Forecasting cross-border power transmission capacities in Central Western Europe using artificial neural networks

• Main Authors: Hazem Abdel-Khalek, Mirko Schäfer, Raquel Vásquez, Jan Frederick Unnewehr, Anke Weidlich
• Format: Article
• Language: English
• Published: SpringerOpen 2019-09-01
• Series: Energy Informatics
• Subjects: Flow-based market coupling; Cross-border electricity trading; Capacity calculation; Maximum bilateral exchanges; Artificial neural networks
• Online Access: http://link.springer.com/article/10.1186/s42162-019-0094-y

Abstract Flow-based Market Coupling (FBMC) provides welfare gains from cross-border electricity trading by efficiently providing coupling capacity between bidding zones. In the coupled markets of Central Western Europe, common regulations define the FBMC methods, but transmission system operators keep some degrees of freedom in parts of the capacity calculation. Besides, many influencing factors define the flow-based capacity domain, making it difficult to fundamentally model the capacity calculation and to derive reliable forecasts from it. In light of this challenge, the given contribution reports findings from the attempt to model the capacity domain in FBMC by applying Artificial Neural Networks (ANN). As target values, the Maximum Bilateral Exchanges (MAXBEX) have been chosen. Only publicly available data has been used as inputs to make the approach reproducible for any market participant. It is observed that the forecast derived from the ANN yields similar results to a simple carry-forward method for a one-hour forecast, whereas for a longer-term forecast, up to twelve hours ahead, the network outperforms this trivial approach. Nevertheless, the overall low accuracy of the prediction strongly suggests that a more detailed understanding of the structure and evolution of the flow-based capacity domain and its relation to the underlying market and infrastructure characteristics is needed to allow market participants to derive robust forecasts of FMBC parameters.