Speaker
Description
Offshore wind farms play an important role in the transition toward carbon-free energy production, but their variable and uncertain power output, depending on the wind conditions, presents significant operational challenges. To mitigate fluctuations in the grid, transmission system operators and regulatory authorities may impose various constraints on the power injection into the grid. Complying with these requirements may necessitate the integration of a battery energy storage system (BESS) together with advanced supervisory control.
In this talk, a stochastic control strategy is presented for a hybrid wind-battery plant operating under time-dependent grid constraints, motivated by a real-world use case. The proposed approach maximizes the economic value of the plant, while ensuring compliance with both operational limitations and grid constraints. It is demonstrated that combining machine-learning-based wind farm power forecasts with a stochastic model predictive control (SMPC) framework can substantially improve the profitability of the hybrid renewable energy plant.
