Optimal Sizing of Networked Microgrid using Game Theory considering the Peer-to-Peer Energy Trading

This paper proposes a peer-to-grid (P2G) energy trading combined with peer-to-peer (P2P) energy trading scheme based on a cooperative game theoretical technique to optimize sizes of the generation resources and battery, and achieve maximum payoff from a networked microgrid. The selected architecture consists of two microgrids in which both microgrids contain solar panels, wind turbines, and batteries to meet the requirements of the load. In the first stage, a game theory technique based on particle swarm optimization (PSO) method is used to find the optimum sizes of the generation resources and batteries considering the conventional P2G combined with P2P energy trading. In the second stage, considering two energy trading scenarios including P2G and P2G combined with P2P capability, the maximum payoffs of both microgrids are optimized and compared.

[1]  P Porta,et al.  "Pasinetti, Luigi" for the International Encyclopaedia of the Social Sciences , 2007 .

[2]  Zwe-Lee Gaing,et al.  Particle swarm optimization to solving the economic dispatch considering the generator constraints , 2003 .

[3]  Juho Hamari,et al.  The sharing economy: Why people participate in collaborative consumption , 2016, J. Assoc. Inf. Sci. Technol..

[4]  Roger B. Myerson,et al.  Game theory - Analysis of Conflict , 1991 .

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

[6]  Ali,et al.  Comparative Study on Game-Theoretic Optimum Sizing and Economical Analysis of a Networked Microgrid , 2019, Energies.

[7]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[8]  Thomas Kunz,et al.  An optimal P2P energy trading model for smart homes in the smart grid , 2017 .

[9]  Mohammad Shahidehpour,et al.  DC Microgrids: Economic Operation and Enhancement of Resilience by Hierarchical Control , 2014, IEEE Transactions on Smart Grid.

[10]  Lei Wu,et al.  Optimal Operation for Community-Based Multi-Party Microgrid in Grid-Connected and Islanded Modes , 2018, IEEE Transactions on Smart Grid.

[11]  Xinghuo Yu,et al.  Energy-Sharing Provider for PV Prosumer Clusters: A Hybrid Approach Using Stochastic Programming and Stackelberg Game , 2018, IEEE Transactions on Industrial Electronics.

[12]  Jianhui Wang,et al.  Self-Healing Resilient Distribution Systems Based on Sectionalization Into Microgrids , 2015, IEEE Transactions on Power Systems.

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

[14]  Munther A. Dahleh,et al.  Demand Response Using Linear Supply Function Bidding , 2015, IEEE Transactions on Smart Grid.

[15]  Lalit Goel,et al.  Power system planning — a reliability perspective , 1995 .

[16]  Wei Zhou,et al.  A novel optimization sizing model for hybrid solar-wind power generation system , 2007 .

[17]  Aneesh Krishna,et al.  Game Theory-Based Requirements Analysis in the i* Framework , 2018, Comput. J..

[18]  Yassine Mhandi,et al.  Impact of clustering microgrids on their stability and resilience during blackouts , 2015, 2015 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE).

[19]  S Kuruseelan,et al.  Peer-to-Peer Energy Trading of a Community Connected with an AC and DC Microgrid , 2019, Energies.

[20]  Shengwei Mei,et al.  Nonlinear Control Systems and Power System Dynamics , 2001, The Springer International Series on Asian Studies in Computer and Information Science.

[21]  Hoay Beng Gooi,et al.  Peer-to-Peer Energy Trading in a Prosumer-Based Community Microgrid: A Game-Theoretic Model , 2019, IEEE Transactions on Industrial Electronics.