Energy storage technologies have been rapidly evolving in recent years. Energy storage plays different roles in various scenarios. For electricity consumers, they are concerned with how to use the energy storage system (ESS) to reduce their costs of electricity or increase their profits. In this paper, a stochastic optimization method for energy storage sizing based on an expected value model for consumers with Photovoltaic Generation (PV) is proposed. Firstly, the Gaussian mixture model clustering method is used to cluster the historical load and PV data and calculate the probability of each cluster. Secondly, the optimal model of total system profit is established. Finally, according to the expected value model, the optimal ESS power and capacity are determined. Two case studies are used to demonstrate the calculation of optimal ESS capacity. The results obtained by the method proposed in this paper are compared with the results produced by the deterministic method. Through the analysis and comparison, the validity and superiority of the method proposed in this paper are verified. The profits obtained by the method proposed in this paper are 0.87% to 127.16% more than the deterministic method.

The energy storage system of PV power plant can improve the accommodating ability of photovoltaic (PV) generation. The coordinated control of hybrid energy storage system (HESS) becomes one of the urgent problems to be solved. This study proposes a coordinated control strategy of HESS with complementary features to improve the accommodating ability of PV. In the 1 min time scale, a time window selection method is proposed to control the super conductor to smooth the short‐term fluctuation of the PV. In the 1 h time scale, the lithium‐ion battery output model maximising the system economic benefit is established, and the particle swarm optimisation algorithm is used to solve the model. Based on the result of 1 h time scale, the objective function of reducing the fluctuation of electric power consumption is established and solved on the 15 min time scale. The effectiveness of the proposed control strategy is verified by simulation. The coordinated operation of HESS improves the utilisation rate of the PV power generation.