globalchange  > 气候减缓与适应
DOI: 10.1109/TSTE.2018.2825780
WOS记录号: WOS:000454223400001
论文题名:
Predictive Risk Analytics for Weather-Resilient Operation of Electric Power Systems
作者: Dehghanian, Payman1; Zhang, Bei2; Dokic, Tatjana3; Kezunovic, Mladen3
通讯作者: Dehghanian, Payman
刊名: IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
ISSN: 1949-3029
出版年: 2019
卷: 10, 期:1, 页码:3-15
语种: 英语
英文关键词: Weather ; forecast ; risk ; decision making ; topology control ; vulnerability ; mitigation
WOS关键词: CLIMATE-CHANGE ; FORECAST ; INFRASTRUCTURE ; FRAMEWORK ; IMPACTS
WOS学科分类: Green & Sustainable Science & Technology ; Energy & Fuels ; Engineering, Electrical & Electronic
WOS研究方向: Science & Technology - Other Topics ; Energy & Fuels ; Engineering
英文摘要:

Day-to-day operation of the electricity grid generation, transmission, and distribution is environmentally driven and closely dependent on evolving weather patterns. This paper introduces several new weather-driven analytics for accurate spatial-temporal electricity generation forecasts, asset health and reliability assessment, probabilistic load forecasts, and electricity market simulations. A new risk metric is suggested, which accounts for the weather hazards, grid vulnerability, and financial consequences in the face of changing weather patterns and associated meteorological predictions over time. New mitigation formulations for power system topology control through transmission line switching for fast and timely recovery of the weather-caused electricity outages are suggested. The proposed decision support tool enables the operators to predictively evaluate the high-risk weather threats and consequently plan on how to safeguard the grid when exposed to forecasted weather-driven incidents. The efficiency of the proposed toolset is illustrated by application to a part of the IEEE 73-Bus test system.


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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/124801
Appears in Collections:气候减缓与适应

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作者单位: 1.George Washington Univ, Dept Elect & Comp Engn, Washington, DC 20052 USA
2.GE Energy Consulting, Schenectady, NY 12345 USA
3.Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA

Recommended Citation:
Dehghanian, Payman,Zhang, Bei,Dokic, Tatjana,et al. Predictive Risk Analytics for Weather-Resilient Operation of Electric Power Systems[J]. IEEE TRANSACTIONS ON SUSTAINABLE ENERGY,2019-01-01,10(1):3-15
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