Optimal Scheduling of Microgrids Based on a Two-Population Cooperative Search Mechanism

Aiming at the problems of high-dimensional nonlinear constraints, multi-objective conflicts, and low solution efficiency in microgrid optimal scheduling, this paper proposes a multi-objective Harris Hawk-Grey Wolf hybrid intelligent algorithm (IMOHHOGWO). The problem of balancing the global exploration and local exploitation of the algorithm is solved by introducing an adaptive energy factor and a nonlinear convergence factor; in terms of the algorithm's exploration scope, the stochastic raid strategy of Harris Hawk optimization (HHO) is used to generate diversified solutions to expand the search scope, and constraints such as the energy storage SOC and DG outflow are finely tuned through the α/β/δ wolf bootstrapping of the Grey Wolf Optimizer (GWO). It is combined with a simulated annealing perturbation strategy to enhance the adaptability of complex constraints and local search accuracy, at the same time considering various constraints such as power generation, energy storage, power sales, and power purchase. We establish the microgrid multi-objective operation cost and carbon emission cost objective function, and through the simulation examples, we verify and determine that the IMOHHOGWO hybrid intelligent algorithm is better than the other three algorithms in terms of both convergence speed and convergence accuracy. According to the results of the multi-objective test function analysis, its performance is superior to the other four algorithms. The IMOHHOGWO hybrid intelligent algorithm reduces the grid operation cost and carbon emissions in the microgrid optimal scheduling model and is more suitable for the microgrid multi-objective model, which provides a feasible reference for future integrated microgrid optimal scheduling.