Optimal scheduling of islanded microgrid based on improved MOSSA

Microgrid systems are widely used in various scenarios. However, there is great uncertainty in the output of wind and solar units. How to maximize both the economic and environmental benefits of microgrid is an important issue. To achieve this, considering consumption of renewable energy and total cost, a multi-objective optimization model for islanded microgrid is established. The classical optimization algorithm has insufficient ability to approach the optimal solution in each dimension when facing multi-objective optimization. Thus, this paper introduces the fuzzy clustering mechanism and the non-dominated sorting method to obtain the improved multi-objective sparrow search algorithm (MOSSA). Taking data of renewable energy units and loads from a certain microgrid as test case, the Pareto frontier is searched by improved MOSSA. The experimental results show that the proposed method can effectively reduce the economic cost and improve the absorption capacity of wind power and photovoltaic energy.