Given the increasing investment in resources with relatively short-term energy limits, such as battery energy storage, a key emerging question for capacity adequacy assessment is how to include such resources in system studies. Because a storage system’s state of charge varies during normal operation, and it is not necessarily able to discharge at its nameplate power and energy at a particular moment—unlike the assumption commonly used for conventional generation—a method to assess the likelihood that storage can provide capacity in a system when a capacity shortage is likely to occur is necessary. This work applied an advanced algorithm to replicate the dispatch of energy storage combined with a probabilistic assessment of the risk of insufficient capacity to estimate the contribution of such resources. The method leveraged the commonly used effective load-carrying capability (ELCC) method commonly used for renewable generation capacity assessments.
By demonstrating the approach on two test systems, a greater understanding of the range of possible capacity values associated with energy storage was gained and indicated the impact of energy storage duration, system net load profile, and capacity incentives on the capacity value of such resources. A comparison was conducted with a range of similar studies to highlight the similarities and differences among several of the methods that have been applied in practice so far.