A Two-Stage Scheduling Optimization Model and Corresponding Solving Algorithm for Power Grid Containing Wind Farm and Energy Storage System Considering Demand Response

Abstract

In allusion to the effects on system stability brought by wind power uncertainty, the energy storage system and demand response are led into the optimal dispatching of power grid containing wind farms. Firstly, the interval method is utilized to simulate the scene of wind farm and a Kantorovich distance based scene cut strategy is constructed; secondly, the demand response and energy storage system are led into the demand side and generation side respectively; thirdly, combining with two-stage optimization theory and taking the day-ahead predicted wind power and ultra-short term predicted wind power as random variable and its implementation a two-stage scheduling optimization model for wind farm and energy storage system, in which the demand response is taken into account, is constructed. To solve the constructed model, the chaos searching is led into traditional binary particle swarm optimization (PSO) algorithm to a construct chaotic binary PSO algorithm; finally, the simulation based on IEEE 36-bus 10-machine system, to which a wind farm with capacity of 650 MW is connected, is performed. Simulation results show that the global optimal solution can be obtained by chaotic binary PSO algorithm, thus this algorithm is suitable to solve the two-stage scheduling optimization model for wind farm and energy storage system; utilizing the synergetic effect of demand response with energy storage system the uncertainty of wind power can be suppressed and the wind energy utilization efficiency can be improved, meanwhile the coal consumption for grid power generation can be reduced, so the comprehensive benefits of the proposed strategy are obvious.