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A Bayesian approach to determining the feasibility of electricity capacity expansion plans

Presentation Date
Thursday, December 10, 2020 at 4:00am



Forward-looking models of electricity capacity expansion are extensively used to provide guidance to stakeholders about the type and magnitude of investments in power sector infrastructure and vulnerabilities of existing infrastructure under various climate and socioeconomic scenarios. A feasibility evaluation of model-based scenarios of the future has generally been conducted in two ways: 1) comparison to model-based scenarios of the future against historical trends, and 2) an integrated approach that captures the complex processes that affect technology deployment in the power sector for a given scenario. We propose a third that builds off of the advantages of (2) by evaluating the limits of feasibility of a target area under varying assumptions and describing under what conditions an expansion plan may or may not be realistic. We set our initial prior using a beta distribution that uses suitable and unsuitable grid cells as a proxy for success and failures for siting in a target region. We then evaluated differing conditions (e.g., buffer size, different technology siting order, etc.) that impact siting for a given suitability per technology using MCMC to get a robust understanding of the feasibility of being able to achieve an expansion plan under varying future conditions within a certain credibility interval.

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