A Convex Economic Model Predictive Control Framework for Hydraulic-Drivetrain Wind Turbines and Farms

Abstract

In the mission to slow down global warming, the replacement of fossil fuels by renewable energy resources is key. A tipping point in the adoption of renewable energy resources is notable, as they are becoming economically more viable. Offshore wind energy is considered essential in realizing the greenhouse gas emission reduction targets. However, high construction, installation, and maintenance costs cause offshore wind to remain in competition with fossil fuel-based energy. Therefore, further reduction of offshore wind energy costs is crucial.

The most common method of wind energy cost reduction is the upscaling of nominal power ratings by increasing the size of the rotor. However, an alternative way for attaining cost reductions might be the employment of a radically different hydraulic drivetrain concept. Hydraulic-drivetrain wind turbines have the potential of lowering the construction and maintenance cost of wind farms by using a shared hydraulic network. Hydrostatic power, generated by individual wind turbines, is transmitted to a central location where electrical power is collectively generated. However, challenges arise in the control of hydraulic-drivetrain wind farms, e.g., limited pump torque controllability and increasingly complex coupled dynamics for a rising amount of employed wind turbines.

Current control strategies for hydraulic-drivetrain wind farms are developed based on classical control methods. These methods become less suitable for maximizing the collective power of larger wind farms. Therefore, this thesis presents a modification of the existing convex economic model predictive control (CEMPC) framework for conventional wind turbines, such that it becomes compliant to the domain of single-turbine and multi-turbine hydraulic-drivetrain wind farms. The CEMPC method provides computational tractability by circumventing the nonlinear nature of the dynamics. A novelty in this work is that the CEMPC framework is scalable for the control of multiple wind turbines. Moreover, an additional algorithm is proposed to extend the applicability of this framework to control wind turbines containing digital displacement pumps.

In a simulation study, the performances of the developed CEMPC framework applied to a single-turbine and two-turbine hydraulic-drivetrain wind farm are compared for different wind speed scenarios. In all scenarios, the proposed CEMPC framework shows its ability to adequately control the employed wind turbines. When comparing the obtained power production efficiencies with a conventional wind farm employing NREL 5-MW reference turbines, the efficiency of the hydraulic-drivetrain wind farm is 10%-17% lower compared to the reference wind farm. To reduce the levelized costs of offshore wind energy, the hydraulic-drivetrain wind farm concept has to provide at least an equivalent cost reduction over the lifetime of the wind farm. The scalability in the number of controlled turbines makes the proposed CEMPC framework for hydraulic-drivetrain wind farms a promising candidate for realizing the necessary cost reduction.