Optimal wind farm energy and reserve scheduling incorporating wake interactions

This paper proposes a novel approach for optimal energy and reserve scheduling of wind farms by explicitly modelling wake interactions to enhance market participation and operational efficiency. Conventional methods often neglect wake effects, relying on power curve estimations that represent an upper limit and reduce market performance. To address this, a two-stage stochastic programming framework is developed, integrating a wake-aware power estimation model within the FLORIS simulation software. Wind and reserve uncertainties are addressed through scenario generation and reduction, enabling wind power producers to optimise participation in day-ahead energy and ancillary services markets, with particular focus on the Frequency Restoration Reserve (FRR). The wake-aware model provides more realistic power output predictions based on site-specific wind and atmospheric conditions, improving scheduling accuracy and reducing imbalance penalties. Wake steering is further employed to mitigate wake-induced losses and increase income through participation in ancillary services. The proposed approach is evaluated through a case study of the London Array offshore wind farm participating in the Great Britain (GB) electricity markets. Results show that conventional methods estimate production 12%–13% higher, leading to imbalance penalties and 3% lower revenue compared with the wake-aware approach accounting for wake interactions. Moreover, the steering-enhanced approach yields an additional 1%–2% increase in income relative to the wake-aware baseline. These findings underscore the value of accounting for wake interactions in wind farm scheduling and demonstrate the economic and operational benefits of active wake management, offering insights for improving grid stability and profitability as wind penetration continues to rise.