Research on multi-objective microgrid optimization scheduling based on improved dung beetle optimization algorithm

Traditional intelligent algorithms have many problems such as easily falling into a local optimum, slow convergence speed, and poor optimization accuracy in the multi-objective optimization dispatch of microgrid. Therefore, a novel IDBO algorithm based on chaotic mapping and adaptive t-distribution variation is proposed. Firstly, the weights of economic cost, environmental cost, and exchange power penalty cost are determined by the analytic hierarchy process, and the multiobjective problem is transformed into a single comprehensive goal for solution. Secondly, the Circle chaotic mapping is used to make the population initialization more thorough, the golden sine strategy is integrated into the rolling ball behavior to improve the optimization accuracy, and the adaptive t-distribution is introduced to improve the algorithm's global search ability and escape from the local optimum. The comparative analysis results show that the proposed improved dung beetle algorithm has fast convergence speed, high optimization accuracy, and can effectively reduce the operating cost of the microgrid. The comparative evaluation reveals that the newly proposed dung beetle algorithm enhancement features swift convergence rates, enhanced optimization accuracy, and significantly mitigates the operational costs associated with microgrid systems.