Comparative Study on Game-Theoretic Optimum Sizing and Economical Analysis of a Networked Microgrid

In this paper, two techniques of game theory are considered for sizing and comparative analysis of a grid-connected networked microgrid, based on a multi-objective imperialistic competition algorithm (ICA) for system optimization. The selected networked microgrid, which consists of two different grid-connected microgrids with common electrical load and main grid, might have different combinations of generation resources including wind turbine, photovoltaic panels, and batteries. The game theory technique of Nash equilibrium is developed to perform the effective sizing of the networked microgrid in which capacities of the generation resources and batteries are considered as players and annual profit as payoff. In order to meet the equilibrium point and the optimum sizes of generation resources, all possible coalitions between the players are considered; ICA, which is frequently used in optimization applications, is implemented using MATLAB software. Both techniques of game theory, Shapley values and Nash equilibrium, are used to find the annual profit of each microgrid, and results are compared based on optimum sizing, and maximum values of annual profit are identified. Finally, in order to validate the results of the networked microgrid, the sensitivity analysis is studied to examine the impact of electricity price and discount rates on maximum values of profit for both game theory techniques.

[1]  R. Ramakumar,et al.  Loss of power supply probability of stand-alone wind electric conversion systems: a closed form solu , 1990 .

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

[3]  R. Ramakumar,et al.  Loss of Power Supply Probability of Stand-Alone Photovoltaic Systems: A Closed Form Solution Approach , 1991, IEEE Power Engineering Review.

[4]  Anastasios G. Bakirtzis,et al.  Design of a stand alone system with renewable energy sources using trade off methods , 1992 .

[5]  G. Zaccour,et al.  Time-consistent Shapley value allocation of pollution cost reduction , 1999 .

[6]  Y. Hongxing,et al.  Design of a building-integrated photovoltaic system in Hong Kong , 1999 .

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

[8]  T. Lie,et al.  Application of the Shapley Value on transmission cost allocation in the competitive power market environment , 2002 .

[9]  N. Jia,et al.  Profit allocation of independent power producers based on cooperative Game theory , 2003 .

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

[11]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[12]  Yonghua Song,et al.  A Comprehensive Review on the Development of Sustainable Energy Strategy and Implementation in China , 2010, IEEE Transactions on Sustainable Energy.

[13]  Reza Tavakkoli-Moghaddam,et al.  Imperialistic Competitive Algorithm for Solving a Dynamic Cell Formation Problem with Production Planning , 2010, ICIC.

[14]  M. Ehsan,et al.  Possibilistic Evaluation of Distributed Generations Impacts on Distribution Networks , 2011, IEEE Transactions on Power Systems.

[15]  Liu Feng A Game Theory Based Planning Model and Analysis for Hybrid Power System with Wind Generators-Photovoltaic Panels-Storage Batteries , 2011 .

[16]  Heikki N. Koivo,et al.  Multiobjective optimization using modified game theory for online management of microgrid , 2011 .

[17]  Joakim Widen,et al.  Correlations Between Large-Scale Solar and Wind Power in a Future Scenario for Sweden , 2011, IEEE Transactions on Sustainable Energy.

[18]  Shengwei Mei,et al.  Game Approaches for Hybrid Power System Planning , 2012, IEEE Transactions on Sustainable Energy.

[19]  D. M. Vilathgamuwa,et al.  Design of a Least-Cost Battery-Supercapacitor Energy Storage System for Realizing Dispatchable Wind Power , 2013, IEEE Transactions on Sustainable Energy.

[20]  Gevork B. Gharehpetian,et al.  Game-theoretic approach to cooperative control of distributed energy resources in islanded microgrid considering voltage and frequency stability , 2013, Neural Computing and Applications.

[21]  Yang Liu,et al.  Game Optimization Theory and Application in Distribution System Expansion Planning, Including Distributed Generation , 2013 .

[22]  Nicanor Quijano,et al.  Dynamic Population Games for Optimal Dispatch on Hierarchical Microgrid Control , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

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

[24]  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).

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

[26]  Ali Ahmadian,et al.  Optimal Storage Planning in Active Distribution Network Considering Uncertainty of Wind Power Distributed Generation , 2016, IEEE Transactions on Power Systems.

[27]  Leonardo Frizziero,et al.  Integrating QFD and TRIZ for innovative design , 2017 .

[28]  Nian Liu,et al.  Multi-Party Energy Management for Networks of PV-Assisted Charging Stations: A Game Theoretical Approach , 2017 .

[29]  Hongyu Lin,et al.  An Income Distributing Optimization Model for Cooperative Operation among Different Types of Power Sellers Considering Different Scenarios , 2018, Energies.

[30]  Wei Cui,et al.  Modeling Interprovincial Cooperative Energy Saving in China: An Electricity Utilization Perspective , 2018 .

[31]  Jovica V. Milanovic,et al.  Application of game theoretic approaches for identification of critical parameters affecting power system small-disturbance stability , 2018 .

[32]  Xiaofeng Liu,et al.  Energy Management for Community Energy Network with CHP Based on Cooperative Game , 2018 .

[33]  Sanjay Thakur,et al.  A strategical game theoretic based demand response model for residential consumers in a fair environment , 2018 .

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

[35]  Ahmet Ünveren,et al.  Multi-objective imperialistic competitive algorithm with multiple non-dominated sets for the solution of global optimization problems , 2017, Soft Computing.

[36]  Shiny Abraham,et al.  A Method for Distributed Control of Reactive Power and Voltage in a Power Grid: A Game-Theoretic Approach , 2018 .

[37]  Azam Khalili,et al.  A distributed game-theoretic demand response with multi-class appliance control in smart grid , 2019, Electric Power Systems Research.