Reference : An Artificial Intelligence Solution for Electricity Procurement in Forward Markets
Scientific journals : Article
Engineering, computing & technology : Computer science
Engineering, computing & technology : Energy
Business & economic sciences : Finance
Engineering, computing & technology : Computer science
Engineering, computing & technology : Energy
Business & economic sciences : Finance
http://hdl.handle.net/2268/248421
An Artificial Intelligence Solution for Electricity Procurement in Forward Markets
English
Théate, Thibaut mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids >]
Mathieu, Sébastien [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids >]
Ernst, Damien mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids >]
Dec-2020
Energies
Multidisciplinary Digital Publishing Institute (MDPI)
13
23
Yes (verified by ORBi)
International
1996-1073
Switzerland
[en] Artificial intelligence ; Deep learning ; Electricity procurement ; Forward/Future markets
[en] 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.
Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science
F.R.S.-FNRS - Fonds de la Recherche Scientifique
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/2268/248421
10.3390/en13236435
Citation: Théate, T.; Mathieu, S.; Ernst, D. An Artificial Intelligence Solution for Electricity Procurement in Forward Markets. Energies 2020, 13, 6435.

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