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
Creators: name: Corcoran, James Sean
name: 0000-0003-4936-1811
Publisher: name: The University of Texas at Austin
Year: 2022
Subjects: Electricity
Investment planning
Capacity expansion modeling
Financial modeling
Risk perception
Renewable energy
Tax credit policy

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 financing learning rates can also have a material effect on system outcomes, increasing wind and solar deployment significantly through 2050. Secondly, a policy analysis, enabled by the improved financing representation developed in the first objective, is completed to predict system outcomes from a move to Direct Pay in a 10-year tax credit extension, recently proposed as part of the Biden Administration’s Build Back Better infrastructure package. Through the construction of a Discounted Cash Flow (DCF) model to measure the impact of a switch to refundable tax credits on a project’s debt capacity, the study demonstrates how the policy would increase renewable technologies’ access to lower-cost capital, enabling further deployment. ReEDS modeling finds that Direct Pay’s primary advantage lies in accelerating the deployment of wind and solar over the period 2022-2030, which in turn accelerates the decarbonization of the power sector. This switch to Direct Pay results in cumulative emissions reductions of 24% through 2050, compared to current nonrefundable tax credits, due to the reduction in financing costs alone. This reduction increases to 35% when including the reductions in soft costs that are possible from the move to a more simplified capital structure