Given the increasing significance of energy storage in future energy systems, it would be valuable to resource planners to have more information about how this technology can be effectively integrated into their long-term planning studies and modeling tools. Assessing energy storage systems is complex due to the variety of storage technology types, dependencies on state-of-charge, wide operational space across temporal and spatial scales, and potential for providing multiple services. Existing analytical tools often require simplifications related to energy storage technology and the level of spatial and temporal detail. However, these simplifications may lead to inaccurate evaluations, potentially resulting in either underestimation or overestimation of storage resources in planning studies.
This project aims to highlight the trade-offs between enhanced spatio-temporal resolution and model complexity with respect to energy storage. To achieve this, a utility-level capacity expansion planning tool is utilized to assess the impact of these simplifications on planning model results. Specifically, this study focuses on storage deployment, including installed capacity, technology type, location within the network, and build year, all within the context of decarbonization. Results show that longer duration storage technologies are built in scenarios with increased temporal resolution. Significant variation in storage portfolios are also observed across different levels of model spatial and temporal resolution. These variations suggest that spatio-temporal simplifications within capacity expansion models, aimed at reducing potentially lengthy run times, need to be carefully considered by planners.
Authors Karen Tapia-Ahumada