Wilson Ricks

Multi-day energy storage (MDS), a subset of long-duration energy storage, may become a critical technology for the decarbonization of the power sector, as current commercially available Lithium-ion battery storage technologies cannot cost-effectively shift energy to address multi-day or seasonal variability in demand and renewable energy availability. MDS is difficult to model in existing energy system planning models (such as electricity system capacity expansion models (CEMs)), as it is much more dependent on an accurate representation of chronology than other resources. Techniques exist for modeling MDS in these planning models; however, it is not known how spatial and temporal resolution affect the performance of these techniques, creating a research gap. In this study we examine what spatial and temporal resolution is necessary to accurately capture the full value of MDS, in the context of a continent-scale CEM. We use the results to draw conclusions and present best practices for modelers seeking to accurately model MDS in a macro-energy systems planning context. Our key findings are: (1) modeling MDS with linked representative periods is crucial to capturing its full value, (2) MDS value is highly sensitive to the cost and availability of other resources, and (3) temporal resolution is more important than spatial resolution for capturing the full value of MDS, although how much temporal resolution is needed will depend on the specific model context.

GenX is a highly-configurable, open source electricity resource capacity expansion model that incorporates several state-of-the-art practices in electricity system planning to offer improved decision support for a changing electricity landscape. GenX is a constrained linear or mixed integer linear optimization model that determines the portfolio of electricity generation, storage, transmission, and demand-side resource investments and operational decisions to meet electricity demand in one or more future planning years at lowest cost, while subject to a variety of power system operational constraints, resource availability limits, and other imposed environmental, market design, and policy constraints. GenX features a modular and transparent code structure developed in Julia + JuMP. The model is designed to be highly flexible and configurable for use in a variety of applications from academic research and technology evaluation to public policy and regulatory analysis and resource planning.

Enhanced geothermal systems (EGS) are one of a small number of emerging energy technologies with the potential to deliver firm carbon-free electricity at large scale, but are often excluded from macro-scale decarbonization studies due to uncertainties regarding their cost and resource potential. Here we combine empirically-grounded near-term EGS cost estimates with an experience curves framework, by which costs fall as a function of cumulative deployment, to model EGS deployment pathways and impacts on the United States electricity sector from the present day through 2050. We find that by initially exploiting limited high-quality geothermal resources in the western US, EGS can achieve early commercialization and experience-based cost reductions that enable it to supply up to a fifth of total US electricity generation by 2050 and substantially reduce the cost of decarbonization nationwide. Higher-than-expected initial EGS costs could inhibit early growth and constrain the technology’s long-run potential, though supportive policies can counteract these effects.  

Scheduled maintenance is likely to be lengthy and therefore consequential for the economics of fusion power plants. The maintenance strategy that maximizes the economic value of a plant depends on internal factors such as the cost and durability of the replaceable components, the frequency and duration of the maintenance blocks, and the external factors of the electricity system in which the plant operates. This paper examines the value of fusion power plants with various maintenance properties in a decarbonized United States Eastern Interconnection circa 2050. Seasonal variations in electricity supply and demand mean that certain times of year, particularly spring to early summer, are best for scheduled maintenance. Seasonality has two important consequences. First, the value of a plant can be 15% higher than what one would naively expect if value were directly proportional to its availability. Second, in some cases, replacing fractions of a component in shorter maintenance blocks spread over multiple years is better than replacing it all at once during a longer outage, even through the overall availability of the plant is lower in the former scenario.