4.6 Article

Multi-phase assessment and adaptation of power systems resilience to natural hazards

期刊

ELECTRIC POWER SYSTEMS RESEARCH
卷 136, 期 -, 页码 352-361

出版社

ELSEVIER SCIENCE SA
DOI: 10.1016/j.epsr.2016.03.019

关键词

Electric power systems; Floods; Fragility curves; Resilience; Resiliency; Windstorms

资金

  1. Resilient Electricity Networks for Great Britain (RESNET) project
  2. Engineering and Physical Sciences Research Council (EPSRC), UK [EP/I035781/1]
  3. CONICYT, Chile under Fondecyt [1141082]
  4. EPSRC [EP/N034899/1, EP/I035757/1, EP/I035781/1] Funding Source: UKRI
  5. Engineering and Physical Sciences Research Council [EP/N034899/1, EP/I035757/1, EP/I035781/1] Funding Source: researchfish

向作者/读者索取更多资源

Extreme weather hazards, as high-impact low-probability events, have catastrophic consequences on critical infrastructures. As a direct impact of climate change, the frequency and severity of some of these events is expected to increase in the future, which highlights the necessity of evaluating their impact and investigating how can systems withstand a major disruption with limited degradation and recover rapidly. This paper first presents a multi-phase resilience assessment framework that can be used to analyze any natural threat that may have a severe single, multiple and/or continuous impact on critical infrastructures, such as electric power systems. Namely, these phases are (i) threat characterization, (ii) vulnerability assessment of the system's components, (iii) system's reaction and (iv) system's restoration. Second, multi-phase adaptation cases, i.e. making the system more robust, redundant and responsive are explained to discuss different strategies to enhance the resilience of the electricity network. To illustrate the above, this time-dependent framework is applied to assess the impact of potential future windstorms and floods on a reduced version of the Great Britain's power network. Finally, the adaptation cases are evaluated to conclude in what situations a stronger, bigger or smarter grid is preferred against the uncertain future. (C) 2016 Elsevier B.V. All rights reserved.

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