Energy Storage Valuation
This wiki page identifies key gaps in the current field of energy storage modeling tools, characterizes key differences between energy storage modeling and valuation tools, and identifies where specific tools fall in this characterization. In addition, this report presents the beginnings of a quantitative benchmarking and comparison exercise between select tools meant to highlight their modeling differences and help the reader understand where certain tools are most applicable. This exercise will be expanded in future publications. The results show broad alignment between tools’ modeling approaches, and the key differences come out in scope, usability, and how the modeling approach (for example, perfect foresight dispatch optimization) is applied. These differences show up strongly in the quantitative benchmarking results, where there is less alignment despite similar modeling approaches owing to differences in implementation and applicability.
- Broad similarity exists between the modeling approaches of currently available energy storage valuation tools (with some exceptions), but this similarity does not necessarily extend to quantitative results owing to differences in the implementation and applicability of the tools.
- EPRI and others have made progress toward filling the gaps in valuation tool capability, but key gaps remain, and new gaps have opened as new needs have been highlighted.
- In the future, broad-scope, standardized benchmarking of energy storage valuation tools will help bring the field into an alignment that does not always exist currently.
Energy storage valuation tools can be used to make critical decision around energy storage, including where to locate energy storage, how big to size the best power and energy capacity for a storage system, what applications make the most sense for a particular system, which technical solution to select from a set of technology offerings, how to pair the storage system with other resources, etc. Energy storage is often characterized by high cost and high flexibility/value, so modeling the financial performance of the storage can be a sensitive, uncertain process. Unlike with other power technologies, modeling energy storage requires complicated approaches necessitated by its energy limit that increase uncertainty relative to an energy-unlimited resource. Users may rely on the results from a valuation tool to support decisions on which millions of dollars depend, so robust, case-specific valuation that internalizes these sources of uncertainty and allows for an understanding of cost, reward, and risk tradeoffs is a critical part of the storage decision making process.