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Toward Scalable Stochastic Unit Commitment - Part 1: Load Scenario Generation

Yonghan Feng(yhfeng***at***iastate.edu)
Ignacio Rios(ignacio.rios.u***at***gmail.com)
Sarah Ryan(smryan***at***iastate.edu)
Kai Spurkel(kai.s88***at***web.de)
Jean-Paul Watson(jwatson***at***sandia.gov)
Roger J-B Wets(rjbwets***at***ucdavis.edu)
David Woodruff(dlwoodruff***at***ucdavis.edu)

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 processes. 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 for New England (ISO-NE). The accuracy of 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.

Keywords: Stochastic programming. Stochastic unit commitment. Scenario tree generation.

Category 1: Stochastic Programming


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Entry Submitted: 05/16/2014
Entry Accepted: 05/19/2014
Entry Last Modified: 05/16/2014

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