Hierarchical coordination and power generation optimization for interacted wind farms of large-scale wind farm cluster

Abstract

To overcome the real-time computation and scalability issues caused by the high-dimensional decision space, nonlinearity in the generation optimization of large-scale wind-farm clusters, this paper proposes a Two-tier hierarchical framework that exploits inter-farm wake steering. Specifically, at the lower tier, each wind farm follows the power reference issued by the upper level while at the same time trying to maximize the wake-deficit velocity that reaches the inflow of downstream farms by adjusting the yaw angle of each turbine. Deep neural networks are trained to learn farm-level optimization and calculate the inter-farm wake, and act as interfarm wake models for upper tier optimization problem. At the upper tier, the controller manages the wake interactions among neighboring farms through regulating the power set-points of each farm under the given ambient wind conditions, to maximize the total power output of the whole cluster. Numerical experiments performed on a Chinese wind farm cluster confirm that the proposed framework achieves the desired optimization performance while significantly lowering computational cost and enhancing scalability compared with conventional centralized optimization.