Massimo La Scala

Politecnico di Bari – Italy

Overview: the story of a transition

Smart grid & Smart cities applications

Optimization issues

Tangible multiple energy carrier grids, focus on grids (long term investments)

Experience gained by our research group

Implemented projects

Lessons learned & future work

Recent projects (power system group at Politecnico di Bari)

Smart Grids for multi-utilities: Poliba, AMGAS SpA, AMET SpA, 1.3

M€ 2009-2013.

“SOLAR”, Università del Salento & Poliba 14 M€, 2011-2015

METERGLOB – 2.2 M€, 2011-2015.

RES NOVAE – PON Smart Cities con ENEL, GE, IBM, Università di

Cosenza, ENEA 23,4 M€ 2012-(2015).

Companies Cluster (9) 2014 -2017, “Energy Routers and cloud

computing for smart grids”, 2.5 M€.

Lab Innovative conversion processes PrInCE 12.4 M€ 2012-2015

Lab ZERO (Zero Emission Lab)”, Politecnico di Bari e ENEA, 2.5 M€,

2014-(2017).

… others

Smart Grids Functionality

Adaptive relays (DG frequency relays ) Anti – Islanding Fast faulted line identification Logic selectivity (interruption < 1’’ ) Voltage Regulation (DG partecipation to VVO)

Puglia 2600 MW PV, 2400 MW wind 170 M€ project (2014-2018) N. 202 primary substation/satellite Centers N. 1.400 MT Lines N. 7.900 controlled secondary substations N. 30.000 Smart Info N. 130 Charging infrastructure

Puglia Active Network a regional Smart Grid

Big Companies Role

Electrical Distribution Systems

In the path towards smart grids, distribution systems face the greatest challenge

traditionally passive networks

built with a straightforward radial (or multi-radial) configuration

minimal ability of monitoring and controlling power flows

Luckily, distribution systems undergo profound modifications due to:

distributed energy resources (DERs),

smart metering (in Italy 2nd massive deployment) ,

storage/PHEVs

AMET MV Distribution in Trani Trani : 56000 inhabitants

900 buses, 1000 lines, 500 loads nodes,

100 remotely controllable disconnectors,

35 MW load peak 60 MW PV requested

Upgrading AMET Distribution Control center

SCA

DA

°°

ODPF

TP

SE

ONR

VVO

FPFS

CVP

SC

AR

SM

ADMS

• CVR: Conservative Voltage Regulation

• SE: State Estimator

• VVO: Voltage – Var Optimization

• SMS: Storage Management System

• ODPF: Optimal Distribution Power Flow

• EDA: Environmental Data Acquisition

• ONR: Network Reconf iguration

• MMS: Maintanance Monitoring System

• AR: Adaptive Relaying

• CA: Contingencies Analysis

• SM: Switch Management

• FPFS: directional Fault and Power

Failure System

• SDF: Supply and Demand Forecast

• TP: Topology Processor

• SC: Short Circuit analysis

• CVP: Capacitor/Voltage regulator

Placement

• OTS: Operator Training Simulator

DISTRIBUTION NETWORK

• ULTC: UnderLoad Tap Changer

• RCS: Remote Controlled Switches

• DG: Distributed Generation

• SCs: Switching Capacitors

• SF: Storage Facilities

AMI

• MDI: Meter Data Integration

• AMR: Automatic Meter Reading

SDF

CA

SMS

CVR

EDA

OTS

Off-line

Real time concentrator

AMR

AMR

AMR

concentrator

SERVER

MDI

GIS INTERFACE

MMS

EnvironmentalMonitoring

Stations

CONTROL CENTER

ADMS

Signals from RTUs

AMI

DISTRIBUTION NETWORK

RCS

SCs

ULTC

DG

DGSF

A basic piece of SW: The Three Phase Optimal Power Flow

Optimizes active and reactive control resources in the presence of unbalanced conditions

Controls both three-phase loads/generation and single-phase ones

Unbalanced conditions

Object-oriented environment, improved representation of loads and other components

Include new control variables and devices

Load curtailment / active power control

Volt-Var Optimation (VVO) ( Controlling DG and tap changers )

Conservative Voltage Regulation (CVR)

Another piece of SW: ONR formulation

Optimal Network Reconfiguration code evaluates the necessary switching maneuvers for implementing the best grid configuration which minimizes losses or generation curtailment during congestion management (or increases Host capacity)

ONR is a MINLP formulated as

),,(Cmin obj,

uxVux

subjected to

Cobj is the objective function to be minimized

V is the set of nodal voltage

x is the set of continuous control variables (for example generated active and reactive power)

u is the set of discrete control variables (i.e. the open/closed status of each switch)

0),,(f uxV 0),,(g uxV

ONR algorithm

The problem is solved by

decomposing the MINLP

into two problems

A simulated annealing

module searches and

selects radial network

configurations, whereas a

nested code performs

Distribution Three-Phase

Load Flow (DTLF) or solves

a more complex optimization

problem (for example a

TDOPF)

select or detect initial

configuration

k=0 u=u0

evaluate objective function C

through DTLF or TDOPF

k=k+1

close a random switch

search all switches that can be opened; open one

randomly and set new configuration u=uk

is uk a new

configuration

?

evaluate objective function Ck

through DTLF or TDOPF

Ck< Ck-1?

yes

no

yes

accept new configuration uk

and reduce temperature T

no select a random number R and

set probability P(T)

R< P(T)?yes

T< Tmin?

yes

STOP

no discard new configuration and

restore old one

u=uk-1

no

Congestion management

32 MW produced by PV causing line congestions

Generation curtailment might be necessary

In this case, ONR finds an optimal solution with no generation curtailment

0 100 200 300 400 500 600 700 800 9000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

# line

I / Im

ax [p.u

.]

0 5 10 15 20 25 300

0.5

1

1.5

2

2.5

iteration #

C [p

.u.]

0 100 200 300 400 500 600 700 800 9000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

# line

I / Im

ax [p.u

.]

befo

re O

NR

aft

er

ON

R

ENEL Distribuzione, IBM, GE, ENEA , Poliba, UniCal,

SMEs, 23.4 M€

LV Grid: a Grey area of the grid

Most of challenges: DER, PV, EVs, residential DR, ….

Voltage and currents profiles are not known and almost never monitored, except for aggregated measurements at MV/LV interface

Few measurements are used to represent the state of hundreds of nodes and branches that are connected to the main MV feeder.

Load and generation imbalance is in general undetermined at LV level.

The exact location of single-phase objects (loads and generators) is not known at central level and, sometimes, not known at all.

LV active/reactive control (District Level -Bari)

case num. active

resources

num. reactive

resources

active

control

[ΔkW]

reactive

Control

[ΔkVAr]

D1 48 21 82.12 7.02

D2 48 0 83.33 0.00

D3 48 63 82.10 27.51

case num. active

resources

num. reactive

resources

active

control

[ΔkW]

reactive

control

[ΔkVAr]

losses

[%]

E1 0 63 0.00 144.41 6.39

E2 48 0 132.89 0.00 6.24

E3 48 63 99.21 150.29 6.15

Load curtailment for congestion relief (Street Disconnectors cabinets remotely controlled)

Active/reactive control for power losses

LV SCADA ? Smart meters available (in Italy 2nd deployment, Open Fiber, Fiber to

the Home, 1Gbps, Bari almost completed)

Automated Measurement Infrastructure (AMI)

Relevant number of non synchronized measurements from SMs Robust State estimation and large use of pseudomeasurements

New codes should deal with complexity of multi-phase unbalanced models

Few Low Voltage Distribution State Estimation tested on small systems

First trials of auto-detected inventory of LV grids; tests carried out to identify where a smart meter is physically located (both phase and line) [J. Varela et. al. 2015]

Smart charging EV stations

EVs can be a problem for urban grids

But the new infrastructure may become a monitoring and controlling resource on the LV grid

A car is parked for the 96% of the day, EV-charging power can be considered at least as a flexible load to be dispatched at occurrence along a wide time interval.

or in some cases (V2G) can be employed as a source

creation of innovative ancillary services, for ex. power balancing in the presence of RES

Optimal amount of EV-charging power in the presence of line and transformer congestions, voltage drops, power counterflows, phase imbalance, etc.

Charging Infrastructure Remote Management Enabling market players in providing services to the customers

Optimal charging Vision

More LV components Can we use more power electronics in LV ? Easy?

Custom power for PQ ( DVR, Dstatcom, UPQC,…)

New LV distribution topologies ( more meshes, cellular, microgrids)

Power flow control (active and reactive) in LV (B2B, UPFC between feeders)

Utility-Microgrid Load Sharing by B2B

Power electronics Voltage regulators

New smart charging stations: more active pedestals (reactive control, dispersed storage, pollution control, IoT, etc.)

V2G

DC grids ? Smart Transformer

«Food distribution, energy provision, water supply, waste removal, information technology and susceptibility to pandemics are all the Achilles heels of cities»

( World Bank 2010)

Power Smart Grids show the promise to integrate electricity generated by renewables into the energy mix, and allow a feasible e-mobility, to provide efficient and reliable widespread demand response, to utilize storage for peak reduction and shifting, etc.

1/3 of final energy use is electricity in towns

Why Smart Cities ?

Smart Energy Grids & Smart Cities

The expansion of the Smart Grid term is usually addressed as Multi-Energy System, Smart Energy System or briefly Smart Energy Grids

The multiple energy carrier approach takes into account all relevant energy carriers ( electricity, natural gas, liquid fuels) and services such as (heating, cooling, transportation, etc.) and multiple storage options (electric, thermal, hydraulic head, hydrogen,…)

This approach can provide more flexibility in the optimization and planning tools for increased energy efficiency and sustainability

A hybrid energy system (district)

gas turbine

turbine PV

batterystoragesystem

pump

water reservoir

electricloads

thermalloads

gas boiler

grid supply / market

natural gas distribution

grid

Proposed control architecture (I)

system state

(power, storage

and fuel

availability) DB parameters

time varying

trajectories

formulation and solution of a

discrete optimal control problem

set-points of

all controlled

devices

field data

real time

control

Mathematical formulation (I)

The optimization problem aims to minimize operative

costs along a selected time window T,

with px being the instant injected or demanded power, and

subject to energy balancing equality constraints

and technical minimum and maximum constraints

T

tx

xx ttpcmin0

d)(p

t

tpkt

tpke

xxx

xxx

0)(

0)(

x,tptpp xxx maxmin )(

Mathematical formulation (II)

The introduction of storage units the following differential

equations and constraints related to the generic qs quantity

of energy stored must be added to the formulation

with

and

Inequality constraints take into account the limitations on

storing capability

The minimum state of charge (SOC) can be constrained

stqtfq sss ))(),(( p

0(0) ss Qq

s,tqtqq sss maxmin )(

Predictive Dispatch Across Time of Hybrid Isolated Power Systems

Developed within the project “GEI5 – Green Energy Island” with two industrial partners

Project aimed at designing a pre-fab stand-alone micro grid based on a hybrid energy system configuration

Optimization of a complex system composed by many competing generation and storage technologies

Applications: dispersed generation, civil protection, remote installations (oil wells), armed forces, etc.

This methodology was applied on energy hubs and multi energy carrier systems (including heat and cool balancing equations)

Isolated Hybrid energy systems

Urban energy optimization

Urban Energy hub or grids of energy hubs

The components within the hub can

establish redundant connections between

inputs and outputs.

Side effects: Optimal Gas Flow

SCADA implementation

OGF equivalent to OPF in power

Some simulation tools

Infrastructure DER presence

Simulation of Control

Simulation time step

MESCOS Electrical-Thermal

Yes Yes

(complex) Second

CITYSIM Thermal Yes No Hourly

ENERGYPLUS Thermal Yes No Minute

ENERGYPLAN

Electrical-Thermal-Transport

Yes Yes (simple) Hourly

HYBRID2 Electrical Yes Yes

(complex) 5

minutes

GRID-LAB Electrical (detailed)

Yes Yes

(complex) Sub-

seconds

RETSCREEN Electrical-Thermal

Yes Yes (simple) Hourly

RAPSIM Electrical (detailed)

Yes No Minutes

Need for more inter-related infrastructural issues (how the operation or the demand on one infrastructure affects the others)

Concurrent optimization of more services: ex. Mini-hydro, gas turbo-expanders and compressors, e-mobility, gas-resiliency (buried, alternative to electricity), etc.

Buildings modelling, urban microclimate

Side effects: Suburban District Regeneration

“San Paolo” district (Bari, Italy)

Inspiration from the “Appleseed project”: reduce energy costs to create business opportunities

District optimization & design : integrating short term operation for long term planning

The overall problem: trigeneration + on-site hydrogen production

District optimization & design : integrating short term operation for long term planning

uzz

,max ROI

N

k

kk RCmin1

),(-),( uzuzu

0uzh ,

0uzg ,

subject to

Decomposition of the overall problem into a two-stage optimization

Barriers

Insufficient knowledge and skills for interdisciplinary collaboration (synthesis of multiple disciplines), training in conducting interdisciplinary research

Limited funding available for interdisciplinary research, specialization is favored

Funding criteria not fit for measuring interdisciplinary research

Publication process, academic promotion favors mono-disciplinary research and specific bibliometric indices

University systems not fully able to cultivate interdisciplinary researchers

Early and mid career energy researchers suffer most when choosing interdisciplinary studies

Interdiscplinary power system research

Power people can give a contribution? (rhetoric question…)

Power system realm has always been involved in, at least, a multidisciplinary approach (additive juxtaposition of disciplines)

Power industry has always paid a special attention to social issues (authorization processes, national energy policies, privacy issues, etc.)

Methodologies developed in the power area can be effectively utilized in other realms.

Different energy infrastructures share the same needs for more automation, optimization in operations, better performances and efficiency.

Large use of optimization tools.

Living labs are based on the:

participation and involvement of final users early on in the innovation process;

experimentation and demonstration of scientific and technological outcomings into the real world;

experimentation of new governance models for including users in the innovation process.

These principles constitute the pillars of the LabZERO organization.

• Since the proposal, LabZERO is based on a close cooperation with thirty initial partners including large companies and SMEs (small medium enterprises) as industrial developers, public territorial bodies, public administrations and Municipalities as potential users of know-how and demonstrators, industrial associations for the dissemination of results.

ELECTRICITY RTDS & Fast Prototyping

Smart Mobility, Storage & Smart Grids;

Grid Analysis and Testing Microgrid Test Facility

MECHANICS Eco-friendly refrigeration;

Wind turbines prototyping; Biomass Plants Testing

ENEA Solar heating & cooling;

Nanocomposites and nanostructured materials

BUILDINGS Mechanical and

thermohygrometric characterization of materials

and structures; Non destructive testings

Smart Grids, MV/LV Distribution Automation, Protections The test facility consists of a microgrid which can be operated in both stand-alone and grid-connected mode. The microgrid in LabZERO consists of the following components: • SCADA system; • photovoltaic generator; • interchangeable mini-wind turbines; • cluster of fans dispatchable load through inverter

control; • controllable loads; • Battery Energy Storage System based on Li Fe

PO4 technology equipped with a local controller for PQ and PF control;

• V2G recharging pedestal for EVs; • small size biomass-fired combined-cycle

cogenerator The microgrid is interfaced with the OPAL RTDS to execute PHIL (Power Hardware in the Loop) experimentations, so it is possible to connect to the test facility any (simulated) power component.

More details in: • S. Bruno, S. Lamonaca, G. Rotondo, U. Stecchi, M. La Scala, “Unbalanced Three-phase

Optimal Power Flow for Smart Grids”, IEEE Trans. On Industrial Electronics Vol. 58, n. 10, October, pp. 4504-4513.

• M. Bronzini, S.Bruno, M. La Scala, R. Sbrizzai, “Coordination of Active and Reactive Distributed Resources in a Smart Grid” , PowerTech 2011, Trondheim, 19-23 June, 2011.

• S. Bruno, M. La Scala, U. Stecchi, “Monitoring and Control of a Smart Distribution Network in Extended Real-Time DMS Framework”, Cigré International Symposium - Bologna, Italy, September 13-15, 2011.

• S. Bruno, S. Lamonaca, M. La Scala, U. Stecchi, "Integration of Optimal Reconfiguration Tools in Advanced Distribution Management System", IEEE PES Innovative Smart Grid Technologies Europe 2012, October 14 -- 17, 2012, Berlin, Germany.

• M. La Scala, A. Vaccaro, A.F. Zobaa, “ A Goal Programming Methodology for Multiobjective Optimization of Distributed Energy Hubs Operation” Applied Thermal Energy, vol. 71, p. 658-666, ISSN: 1359-4311.

• S. Bruno, M. Dassisti, M. La Scala, M. Chimienti, C. Cignali, E. Palmisani, “Predictive Dispatch Across Time of Hybrid Isolated Power Systems”, IEEE Transaction on Sustainable Energy, Vol. 5, No. 3, pp. 738-746, July 2014.

• S. Bruno, M. Dassisti, M. La Scala, M. Chimienti, G. Stigliano, E. Palmisani, “Managing Networked Hybrid-Energy Systems: A Predictive Dispatch Approach”, 19th World Congress of the International Federation of Automatic Control (IFAC 2014), August 24-29, 2014, Cape Town, South Africa.

• M. La Scala, Editor, From Smart Grids to Smart Cities. ISBN 978-1-84821-749-2, 54 pp., Wiley-ISTE, 2017.