Multiplayer Nash–Stackelberg Game Analysis of Electricity Markets With the Participation of a Distribution Company

The integration of a large number of distributed energy resources into the distribution system poses operational challenges and creates opportunities to generate profits in the electricity market. In this study, a multileader one-follower model is built to elaborate the Nash–Stackelberg game of the electricity market, where a distribution company participates in a competition with a generation company. The distribution company, as one leader, decides on the bidding price and quantity to maximize its profits in the market by integrating the distributed energy resources of multiple distribution systems after satisfying the local demand, whereas the generation company, as another leader, participates in the competitive market. As the follower, the independent system operator clears electricity markets. Moreover, a tailored algorithm is proposed to obtain the global Nash equilibrium point of the multileader one-follower Nash–Stackelberg game model. First, the proposed game model is transformed into a generalized Nash equilibrium game model; then, the transformed game model is converted to a potential game model for a solution. Finally, case studies on a constructed transmission and distribution joint system and a practical transmission and distribution joint system are performed to demonstrate the effectiveness and scalability of the proposed model and algorithm.

[1]  M. Erol-Kantarci,et al.  Stochastic Demand Response Management Using Mixed-Strategy Stackelberg Game , 2022, IEEE Systems Journal.

[2]  Yuhong Mo,et al.  Operational flexibility enhancements using mobile energy storage in day-ahead electricity market by game-theoretic approach , 2021 .

[3]  Shiyan Hu,et al.  Two-Layer Game Theoretic Microgrid Capacity Optimization Considering Uncertainty of Renewable Energy , 2021, IEEE Systems Journal.

[4]  Pierluigi Siano,et al.  Game Theory-Based Energy-Management Method Considering Autonomous Demand Response and Distributed Generation Interactions in Smart Distribution Systems , 2021, IEEE Systems Journal.

[5]  Mohammad Shahidehpour,et al.  An Operation Model for Distribution Companies Using the Flexibility of Electric Vehicle Aggregators , 2021, IEEE Transactions on Smart Grid.

[6]  Joao P. S. Catalao,et al.  Bi-level optimization model for the coordination between transmission and distribution systems interacting with local energy markets , 2021, International Journal of Electrical Power & Energy Systems.

[7]  Pouria Sheikhahmadi,et al.  The participation of a renewable energy-based aggregator in real-time market: A Bi-level approach , 2020 .

[8]  F. Moghimi,et al.  Optimal scheduling of resources for a price-maker distribution company in electricity markets considering network component failures , 2020 .

[9]  Jianzhong Wu,et al.  Framework design and optimal bidding strategy for ancillary service provision from a peer-to-peer energy trading community , 2020, Applied Energy.

[10]  Pierluigi Siano,et al.  Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing , 2020 .

[11]  Taghi Barforoushi,et al.  A short-term decision-making model for a price-maker distribution company in wholesale and retail electricity markets considering demand response and real-time pricing , 2020 .

[12]  Chaorui Zhang,et al.  Optimal Bidding Strategy of Prosumers in Distribution-Level Energy Markets , 2020, IEEE Transactions on Power Systems.

[13]  Javier Contreras,et al.  EPEC approach for finding optimal day-ahead bidding strategy equilibria of multi-microgrids in active distribution networks , 2020 .

[14]  Bruno Francois,et al.  Risk management model for simultaneous participation of a distribution company in Day-ahead and Real-time markets , 2020 .

[15]  Joao P. S. Catalao,et al.  A Risk-Based Decision Framework for the Distribution Company in Mutual Interaction With the Wholesale Day-Ahead Market and Microgrids , 2020, IEEE Transactions on Industrial Informatics.

[16]  Xianglong Liu,et al.  Optimal planning of AC-DC hybrid transmission and distributed energy resource system: Review and prospects , 2019, CSEE Journal of Power and Energy Systems.

[17]  Farhad Samadi Gazijahani,et al.  Game Theory Based Profit Maximization Model for Microgrid Aggregators With Presence of EDRP Using Information Gap Decision Theory , 2019, IEEE Systems Journal.

[18]  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.

[19]  K. Afshar,et al.  Optimal bidding strategy of wind power producers in pay-as-bid power markets , 2018, Renewable Energy.

[20]  Salah Bahramara,et al.  A risk-based approach for modeling the strategic behavior of a distribution company in wholesale energy market , 2018 .

[21]  Jianhui Wang,et al.  Real-Time Procurement Strategies of a Proactive Distribution Company With Aggregator-Based Demand Response , 2018, IEEE Transactions on Smart Grid.

[22]  Mohammad S. Obaidat,et al.  Distributed Home Energy Management System With Storage in Smart Grid Using Game Theory , 2017, IEEE Systems Journal.

[23]  J. Contreras,et al.  Modeling strategic behavior of distribution company in wholesale energy and reserve markets , 2017, 2017 IEEE Manchester PowerTech.

[24]  Magnus Korpås,et al.  Strategy-making for a proactive distribution company in the real-time market with demand response , 2016 .

[25]  Pierre Pinson,et al.  Trading strategies for distribution company with stochastic distributed energy resources , 2016 .

[26]  Sairaj V. Dhople,et al.  Scalable Optimization Methods for Distribution Networks With High PV Integration , 2016, IEEE Transactions on Smart Grid.

[27]  Joshua A. Taylor Convex Optimization of Power Systems , 2015 .

[28]  Enrique Mallada,et al.  Optimal Load-Side Control for Frequency Regulation in Smart Grids , 2014, IEEE Transactions on Automatic Control.

[29]  M. Fotuhi‐Firuzabad,et al.  A Stochastic Framework for Short-Term Operation of a Distribution Company , 2013, IEEE Transactions on Power Systems.

[30]  Ankur A. Kulkarni,et al.  A Shared-Constraint Approach to Multi-Leader Multi-Follower Games , 2012, 1206.2968.

[31]  A. Conejo,et al.  Pool Strategy of a Producer With Endogenous Formation of Locational Marginal Prices , 2009, IEEE Transactions on Power Systems.

[32]  José Fortuny-Amat,et al.  A Representation and Economic Interpretation of a Two-Level Programming Problem , 1981 .

[33]  L. Shapley,et al.  Potential Games , 1994 .