A Forecasting Model for the Prediction of System Imbalance in the Greek Power System

Abstract

Forecasting imbalance volumes are of great importance for the different actors in electricity markets. From a transmission system operator (TSO) perspective, balancing supply and demand in real-time is one of the main operational tasks to ensure the safe and reliable operation of the power system, while market participants also use forecasting tools to enhance their participation strategy in electricity wholesale markets. Over the last few years, the increasing integration of renewable energy sources into the power system has created additional complexity for the problem of accurately determining the imbalance volume. In the present work, a case study of the Greek balancing market is presented and analysed. Different algorithms and a set of external predictors are adopted both from the market and operational perspective and compared for two different forecasting horizons.

Keywords

• system imbalance
• electricity markets
• time-series forecasting
• machine learning
• imbalance forecasting