Optimal scheduling of independent microgrid based on multi-objective improved gray wolf algorithmaper

Microgrids contain a variety of micropower sources and need to meet many constraints, and the traditional gray wolf algorithm is easy to fall into local optimum, so this paper proposes a multi-objective improved gray wolf algorithm (MLGWO) for microgrid economic optimization scheduling. In order to solve the problems of precocious maturity and local convergence, the improved algorithm first uses the tent chaotic map to generate the initial population, and the Levy flight strategy increases the search space to enhance the diversity of followers. Secondly, In order to balance the global search and local development of the algorithm, a nonlinear adjustment is set for the convergence factor. Finally, the adaptive t-distribution variation is used to effectively jump out of the local optimal to improve the solution accuracy. Under the benchmark functions and the microgrid economic optimization scheduling model with the lowest daily operating cost, the improved gray wolf algorithm is compared with the gray wolf algorithm and the particle swarm optimization algorithm. The simulation results show that the improved gray wolf algorithm has certain feasibility and practicability.