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Toward scalable stochastic unit commitment. Part 1: load scenario generation

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Abstract

Unit commitment decisions made in the day-ahead market and during subsequent reliability assessments are critically based on forecasts of load. Traditional, deterministic unit commitment is based on point or expectation-based load forecasts. In contrast, stochastic unit commitment relies on multiple load scenarios, with associated probabilities, that in aggregate capture the range of likely load time-series. The shift from point-based to scenario-based forecasting necessitates a shift in forecasting technologies, to provide accurate inputs to stochastic unit commitment. In this paper, we discuss a novel scenario generation methodology for load forecasting in stochastic unit commitment, with application to real data associated with the Independent System Operator of New England (ISO-NE). The accuracy of the expected load scenario generated using our methodology is consistent with that of point forecasting methods. The resulting sets of realistic scenarios serve as input to rigorously test the scalability of stochastic unit commitment solvers, as described in the companion paper. The scenarios generated by our method are available as an online supplement to this paper, as part of a novel, publicly available large-scale stochastic unit commitment benchmark.

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Acknowledgments

Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under Contract DE-AC04-94-AL85000. This work was funded by the Department of Energy’s Advanced Research Projects Agency - Energy, under the Green Energy Network Integration (GENI) project portfolio. The authors would like to thank Dr. Eugene Litvinov and his research group at ISO-NE (in particular, Bill Callan) for their assistance with ISO-NE system and data sources.

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Authors and Affiliations

  1. Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, USA

    Yonghan Feng & Sarah M. Ryan

  2. University of Chile, Santiago, Chile

    Ignacio Rios

  3. University of Duisburg-Essen, Essen, Germany

    Kai Spürkel

  4. Analytics Department, Sandia National Laboratories, Albuquerque, NM, USA

    Jean-Paul Watson

  5. Department of Mathematics, University of California Davis, Davis, CA, USA

    Roger J.-B. Wets

  6. Graduate School of Management, University of California Davis, Davis, CA, USA

    David L. Woodruff

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  1. Yonghan Feng
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  2. Ignacio Rios
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  3. Sarah M. Ryan
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  4. Kai Spürkel
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  5. Jean-Paul Watson
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  6. Roger J.-B. Wets
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  7. David L. Woodruff
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Correspondence to Jean-Paul Watson.

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Feng, Y., Rios, I., Ryan, S.M. et al. Toward scalable stochastic unit commitment. Part 1: load scenario generation. Energy Syst 6, 309–329 (2015). https://doi.org/10.1007/s12667-015-0146-8

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  • DOI: https://doi.org/10.1007/s12667-015-0146-8

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