Energy trading in the distribution system using a non-model based game theoretic approach

Abstract A comprehensive energy trading market is proposed at the distribution level using a model-free, game theoretic approach. The proposed market is modeled as a non-cooperative, multiplayer game, and a Nash equilibrium solution is obtained using an extremum seeking algorithm. For the game model, a non-cooperative market structure is proposed with an embedded notion of each player’s reputation. Besides that, an index is proposed that tracks the commitments of each player, rewards them according to their past behavior, and improves market reliability. Moreover, the proposed index, referred to as a Market Reputation Index, promotes fairness by rewarding good players and also encourages installation of an energy management system with accurate forecasting. A new, low-complexity, model free approach (i.e., Extremum seeking) is demonstrated to model Nash seeking behavior of players and a unique Nash equilibrium solution is sought for the proposed game. Detailed case studies demonstrate how the game is setup and the convergence to Nash equilibrium is achieved. Results are analyzed to show the usefulness of the market reputation index in improving reliability and fairness. It is also shown that the proposed market results in an increased local generation, higher payoffs (profits) for the participating players, and lower market clearing prices.

[1]  Meng Cheng,et al.  Peer-to-Peer energy trading in a Microgrid , 2018, Applied Energy.

[2]  H. R. Pota,et al.  Online energy management strategy for islanded microgrids with feedback linearizing inner controllers , 2017, 2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia).

[3]  Osama A. Mohammed,et al.  Multiagent-Based Optimal Microgrid Control Using Fully Distributed Diffusion Strategy , 2017, IEEE Transactions on Smart Grid.

[4]  Wei Lin,et al.  Game-theory based trading analysis between distribution network operator and multi-microgrids , 2019 .

[5]  Ruben Tapia-Olvera,et al.  Optimal Economic Dispatch in Microgrids with Renewable Energy Sources , 2019, Energies.

[6]  Osama A. Mohammed,et al.  Real-Time Implementation of Multiagent-Based Game Theory Reverse Auction Model for Microgrid Market Operation , 2015, IEEE Transactions on Smart Grid.

[7]  Xinghuo Yu,et al.  Risk-Averse Energy Trading in Multienergy Microgrids: A Two-Stage Stochastic Game Approach , 2017, IEEE Transactions on Industrial Informatics.

[8]  Tansu Alpcan,et al.  Game-Theoretic Frameworks for Demand Response in Electricity Markets , 2015, IEEE Transactions on Smart Grid.

[9]  Mengmeng Yu,et al.  Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach , 2017 .

[10]  Long Bao Le,et al.  Risk-Constrained Profit Maximization for Microgrid Aggregators With Demand Response , 2015, IEEE Transactions on Smart Grid.

[11]  H. Vincent Poor,et al.  Energy Storage Sharing in Smart Grid: A Modified Auction-Based Approach , 2015, IEEE Transactions on Smart Grid.

[12]  Gordon Lightbody,et al.  An advanced retail electricity market for active distribution systems and home microgrid interoperability based on game theory , 2018 .

[13]  Fangxing Li,et al.  Hardware Design of Smart Home Energy Management System With Dynamic Price Response , 2013, IEEE Transactions on Smart Grid.

[14]  Hussein T. Mouftah,et al.  A distributed game theoretic approach to energy trading in the smart grid , 2015, 2015 IEEE Electrical Power and Energy Conference (EPEC).

[15]  Wencong Su,et al.  A game-theoretic economic operation of residential distribution system with high participation of distributed electricity prosumers , 2015 .

[16]  Mehdi Savaghebi,et al.  Secondary Control Scheme for Voltage Unbalance Compensation in an Islanded Droop-Controlled Microgrid , 2012, IEEE Transactions on Smart Grid.

[17]  Nicanor Quijano,et al.  Population Games Methods for Distributed Control of Microgrids , 2015, IEEE Transactions on Smart Grid.

[18]  Wei Wei,et al.  Energy Pricing and Dispatch for Smart Grid Retailers Under Demand Response and Market Price Uncertainty , 2015, IEEE Transactions on Smart Grid.

[19]  Saeed Rahmani Dabbagh,et al.  Risk-based profit allocation to DERs integrated with a virtual power plant using cooperative Game theory , 2015 .

[20]  Yu Yan,et al.  Game-theory-based electricity market clearing mechanisms for an open and transactive distribution grid , 2015, 2015 IEEE Power & Energy Society General Meeting.

[21]  Suryanarayana Doolla,et al.  Demand Response in Smart Distribution System With Multiple Microgrids , 2012, IEEE Transactions on Smart Grid.

[22]  Miroslav Krstic,et al.  Nash Equilibrium Seeking in Noncooperative Games , 2012, IEEE Transactions on Automatic Control.

[23]  Ganguk Hwang,et al.  Event-Driven Energy Trading System in Microgrids: Aperiodic Market Model Analysis With a Game Theoretic Approach , 2017, IEEE Access.

[24]  A. Huang,et al.  A game theoretic framework for a next-generation retail electricity market with high penetration of distributed residential electricity suppliers , 2014 .

[25]  Xiaofeng Liao,et al.  Reinforcement Learning for Constrained Energy Trading Games With Incomplete Information , 2017, IEEE Transactions on Cybernetics.

[26]  Moshe Zukerman,et al.  Distributed Energy Trading in Microgrids: A Game-Theoretic Model and Its Equilibrium Analysis , 2015, IEEE Transactions on Industrial Electronics.

[27]  Hongming Yang,et al.  Dynamic Cournot game behavior of electric power providers in retail electricity market , 2005, IEEE Power Engineering Society General Meeting, 2005.

[28]  Tao Chen,et al.  A game theoretic approach to analyze the dynamic interactions of multiple residential prosumers considering power flow constraints , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[29]  Jianzhong Wu,et al.  A game theoretic approach for peer to peer energy trading , 2019, Energy Procedia.

[30]  H. Vincent Poor,et al.  A Motivational Game-Theoretic Approach for Peer-to-Peer Energy Trading in the Smart Grid , 2019, Applied Energy.

[31]  Cheng Wang,et al.  Energy Sharing Management for Microgrids With PV Prosumers: A Stackelberg Game Approach , 2017, IEEE Transactions on Industrial Informatics.

[32]  H. Vincent Poor,et al.  Price Discrimination for Energy Trading in Smart Grid: A Game Theoretic Approach , 2015, IEEE Transactions on Smart Grid.

[33]  Na Li,et al.  Real-Time Energy Management in Microgrids , 2017, IEEE Transactions on Smart Grid.

[34]  Mahmud Fotuhi-Firuzabad,et al.  Application of Game Theory in Reliability-Centered Maintenance of Electric Power Systems , 2017, IEEE Transactions on Industry Applications.

[35]  Lang Tong,et al.  Dynamic Pricing and Distributed Energy Management for Demand Response , 2016, IEEE Transactions on Smart Grid.

[36]  Anirban Mahanti,et al.  Competitive Energy Trading Framework for Demand-Side Management in Neighborhood Area Networks , 2015, IEEE Transactions on Smart Grid.

[37]  Alejandro Ribeiro,et al.  Demand Response Management in Smart Grids With Heterogeneous Consumer Preferences , 2015, IEEE Transactions on Smart Grid.

[38]  Chen Chen,et al.  Coalitional game theory based local power exchange algorithm for networked microgrids , 2019, Applied Energy.

[39]  Jian-xing Ren,et al.  Benefit allocation for distributed energy network participants applying game theory based solutions , 2017 .

[40]  Z. X. Jing,et al.  A Stackelberg game approach for multiple energies trading in integrated energy systems , 2017 .

[41]  R. Myerson Nash Equilibrium and the History of Economic Theory , 1999 .

[42]  Anuradha M. Annaswamy,et al.  A real-time demand response market through a repeated incomplete-information game , 2018 .

[43]  Mousa Marzband,et al.  Optimal energy management system based on stochastic approach for a home Microgrid with integrated responsive load demand and energy storage , 2017 .

[44]  Ervin Bossanyi,et al.  Wind Energy Handbook , 2001 .