Expectations for the future role of energy storage resources in the electric sector have increased in recent years, as technological developments have been accompanied by policy support. However, energy storage technologies have complex cost, value, and performance characteristics that make them challenging to model.
This analysis aims to determine which features that are not commonly represented in existing long-term capacity planning models may, if included, materially alter key decisions related to how much energy storage is expected to be cost-effective. Using an integrated model of capacity planning and operations, the analysis varies the inclusion of five features to understand how model complexity can impact planning insights: degradation, grid (network) modeling, ancillary services, subhourly temporal resolution, and uncertainty.
Model results indicate that degradation has the largest impact on planning outputs, including those related to energy storage deployment and operations. Other features have smaller impacts on investment and dispatch outcomes. Grid modeling does not alter cost-effective levels of energy storage in the conventional capacity planning setting with a fixed planning reserve margin, though it impacts transmission planning decisions on the locational placement of resources. The extent of cost-effective battery storage capacity in these scenarios is primarily driven by capacity needs, but energy time-shifting represents a non-trivial fraction of the system value of storage.
Long-term electric sector capacity planning models continue to evolve to more comprehensively assess the potential value of energy storage and other emerging technologies. Model formulation decisions entail tradeoffs between the accuracy of the representation and model parsimony, and determining which model details matter can help to prioritize efforts and to ensure the appropriate valuation of resources under wide range of possible futures.
Authors Adam Diamant and John Bistline