Quantifying value and representing competitiveness of electricity system technologies in economic models

• Published in: Applied energy 2023-01, Vol.329, p.120132, Article 120132
• Main Authors: Mowers, Matthew, Mignone, Bryan K., Steinberg, Daniel C.
• Format: Article
• Language: English
• Subjects: Electricity model; LCOE; Regional Energy Deployment System (ReEDS); Technology competitiveness; Value factor
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2022.120132

•Value and cost must be considered for all technologies in evaluating competition.•We quantify technology value at market shares above 50%.•We quantify wind and solar value at combined market shares above 90%.•We explore how technology interactions affect technology value.•We propose methods of parameterizing value in large-scale models. Evaluating competition between electricity technologies is challenging because it depends on both their costs and their values. While technology costs can typically be estimated from projections of the cost components—capital, fuel, and O&M—estimating a technology’s value is more complex due to its dependence on its contributions to multiple different grid services, each with prices that can vary substantially over space and time. In this work, using an electricity model of the contiguous United States, we develop relationships between relative value and share of total generation for major electricity generation technologies which, when paired with projections of technology costs, can be used to estimate technology competitiveness. We identify significant differences in the relationship between relative value and generation share for variable renewable energy (VRE) and non-VRE sources, but we demonstrate that all technologies require consideration of their dynamic values (in addition to cost) when evaluating competitiveness. In addition, we demonstrate that relative value of a technology is substantially impacted by not only its own generation share but also other aspects of the system state, in particular the mix of other technologies present in the system. Finally, we use the developed relative value relationships in combination with projections of future technology costs in a coarse resolution model that competes technologies based on a comprehensive competitiveness metric: profitability-adjusted LCOE (PLCOE). We show that this simple representation of technology competition approximately recovers the generation mix from a detailed model, which is not possible using LCOE alone. Such an approach can be used to improve the representation of technology competition in coarse-resolution models such as integrated assessment models, for which simplified metrics are often needed.