A two-stage scheduling optimization model and solution algorithm for wind power and energy storage system considering uncertainty and demand response

Abstract

To reduce the influence of wind power output uncertainty on power system stable operation, demand response (DRPs) and energy storage system (ESSs) are introduced while solving scheduling optimization problems of system with wind power. To simulate wind power scenarios, this paper used interval method to generate the initial scenario set, and construct scenario reduction strategy based on Kantorovich distance. Then, DRPs and ESSs are respectively introduced in the demand-side and generation side, taking wind power day-ahead forecasting and ultra-short-term forecasting as a random variable and its implementation, a two-stage scheduling optimization model for wind energy storage systems is construct combined with two stage optimization theory. To solve the proposed model, the ergodic of chaos search is applied to improve the inadequate that binary particle swarm algorithm may fall into local optimum, chaotic binary particle swarm optimization algorithm is proposed. Finally, example simulation is made in the IEEE36 node 10 machine systems to analyze the influence of energy storage system and demand response on system’s wind power consumptive capacity. The result shows chaotic binary particle swarm algorithm can get a global optimal solution, applicable to solve wind power energy storage systems two-stage model. The synergies of DRPs and ESSs can be used to suppress wind power uncertainty, improve the utilization efficiency of wind power, and reduce coal consumption level with significantly overall efficiency.