Abstract
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.
Highlights
In recent years, to control the increasing requirements of electricity price and demand, different renewable methods of energy generation have been of great interest
The annual profit of the each microgrid was found using Nash equilibrium and Shapely values, and a comparative analysis was carried out among the game-theoretic techniques to identify the maximum profit of the networked microgrid
A simulation model was made in MATLAB software and used a population-based imperialistic competition algorithm
Summary
To control the increasing requirements of electricity price and demand, different renewable methods of energy generation have been of great interest. Novel research studies have shown that generation through renewable energy is the modern way and that environmental concerns are another reason to increase the rapid use of such methods [1,2]. The solution to control the intermittent type of generation is the addition of different kinds of energy storage systems [3,4]. Generation through renewable resources is not the only reliable, safe, and economical way, but it is a most encouraging and leading way to develop a modern form of power generation. Maximum utilization of generation resources, and efficient performance of a generation system is guaranteed by considering the right optimization method [7]. In [8], a new strategy using evaluation
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