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

Date
2015-04-01
Authors
Feng, Yonghan
Rios, Ignacio
Ryan, Sarah
Spurkel, Kai
Watson, Jean-Paul
Wets, Roger
Woodruff, David
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Ryan, Sarah
Person
Research Projects
Organizational Units
Journal Issue
Series
Abstract

Unit commitment decisions made in the day-ahead market and during subsequent reliability assessments are critically based on forecasts of load. Tra- ditional, deterministic unit commitment is based on point or expectation-based load forecasts. In contrast, stochastic unit commitment relies on multiple load sce- narios, with associated probabilities, that in aggregate capture the range of likely load time-series. The shift from point-based to scenario-based forecasting necessi- tates a shift in forecasting technologies, to provide accurate inputs to stochastic unit commitment. In this paper, we discuss a novel scenario generation method- ology 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 the expected 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 com- mitment 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.

Description

This is a manuscript of an article from Energy Systems (2015). The final publication is available at Springer via http://dx.doi.org/10.1007/s12667-015-0146-8. Posted with permission.

Keywords
Citation
DOI
Collections