Day-Ahead Operation Optimization Model of User-Side Cloud Energy Storage Considering Wind Power and Photovoltaic Uncertainty

Abstract

With the development and deepening of energy Internet and new energy applications, cloud energy storage (CES) gradually shows its good applicability and economy, which provides an effective way to solve the increasingly prominent intermittent and fluctuant problems of user-side wind turbines due to the deepening of renewable resource permeability. In this context, this paper proposes a user-side day-ahead economic scheduling model of CES considering wind power uncertainty. First, according to the characteristics of CES, the operation model containing CES on the user side is established to analyze the energy conversion relationship between it and physical energy storage on the operator-side. Then, a two-stage robust optimization (RO) model of min-max-min structure was constructed based on the RO method to characterize the renewable resource uncertainty, and the adjustable parameters were introduced to ensure the economy of the model. Finally, the strong duality theory and the column and constraint generation algorithm are combined to solve the above model. The example results verify that the user-side CES resources can be more effectively utilized and the user energy cost can be significantly reduced by characterizing the wind-power output uncertainty.