Perceptions of risk in increasingly capital-intensive electricity grids : measuring the impacts of accurate cost of capital representation on investment planning for future energy systems
type: CreativeWork
description: The U.S. electric grid is experiencing unprecedented change as the system continues a path toward a more diversified and decarbonized generation mix, with increased investment in wind, solar, storage and other energy transition technologies. Concurrently, market structures continue their evolution away from highly regulated monopolies and towards competitive markets, expanding the share of deployable capital that engages with the power sector and introducing a broader range of investor classes and preferences. These two factors create complex financing dynamics that ultimately determine which set of technologies are deployed onto the grid. However, many of the leading tools used by stakeholders to guide long-term system planning do not sufficiently account for these dynamics with oversimplified financing cost representations. Through the modeling of the ERCOT electric grid using the Regional Energy Deployment System (ReEDS), this study assesses the significance of improved representation of financing costs for the various stakeholders who interact with large-scale capacity expansion models. This is achieved through the exploration of two distinct but connected research objectives, the first being relevant to energy modelers and the second to policymakers. First, the impacts of representing financing costs in a more sophisticated manner across technologies and over time are measured through the investigation into empirically observed financing dynamics, including (1) the general level of risk associated with the electric power sector, (2) differences in technology-specific risk perception, (3) financing improvements as investors gain familiarity with specific technologies, and (4) operational risk priced into financing “hurdle rates.” ReEDS analysis finds that general system risk does matter, as a system modeled with a uniform 10% discount rate deploys 44% less renewable energy capacity compared to one with a market-weighted uniform 6.02% discount rate. Implementing …
abstract: The U.S. electric grid is experiencing unprecedented change as the system continues a path toward a more diversified and decarbonized generation mix, with increased investment in wind, solar, storage and other energy transition technologies. Concurrently, market structures continue their evolution away from highly regulated monopolies and towards competitive markets, expanding the share of deployable capital that engages with the power sector and introducing a broader range of investor classes and preferences. These two factors create complex financing dynamics that ultimately determine which set of technologies are deployed onto the grid. However, many of the leading tools used by stakeholders to guide long-term system planning do not sufficiently account for these dynamics with oversimplified financing cost representations. Through the modeling of the ERCOT electric grid using the Regional Energy Deployment System (ReEDS), this study assesses the significance of improved representation of financing costs for the various stakeholders who interact with large-scale capacity expansion models. This is achieved through the exploration of two distinct but connected research objectives, the first being relevant to energy modelers and the second to policymakers. First, the impacts of representing financing costs in a more sophisticated manner across technologies and over time are measured through the investigation into empirically observed financing dynamics, including (1) the general level of risk associated with the electric power sector, (2) differences in technology-specific risk perception, (3) financing improvements as investors gain familiarity with specific technologies, and (4) operational risk priced into financing “hurdle rates.” ReEDS analysis finds that general system risk does matter, as a system modeled with a uniform 10% discount rate deploys 44% less renewable energy capacity compared to one with a market-weighted uniform 6.02% discount rate. Implementing …
author: Corcoran, James Sean
language: en
keywords: Electricity, Investment planning, Capacity expansion modeling, Financial modeling, Risk perception, Renewable energy, Tax credit policy
date published: 2022-05-11
date modified: 2026-07-24T05:01:06Z
provider: University of Texas
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: The University of Texas at Austin

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2152/115220

PropertyValue
property ID: DOI
value: 10.26153/tsw/42121