Optimize the energy storage system with an artificial intelligence-based control strategy

Abstract

Abstract Currently, energy storage systems adopt control strategies based on the crossover approach despite their limited generalization performance. To improve the control effect of the control strategy, the limitation of the SOC of the energy storage element is added. This adds smoothness to the system’s output power, enabling the energy storage element to distribute the power reasonably. Meanwhile, we propose and introduce a DPI control strategy based on PSO optimization into the control strategy of the second-order low-pass filtering method, thereby further enhancing and optimizing the original control strategy. Taking the investment cost of the energy storage system and the stable operation of the system as the objective functions, the constraint function is determined according to the parameters, the PSO algorithm is used to construct a two-layer optimization model of the energy storage system, and the FCEM is introduced to determine the objective weights. Based on the constructed model, an arithmetic example analysis of the energy storage system is carried out using artificial intelligence. Under the steady-state analysis of Buck mode, the low voltage side voltage adjustment time based on the PID control strategy is about 0.108s, and the low voltage side voltage adjustment time based on PSOPID is 0.032s, which is reduced by 0.070s compared with the PID control strategy. Also, when optimizing a system that generates electricity from renewable sources on its own, the two goals of minimizing the investment in the energy storage system and making sure it runs smoothly are met when the system’s fixed power is equal to 6.58 MW and its capacity is less than 267 MWh.