Research on Microgrid Power Dispatch Optimization Based on an Improved Parrot Optimization Algorithm in Cloud Environments

To address the issues of high operational costs and load factors in microgrids under current renewable energy conditions, this study proposes a grid scheduling strategy based on a parrot optimization algorithm incorporating chaotic and adaptive weighting. First, a scheduling model based on power operation costs and load rates in cloud-based microgrids is constructed. Second, logistic chaos is employed during the initialization of the parameter optimization algorithm to increase population diversity, whereas an adaptive weight adjustment strategy balances global and local exploration capabilities. Finally, simulation experiments validate the algorithm’s performance. Compared with the ACO, PSO, and PO algorithms, it reduces costs and power load factors by 63.4%, 45.7%, 8.3%, and 6%, respectively, in scenarios with small numbers of users and by 37.4%, 34.4%, 23.6%, and 6%, respectively, in scenarios with large numbers of users. 23.6%, 9.51%, 9.51%, and 1.18%, respectively. This demonstrates its ability to effectively reduce operational costs and lower power load rates, indicating significant practical value.