The integration of renewable energy sources, especially offshore wind farms, poses a challenge to the stability of the electricity

grid. As the proportion of wind power grows, the variability and uncertainty of wind power generation leads to frequent grid

overloads, which require curtailment and result in significant energy losses. In addition, the increase in renewable generation

leads to higher intra-day and day-ahead market volatility. Both challenges can be addressed with storage, which allows for

economically optimized portfolio management and has the potential to alleviate congestion by acting as a buffer. However,

current control strategies often neglect the inherent uncertainties in both wind power forecasts and grid load dynamics.

This study tackles the critical issue of how to effectively manage co-located battery storage systems in wind park-grid setups

under uncertain conditions to reduce energy losses caused by frequent grid overloads. We explore the potential of incorporating

probabilistic forecasts into battery management strategies, which may improve the mitigation of overload-induced energy losses

compared to conventional deterministic methods. In addition, our studies analyze current market effectiveness, concerning energy

losses, and how regulations need to be adapted under changing revenue scenarios.

Tested on real world wind park data, we show that while even a simple rule-based strategy can significantly enhance the

effective energy generation due to buffering in the battery storage system, only an optimization strategy can truly trigger the

economic potential. Above that we demonstrate that an advanced stochastic optimization strategy, based on probabilistic day-

ahead wind energy forecasts, can significantly reduce price risks with respect to intraday marketing compared to a simpler

approach based on a deterministic day-ahead wind energy forecast alone. In a setup with a 1 EUR/MWh risk charge on intraday

prices relative to day-ahead prices, for instance, switching from deterministic to probabilistic forecasts improves overall market

gain by 0.1%. The advantage grows to up to 5% at a 100 EUR/MWh risk charge, assuming a battery sized at 33% of the wind

farm’s nominal power.

These findings indicate that accounting for forecast uncertainty not only enables more efficient use of wind energy, challenging

the prevailing reliance on deterministic models in battery dispatch, but also allows market participants to achieve greater financial

benefits through probabilistic forecasting in the future development of energy systems. This has important implications for the

design of future renewable energy systems and regulatory adaption, where flexible storage operation will be critical to manage

growing shares of variable generation. By providing a robust simulation model validated with real-world data, this work con-

tributes to the advancement of predictive control in energy systems, supporting the broader goal of increasing renewable energy

integration while maintaining grid reliability.