Establishing best practices for modeling multi-day energy storage in deeply decarbonized energy systems

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.