Master's Thesis : Improvement of decision making for trading in wholesale electricity market

Abstract

It is common practice for risk-averse industrial companies to reduce their exposure to the volatile prices of the spot market by securing a base load supply on the year-ahead electricity market. While many research efforts have been put in designing strategies to interact with several markets and assets, the case of small industrial consumers bound to a block-size constrained click-by-click contract for the year-ahead market is overlooked in literature. This Master thesis seeks to explore this gap and aims at improving the purchase decision making process of such electricity consumers. Multivariate probabilistic forecasting is investigated as a mean to complement the trader's expertise. Compelling results are that year-ahead electricity prices expose random-like patterns which make future price inference extremely difficult. A comparison study of several time series model suggests that training global deep learning models on related time series noticeably improves the forecast accuracy but that simpler models produce better calibrated prediction intervals.