Reference : An Artificial Intelligence Solution for Electricity Procurement in Forward Markets
E-prints/Working papers : Already available on another site
Engineering, computing & technology : Energy
Business & economic sciences : Finance
Engineering, computing & technology : Computer science
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 mailto [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 >]
Jun-2020
No
[en] Artificial intelligence ; Deep learning ; Electricity procurement ; Forward markets
[en] Retailers and major consumers of electricity generally purchase a critical percentage of their estimated electricity needs years ahead on the forward markets. 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, 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 strategy. Basically, a new purchase operation is advised when this mathematical indicator hits the trigger associated with the market direction predicted by the forecaster. On average, the proposed approach surpasses benchmark procurement strategies and achieves a reduction in costs of 1.65% with respect to the perfectly uniform reference procurement strategy achieving the mean electricity price. Moreover, in addition to automating the electricity procurement task, this algorithm demonstrates more consistent results throughout the years compared to the benchmark strategies.
Montefiore Institute of Electrical Engineering and Computer Science - Montefiore Institute
Fonds de la Recherche Scientifique - F.R.S.-FNRS
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/248421
Preprint submitted to the Elsevier journal named "Journal of Commodity Markets".
https://arxiv.org/abs/2006.05784

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