Operating Dynamic Reserve Dimensioning Using Probabilistic Forecasts

Costilla-Enriquez, Napoleon; Ortega-Vazquez, Miguel A.; Tuohy, Aidan; Motley, Amber; Webb, Rebecca

doi:10.1109/tpwrs.2022.3163106

Operating Dynamic Reserve Dimensioning Using Probabilistic Forecasts

Journal Article · Tue Mar 29 00:00:00 EDT 2022 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/tpwrs.2022.3163106· OSTI ID:1986481

^[1]; ^[2]; ^[2]; Motley, Amber ^[3]; Webb, Rebecca ^[3]

Arizona State University, Tempe, AZ (United States); Electric Power Research Institute (EPRI)
Electric Power Research Institute (EPRI), Palo Alto, CA (United States)
California Independent System Operator, Folsom, CA (United States)

The rapid integration of variable energy sources (VRES) into power grids increases variability and uncertainty of the net demand, making the power system operation challenging. Operating reserve is used by system operators to manage and hedge against such variability and uncertainty. Traditionally, reserve requirements are determined by rules-of-thumb (static reserve requirements, e.g., NERC Reliability Standards), and more recently, dynamic reserve requirements from tools and methods which are in the adoption process (e.g., DynADOR, DRD, and RESERVE, among others). While these methods/tools significantly improve the static rule-of-thumb approaches, they rely exclusively on deterministic data (i.e., best guess only). Consequently, these methods disregard the probabilistic uncertainty thresholds associated with specific days and their weather conditions (i.e., best guess plus probabilistic uncertainty). This work presents practical approaches to determine the operating reserve requirements leveraging the wealth information from probabilistic forecasts. Proposed approaches are validated and tested using actual data from the CAISO system. Furthermore, results show the benefits in terms of risk reduction of considering the probabilistic forecast information into the dimensioning process of operating reserve requirements.

View Accepted Manuscript (DOE)

Research Organization:: Electric Power Research Institute (EPRI), Palo Alto, CA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: EE0008601

OSTI ID:: 1986481

Journal Information:: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 1 Vol. 38; ISSN 0885-8950

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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