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, 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.Comment: Scientific article accepted for publication in the Energies journal edited by MDP

Methodological Development for Electrical Cost Optimization in Cement Factories by Using Artificial Intelligence

Las fábricas de cemento son plantas que presentan unos consumos energéticos muy importantes, tanto térmicos como eléctricos. Esta tesis doctoral tiene como objetivo desarrollar una metodología dirigida a minimizar el coste eléctrico de una fábrica de cemento, haciendo uso de distintas herramientas de Inteligencia Artificial -Redes Neuronales, Algoritmos Backpropagation y Algoritmos Genéticos, fundamentalmente. El objetivo de la tesis doctoral se alcanzará abordando de manera simultánea tres cuestiones diferentes que afectan al coste eléctrico: 1.- Optimización del coste eléctrico operacional. Se trata de reducir el consumo eléctrico de una fábrica de cemento, operando sobre ciertas variables del proceso productivo. 2.- Optimización de los precios eléctricos regulados. Se trata de mejorar la eficiencia del coste eléctrico en una fábrica de cemento, desplazando el consumo de electricidad a los periodos en los que esta tiene menor coste. 3.- Optimización de la compra de electricidad en el mercado

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, 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