考虑新能源不确定出力的有功无功协调降损控制策略研究

Research on Active and Reactive Power Coordination Loss Reduction Control Strategy Considering the Uncertain Output of New Energy

在高比例新能源接入电力系统的背景下,电网安全、经济的运行面临着巨大挑战。该文考虑风光出力的不确定性,建立有功无功协调优化的多目标优化模型。运用拉丁超立方抽样(latin hypercube sampling,LHS)场景生成法和Kantorovich距离的场景缩减技术,得到典型日的风电和光伏出力;建立以系统运行有功网损和电压偏移最小的多目标综合优化调度模型,采用传统的鲸鱼优化算法(whale optimization algorithm,WOA)与粒子群优化(particle swarm optimization,PSO)相结合的鲸鱼群(WOA-PSO)算法求解该模型,得到Pareto解的分布;根据优化结果制定无功补偿装置的投切功率以及储能系统充放电策略,达到提升系统电压稳定性与平缓新能源出力波动的效果,减少功率在线路上的损耗,实现电网的安全、节能运行;在改进IEEE30节点系统上进行实验分析,验证所提模型与求解算法的有效性与合理性。

Under the background of high proportion of new energy connected to the power system,the safety and economic operation of the power grid is facing huge challenges.This paper establishes a multi-objective optimization model for active and reactive power coordination optimization considering the uncertainty of wind and solar output.First,the typical day wind and photovoltaic outputs are obtained using the Latin hypercube sampling scene generation method and the Kantorovich distance scene reduction technique.On this basis,a multi-objective comprehensive optimization scheduling model is established with the minimum active network loss and voltage offset in the system operation,a whale particle swarm optimization model solving algorithm is proposed that combines the whale optimization algorithm(WOA)and particle swarm optimization(PSO).The whale particle swarm optimization algorithm is used to solve the model,and the distribution of Pareto solutions is obtained.According to the optimization results,the switching power of the reactive power compensation device and the charging and discharging strategy of the energy storage system are formulated to improve the system voltage stability and smooth the fluctuation of the new energy output,reduce the power loss on the line,and realize the safe and energy-saving operation of the power grid.Finally,the verification analysis is carried out on the improved IEEE30 node system,which proves the validity and rationality of the proposed model and solution algorithm.