A Coordinated Peak Shaving Strategy Using Neural Network for Discretely Adjustable Energy-Intensive Load and Battery Energy Storage

Abstract

The large-scale wind power introduces the challenge of the power demand and generation balancing. Energy-intensive load (EIL) is a promising option for peak shaving since it can change its production time and power demand without affecting its overall production. However, EIL which is discretely adjustable is unable to track the net load in real time. A two-stage complementary peak shaving strategy of EILs with the aid of battery energy storage systems (BESSs) is proposed to address this issue. This paper establishes an optimization model with the minimum system operation costs and wind curtailment costs as the objective function, in which EIL operation constraints and BESS power and energy balance constraints are added to the unit commitment model. And the neural network algorithm is used to solve this optimization problem. Finally, a system with a high proportion of wind power is adopted to analyze the functions of EIL and BESS in the method. It is verified that the proposed strategy can effectively reduce the amount of wind curtailment and the operation costs of the system.