A Combination of Genetic Algorithm and Particle Swarm Optimization for Power Systems Planning Subject to Energy Storage

Document Type : Original Research (Full Papers)


Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran


With the ever-increasing growth of electrical energy consumption in different fields of a power plant, expanding strategies in power plants is a vital, important and inevitable action. Generally, greenhouse gas emissions can be reduced by replacing wind energy instead of using fossil fuels in power plants for electricity generation. A physical system that is capable of harnessing energy for distribution and compensation electricity at a desired and determined later time is called a typical energy storage system. In this paper, a proper optimization method for expansion planning of electrical energy storage is presented. Since the meta-heuristic algorithms are a very suitable tool for optimization purposes, a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO) technique are used in this research. The main objective of the optimization problem is to increase the energy storage. The implementation of the proposed method is performed using MATLAB and GAMS tools. The simulation results strongly validate the correctness and effectiveness of the proposed method.