Real-time scheduling strategy for distributed microgrids based on improved particle swarm optimization and fuzzy logic

Real-time scheduling of microgrids is crucial for improving energy utilization efficiency and system stability, but the uncertainty of renewable energy sources and the distributed nature of the system pose great challenges. In this paper, a distributed microgrid real-time scheduling strategy based on the combination of improved particle swarm optimization (IPSO) and fuzzy logic control (FLC) is proposed. First, an integrated microgrid model containing renewable energy, conventional generation, energy storage and controllable loads is constructed. Second, the IPSO algorithm with adaptive inertia weights and chaotic perturbations was designed to enhance the global search capability and convergence performance. Then, a fuzzy rule-based FLC system is developed to dynamically adjust the IPSO parameters and improve the adaptability of the algorithm to uncertainty. The combination of IPSO and FLC can effectively deal with the complexity and uncertainty in microgrid scheduling problems. Finally, a distributed optimization framework is constructed based on the consistency algorithm to realize the cooperative optimization of large-scale microgrid system. Simulation results show that the hybrid IPSO-FLC algorithm proposed in this paper has significant improvement in convergence speed, solution quality and computational efficiency. In addition, the proposed multi-timescale scheduling strategy effectively balances day-ahead planning and real-time adjustment, enhancing the flexibility and robustness of microgrid operation. This study provides new ideas for solving the real-time scheduling problem of large-scale distributed microgrids, which is of great significance for promoting the development of smart grid.