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上海电力大学电气工程学院,上海市 200090
李振坤(1982—),男,通信作者,博士,教授,硕士生导师,主要研究方向:配电网规划及运行控制、分布式电源并网及微电网、主动配电网技术等。E-mail:lzk021@163.com
钱晋(1996—),男,硕士研究生,主要研究方向:电动汽车并网和配电网阻塞管理。E-mail:457268674@qq.com
符杨(1968—),男,博士,教授,博士生导师,主要研究方向:风力发电和电力系统规划。E-mail:mfudong@126.com
收稿日期:2021-01-14,
修回日期:2021-04-23,
网络出版日期:2021-07-02,
纸质出版日期:2021-10-10
移动端阅览
李振坤,钱晋,符杨等.基于负荷聚合商优选分级的配电网多重阻塞管理[J].电力系统自动化,2021,45(19):109-116.
LI Zhenkun,QIAN Jin,FU Yang,et al.Multiple Congestion Management for Distribution Network Based on Optimization Classification of Load Aggregators[J].Automation of Electric Power Systems,2021,45(19):109-116.
李振坤,钱晋,符杨等.基于负荷聚合商优选分级的配电网多重阻塞管理[J].电力系统自动化,2021,45(19):109-116. DOI: 10.7500/AEPS20210114006.
LI Zhenkun,QIAN Jin,FU Yang,et al.Multiple Congestion Management for Distribution Network Based on Optimization Classification of Load Aggregators[J].Automation of Electric Power Systems,2021,45(19):109-116. DOI: 10.7500/AEPS20210114006.
通过负荷聚合商(LA)充分挖掘负荷侧可控资源是实现配电网阻塞管理的重要手段。首先,针对LA在规模、可靠性、阻塞改善效果等多个方面存在的较大差异的问题,文中提出了基于LA优选分级的配电网多重阻塞管理方法。然后,定义了阻塞管理有效性、贡献度和违约率3个指标衡量LA阻塞管理可靠度水平,并通过灰色定权聚类对其进行优先级划分。为了计及分布式电源出力不确定性,建立了配电网阻塞管理机会约束规划模型,模型兼顾阻塞管理可靠性和经济性,并用随机模拟粒子群算法进行了求解。最后,通过算例仿真验证了所提方法的有效性。
Fully exploiting the load-side controllable resources through the load aggregators (LAs) is an important way to realize the congestion management in the distribution network. Firstly
in response to the problem of huge differences of LAs in several aspects
such as scale
reliability and congestion improvement effects
this paper proposes a multiple congestion management method for distribution networks based on optimization classification for LAs. Then
three indicators are defined to measure the reliability level of LA congestion management
such as congestion management efficiency
contribution and default rate
which are prioritized by the grey fixed weight clustering. In order to take into account the uncertainty of distributed power supply
an opportunity constraint planning model of congestion management in the distribution network is established. The model is solved by a stochastic simulation particle swarm algorithm
taking into account the reliability and economy of congestion management. Finally
the effectiveness of the proposed method is verified by case simulation.
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