The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets

TL;DR

This paper evaluates how online and offline machine learning algorithms for demand prediction influence long-term investment decisions and generator utilization in decentralized electricity markets, highlighting the benefits of online methods.

Contribution

It introduces a novel integration of demand prediction algorithms within an agent-based model to assess long-term market impacts and compares their effectiveness in reducing errors and costs.

Findings

Online algorithms reduce mean absolute error by 30%.

Improved demand predictions lower national grid reserve needs.

Prediction errors significantly affect 17-year investment and electricity mix.

Abstract

Electricity supply must be matched with demand at all times. This helps reduce the chances of issues such as load frequency control and the chances of electricity blackouts. To gain a better understanding of the load that is likely to be required over the next 24h, estimations under uncertainty are needed. This is especially difficult in a decentralized electricity market with many micro-producers which are not under central control. In this paper, we investigate the impact of eleven offline learning and five online learning algorithms to predict the electricity demand profile over the next 24h. We achieve this through integration within the long-term agent-based model, ElecSim. Through the prediction of electricity demand profile over the next 24h, we can simulate the predictions made for a day-ahead market. Once we have made these predictions, we sample from the residual distributions and perturb the electricity market demand using the simulation, ElecSim. This enables us to understand the impact of errors on the long-term dynamics of a decentralized electricity market. We show we can reduce the mean absolute error by 30% using an online algorithm when compared to the best offline algorithm, whilst reducing the required tendered national grid reserve required. This reduction in national grid reserves leads to savings in costs and emissions. We also show that large errors in prediction accuracy have a disproportionate error on investments made over a 17-year time frame, as well as electricity mix.