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文章摘要
基于机会约束规划的广义电源多目标优化配置
Multi-objective Optimal Configuration of Generalized Power Sources Based on Chance Constrained Programming
Received:March 24, 2016  Revised:April 13, 2016
DOI:
中文关键词: 广义电源  机会约束规划 蒙特卡洛模拟  多目标优化配置 置信水平
英文关键词: generalized  power sources, chance  constrained programming, monte  carlo simulation, multi-objective  optimal Configuration, confidence  level
基金项目:国家自然科学基金(51177010)
Author NameAffiliationE-mail
ZHANG Hengwei* Lianyungang Electric Supply Company 1508040128@qq.com 
ZHANG Wang School of Electrical Engineering,Northeast Dianli University 1365441586@qq.com 
WANG Hongxing Lianyungang Electric Supply Company 1508040121@qq.com 
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中文摘要:
      针对配电网间歇性电源渗透水平逐渐增大、分布式电源和电容器组分开规划的现状,将能提供有功功率和无功功率的电源统称为广义电源。考虑风电、光伏出力的随机性,以投资效益、污染气体排放和反映系统供电可靠性的支路电压稳定裕度为优化目标,建立基于机会约束规划的配电网广义电源多目标配置模型,采用内嵌蒙特卡洛模拟的改进多目标粒子群算法对模型求解。IEEE-33节点系统的仿真结果表明,协调优化配置配电网有功无功资源,在提高资源利用率的同时也降低了污染气体排放量,并且从概率的角度对电压质量进行评估,辅助规划人员进行科学决策。
英文摘要:
      According to the current situation that the level of penetration of intermittent power sources is gradually increasing and the distributed generation(DG) and the capacitor group(CG) are separate planning in the distribution network, the power sources of active power and reactive power can be referred to as generalized power supply(GPS). Output randomness of wind power and photovoltaic power output are considered in this paper. Multi-objective optimal configuration model of generalized power sources is established in distribution system based on chance constrained programming, which takes investment benefit, pollution emissions and branch voltage stability margin as optimal targe. Improved multi-objective particle swarm optimization algorithm-Monte Carlo simulation solves this model. The simulation results of IEEE-33 bus system demonstrate that coordinately Optimizing active and reactive power resources of distribution network can not only improve resource utilization but also reduce pollution emissions and evaluating voltage quality from the viewpoint of probability assists planners in making scientific decisions.
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