Optimal scheduling of microgrids based on improved dung beetle optimization algorithm

In order to achieve the economic and environmental protection operation of microgrids, and at the same time to solve the problem that the dung beetle optimisation algorithm is prone to falling into local optimal solutions, an improved dung beetle optimisation algorithm is proposed. Firstly, the lens imaging reverse learning strategy and the spiral search strategy in the whale optimisation algorithm are used to expand the search range of the algorithm and increase the diversity of individuals in order to improve the stability of the algorithm and the quality of the solution. Second, the optimal value guidance strategy is used to improve the search efficiency of the algorithm, accelerate the convergence speed, and further improve the algorithm's optimisation accuracy. Finally, the improved algorithm is applied to the optimal scheduling model in microgrid grid-connected mode for simulation experiments. The simulation results show that the improved dung beetle optimisation algorithm can effectively reduce the total daily operating cost of the microgrid and improve the stability of the system operation compared with the original algorithm.