Optimal scheduling for user-side energy storage considering source and load uncertainty

To enhance the economic viability of energy storage applications for users, this paper explores the application of energy storage technology in peak-valley arbitrage and demand management in relation to the tariff structure of large industrial users and the challenge of high demand tariffs. A scheduling model is proposed to address the uncertainty in source-load data. The probability distribution of output, taking source-load correlation into account, is derived using kernel density estimation and copula method. Monte Carlo sampling is utilized to generate uncertainty scenarios, and typical scenarios are identified through clustering algorithms to establish a dispatch model for user-side energy storage economics considering source-load uncertainty. Through the application and analysis of real-world cases, the model can effectively formulate practical energy storage operation strategies, maximize economic benefits, and ensure stable system operation.