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文章摘要
计及分布式电源接入的配电网重构
Network reconfiguration in distribution networks considering distributed generations
Received:February 27, 2018  Revised:February 27, 2018
DOI:
中文关键词: 分布式电源  配电网重构  支路潮流模型  凸优化  混合整数二阶锥
英文关键词: distributed generation  network reconfiguration  active distribution network  distflow equations  convex optimization  mixed integer second-order cone programming
基金项目:国家自然科学基金(51667007);中国南方电网公司重点科技项目(GZKJQQ00000417)
Author NameAffiliationE-mail
Xu Yutao* Electric Power Research Institute of Guizhou Power Grid Co Ltd 95616048@qq.com 
Tan Zhukui Electric Power Research Institute of Guizhou Power Grid Co Ltd tanzk@163.com 
Yuan Xufeng Electric Power Research Institute of Guizhou Power Grid Co Ltd ee.xfyuan@foxmail.com 
Xie Baiming Electric Power Research Institute of Guizhou Power Grid Co Ltd 13908504591@139.com 
Chen Yufeng Beijing Sifang Automation Co,Ltd,Beijing chenyufeng_zw@sf-auto.com 
Wu Heng Beijing Sifang Automation Co,Ltd,Beijing wuheng@sf-auto.com 
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中文摘要:
      分布式电源的接入使得配电网重构需要考虑更多的安全因素。基于配电网支路潮流模型,建立以重构周期内网络有功损最低,以满足分布式电源接入下网络运行安全为约束的配电网重构。为有效求解该重构数学模型,利用凸松弛方法将原问题中二次项进行松弛,使之形成混合整数二阶锥规划形式,从而可利用YALMIP商业软件进行高效求解。最后,通过改进的IEEE 33节点测试系统进行仿真分析,并与现有基于粒子群算法及启发式算法的配电网重构方法进行对比,结果表明基于凸优化技术的重构结果不仅能够有效避免算法陷于局部最优,而且稳定性好、计算效率高。
英文摘要:
      The injections of distributed generation make distribution network reconfiguration need to consider more security. In this paper, an network reconfiguration with the lowest active power loss in the reconstruction period and the network operation safety as the constraint is established in distribution networks based on the distflow equations. In order to solve the proposed mathematical model effectively, the convex relaxation method is used to relax the quadratic terms into mixed integer second-order cone programming form, and then the proposed mathematical model of network reconfiguration could be solved using YALMIP effectively. Finally, the feasibility and effectiveness of the proposed network reconfiguration is verified via the modified IEEE 33-bus test system, and the simulation results show that the proposed reconfiguration based on convex optimization is better in global optimization ability, algorithm stability and computation efficiency than that based on particle swarm optimization (PSO) and other heuristic algorithms.
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