Data Reconciliation in Electricity Markets : Implementing and Testing a Physics-Informed Optimization Framework to Correct Data Inconsistencies on the ENTSO-E Transparency Platform

Abstract

Climate goals and geopolitical shifts have forced the European electricity system into transition. High-quality electricity data is a critical success factor for stakeholders in this transition. This thesis examines the quality of publicly available electricity data on the ENTSO-E (The European Network of Transmission System Operators for Electricity) Transparency Platform, an important resource in the evolving European electricity landscape. The primary focus is the assessment of the internal consistency of 2021 ENTSO-E Transparency Platform data. To address identified inconsistencies, we apply a physics-informed reconciliation framework that incorporates a non-linear optimization model. This approach is designed to enhance data processing efficiency, potentially benefiting stakeholder decisionmaking and analysis. Our investigation identifies notable inconsistencies in the ENTSO-E Transparency Platform’s data across various zones. Specifically, discrepancies in production, consumption, and transmission data challenge the expected physical relationships, suggesting potential errors in one or more data categories. The proposed reconciliation framework has demonstrated promise in rectifying these issues, showing effectiveness in testing. Nevertheless, the model requires further refinement, especially in parameterization and handling data from geographically adjacent zones. The findings in the thesis can be valuable for readers considering using the physics-informed data reconciliation framework. It gives an understanding of the framework’s strengths and weaknesses in the European context and points to key areas for further research, such as applications for emissions tracking.