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

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  • Mc Garrigle, E.V.
  • Leahy, P.G.

Abstract

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.

Suggested Citation

  • Mc Garrigle, E.V. & Leahy, P.G., 2015. "Quantifying the value of improved wind energy forecasts in a pool-based electricity market," Renewable Energy, Elsevier, vol. 80(C), pages 517-524.
    • Handle: RePEc:eee:renene:v:80:y:2015:i:c:p:517-524
    DOI: 10.1016/j.renene.2015.02.023
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    Cited by:

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    7. Davis, Dominic & Brear, Michael J., 2024. "Impact of short-term wind forecast accuracy on the performance of decarbonising wholesale electricity markets," Energy Economics, Elsevier, vol. 130(C).
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    13. Sewdien, V.N. & Preece, R. & Torres, J.L. Rueda & Rakhshani, E. & van der Meijden, M., 2020. "Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting," Renewable Energy, Elsevier, vol. 161(C), pages 878-892.
    14. Dehghani, Hamed & Vahidi, Behrooz & Hosseinian, Seyed Hossein, 2017. "Wind farms participation in electricity markets considering uncertainties," Renewable Energy, Elsevier, vol. 101(C), pages 907-918.
    15. Devlin, Joseph & Li, Kang & Higgins, Paraic & Foley, Aoife, 2017. "Gas generation and wind power: A review of unlikely allies in the United Kingdom and Ireland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 757-768.
    16. 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.
    17. Hung, Tzu-Chieh & Chong, John & Chan, Kuei-Yuan, 2017. "Reducing uncertainty accumulation in wind-integrated electrical grid," Energy, Elsevier, vol. 141(C), pages 1072-1083.
    18. Yu-Jen Chen & Y. C. Shiah, 2016. "Experiments on the Performance of Small Horizontal Axis Wind Turbine with Passive Pitch Control by Disk Pulley," Energies, MDPI, vol. 9(5), pages 1-13, May.

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