Research on Energy Storage Locating and Sizing based on Improved Manta Ray Foraging Optimization Algorithm

Energy storage has the characteristics of strong flexibility and fast response, which can effectively alleviate the load fluctuation, voltage instability, harmonic pollution and other problems caused by the high proportion of new energy access. In this paper, a power system energy storage locating and sizing based on bi-level model and improved manta ray foraging optimization algorithm is proposed. First of all, a bi-level locating and sizing model is established to minimize energy storage investment cost, daily average voltage fluctuation and daily average load fluctuation. Secondly, the elite reverse learning strategy and adaptive tumbling factor are used to improve the manta ray foraging optimization algorithm, and the improved algorithm is used to solve the model. Finally, the proposed method is simulated by taking the IEEE39 node system with high proportion of new energy as an example. The results prove that the proposed method has faster operation speed and higher accuracy, and the locating and sizing selection scheme can effectively reduce the system investment cost and significantly reduce the system voltage and load fluctuation.