Distributed power grid integration contributes to both the reduction of greenhouse gas emissions and the protection of the environment. Nevertheless, the uncertainty and volatility associated with the production of clean renewable energy adds additional challenges to microgrid dispatch. The paper presents an adaptive mutant bird swarm algorithm and suggests a comparison mechanism based on population fitness variances and optimal values in order to overcome the shortcomings of BSA, in particular its tendency to self-correct into local optimum and slow convergence speed. First, the algorithm determines if the population is in the local optimal state. The local optimal individual is then subjected to Cauchy mutation in order to determine the optimal value again. This improves the accuracy and speed of the BSA. Based on simulation results, the improved algorithm has higher optimization accuracy and faster optimization speed, which demonstrates the effectiveness and advancement of the algorithm proposed in this research.
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