An Artificial Intelligence Solution for Electricity Procurement in Forward Markets
Retailers and major consumers of electricity generally purchase an important percentage of their estimated electricity needs years ahead in the forward market. This long-term electricity procurement task consists of determining when to buy electricity so that the resulting energy cost is minimised,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/23/6435 |
| _version_ | 1797545595534049280 |
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| author | Thibaut Théate Sébastien Mathieu Damien Ernst |
| author_facet | Thibaut Théate Sébastien Mathieu Damien Ernst |
| author_sort | Thibaut Théate |
| collection | DOAJ |
| description | Retailers and major consumers of electricity generally purchase an important percentage of their estimated electricity needs years ahead in the forward market. This long-term electricity procurement task consists of determining when to buy electricity so that the resulting energy cost is minimised, and the forecast consumption is covered. In this scientific article, the focus is set on a yearly base load product from the Belgian forward market, named calendar (CAL), which is tradable up to three years ahead of the delivery period. This research paper introduces a novel algorithm providing recommendations to either buy electricity now or wait for a future opportunity based on the history of CAL prices. This algorithm relies on deep learning forecasting techniques and on an indicator quantifying the deviation from a perfectly uniform reference procurement policy. On average, the proposed approach surpasses the benchmark procurement policies considered and achieves a reduction in costs of 1.65% with respect to the perfectly uniform reference procurement policy achieving the mean electricity price. Moreover, in addition to automating the complex electricity procurement task, this algorithm demonstrates more consistent results throughout the years. Eventually, the generality of the solution presented makes it well suited for solving other commodity procurement problems. |
| first_indexed | 2024-03-10T14:18:30Z |
| format | Article |
| id | doaj.art-7e30a428c0d044e9865a88e2e24883dc |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T14:18:30Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-7e30a428c0d044e9865a88e2e24883dc2023-11-20T23:36:24ZengMDPI AGEnergies1996-10732020-12-011323643510.3390/en13236435An Artificial Intelligence Solution for Electricity Procurement in Forward MarketsThibaut Théate0Sébastien Mathieu1Damien Ernst2Montefiore Institute, University of Liège, Allée de la Découverte 10, 4000 Liège, BelgiumMontefiore Institute, University of Liège, Allée de la Découverte 10, 4000 Liège, BelgiumMontefiore Institute, University of Liège, Allée de la Découverte 10, 4000 Liège, BelgiumRetailers and major consumers of electricity generally purchase an important percentage of their estimated electricity needs years ahead in the forward market. This long-term electricity procurement task consists of determining when to buy electricity so that the resulting energy cost is minimised, and the forecast consumption is covered. In this scientific article, the focus is set on a yearly base load product from the Belgian forward market, named calendar (CAL), which is tradable up to three years ahead of the delivery period. This research paper introduces a novel algorithm providing recommendations to either buy electricity now or wait for a future opportunity based on the history of CAL prices. This algorithm relies on deep learning forecasting techniques and on an indicator quantifying the deviation from a perfectly uniform reference procurement policy. On average, the proposed approach surpasses the benchmark procurement policies considered and achieves a reduction in costs of 1.65% with respect to the perfectly uniform reference procurement policy achieving the mean electricity price. Moreover, in addition to automating the complex electricity procurement task, this algorithm demonstrates more consistent results throughout the years. Eventually, the generality of the solution presented makes it well suited for solving other commodity procurement problems.https://www.mdpi.com/1996-1073/13/23/6435artificial intelligencedeep learningelectricity procurementforward/future market |
| spellingShingle | Thibaut Théate Sébastien Mathieu Damien Ernst An Artificial Intelligence Solution for Electricity Procurement in Forward Markets Energies artificial intelligence deep learning electricity procurement forward/future market |
| title | An Artificial Intelligence Solution for Electricity Procurement in Forward Markets |
| title_full | An Artificial Intelligence Solution for Electricity Procurement in Forward Markets |
| title_fullStr | An Artificial Intelligence Solution for Electricity Procurement in Forward Markets |
| title_full_unstemmed | An Artificial Intelligence Solution for Electricity Procurement in Forward Markets |
| title_short | An Artificial Intelligence Solution for Electricity Procurement in Forward Markets |
| title_sort | artificial intelligence solution for electricity procurement in forward markets |
| topic | artificial intelligence deep learning electricity procurement forward/future market |
| url | https://www.mdpi.com/1996-1073/13/23/6435 |
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