Research on Microgrid Scheduling Based on Improved Crow Search Algorithm

Abstract

Considering the characteristics of distributed power in microgrid, in order to maximize the advantages of distributed power generation technology in economy, environment, and energy, a multiobjective dispatching model of microgrid is proposed under the condition of satisfying system constraints and considering the operating costs and environmental costs of microgrid. The crow search algorithm (CSA) has the advantages of less parameter setting, simple implementation, and strong optimization ability and is often used in theoretical analysis and practical engineering applications. However, its disadvantage is that crows only search for candidate solutions according to their own experience, and their development ability is poor, especially for solving high-dimensional functions. In order to overcome these shortcomings, an improved crow search algorithm (CSA-PSO) is proposed based on the particle swarm optimization (PSO) algorithm. The two main improvements are as follows: (1) in order to avoid the blind selection of crow in search, the global optimal solution is adopted to modify the solution search equation to guide the search of new candidate solutions, so as to improve the development ability; (2) introducing a levy flight strategy to improve the single search mechanism of the CSA. In order to verify the performance of the CSA-PSO algorithm, 17 benchmark functions are simulated with other intelligent algorithms. The results show that the CSA-PSO algorithm has a good optimization effect in search accuracy, convergence speed, and robustness. Finally, this algorithm and other five algorithms are applied to the optimal scheduling problem of microgrid. By solving the objective function, the optimal scheduling output scheme of each distributed power supply is obtained. By using the CSA-PSO algorithm, the total operating cost of microgrid is reduced by at least 21.5%, which further verifies the effectiveness of the CSA-PSO algorithm.