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Quantifying the value of probabilistic forecasting for power system operation planning

Author

Listed:
  • Wang, Qin
  • Tuohy, Aidan
  • Ortega-Vazquez, Miguel
  • Bello, Mobolaji
  • Ela, Erik
  • Kirk-Davidoff, Daniel
  • Hobbs, William B.
  • Ault, David J.
  • Philbrick, Russ

Abstract

A recent key research area in renewable energy integration is the development of tools and methods to capture and accommodate the uncertainty associated with the forecast errors. While the research community has proposed numerous methods to improve the accuracy of probabilistic forecasts, their application to operational planning is still an open question. This work applies dynamic reserve determination methods to solar probabilistic forecasts and then feed them to a commercial production cost model simulator to assess the value of capturing the uncertainty endogenously in the reserve determination process. Testing is carried out on a calibrated real-size system representing the Southern Company for medium, and high solar penetration levels. Numerical results demonstrate the benefits that can be attained by explicitly modeling probabilistic uncertainty in terms of operating cost, and enhanced system reliability which is measured as the quantity of balancing and reserve violations. Additionally, these methods and results can pave the way for system operators to adopt probabilistic forecasting to draw the operating plans of the system, and this allowing the successful integration of variable renewable energy sources.

Suggested Citation

  • Wang, Qin & Tuohy, Aidan & Ortega-Vazquez, Miguel & Bello, Mobolaji & Ela, Erik & Kirk-Davidoff, Daniel & Hobbs, William B. & Ault, David J. & Philbrick, Russ, 2023. "Quantifying the value of probabilistic forecasting for power system operation planning," Applied Energy, Elsevier, vol. 343(C).
    • Handle: RePEc:eee:appene:v:343:y:2023:i:c:s0306261923006189
    DOI: 10.1016/j.apenergy.2023.121254
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    1. James E. Matheson & Robert L. Winkler, 1976. "Scoring Rules for Continuous Probability Distributions," Management Science, INFORMS, vol. 22(10), pages 1087-1096, June.
    2. Yan, Xingyu & Abbes, Dhaker & Francois, Bruno, 2017. "Uncertainty analysis for day ahead power reserve quantification in an urban microgrid including PV generators," Renewable Energy, Elsevier, vol. 106(C), pages 288-297.
    3. Li, Binghui & Feng, Cong & Siebenschuh, Carlo & Zhang, Rui & Spyrou, Evangelia & Krishnan, Venkat & Hobbs, Benjamin F. & Zhang, Jie, 2022. "Sizing ramping reserve using probabilistic solar forecasts: A data-driven method," Applied Energy, Elsevier, vol. 313(C).
    4. Wang, Qin & Wu, Hongyu & Florita, Anthony R. & Brancucci Martinez-Anido, Carlo & Hodge, Bri-Mathias, 2016. "The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales," Applied Energy, Elsevier, vol. 184(C), pages 696-713.
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    1. Rafael Alvarenga & Hubert Herbaux & Laurent Linguet, 2023. "On the Added Value of State-of-the-Art Probabilistic Forecasting Methods Applied to the Optimal Scheduling of a PV Power Plant with Batteries," Energies, MDPI, vol. 16(18), pages 1-24, September.

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