Wind turbine operational optimization considering revenue and fatigue objectives

Abstract

Wind energy holds significant importance in achieving the energy transition towards a sustainable, carbon-free energy future. The core technology has matured over the last decades, resulting in a substantial increase in installed capacity. While these advancements have led to considerable cost reductions, they have also brought about reductions in mechanisms supporting the economic viability of wind energy projects. Additionally, wind energy is required to play a more substantial role in supporting the electrical network, traditionally done by conventional generation technologies. However, wind energy differs fundamentally from these technologies in terms of cost allocation through the project's lifecycle and the ability to forecast and regulate energy production. These factors have given rise to new challenges for wind energy research, expanding its focus on topics such as enhancing grid integration, increasing profitability, and better utilization of existing farms through lifetime extension. The objective of this thesis is to support these goals by introducing a methodology for the management of long-term operational objectives, in terms of revenue and fatigue loading, based on adaptive control considering wind conditions and electricity prices. Currently, wind turbines produce power according to the wind conditions, operating in a single mode for most of their operational life. However, wind turbines have the capability to adjust their power output. They can operate in down-regulation, producing lower power than the baseline mode, which also decreases structural loading. Moreover, they can operate in power-boosting mode, which allows for exceeding the baseline power level at higher wind speeds with a tradeoff in structural loading. Additionally, modern turbines are capable of individually adjusting the pitch angle of each blade, enabling individual pitch control (IPC), which can reduce structural loading with the penalty of increased pitch actuator usage. These functionalities require mainly software modifications and can be applied to most machines, allowing for switching between modes according to an external decision-maker. The active management of power output and fatigue loading over time (e.g, in hourly intervals) according to wind conditions and electricity prices enables operational strategies for optimizing long-term objectives. Fatigue consumption and revenue generation can be effectively redistributed over time and conditions in an optimal manner, providing additional flexibility to both wind farm owners and grid operators. Exploring this research objective requires a multidisciplinary approach encompassing aspects of controller design, surrogate modeling, and optimization. As a first step, a multi-mode controller is synthesized, allowing adjustment of the power level between 50% and 130% of the baseline level and the optional application of an IPC loop. Two configurations are considered to assess the impact of the choice of down-regulation trajectory on structural load reduction. The IPC loop is based on an individual blade control (IBC) approach involving an independent controller for each blade. In the next step, a data-driven surrogate model is created based on mid-fidelity aeroelastic simulations. Two regression approaches are considered: a spline-based interpolation and a Gaussian process regression (GPR). The two methods performed very similarly with low uncertainty in their predictions, possibly due to the dense factorial sampling considered. The surrogate model is utilized to develop an evaluation-optimization framework for long-term operational strategies. As a monitoring tool, it resembles a digital twin, enabling the tracking of fatigue consumption across all components, revenue accumulation, and other metrics potentially useful for condition monitoring purposes based on input time series of wind and prices. Two optimization approaches are developed within this framework. The first employs as input the mean wind distribution, aiming to optimize the controller mode per wind speed, thereby creating a long-term operational schedule according to wind conditions. The second approach leverages forecasts of wind speeds and electricity prices to assign the optimal control mode per time step within the designated horizon. The proposed method is evaluated using two multi-year datasets, each representing distinct boundary conditions with regard to wind and price dynamics. Two optimization scenarios are considered, reflecting potential business cases: revenue maximization with a constraint on fatigue damage accumulation and fatigue damage minimization with constraints on cumulative revenue. This methodology is employed to assess the applicability and performance of the proposed operational approach for different objectives, considering both fixed and fluctuating market prices. Furthermore, it investigates the impact of controller design, optimization approach, and boundary conditions on the optimization outcomes. The results indicate that the method is able to concurrently optimize the revenue and fatigue loads in the long term. The two optimization approaches exhibited differences in terms of improvements achieved and the distribution of fatigue and revenue. Fatigue reduction was possible across all wind turbine components, suggesting that it can enable lifetime extension for the entire system while maintaining profitability. Revenue maximization cases showed higher dependency on the optimization approach and the maximum power boosting level considered. For the forecast-based optimization, the length of the horizon considered proved to be critical. For all cases, the most influential factor determining the extent of improvement was found to be the boundary wind and market dynamics. Overall, this thesis demonstrates that there is potential for optimizing long-term objectives using adaptive control, and it is worth exploring it further to address current research challenges in wind energy.