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The Financial Effect of the Electricity Price Forecasts’ Inaccuracy on a Hydro-Based Generation Company

Author

Listed:
  • Umut Ugurlu

    (Management Engineering Department, Istanbul Technical University, Besiktas, Istanbul 34367, Turkey)

  • Oktay Tas

    (Management Engineering Department, Istanbul Technical University, Besiktas, Istanbul 34367, Turkey)

  • Aycan Kaya

    (Industrial Engineering Department, Istanbul Technical University, Besiktas, Istanbul 34367, Turkey)

  • Ilkay Oksuz

    (Biomedical Engineering Department, King’s College London, London SE1 7EU, UK)

Abstract

Electricity price forecasting has a paramount effect on generation companies (GenCos) due to the scheduling of the electricity generation scheme according to electricity price forecasts. Inaccurate electricity price forecasts could cause important loss of profits to the suppliers. In this paper, the financial effect of inaccurate electricity price forecasts on a hydro-based GenCo is examined. Electricity price forecasts of five individual and four hybrid forecast models and the ex-post actual prices are used to schedule the hydro-based GenCo using Mixed Integer Linear Programming (MILP). The financial effect measures of profit loss, Economic Loss Index (ELI) and Price Forecast Disadvantage Index (PFDI), as well as Mean Absolute Error (MAE) of the models are used for comparison of the data from 24 weeks of the year. According to the results, a hybrid model, 50% Artificial Neural Network (ANN)–50% Long Short Term Memory (LSTM), has the best performance in terms of financial effect. Furthermore, the forecast performance evaluation methods, such as Mean Absolute Error (MAE), are not necessarily coherent with inaccurate electricity price forecasts’ financial effect measures.

Suggested Citation

  • Umut Ugurlu & Oktay Tas & Aycan Kaya & Ilkay Oksuz, 2018. "The Financial Effect of the Electricity Price Forecasts’ Inaccuracy on a Hydro-Based Generation Company," Energies, MDPI, vol. 11(8), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2093-:d:163292
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    References listed on IDEAS

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    Cited by:

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    4. Beltrán, Sergio & Castro, Alain & Irizar, Ion & Naveran, Gorka & Yeregui, Imanol, 2022. "Framework for collaborative intelligence in forecasting day-ahead electricity price," Applied Energy, Elsevier, vol. 306(PA).
    5. Gandhi, Oktoviano & Zhang, Wenjie & Kumar, Dhivya Sampath & Rodríguez-Gallegos, Carlos D. & Yagli, Gokhan Mert & Yang, Dazhi & Reindl, Thomas & Srinivasan, Dipti, 2024. "The value of solar forecasts and the cost of their errors: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    6. Orlando Joaqui-Barandica & Diego F. Manotas-Duque, 2023. "How do Climate and Macroeconomic Factors Affect the Profitability of the Energy Sector?," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 444-454, July.
    7. Philip Beran & Arne Vogler, 2021. "Multi-Day-Ahead Electricity Price Forecasting: A Comparison of fundamental, econometric and hybrid Models," EWL Working Papers 2102, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Oct 2021.

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