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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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    1. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.
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    3. Mc Garrigle, E.V. & Deane, J.P. & Leahy, P.G., 2013. "How much wind energy will be curtailed on the 2020 Irish power system?," Renewable Energy, Elsevier, vol. 55(C), pages 544-553.
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    5. Higgins, P. & Foley, A.M. & Douglas, R. & Li, K., 2014. "Impact of offshore wind power forecast error in a carbon constraint electricity market," Energy, Elsevier, vol. 76(C), pages 187-197.
    6. Weber, Christoph, 2010. "Adequate intraday market design to enable the integration of wind energy into the European power systems," Energy Policy, Elsevier, vol. 38(7), pages 3155-3163, July.
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    Cited by:

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    2. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    3. Rasku, Topi & Miettinen, Jari & Rinne, Erkka & Kiviluoma, Juha, 2020. "Impact of 15-day energy forecasts on the hydro-thermal scheduling of a future Nordic power system," Energy, Elsevier, vol. 192(C).
    4. Holmberg, Pär & Tangerås, Thomas & Ahlqvist, Victor, 2018. "Central- versus Self-Dispatch in Electricity Markets," Working Paper Series 1257, Research Institute of Industrial Economics, revised 27 Mar 2019.
    5. 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.
    6. Lyons, Selina & Whale, Jonathan & Wood, Justin, 2018. "Wind power variations during storms and their impact on balancing generators and carbon emissions in the Australian National Electricity Market," Renewable Energy, Elsevier, vol. 118(C), pages 1052-1063.
    7. 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.
    8. Notton, Gilles & Nivet, Marie-Laure & Voyant, Cyril & Paoli, Christophe & Darras, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2018. "Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 87(C), pages 96-105.
    9. 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.
    10. 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.
    11. Lujano-Rojas, J.M. & Osório, G.J. & Matias, J.C.O. & Catalão, J.P.S., 2016. "A heuristic methodology to economic dispatch problem incorporating renewable power forecasting error and system reliability," Renewable Energy, Elsevier, vol. 87(P1), pages 731-743.
    12. 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.
    13. David Schönheit & Dominik Möst, 2019. "The Effect of Offshore Wind Capacity Expansion on Uncertainties in Germany’s Day-Ahead Wind Energy Forecasts," Energies, MDPI, vol. 12(13), pages 1-23, July.
    14. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
    15. Antonello Rosato & Rosa Altilio & Rodolfo Araneo & Massimo Panella, 2017. "Prediction in Photovoltaic Power by Neural Networks," Energies, MDPI, vol. 10(7), pages 1-25, July.
    16. 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.
    17. Ruhnau, Oliver & Hennig, Patrick & Madlener, Reinhard, 2020. "Economic implications of forecasting electricity generation from variable renewable energy sources," Renewable Energy, Elsevier, vol. 161(C), pages 1318-1327.
    18. 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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