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
1
Outline
• RSE S.p.A.
• RES diffusion & scenario in Italy
• Active Networks / Control System
• Test cases
• Conclusion / Next activities
2
RSE S.p.A.
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…
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RES diffusion & scenario in Italy
4
National Action Plan on Renewable Energy Resource
Electricity balance 2012 – net grid request (Source: Terna)
IMPORT
13 %
Main contribution is expected by the electric sector to
reach the overall national target of 17 %
Hydro
340 TWh
TRADITIONAL
60 %
RES
27%
Solar
2020 targets
RES-electric
Wind
Bio
Geo
26.39%
RES-total
“mimimum trajectory”
http://ec.europa.eu/energy/renewables/transparency_platform/action_plan_en.htm
5
DG-RES Diffusion
14,8 PV
Connected Power [MW]
Cumulative Data
(source: ENEL D.)
(62,5 %)
Cumulative PV installed up today: 18.2 GW
Sun
Wind
Geographical distribution of
renewable resources
HV
MV
(source: RSE)
(source: re.jrc.ec.europa.eu/pvgis/cmaps/eur.htm/
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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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Advanced control approach
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
Develop and verify control functions
‘slow’
actions
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Centralized control
HV
Topology
MV busbar
Measurements
from field
Cyclic restart
/ on event
Forecast
Network data
Dynamic
analysis
Network State
OK
Critical state
Constraints
(Static &
Dynamics)
Setpoint
field
to field
SETPOINT
NO
load
generator
controllable gen (/load)
storage unit
Optimization
OLTC+DER+Storage
DISCoVER
STORE DATA
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Regulation resources
cost
•
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)
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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
 NG

min    Gi ( pi )    Ci (ci ) 
p ,c
i 1
 i1

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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
Active network:
• Summer Sunday
• Voltage range: 0.96-1.05 [p.u.]
• 24 periods of 1 h
• OLTC
• Reactive power from PV generators
• Storage on feeder B
• Capability ±1MW ±1MVAr
Baseline:
• Capacity 2 MWh
• No resources available
• Initial and final charge: 1 MWh
• Free voltage limit
• Intermediate costs for all the
• MV bus-bar fixed to 1.035 [p.u.]
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.
Charge
Maximum power: 1 MW.
Discharge
Discharge
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Voltage in the optimized case
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Others advantages
Power injected by HV network
2 MWh
Reduction of active losses
Cases
NO
OPTIMIZATION
STORAGE
IN B
STORAGE
IN A
DIFFERENCE
Active
losses [kWh]
5036
4771
4543
265-493
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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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Thanks for your kind attention
Giacomo Viganò
[email protected]
Diana Moneta
[email protected]
…questions?
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