F. Pilo

Novel planning techniques for the optimal allocation of DSOs owned energy storage

Prof. Fabrizio Pilo, Ph.D.

Department of Electrical and Electronic Engineering – University of Cagliari - Italy

The Norwegian Smart Grid Conference19 - 20 September 2017Clarion Hotel Congress Trondheim

F. Pilo

• Introduction• Overview of regulatory framework on storage in EU• Italian regulation for storage

• Private storage (distribution system)• TSO • DSO

• CBA and MO for storage allocation• Preliminary results• Conclusions

Index

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Storage and DSO

yes

Is the storage unit qualifiable for the ancillary services

market(included its size is

above the minimum power threshold)?

Is the DSO able to demonstrate, through a CBA case (ex-ante approved methodology), the cost-effectiveness of this storage

application?

noCBAnegative

(A simplified CBA methodology is

envisaged for LV applications)

Are the rules enabling Distributed Resources to take part to the ancillary service market defined?

Is the storage application connected to MV network?

no

no

yes(Storage treatedexactly as DG)

NOT ALLOWEDyesCBApositive

ALLOWED

AEEGSI decision 646/2015; compare with :CEER «New role of DSOs» conclusionsC15-DSO-16-03

yes(Smart grid functions are present)

yes (MV)

no(Storage cannot participate to the market)

noSmart Functionalities for

Observability and Voltage Regulation are already active on the given MV

network?

• DSOs may be allowed to own storage for network operation

• Transient condition• Market should not be stopped • CBA must be positive• Storage is remunerated (WACC)

• Economic CBA not affordable for small DSOs

• Standardization issues

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F. Pilo

• Multi-objective optimization• Storage optimal position, size

(power and energy) and the daily control law for any representative network

• Set of Pareto optimal with no a priori choice of benefits

• CBA applied to the solutions of the Pareto front

• Clustering applied to the results

• Simple to use look-up tables as final output

Novel planning techniques for DES allocation

A B1 B2 C1 C2 C3 C4

T1 NO NO YES NO NO YES YES

T2 NO NO NO NO NO NO NO

T3 NO NO NO NO NO NO NO

T4 NO NO NO NO NO NO NO

T5 NO NO NO NO NO NO NO

T6 NO NO NO NO NO NO NO

T7 NO NO NO NO NO NO NO

T8 NO NO NO NO NO NO NO

T9 NO NO NO NO NO NO NO

T10 NO NO NO NO NO NO NO

T11 NO NO NO NO NO NO NO

T12 NO NO NO NO NO NO NO

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Input data for project definitionNmax_DES ; [Pmin , Pmax] ; [dmin , dmax]

Selection of Objectives in MO

r = 1

Input data from rth network (topology, generation and consumption

MO optimization. The Pareto set of optimal solutions is found

CBA applied to the Pareto set

Positive CBA are grouped into clusters

r = Nnetworks ?

r = r + 1

Look up table for final decision

STOP

F. Pilo

Benefits from DES – all monetary?

• Benefits (not all monetized) • Investment deferral

• Reduction of energy losses

• Power Congestions• Reactive power compensation

• Voltage regulation

• Service continuity and resiliency• RES integration (less curtailment)

• Black start

• Unbalances (in Italy only LV)

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• Multi-objective optimization

• NSGA-II used for finding the Pareto front

• Real coding (NEW!) to include also daily energy scheduling

• Daily pattern (24 hours) for storage

F. Pilo

Clustering of resultsInput data for project definition

Nmax_DES ; [Pmin , Pmax] ; [dmin , dmax]

Selection of Objectives in MO

r = 1

Input data from rth network (topology, generation and consumption

MO optimization. The Pareto set of optimal solutions is found

CBA applied to the Pareto set

Positive CBA are grouped into clusters

r = Nnetworks ?

r = r + 1

Look up table for final decision STOP

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Example - Representative rural networks

HV/MV substation

MV/LV trunk node

MV/LV lateral node

Trunk branch

Emergency connection

Lateral branch

DG (existing PV)

DG ( new PV )

HV/MV substation

MV/LV trunk node

MV/LV lateral node

Trunk branch

Emergency connection

Lateral branch

DG (existing PV)

DG ( new PV )

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Acceptation 80%• Small Sizes (T1, Pn <

500 kW e dn < 5 h )• Overhead Rural

with high shares of renewables (PV)

A B1 B2 C1 C2 C3 C4

T1 NO NO YES NO NO YES YES

T2 NO NO NO NO NO NO NO

T3 NO NO NO NO NO NO NO

T4 NO NO NO NO NO NO NO

T5 NO NO NO NO NO NO NO

T6 NO NO NO NO NO NO NO

T7 NO NO NO NO NO NO NO

T8 NO NO NO NO NO NO NO

T9 NO NO NO NO NO NO NO

T10 NO NO NO NO NO NO NO

T11 NO NO NO NO NO NO NO

T12 NO NO NO NO NO NO NO

Results

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• Real network examined• The calculations that a DSO can

perform are simulated• The methodology is good if all

positive cases for DSO are judged in the same way in the look up table

• Number of classes is crucial

Testing the methodology with real networks

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Results

Distribution of Pareto optimal solutions with positive CBA

Pareto front distribution of solutions after MO optimization

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• More classes are necessary to better capture the behaviour of underground (urban) contexts

• Tuning of the algorithms is under completion

• Very promising results

• Sensitivity analysis

Cross check and validation

A B1 B2 C1 C2 C3 C4

T1 NO NO YES NO NO YES YES

T2 NO NO NO NO NO NO NO

T3 NO NO NO NO NO NO NO

T4 NO NO NO NO NO NO NO

T5 NO NO NO NO NO NO NO

T6 NO NO NO NO NO NO NO

T7 NO NO NO NO NO NO NO

T8 NO NO NO NO NO NO NO

T9 NO NO NO NO NO NO NO

T10 NO NO NO NO NO NO NO

T11 NO NO NO NO NO NO NO

T12 NO NO NO NO NO NO NO

A B1 B2 C1 C2 C3 C4

T1 YES NO YES NO NO YES YES

T2 NO NO NO NO NO NO YES

T3 YES NO NO NO NO NO NO

T4 NO NO NO NO NO NO NO

T5 NO NO NO NO NO NO NO

T6 NO NO NO NO NO NO NO

T7 NO NO NO NO NO NO NO

T8 NO NO NO NO NO NO NO

T9 NO NO NO NO NO NO NO

T10 NO NO NO NO NO NO NO

T11 NO NO NO NO NO NO NO

T12 NO NO NO NO NO NO NO

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• The regulation for enabling DSO to own storage is still on the way

• EU directives (winter package 2016) are not in favor to allow DSO owning storage

• Derogations are allowed under strict conditions (Italy)

• DES can be remunerated only if CBA is positive (for the society).

• Italian Regulator financed a research project for finding the conditions that can entitle DSO to own storage as regulated bodies and obtain remuneration of investments

• A methodology based on MO genetic algorithms, CBA, and clustering techniques presented

• The more complex the methodology the simpler the application. It will be as simple as using a look up table. Designed for small DSO and LV applications also.

• Results showed that only few cases exist where DES is more convenient than other investments and only for very small scale (<500 kW)

Conclusions

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Thank you!

Questions?

pilo@diee.unica.itgiuditta.pisano@diee.unica.it

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