mocci – it – rif session 5 – paper 999

Frankfurt (Germany), 6-9 June 2011

MOCCI – IT – RIF Session 5 – Paper 999

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



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

Author Name – Country – RIF Session ….. – Paper ID

Introduction

Smart Grid is the solution for a sustainable energy future



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.=S

P, Q, V P, Q, V

P, Q

, V

P, Q P, -QP, Q

DMS

CHP

PVWT==SS

P, Q, V P, Q, V

P, Q

, V

P, Q P, -QP, Q

DMS

CHP

PVWT

=S

P, Q, V P, Q, V

P, Q

, V

P, Q P, -QP, Q

DMS

CHP

PVWT==SS

P, Q, V P, Q, V

P, Q

, V

P, Q P, -QP, Q

DMS

CHP

PVWT

Distribution planning of ADNs Distribution planning of ADNs

Active Distribution Networks (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.


System stakeholders and Goals Producers (DER owners)

Energy production/selling maximization

Earning money from RES incentives

Low connection charges Network availability


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 Regulator’s Prescriptions ADN CAPEX and OPEX

System stakeholders have conflicting goals:

compromise solutions are necessary.


Multi-Objective (MO) methods: provide a set of optimal solutions (Pareto set) instead of a single optimal solution of the traditional techniques.

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.

Multi-Objective Programming

In recent works:


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.


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).

Aim of the Study

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.

Main novelty of the present paper:


Active operation can help solve tensions caused by investors and DSO contrasting goals, direct consequence of the regulatory mechanism

adopted.


ScenariosScenario

ADNImplementation

DER Investor responsibility

Use of systemcharge

A.1 no no noB.1 GC(P) committed Energy curtailedB.2 GC(P) remunerated noC.1 DG Control (P&Q) committed Energy curtailedC.2 DG Control (P&Q) remunerated noD DSM remunerated noE RCF no noF GC+DSM+RCF 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;



Civil Society DSOs DER Investors

RES integrationCost 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 (rDSO·CU) Incomes from DG (IConn)Incomes from ancillary services

(IAS)

Expenditure for incentives (EXinc)

Civil Society

Distributors

RES Investors

LUDSOConnADNDSO CCrIROF )1(

ConnDGASEnInv CCIIOF

incUDSOADNCostsCS EXCrCOF )(

Stakeholders Objective Functions

LLossesCS EOF

100

%1

DGOF DG

CS

(3 different OFs)


DER Investors point of view

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

DER building and operation costs DER building and operation 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).

DER Connection costs DER Connection costs



Case Study

Average PD of 16 MW.

3 existing overhead open loop feeders, several overhead laterals.

Voltage drop problems due to load growth.

CAPEX are 135 k€, 90% reimbursed by the Regulator. Losses < 2%. The balance is positive, 738 k€.

3 HV/MV substations

36 MV/LV (15 trunk - 21 lateral) nodes

Italian 20 kV distribution network

Without new DERs: Without new DERs:



Results

Regulatory environment Scenario AScenario B.1(committed)

Scenario B.2(remunerated)

Scenario C.1(committed)

Scenario C.2 (remunerated)

Scenario D (DSM)

Scenario E(RCF)

Scenario F(P&Q,DSM,RCF)

OFDSO [M€] 1.4 0.6 1.1 0.6 1.2 1.3 1.3 0.9

OFInv [M€] 51.1 33.6 37.5 37.3 38.6 53.6 50.5 41.2

Civil Society (cost) [M€] 4.1 13.8 16.7 14.7 17.3 4.8 4.2 13.8

DG penetration 140 % 174 % 171 % 175 % 171 % 145 % 139 % 171 %

Net DSO CAPEX [k€] 9.5 34.2 34.2 33.7 34.6 4.9 6.6 6.2

EL [MWh] 2.52 6.76 5.80 6.70 5.13 2.67 2.68 6.19

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

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

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

avg. No. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Average OFs values in optimal Pareto sets and significant planning parameters.

Different ScenariosDifferent Scenarios


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

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