An Artificial Intelligence Solution for Electricity Procurement in Forward Markets

TL;DR

This paper presents a deep learning-based algorithm for optimizing long-term electricity procurement in Belgian forward markets, reducing costs and increasing consistency compared to traditional policies.

Contribution

It introduces a novel algorithm that automates electricity procurement decisions using deep learning and deviation indicators, outperforming benchmark policies.

Findings

Cost reduction of 1.65% over benchmark policies

More consistent procurement results across years

Applicable to other commodity procurement problems

Abstract

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.