Probabilistic Forecasting of Imbalance Prices in the Belgian Context

Abstract

Forecasting imbalance prices is essential for strategic participation in the short-term energy markets. A novel twostep probabilistic approach is proposed, with a particular focus on the Belgian case. The first step consists in computing the net regulation volume state transition probabilities. It is modeled as a matrix computed using historical data. This matrix is then used to infer the imbalance prices, since the net regulation volume can be related to the level of reserves activated and the corresponding marginal prices for each activation level are published by the Belgian Transmission System Operator one day before electricity delivery. This approach is compared to a deterministic model, a multi-layer perceptron, and to a widely used probabilistic technique, Gaussian Processes.

Probabilistic forecasting of German electricity imbalance prices

“…The scarce electricity imbalance price forecasting literature focuses on point forecasting [11,12], interval forecasting [11] and probabilistic forecasting [13]. The work of Dumas et al [13] is naturally the closest one to our study. The authors utilize a two-step approach, namely they first calculate the probabilities for the net imbalance and then based on that make predictions regarding the imbalance prices.…”

“…The literature on modelling and forecasting in electricity balancing markets can be split to imbalance forecasting [7][8][9][10], imbalance price forecasting [11][12][13] and the application in trading [8-10, 14, 15]. The scarce electricity imbalance price forecasting literature focuses on point forecasting [11,12], interval forecasting [11] and probabilistic forecasting [13]. The work of Dumas et al [13] is naturally the closest one to our study.…”

“…The authors additionally analyse the impact of the imbalance pricing mechanism on market behaviour, and they conclude that although the system imbalance is similar for different mechanisms, the mechanism that minimizes the imbalance costs for the market is the single price settlement. The literature on modelling and forecasting in electricity balancing markets can be split to imbalance forecasting [7][8][9][10], imbalance price forecasting [11][12][13] and the application in trading [8-10, 14, 15]. The scarce electricity imbalance price forecasting literature focuses on point forecasting [11,12], interval forecasting [11] and probabilistic forecasting [13].…”

Short Term Net Imbalance Volume Forecasting Through Machine and Deep Learning: A UK Case Study

“…As dynamic energy markets are relatively new, there are very few findings in the literature regarding imbalance market forecasting, and even more specifically targeting NIV. Imbalance market's Price Forecasting challenge has been addressed from [13][27] [20] through probabilistic forecasting models, while [7] denoted the importance of NIV as a highly correlated feature to the price. More specifically in [7], a statistical approach has been followed for the calculation of a transition state probability of NIV over historical data.…”

“…Imbalance market's Price Forecasting challenge has been addressed from [13][27] [20] through probabilistic forecasting models, while [7] denoted the importance of NIV as a highly correlated feature to the price. More specifically in [7], a statistical approach has been followed for the calculation of a transition state probability of NIV over historical data. Traditional timeseries algorithms' such as ARIMA were tested in [10] over univariate data, while also conducted autocorrelation and partial autocorrelation analysis in order to explore association between the time periods, to conclude that Feed Forward Networks over multivariate data can achieve higher accuracy because of the problem's complexity.…”