Next Article in Journal
Adaptive Higher-Order Sliding Mode Control of Series-Compensated DFIG-Based Wind Farm for Sub-Synchronous Control Interaction Mitigation
Previous Article in Journal
Design of Kinetic-Energy Harvesting Floors
Open AccessArticle

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

1
Directorate C—Energy, Transport and Climate, Joint Research Centre (JRC), European Commission, Via E. Fermi 2749, 21027 Ispra (V.A.), Italy
2
Kiwi Power, Innovation and Grants Programme, 3 London Wall Buildings, London Wall, London EC2M 5PD, UK
*
Author to whom correspondence should be addressed.
Energies 2020, 13(20), 5420; https://doi.org/10.3390/en13205420
Received: 7 July 2020 / Revised: 28 September 2020 / Accepted: 15 October 2020 / Published: 16 October 2020
(This article belongs to the Special Issue Control and Optimization of Renewable Energy Systems)
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. View Full-Text
Keywords: balance energy market; price forecast; loss of load probability; machine learning; imbalance market; demand response balance energy market; price forecast; loss of load probability; machine learning; imbalance market; demand response
Show Figures

Figure 1

MDPI and ACS Style

Lucas, A.; Pegios, K.; Kotsakis, E.; Clarke, D. Price Forecasting for the Balancing Energy Market Using Machine-Learning Regression. Energies 2020, 13, 5420.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop