Quantifying the value of improved wind energy forecasts in a pool-based electricity market

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McGarrigle, Edward V.
Leahy, Paul G.
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This work illustrates the influence of wind forecast errors on system costs, wind curtailment and generator dispatch in a system with high wind penetration. Realistic wind forecasts of different specified accuracy levels are created using an auto-regressive moving average model and these are then used in the creation of day-ahead unit commitment schedules. The schedules are generated for a model of the 2020 Irish electricity system with 33% wind penetration using both stochastic and deterministic approaches. Improvements in wind forecast accuracy are demonstrated to deliver: (i) clear savings in total system costs for deterministic and, to a lesser extent, stochastic scheduling; (ii) a decrease in the level of wind curtailment, with close agreement between stochastic and deterministic scheduling; and (iii) a decrease in the dispatch of open cycle gas turbine generation, evident with deterministic, and to a lesser extent, with stochastic scheduling.
Wind forecas , Unit commitment , Economic dispatch , PLEXOS , Autoregressive moving average , Curtailment , Stochastic scheduling
McGarrigle, Edward V.; Leahy, Paul G. (2015) 'Quantifying the value of improved wind energy forecasts in a pool-based electricity market'. Renewable Energy, 80 (8):517-524.
Copyright © 2015 Elsevier Inc. All rights reserved. NOTICE: this is the author’s version of a work that was accepted for publication in Renewable Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Renewable Energy [Volume 80, August 2015, Pages 517–524] http://dx.doi.org/10.1016/j.renene.2015.02.023