A Data-Driven Method for Adaptive Reserve Requirement Estimation via Probabilistic Net Load Forecasting

Cong Feng, Mucun Sun, Jie Zhang, Kate Doubleday, Bri-Mathias Hodge, Pengwei Du

Research output: Contribution to conferencePaperpeer-review

4 Scopus Citations


The University of Colorado Boulder Boulder, CO, 80309, USA With the increasing penetration of renewable energy, power systems are subject to more uncertainty. This makes power system reserve scheduling more challenging. Most of the current reserve requirement determination methods calculate reserve requirements based on historical data, which does not consider the real-time or future system uncertainty. In this paper, a data-driven method is developed to determine the non-spinning reserve requirement (NSRR) in the Electric Reliability Council of Texas (ERCOT) system. The method follows the procedure of the current ERCOT method while adaptively determining the NSRR based on probabilistic net load forecasts. Case studies with two years of ERCOT data show that the developed method significantly reduces the NSRR by introducing an adaptive temporal resolution and update rate. Sensitivity analysis with different forecasting and percentile thresholds indicates the flexibility of the developed method.

Original languageAmerican English
Number of pages5
StatePublished - 2020
Event2020 IEEE Power & Energy Society General Meeting (PESGM) - Montreal, Canada
Duration: 2 Aug 20206 Aug 2020


Conference2020 IEEE Power & Energy Society General Meeting (PESGM)
CityMontreal, Canada

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

NREL Publication Number

  • NREL/CP-5D00-79013


  • Non-spinning reserve
  • Probabilistic forecasting
  • Reserve requirement


Dive into the research topics of 'A Data-Driven Method for Adaptive Reserve Requirement Estimation via Probabilistic Net Load Forecasting'. Together they form a unique fingerprint.

Cite this