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

Frankfurt (Germany), 6-9 June 2011 MOCCI – IT – RIF Session 5 – Paper 999  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

Frankfurt (Germany), 6-9 June 2011 MOCCI – IT – RIF Session 5 – Paper 999  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 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.

Frankfurt (Germany), 6-9 June 2011 System stakeholders and Goals  Producers (DER owners)  Energy production/selling maximization  Earning money from RES incentives  Low connection charges  Network availability MOCCI – IT – RIF Session 5 – Paper 999  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.

Frankfurt (Germany), 6-9 June 2011 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: MOCCI – IT – RIF Session 5 – Paper 999 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.

Frankfurt (Germany), 6-9 June 2011 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: MOCCI – IT – RIF Session 5 – Paper 999 Active operation can help solve tensions caused by investors and DSO contrasting goals, direct consequence of the regulatory mechanism adopted.

Frankfurt (Germany), 6-9 June 2011 Scenarios Scenario ADN Implementation DER Investor responsibility Use of system charge A.1no B.1GC(P)committedEnergy curtailed B.2GC(P)remuneratedno C.1DG Control (P&Q)committedEnergy curtailed C.2DG Control (P&Q)remuneratedno DDSMremuneratedno ERCFno FGC+DSM+RCFremuneratedno  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

Frankfurt (Germany), 6-9 June 2011 Civil SocietyDSOsDER Investors RES integration Cost of network upgrading [(1  r DSO )·C U ] Building and operation (C DG ) Energy Losses (E L )Cost of energy losses (C L )Cost of connection (C Conn ) ADN OPEX(C ADN )Incomes for ADN (R ADN )Incentives (I En ) Asset management (r DSO ·C U )Incomes from DG (I Conn ) Incomes from ancillary services (I AS ) Expenditure for incentives (EX inc ) Civil Society Distributors RES Investors Stakeholders Objective Functions (3 different OFs)

Frankfurt (Germany), 6-9 June 2011 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  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 MOCCI – IT – RIF Session 5 – Paper 999

Frankfurt (Germany), 6-9 June 2011 Case Study  Average P D 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: MOCCI – IT – RIF Session 5 – Paper 999

Frankfurt (Germany), 6-9 June 2011 Results Regulatory environmentScenario 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 (P&Q,DSM,RCF) OF DSO [M€] OF Inv [M€] Civil Society (cost) [M€] DG penetration 140 %174 %171 %175 %171 %145 %139 %171 % Net DSO CAPEX [k€] E L [MWh] PBT (mean value) [years] Wind plants avg. power 3758 kW3928 kW3760 kW2677 kW 2881 kW3295 kW avg. No PV plants avg. power 961 kW748 kW879 kW955 kW 945 kW955 kW avg. No Biomass plants avg. power kW 00 avg. No. 0.0 Average OFs values in optimal Pareto sets and significant planning parameters. Different Scenarios

Frankfurt (Germany), 6-9 June 2011 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