Framework for capacity credit assessment of electrical energy storage and demand response. Issue 9 (1st June 2016)

• Record Type: Journal Article
• Authors: Zhou, Yutian; Mancarella, Pierluigi; Mutale, Joseph
• Is Part Of: IET generation, transmission & distribution. Volume 10:Issue 9(2016)
• Page Start: 2267
• Page End: 2276
• Publication Date: 2016-06-01
• Subjects: energy storage -- numerical analysis -- smart power grids -- power markets -- reliability; capacity credit assessment -- electrical energy storage -- demand response -- EES -- DR -- generation‐oriented concept -- capacity credit -- CC metric -- system reliability -- numerical algorithms -- specific technology‐agnostic models -- energy capacity -- power ratings -- payback characteristics -- customer flexibility -- IEEE RTS -- smart grid planning -- market development; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2015.0458
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : The use of electrical energy storage (EES) and demand response (DR) to support system capacity is attracting increasing attention. However, little work has been done to investigate the capability of EES/DR to displace generation while providing prescribed levels of system reliability. In this context, this study extends the generation‐oriented concept of capacity credit (CC) to EES/DR, with the aim of assessing their contribution to adequacy of supply. A comprehensive framework and relevant numerical algorithms are proposed for the evaluation of EES/DR CC, with different 'traditional' generation‐oriented CC metrics being extended and a new CC metric defined to formally quantify the capability of EES/DR to displace conventional generation for different applications (system expansion, reliability increase etc.). In particular, specific technology‐agnostic models have been developed to illustrate the implications of energy capacity, power ratings, and efficiency of EES, as well as payback characteristics and customer flexibility (that often also depend on different forms of storage available to customers) of DR. Case studies are performed on the IEEE RTS to demonstrate how the different characteristics of EES/DR can impact on their CC. The framework developed can thus support the important debates on the role of EES/DR for smart grid planning and market development.