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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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F. Pilo
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?
[email protected]@diee.unica.it
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