Optimization of active distribution networks: design and analysis of significative case studies for enabling

control actions of real infrastructures

D.Moneta*, Giacomo Viganò**, Gianluca Alimonti***, Paolo Mora*

* RSE Ricerca sul Sistema Energetico SpA - Italy** Università degli Studi di Milano – Italy

***INFN & Università degli Studi, Milano - Italy

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Outline

• RSE S.p.A.• RES diffusion & scenario in Italy

• Active Networks / Control System

• Test cases

• Conclusion / Next activities

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RSE S.p.A. (formerly CESI RICERCA SpA, ERSE SpA) has been established at the end of 2005, with the mission to take over funded research activities of national and international interest focused on electricity and energy sector and it started operating on January 1st 2006.

RSE S.p.A. is currently owned by GSE, a publicly-owned company promoting and supporting renewable energy sources in Italy.

• ~320 researchers and technicians in 4 departments

• research on all aspects of energy sectors: security, power supply, regulation…

RSE S.p.A.

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RES diffusion & scenario in Italy

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National Action Plan on Renewable Energy Resource

“mimimum trajectory”http://ec.europa.eu/energy/renewables/transparency_platform/action_plan_en.htm

Main contribution is expected by the electric sector to

reach the overall national target of 17 %Hydro

Solar

Wind

BioGeo

IMPORT 13 %

TRADITIONAL 60 %

Electricity balance 2012 – net grid request (Source: Terna)

2020 targets

26.39%

RES-electric

RES-total

340 TWh

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DG-RES Diffusion

MV

HV

(source: re.jrc.ec.europa.eu/pvgis/cmaps/eur.htm/

(source: RSE)

Sun Wind

Connected Power [MW]Cumulative Data(source: ENEL D.)

14,8 PV

(62,5 %)

Geographical distribution of renewable resources

Cumulative PV installed up today: 18.2 GW

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Active Networks / Control System

RSE experience

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Active distribution networks

•Active distribution networks (ADNs) have systems in place to control a combination of distributed energy resources (DERs), defined as

generators, loads and storage. •Distribution system operators (DSOs) have the possibility of managing the electricity

flows using a flexible network topology (and DERs). •DERs take some degree of responsibility for system support, which will depend on a

suitable regulatory environment and connection agreement• Council on Large Electric Systems (CIGRE) WG C6.11.

The Italian Authority set a threshold of at least 1% (5%) of hour per year where power flow inversion towards HV must occur for MV feeders to be considered as ‘active networks’

[delibera ARG/elt 39/10]

• From simple “connection” to “integration” of DG

• Develope of advanced control approach

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Objectives:

• Maximise DG diffusion of renewable resources.

• Assure high quality power levels.

• Increase the stability and security of the network.

• Open market opportunities.

Two different approaches:

• diffuse control (local)• centralized control

Advanced control approach

Develop and verify control functions

‘slow’ actions

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Centralized control

load

generator

controllable gen (/load)

storage unit

HV

MV busbar

STORE DATA

SETPOINT

Network State

Critical state

Network data

OptimizationOLTC+DER+Storage

Setpointto field

OK

NO

Forecast

Constraints(Static &

Dynamics)

Cyclic restart/ on event

Dynamicanalysis

Measurements from field

Topology

Network State

Critical state

Network data

OptimizationOLTC+DER+Storage

Setpointto field

OK

NO

Forecast

Constraints(Static &

Dynamics)

Cyclic restart/ on event

Dynamicanalysis

Measurements from field

Topology

field

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DISCoVER

Regulation resources

• On Load Tap Changer (OLTC):– operated by DSO

• Capacitor bank– operated by DSO

• Reactive power injection/absorption by ‘controllable’ generators (subset of DERs)

• Active power modulation of “controllable” generators (subset of DERs)

• Storage: – operated by DSO (integral constraint on 24 h period)

Necessary data:- capability curves of controllable resources- costs of resources operated by the DSO- rewards of Ancillary Services (for DERs)

cost

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Optimization Procedure: DISCoVER

• The main part of the DISCoVER algorithm is represented by an optimal

power flow (OPF), that determines the admissible working point, ‘optimal’

according to the criteria defined in the objective function.

• The objective of the optimization procedure is to determine, starting from a

set of operating values, fixed for a series of time periods, an admissible

condition for a MV network in presence of DG, with the minimum

dispatching cost for the DSO.

• That OPF calculates network losses, too, which are then evaluated at the

marginal cost of energy.

NC

iiCi

NG

iiGi

cpcp

11,

)()(min

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Test cases

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Test network

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Design of the cases

Different case studies are implemented changing:• Topology of the network: the counter-alimentation of some branch

are studied• Day of reference for load and generation (for example):

Winter weekday Summer Sunday

7.75 MWh

• Voltage and current constraints• Number and length of the time intervals

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• Set of available resources: • OLTC• Set of DG and their capability limits• Storage

• Characteristic of the storage:• Position: feeders A-B-C, different node• Capability and capacity• Initial and final charge

• Cost of the resources (active and reactive power)

Case analyzed: the case analized have the following characteristic:• Normal network • Summer Sunday• 24 periods of 1 h

Baseline:• No resources available• Free voltage limit• MV bus-bar fixed to 1.035 [p.u.]

Active network:• Voltage range: 0.96-1.05 [p.u.]• OLTC• Reactive power from PV generators• Storage on feeder B

• Capability ±1MW ±1MVAr• Capacity 2 MWh• Initial and final charge: 1 MWh

• Intermediate costs for all the resources

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Voltage in the baseline case

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Optimization algorithm output

Reactive power from DG BUS-BAR voltage

Charge of the storage.

Capacity of the storage: 2 MWh.

Maximum power: 1 MW.Charge

Discharge Discharge

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Voltage in the optimized case

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Others advantages

2 MWh

Cases NO OPTIMIZATION

STORAGE IN B

STORAGE IN A DIFFERENCE

Active losses [kWh] 5036 4771 4543 265-493

Power injected by HV network

Reduction of active losses

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Conclusions

Next activities

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Conclusion & Next activities

• Diffusion of Distributed Generation requires new strategies to ensure reliable and economic operations

• RSE methodologies permit to manage an Active MV network and to evaluate results of different control strategies depending on: – number, siting and sizing of controllable DERs,– cost of internal resources (storage, especially),– rewards for Ancillary services offered by controllable resources

(generators and loads).

• Implement new functionality (i.e. variable loads)

• Comparison between different approaches (centralized vs local control) and different management of the resources.

• It’s necessary to adopt standard interfaces for network description and to exchange information with DERs.

• Demo on real networks (national projects, EU project GRID4EU)

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…questions?

Giacomo Viganò giacomo.vigano@studenti.unimi.it

Diana Monetadiana.moneta@rse-web.it

Thanks for your kind attention

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