摘要
风电输出具备明显随机性且呈现较大波动的状态,这会增加负荷预测的难度。为了解决传统粒子群(PSO)算法风电出力鲁棒调度较低的问题,设计了一种基于遗传算法(GA)优化传统粒子群(GA-PSO)算法的风电高渗透率电网优化调度方法。研究结果表明改进GA-PSO算法保留了标准粒子群快速收敛的特点,避免产生局部最优问题,获得更精确计算结果,充分满足了实际电网调度需求,确保电网具有安全稳定的运行控制性能。逐渐增加风电场数量后,形成了更广泛的风电场分布区域,显著增加了空间不确定性,降低了风电出力不确定性,获得更小系统风险成本。该方法具备PSO快速收敛优势,消除了粒子存在局部最优的缺陷。
The wind power output has obvious randomness and presents large fluctuation,which increases the difficulty of load forecasting.In order to solve the problem that the traditional particle swarm optimization(PSO)algorithm has low robust scheduling of wind power output,an optimal scheduling method of wind power grid with high permeability based on genetic algorithm(GA)optimized PSO algorithm(GA-PSO)is designed.The results show that the optimized GA-PSO algorithm retains the fast convergence of the standard particle swarm,avoids the local optimal problem,obtains more accurate calculation results,fully meets the actual power grid scheduling requirements,and ensures that the power grid achieves safe and stable operation control performance.After gradually increasing the number of wind farms,a wider distribution area of wind farms is formed,which significantly increases the spatial uncertainty,reduces the uncertainty of wind power output,and achieves a smaller system risk cost.This method has the advantage of rapid convergence of PSO and eliminates the defect of local optimization.
作者
杨易
李峰
YANG Yi;LI Feng(State Grid Liaoyang Power Supply Company,Liaoyang 111000,China;School of Electrical Engineering,Dalian University of Technology,Dalian 116081,China)
出处
《微型电脑应用》
2025年第2期281-283,287,共4页
Microcomputer Applications
关键词
电网
风电
优化调度
粒子群算法
遗传算法
鲁棒性
风险成本
power grid
wind power
optimal scheduling
particle swarm optimization
genetic algorithm
robustness
risk cost
