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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.

For the grid connection of offshore wind farms today, in many cases a high-voltage direct current (HVDC) connection to the shore is implemented. The scheduled maintenance of the offshore and onshore HVDC stations makes up a significant part of the operational costs of the connected wind farms. The main factor for the maintenance cost is the lost income from the missing energy yield (indirect maintenance costs). In this study, we show an in-depth analysis of the used components, maintenance cycles, maintenance work for the on- and offshore station, and the risks assigned in prolonging the maintenance cycle of the modular multilevel converter (MMC). In addition, we investigate the potential to shift the start date of the maintenance work, based on a forecast of the energy generation. Our findings indicate that an optimized maintenance design with respect to the maintenance behavior of an HVDC energy export system can decrease the maintenance-related energy losses (indirect maintenance costs) for an offshore wind farm to almost one half. It was also shown that direct maintenance costs for the MMC (staff costs) have small effect on the total maintenance costs. This can be explained by the fact that the additional costs for maintenance staff are two orders of magnitude lower than the revenue losses during maintenance.