计及负荷和调配时间不确定性的防灾应急电源优化配置
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  • 英文篇名:Optimal Allocation of Emergency Power Sources Considering Uncertainties of Loads and Dispatching Time Periods
  • 作者:王志奎 ; 孙磊 ; 林振智 ; 文福拴 ; 易仕敏 ; 张宏
  • 英文作者:WANG Zhikui;SUN Lei;LIN Zhenzhi;WEN Fushuan;YI Shimin;ZHANG Hong;College of Electrical Engineering,Zhejiang University;Guangdong Power Grid Co.Ltd.;State Grid Hunan Electric Power Corporation Maintenance Company;
  • 关键词:防灾应急电源 ; 优化配置 ; 不确定性 ; 区间数 ; 鲁棒优化
  • 英文关键词:emergency power source;;optimal allocation;;uncertainty;;interval value;;robust optimization
  • 中文刊名:DLXT
  • 英文刊名:Automation of Electric Power Systems
  • 机构:浙江大学电气工程学院;广东电网有限责任公司;国网湖南省电力有限公司检修公司;
  • 出版日期:2017-12-12 15:36
  • 出版单位:电力系统自动化
  • 年:2018
  • 期:v.42;No.624
  • 基金:国家自然科学基金资助项目(51377005);; 国家重点研发计划资助项目(2016YFB0900105);; 浙江省自然科学基金资助项目(LY17E070003)~~
  • 语种:中文;
  • 页:DLXT201802005
  • 页数:8
  • CN:02
  • ISSN:32-1180/TP
  • 分类号:40-47
摘要
为最大限度地降低自然灾害可能导致的停电损失,合理配置防灾应急电源至关重要。现有的关于防灾应急电源的研究大多侧重于优化调度城市区域中已有的防灾应急电源,而应该如何对防灾应急电源进行合理配置则研究较少,且没有系统考虑用户负荷及因交通拥堵、道路破坏等引起的应急电源调配时间不确定性的影响。在此背景下,建立了计及负荷和调配时间不确定性的防灾应急电源优化配置模型,并以最大化城市区域的社会效益为优化目标。采用径向区间数的形式(即区间数的两端设定相应的波动半径)来更合理地描述调配时间的不确定性,并将其转化为悲观子模型和乐观子模型,之后分别采用鲁棒优化方法求解。最后,用算例说明了所述模型和方法的基本特征。
        It is crucial to appropriately allocate emergency power sources for reducing the outage cost to the greatest extent in emergency circumstances.Existing publications on emergency power sources are focused on optimal scheduling of emergency power sources in urban areas,while less attention has been paid to reasonable allocations of emergency power sources and the impacts of uncertain load demands and dispatching time periods of emergency power sources caused by some factors such as traffic jam and road damage have not yet been addressed.Given this background,an optimization model for allocating emergency power sources is presented with uncertain load demands and dispatching time periods considered,with an objective of maximizing the social welfare.The well-established radial interval represented by the fluctuation radius for the boundaries of an interval value is employed to more appropriately characterize the uncertainties involved.The optimization model is next transformed into a pessimistic sub-model and an optimistic sub-model for speeding the solving process,and each of the submodels is then solved by a robust optimization method.Finally,case studies are served for demonstrating the developed model and method for allocating emergency power sources.
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