Trading Small Prosumers Flexibility in the Energy and Tertiary Reserve Markets

This paper addresses the participation of an aggregator of small prosumers in the energy and tertiary reserve markets. A two-stage stochastic optimization model is proposed to exploit the load and generation flexibility of the prosumers. The aim is to define energy and tertiary reserve bids to minimize the net cost of the aggregator buying and selling energy in the day-ahead and real-time markets, as well as to maximize the revenue of selling tertiary reserve during the real-time stage. Scenario-based stochastic programming is used to deal with the uncertainties of photovoltaic power generation, electricity demand, outdoor temperature, end-users’ behavior, and preferences. A case study of 1000 small prosumers from MIBEL is used to compare the proposed strategy to two other strategies. The numerical results show that the proposed strategy reduces the bidding net cost of the aggregator by 48% when compared to an inflexible strategy typically used by retailers.

[1]  P. Pinson,et al.  Generation and evaluation of space–time trajectories of photovoltaic power , 2016, 1603.06649.

[2]  Filipe Joel Soares,et al.  Optimal supply and demand bidding strategy for an aggregator of small prosumers , 2017 .

[3]  Zhi Zhou,et al.  Survey of U.S. Ancillary Services Markets , 2016 .

[4]  Hamed Mohsenian-Rad,et al.  Optimal Demand Bidding for Time-Shiftable Loads , 2015, IEEE Transactions on Power Systems.

[5]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[6]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

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

[8]  João P. S. Catalão,et al.  Plug-In Electric Vehicles Parking Lot Equilibria With Energy and Reserve Markets , 2017, IEEE Transactions on Power Systems.

[9]  Saeed Rahmani Dabbagh,et al.  Risk Assessment of Virtual Power Plants Offering in Energy and Reserve Markets , 2016, IEEE Transactions on Power Systems.

[10]  H. Madsen,et al.  Benefits and challenges of electrical demand response: A critical review , 2014 .

[11]  Pierre Pinson,et al.  Optimal Offering Strategies for Wind Power in Energy and Primary Reserve Markets , 2016, IEEE Transactions on Sustainable Energy.

[12]  F. J. Soares,et al.  Optimized Demand Response Bidding in the Wholesale Market under Scenarios of Prices and Temperatures , 2015, 2015 IEEE Eindhoven PowerTech.

[13]  Stamatis Karnouskos,et al.  An energy market for trading electricity in smart grid neighbourhoods , 2012, 2012 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST).

[14]  Anastasios G. Bakirtzis,et al.  Optimal Bidding Strategy for Electric Vehicle Aggregators in Electricity Markets , 2013, IEEE Transactions on Power Systems.

[15]  Shin Nakamura,et al.  Autonomous cooperative energy trading between prosumers for microgrid systems , 2014, 39th Annual IEEE Conference on Local Computer Networks Workshops.

[16]  Ilan Momber,et al.  Risk Averse Scheduling by a PEV Aggregator Under Uncertainty , 2015, IEEE Transactions on Power Systems.

[17]  Iliana Ilieva,et al.  An econometric analysis of the regulation power market at the Nordic power exchange , 2014 .

[18]  Manuel A. Matos,et al.  Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part II: Numerical analysis , 2013 .

[19]  Filipe Joel Soares,et al.  A STOCHASTIC MODEL TO SIMULATE ELECTRIC VEHICLES MOTION AND QUANTIFY THE ENERGY REQUIRED FROM THE GRID , 2011 .

[20]  Mohamed A. El-Sharkawi,et al.  Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services , 2012, IEEE Transactions on Smart Grid.

[21]  Zancanella Paolo,et al.  Demand response status in EU Member States , 2016 .

[22]  Manuel A. Matos,et al.  A bottom-up approach to leverage the participation of residential aggregators in reserve services markets , 2016 .

[23]  Yin Xu,et al.  Strategic Bidding and Compensation Mechanism for a Load Aggregator With Direct Thermostat Control Capabilities , 2018, IEEE Transactions on Smart Grid.

[24]  Manuel A. Matos,et al.  Availability and Flexibility of Loads for the Provision of Reserve , 2015, IEEE Transactions on Smart Grid.

[25]  Asgeir Tomasgard,et al.  Prosumer bidding and scheduling in electricity markets , 2016 .

[26]  Ye Cai,et al.  Self-Sustainable Community of Electricity Prosumers in the Emerging Distribution System , 2017, IEEE Transactions on Smart Grid.

[27]  Filipe Joel Soares,et al.  Optimized Bidding of a EV Aggregation Agent in the Electricity Market , 2012, IEEE Transactions on Smart Grid.

[28]  Filipe Joel Soares,et al.  Integration of Electric Vehicles in the Electric Power System , 2011, Proceedings of the IEEE.

[29]  Francisco Alberto Campos Fernández,et al.  Joint energy and reserve markets: current implementations and modeling trends , 2014 .