A chance-constrained programming based energy storage system sizing model considering uncertainty of wind power

The uncertainty and intermittence of wind power brings challenges to power systems, especially at high levels of penetration. In order to increase the acceptable grid-connected capacity for wind power, the output fluctuation is requested to be limited by improving forecast precision or using energy storage. This paper is focused on sizing battery energy storage system (BESS) for wind farm applications, so as to keep the differences between the combined wind/BESS output and the predefined profile within a required limit. Mathematical model for seeking the optimal size is developed based on chance-constrained programming. Genetic algorithm is used to solve the optimization problem, and Monte-Carlo method is applied to deal with the chance-constrained question.