Optimal scheduling study of microgrids based on multistrategy improved sardine algorithm

To enhance the utilization efficiency of wind and photovoltaic power generation in microgrids, this study develops an optimal scheduling model that incorporates multiple operational constraints, including power generation, energy storage, and electricity transactions while aiming to minimize the combined economic and environmental costs. In response to the common limitations of existing meta‑heuristic algorithms, such as premature convergence to local optima and insufficient solution accuracy when solving such models, a multi‑strategy improved sardine optimization algorithm is proposed. By integrating the core mechanisms of the sardine optimization algorithm and particle swarm optimization, along with four enhancement strategies, the proposed method achieves complementary advantages and significantly improves solution precision. Using twelve test functions from the CEC2022 benchmark set, a comparative evaluation of eight algorithms demonstrates that the proposed method outperforms others in both convergence speed and accuracy. When applied to the microgrid scheduling model, the algorithm effectively reduces the total system cost compared to traditional SOA, GWO, and PSO approaches, offering a novel and effective methodology for microgrid optimization dispatch.