Probabilistic Forecasting of Imbalance Prices in the Belgian Context

TL;DR

This paper introduces a novel two-step probabilistic method for forecasting imbalance prices in the Belgian energy market, leveraging historical data and reserve activation levels to improve prediction accuracy.

Contribution

It presents a new probabilistic approach that models net regulation volume transitions and infers imbalance prices, specifically tailored for the Belgian market context.

Findings

The proposed method outperforms deterministic and Gaussian Process models.

It effectively captures the probabilistic nature of imbalance prices.

The approach provides more accurate risk assessments for market participants.

Abstract

Forecasting imbalance prices is essential for strategic participation in the short-term energy markets. A novel two-step probabilistic approach is proposed, with a particular focus on the Belgian case. The first step consists of 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 a widely used probabilistic technique, Gaussian Processes.