Price Forecasting for the Balancing Energy Market Using Machine-Learning Regression

Alexandre Lucas,Evangelos Kotsakis,Konstantinos Pegios,Dan Clarke

doi:10.3390/en13205420

Abstract

The importance of price forecasting has gained attention over the last few years, with the growth of aggregators and the general opening of the European electricity markets. Market participants manage a tradeoff between, bidding in a lower price market (day-ahead), but with typically higher volume, or aiming for a lower volume market but with potentially higher returns (balance energy market). Companies try to forecast the extremes of revenues or prices, in order to manage risk and opportunity, assigning their assets in an optimal way. It is thought that in general, electricity markets have quasi-deterministic principles, rather than being based on speculation, hence the desire to forecast the price based on variables that can describe the outcome of the market. Many studies address this problem from a statistical approach or by performing multiple-variable regressions, but they very often focus only on the time series analysis. In 2019, the Loss of Load Probability (LOLP) was made available in the UK for the first time. Taking this opportunity, this study focusses on five LOLP variables (with different time-ahead estimations) and other quasi-deterministic variables, to explain the price behavior of a multi-variable regression model. These include base production, system load, solar and wind generation, seasonality, day-ahead price and imbalance volume contributions. Three machine-learning algorithms were applied to test for performance, Gradient Boosting (GB), Random Forest (RF) and XGBoost. XGBoost presented higher performance and so it was chosen for the implementation of the real time forecast step. The model returns a Mean Absolute Error (MAE) of 7.89 £/MWh, a coefficient of determination (R2 score) of 76.8% and a Mean Squared Error (MSE) of 124.74. The variables that contribute the most to the model are the Net Imbalance Volume, the LOLP (aggregated), the month and the De-rated margins (aggregated) with 28.6%, 27.5%, 14.0%, and 8.9% of weight on feature importance respectively.

Highlights

The political guidelines for the new European Commission (2019–2024) propose a EuropeanGreen Deal, that puts Europe at the forefront, to become the world’s first climate neutral continent [1].The role of electricity is expected to increase in the coming decade and by 2050 all scenarios contain high end-use electrification, which would mean 53% by 2050, from 20% in 2018 of electricity in final energy consumption share [2]
This study presents the development of a multi-regression model, testing three machine learning algorithms, Gradient Boosting (GB), Random Forest (RF) and Extreme Gradient Boosting (XGBoost), presenting a combined approach of several categories according to Aggarwal et al 2009 [7] classification
In order to focus on a specific framework, in this study the United Kingdom (UK) Energy Balancing Market is address, which is managed by ELEXON [45]

Summary

Introduction

The political guidelines for the new European Commission (2019–2024) propose a EuropeanGreen Deal, that puts Europe at the forefront, to become the world’s first climate neutral continent [1].The role of electricity is expected to increase in the coming decade and by 2050 all scenarios contain high end-use electrification, which would mean 53% by 2050, from 20% in 2018 of electricity in final energy consumption share [2]. Besides the dynamic and highly complex traded value of electricity as a commodity, which is subject to multiple intrinsic and extrinsic variables, trying to predict the market with a high degree of confidence has many benefits for all market participants Such benefits arise from setting up their respective risk-adjusted bidding strategies and ensuring cost-optimal generation activities, to formulating evidence and insights on the economic development of a country’s power sector, as well as ensuring the reliable security and safe operation of the whole electricity system [5,6]. The functioning of the balancing market is more sophisticated, as it acts as a bridge between the financial transactions (electricity market) and the physical transactions (the power system) It is the last opportunity for all parties to state a position (load/generation decrease or increase needs/availability), for each settlement period. ELEXON serves around 470 market participants and settled around 44 TWh in balancing actions and partys’ imbalance volumes in the reported years of 2018/2019 [46]

Methods

Results

Conclusion