dD1.2 Report of development for flexible MV-network...

dD1.2 Report of development for flexible MV-network operation

Advanced MV network operations using a multi agent system


30 October 2013 2/92

ID & Title : dD1.2 Report of development for flexible MV-network operation

Version : V1.0 Number of pages : 92

Short Description

1) Introduction and scope of the document 2) Update deliverable dD1.1 3) Description of requirements and functionality of the overall system 4) Autonomous multi agent system 5) RTU communication

6) Risk management year 2

Revision history

Version Date Modifications’ nature Author

V0.1 23/04/2013 First draft Anton Shapovalov

V0.2 11/07/2013 Internal Review René Lorenz

V0.10 22/08/2013 Ready for review René Lorenz

V1.0 18/10/2013 Final version René Lorenz

Accessibility

Public Consortium + EC Restricted to a specific Group + EC Confidential + EC

If restricted, please specify here the group

Owner / Main responsible

Name (s) Function Company Visa

Dr. Lars Jendernalik Technical Manager DEMO1 Westnetz GmbH Dr. Lars Jendernalik

Author (s) / Contributor (s) : Company name (s)

RWE Deutschland AG Westnetz GmbH (RWE DSO) ABB Deutschland AG TU Dortmund

Reviewer (s) : Company name (s)

Company Visa

ENEL Review validated by Technical Committee on 18/10/2013

Approver (s) : Company name (s)

Company Visa

CEZ, ENEL, ERDF, IBERDROLA, VATTENFALL Approved by Steering Committee on 18/10/2013

Work Package ID: DEMO1 Task ID: DEMO1.2.1, 1.2.2, 1.2.3, 1.2.4


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Executive summary

This document describes the activities of the demonstration project DEMO1 “Advanced MV

network operations using a multi agent system” in the period of the second project year.

The high share and still massive increasing amount of distributed generation, predominantly wind

and photo-voltaic set new challenges to the DSO‟s. In order to provide hosting capacity to integrate

these resources huge investments in grid infrastructure are required. Grid operation and grid

observation becomes more complex since power flows become less predictable. At present in

Germany there are hardly any surveillance facilities or grid automation in place in medium voltage

networks.

DEMO1 addresses these challenges with the demonstrator to be built up in the area of “Reken”,

located in North-Rhine-Westphalia. The considered grid is well selected since it shows already

today a balance between installed generation power and maximum demand. Further increase in

renewables to be connected is forecasted.

The grid area of Reken consists of around 120 secondary substations. In the first year a total

number of about 15 stations was selected to be equipped with switching facilities – so called

switching agents.

The following objectives are targeted:

– Integrating an increasing number of decentralized energy resources (DER) in the medium-

voltage (MV) network and underlying low-voltage (LV) networks

– Achieving higher reliability, shorter recovery times after grid failures

– Avoiding unknown overloads and voltage violations

– Fulfilling the needs of surveillance and remote-control in MV-networks

– Reducing network losses

These objectives led to the question of a minimum number of necessary switching operations per

year. The standard load switching gear is not capable of the estimated number. Therefore the

primary switching hardware also has to be replaced leading to a significant increase of the specific

costs of this solution.

Due to the principle project idea of innovative but also economical grid solutions this necessary

equipment was again discussed at the beginning of the second project year regarding terms of

cost-benefit-analysis. This led to a solution where the most expensive parts of the equipment – the

switching agents with their replaced switching gear – are only installed in those grid areas with the

most benefit in the context of the above shown objectives. The final analysis now shows the

installation of 7 switching agents gaining the most benefit of this innovative switching solution.


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Regarding the demonstrative character of this project the main activity of this period was the

preparation of all necessary requirements to ensure the construction of an automated multi agent

system in the field in the next project period. To fulfil this objective, several subtasks were solved.

The principle idea of a multi agent system is based on an autonomous interaction between these

agents and their responsibility for a defined part of the MV network. The agents are divided into the

two groups: Switching Agents and Measuring Agents. Switching Agents can use the switch gear of

their secondary substation whereas Measuring Agents provide measured values to the Switching

Agents. The possibility of autonomous switching provides dynamic topology reconfiguration which

is a new concept of operation.

Based on the systematic results of the first project year the principles of the multi agent system

were finalized. The project discussions showed that the completely decentralized approach (where

the system intelligence is divided between the switching agents) will be very complex. Therefore a

two-step approach was chosen: in the first step the system intelligence will be concentrated in the

control centre steering the switching agents. The second step will be an implementation of the

above shown decentralized approach. This two-step approach has several advantages: the central

approach is still a decentralized solution regarding from the central grid control centre. Furthermore

the first step will be possible without larger implementation risks. The change between both steps

will be a change of the multi agent system software, the hardware will remain the same.

The tasks of the second project year finalized the principle central approach and the underlying

communication structure. The system structure and their related algorithms were basically

implemented in the RTUs and tested in laboratory tests. The primary hardware equipment was fully

specified and finally ordered. The risk management was detailed regarding the upcoming field

construction subtasks. The field implementation of the hardware components will be one of the

main tasks of the first half of the third project year. In parallel the completely decentralized

approach will be further elaborated and developed. First experiences gained from the field- and

lab-tests of the centralized approach will be considered in order to eventually identify the most

promising approach for the communication scheme of a multi-agent-system.


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Table of Contents

EXECUTIVE SUMMARY ............................................................................. 3

LIST OF FIGURES & TABLES ..................................................................... 7

1 INTRODUCTION AND SCOPE OF THE DOCUMENT .............................. 10

1.1 Scope of the document ........................................................................................................ 10

1.2 Structure of the document ................................................................................................... 10

1.3 Notations, abbreviations and acronyms .............................................................................. 11

1.4 Definitions and Explanations ............................................................................................... 12

2 UPDATE DELIVERABLE DD1.1 .............................................................. 13

2.1 Focus on most benefical area ............................................................................................. 13

2.2 Two step approach of implementation ................................................................................ 17

2.3 Use Case - Grid losses ........................................................................................................ 17

2.3.1 Concept of loss measurement .............................................................................................. 17

2.3.2 Approach of simulations ....................................................................................................... 20

3 DESCRIPTION OF REQUIREMENTS AND FUNCTIONALITY OF THE

OVERALL SYSTEM ................................................................................ 24

3.1 Hardware specification of the multi agent system ............................................................... 24

3.1.1 Concept of the switching agent ............................................................................................ 24

3.1.2 Market analysis of primary equipment .................................................................................. 28

3.1.3 Concept of the measurement part of s-agents and m-agents............................................... 28

4 AUTONOMOUS MULTI AGENT SYSTEM ............................................... 34

4.1 Concept ............................................................................................................................... 34

4.1.1 Architecture of the autonomous system ............................................................................... 34

4.1.2 Control and Decision module ............................................................................................... 35

4.1.3 Post-fault operation .............................................................................................................. 42

4.1.4 Topology Optimization .......................................................................................................... 44

4.1.5 Time series forecasting ........................................................................................................ 49

4.1.6 Reduced Network model ...................................................................................................... 53

4.1.7 Data Storage ........................................................................................................................ 58

4.2 Laboratory model ................................................................................................................. 59

4.2.1 RTU Hardware ..................................................................................................................... 60

4.2.2 Grid Model ............................................................................................................................ 60

4.2.3 Model coupling ..................................................................................................................... 70

4.3 Current status and outlook .................................................................................................. 72

5 RTU COMMUNICATION ........................................................................ 74

5.1 Communication overview .................................................................................................... 74

5.2 VPN communication ............................................................................................................ 76


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5.2.1 Central communication ......................................................................................................... 76

5.2.2 Peer to Peer communication ................................................................................................ 76

5.3 IEC 60870-5-104 ................................................................................................................. 77

5.3.1 Overview 77

5.3.2 Central approach .................................................................................................................. 79

5.3.3 Peer 2 Peer approach .......................................................................................................... 80

5.3.4 Peer2Peer Database ............................................................................................................ 82

6 RISK MANAGEMENT YEAR 2 ................................................................ 84

7 REFERENCES ........................................................................................ 86

7.1 Project Documents .............................................................................................................. 86

7.2 External documents ............................................................................................................. 86

8 APPENDIX ............................................................................................. 88

8.1 Fault detection ..................................................................................................................... 88

8.2 Load/Generation models ..................................................................................................... 89


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List of figures & tables

Figure 1: Final selection of separation points.............................................................................. 13

Figure 2: Actual state of separation points ................................................................................... 14

Figure 3: Procedure of calculation ................................................................................................... 15

Figure 4: Number of switching actions .......................................................................................... 15

Figure 5: Actual extension plan ......................................................................................................... 16

Figure 6: Chosen feeder in the selected grid area ...................................................................... 18

Figure 7: Example of a m-agent for grid losses ........................................................................... 19

Figure 8: Example of an installation position in a secondary substation ......................... 20

Figure 9: Effective feed-in capacity of a wind reference unit in Reken ............................. 21

Figure 10: Projected feed-in capacity of wind energy in Reken ........................................... 21

Figure 11: Diagram of wind ................................................................................................................ 22

Figure 12: Diagram of PV ..................................................................................................................... 22

Figure 13: Diagram of load .................................................................................................................. 22

Figure 14: Circuit diagram of the new switchgear in walk-in substations ....................... 25

Figure 15: Circuit diagram of a new MV/LV substation .......................................................... 26

Figure 16: Circuit diagram of the switchboard solution .......................................................... 28

Figure 17: Voltage taps and current transducer for m-agent or s-agent .......................... 29

Figure 18: Principle structure of a m-agent and s-agent ......................................................... 30

Figure 19: Top view of a secondary substation with installation space for a s-agent . 31

Figure 20: Picture of a control cabinet for a m-agent ............................................................... 32

Figure 21: 560CVD11 multimeter .................................................................................................... 33

Figure 22: Drawing of the measurement concept in the primary substation ................. 33

Figure 23: Architecture of the centralized autonomous system .......................................... 35

Figure 24: State machine representation of the system internal states ............................ 36

Figure 25: State limits for the medium-voltage network defined by the DSO ................ 37

Figure 26: Secure state and its transitions ................................................................................... 38

Figure 27: Algorithm for handling the endangered state level 1 ......................................... 39

Figure 28: Qualitative voltage behaviour in the endangered state level 1 and the system reaction ....................................................................................................................................... 40

Figure 29: internal logic of the endangered state level 2 ........................................................ 41

Figure 30: Qualitative behaviour of a voltage measurement when reaching the endangered state level 2 and the consequence of the control actions ............................... 41

Figure 31: Algorithm of the FDIR module ..................................................................................... 42

Figure 32: Short circuit case and the corresponding indication .......................................... 43

Figure 33: Flowchart of the SEM algorithm .................................................................................. 47

Figure 34: Dynamic optimization for an examplary network ............................................... 49

Figure 35: Principle of observations weighting .......................................................................... 50

Figure 36: Measurement and forecast of an active power time series (Exponential Smoothing) ................................................................................................................................................ 52


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Figure 37: Measurement and forecast of an active power time series (Multiple Regression + ARIMA) ............................................................................................................................ 53

Figure 38: Basic principle of the network reduction ................................................................ 56

Figure 39: Complete and reduced Reken topology ................................................................... 57

Figure 40: Reduced model precision for the 130 node Reken grid ..................................... 57

Figure 41: Concept of the laboratory model ................................................................................ 59

Figure 42: Basic principle of the grid modelling with aggregated LV grid ....................... 61

Figure 43: Exemplary sector of Reken MV grid modelled in MATLAB®/Simulink® .... 62

Figure 44: Principle of the phasor simulation method [16] ................................................... 63

Figure 45: Model of a secondary substation in Simulink® ...................................................... 63

Figure 46: Model of the controlled current source .................................................................... 64

Figure 47: Typical household load profiles .................................................................................. 65

Figure 48: Biogas plant time series. Top: five different units; Bottom: cumulated feed-in .................................................................................................................................................................... 66

Figure 49: Day-long elevation angle course ................................................................................. 67

Figure 50: Day-long photovoltaic feed-in ...................................................................................... 68

Figure 51: Converted wind speed measurements in the relevant region ........................ 69

Figure 52: Wind power coefficient curve ...................................................................................... 69

Figure 53: Active power output of a wind turbine model for the given time period ... 70

Figure 54: Architecture of the laboratory model ....................................................................... 71

Figure 55: Picture of the laboratory set-up .................................................................................. 72

Figure 56: Communication layer ...................................................................................................... 74

Figure 57: Central communication .................................................................................................. 76

Figure 58: Peer to Peer communication ........................................................................................ 77

Figure 59: Hierarchical communication ........................................................................................ 79

Figure 60: Peer2Peer Communication to neighbors ................................................................. 80

Figure 61 : Data routing ....................................................................................................................... 81

Figure 62: Risk Matrix Year 1 ............................................................................................................. 85

Figure 63: Risk Matrix Year 2 ............................................................................................................. 85


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Table 1: List of deliverable dD1.2 ..................................................................................................... 10

Table 2: Scenario matrix ...................................................................................................................... 23

Table 3: Overview of the existing optimization techniques ................................................... 45

Table 4: Key properties of both forecasting methods .............................................................. 53

Table 5: Exemplary line data .............................................................................................................. 59

Table 6: RWE ASDU/IOA address scheme .................................................................................... 78

Table 7: RWE Data model .................................................................................................................... 79

Table 8: RTU connection matrix ....................................................................................................... 81

Table 9: Actual list of risks .................................................................................................................. 84

https://grid4eu.sharepoint.com/demo1/Shared%20Documents/Dokumentation/Deliverable%20dD1.2/2013-10-18_Grid4EU_Deliverable_dD1.2_V12.docx#_Toc370136202

https://grid4eu.sharepoint.com/demo1/Shared%20Documents/Dokumentation/Deliverable%20dD1.2/2013-10-18_Grid4EU_Deliverable_dD1.2_V12.docx#_Toc370136203


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1 Introduction and scope of the document

1.1 Scope of the document The scope of the document is the description of all DEMO1-activities in the second project year.

The delivery date of the document is M24 (end of month 24).

Task

number

Deliverable

number Deliverable Deliverable description and responsibilities

Delivery

date

1.2.1

dD1.2

Report of development for

flexible MV-network operation

1) Introduction and scope of the document

2) Update of the technical description of the

deliverable dD1.1

3) Description of requirements and functionality of the overall system

4) Autonomous multi agent system

5) RTU communication 6) Risk management year 2

M24

1.2.2

1.2.3

1.2.4

1.2.5

1.2.6

Table 1: List of deliverable dD1.2

Table 1 shows that the deliverable dD1.2 “Report of development for flexible MV-network

operation” consists of different tasks. All the tasks are described separately in the document.

1.2 Structure of the document

The structure of the document is according to the tasks listed in Table 1: List of deliverable dD1.2

1) Introduction and scope of the document

2) Update of the technical description of the deliverable dD1.1

3) Description of requirements and functionality of the overall system

4) Autonomous multi agent system

5) RTU communication

6) Risk management year 2


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1.3 Notations, abbreviations and acronyms

CC-Agent Control Centre-Agent

DER Distributed Energy Resources

DG Distributed Generators

DSO Distribution System Operator

EU European Union

IAF Infrastructure Agent Function

KPI Key Performance Indicator

MAF MV-Agent Function

M-Agent Measuring-Agent

MAS Multi Agent System

PC Project Coordinator

PLC Programmable Logic Controller

PLC Power Line Carrier

PV Photovoltaic

RES Renewables

RTU Remote Terminal Unit

S-Agent Switching-Agent

SCADA Supervisory Control and Data Acquisition

SCC Special Contract Customer

SEM Switch Exchange Method

SGAM Smart Grid Architecture Model

SGCG Smart Grid Coordination Group

SSOM Sequential Switch Opening Method

SSS Secondary Substation

TM Technical Manager

UPS Uninterrupted Power Supply

VPN Virtual Private Network


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1.4 Definitions and Explanations

Generic Networks – Generic Networks are theoretically assumed structures, which are

common in MV-networks of a Distribution System Operator (DSO). The generic network

consists of typically used equipment. The network is tested with various scenarios of load

and feed-in, especially the possible location of the circuit breakers has to be analysed.

Scenario – A certain constellation of different parameters of load and feed-in. Different

scenarios have to be mastered by the tested network. Also a development of load and

feed-in over several years give multiple scenarios.

Switch state – This state represents the actual topology of the grid, depending on the

actual circuit breaker state. For example a ring structure with three breakers could have

three practical switch states, which give a radial structure and not a meshed structure

(closed ring topology and / or “islanding” needs to be avoided).

Limit value violation – under/over crossing of an allowed operative state value. Allowed

state values are:

voltage limits: +/- 10% from the nominal voltage

current limits: the current in a line must not exceed 100% of the nominal

current

Primary hardware – Equipment that is directly used for the energy transport within a

substation, e.g. the electromechanical components

Secondary hardware – Equipment that is used for safety, monitoring, control, automation

and communication tasks, e.g. the RTU or communication modules

Circuit Breaker - A circuit breaker is an automatically operated electrical switch designed

to protect an electrical circuit from damage caused by overload or short circuit.

Walk-in substation – a secondary substation where the primary and secondary hardware

equipment is located inside a small building. Maintenance and manual operation are done

within this building in contrast to typical compact substations where the personal is

working from outside of the station housing.


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2 Update deliverable dD1.1

2.1 Focus on most benefical area

In the deliverable dD1.1 we have described two different methods to place the agents: the heuristic

approach by the Technical University of Dortmund and the operational approach for finding an

optimal location of the circuit breakers by RWE. At the end the operational approach by RWE was

chosen. As you can see in Figure 1, seventeen separation points were planned in the first

deliverable dD1.1.

Figure 1: Final selection of separation points

One important issue of the first year was to estimate the number of expected switching operations.

Analysis and simulations have shown a higher needed number of switching operations as planned

before. Approximately 700 switching operations per year were expected for a single separation

point. So far, only circuit breakers with motor drives were considered.

The implemented circuit breakers have a M1000-classification. This means that 1000 mechanical

switching operations, but only 100 switching operations under load are possible per lifetime. At the

moment there is no technical solution to solve the problem with the restricted number of switching

operations. The result is the need of power circuit breakers and significant additional expenses for

reinforcement of primary hardware.

SP Method 2

SP Method 1

Chosen SP

Shift to near SP


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Due to the higher costs for reinforcement of primary hardware, the focus will be on the most

beneficial areas to get a positive business case (with use of power circuit breakers). Network

calculations and simulations by the Technical University of Dortmund have shown, that a great

effect will be reached with a few separation points (s-agents).

This subchapter describes the procedure to identify the most beneficial area in the demonstration

grid of Reken. In this regard the changes in the current topology compared to the previously

planned allocation of agents are necessary. Due to operational reasons four separation points

were rejected and one separation point was complemented. Operational reasons are for example

static separation points with no considerable effects or separation points close to special contract

customers that should only be used for failure management (minimizing the impacts on this

sensible customer group). These minor changes lead to the following data base.

Figure 2: Actual state of separation points

A few scenarios (load and feed-in) for one year were defined, corresponding to the actual state.

These data consists of

o 15-minutes intervals

o reference time series of real measured generators

o mean load curves, created from standard load curves

o real installed decentralised generators.


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The whole year is analysed. For each time interval the following steps are performed (Figure 3).

Figure 3: Procedure of calculation

After these steps are performed for every time interval, the switching activities are analysed. For

each switching agent the state change (closed->opened OR opened->close) is counted.

Figure 4: Number of switching actions


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Based on this evaluation the most beneficial area in the demonstration grid was identified. The

result is shown in Figure 5. This picture shows seven most important separation points. Four

separation points will be realized as a switchboard solutions close to the MV/LV substation, two by

the replacement of the complete MV/LV substation and one by the replacement of the MV

switchgear in walk-in substations. These three different concepts for station reinforcement of

primary hardware (s-agents) will be tested in DEMO1.

Figure 5: Actual extension plan

The red circle in Figure 5 symbolises the replacement of the MV switchgear in walk-in substations

or the replacement of the complete MV/LV substation. The drawing x symbolises the switchboard

solution close to the MV/LV substation. The different concepts will be descripted separately in

another chapter (chapter 3.1.1).

In the first year (see deliverable dD1.1) we planned to implement seventeen s-agents. Now one

year later we have to correct the number of separation points (s-agents) due to the previous

explanations. The aim is, that we can reach round about 80 per cent of the benefit with the reduced

number of separation points (s-agents). The business case becomes better, because of less

station reinforcement of primary hardware as planned before. And furthermore no changes on

testing the algorithm and the communication structure or on topics like scalability and replicability

are necessary.

The number and location of m-agents is not changed in comparison to the first year deliverable.


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2.2 Two step approach of implementation

The initial idea stated in dD1.1 was to develop a fully decentralized autonomous control system.

This would exhibit decentralized logic and decision making as well as peer-to-peer communication.

In the course of the second project year some new insights have been made:

o Traditionally DSOs rely on centralized logics placed in the network control centres, so it is

hard to migrate to a fully decentralized alternative

o Chosen control action (switching) causes non-local changes in the systems state

o More global system insight is probably needed to guarantee secure operation

o Power flow computation is needed

These points made clear that a reliable fully decentralized concept would be very challenging and

an alternative approach could enable an easier and faster access to the implementation phase.

The decentralized implementation should not be completely disregarded, but postponed. In the

meanwhile a centralized approach, which seems to be more distinct, should be implemented.

The proposed approach has some similarities with a classical SCADA system like centralized data

acquisition and centralized decision making. Though, some features make it innovative. The

system should work in an autonomous way and make decisions based on manageable models.

Some interesting and not foreseen aspects have been identified in the second project year:

o Load flow model for a not fully monitored power system is required

o Optimization techniques require insight in a specific time horizon

System state forecast should be applied

In the following, we plan to proceed in a two-step manner. Currently the centralized system

implementation is being carried out. Also a laboratory test set-up with real RTU units is being

realized. In the second step, which will be by October 2013, we start to develop and implement

some decentralized functions. It has been decided that not all system functionalities have to be

necessarily decentralized. The most important and interesting aspects are peer-to-peer

communication, decentralized post-fault operation, decentralized topology detection and state

identification. We plan to combine these new aspects with the results from the central

implementation, so that the system will have both kinds of functionalities – centralized as well as

decentralized. One of the most interesting research questions will be: what approach can show the

best performance?

2.3 Use Case - Grid losses

2.3.1 Concept of loss measurement

In this chapter the loss measurement concept is described. Reconstruction work and additional


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measurements are necessary in the primary substation. The objective is to recognize the grid

losses in the distribution grid of Groß Reken. Additionally it is planned to identify the grid losses in

detail for one selected MV feeder. Figure 6 shows the selected feeder (pink colour).

Figure 6: Chosen feeder in the selected grid area

The transformation losses of the MV/LV transformers and the line losses in the MV network will be

measured with the help of the m-agents, which are located in each secondary substation. The m-

agents for loss detection are different to the m-agents planned for detection of unknown overloads

and voltage violations (see 3.1.3). Furthermore the line losses in the underlying LV network will be

identified in a few secondary substations of the selected feeder.

Based on these collected data the expected grid losses will be estimate for the distribution grid of

Reken. The results of the estimation can be used for a comparison with the results by simulation.

The simulation and the results will be described in the next chapter (2.3.2).

The voltage will be measured with an accuracy of 1 %, the current with an accuracy of 0.5 %.

These values are quite small, this means that really high-end equipment is deployed for the

measurement concept.

2.3.1.1 Concept of the measurement in the primary substation

All switching bays of the primary substation of Groß Reken have to be upgraded in the same way.

An additional current transducer and the multimeter 560CVD11 have to be implemented in each


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switching bay. This multimeter is also integrated in the RS485-bus, so that the values can be

concentrated in the control centre (RTU) and transmitted to the central SCADA system for

analysing and archiving data. An expected accuracy of 1 % can be achieved with this

methodology. The same procedure is also described in chapter 3.1.3.1.

2.3.1.2 Concept of the measurement agent for grid losses

The structure of the loss measurement agent leans heavily on the structure of the m-agent. The

structure of the m-agent is described separately in section 3.1.3. Therefore, here are only the

essential differences characterised.

The objective is to measure the power (in detail voltage and current) on the secondary side of the

transformer. So all measurements in the MV network and the associated measuring components

(transmitter, transducer) will be obsolete. The measurement on the LV side is performed as

described later in the report (chapter 3.1.3). The power values will not be measured in the

secondary substations in case of failure. In this way, an additional battery supply is not necessary.

Binary I/O´s are also not necessary. In total less components are responsible for a reduction of the

installation dimensions. This allows, at least partly, to install the m-agent directly in the secondary

substation. A maximum dimension of (W x H x D) = (360 mm x 254 mm x 165 mm) is provided.

Figure 7 and Figure 8 are showing the different variants of installation in the secondary substation.

The loss measurement agent will be realized with components of ABB (FIONA) in this project. It is

possible to achieve an accuracy of 1 % with the implemented current transducers.

Figure 7: Example of a m-agent for grid losses


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Figure 8: Example of an installation position in a secondary substation

2.3.2 Approach of simulations

This chapter describes the procedure to determine the grid losses in a distribution grid. In order to

recreate the supply over the course of a year, it is necessary to evaluate reference units of

renewable energy resources (PV, wind, biomass). The reference values are normed. These

normed factors are used to calculate the actual amount of feed-in in 15-minute values, with the

installed feed-in capacity. The load is calculated with the help of standard load profiles, also in 15-

minute values.

Figure 9 shows the effective feed-in capacity of a selected wind reference unit in Reken. The feed-

in capacity is measured in 15-minute-values.


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Figure 9: Effective feed-in capacity of a wind reference unit in Reken

The data of the reference unit are used to extrapolate the full feed-in capacity of wind energy in

Reken. The result of a selected day is shown in Figure 10.

Figure 10: Projected feed-in capacity of wind energy in Reken

All possible scenarios are simulated in NEPLAN (NEPLAN is the standard grid planning tool at

RWE). To minimize the number of simulated scenarios, the renewable energy resources (PV, wind,

biomass) and load are divided in different clusters. The values of biomass are not considered here

and expected durable at 100 %, because they remain nearly constant during the year. The

following pictures (Figure 11, Figure 12, Figure 13) are showing the defined four clusters for PV,

wind and load.


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Figure 11: Diagram of wind

Figure 12: Diagram of PV

Figure 13: Diagram of load

Thus, a number of 64 (4x4x4) different scenarios is possible. With the help of a scenario matrix

(Table 2), the frequency of each scenario is counted. Each scenario with a frequency of more than

100 is simulated twice in NEPLAN.


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Table 2: Scenario matrix

The first simulation is a determination of grid losses with an load optimized grid plan (given factors

for load and feed-in). The second simulation is a optimization of the separation points with the

same factors and a subsequently determination of the grid losses.

The comparison of these two simulations shows the savings potential. The savings potential

means the reduction of grid losses and at the end of the day the monetary gain: Especially the

scenarios with strong supply and low load resulting in very low total grid losses.

Subsequently, with the help of the determined reference topology (optimized load case), an

increase of the power supply will be simulated under attention of voltage violations and current

limits. By the automated switching operation of the grid topology the voltage violations and

overloads should be adhered.

The resulting grid situation has to be checked with the factors of the load case. Therefore the

voltage violations, unknown overloads and additionally the potential of savings has to be

considered in detail.

The factors to simulate the feed-in scenario will be increased as long as no voltage violation or

unknown overload is located. Thus, the time period up to a necessary grid expansion and the

associated costs has to be determined.


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3 Description of requirements and functionality of the overall system

3.1 Hardware specification of the multi agent

system

3.1.1 Concept of the switching agent

The following chapter describes the three different concepts for station reinforcement of primary

hardware. As mentioned before, these concepts are:

o the replacement of the MV switchgear in walk-in substations

o the replacement of the complete MV/LV substation

o a switchboard solution close to the MV/LV substation

The requirements for the switching technology concerning the expected number of switching

operations per year and the useful life time are the same for all three concepts. The power circuit

breakers have to be designed for 1.000 switching operations per year (under load) and a useful life

time of 20 years. These two points were advertised by the invitation.

The manufacturers were encouraged to name their maximum number of switching operations for

the different types of the MV switchgear. The result was, that no manufacturer could guarantee the

expected number of 20.000 switching operations over 20 years. Furthermore the manufacturers

have to guarantee that their components fulfil the RWE-standards.

3.1.1.1 Replacement of the MV switchgear in walk-in substations

The first concept describes the replacement of the MV switchgear in walk-in substations. The

actual existing air-insulated switchgear will be replaced by a new sulphur hexafluoride (SF6)-

insulated switchgear in the MV/LV substation.

Following steps have to be conducted for a technical implementation:

o Dismantling and a proper disposal of the existing 10 kV air-insulated switchgear.

o Creating a temporary 10 kV solution to ensure the local power supply.

o The new switchgear consists of three switching bays and a transformer. The switches of

the s-agents will be equipped with motor drives. Additionally status signals will be used to

transmit the currently switching state.

o Connection of the motor drives and feedback of the switching state to the SCADA system.

o In every switching bay the measured values (for the actual current and voltage level) have

to be acquired for the data transmission with an accuracy of 1 %. Also the acquisition and

announcement of short and earth faults will be required.


30 October 2013 25/92

o Possibility to connect 10 kV lines (cables) with a size of 500 mm² in a walk-in substation.

o Delivery and installation of an uninterrupted power supply (UPS) with a rectifier to supply

the motor drives of switches in case of a grid failure. The two components are involved in

the monitoring process. The UPS shall buffer 3 switching operations for a time period of 3

hours.

o Installation of a box (600 mm x 600 mm) to connect the remote control technology to the

switchgear and power supply.

o Pressure calculation for the secondary substation housing.

Figure 14: Circuit diagram of the new switchgear in walk-in substations

3.1.1.2 Replacement of the complete MV/LV substation

The second concept describes the replacement of the complete MV/LV substation. The actual

existing secondary substation will be replaced by a new MV/LV compact substation.


o Delivery and installation of a complete 10-kV secondary substation (including a 400-kVA

transformer)


30 October 2013 26/92

o 10 kV switchgear will be equipped with two switching bays for cables and one for the

transformer. The MV switches have also motor drives and status signals to transmit the

actual switching state.

o Connection of the motor drives and feedback of the switching state to the SCADA system.

o In every switching bay the measured values (for the actual current and voltage level) have

to be acquired for the data transmission with an accuracy of 1 %. Also the acquisition and


o Possibility to connect 10 kV lines (cables) with a size of 500 mm² in a substation.

Introduction of the cables into the substation by sealing packing HIS 150.




hours.



Figure 15: Circuit diagram of a new MV/LV substation


30 October 2013 27/92

3.1.1.3 Switchboard solution close to the MV/LV substation

The third concept describes the switchboard solution close to the MV/LV substation. The actual

existing secondary substation will be untouched. An additional switchboard will be placed close to

the station. The function is to operate (open/close) the line in one direction.


o Delivery and installation of complete 10-kV switchboards

o The switchboard housing is made of concrete, plastic or comparable materials for the

switching equipment.

o The switchgear will be equipped with one switching bay for a cable and one recital bay with

earth switch. The switch is equipped with a motor drive and status signal to transmit the

actual switching state.

o Connection of the motor drives and feedback to the transmission technique.

o In the switching bay the measured values (for the actual current and voltage level) have to

be acquired for the data transmission with an accuracy of 1 %. Also the acquisition and


o Possibility to connect 10 kV lines (cables) with diameter of 500 mm² in a substation.

Introduction of the cables into the substation by sealing packing HIS 150.




hours.



o Installation of a LV distribution panel in the switchgear for grid connection


30 October 2013 28/92

Figure 16: Circuit diagram of the switchboard solution

3.1.2 Market analysis of primary equipment

The idea was to modify the circuit breakers in the MV network to round about 10.000 electrical

switching operations. Due to this topic we had a lot of discussions with several manufacturers.

Currently available circuit breakers have a M1000-classification.

As mentioned before, all circuit breakers in the demonstration grid of Groß Reken have also a

M1000-classification. If these types of circuit breakers should be used for 10.000 switching

operations for example, it would be necessary to upgrade the mechanical system. In this case, it

was even a new development of a circuit breaker. Additionally these circuit breakers are not

designed for 10.000 electrical switching operations. An adjustment of the chambers and

extinguishing system would raise a great effort and means also a new development.

In summary one can say that an adjustment or modification of the circuit breakers is neither

technically nor economically feasible within this project scope. For applications with a high

switching frequency, furthermore the existing power circuit breakers have to be used.

3.1.3 Concept of the measurement part of s-agents

and m-agents

Basically active and reactive power values of all feeders are needed in a secondary substation.

This means that the voltage level of the MV busbar and the current of each feeder of the selected


30 October 2013 29/92

secondary substations must be measured. Additionally the switching agents must be equipped with

a control option. These requirements are responsible for the secondary technical structure of the s-

agents and m-agents.

The secondary hardware of the s-agents and m-agents is basically constructed similarly. It consists

essentially of the following components:

o battery

o power supply unit

o safety devices (fuses)

o Remote Terminal Unit (RTU) with binary I/Os

o GPRS modem

o measurement inputs for current from each feeder

o measurement inputs for current and voltage on LV side

o current transducer in each feeder

o current transducer on LV side of the (secondary substation) transformer

o voltage taps to measure voltage level on LV side and providing the supply voltage

Figure 17: Voltage taps and current transducer for m-agent or s-agent


30 October 2013 30/92

Figure 18 shows an example of the standard product of ABB (type FIONA) with the principle

structure of the m-agent and s-agent.

Figure 18: Principle structure of a m-agent and s-agent

The power supply of components is secured by voltage taps on the LV side in the secondary

substation. Therefore, the voltage is 230 V (AC). The power supply unit converts 230 V (AC) into

24 V (DC). This voltage is necessary, because all secondary technical components are operating

with 24 V (DC).

Depending on the number of feeders in a secondary substation, the corresponding number of

measurement components and current transducers will be deployed.

The voltage values will be directly ascertained in the switching bays of the s-agents with the use of

capacitive voltage taps. So the power values can be determined directly in the feeders and

transmitted to the RTU.

In secondary substations, where only m-agents are planned, therefore no capacitive voltage taps

are existing. A upgrade of these substations with voltage transducers would be very expensive and

because of that another methodology will be used. The measured voltage value on the LV side will

be used to determine the power values on the MV side. This LV value is used in consideration of

the transmission ratio and the losses of the MV/LV transformer to calculate the voltage value which

exists on the primary side of the transformer.

Another difference between m-agents and s-agents is the dimension of the battery. In error-free


30 October 2013 31/92

operation can be assumed that the supply voltage for the components of secondary equipment is

available. If the supply is interrupted in case of error, it may be necessary to perform switching

operations in the local distribution grid. Due to these switching operations, the battery in the s-

agents is designed in a way, that there is sufficient capacity for at least two hours of operation and

three switching operations.

It has to be ensured that after the interruption of the supply voltage a message about the short

circuit or ground fault is transmitted by the m-agent securely. From previous experience with the

GSM transmission a further supply of the system of maximum 10 minutes is sufficient. A

transmission of measured values is no longer necessary, because all measured values are zero.

For reasons of IT-security the RTU sets up an encrypted connection via IPSec encryption. The

necessary certificates are predefined by the respective DSO, in case of DEMO1 by RWE. The

GPRS modem transmits the encrypted data. Based on this methodology, there are no special

requirements for the functionality of the modems.

For data transfer the communication protocol in accordance with IEC 60870-5-104 is used in the

project. Additionally RWE requires the observation of a special RWE-profile.

The installation slot for the secondary equipment has to be chosen for easily maintenance access

in the secondary substation. A space requirement with a maximum dimension of (W x H x D) =

(600mm x 600mm x 400mm) is provided for the s-agents. Separate cabinets will be placed next to

the secondary substations by the m-agents. The internal dimension of these cabinets are (W x H x

D) = (600mm x 600mm x 300mm) at minimum.

Figure 19: Top view of a secondary substation with installation space for a s-agent


30 October 2013 32/92

Figure 20: Picture of a control cabinet for a m-agent

3.1.3.1 Concept of the measurement in the primary substation

Currently there is only a single-phase current measurement located in the primary substation. In

addition, protective cores are inserted that are connected only to the protective devices at the

moment. For the algorithms that are used in the project, the active and reactive power values are

essential. Therefore the measurements must be upgraded in the affected switching bays of the

primary substation. That‟s why current transducers (type RITZ ZKSW 70 1/1A Kl.05 10 VA) are

deployed in the existing protection cores of the switching bays. The voltage will be measured at the

MV busbar and transmitted via an existing ring line to each switching bay. Direct-measurement-

facilities are employed in the affected switching bays, which are supplied with the voltage value of

the ring line and the current measurement of the transducer. The ABB unit 560CVD11 is used for

the measurement. This multimeter is supplied with 220 V (DC), because it is the only secured

auxiliary voltage supply in the primary substation.


30 October 2013 33/92

Figure 21: 560CVD11 multimeter

This ABB unit will be implemented in each switching bay and connected via RS485-interface and

modbus protocol to the control centre (RTU). The following picture (Figure 22) shows the

measurement concept in the primary substation.

Figure 22: Drawing of the measurement concept in the primary substation


30 October 2013 34/92

4 Autonomous multi agent system

This chapter introduces the concept and the implementation of the autonomous control and

operation system for use in electrical medium-voltage networks. First, the overall system

architecture is described and the essential functional modules are explained in detail. In the

following part, the laboratory implementation and the concept of the hardware-in-the-loop simulator

are presented.

4.1 Concept

4.1.1 Architecture of the autonomous system

The overall system is based on the central architecture approach, which means that the system

logic and decision making entity is placed at the so called master agent or control centre (CC)

placed in the Reken primary substation. The control centre receives measurements from the slave

agents and is able to give switching commands in order to reconfigure the network topology.

Furthermore CC is connected to the SCADA system for the reason of information transparency and

in case of the need to control the system remotely. Also, occasionally some network topology

information update from SCADA is needed.

One major benefit of the central approach is the security of switching actions: because of the

central supervised switching process and the underlying optimization algorithm (see chapter 4.1.4)

no illegal topology configurations can be achieved. This proceeding corresponds to the current

manually operation philosophy: when switching actions are planned, the executing personal has to

receive a switching permission from the network control centre.

As illustrated in Figure 23, the structure of the control centre logic is subdivided in following entities:

o Control and decision module

o Forecast module

o Execution module

o Topology optimization module

o Post-fault operation module (FDIR)

o Data storage/SCADA Interface

All of these blocks are interconnected through communication or data channels.

For better understanding of the logical organization the secure operation case is considered. Slave

agents acquire the current measurements and transmit them to the CC. At the CC side the control

and decision module analyses incoming data. No state violations are detected and the

measurements are passed over to the forecast module.


30 October 2013 35/92

Figure 23: Architecture of the centralized autonomous system

The latest forecast is updated and the deviation from the last forecast is calculated. The forecast

update is given back to the decision module. The decision module initializes a new optimization

process in order to update the switching action plan for the current day and the day ahead. The

topology optimization requires a new nodal power forecast for the optimization horizon. After

computing the new switching plan, it is forwarded to the control and decision module. The old

switching schedule is now updated and passed to the execution module. This module is

responsible for the proper switching procedure and its supervision. In the meanwhile the

measurements and the switch state updates are transmitted to the SCADA system. Also if some

topology updates, e.g. due to network reinforcement, occur – the SCADA system sends this

information to the CC.

In the following sections, a more detailed explanation of every single logical module is given.

4.1.2 Control and Decision module

The control and decision module (CD) contains the overall system logic and is responsible for the

control actions organization. The system differentiates between different internal states, which are

always given by the present measurements and by the short circuit indication signals. There are

two types of internal states: non-faulty and faulty operation. The first one is subdivided into secure

Grid

Slave agent

Control and Decision

Forecast

Optimization

FDIR

Execution

Data storage

Control centre

SCADA

‚Actor’

‚Storage’

Data exchange

Communication channel


30 October 2013 36/92

state, endangered state level 1 and endangered state level 2. These states are defined in 4.1.2.2.

After a short circuit appeared, the system is at the faulty operation state. Failure isolation and

system restoration have to be carried out. So the state is set back to the most common secure

mode. Figure 24 illustrates all possible system states and their transitions. Some of the states are

looped, which means that the system remains for a longer period of time in secure or endangered

state level 1. It is assumed that it is always a possibility to avoid critical overloads or over-voltages

due to the load flow situation. So the endangered state level 2 is not looped.

Figure 24: State machine representation of the system internal states

Every internal state requires some specific actions. Typically measurements acquisition and

analysis of the measurements is performed continuously. Based on that, a transition between two

states can be decided.

For better understanding the non-faulty internal states, an overview about the state variables limits

is given.

4.1.2.1 State limits

A state of an electrical network is defined by its branch currents (or in following line loadings) and

nodal voltages. The described control system acts at the medium-voltage level. That means that

voltage changes at the substations affect the low-voltage customers. By the norm DIN EN 50160

the voltage level at the end customer‟s node has to be held in the ±10% range in respect to the

nominal voltage (0.4 kV for the low-voltage level). Depending on this requirement, RWE defined

viable limits for the MV-network operation (see Figure 25). These limits should not be violated while

operating the network.

The state values beyond the allowed limits belong to the endangered state level 2 (red areas in

Figure 25). This state has to be left very fast for preventing possible violations in the underlying

secure state

endangeredstate level 1

endangeredstate level 2

fault detection

restoration

isolation

faulty operationnon-faulty operation


30 October 2013 37/92

low-voltage network. Also the line loading of over 100% of line‟s thermal rating leads to the

endangered state level 2.

Basically all the states below the limits of the endangered state level 2 are considered as not

critical. However, it is proper to define some security margin which has to be passed through

before causing critical violations. The voltage margin of 1% from the critical limits and the line

loading between 80% and 100% is defined as endangered state 1. This state is allowed to be

driven accidently. The remaining voltage and current range are building the secure state, which is

preferred to be driven all the time.

Figure 25: State limits for the medium-voltage network defined by the DSO

4.1.2.2 Non-faulty operation

Now the main principles of the non-faulty state transitions and the inner logic of every state are

described. In chapter 4.1.3 the post-fault FDIR module is explained more in detail.

Secure State

The secure state can be changed to every other state (Figure 26). Also it is possible to get from

other states into secure state. Basically continuous measurements supervision is carried out. In the

meanwhile the losses optimal network configuration is calculated and a switching job plan is set.

So if nothing extraordinary happens, the switching jobs are performed.


30 October 2013 38/92

Figure 26: Secure state and its transitions

Endangered State Level 1

As described by the state limits, the endangered state level 1 is taken, when the system state

variables are not yet hard violated, but may run into the not permitted range. That‟s why a

mechanism which considers the staying time in this state and the next forecast value is

established. The flow chart of the algorithm is given in Figure 27.

measurement

Secure

regular optimization

switching actions

Endangered lvl 1

Faulty operation

Endangered lvl 2


30 October 2013 39/92

Figure 27: Algorithm for handling the endangered state level 1

As soon as one of the measured values access the endangered level 1, an integration process is

started. A threshold is defined for remaining in the state. When the integrated threshold is reached

two possible decisions can be made: whether to let the system remain for a while in the

endangered state level 1 or to leave this state by means of topology reconfiguration. The decision

is supported by the information provided from the forecast tool. It gives voltages and current

forecasts for the next 15 minutes interval. If the forecast indicates that the system would remain in

the same endangered state, an optimization process is started and a new topology is evaluated in

order to prevent a longer staying in this state. Otherwise no actions are done and the integrator is

reset. Also some planned switching job should be considered in order not to perform extra

switching. State transitions to and from all other states are possible. State supervision is carried out

by measurements acquisition.

Integrator limit crossed?

Is forecast secure?

switching operation

yes

yes

Is another switching operation planned during

the next 5 minutes?

no

no

start integrator

yes

Endangered lvl 1

no

Secure

Faulty operation

Endangered lvl 2measurement

reset integrator


30 October 2013 40/92

The principle of the variable integration and the usage of the forecast are illustrated in Figure 28.

Figure 28: Qualitative voltage behaviour in the endangered state level 1 and the system reaction

Every measurement needs its own integration process and though the state supervision is carried

out in parallel for every state variable. The most critical state of one of the variables corresponds to

the state of the entire system.

Endangered State Level 2

As already mentioned, as soon as the endangered level 2 state is reached a control action has to

be applied immediately. The flowchart for the endangered state level 2 is given in Figure 29.

This state assumed to be reached only from the secure state or from the endangered state level 1.

A transition from the post-fault state to the endangered state level 2 is unlikely, because the logic of

the post-fault state doesn‟t allow leaving the state before not guaranteed the transition to at least

endangered state level 1 or at the best to the secure state. After performing the topology

reconfiguration, the system has to be set to the one of the allowed states.

In the following a qualitative example of the time dependent system behaviour is shown in Figure

30. After a limit violation is detected the autonomous control system requires some time to

compute the new optimal topology. When the switching job is generated a sequence of topology

changes is performed. As the new optimal topology is applied, the system is driven out from the not

permitted state.

integrator trips, noneed for actionbecause of forecast

endangered lvl 2

endangered lvl 1

secure

Limit violation end lvl 1 -> integrator starts

V

t

forecast

measured voltage


30 October 2013 41/92

Figure 29: internal logic of the endangered state level 2

Figure 30: Qualitative behaviour of a voltage measurement when reaching the endangered state level 2 and the consequence of the control actions

Endangered lvl 1

Faulty operation

Secure

optimization

Endangered lvl 2

switching operation

Short circuit appeared?

no

yes

forecast

measured voltage

V

t

endangered lvl 2

endangered lvl 1

secure

Limit violation end lvl1 -> need for action

switchingsequence


30 October 2013 42/92

4.1.3 Post-fault operation

The FDIR module defines the system behaviour in case of the occurred short circuit fault (SC). The

autonomous multi agent system does not replace the protection system, which acts in the

traditional way by opening circuit breakers at the primary substation when detecting overcurrent.

So the entire interconnected faulty network region is disconnected. The autonomous system has

the task to identify the fault location between two measuring agents and to restore the part of the

affected area in a short time. The process is typically subdivided in three phases (FDIR):

1. fault detection

2. fault isolation

3. restoration

In Figure 31 the overall algorithm of the FDIR module is given. The transition to the faulty state

exists from all the other states. The transition from the faulty state is only given to the secure and

endangered state level 1.

Figure 31: Algorithm of the FDIR module

Short circuit

Isolating SC-area,lock switch for safety reason

Possible to restore existing topology without sc-area

LF-calculation

yes

Is topology secure?

Execute switching algorithm

yes

Optimizationno

no

Is new topology in endangered lvl 1 or

secure?

Execute switching algorithm

yes

Delay-block

SC-detection

Endangered lvl 2

Secure

Endangered lvl 1


30 October 2013 43/92

FDIR is a well-known procedure, already applied in some other autonomous systems [1]. The

essential difference to the other approaches is considering an amount of installed decentralized

generation in the grid. Due to this fact, the restoration procedure becomes more complicated. Thus

it requires a better understanding of the system state after performing switching actions. So a load

flow and the topology optimisation are involved. In following the single steps of the algorithm are

demonstrated for the exemplary topology in Figure 32.

Figure 32: Short circuit case and the corresponding indication

4.1.3.1 Fault Detection

Only a line segment between two switching agents can be disconnected in order to restore the rest

of the system. Because of that the SC indicators of the switching agents have to be analysed (see

Figure 32).

The fault detection algorithm (flow chart in Appendix 1: Flow chart of the algorithm for fault

detection) starts at the primary substation bus bar and goes through the path of the positive short

circuit indications. As soon as the SC flags don‟t appear anymore the faulty segment is found. In

the given example it would be the segment E-D. The algorithm uses neighbourhood relations of the

switching agents.

4.1.3.2 Isolation

To the faulty segment belong a couple of switching agents. These agents and their switches are

identified in the fault detection phase. A direct commando to disconnect the switches performs the

isolation of the faulty line.

A

B

C

D E

F

G

Feeder 2 Feeder 1

0

1

0

0

0

0

0

0 0

1

1

0

1 0

1

0


30 October 2013 44/92

4.1.3.3 Restoration

The steps of the restoration process are described in Figure 31. Beside the optimization based

restoration a fast variant is given: this consists in closing the circuit breaker at the primary

substation. In the given example this would be probably the easiest way to restore the most part of

the network without involving any additional switching. Nevertheless this method needs a validation

of the system state after performing the restoration. This is carried out by means of a simple load

flow computation and analysing the resulting state variables. If the supposed system state is

secure, this fast restoration is applied.

Otherwise an optimization based variant is chosen. The restoration process has two requirements

– to find a new valid topology after the fault isolation and to avoid over-voltages and over-currents

in the restored area. This is a typical task for the topology optimization routine. In case of not

finding an appropriate solution, the system may wait and perform the optimization again. As soon

as the expected restored state is secure or endangered level 1, the switching execution is initiated.

4.1.4 Topology Optimization

Network topology optimization by reconfiguration is a well-known approach at the distribution

network level. A typical application field for this optimization is network planning. Assuming a

specific load and a generation structure an optimal open switch configuration for a radial network

can be found in order to minimize a certain objective function.

Another topology optimization application is carried out indirectly while operating the network and

conducting some maintenance works. In such a case a part of the network has to be reconnected

so that the system state values are still within valid limits. So an operating engineer proposes a

reconfiguration scheme for the duration of the maintenance. Usually the new topology is either

calculated with a load flow tool or estimated by the operational experience.

In GRID4EU DEMO1-project the autonomous control system acts only by closing or opening circuit

breakers1. This special control action affects the topology directly. A reliable optimization tool for

evaluating an optimal topology and a switching sequence is thus needed to provide an analytical

basis for the control actions, which are by their nature strongly non-linear and affect plenty of nodal

voltages and line currents.

The boundary conditions of the optimization problem are given by the operational requirements:

o The network topology has to remain unmeshed or radial

o Islanding through switching actions has to be avoided

o The voltages and currents are to be hold in the given limits (see chapter 4.1.2.1)

o The number of switching actions should remain low

The objective function assumed for the optimization problem is the network total losses. Even if the

exact losses cannot be obtained because of the lack of measurements a qualitative losses figure

obtained by the reduced model still can be used. The reduced network model is used in order to

1 In following, for the sake of simplicity, called “switches”


30 October 2013 45/92

provide a mathematical base for load flow computations on a not fully observed network. The

detailed description of this concept is given in the chapter 4.1.6.

In following an overview about the state of the art optimization techniques is given and the selected

method for the implementation in the project is described. This method refers to the so called static

optimization for a given system state snapshot. In order to compute a quasi-optimal switching plan

for a day, a dynamic optimization has to be carried out.

4.1.4.1 Review and selection of the optimization technique

Finding an optimal switch configuration in order to minimize a given objective function may be a

computational intensive task. The more switches are involved in the optimization the more complex

is the search of an optimal solution. In general, it is not possible to prove the absolute optimality of

a found topology configuration. Though, optimal topology reconfiguration techniques for radial

distribution networks are being developed since 80‟s. A good literature overview is given in [2] and

also in [3].

In Table 3 a summarized overview of the different techniques is given. The developed approaches

can be subdivided in mathematical programming, heuristics and methods from artificial intelligence

(AI). Also sometimes a possibility exists to list all possible topologies and to search over the entire

set. This naïve method is only applicable for small optimization problems and gets more

problematic yet for more than 15 switches2.

Considering the future implementation of the selected method on the RTU in PLC code the

complexity and needed time of the existing approaches have been ranked in a qualitative way.

Table 3: Overview of the existing optimization techniques

AI methods like genetic algorithms and particle swarm optimization are rather not applicable

because of their computational intensive nature. They are based on thousands of load flow

computations and require much virtual memory and time. The methods of mathematical

2 This reference number is evaluated by own experience

Naïve approachMathematical

programmingheuristics

'intelligent'

methods

some typical

methods

try all solutions dynamic programming

Branch exchange, heuristic rules

Genetic

algorithms,

Particle Swarm

Optimization

complexity + ++ + +++

quality of

solution + ++ + ++

needed time + ++ ++ +++


30 October 2013 46/92

programming are applied in the field of feeder planning and the formulation is not always directly

transferable to the problem of the reconfiguration. The quality of solution of heuristic methods

seems to be slightly inferior to the other methods. Thus their simplicity and easiness of the

implementation make these methods attractive. For the use in the project a method from the

category of the heuristics has been chosen.

Heuristics in general doesn‟t deliver an optimal solution. They are often based on intuitive rules,

like opening and closing switches and detecting lowest currents. One of such techniques is being

used in the network planning by RWE (see the first year deliverable dD1). In the literature it is

known as sequential switch opening method (SSOM) [4]. The approach chosen for the current

implementation is of the switch exchange type (SEM) and bases on [5]. This technique shows often

similar results like SSOM, though in some cases slightly better results are provided. The main

benefit towards the SSOM is the ability to understand the sequence of switching. Both approaches

make use of load flow computation.

4.1.4.2 Load flow computation

Load flow computation is a basic tool used for the topology optimization or system state

computation. Typically this mathematical method requires some input data:

o Network topology

o Nodal active and reactive power

The complete network topology is known by the DSO and can be directly used. However a

fundamental problem is the fact that the given distribution network is not fully observed. Thus the

information about the nodal power values at the unobserved nodes is missing and the load flow

computation can‟t be directly applied to the complete network topology in operation.

There are some few approaches in the literature to handle this typical problem of less

measurement. [6] uses an assumption that the total load of the unobserved network segment is

distributed either equally at all the secondary substations or in an increasing manner. In this way it

is possible to generate complete input data for the load flow computation. Another approach given

by [7] consists in performing a generalized state estimation for an underdetermined system. Also

here the output from the state computation contains deviations to the real state due to

uncertainties. Although this approach seems to be generally applicable, its scalability to the

systems with several more nodes is not clear.

The approach used here is built on the usage of the reduced network model which is described in

detail in chapter 4.1.6. The two main benefits of this approach concerning the implementation on

the RTU are the reduction of the system complicity3 and thus faster computation and the

mathematical reproduction of the system behaviour at the measured nodes.

The suggested load flow algorithm is the Gauß-Seidel [8] load flow. Its main benefit towards the

most common used Newton-Raphson algorithm is the absence of the matrix inversion which would

involve extra computational burden when implemented on the RTU. The convergence behaviour

and the fastness of the algorithm implementation show good results.

3 Which means reducing the number of nodes and branches of the network model


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4.1.4.3 Static optimization

The problem of static optimization consists of finding a switch configuration for a given static load

flow case so that an objective function is minimized. The applied method is the SEM based on [5].

The flowchart of the algorithm is presented in Figure 33.

Given a radial operated network with N open switches a random opened switch is virtually closed.

This causes a closed looped which contains several closed switches. A load flow computation is

performed to determine the closed switch leading the minimal current. This switch is the candidate

for exchanging the previously opened switch in the loop. After it is opened an additional load flow is

computed. When no violations are detected the objective function of this new topology is compared

with the older value of the objective function. If the objective function has been reduced by the

switch exchange, the procedure should be repeated again. This time another random switch is

chosen. Usually about 2N are sufficient until no more objective function reduction happens. Thus

the final „optimal‟ topology is found.

Some additional checks are needed. They are not described in detail here. E.g. one of them would

be avoiding the successive closing of the same switch.

It is notable that the random choosing of the switch to close is fully sufficient. Whereas other

supposed criteria like voltage difference at the opened switch would lead to the same solution [5].

Figure 33: Flowchart of the SEM algorithm

close a random opened

switch

find the corresponding

loop

open a new optimal

switch (Imin)

objective fucntion

reduced?

limit violations? cancelY

N

Y

N

END

START


30 October 2013 48/92

While performing virtually the closing and opening of the switches the sequence of the switch

exchange is noted. When optimizing the network topology for different load flow situations a

switching sequence is developed. In following it is called switching function. A network with N open

switches will have N time-dependent switching functions.

4.1.4.4 Dynamic optimization

In the context of the preventive and losses optimal network reconfiguration a horizon for optimizing

of the switching function is defined as 24 hours. The optimizing horizon is subdivided in 15 minutes

intervals. That means 96 static optimizations are carried out. Input data for the static optimization of

future time steps is provided by the forecast (see chapter 4.1.5). Due to volatile behaviour of nodal

active and reactive power time series and also due to the forecast errors too many switching

actions can be suggested when performing optimization for every 15 minutes interval. For that

reason the 24 hours switching functions have to be smoothened to some extent.

One effective way to minimize switching actions is the application of the slightly modified moving

average filter to the switching function. In Figure 34 an example for the network topology with two

opened switches is given. The reference topology corresponds to opened switches 4 and 16. The

non-optimized switching functions (blue and green in Figure 35) show different behaviour:

o Switching function 1 remains constant. This means the initially opened switch 4 is optimal

for the entire horizon

o Switching function 2 has some relatively short steps which let the switch number 16 be

closed and reopened plenty of times

Applying the moving average filter means substituting every value with the median value of a

certain interval. The length of this interval is essential for the smoothening strength. For the

„window length‟ of > 10 all the switching actions except of the {switch16-switch9} at the time

intervals 80 to 90 are neglected (compare with the red curve). Hence the number of switching

actions is strongly reduced and the lifetime of the circuit breakers is been extended.

Figure 34: Schematic switch configuration of the exemplary network


30 October 2013 49/92

Figure 35: Dynamic optimization for an examplary network

The main disadvantage of this approach is neglecting of the losses reduction potential

corresponding to the switching actions. As seen from Figure 35 below the losses reduction effect in

the interval 0-70 is being lost after performing dynamic optimization.

This issue is being currently considered. The method will be extended by involving the loss

reduction information.

4.1.5 Time series forecasting

A wanted and essential property of the multi agent system is its non-reactive behaviour. This

means, that the system does not react immediately to any limit value violation of the

measurements with a switching operation, but foresees a possible return of the system in the

secure state by its own.

To gain knowledge of the future system state4 a forecasting tool is necessary. Within the scope of

the project two different forecasting methods are considered:

1. Double Seasonal Exponential Smoothing

2. Multiple Regression + ARIMA (Auto Regressive Integrated Moving Average)

The double seasonal exponential smoothing method is purely based on measured values and

4 For the power flow computation a forecast of the nodal power values and also line power flow forecasts are needed. From the power flow computation nodal voltages and branch current forecasts are evaluated.

0 10 20 30 40 50 60 70 80 900

0.5

1

1.5

2x 10

5

15 minutes time intervals

loss

es

red

ucti

on

in W

0 10 20 30 40 50 60 70 80 900

5

10

15

20

15 minutes time intervals

ope

n s

wit

ch #

switching function 1

switching function 2

optimized switching function 2


30 October 2013 50/92

combines the advantages of low computing power demand and low memory space requirements,

so that it is suitable for running on RTUs.

On the other hand the Regression + ARIMA forecasting method has got the benefit that additional

information like weather data can be involved, so that a high forecasting quality can be achieved.

In the following sections these two concepts are briefly introduced and their advantages and

disadvantages are described. Finally both methods are compared with respect to their forecasting

accuracy by using real measured data.

4.1.5.1 Double Seasonal Exponential Smoothing

The Exponential Smoothing method is purely based on measured values. It is based on the

assumption, that the Time Series contains all necessary information.

In the Exponential Smoothing method forecasts are weighted averages of past observations. The

weights decay exponentially as the observations get older, so that the most recent observations

get the highest associated weights. Formula (1) describes the principle of the simple exponential

smoothing method [9]:

𝑦 𝑡+ℎ |𝑡 = 𝛼 ∙ 𝑦𝑡 + 1 − 𝛼 ∙ 𝑦 𝑡|𝑡−ℎ

(1)

According to the formula the forecast for time t+h given all information up to t is equal to a weighted

average between the most recent observation yt and the forecast for time t given all information up

to t-h. In Figure 36 the weights assigned to the observations are shown exemplarily. The more

recent the single observation the higher is its influence on the next forecast.

Figure 36: Principle of observations weighting

In order to be able to consider seasonal patterns in time series, the simple exponential smoothing

method needs to be extended. Within the scope of the project the double seasonal exponential

smoothing method is considered, which is suitable for series with two seasonal patterns.

In this method the three time series components – Level St, within-day seasonality Dt and within-

week seasonality Wt – are smoothed separately. The forecast for time t+h is obtained by

multiplying these three components:

(2)

Weights assigned to observations

t t-16 t-36 t-56 t-76 t-96

time axis

weig

ht


30 October 2013 51/92

𝑦 𝑡+ℎ |𝑡 = 𝑆𝑡|𝑡 ∙ 𝐷𝑡−𝑠1+ℎ|𝑡 ∙ 𝑊𝑡−𝑠2+ℎ|𝑡

The components are calculated using formulas (3)-(5) [10]

𝑆𝑡 = 𝛽 ∙ 𝑦𝑡

𝐷𝑡−𝑠1∙ 𝑊𝑡−𝑠2

+ 1 − 𝛽 𝑆𝑡−1 (3)

𝐷𝑡 = 𝛿 ∙ 𝑦𝑡

𝑆𝑡 ∙ 𝑊𝑡−𝑠2

+ 1 − 𝛿 𝐷𝑡−𝑠1 (4)

𝑊𝑡 = 𝜔 ∙ 𝑦𝑡

𝑆𝑡 ∙ 𝐷𝑡−𝑠1

+ 1 −𝜔 𝑊𝑡−𝑠2 (5)

Where β, δ and ω are the smoothing parameters. Applying the method to a quarter-hourly time

series, the seasonal indices s1 and s2 of the two seasonalities would be set to s1 = 96 and s2 = 7*96

= 672.

4.1.5.2 Multiple Regression + ARIMA

In contrast to the Exponential Smoothing method the Multiple Regression (MR) considers external

factors, so that e.g. weather data can be taken into account when calculating the forecasts. The

multiple regression method is based on the assumption, that the forecast variable has a linear

relationship with several explanatory variables. The forecast variable Y is calculated from a

functional relation between the explanatory variables Ai and a residual error ε:

𝑌 = 𝑓 𝐴1,𝐴2,… ,𝐴𝑝 , 휀 (6)

The explanatory variables Ai are weighted with regression coefficients γi. The aim of the method is

to determine the regression coefficients γi using the method of the smallest error squares. The

solution is given by the linear model:

𝑌 = 𝛾0 + 𝛾1 ∙ 𝐴1 + 𝛾2 ∙ 𝐴2+. . + 𝛾𝑝 ∙ 𝐴𝑝 + 휀 (7)

Within the scope of the project the explanatory variables day, time, combination of day and time,

solar radiation, wind speed (quadratic) and temperature are considered.

Due to the fact, that the forecast variable is calculated considering only external factors and

neglecting time dependencies within the time series, an ARIMA-model is used subsequently to

analyse the MR‟s residual error.

An Auto Regressive Integrated Moving Average (ARIMA) method aims to describe the

autocorrelations in the time series data [9]. The Auto Regressive (AR) part forecasts the variable of

interest using a linear combination of past values of the variable (instead of explanatory variables):


30 October 2013 52/92

𝑌𝑡 = 𝜙0 + 𝜙1 ∙ 𝑌𝑡−1 + 𝜙2 ∙ 𝑌𝑡−2+. . +𝜙𝑝 ∙ 𝑌𝑡−𝑝 + 휀𝑡 (8)

In contrast to the AR part the Moving Average (MA) method predicts the variable of interest using a

linear combination of past forecast errors:

𝑌𝑡 = 𝜃0 + 휀𝑡 + 𝜃1 ∙ 휀𝑡−1 + 𝜃2 ∙ 휀𝑡−2+. . +𝜃𝑝 ∙ 휀𝑡−𝑝 (9)

The ARIMA method is applied to the residual error ε of the multiple regression model to search for

structures resp. dependencies in the time series. With the help of the AR method the current

residual error is explained by previous residual errors. Due to the MA method the current residual

error is explained by errors, which were made when forecasting the previous residual errors.

4.1.5.3 Comparison of both forecasting methods

The Double Seasonal Exponential Smoothing and the Multiple Regression + ARIMA forecasting

methods were compared with respect to their accuracy using real 15-minutes active nodal power

time series of secondary substations with corresponding weather data. Three weeks of the

measurement data were used for training and the fourth week was forecasted.

In Figure 37 and Figure 38 the measured time series and the predicted time series (one-step-

forecast) of each method are shown for one secondary substation.

Figure 37: Measurement and forecast of an active power time series (Exponential Smoothing)

forecast

measurement


30 October 2013 53/92

Figure 38: Measurement and forecast of an active power time series (Multiple Regression + ARIMA)

It can be seen that both forecasting methods provide a comparable accuracy. The following Table

4 summarises the key properties of both concepts.

Double Seasonal ES Multiple Regression + ARIMA

low data demand

high data demand

(as much historical data as possible)

forecast only from running measurements possibility to involve additional information

(weather forecast...)

simple implementation sophisticated implementation

low hardware requirements high hardware requirements

Table 4: Key properties of both forecasting methods

Due to the low computing power demand and the low memory space requirements the Double

Seasonal Exponential Smoothing method is suitable for running on RTUs. In contrast the Multiple

Regression + ARIMA forecasting method requires a PC system, because of its sophisticated

hardware demands. Based on the simulation results it was decided to use the Double Seasonal

Exponential Smoothing method within the laboratory model.

4.1.6 Reduced Network model

As mentioned before, a viable network model for the state identification is needed. The state

identification is based on the load flow computation (LFC). LFC requires all nodal power values. On

forecast

measurement


30 October 2013 54/92

the distribution network level there are often only few measurements due to the high number of

nodes. Hence to perform a LFC some assumptions about the unobserved network areas are

needed. One possibility of dealing with this problem is to reduce these areas. The resulting network

model has to reproduce the same electrical behaviour in the measured areas.

From the literature different methods for network reduction are known. In [11] and [12] approaches

are suggested, which imply complete knowledge of the current network state. Thus, such methods

are not applicable for not fully observed systems.

Also in [13] some ideas of network simplification are described, which are yet based on grouping of

loads and pruning of unmeasured laterals5. Contrariwise, in [6] the aggregated load of the

unobserved area is distributed to the complete topology. This approach has been already

successful applied for an online control system. However, it is assumed that the unobserved areas

mainly consist of loads.

A more general approach, which is not bound on a particular load flow case, is described in [14].

As well as neglecting laterals, aggregating of nodal power is applied. For the calculation of the

equivalent line parameters a simple topology reduction principle is suggested.

In the scope of the GRID4EU DEMO1-project a systematic approach for reducing distribution

network topologies based on the ideas in [14] has been developed. An important extension is done

by assuming measurements at the laterals and considering the interconnections between different

feeders. A detailed illustration and an accuracy discussion are given in [15].

The application of the reduced model takes place in several areas of the autonomous control

system. It is always used in combination with the load flow computation and occurs in:

o Topology optimization module

o FDIR module

o For the computation of the system state from the power forecast

The reduced network model is computed once offline and is used as long as no network topology

changes, e.g. network extension, occur. When the topology has been changed, an updated model

has to be provided to the system.

In following the basic principle of the network reduction is described. Then the algorithm accuracy

is evaluated. Finally, the idea of the topology data exchange interface with the SCADA system is

illustrated.

4.1.6.1 Basic principle of the network reduction

The aim of the network reduction is to produce an equivalent behaviour at the interface to the non-

reduced part of the network, while simplifying the network topology. The detailed system state in

the reduced network areas is not regarded. For the network reduction some measurements at

critical nodes are necessary. In practice, the number and placement of measurements can be

carried out by the expertise of the DSO.

5 Laterals are shorter lines connected to the main feeder and supplying few secondary substations


30 October 2013 55/92

A typical constellation in distribution networks is a feeder line with several secondary substations

(see Figure 39), which are interpreted as nodes. At the nodes 1, 2 and 5 the voltages, active and

reactive power, line currents and the corresponding line power flows are measured. Due to the two

flow measurements at both ends of the unobserved area, between the nodes 2 and 5, the total

power feed-in of the nodes 3 and 4 can be calculated. The influence of network losses is

neglected.

The unobserved area is replaced by an equivalent node (3‟) and two lines (2-3‟ and 3‟-4‟). At the

equivalent node the total measured power S is cumulated. Generally, the -equivalent circuit

parameters of the equivalent lines can be calculated as weighted mean values of the original

lines [14]:

i

itotal,redtotal,red

i

i'i

'red

total,red

i

i'i

'red

total,red

i

i'i

'red

lll

lB

B

l

lX

Xl

lR

R

(10)

Where 'redR , '

redX and 'redB are respectively equivalent line resistance, reactance, susceptance

and total,redl is the total line length. is the set of original lines, which are being reduced.

The position of the equivalent node can be determined with two practical approaches [14]

o equidistant positioning, where both equivalent lines are of the same length total,redl2

1

o terminal current dependent positioning can be applied regarding the ratio of the

measured currents:

|I|

|I|

l

l

right

left

left,red

right,red (11)

Where “left” and “right” means the relative position of both reduced lines and of the measurements.

The first approach has the advantage of its simplicity and can be applied directly. The second

approach requires continuous model adaption through measurements. Thus, the model accuracy

increases.


30 October 2013 56/92

Figure 39: Basic principle of the network reduction

4.1.6.2 Accuracy results

For testing the concept of the reduced network the considered part of Reken medium-voltage grid

has been chosen. The original complete topology consists of 130 nodes and 128 lines. In contrast,

the reduced model has only 29 nodes and 27 lines. This is a remarkable complexity reduction,

when considering online power flow computations (see Figure 40).

To determine the accuracy of the presented method 10000 power flow calculations are performed

by varying the nodal powers. The nodal powers are generated by uniformly distributed random

numbers, which are scaled to the typical range of the installed capacities and loads. The reactive

power is assumed to a 90.cos by the load. The distributed energy resources have no reactive

power injection with 1cos . After every power flow computation, voltage and current deviations

to the complete network model are calculated. The deviation statistics are presented in a box plot

(Figure 41):

o The “box” includes 50% of all deviation values

o The median is the mark in the middle of the box

The whiskers on both ends of the box mark the entire value range of the deviations

1 2 4‟S1 S2 SΣ S5

3‟

~

1 2 3 4 5

~~

1 2 3‟ 4‟

~~

measurement

~

~ loadgenerator


30 October 2013 57/92

Figure 40: Complete and reduced Reken topology

Figure 41: Reduced model precision for the 130 node Reken grid

-1000 -500 0 500 1000-600

-400

-200

0

200

400

600

800

1000

A

B

C

D

E

F

G

H

I J

K

L

MN

measured

nodes

unmeasured

nodes

equivalent

nodes

complete topology reduced topology

equivalent

lines

original

lines

0

0.005

equidistant positioning

A B C D E F G H I J K L M N0

0.005

current dependent positioning

abso

lute

volt

age

devia

tio

ns

in p

.u.

node index

0

0.04

0.08

measured lines

abso

lute

curr

ent

devia

tio

n in

p.u

.


30 October 2013 58/92

Both described equivalent node positioning approaches are applied and compared. In the case of

equidistant positioning (Figure 41 top), the voltage deviations reach nearly 0.7% in some seldom

cases. This is a relatively high deviation, which occurs on the distant lateral node “N”. It can be

explained due to wrong placement of the connection point of the reduced lateral to the reduced

main feeder. The precision can be improved significantly by applying the current dependent

positioning of the equivalent nodes. In this case all the median values of the voltage deviations do

not exceed 0.5%.

Current deviations are almost not affected by the positioning approach. Most deviations tend to be

less than 8%.

Considering voltage violations as the most common and critical, the accuracy of the esteemed

voltages has to be good. If the accuracy can be held under 0.5%, this is supposed to be not much

worse than the typical measurement equipment accuracy.

4.1.6.3 Integration into the autonomous system

Network reduction is a process which doesn‟t have to be carried out within the autonomous

system. It is assumed that the number of agents, the line parameters and the number of secondary

substations stay constant for a longer time. So the corresponding reduced model used by the

autonomous system hasn‟t to be changed. As soon as any external changes appear – e.g. a new

secondary substation has been built – the model has to be adapted as well.

The up to date network topology is documented by RWE in form of a NEPLAN file. This NEPLAN

file is provided for both DSO control centre and the planning department. All the changes in the file

are synchronised via a data management system. In future every file changing should trigger a

software routine which would perform an offline network model reduction. This routine is not a part

of the autonomous system and could be integrated into the interface between the SCADA system

and the autonomous multi agent control system. Currently ABB investigates the possibility of

importing text files with updated topology into the PLC code placed at the RTU.

4.1.7 Data Storage

In order to guarantee proper interaction of different logical modules a concept of electrical network

data and state representation is needed.

The entire data can be classified as dynamic and static data: Static data refers to the topology

information (line parameters, nodal admittance matrix, etc.) and to the state limits. Dynamic data

are measurements, indications and system‟s topology state (status of the switches). It is assumed

that the static data can be only changed from the superior SCADA system. A typical example

would be an extension by an additional measuring agent. The dynamical data is being changed

permanently through measurements and switching operations.

One of the most important data structures is the line table with the line parameters (Table 5).


30 October 2013 59/92

node i node j switch i switch j R/Ohm/km X/Ohm/km B/µS/km L/km Imax/A

7 5 1 1 0.169 0.111 127.5 0.75 316

… … … … … … … … …

Table 5: Exemplary line data

In the line table the line interconnections between nodes are given. Of great importance is the

information about the electrical parameters of the line (R, X, B). This is necessary to build the nodal

admittance matrix of the system in order to perform load flow computation. In PLC language, used

at RTU560, such table is represented by a structure object.

4.2 Laboratory model

Another challenge of the DEMO1-project year two has been a development of the laboratory test

model. While the above described concept of the autonomous system has been modelled to a

great extent in MATLAB® environment, the actual system will run on the RTUs. Thus the

functionalities have to be implemented in PLC (programmable logic controller) language, real

communication has to be considered and some test data has to be provided to the system. For this

aim a testing environment framework is needed. Such a framework would integrate the RTU

network, which simulate the agent network of the equipped substations. Also a source of

measurements for every simulated substation is needed - this task is carried out by a network

simulator.

The idea behind the laboratory setup is illustrated in Figure 42.

Figure 42: Concept of the laboratory model

hardware layer

Smart Grid applications

in PLC* programming language

software layer

network model

MATLAB/Simulink

measurements

control

signals

* programmable logic controller

control

centre


30 October 2013 60/92

The introduced network consists of two logical layers: hardware and software. Both of them are

coupled by communication links and can affect each other. Hence a so called Hardware-in-the-loop

simulation is performed.

The Hardware layer is given by real automation equipment from ABB. The task of these Remote

Terminal Units is to receive measurements from the grid and to transmit signals to installed circuit

breakers. In the simulation real grid is represented by a software based grid model in

MATLAB®/Simulink

®. Based on the load and generator structure of the simulated network,

synthetic time series are created offline. These nodal power time series represent the network

behaviour. The grid model produces quasi real time signals.

In the following chapters the hardware and the software layer as well as the communication

between them are described more detailed.

4.2.1 RTU Hardware

Complete agent software will run on the ABB-RTU560 platform. A remote terminal unit is a

microprocessor-controlled electronic device, which can be programmed in the PLC-Language

(programmable logic controller). A RTU560 has digital and analog inputs and binary outputs. For

the given application of the multi agent control system, the input signals are corresponding to the

measurements from the field and the output signals are the obtained switching commands.

In order to analyse the performance, four devices of the RTU560 are used in the laboratory model.

One of the RTUs is used as the control centre, which collects all the measurements from the slave

RTUs. An alternative option is the direct coupling of the master RTU to the grid model.

The power supply of 24 VDC is provided for all RTUs. Typically the measurements in a secondary

substation are provided by an ABB CVD device (multimeter). In the laboratory set-up the

measurements are transmitted via Ethernet as IEC 60870-5-104 or DNP3 messages (depending

on the measurement acquisition concept).

Measurement values are initially provided by a MATLAB®/Simulink

® model. They are then sent to

the RTUs via different software interfaces that are described in the later chapter. The control output

of the autonomous system is sent back from the RTU to the MATLAB®/Simulink

® model.

4.2.2 Grid Model

In the laboratory environment the real power system is represented by software grid model. The

task of the grid model is to produce measuring signals and to react to switching actions calculated

by RTU agents.

The model is implemented in the MATLAB®/Simulink

® environment by using the SimPowerSystems

library in addition of some self-developed models. The phasor simulation method is applied. It

enables time series simulation and provides fast computation. The coupling with the hardware

layer is provided by the OPC software interface (see chapter 4.2.3). Necessary OPC blocks are

connected with power system model blocks.

Since the autonomous multi agent system is acting on the medium-voltage level, the modelling is


30 October 2013 61/92

focused on that level. Underlying low-voltage networks are not modelled explicitly. Every MV

substation features a MV/LV transformer and aggregated load and generation units (see Figure

43).

Figure 43: Basic principle of the grid modelling with aggregated LV grid

The single generator and load time series are computed offline, previous to the simulation. The

underlying models use weather data and standard load curves as input. A detailed descriptions of

the models is given in chapter 4.2.2.3.

In Figure 44 a sector from the Reken MV grid is illustrated. The essential model components are -

lines (cable type as well as overhead lines), secondary substations and breakers. For controlling

the breakers at simulation runtime a connection to the OPC interface is needed. In Figure 44

constants blocks are used for test purpose. Secondary substation block is a subsystem with

individual input data. Detailed information about its modelling follows in chapter 4.2.2.2.

MV grid

LV grid

~

...

~

...

...~

...


30 October 2013 62/92

Figure 44: Exemplary sector of Reken MV grid modelled in MATLAB®/Simulink

®

4.2.2.1 Phasor simulation

Because of the focus on the time dependent behaviour of the modelled power system, the phasor

solution method is chosen. Simulink® environment provides this method for computation of

magnitudes and phases of all steady state voltages and currents, without considering transient

system dynamics. Thus a fix frequency is considered.

Figure 45 illustrates the relation between continuous time series signal and its phasor

representation. While the differential equations describing the system behaviour are substituted by

a set of algebraic equations, the sinusoidal voltages and currents are replaced by complex

numbers [16]. This property leads to a much faster solution.


30 October 2013 63/92

Figure 45: Principle of the phasor simulation method [16]

4.2.2.2 Model of the secondary substation

As mentioned before, the model of a secondary substation is a slef-made Simulink® block. The

idea behind the model is to simulate power consumption or generation by applying a controlled

current source (CCS). This element is set by a load or generation profile from a predefined vector.

In Figure 46 the circuit of a secondary substation is depicted. The time series vectors are given by

“P_load” and “P_PV”. In every simulation step power values in Watt are read from these vectors.

Figure 46: Model of a secondary substation in Simulink®

Two CCS, corresponding to a load and a PV generator, are connected in parallel. A high rated RC

Element is adjusted to simulate the inner impedance of the current source. A MV/LV transformer

connects the low-voltage sources with the medium-voltage level.

The principle of CCS is depicted in Figure 47. A current source is connected between the neutral

phase N and the phase P. “Set point” input defines the power value. With the gain factor K a set

point current is calculated. Thereby the assumption about the constant voltage of 230 V is made.

This type of modelling refers to the constant current type load [17].


30 October 2013 64/92

Figure 47: Model of the controlled current source

With the block “U_load_1” complex voltage measurement is fulfilled. The voltage angle information

is extracted for the setting of the predefined power angle. The angle measurement isn‟t allowed to

be directly forwarded to the current source input because of their mutual dependency (so called

algebraic loop). Hence this signal is delayed with a unit delay block.

After building the difference between the predefined angle “Phi_load” and the measured angle, the new complex current is set.

4.2.2.3 Load and Generator models

In this subchapter models of load and generation units used in the network simulator are

presented. The synthetic generated data bear on historical weather time series or standard load

profiles. This raw data with different time resolution is used as input for generating active power

time series. These are applied at simulation runtime to CCS.

All introduced concepts are implemented as functions in MATLAB®. They can be called

automatically before starting a simulation. A detailed overview of the algorithms, including

flowcharts and input/output parameter, is given in Appendix. In following, main ideas of modelling

load, biogas plant, photovoltaic generator and wind turbine active power time series are presented.

The developed load model is based on the accumulation of the standard load profiles, which are

often used by DSOs. The standard load profiles (SLP) define typical energy consumption of private

household loads as well of industrial or rural loads. Here private household models are used. For

every season and every typical day of the week a SLP is given in 15 minutes intervals. To provide

a needed time resolution the raw data is being interpolated.

The number of households belonging to a substation has to be specified. For every household a

SLP is assumed so that the number of inhabitants is randomly set and also some normal

distributed noise added. All the household profiles are accumulated to the substation profile. In

Figure 48 some load time series for different numbers of households are presented.


30 October 2013 65/92

Figure 48: Typical household load profiles

Biogas plant model

The biogas plant model used for the network simulator is based on the yearlong measurements

from a reference biogas plant, provided by RWE. Multiple plants connected at one substation can

be also modelled.

The main idea is to provide quasi-random plant behaviour by randomly choosing an extract from

the yearly reference measurement. For multiple plants random extracts are chosen. Usually the

power feed-in of a biogas plant is nearly constant. Though, since the dataset contains days with

different feed-in behaviour due to variable methane production, the superposed feed-in of multiple

units gets more realistic in this way.

In the upper plot of Figure 49 five day-long biogas plants time series are presented. Two of the

units are part time disconnected from grid, e.g. due to maintenance work. The others have

constant power outputs of about 250 kW all day long. The lower plot of Figure 49 shows the total

active power of all biogas plants. The part, where two plants are disconnected, depicts a big power

drop.


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Figure 49: Biogas plant time series. Top: five different units; Bottom: cumulated feed-in

Photovoltaic feed-in model

The suggested model provides the electrical output power of solar power systems. Required input

parameters are the number of households with photovoltaic systems and their corresponding

surface areas, the start and stop time as well as the step size (for more details see Appendix 4:

Flowchart of PV time series generation).

First the geographic coordinates have to be set. For the assumed grid model the coordinates for

Reken (latitude 𝜙 ≈ 51.8° and longitude 𝜆 ≈ 7.0°) are predefined. After that, solar power system

specifications like deviation from the south axis, work angle and efficiency are initialized. Efficiency

can be separated into two parts, where the first part describes the efficiency of the photovoltaic-

module (e.g. for polycrystalline silicon: 𝜂𝑝𝑣 ≈ 15%) and the second one the efficiency of the inverter

(e.g. 𝜂𝑖𝑛𝑣 ,𝑚𝑎𝑥 ≈ 95%).

To compute the insolation onto an arbitrary aligned surface, in a first step, a mathematical

description of the course of the sun is needed. For that the following two equations taken from [18]

are used, where 𝛽𝑆 is the elevation and 𝛼𝑆 the azimuth angle of the sun:

𝛽𝑆 = arcsin 𝑠𝑖𝑛 𝜙 ⋅ 𝑠𝑖𝑛 𝛿 + 𝑐𝑜𝑠 𝜙 ⋅ 𝑐𝑜𝑠 𝛿 ⋅ 𝑐𝑜𝑠 𝜔 (12)

𝛼𝑆 = 𝐶1 ⋅ arctan

sin𝜔

sin𝜙 ⋅ cos𝜔 − cos𝜙 ⋅ tan 𝛿 + 𝐶2 1 − 𝐶1𝐶3 ⋅ 90° (13)

The position of the sun is determined by the declination angle 𝛿, which describes the angle

between the sun and the equatorial plane and the hour angle 𝜔, which is 15° per hour and

dependent on the solar time. The solar time is calculated by means of longitude 𝜆 and the number

of the day within a year. 𝐶1, 𝐶2 and 𝐶3 are constants, which are dependent on the latitude 𝜙, the

declination angle 𝛿 and the hour angle 𝜔. In Figure 50 you can see curve shape of the elevation

angle, which describes the altitude of the sun from sunrise to sunset.


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Figure 50: Day-long elevation angle course

In order to calculate the incident angle 𝜃 of the direct insolation on an arbitrary aligned surface the

following trigonometric relationships (eq. (15) and (15) [18]) are used:

cos 𝜃 𝛼,𝛽 = cos𝛽 sin𝜙 − sin𝛽 cos𝜙 cos𝛼 sin 𝛿

+ cos𝛽 cos𝜙 + sin𝛽 sin𝜙 cos𝛼 cos 𝛿 cos𝜔

+ sin𝛼 sin𝛽 cos𝛿 sin𝜔 (14)

cos 𝜃 𝛼,𝛽 = cos𝛽 sin𝛽𝑆 + sin𝛽 cos𝛽𝑆 cos 𝛼𝑆 − 𝛼 (15)

The incident angle 𝜃 describes the angle between the surface normal of the PV-Module and the

position of the sun.

Finally the direct insolation on an arbitrary aligned surface can be calculated with eq. (16) [18]:

𝐼 𝛼,𝛽 = 𝐼0 ⋅ cos 𝜃 ⋅ exp −

𝑇𝐿 ⋅ 𝑚

0.9 ⋅ 𝑚 + 9.4 (16)

Eq. (16) contains the extraterrestrial radiation intensity 𝐼0 (calculated from the solar constant

𝐸0 = 1367 𝑊/𝑚2), the Linke turbidity factor 𝑇𝐿 of the atmosphere and the air mass coefficient 𝑚.

However the curves generated with eq. (16) are ideal, because realistic influences like particles,

steam (clouds) and other outside influences, which are responsible for insolation drops, are not

considered. These influences are implemented by subtracting a random amount from the direct

insolation.

By multiplying the insolation 𝐼 calculated above with the surface 𝐴 of the solar power system and

the efficiency 𝜂 the electrical power output 𝑃𝑜𝑢𝑡 of the system is obtained with eq. (17):

𝑃𝑜𝑢𝑡 = 𝐴 ⋅ 𝜂 ⋅ 𝐼 (17)

To straighten the curve shape in the area of drops, the data is interpolated.

Figure 51 shows an example of three substations that are fed by different numbers of solar power


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systems with different collector areas. Before sunrise and after sunset the output power is set to

zero. The figure also shows the mentioned power drops caused by shadowing due to clouds and

other outer influences. Furthermore the peaks of the curves are not completely cantered. The

reason for these small displacements is the different alignment of the surfaces of the PV modules.

Figure 51: Day-long photovoltaic feed-in

Wind-Power-Plant-Model

For the evaluation of wind power feed-in a weather data input is needed. Thus wind speeds for a

requested measuring site and time range, provided by "Deutscher Wetterdienst (DWD)" [21], are

used. It contains the wind speeds metered in 10 m height for different locations. To obtain the wind

speeds at hub height, data have to be converted with equation (18) [18]:

vwind =log

z2

z0

log z1

z0 ⋅ windspeed10m (18)

Where vwind is the wind speed at hub height z2 and windspeed10m the wind speed at 10 m height

(z1). Furthermore the roughness class (or roughness length z0) has to be specified. For example

the roughness length for a landscape with some houses and hedges, bushes and trees or at least

250 m open space is z0 = 0.2 m [19]. The converted wind speed data can be further applied to the

mathematical wind turbine model.

In Figure 52 you can see the wind speeds measured in Münster-Osnabrück on 17th to 20th May

2012. On May the 18th a top speed of nearly 8 m/s (that means ca. 28 km/h) is reached.


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Figure 52: Converted wind speed measurements in the relevant region

The computation of the active power output of a wind turbine is done with a deposited power

coefficient curve (cp over wind speed) (see Figure 53). The power coefficient cp describes the ratio

between the wind turbine output power P and the total wind power P0. The ideal power coefficient

cp,max defines that part, which can maximally be drawn from the wind with an ideal wind turbine. It

will amount to cp,max =16

27≈ 0.59, if the wind speed ratio between the wind speed behind (v2) the

turbine and in front of it (v1) is v2/v1 = 1/3. Since these are theoretical figures, the ideal value is

never reached (see Figure 53).

Figure 53: Wind power coefficient curve


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Each occurring wind speed is compared with the cp-curve, so that one cp-value is obtained. With

that value, the corresponding wind speed v, the density of air ρair and the rotor sweep A, the wind

turbine output power is computed with equation (19):

Pw = 0.5 ⋅ ρ ⋅ A ⋅ cp value⋅ v3

(19)

In Figure 54 you can see the results of electrical output power for a wind turbine with a rotor sweep

of A = 4000 m² for the wind speeds presented above. It can be stated that wind speeds below 2

m/s are insufficient to generate active power output.

Figure 54: Active power output of a wind turbine model for the given time period

4.2.3 Model coupling

In this project, in order to simulate, monitor and control real world grid application by RTU 560s, we

have a program chain. This chain is in the following,

o MATLAB®/Simulink® is used to simulate the real world like grid application.

o OPC server is used to establish connection between MATLAB®/Simulink® and PCU400

server.

o PCU400 server is used to establish connection between Matrikon OPC server and

RTU560s.

o RTU560s are hardware modules to observe and control grid.

An overview of the particular interconnections is illustrated in Figure 55.

The grid model part of the simulation is carried out on a PC. This part introduces the data

exchange via OPC interface (OPC = Object Linking and Embedding (OLE) for Process

Control) [20]. The grid model acts as an OPC client and communicates with the software OPC

Server. OPC has Analog Measured value Input (AMI) tags (voltage, current, real and reactive


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power) which are used to observe grid simulation in MATLAB®/Simulink

®. One can configure OPC

AMI tags on CSV excel sheet. An important point here is that, tags are supposed to be consistent

on each part of OPC-PCU400-RTU560 chain.

PCU400 is a software application by ABB which establishes the connection between OPC server

and Rtu560. PCU400 has also web interface which lets users to observe indicators and AMI`s by

local network and IP connections. Alarms and Events can also be observed.

For simulating the measurements of a single RTU, PCU400 provides DNP3 communication lines

(Distributed Network Protocol). This imitates local measurements acquisition of an RTU.

Figure 55: Architecture of the laboratory model

Excel sheets are used to configure tags in PCU400. Those tags are short circuit indicators and

AMIs. One specific excel tool converts these excel sheets to XML files which include all relevant

information belonging to particular communication line. Generated XML files are supposed to be

copied to configuration files under PCU400 folder.

PCU400 has clock server which enables PCU400 to connect general system clock (if available) in

order to increase system consistency.

RTU RTU RTU

simulation PC

OPC client

network model (Matlab/Simulink)

OPC Server (Matrikon)

PCU 400

OPC client

DN

P 3

DN

P 3

DN

P 3

DN

P 3

104

104

IEC 6

0870-5-1

04

RTU

(control center)

104

local signals

remote signals

software

hardware


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Last part of the communication chain is RTUs. Slave RTUs forward their local data via IEC 60870-

5-104 protocol to the master RTU placed in the control centre. This is performed automatically due

to the slave RTU‟s configuration. As mentioned in the RTU560 configuration, RTU web-interface

enables us to observe all measurements and connections from Master and Slave RTUs. As in the

program demo, AMI or circuit indicator changes in MATLAB®/Simulink

® are supposed to be

observable through the OPC server, PCU400 web-interface, and RTU560 web-interface.

In Figure 56 an insight in the laboratory set-up is depicted. On the left, the RTU network can be

recognized. On the right, the Simulink® grid model running on a PC system is visible. Both

simulation domains are connected via Ethernet switch.

Figure 56: Picture of the laboratory set-up

4.3 Current status and outlook

By the middle of August 2013 different implementation works are still in progress. Some of the

concepts like topology optimization are approved in MATLAB®

environment and have to be

transferred into PLC code partially. Some other modules are being directly developed in PLC. By

the end of September it is planned to test the whole simulation chain including the RTU logic.

Currently the data transition from the network model up to the RTU is already established. Also the

backward path – switching command from RTU to the Simulink® grid model is already working. The

most time consuming part of the work, the completion of all software modules in PLC, still remains.

Though the following essential packages are already implemented and fulfil their functions:

o State machine

o Load flow computation


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o Static topology optimization

The next steps of the laboratory implementation and test phase will be:

o Finalizing basis RTU software implementation and testing

o Involving real measured data for more realistic scenarios

o Developing and implementing of decentralized concepts


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5 RTU communication

5.1 Communication overview

A stable communication between the agents is essential for the correct working of the complete

MAS. Beside the pure communication stability some other points must also be taken in account:

o High availability

o Standard components, availability in the market

o Cyber security

o Easy maintainability

o Reasonable initial costs

o Predictable operating costs

In addition to this, one very important item is that the communication technology should be well

known and already used within RWE. Finally it was decided to use GSM as basis for the

communication infrastructure. The used services are GPRS and UMTS.

To meet security polices it is necessary to introduce an encrypted communication. This is done

with a dedicated VPN within the RWE network. To avoid any potential hole in the security concept

the encryption is already done in the RTU.

In such complex system it is important to distinguish between different layers of communication

relationship, see Figure 57.

Figure 57: Communication layer

Agent Agent

VPN

TCP/IP TCP/IP

VPN

Network

VPN-Network

Agent-Network RTU RTU SCADA


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As basic layer the traditional TCP/IP communication will be used. The main objective is to

guarantee a stable, reliable communication infrastructure. The second layer is the VPN which

spans a closed network between all partners. This is a vital part of the communication to ensure

that the agent-system cannot be attacked from outside.

The communication between the agents can be seen as a communication over a dedicated

network, in this case called the “Agent-Network”. Over this virtual network only agent information

will be exchanged.

Also the RTU information, like indications, will go through all these levels. From the RTU point of

view everything works as a transparent network. Moreover the communication towards SCADA is

using this infrastructure.

As well as for the basic communication layers, there must be a decision done, which protocol will

be used for agent to agent communication. This decision follows almost the same rules as for

choosing the communication technology itself. In addition the technical capabilities of the SCADA

system must be taken in account. As described earlier in chapter 3.1.3 RWE is using IEC 60870-5-

104 for communication between SCADA and RTUs. Due to this restriction no other communication

protocol can be used between master agent in the primary substation of Groß Reken and the

SCADA system. To ensure consistency it was decided to use IEC 60870-5-104 in all other

communication links as well.


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5.2 VPN communication

As described in chapter 2.2 there are two possible approaches for the MAS:

o Central, with communication towards one single “Master” Agent

o Decentral, with peer to peer communication between neighbours

The next chapters will show the differences between the two structures.

5.2.1 Central communication

Figure 58 shows the communication structure of a hierarchic network using VPN. As described in

5.1 we have as basic level the TCP/IP data flow which is always over the VPN-router in the

communication centre. The second layer, the encrypted VPN, is always established from the RTU

towards the VPN-router. After the RTU is connected to the VPN the logical IEC 60870-5-104

connections can be established between the agents and master agent and also SCADA system.

This is always initiated by the master or SCADA (controlling station).

Primary substation Groß-Reken

SCADA

IEC 104 Agent n...

Connection request from A to B

(A = Controlling station)

Monitoring direction from B to A

(B = Controlled station)

Communication Center

Firewall Firewall

VPN-Router

Legend:Logic IEC 60870-5-104 ConnectionIPsec VPN tunnelPhysical data flow (TCP/IP)

RTU560, control centre

AgentMappingSCADA

Information

A B

GPRS-

Network

Agent 1 Agent n

Figure 58: Central communication

5.2.2 Peer to Peer communication

Figure 59 shows the communication structure of a P2P-network using VPN. Again we have the

TCP/IP and VPN connections. The difference to the standard approach is now, that in principle all

agents can talk to each other.


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Figure 59: Peer to Peer communication

Physically all traffic is still going over the VPN-router. As well as in the centralized concept, always

the controlling station is initiating the IEC 60870-5-104 connection to the so called controlled

station. We will later on see, that this does not mean, that only the controlled station is allowed to

send information to the controlling station.

5.3 IEC 60870-5-104

5.3.1 Overview

The information between partner RTU‟s within this system is exchanged via IEC 60870-5-104. This

exchange of data is not limited to the measured information, like status of a switch or the voltage,

also all information about the status of the agents will be transferred by using this protocol.

Again we have to distinguish between different communication links:

o Communication between Agents (Peer to Peer)

o Communication between Agents and Master Agent

o Communication between Master Agent and SCADA

The simplest way of exchanging the data is of course the classical approach of a data concentrator

in the substation and connecting all agents (RTUs) in the field to this master agent. In that case all

monitoring data is send to the master agent, and only control data from master agent to the

Primary substation Groß-Reken

Agent n...

GPRS – Network

Connection request from A to B

(A = Controlling station)

Monitoring direction from B to A

(B = Controlled station)

Communication Center

Firewall Firewall

VPN-Router

Legend:Logic IEC 60870-5-104 ConnectionIPsec VPN tunnelPhysical data flow (TCP/IP)

RTU560, Control Centre

AgentMappingSCADA

Information

A B

Agent 1 Agent n


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“normal” agents. The consolidated data is then transferred to the SCADA system. This is the

standard hierarchical approach (see chapter 5.2.1).

A little bit more complicated is the peer to peer (P2P) communication, where in principle all agents

can talk among each other. Also the monitoring and control direction is no longer fixed. All peers

can be controlled and controlling station at the same time. Obviously it is not possible to handle this

naïve approach in regards of configuration and maintenance, because RTUs have not the

capabilities and power like standard PC. The configuration effort is, even if only a few agents are in

the system, immense. It is for example necessary to configure each possible link between the

agents. Also maintaining such a system is a mess, because adding one simple agent causes

configuration of a new link in all other. To reduce complexity it is necessary to reduce the number

of communication links. This is done by limiting the communication links to links between

neighbours in the MV-grid. This method is described more detailed in chapter 5.2.2.

Another issue is the addressing inside the IEC 60870-5-104. The address scheme between master

agent and SCADA system is fixed by the RWE profile:

The address scheme used by RWE is using the structured address mode. This means, that every

field has a meaning, for example the 12 MSB of the common address of ASDU contain the station

number. This is a unique number within RWE. The advantage of this structured address is, that it is

very easy to assign a telegram to a station, and at the end to an object within the station. The

disadvantage could be, that in some cases it is not possible to assemble several message in an

optimal way in one telegram. This increases the transferred data volume and could lead in higher

costs.

The following table shows the used objects and their addresses.

Table 6: RWE ASDU/IOA address scheme


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5.3.2 Central approach

The central approach is a hierarchical design where we have a SCADA system at the top, a data

concentrator in the middle and the agents (RTUs) at the bottom (see Figure 60).

SCADA

Data concentrator

RTU

Control Centre

Agents

Figure 60: Hierarchical communication

The communication between all levels is independent of each other. The instance on the higher

level is always the controlling station of the lower level (SCADA->Control Centre->Agents), where

the instance on the lower level is the controlled station.

Table 7: RWE Data model


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This approach is an absolute standard and will not be discussed further.

5.3.3 Peer 2 Peer approach

A peer-to-peer (P2P) network is a decentralized and distributed network architecture, with nodes

which can act as both suppliers and consumers of a service, in contrast to the centralized

approach which was described in chapter 5.2.1. As already discussed it is not possible to run a real

classical peer to peer approach, where every agent can talk to every other agent. Due to

restrictions in sense of configuration and manageability the number of connections must be

reduced. This is done by allowing connection only to the neighbors on the MV-line.

Figure 61: Peer2Peer Communication to neighbors

In Figure 61 there is an example of a simple MV-network with some few nodes. In this picture we

can see, that RTU 11 is talking to RTU 21, RTU 22, RTU 23 and RTU 24, but has no connection to

RTU 12 and the RTU 25. For RTU 22 the things are much easier, here we have only one

connection between RTU 11 and RTU 22. In sum the number of connections is reduced, and the

configuration of one RTU is much simpler, because it is enough to configure only the neighbors

and not all existing RTUs in the networks.

Another point is the definition, who is controlling station and who is controlled station. This is a

configuration issue and must be done very carefully even though it seems to be very simple.

One way to ensure that all connections are correct configured is to create a matrix with all RTUs

and their connections (see Table 8).


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RTU 11 RTU 12 RTU 21 RTU 22 RTU 23 RTU 24 RTU 25

RTU 11 RTU 21 RTU 22 RTU 23 RTU 24

RTU 12

RTU 23 RTU 24 RTU 25

RTU 21 RTU 11

RTU 25

RTU 22 RTU 11

RTU 23 RT11 RTU 12

RTU 24

RTU 24 RTU 11 RTU 12

RTU 23

RTU 25

RTU 12 RTU 21

Table 8: RTU connection matrix

For the RTU configuration we need only the red part of the matrix. In detail it means, that for

example RTU 11 is controlling station of RTU21, 22, 23 and 24. RTU21, 22, 23 and 24 are in this

case the controlled stations. These configurations must now be stored in all agents. Obviously this

relationship is critical because, if for example for the connection between RTU11 and RTU22 both

RTUs configured as controlled station, there will be no connection between both RTUs established.

The next step in this approach is do define, how the data transfer should be done. Under the

premise that all agents should have all information about the whole MV-network it is not enough to

transfer only their own data between the agents, but also all other information from the network. To

implement this, the so called IEC 60870-5-104 “reverse direction” is used. Usually the information

flow is always from the controlled to the controlling station. All measurements, indications and so

on are “created” by the controlled station and transferred to the controlling station, usually a

SCADA system. This is called the monitoring direction. In the control direction, this is the direction

from controlling to controlled station, only commands are transferred. The relationship between

controlled and controlling station is fixed and cannot be changed without changing configuration.

But it is possible to use the reverse direction, which means that also the controlling station is

allowed to send monitoring information. Using this feature we will have a bidirectional data

exchange between two agents.

Figure 62 : Data routing


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Finally the data must be transferred between all agents. This is done by a transparent routing

through all agents. The idea is very simple: all received data is sending out to all direct neighbors,

except that one from where the data was received. The challenge here is to avoid, that data is

circulation endless in the system (see Figure 62).

As an example we will look what will be happen after RTU 22 sends out an analog value to his

neighbor agent RTU11:

RTU11 sends this message to RTU 21, RTU23 and RTU 24. RTU 23 forwards the same message

also to RTU24. In this situation we have the first conflict: RTU 24 receives the same message

twice. The solution, and the first rule, is that, it is not allowed to forward any message which was

already received from another agent. To ensure that, it is necessary that all information is time

stamped and it is not allowed to modify the time information by any agent. All received information

is compared against the latest value and time, and only if the time is newer than the stored the

message will be forwarded.

The second problem area is that in huge agent networks with a high number of agents the

messages will be also received by agents which are far away from the source in the grid and not

interested on this information. To avoid this two approaches are in discussion:

o Counting the number of hops, and stop forwarding after a number of hops is exceeded.

o Clustering agents in groups, where it is not allowed to forward the information to other

groups.

The final decision, which strategy will be used is not yet done. This will be done in the test phase,

after we have better knowledge about the real quantity of data which will be exchange.

5.3.4 Peer2Peer Database

Up to know only the communication itself was considered. The idea was to minimize the

configuration effort and the number of communication links. The engineering should be as simple

as possible. But engineering of a RTU is not only to configure the communication links, it usually

also includes the definition of all data points. It is clear that this cannot be done in our MAS,

because the simple adding of one signal would lead in changing configuration of all agents. This is

not a feasible solution.

To avoid this, a dynamic database will be developed within this project. The idea behind that is

very simple and robust:

o Every received monitoring information creates a database entry in the RTU database.

o Objects which are not updated for a while (e.g. one day) will be deleted from the database

(garbage collection).

The algorithm is also very simple:

o After start-up of an agent and establishing of all connections the standard IEC 60870-5-104

General Interrogation starts.


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o The information received by the all other agents is stored if necessary and forwarded to the

next agent(s).

o Information which was already received will not be forwarded.

After this sequence all agents have the same database, and the same view of the MV-network.

This data base can then be used in the PLC-programs described in chapter 4.


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6 Risk Management Year 2

After the first year of the GRID4EU-project, the project team comes to the conclusion, that all

DEMOs have to use the same methodology for the documentation of the risk management. All

DEMOs have to fill uniform templates with the same procedure. The main issue is to reach a

common understanding in the project. Another aspect is to get a better comparability between the

different DEMOs. Table 9 shows the actual risk list for DEMO1, with one additional risk in

comparison to the previous year (see in deliverable dD1.1).

Table 9: Actual list of risks

Figure 63 shows the risk matrix of the first year. The matrix represents an overview of the risk

management in DEMO1. The matrix is built up with the risks of the first year of the project (see

deliverable dD1.1). The entire methodology is described separately in GWP1 deliverable gD1.1

and in ENEL‟s deliverable dD4.1 of DEMO4. The three different colours are standing for several

risk levels (green (low), yellow (medium), red (high)). A risk is the product of probability, impact and

residual risk.

Nb Description

DEMO1 - 001 Implementation of the agent intelligence

DEMO1 - 002 Software development

DEMO1 - 003 Communication structure

DEMO1 - 004 Scalability and replication

DEMO1 - 005 Hardware upgrade of substations

DEMO1 - 006 Substation space difficulties

DEMO1 - 007 Heat conditions in substations

DEMO1 - 008 RWE security standards

DEMO1 - 009 Network penetration within the project run time

DEMO1 - 010 Real network operation failures

DEMO1 - 011 Technical requirements of switching agents


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Figure 63: Risk Matrix Year 1

Figure 64 shows the risk matrix of the second year. This risk matrix is built up with the actual risk

list (Table 9). As described before, one additional risk compared to the first year of the project was

identified. Nevertheless, in comparison to the matrix of the first year (Figure 63), you can see a

decreasing risk level.

Figure 64: Risk Matrix Year 2

For more details, please refer to the document “GRID4EU Year 2 Detailed Risk Reports”, where all

reported risks are detailed by Work Package (DEMO and GWP) in a common risk report template.

A decreasing risk level is also the expectation for the upcoming years. The result should be no

remaining risk at the end of the project in 2015.


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7 References

7.1 Project Documents

List of reference document produced in the project or part of the grant agreement

[DOW] – Description of Work

[GA] – Grant Agreement

[CA] – Consortium Agreement

7.2 External documents

[1] D. M. Staszesky, “Use of virtual agents to effect intelligent distribution automation,” in Proc.

IEEE Power Eng. Soc. General Meeting, 2006, pp. 1–6.

[2] K. Nara, Y. Mishima and T. Satoh, “Network Reconfiguration for Loss Minimization and Load

Balancing,” IEEE Power Engineering Society General Meeting, v. 4, Jul. 2003

[3] T. Gönen and I. J. Ramírez-Rosado, “Review of distribution system planning models: A model

for optimal multistage planning,” Proc. Inst. Elect. Eng., vol. 133, no. 7, pp. 397–408, Nov.

1986

[4] D. Shirmohammadi, H. W. Hong; “Reconfiguration of electric distribution networks for resistive

line losses reduction”, IEEE Trans. on PWRD, Vo1.4. Na.2, 1492-1498, April 1989

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8 Appendix

8.1 Fault detection

Appendix 1: Flow chart of the algorithm for fault detection

Start

Does the outgoing line n have a sc-flag

Does the outgoing line n have a

neighbour agent

yes

Short-circuit foundno

n := n +1 no

m := 1

yes

Does the neighbour agent m have a sc-flag

yes

Are there further neighbour agents

no

m := m + 1

yes

n := 1

Short-circuit found


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8.2 Load/Generation models

Algorithm for household load accumulation

[data] = load_data(nr_of_houses, nr_of_stations, day, month, starttime, t, t_step)

Appendix 2: Flowchart of load accumulation

Read load data for the chosen dates

Calculate load power for each household h connected to substation s:

define number of persons of household h

randomly choose one load profile for household h

modify each power value with a standard deviation of 0.5%

multiply the power curve with the number of inhabitants

sum up the power of every household to the total power of substation s

Start

Output a matrix with a time-vector and the load power of all substations

End

For each substation s

For each household h

For each date


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Biogas plant model

[P_out] = biogas(nr_of_plants, starttime, stoptime, stepsize)

Appendix 3: Flowchart of biogas plant time series generation

Read data from one reference plant and save it into workspace

Calculate power for the given number of biogas plants:

the required data for every biogas plant is chosen randomly within every iteration

Start

Interpolate data

End

Output data

For each biogas plant


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Photovoltaic power plant model

[t, data_solar] = solar_power(hh, A, starttime, stoptime, stepsize)

Appendix 4: Flowchart of PV time series generation

Initialize:

Geographic coordinates

Specifications of the solar power system (deviation from the south axis, work angle, efficiency)

Start

Calculate the course of the sun over one day of a requested date for Reken

Calculate the angle 𝜃 (or 𝑐𝑜𝑠(𝜃)) between the surface normal of the

PV-Module (with its alignment) and the position of the sun.

Simulate the influence of clouds, by subtracting a random amount from the direct insolation.

Calculate the direct insolation onto the currently considered PV-Module

Interpolate data

End

Output data

Multiply the surface of the solar power system with the direct insolation and the efficiency to obtain the power output

For each hour angle

For each solar power system


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Wind power plant model

[timestep windspeed] = wind_data(date_input_beg,date_input_end)

Appendix 5: Flowchart for wind speed conversion

[Pw] = wind_power_timeseries(c_p, v)

Appendix 6: Flowchart for wind power time series generation

With the deposited power coefficient curve (𝑐𝑝 vs.

wind speed) and the inputted wind speed the corresponding 𝑐𝑝 -values is obtained.

Start

Pw = 0.5 ⋅ ρair ⋅ A ⋅ cp value⋅ v3

The output power of the wind turbine is computed with the power equation:

End

Output data

For each wind speed

Load data from file: DWD_Wind_Data, which contains the

wind speeds measured at 10 m height provided by "Deutscher Wetterdienst (DWD)"

Start

Convert wind speeds to hub height

End

Output data

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