考虑电动汽车不确定性因素的配电网分布式电源优化布置

王满商; 李正明; 汪洋

引用本文:	王满商,李正明,汪洋.考虑电动汽车不确定性因素的配电网分布式电源优化布置[J].电力系统保护与控制,2019,47(1):67-72.
	WANG Manshang,LI Zhengming,WANG Yang.Distribution network distributed power supply configuration considering the uncertainties of electric vehicle[J].Power System Protection and Control,2019,47(1):67-72

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考虑电动汽车不确定性因素的配电网分布式电源优化布置
王满商^1,2,李正明¹,汪洋¹
(1.江苏大学电气信息工程学院，江苏镇江 212013;2.国网镇江供电公司，江苏镇江 212001)

摘要:

分布式电源和电动汽车的规模化接入对配电网经济运行和电能质量产生了较大影响。分布式电源和电动汽车的功率具有不确定性。为实现分布式电源的合理配置，提出了一种考虑电动汽车不确定性因素的分布式电源优化布置方法。首先，以网络损耗最小、电压偏移最小和系统稳定性高为优化目标，利用机会约束规划方法建立分布式电源优化配置模型。然后，采用支持向量机算法和多目标粒子群算法对上述模型进行求解，得到其Pareto解集。以IEEE 37节点配电网为例对所提模型进行验证，结果表明该模型可以有效得到合理的配置方案。

关键词: 分布式电源电动汽车不确定性多目标规划支持向量机多目标粒子群

DOI：10.7667/PSPC171808

分类号:

基金项目:国家自然科学基金项目资助(51477070)

Distribution network distributed power supply configuration considering the uncertainties of electric vehicle

WANG Manshang^1,2,LI Zhengming¹,WANG Yang¹

(1. School of Electrical Information and Engineering, Jiangsu University, Zhenjiang 212013, China;2. State Grid Zhenjiang Power Supply Company, Zhenjiang 212001, China)

Abstract:

The large-scale access of distributed generation and electric vehicles has a great impact on the economic operation and power quality of distribution network. Distributed power and electric vehicle power have uncertainties. In order to realize the rational distribution of distributed power, this paper presents a distributed power optimization layout method considering the uncertainties of electric vehicles. First of all, the optimal model of distributed generation is established by using the opportunistic constrained programming method with the least network loss, the minimum voltage offset and the high stability of the system. The SVM algorithm and the multi-objective particle swarm optimization algorithm are used to solve the above model, Pareto solution set is obtained. Finally, the model proposed in this paper is validated by IEEE37 node distribution network. The results show that the model can effectively get a reasonable configuration scheme. This work is supported by National Nature Science Foundation of China (No. 51477070).

Key words: distributed power supply electric vehicle uncertainty multi-objective programming support vector machine multi-objective particle swarm

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