A Probabilistic Transmission Planning Framework for Reducing Network Vulnerability to Extreme Events
The restructuring of electric power industry has brought in plenty of challenges for transmission expansion planning (TEP), mainly due to uncertainties. The commonly used probabilistic TEP approach requires the network to meet an acceptable risk criterion. However, a series of blackouts in recent years caused by extreme weather-related events have raised the concerns about network vulnerability through calculating the expected risk value. In this paper, we have proposed the concept that TEP should be economically adjusted in order to make network less vulnerable to extreme events (EEs) caused by climate change, e.g., floods or ice storms.
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Resilience Assessment of Distribution Systems Integrated With Distributed Energy Resources
The resilience of electric systems is receiving growing attention due to their increased vulnerability to infrastructure damages and widespread outages from frequent extreme climactic conditions attributed to global warming effects. Resilience evaluation methods should recognize the uncertainties and correlations in the performance variations of different types of energy resources, load characteristics, extreme events and their impacts on the grid elements.
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Stochastic Generation Capacity Expansion Planning Reducing Greenhouse Gas Emissions
With increasing concerns about greenhouse gas emissions, a least-cost generation capacity expansion model to control carbon dioxide (CO 2) emissions is proposed in this paper. The mathematical model employs a decomposed two-stage stochastic integer program. Realizations of uncertain load and wind are represented by independent and identically distributed (i.i.d.) random samples generated via the Gaussian copula method. Two policies that affect CO 2 emissions directly and indirectly, carbon tax and renewable portfolio standard (RPS), are investigated to assess how much CO 2 emissions are expected to be reduced through those policies.
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