Abstract:
This study presents a novel optimization method for the design of a hybrid microgrid system, consisting of wind turbines, photovoltaic systems, battery energy storage systems, and diesel generators. A Continuous Grey Wolf Optimization (CGWO) algorithm is proposed to tackle the challenges of nonlinearity and stochastic disturbances in the system’s capacity configuration. The CGWO enhances the traditional Grey Wolf Optimization (GWO) by incorporating an improved convergence factor and a dynamic weighting strategy, significantly increasing convergence speed and solution quality. A case study is conducted to evaluate four power supply schemes for the microgrid. Results indicate that Scheme 3 achieves the lowest total cost and environmental conversion expenses, with reductions of 30.12% and 59.7% compared to Scheme 1, and 16.74% and 39.84% compared to Scheme 2, respectively. In addition, the CGWO reduces diesel generator usage by 23.78% compared to the GWO and 22.04% compared to Particle Swarm Optimization (PSO), while decreasing power shortages by 62.09% and 60.25%, respectively. These findings highlight the CGWO’s effectiveness in optimizing microgrid configurations, balancing cost, sustainability, and reliability. The proposed method provides valuable insights for designing cost-efficient and environmentally sustainable energy systems.
