Double-layer optimal microgrid dispatching with price response using multi-point improved gray wolf intelligent algorithm

Optimal dispatch in power systems is a complex mathematical model of nonlinear programming with many physical constraints, which is difficult to solve by conventional methods. Thus, intelligent algorithms are now viable options for resolving the nonlinear scheduling issues of microgrids. In this paper, we propose a double-layer optimization strategy based on the multi-point improved gray wolf algorithm (MPIGWO). The inner layer optimizes load profiles with time-of-use tariffs. The outer one achieves a fast search for the optimal solution and prevents getting stuck in a local optimum, which improves the gray wolf algorithm significantly. First, the Bernoulli map is used to randomly generate the initial population. Second, the efficiency of optimization can be improved by modifying the attenuation factor based on the Sin function and then updating the exact weight factor of the position to reasonably select the best position. Finally, an improved dimensional learning-based hunting (IDLH) search strategy is employed to determine the optimal solution. The numerical case study shows that the proposed double-layer optimization strategy can implement dynamic scheduling for distributed power sources while lowering the costs of economic operation and environmental protection significantly.