pp. 157-175, September 1997.

[100] F. Soares, "Impact of the deployment of electric vehicles in Grid operation and

expansion" .

[101] J. Yong, V. Ramachandaramurthy, K. Tan, N. Mithulananthan, “A review on the

state-of-the-art technologies of electric vehicle, its impacts and prospects”.

[102] Youjie Ma, Bin Zhang, Xuesong Zhou , An overview on impacts of electric vehicles

integration into distribution network, 2015 IEEE International Conference on

Mechatronics and Automation (ICMA).

[103] W. Kempton, and J. Tomic, “Vehicle-to-grid power fundamentals: Calculating

capacity and net revenue”, Journal of Power Sources, vol. 144, no. 1, pp. 268-279,

June 2005.

[104] mpacts of classified electric vehicle charging derived from driving patterns to the LV

distribution network ; Fan Yi; Pingliang Zeng; Shuang Yu; Chenghong Gu; Jiangtao

Li; Chenchen Yuan; Furong Li ; 2014 IEEE PES General Meeting | Conference &

Expos.

[105] O.B. Tor and M. Shahidehpour, “Electric Power Distribution Asset Management,”, in

proc. of the 4th Int. Conf. on Electrical and Electronics Engineering ELECO, vol 5,

2005.

[106] SCADA –BT – Desenvolvimento de um Sistema de Comando e Controlo Adequado

às Smart Grids em Baixa Tensão QREN nº 24661 Scope and applicability of a LV

SCADA.

[107] Ergon Energy distribution annual planning report, part A, 2014.

[108] Towards a best practice asset management framework for electrical power

distribution organisations Sohail Abdul Khaliq; Muhammad Nateque Mahmood;

Narottam Das Power and Energy Engineering Conference (APPEEC), 2015 IEEE PES

Asia-Pacific.

[109] Short-term aggregated load and distributed generation forecast using fuzzy

grouping approach; Matej Rejc; Alfred Einfalt; Tobias Gawron-Deutsch; Smart

Electric Distribution Systems and Technologies (EDST), 2015 International

Symposium on.

[110] Hagan, M. T.; Behr, S. M., , "The Time Series Approach to Short Term Load

Forecasting,", Power Systems, IEEE Transactions on , vol.2, no.3, pp.785-791, Aug.

1987.


[111] Villalba, S.A.; Bel, C.A., , "Hybrid demand model for load estimation and short term

load forecasting in distribution electric systems,", Power Delivery, IEEE Transactions

on , vol.15, no.2, pp.764-769, Apr 2000.

[112] Yang, J., Stenzel, J., “Short-term load forecasting with increment regression

tree,”Electric Power Systems Research, Volume 76, Issues 9- 10, June 2006,.

[113] Moghaddas-Tafreshi, S.M.; Farhadi, M., "A linear regression-based study for

temperature sensitivity analysis of Iran electrical load," Industrial Technology,

2008. ICIT 2008. IEEE International Conference on , vol., no., pp.1-7, 21-24 April

2008.

[114] Peng, T.M.; Hubele, N.F.; Karady, G.G., "Advancement in the application of neural

networks for short-term load forecasting," Power Systems, IEEE Transactions on ,

vol.7, no.1, pp.250-257, Feb 1992.

[115] H. Cho, Y. Goude, X. Brossat, and Q. Yao, “Modeling and forecasting daily electricity

load curves: a hybrid approach,” Journal of the American Statistical Association,

vol. 108, no. 501, pp. 7–21, 2013..

[116] A. Kavousi-Fard, “A new fuzzy-based feature selection and hybrid tlaann modelling

for short-term load forecasting,” Journal of Experimental & Theoretical Artificial

Intelligence, vol. 25, no. 4, pp. 543–557, 2013.

[117] R. Bessa, C. Moreira, B. Silva, M. Matos, “Handling renewable energy variability and

uncertainty in power systems operation”.

[118] H. Chitsaz, N. Amjady, and H. Zareipour, “Wind power forecast using wavelet

neural network trained by improved Clonal selection algorithm,” Energy Convers.

Manage., vol. 89, pp. 588–598, Jan. 2015..

[119] D. Z. Huang, R. X. Gong, and S. Gong, “Prediction of wind power by chaos and BP

artificial neural networks approach based on genetic algorithm,” J. Elect. Eng.

Technol., vol. 10, no. 1, pp. 41–46, 2015..

[120] [Online]. Available: http://www.electricaltechnology.org/wp-

content/uploads/2015/09/SCADA-MAster-Control-Station-Center.png .

[121] Electricity Networks Association , “Active Network Management Good Pratice

Guide,” 2015.

[122] Imperial College London, “Network State Estimation and Ooptimal Sensor

Placement, Report C4 for the "Low Carbon London",” 2014.

[123] European Electricity Grid Initiative , “Gridplus - EEGI,” [Online]. Available:

http://www.gridplus.eu/eegi. [Accessed November 2016].

[124] DISCERN - Final report, [Online]. Available:

http://www.discern.eu/datas/Discern_Final_Report.pdf . [Accessed November

2016].

[125] “CORDIS, Sustainable Project,” [Online]. Available:

http://cordis.europa.eu/docs/results/308/308755/final1-sustainable-d1-4-project-

book.pdf. [Accessed November 2016].


[126] “UPGRID project,” [Online]. Available: http://upgrid.eu/. [Accessed November

2016].

[127] “CitInES Project,” [Online]. Available: http://www.citines.com/. [Accessed

November 2016].

[128] CitInES, “Design of a decision support tool for sustainable, reliable and cost-

effective energy strategies in cities and industrial complexes,” 2014.

[129] Godinho Matos, P., Lbano Monteiro, P., So Miguel Oliveira, M., Aires Messias, A.,

Veiga, A. M., & Daniel, P. R. (2013). InovGrid, a smart vision for a next generation

distribution system. In 22nd International Conference and Exhibition on Electricity

Dist.

[130] “Norwegian Smartgrid Centre (NSGC)” [Online]. Available:

https://smartgrids.no/english . [Accessed November 2016].

[131] ETP SmartGrids, “National and Regional Smart Grids initiatives in Europe,” ETP

SmartGrids, 2016.

[132] ETP SmartGrids, “National and Regional Smart Grids initiatives in Europe,” ETP

SmartGrids, 2016.

[133] W. P, “EDL21/EDL40-Zähler-Konzept, Erfahrungsbericht, Entwicklungstendenzen,”

2009.

[134] RWE Deutschland - Smart Operator, [Online]. Available:

http://www.rwe.com/web/cms/de/1943232/rwe-

deutschland/energiewende/intelligente-netze/smart-operator/. [Accessed 07

November 2016].

[135] “IEEE Guide for Smart Grid Interoperability of Energy Technology and Information

Technology Operation with the Electric Power System (EPS), End-Use Applications,

and Loads,” IEEE Std 2030-2011, pp. 1–126, Sep. 2011., [Online].

[136] CEN, CENELEC, and ETSI, “Smart Grid Coordination Group, Smart Grid Reference

Ar-chitecture,” Nov. 2012., [Online].

[137] CEN, CENELEC, and ETSI, “Smart Grid Coordination Group, Smart Grid

Interoperabil-ity,” Oct. 2014.

[138] da Cunha, L. V., Abreu, C., Teixeira, J. C., & Guimaraes, M. E. (2009). Addressing

microgeneration in Portugal - implementing effective technical and commercial

solutions for network integration and market operation, according to new regulatory

issues. IE, [Online].

[139] R. Prata, H. Craveiro, C. A. Santos and E. Quaresma, "SmartGrid role in reducing

electrical losses — The InovGrid experience," Electrical Power Quality and Utilisation

(EPQU), 2011 11th International Conference on, Lisbon, 2011, pp. 1-6.

[140] H. Kirkeby and M. Kolstad, “Nasjonalt potensiale for spenningsregulering,” SINTEF

Energy Research, 2016.

[141] H. Kirkeby, “Beste praksis for trinnkobler i krafttransformator og

spenningsregulator i DG-enheter,” SINTEF Energy Research, 2015.


[142] M. L. Kolstad, “Alternative tiltak for å øke tilknytningskapasitet i distribusjonsnett,”


[143] Lovdata, “Forskrift om måling, avregning, fakturering av nettjenester og elektrisk

energi, nettselskapets nøytralitet mv,” 1999 (last edited September 1st 2016).

[Online]. Available: https://lovdata.no/dokument/SF/forskrift/1999-03-11-301.


[144] A. Venjum and C. Å. Hagen , “Smarte målere (AMS) - Status og planer for

installasjon og oppstart per 1. kvartal 2015,” NVE, Oslo, 2015.

[145] Aidon, [Online]. Available: http://www.aidon.com/nb/aktuelt/beyond-metering-

nyhetsbrev/nyhetsbrev-januar-2016/kontraktene-er-signert-thor-erik-gjor-et-

tilbakeblikk/. [Accessed November 2016].

[146] Elhub (Statnett), “About Elhub (English),” n.d. [Online]. Available:

http://elhub.no/en/elhub. [Accessed 1 November 2016].

[147] Sinusmagasinet, “ABB og Lyse Elnett planlegger viktig pilotprosjekt,” 26 February

2015. [Online]. Available:

http://www.sinusmagasinet.no/artikler/2015/februar/kraftnettet_i_soer-

rogaland_blir_smartere_/1268. [Accessed 26 October 2016].

[148] Lyse Elnett, “Stavanger sentrum får framtidens strømnett,” n.d. [Online]. Available:

https://www.lysenett.no/smartnett/. [Accessed 26 October 2016].

[149] Accenture, WWF, “Solkraft i Norge – Fremtidige muligheter for verdiskaping,” 2016.

[150] H. Sæle and O. Grande, “Demand Response From Household Customers:

Experiences From a Pilot Study in Norway,” IEEE TRANSACTIONS ON SMART GRID,

2011.

[151] Thema Consulting Group, “Forbrukerfleksibilitet og styring av forbruk –-

pågående aktiviteter,” NVE, 2015.

[152] Enova, [Online]. Available: www.enova.no/ams. [Accessed November 2016].

[153] Norwegian EV Association, [Online]. Available: http://elbil.no/elbil-2/elbilens-

fordeler/. [Accessed November 2016].

[154] Norsk elbilforening (The Norwegian EV Association), “Elbilens fordeler,” n.d.

[Online]. Available: http://elbil.no/elbil-2/elbilens-fordeler/. [Accessed 31 October

2016].

[155] C. H. Skotland, E. Eggum and D. Spilde, “Hva betyr elbiler for strømnettet?,” NVE,

2016.

[156] “Statnett's asset management,” [Online]. Available:

http://www.statnett.no/Global/Dokumenter/Om%20Statnett/Anskaffelse/Governing

%20policy%20for%20Stanett's%20asset%20management.pdf. [Accessed

November 2016].

[157] A. Økland, H. M. Gabriel, A. Ekambaram and S. B. Halvorsen, “Characteristics of

different approaches to and frameworks for maintenance optimization

methodologies - Case 1: Electricity Networks,” OPTIRAIL Deliverable 2.1, 2013.


[158] CENS, [Online]. Available: https://www.sintef.no/projectweb/kile_uk/ . [Accessed

November 2016].

[159] A. Ørnes, “Master's thesis: Bruk av AMS-data i forbindelse med nettplanlegging i

distribusjonsnett,” Norwegian University of Science and Technology, NTNU, 2014.

[160] SINTEF, “DeVID - Demonstration and verification of intelligent distribution grids,”

[Online]. Available: https://www.sintef.no/en/projects/devid-demonstrasjon-og-

verifikasjon-av-intelligent/.

[161] H. Sæle, Ø. Sagosen and J. Bjørndalen, “Norsk driftssentralstruktur - Funksjon,

kostnadsforhold og fremtidig utvikling,” SINTEF Energy Research, Trondheim, 2014.

[162] K. M. E. Grindheim, “Brukergrensesnitt og visualisering i fremtidens driftssentral for

smarte nett,” Norwegian Univeristy of Science and Technology, Trondheim, 2014.

[163] K. Sand and P. Heegaard, “Next Generation Control Centres - state of art and

future scenarios (version 2.0),” Norwegian University of Science and Technology,

Trondheim, 2015.

[164] BET, “Kurzgutachten - Auswirkungen des geplanten Messstellenbetriebsgesetzes

auf das Energiedatenmanagement für betriebliche Anwendungen und

Abrechnungszwecke des Verteilnetzbetreibers,” Apr. 2016., [Online].

[165] Electricity North-West, “Low Voltage Integrated Automation: Closedown Report“,

2015, Available: http://www.enwl.co.uk/docs/default-source/future-lovia/lovia-

close-down-report-final-2.pdf?sfvrsn=2

[166] Hollingworth. D, “Proposals for a voltage control policty from CLNR learning“,

Northern Powergrid, 2014, Available: http://www.networkrevolution.co.uk/wp-

content/uploads/2014/12/CLNR-L257-CLNR-Voltage-Control-Policy-v1.0.pdf

[167] Duran. A, Lloyd. I, et al, “Lessons Learned Report: Enhanced Automatic Voltage

Control“, Northern Powergrid, 2014, Available:

http://www.networkrevolution.co.uk/wp-content/uploads/2014/12/CLNR-EAVC-

Lessons-Learned-Report-V2.pdf

[168] Bale. P, “Low Carbon Hub: Project Closedown Report“, Western Power Distribution,

2015, Available: https://www.westernpowerinnovation.co.uk/Document-

library/2015/CNT2002-LLCH-Close-Down-Report_v1-0-Final.aspx

[169] Yi. J, Wang. P, “Distribution Voltage Control Using Energy Storage and Demand

Side Response“, Proc. IEEE PES Innovative Smart Grid Technologies Europe, Berlin,

2012

[170] Western Power Distribution, “Network Equilibrium: LCN Fund Tier 2 Bid“, 2014,

Available:

http://www.smarternetworks.org/Files/Network_Equilibrium_141124160604.pdf

[171] Gough. S, “Voltage Control System Demonstration Project: Closedown

Report“,Western Power Distribution, 2014, Available:

https://www.ofgem.gov.uk/sites/default/files/docs/2015/03/wpdt1003_-

_voltage_control_system_demonstration_close-down_report_final_08.12.14_0.pdf


[172] Barteczo-Hibbert. C, Slater. S, et al, “Customer-Led Network Revolution Insight

Report: Baseline Domestic Profile“, Northern Powergrid, 2015, Available:

http://www.networkrevolution.co.uk/wp-content/uploads/2015/02/Insight-Report-

TC1a.pdf

[173] UK Power Networks, “Use of Smart Meter Information for Network Planning and

Operation“, 2014, Available:

http://innovation.ukpowernetworks.co.uk/innovation/en/Projects/tier-2-

projects/Low-Carbon-London-(LCL)/Project-

Documents/LCL%20Learning%20Report%20-%20C1%20-

%20Use%20of%20smart%20meter%20information%20for%20network%20plannin

g%20and%20operation.pdf

[174] UK Power Networks, “Development of Network Network Design and Operation

Practices“, 2014, Available:



Documents/LCL%20Learning%20Report%20-%20D1%20-

%20Development%20of%20new%20network%20design%20and%20operation%20

practices.pdf

[175] UK Power Networks, “Low Carbon London: Project Closedown Report“, 2015,

Available:

http://www.smarternetworks.org/Files/Low_Carbon_London_150701162635.pdf

[176] Krishnan. H. K, Cross. J, et al, “My Electric Avenue: Evaluation of Power Line

Carrier Communication for Direct Control of Electric Vehicle Charging“, Scotish and

Southern Energy, 2015, Available:

http://myelectricavenue.info/sites/default/files/PLC%20Communication%20Reliabili

ty%20Report.pdf

[177] Northern Powergrid, “NPS/002/024 Technical Specification for Fibre Optic Cables

and Fibre Wrap“, 2015 Available:

https://www.northernpowergrid.com/asset/1/document/483.pdf

[178] Evans. G, MacLeman. D, “Demonstrating the Benefits of Monitoring LV Networks

with Embedded PV Panels and EV Charging Point“, Scottish and Southern Energy

Power Distribution, 2013, Available:

http://www.smarternetworks.org/Files/Benefits_of_Monitoring_LV_Networks_1303

27132144.pdf

[179] Sidebotham. L, Customer-Led Network Revolution: Closedown Report“, Northern

Powergrid, 2015, Available: http://www.networkrevolution.co.uk/wp-

content/uploads/2015/01/CLNR-G026-Project-Closedown-Report-2301152.pdf

[180] Blair. S, Booth. C, “Analysis of the Technical Performance of C2C Operation for HV

Networks“, University of Strathclyde, 2014, Available:

http://www.enwl.co.uk/docs/default-source/c2c-key-documents/technical-

performance-report.pdf?sfvrsn=4

[181] Electricity North-West, “Capacity to Customers: Project Closedown Report“, 2015,

Available: http://www.enwl.co.uk/docs/default-source/c2c-key-documents/c2c-

closedown-report-aug-2015.pdf?sfvrsn=8

[182] UK Power Networks, “Industrial and Commercial Demand Response for Outage

Management and as an Alternative to Network Reinforcement“, 2014, Available:

http://innovation.ukpowernetworks.co.uk/innovation/en/Projects/tier-2-projects/Low-Carbon-London-(LCL)/Project-Documents/LCL%20Learning%20Report%20-%20C1%20-%20Use%20of%20smart%20meter%20information%20for%20network%20planning%20and%20operation.pdf





http://innovation.ukpowernetworks.co.uk/innovation/en/Projects/tier-2-projects/Low-Carbon-London-(LCL)/Project-Documents/LCL%20Learning%20Report%20-%20D1%20-%20Development%20of%20new%20network%20design%20and%20operation%20practices.pdf





http://myelectricavenue.info/sites/default/files/PLC%20Communication%20Reliability%20Report.pdf

http://myelectricavenue.info/sites/default/files/PLC%20Communication%20Reliability%20Report.pdf

http://www.enwl.co.uk/docs/default-source/c2c-key-documents/technical-performance-report.pdf?sfvrsn=4

http://www.enwl.co.uk/docs/default-source/c2c-key-documents/technical-performance-report.pdf?sfvrsn=4




Documents/LCL%20Learning%20Report%20-%20A4%20-

%20Industrial%20and%20Commercial%20Demand%20Side%20Response%20for

%20outage%20management%20and%20as%20an%20alternative%20to%20netwo

rk%20reinforcement.pdf

[183] D. -I. J. Büchner, D. -I. J. Katzfey, O. Flörcken, U. -P. D. -I. A. Moser, D. -I. H.

Schuster, S. Dierkes, T. v. Leeuwen, L. Verheggen, D. -I. M. Uslar und M. v.

Amelsvoort, „Moderne Verteilnetze für Deutschland,“ BMWi, 2014.

[184] C. Hille, G. Narkus, F. Potratz, S. Schrader, C. Matrose, B. Hörpel, H. Harms, J.

Kampik und A. Schnettler, „Technologieoptionen für den Verteilungsnetzausbau in

Deutschland - Marktanalyse und Bewertung,“ P3 Energy, Aachen, 2013.

[185] S. Berlin, „Micro Grids - Keimzelle des Berliner Smart Grids,“ [Online]. Available:

http://www.stromnetz.berlin/de/micro-grids.htm. [Zugriff am 23 01 2017].

[186] E. Mahnke, J. Hühlenhoff und L. Liebland, „Strom Speichern,“ Agentur für

Erneuerbare Energien, 2014.

[187] Bundesgesetzblatt, Verordnung über Vereinbarungen zu abschaltbaren Lasten

(AbLaV), BGBI. I S. 3106, 2016

[188] Bundesgesetzblatt, Verordnung über die Entgelte für den Zugang zu

Elektrizitätsversorgungsnetzen (Stromnetzentgeltverordnung - StromNEV), BGBI. I

S. 3106, 2016

[189] Statista, [Online]. Available:

https://de.statista.com/statistik/daten/studie/265995/umfrage/anzahl-der-

elektroautos-in-deutschland/. [Accessed January 2017].

[190] Statista, [Online]. Available:

https://de.statista.com/statistik/daten/studie/265993/umfrage/anzahl-der-

hybridautos-in-deutschland/. [Accessed January 2017].

[191] Kraftfahrbundesamt, [Online]. Available:

http://www.kba.de/DE/Statistik/Fahrzeuge/Bestand/bestand_node.html. [Accessed

January 2017].

[192] Nationale Platform Elektromobilität, [Online]. Available: http://nationale-plattform-

elektromobilitaet.de/hintergrund/die-ziele/#tabs. [Accessed January 2017].

[193] Bundesregierung, [Online]. Available:

https://www.bundesregierung.de/Content/DE/Artikel/2016/05/2016-05-18-

elektromobilitaet.html. [Accessed January 2017].

[194] Bundesregierung, [Online]. Available:

https://www.bundesregierung.de/Content/DE/Artikel/2016/05/2016-05-18-

elektromobilitaet.html. [Accessed January 2017].

[195] Handesblatt, [Online]. Available:

http://www.handelsblatt.com/unternehmen/industrie/elektromobilitaet-

deutschland-hinkt-deutlich-hinterher/19199348.html. [Accessed January 2017].



elektromobilitaet.de/themen/ladeinfrastruktur/#tabs. [Accessed January 2017].


elektromobilitaet.de/fileadmin/user_upload/Redaktion/NPE_AG3_Statusbericht_LIS

_2015_barr_bf.pdf. [Accessed January 2017].

[198] Urquhart. A, “Trial of Orkney Energy Storage Park: Closedown Report“, Scottish

and Southern Energy Power Distribution, 2015, Available:

http://www.smarternetworks.org/Files/Trial_of_Orkney_Energy_Storage_Park_(Pha

se_2)_150708102245.pdf

[199] Kane. L, Ault. G, “A Review and Analysis of Renewable Energy Curtailment Schemes

and Principles of Access: Transitioning Towards Business as Ususal”, Energy Policy,

Vol 72, 2014, pp 67-77

[200] UK Power Networks, “Demonstrating the Benefits of Short-Term Discharge Energy

Storage on an 11 kV Distribution Network“, 2014, Available:


projects/demonstrating-the-benefits-of-short-term-discharge-energy-

storage/Project-Documents/Shor-

term+discharge+energy+storage+closedown+report.pdf

[201] Energy Networks Association, “Active Network Management Good Practice Guide“,

2015, Availabe:

http://www.energynetworks.org/assets/files/news/publications/1500205_ENA_ANM

_report_AW_online.pdf

[202] Baringa Partners, UK Power Networks, “Flexible Plug and Play: Principles of Access

Report“, 2012, Available:

http://www.smarternetworks.org/Files/Flexible_Plug_and_Play_130204092902.pdf

[203] Scott. J, Cleijne. H, “Smart Grid Strategic Review: The Orkney Islands Active

Network Management Scheme“, KEMA, Available:

https://www.ssepd.co.uk/WorkArea/DownloadAsset.aspx?id=991

[204] UK Power Networks, “Flexible Plug and Play: Closedown Report“, 2015, Available:


projects/Flexible-Plug-and-Play-(FPP)/Project-Documents/Close-Down-

Report_Final.pdf

[205] Norris. E, Accelerating Renewable Connections: Project Progress Report December

2014, ScottishPower Energy Networks, 2014, Available:

http://www.smarternetworks.org/Files/ARC_150701161711.pdf

[206] Taljaard. R, Hammond. M, et al, “Standardisation of Curtailment Analysis and the

Implications for Distribution Network Operators and Generators“, Proc. CIRED

Conference, Helsinki, 2016, Available:

http://www.smartergridsolutions.com/media/134525/paper-0344-final-version.pdf

[207] A. J. Schwab, “Elektroenergiesysteme,” Springer Berlin Heidelberg, 2015., [Online].

[208] M. Doktzer and J. Kopec, “Bewerung und Planung von Stromnetzen,” Bonn, Sep. 25

2014.

Defining Planning and Operation Guidelines for European Smart … · 3.3.3 Demand response ......

Documents

Transcript of Defining Planning and Operation Guidelines for European Smart … · 3.3.3 Demand response ......