Research on optimized scheduling of off-grid microgrids based on simulated annealing algorithm

Off-grid microgrids, characterized by significant dynamics and distinct constraints, pose challenges to scheduling practices. This paper advances a research approach to optimize the scheduling of off-grid microgrids based on simulated annealing algorithm(SA). Our study seeks to initially disorganize power source allocation across the microgrid to create a new solution for SA, then accordingly calculate and allocate loads using the greedy algorithm. The adoption of the new load allocation follows the principle of simulated annealing, eventually leading to the optimal solution. We implement scheduling experiments on test microgrids and compare our algorithm with conventional techniques such as linear programming and genetic algorithms. Comparison results demonstrate an effective decrease in total system costs with our algorithm, outperforming the classic methods on performance indicators such as system stability and cost efficiency. Lastly, system services with our algorithm prove to be close to those of state-of-the-art techniques, hence validating the potential advantages and applicability of the proposed method in optimizing off-grid microgrid scheduling.