Capacity Optimization of an Isolated Renewable Energy Microgrid Using an Improved Gray Wolf Algorithm

Abstract

To achieve the goal of allocating the generation capacity of isolated renewable energy system microgrids in a stable, economical, and clean manner, an optimization model considering economic costs, environmental protection, and power supply reliability was established. Compared with the normalization of fixed weight coefficients, a dynamic adaptive parameter method was used in this study to balance the weights of economic, environmental, and stability factors in the objective function. The Levy Flight Strategy, Golden Sine Strategy, and Dynamic Inverse Learning Strategy were embedded to increase algorithm performance for optimization and simulation to address issues such as local optima, slow convergence speed, and lack of diversity commonly associated with traditional Grey Wolf Optimization algorithm. The case analysis shows that the Improved Grey Wolf Optimization algorithm effectively reduces the economic cost of microgrids, enhances environmental performance, and improves system reliability.