基于改进粒子群算法的配电网多目标优化控制
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引用本文:曹锦1,陆飞2,江友华1.基于改进粒子群算法的配电网多目标优化控制[J].电网与清洁能源,2022,38(5):95~103
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作者单位
曹锦1 1. 上海电力大学 电子与信息工程学院 
陆飞2 2. 上海电力大学 电气工程学院 
江友华1 1. 上海电力大学 电子与信息工程学院 
基金项目:上海市自然科学基金(21ZR1424800)
中文摘要:随着光伏机组大量接入配电网,需要在增加间歇性可再生能源使用的同时,保持电力系统的电压稳定。储能技术的快速发展允许部署储能系统来支持电压调节。为了在光伏储能优化出力系统中达到网络损耗和调压措施成双优化的目的,提出了一种改进的Pareto档案粒子群多目标优化算法。在非支配排序环节计算拥挤距离时,加入小生境技术,避免陷入局部最优同时,增加Pareto解集分布的多样性。基于IEEE 30配电网系统测试了所提出的PV-ESS(photovoltaic-energy-storage-system)优化方法。结果证明,该算法对抑制光波动、提高电压稳定性以及降低网损有着良好的表现,进而维护系统运行的稳定性,降低电力行业经济成本。
中文关键词:储能系统  微电网  多目标优化  粒子群算法  Pareto优化
 
Multi-Objective Optimal Control of Distribution Networks Based on Improved Particle Swarm Algorithm
Abstract:As a large number of photovoltaic units are connected to the distribution network, it is necessary to maintain voltage stability of the power system while increasing the use of intermittent renewable energy. Rapid advances in energy storage technology allow the deployment of energy storage systems to support voltage regulation. To achieve the dual optimization of network losses and the cost of voltage regulation measures in a PV energy storage optimized power system, an improved Pareto archival particle swarm multi-objective optimization algorithm is proposed in this paper. The Niche technique is added to the calculation of the congestion distance in the non-dominated ranking link to avoid falling into a local optimum while increasing the diversity of the Pareto solution set distribution. The proposed PV-ESS (photovoltaic energy storage system) optimization method is tested based on the IEEE 30 distribution grid system. The results demonstrate that the algorithm performs well in suppressing light fluctuations, improving voltage stability and reducing network losses, which in turn maintains the stability of system operation and reduces the cost of voltage regulation measures and power industry costs.
keywords:energy storage system  microgrid  multi-objective optimization  particle swarm algorithm  Pareto optimization
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