基于混合整数凸规划的含风力发电机组<br/>配电网无功补偿优化配置

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摘要提出一种基于混合整数凸规划的配电网无功补偿电容优化配置新算法。利用风力发电机组发电功率的多状态离散概率模型,以安装补偿电容所带来的经济效益最大为目标函数,考虑二阶锥松弛后的潮流约束方程和节点电压约束,对含风力发电机组的配电网中补偿电容器的安装位置和容量进行优化配置。基于二阶锥的凸多面体近似等价方法,将配电网无功优化问题描述为混合整数线性规划问题,利用更通用的求解器求得该问题的最优解。在含风电分布式电源配电网中分别对所提方法进行验证,仿真结果表明,该算法计算效率高,且能得到计及风电随机波动影响的最优方案。

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	李静
	戴文战
	韦巍

关键词 ：配电网, 最优潮流, 二阶锥规划, 无功优化

Abstract：A novel mixed integer convex programming for optimal location and sizing of capacitors in the distribution network with wind turbines is proposed in this paper.Considering the intermittent nature of wind power,the power generated by the wind turbines is represented by the multi-states discrete probability model.The location and size of the compensating capacitors in the distribution network containing wind turbines is aimed to maximize the economic benefits considering the conic relaxed power flow constraints and the voltage limit constraints.In addition,based on the tight polyhedral representation of the conic constraints,it is suitable to transfer the problem into a mixed integer linear programming problem and use more widely available software to find the optimal solution.Numerical results on the test networks including distributed wind turbines show that the proposed mixed-integer convex optimization is an effective tool for the size and location of capacitors considering the stochastic turbulence of wind powers.

Key words： Distribution electrical network optimal power flow conic programming reactive power optimization

收稿日期: 2015-03-14 出版日期: 2016-02-25

PACS:

TM315

基金资助:国家高技术研究发展(863)计划(2014AA052001)、国家自然科学基金(61374022,51377142)、浙江省自然科学基金(LQ15F030001)、浙江省海洋可再生能源电气装备与系统技术研究重点实验室开放基金资助项目。

作者简介: 李静女,1983年生,博士,讲师,研究方向为智能计算方法、电力系统规划与可靠性。E-mail:eejing@zjgsu.edu.cn

引用本文:

李静,戴文战,韦巍. 基于混合整数凸规划的含风力发电机组
配电网无功补偿优化配置[J]. 电工技术学报, 2016, 31(3): 121-129. Li Jing, Dai Wenzhan ,Wei Wei. A Mixed Integer Convex Programming for Optimal Reactive Power Compensation in Distribution System with Wind Turbines. Transactions of China Electrotechnical Society, 2016, 31(3): 121-129.

链接本文:

https://dgjsxb.ces-transaction.com/CN/Y2016/V31/I3/121

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