Energy coordinative control of microgrid based on receding horizon optimization with EACOP

According to the topological structure of wind-storage-load complementation microgrids, this paper proposes an energy coordinative optimization method based on the prediction framework to achieve the coordination control of energy allocation and improve the economic benefits of microgrids. In the first place, according to the requirements of the actual constraints, the external characteristic mathematical model of distributed generation (DG) units including wind turbines and storage batteries are established. Meanwhile, the minimum consumption cost from the external grid is set as the objective function, considering the real-time price, wind turbine power and load state construction. Moreover, based on the basic framework of receding horizon optimization, an evolutionary algorithm for complex-process optimization (EACOP) which has a flexible framework structure is applied. This evolutionary algorithm is to figure out the optimum solution in the predictive horizon for the complex non-linear coordination control model of microgrids. The obtained results show that the energy coordinative optimization method takes full advantage of the receding horizon optimization and EACOP to satisfy the requirements of energy coordinative optimization of microgrid systems. The effectiveness and feasibility of the proposed method are verified by examples.