Optimal planning of clustered microgrid using a technique of cooperative game theory

In this paper a cooperative type of game theoretical technique is proposed at the planning stage to model a grid-connected clustered microgrid. In order to make it more applicable, the selected microgrid consists of different combinations of generation resources like wind turbines, solar cells, and batteries. In the game model, generation resources are considered as players and their profit as the payoff. The technique of the Nash bargaining solution is adopted in this study for capacity allocation of generation resources and batteries, and also to maximize the annual profit of individual microgrids and its cluster. As a cooperative game model, all possible coalitions are discussed between the players to find their optimum sizes, and most the suitable one is selected based upon the game theory technique and maximum payoff value. For this purpose, a particle swarm optimization (PSO) algorithm is developed to find the most feasible Nash bargaining solution using MATLAB software. In the simulations and for the system analysis, the realistic electrical load data and weather forecast is taken for a remote town Mount Magnet in Western Australia. Finally, a sensitivity analysis is performed to validate and show the reasonability of the optimized results concerning the proposed clustered system.