To solve the problem of reasonable allocation and storage of renewable energy on the user-side, a method for reducing energy storage application scenarios and an optimization configuration method for energy storage for large industrial users considering the dynamic lifespan of energy storage have been proposed. Using K-means clustering algorithm to reduce the scenario of renewable energy output and load data that have lasted for one year. The objective function is to minimize the sum of the energy storage equipment investment and operation maintenance, and annual transaction costs, taking into account the dynamic lifespan of energy storage. Under constraints such as system power balance and energy storage operation requirements, the optimal configuration of energy storage can be solved under scenario reduction. Simulations were conducted on three types of large industrial users: bimodal users, unimodal users, and stationary users. The results showed that the proposed renewable energy allocation and storage decision-making method was effective, and the economy of stationary users was optimal. The current policy allocation and storage ratio has little impact on the total annual cost of electricity consumption for the same type of large industrial users. Sensitivity analysis was conducted on the factors affecting the total electricity cost, and some conclusions were drawn that are beneficial for user side renewable energy distribution and storage.