考虑风光荷时序波动特性的配电网投资策略多目标优化

Multi-objective optimization of distribution network investment strategy considering the time series fluctuation of wind power generation, photovoltaic power generation and power load

风电、光伏等分布式电源出力和用电负荷具有明显的时序特性,常用的恒定功率模型无法准确体现这一特点。文章构建了风力、光伏发电出力概率模型,应用蒙特卡洛方法对其发电出力和常规用电负荷进行模拟仿真,生成大量场景;采用改进K中心点聚类算法对仿真场景进行缩减,构建典型风光荷联合时序场景集;从各典型场景发生概率出发,以光伏、风力电站投资回报率最大和配电网各节点电压偏差最小为目标,采用改进多目标遗传算法求解光伏、风力电站最优安装位置和容量。最后,以IEEE 33配电系统进行算例分析,验证了所提方法的有效性。

The output of distributed power sources such as wind power and photovoltaic and the size of different types of conventional loads have obvious timing characteristics, which can not be accurately reflected by the commonly used constant power model. Build the probability model of wind power and photovoltaic power generation output, and use Monte Carlo method to simulate its power generation output and conventional load to generate a large number of scenarios;The improved k-medoids clustering algorithm is used to reduce the simulation scene and construct the typical scenery load time series scene set. Based on the occurrence probability of each typical scenario, aiming at maximizing the return on investment of photovoltaic and wind power plants and minimizing the voltage deviation of each node of distribution network, the improved multi-objective genetic algorithm is used to solve the optimal installation location and capacity of photovoltaic and wind power stations. Finally, an example of IEEE 33 distribution system is analyzed to verify the feasibility of the proposed algorithm.