cerf: A Python package to evaluate the feasibility and costs of power plant siting for alternative futures

Abstract

Long-term planning for the electric grid, also referred to as resource adequacy planning, informs decisions about investments in infrastructure associated with policy goals with the ultimate objective to maintain or enhance resilience to potential future vulnerabilities under various natural and human stressors (Alova, 2020; Craig et al., 2018; Jayadev et al., 2020; Khan et al., 2021; Sridharan et al., 2019). Future power plant siting costs will depend on a number of factors including the characteristics of the electricity capacity expansion and electricity demand (e.g., fuel mix of future electric power capacity, and the magnitude and geographic distribution of electricity demand growth) as well as the geographic location of power plants (U. S. Energy Information Administration, 2021). Electricity technology capacity expansion plans modeled to represent alternate future conditions meeting a set of scenario assumptions are traditionally compared against historical trends which may not be consistent with current and future conditions (Iyer et al., 2015; Sluisveld et al., 2015; Wilson et al., 2013). We present the cerf Python package (a.k.a., the Capacity Expansion Regional Feasibility model) which helps evaluate the feasibility of future, scenario-driven electricity capacity expansion plans by siting power plants in areas that have been deemed the least cost option while considering dynamic future conditions [Figure 1]. We can use cerf to gain insight to research topics such as: 1) which future projected electricity expansion plans from models such as GCAM (Calvin et al., 2019; Wise et al., 2019) are possible to achieve, 2) where and which on-the-ground barriers to siting (e.g., protected areas, cooling water availability) may influence our ability to achieve certain expansion scenarios, and 3) evaluate pathways of sited electricity infrastructure build-outs under evolving locational marginal pricing (LMP) based on the supply and demand of electricity from a grid operations model. Vernon et al., (2021). cerf: A Python package to evaluate the feasibility and costs of power plant siting for alternative futures. Journal of Open Source Software, 6(65), 3601. https://doi.