Capacity Credit Evaluation of Generalized Energy Storage Considering Endogenous Uncertainty
<|reference_start|>Capacity Credit Evaluation of Generalized Energy Storage Considering Endogenous Uncertainty: Generalized energy storage (GES), encompassing both physical and virtual energy storage, can provide remarkable but uncertain adequacy flexibility. When assessing GES's contribution to resource adequacy, the literature typically considers exogenous uncertainties (e.g., failures and stochastic response) but overlooks endogenous uncertainties, such as self-scheduling in liberal markets and decision-dependent uncertainty (DDU). In this regard, this paper proposes a novel capacity credit evaluation framework to accurately quantify GES's contribution to resource adequacy, where a sequential coordinated dispatch method is proposed to capture realistic GES operations by coordinating self-scheduling in the day-ahead energy market and real-time adequacy-oriented dispatch in the capacity market. To incorporate DDU of GES (i.e., responsiveness affected by dispatch decisions and prices in capacity market), we present a chance-constrained optimization approach and tractable solution methodologies for real-time dispatch. We propose a practical adequacy assessment method to quantify the impact of DDU on capacity credit by evaluating the consequence of ignoring DDU. Additionally, a novel capacity credit index called equivalent storage capacity substitution is introduced to quantify the equivalent deterministic storage capacity of the uncertain virtual energy storage. Simulations show that the proposed method yields reliable and accurate capacity credit values by accounting for self-scheduling of GES and managing the risk from DDU. Finally, key impact factors of GES's capacity credit are thoroughly discussed, offering valuable insights for the decision-making of capacity market operators.<|reference_end|>
@article{qi2024capacity,
    title={Capacity Credit Evaluation of Generalized Energy Storage under
  Decision-Dependent Uncertainty},
    author={Ning Qi, Pierre Pinson, Mads R. Almassalkhi, Yingrui Zhuang, Yifan Su,
  Feng Liu},
    journal={arXiv preprint arXiv:2406.07338},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07338},
    primaryClass={eess.SY cs.SY}
}