Abstract:

In achieving China’s “dual carbon goals”， it is urgent to promote the transformation of the energy structure towards a distributed energy system dominated by renewable energy. However， its intermittent existence has led to the consumption problem of renewable energy electricity （referred to as “green electricity”）. With the rapid increase in the proportion of renewable energy， its consumption problem has become increasingly prominent. The configuration of energy storage stations in green electricity projects and the implementation of time-of-use electricity pricing policies are vital measures to solve the consumption problem of green electricity. However， the high cost of energy storage batteries has had a negative impact on the configuration of energy torage stations in green electricity projects. It can effectively alleviate the cost dilemma of energy storage by promoting the tiered utilization of power batteries （referred to as “old batteries”） in the energy storage field and implementing a two-part charging model for energy storage leasing.Therefore， wind power generation is used as an example， introducing the cascading utilization of power batteries and a two-part charging model. Regarding the use of new or old batteries and the choice of capacity rental or two-part charging， it designs four strategies： nR strategy （using new batteries， choosing capacity charging）， nT strategy （using new batteries， choosing two-part charging）， sR strategy （using old batteries， choosing capacity charging）， and sT strategy （using old batteries， choosing two-part charging）. At the same time， under the dual uncertainty of the intermittent wind resources and random fluctuations in peak electricity demand， considering the government’s constraints on the configuration of energy storage stations in wind electricity projects， it constructs leasing decision models of energy storage power stations for four strategies. In the construction of the decision model， it uses the intermittency factor u_i=[\mathrm{1},{\mathit{\rho }}_i;\mathrm{0,1}-{\mathit{\rho }}_i] to characterize the intermittency of wind power indicating that the probability of wind power projects generating electricity at rated power k is {\mathit{\rho }}_i in phase i， and the probability of not generating electricity is \mathrm{1}-{\rho }_i. It introduces a capacity retention rate \mathit{\tau } to measure the available capacity of power batteries. It sets the constraint ratio of government energy storage configuration as \mathit{\sigma }， so the capacity of energy storage stations that wind power projects should lease is \mathrm{m}\mathrm{a}\mathrm{x}\left(\mathit{\sigma }k,k_s\right)， where k_s is the leasing capacity of energy storage stations under profit maximization.The specific research steps are as follows　Firstly， using the decision model， the optimal leasing capacity decision is determined for four strategies of energy storage power stations. Secondly， it analyzes the impact of key parameters such as wind power intermittency， generation cost， electricity price， and capacity retention rate on the optimal leasing capacity. it also sort out the relevant properties and management insights. Finally， the advantages and disadvantages of the four strategies are compared from the perspectives of energy storage leasing capacity and maximizing profits for wind power enterprises.

Key words: capacity decision, lease strategy selection, energy storage, cascade utilization of power battery, dual carbon goal