A mixed-integer distributed approach to prosumers aggregation for providing balancing services

Abstract This paper addresses the provision of ancillary services in smart energy systems. A large number of prosumers are aggregated by an Energy Service Provider (ESP) in order to provide a manual Frequency Restoration Reserve (mFRR) service, which consists in offering some degree of flexibility and be willing to provide a power variation over a given time interval upon reception of an explicit manual request by the Transmission System Operator (TSO). The main focus of this paper is to define how the ESP can optimally distribute the requested flexibility effort to the prosumers in the pool, promptly providing the agreed mFRR service upon request of the TSO. In particular, a scalable strategy is proposed, able to account for integer decision variables like on/off commands, while reducing the combinatorial complexity of the problem and preserving privacy of local information via distributed computations. Lead and rebound effects are avoided by maintaining the originally scheduled energy exchange profile before and after the time interval where the TSO request must be satisfied. The simulation results show the effectiveness of the proposed approach in terms of scalability and quality of the obtained feasible solution.

[1]  M. Hadi Amini,et al.  Hierarchical Electric Vehicle Charging Aggregator Strategy Using Dantzig-Wolfe Decomposition , 2018, IEEE Design & Test.

[2]  Paul McNamara,et al.  Hierarchical Demand Response using Dantzig-Wolfe decomposition , 2013, IEEE PES ISGT Europe 2013.

[3]  José Pablo Chaves-Ávila,et al.  Demand response in liberalized electricity markets: Analysis of aggregated load participation in the German balancing mechanism , 2014 .

[4]  Phani Chavali,et al.  A Distributed Algorithm of Appliance Scheduling for Home Energy Management System , 2014, IEEE Transactions on Smart Grid.

[5]  Pierluigi Siano,et al.  Optimal Bidding Strategy for a DER Aggregator in the Day-Ahead Market in the Presence of Demand Flexibility , 2019, IEEE Transactions on Industrial Electronics.

[6]  John Lygeros,et al.  Aggregation and Disaggregation of Energetic Flexibility From Distributed Energy Resources , 2017, IEEE Transactions on Smart Grid.

[7]  A hierarchical approach for balancing service provision by microgrids aggregators , 2020 .

[8]  Hannu Laaksonen,et al.  Optimized Operation of Local Energy Community Providing Frequency Restoration Reserve , 2020, IEEE Access.

[9]  Lijun Chen,et al.  Online Stochastic Optimization of Networked Distributed Energy Resources , 2020, IEEE Transactions on Automatic Control.

[10]  Andrea Michiorri,et al.  Optimal Participation of Residential Aggregators in Energy and Local Flexibility Markets , 2020, IEEE Transactions on Smart Grid.

[11]  Kenli Li,et al.  Minimal Cost Server Configuration for Meeting Time-Varying Resource Demands in Cloud Centers , 2018, IEEE Transactions on Parallel and Distributed Systems.

[12]  Carlos Henggeler Antunes,et al.  Energy management systems aggregators: A literature survey , 2017 .

[13]  Lion Hirth,et al.  The ENTSO-E Transparency Platform – A review of Europe’s most ambitious electricity data platform , 2018, Applied Energy.

[14]  Tariq Samad,et al.  Automated Demand Response for Smart Buildings and Microgrids: The State of the Practice and Research Challenges , 2016, Proceedings of the IEEE.

[15]  Marcello Farina,et al.  Microgrids aggregation management providing ancillary services , 2018, 2018 European Control Conference (ECC).

[16]  Georgios B. Giannakis,et al.  Scalable and Robust Demand Response With Mixed-Integer Constraints , 2013, IEEE Transactions on Smart Grid.

[17]  Alexandre Oudalov,et al.  The Provision of Frequency Control Reserves From Multiple Microgrids , 2011, IEEE Transactions on Industrial Electronics.

[18]  Rodrigo Escobar,et al.  European Union Electricity Markets: Current Practice and Future View , 2019, IEEE Power and Energy Magazine.

[19]  Kenli Li,et al.  Strategy Configurations of Multiple Users Competition for Cloud Service Reservation , 2016, IEEE Transactions on Parallel and Distributed Systems.

[20]  Filipe Joel Soares,et al.  Real-time provision of multiple electricity market products by an aggregator of prosumers , 2019 .

[21]  Gregor Verbic,et al.  A Distributed Algorithm for Demand Response With Mixed-Integer Variables , 2015, IEEE Transactions on Smart Grid.

[22]  Andrea Lodi,et al.  A Decentralized Framework for the Optimal Coordination of Distributed Energy Resources , 2019, IEEE Transactions on Power Systems.

[23]  Marcello Farina,et al.  Design of Aggregators for the Day-Ahead Management of Microgrids Providing Active and Reactive Power Services , 2018, IEEE Transactions on Control Systems Technology.

[24]  Thillainathan Logenthiran,et al.  Demand Side Management in Smart Grid Using Heuristic Optimization , 2012, IEEE Transactions on Smart Grid.

[25]  Nermin Suljanovic,et al.  Performance evaluation of a virtual power plant communication system providing ancillary services , 2017 .

[27]  Maria Prandini,et al.  A decentralized approach to multi-agent MILPs: Finite-time feasibility and performance guarantees , 2017, Autom..

[28]  Iain MacGill,et al.  Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services , 2010, IEEE Transactions on Smart Grid.

[29]  Manuel A. Matos,et al.  Optimization Models for EV Aggregator Participation in a Manual Reserve Market , 2013, IEEE Transactions on Power Systems.

[30]  Evangelos Vrettos,et al.  Robust Energy-Constrained Frequency Reserves From Aggregations of Commercial Buildings , 2015, IEEE Transactions on Power Systems.

[31]  Lion Hirth,et al.  Balancing power and variable renewables: Three links , 2015 .

[32]  F. J. Soares,et al.  Trading Small Prosumers Flexibility in the Energy and Tertiary Reserve Markets , 2019, IEEE Transactions on Smart Grid.

[33]  Antonello Monti,et al.  Distributed Optimization for Scheduling Electrical Demand in Complex City Districts , 2018, IEEE Systems Journal.