Diapositive 1

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Multi-Objective analysis of Regulatory frameworks
for Active Distribution Networks
G. Celli, F. Pilo, S. Mocci, and G. G. Soma
Department of Electrical and Electronic
Engineering University of Cagliari ITALY
MOCCI IT RIF Session 5 Paper 999
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Introduction

• Distribution Systems integrating Distributed
  Energy Resources
• Renewable Energy Sources (RES)
• Consumers are Producers (Prosumers?)
• Medium and Small CHP
• Future
• Plug in electric vehicles
• Storage devices
• Demand response
• Fully liberalized market

Smart Grid is the solution for a sustainable
energy future
Author Name Country RIF Session .. Paper ID
MOCCI IT RIF Session 5 Paper 999
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Active Distribution Networks (ADNs)

• Fundamental step towards Smartgrids
• DERs integrated, not simply connected
• DSO, producers, customers share responsibilities
  for network operation
• Regulation still missing in most cases is the
  key for ADN implementation.

Distribution planning of ADNs

• In intelligent grid era should consider
  opportunities coming from operation (Automation,
  load and DER control, storage) ? network
  investments might be deferred or avoided.
• Planning still answers to why, when, what, and
  where make investments, considering also the
  Active Management.

MOCCI IT RIF Session 5 Paper 999
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System stakeholders and Goals

• The Civil Society (CS)
• Environmental concerned
• DG and RES exploitation
• Energy Losses reduction
• Reliability
• Reasonable Costs
• DSO
• CAPEX OPEX minimization
• Reliability and Efficiency
• To increase revenues
• Fulfill Regulators Prescriptions
• ADN CAPEX and OPEX

• Producers (DER owners)
• Energy production/selling maximization
• Earning money from RES incentives
• Low connection charges
• Network availability

System stakeholders have conflicting goals
compromise solutions are necessary.
MOCCI IT RIF Session 5 Paper 999
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Multi-Objective Programming
Multi-Objective (MO) methods ? provide a set of
optimal solutions (Pareto set) instead of a
single optimal solution of the traditional
techniques.
In recent works
Authors developed a Software tool, based on
Non-dominated Sorting Genetic Algorithm
(NSGA-II), for distribution system planning in
presence of high levels of DG.

• MO optimization aimed at finding the Pareto-set
  of RES placements in planning scenarios
  characterized by
• different regulatory frameworks,
• level of Active Management, and
• incentive mechanisms.

RESULT Active management allows higher DG
shares, without the negative follow up of the
fit and forget policy applied with
unpredictable generation.
MOCCI IT RIF Session 5 Paper 999
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Aim of the Study
Main novelty of the present paper

• Software Planning tool used to perform a MO
  optimization aimed at finding the Pareto-set of
  RES placements in planning scenarios
  characterized by advanced ADN schemes
  (Reconfiguration, Demand side Management, DER as
  active subject, providing system services).

• To simulate the impact of ADN implementation
  level on the development and integration of DER
  in the System,
• To assess the relationship between Regulatory
  environment and the level of ADN implementation.

Active operation can help solve tensions caused
by investors and DSO contrasting goals, direct
consequence of the regulatory mechanism adopted.
MOCCI IT RIF Session 5 Paper 999
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Scenarios
Scenario ADN Implementation DER Investor responsibility Use of system charge
A.1 no no no
B.1 GC(P) committed Energy curtailed
B.2 GC(P) remunerated no
C.1 DG Control (PQ) committed Energy curtailed
C.2 DG Control (PQ) remunerated no
D DSM remunerated no
E RCF no no
F GCDSMRCF remunerated no

• Scenario A is based on the connect and forget
  policy.
• Full incentives mechanism (current Italian
  situation)
• RES earn Green Certificates as a function of the
  energy produced (1 Green Certificate 100
  /MWh). Energy produced by PV is bought at
  special price as high as 300 /MWh, but it cannot
  earn Green Certificates.
• RES refunds by Regulator partially allowed

MOCCI IT RIF Session 5 Paper 999
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Stakeholders Objective Functions
Civil Society DSOs DER Investors
RES integration Cost of network upgrading (1? rDSO)CU Building and operation (CDG)
Energy Losses (EL) Cost of energy losses (CL) Cost of connection (CConn)
ADN OPEX(CADN) Incomes for ADN (RADN) Incentives (IEn)
Asset management (rDSOCU) Incomes from DG (IConn) Incomes from ancillary services (IAS)
Expenditure for incentives (EXinc)
Civil Society
(3 different OFs)
Distributors
RES Investors
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DER Investors point of view
DER building and operation costs

• Building costs are function of DER technology /
  rated capacity
• Operation maintenance costs are function of
  energy produced.

DER Connection costs

• Connection costs calculated according to Italian
  legislation. At distribution level RES owners
• Do not pay for transmission network upgrading
• Pay a flat connection cost, which depends on the
  generator power capacity and the distance from
  HV/LV or MV/LV substations
• Can decide to build the infrastructure by
  themselves. In this case, they can receive money
  back from Regulator (if the connection cost is
  greater than the flat cost).

MOCCI IT RIF Session 5 Paper 999
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Case Study
Italian 20 kV distribution network

• 3 HV/MV substations
• 36 MV/LV (15 trunk - 21 lateral) nodes

• Average PD of 16 MW.
• 3 existing overhead open loop feeders, several
  overhead laterals.
• Voltage drop problems due to load growth.

Without new DERs

• CAPEX are 135 k, 90 reimbursed by the
  Regulator.
• Losses lt 2.
• The balance is positive, 738 k.

MOCCI IT RIF Session 5 Paper 999
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Results
Different Scenarios
Average OFs values in optimal Pareto sets and
significant planning parameters.
Regulatory environment Regulatory environment Scenario A Scenario B.1 (committed) Scenario B.2 (remunerated) Scenario C.1 (committed) Scenario C.2 (remunerated) Scenario D (DSM) Scenario E (RCF) Scenario F (PQ,DSM,RCF)
OFDSO M OFDSO M 1.4 0.6 1.1 0.6 1.2 1.3 1.3 0.9
OFInv M OFInv M 51.1 33.6 37.5 37.3 38.6 53.6 50.5 41.2
Civil Society (cost) M Civil Society (cost) M 4.1 13.8 16.7 14.7 17.3 4.8 4.2 13.8
DG penetration DG penetration 140 174 171 175 171 145 139 171
Net DSO CAPEX k Net DSO CAPEX k 9.5 34.2 34.2 33.7 34.6 4.9 6.6 6.2
EL MWh EL MWh 2.52 6.76 5.80 6.70 5.13 2.67 2.68 6.19
PBT (mean value) years PBT (mean value) years 1.8 2.0 2.1 1.9 2.0 1.8 1.7 2.0
Wind plants avg. power 3758 kW 3928 kW 3760 kW 2677 kW 2677 kW 2881 kW 3295 kW 3295 kW
Wind plants avg. No. 10.1 14.9 12.5 14.9 14.9 13.3 11.2 11.2
PV plants avg. power 961 kW 748 kW 879 kW 955 kW 955 kW 945 kW 955 kW 955 kW
PV plants avg. No. 6.9 3.4 3.1 5.7 5.7 5.0 7.4 7.4
Biomass plants avg. power 0 0 0 20 kW 20 kW 20 kW 0 0
Biomass plants avg. No. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
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Conclusion

• Software planning tool to perform a MO
  optimization algorithm aimed at finding the
  Pareto-set of DER placements in scenarios
  characterized by different AND schemes.
• MO optimization allows finding the good
  compromise solutions for the system stakeholders
  (Civil Society, DER investors and DSOs),
  highlighting the relationship between the
  regulatory environment and the level of Active
  Management implementation.
• The active operation of the system is
  fundamental to limit network investments for the
  necessary network upgrading in the medium term
  without unfair barriers to the integration of
  RES.
• Scenario without active management remuneration
  is preferable, because the reward penalizes too
  much the Regulator .

MOCCI IT RIF Session 5 Paper 999