The financial effect of the electricity price forecasts' inaccuracy on a hydro-based generation company

Umut Ugurlu*, Oktay Tas, Aycan Kaya, Ilkay Oksuz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

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.

Original languageEnglish
Article number2093
JournalEnergies
Volume11
Issue number8
DOIs
Publication statusPublished - Aug 2018

Bibliographical note

Publisher Copyright:
© 2018 by the authors.

Funding

Acknowledgments: Umut Ugurlu and Oktay Tas are supported by the Research Fund of the Istanbul Technical University; project number: SDK-2018-44160. Umut Ugurlu is also partly supported by The Scientific and Technological Research Council of Turkey, 2214/A Program. Ilkay Oksuz is supported by an EPSRC program grant (EP/P001009/1) and the Wellcome EPSRC Center for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences, King’s College London (WE 203148/Z/16/Z). The GPUs used in this research were generously donated by the NVIDIA Corporation. We would like to thank three anonymous referees for their very constructive comments.

FundersFunder number
Wellcome EPSRC Center for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences
King’s College LondonWE 203148/Z/16/Z
Engineering and Physical Sciences Research CouncilEP/P001009/1
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu
Istanbul Teknik ÜniversitesiSDK-2018-44160

    Keywords

    • Electricity price forecasting
    • Hydro-based generation company
    • Mixed integer linear programming
    • Profit loss
    • Self-scheduling

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