Energy Optimal Scheduling of New Energy Hydrogen Production Based on Improved Coyote Algorithm

In view of comparatively large fluctuation of off-grid wind-PV hybrid generation and the poor grid stability brought by using hydrogen energy for electric energy accommodation, based on establishing the frame model of typical off-grid microgrid system, an improved coyote optimization algorithm with high efficient searching performance was adopted, and taking minimum surplus and deficiency of microgrid power as objective function, the power of lithium iron phosphate battery pack and proton exchange membrane (abbr. PEM) electrolyzer were reasonably scheduled and optimized. In order to improve the deficiency of slow convergence speed and easy to fall into local optimum during solving the established model, a coyote growth method with variable dispersion probability and a globally influenced individual exchange method among populations were led in, so, while the convergence rate was increased the possibility of falling into local optimal solution was decreased. Results of calculating example show that the improved coyote algorithm possesses stronger optimization ability and its convergence rate is faster, thus, the energy scheduling optimization in microgrid can be effectively implemented and it has a good application prospect.