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.
Microgrids are emerging to coordinate distributed energy resources and locally increase reliability to expected events and resilience to extreme events. Furthermore, by deploying their inherent flexibility, grid-connected microgrids are capable to provide different services at the system-level too. However, these are often assessed independently and a comprehensive integrated framework that can assess benefits for the whole power system is missing. In this outlook, this paper introduces a system-level assessment framework based on the concept of different duration's reserve services that can be provided by microgrids to the main electricity grid in response to both credible (reliability-oriented) and extreme, possibly unforeseen (resilience-oriented) contingencies. Probabilistic capacity tables accounting for different sources of uncertainty related to both microgrids' operation and occurrences of unfavorable events are built to assess the microgrids' potential capacity contribution to a particular reserve service. Case studies based on representative microgrids and a British test system clearly illustrate and quantify how aggregation of microgrids could provide significant contribution to both short-term reliability and longer-duration resilience services far beyond the simple summation of the individual contributions, thus demonstrating a clear synergic effect, as well as the key role played by different forms of energy storage. The proposed framework can assist policy makers and regulators on the strategic role of microgrids for energy system planning and policy developments, including design of ancillary services markets not only to enhance system reliability but also resilience.
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