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

Umut Ugurlu; Oktay Tas; Aycan Kaya; Ilkay Oksuz

doi:10.3390/en11082093

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

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

^*Bu çalışma için yazışmadan sorumlu yazar

İşletme Mühendisliği Bölümü

Araştırma sonucu: Dergiye katkı › Makale › bilirkişi

17 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Makale numarası	2093
Dergi	Energies
Hacim	11
Basın numarası	8
DOI'lar	https://doi.org/10.3390/en11082093
Yayın durumu	Yayınlandı - Ağu 2018

Bibliyografik not

Publisher Copyright:
© 2018 by the authors.

Finansman

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.

Finansörler	Finansör numarası
Wellcome EPSRC Center for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences
King’s College London	WE 203148/Z/16/Z
Engineering and Physical Sciences Research Council	EP/P001009/1
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu
Istanbul Teknik Üniversitesi	SDK-2018-44160

Belgeye Erişim

10.3390/en11082093

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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

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.",

keywords = "Electricity price forecasting, Hydro-based generation company, Mixed integer linear programming, Profit loss, Self-scheduling",

author = "Umut Ugurlu and Oktay Tas and Aycan Kaya and Ilkay Oksuz",

note = "Publisher Copyright: {\textcopyright} 2018 by the authors.",

year = "2018",

month = aug,

doi = "10.3390/en11082093",

language = "English",

volume = "11",

journal = "Energies",

issn = "1996-1073",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

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T1 - The financial effect of the electricity price forecasts' inaccuracy on a hydro-based generation company

AU - Ugurlu, Umut

AU - Tas, Oktay

AU - Kaya, Aycan

AU - Oksuz, Ilkay

PY - 2018/8

Y1 - 2018/8

N2 - 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.

AB - 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.

KW - Electricity price forecasting

KW - Hydro-based generation company

KW - Mixed integer linear programming

KW - Profit loss

KW - Self-scheduling

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JO - Energies

JF - Energies

IS - 8

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ER -

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

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