Multi-objective optimal scheduling of islanded microgrid based on ISSA

To address the collaborative optimization of environmental protection and economic efficiency in isolated microgrids, an Improved Sparrow Search Algorithm (ISSA) is proposed for economic optimization and scheduling. First, a mathematical model is established with economic cost as the performance indicator. This model incorporates engineering constraints such as power balance and equipment output limits, formulating a multi-objective scheduling optimization problem. Second, the population is initialized using an improved Tent chaotic sequence. The basic sparrow search algorithm is enhanced by introducing a golden-ratio-based sinusoidal strategy into the discoverer phase, enabling thorough local exploration while balancing global and local search capabilities. To avoid premature convergence, a particle swarm velocity update strategy is integrated into the participant position update, improving adaptability to complex optimization problems. Additionally, a dynamic selection adaptive t-distribution mutation operator is introduced to perturb individual positions, enhancing the algorithm's ability to escape local optima. Finally, the ISSA is applied to isolated microgrid dispatching, significantly improving operational economic efficiency while maintaining high clean energy utilization rates. Simulation results validate the rationality and scientific rigor of the proposed strategy.