The objectives of the workshop were to identify models and simulation capabilities that could be used to optimize wind turbines with LAC, to identify reference wind turbine and controller models that could be used for quantifying the benefits of LAC, and to suggest processes for optimizing reference wind turbines with LAC. Additionally, a goal of the workshop was to discuss opportunities to further collaborate on the proposed wind turbine optimization research.

After educational presentations by the workshop organizers providing overviews of LAC and systems engineering, invited presentations were given on the topic of “understanding the benefits of LAC” by researchers, lidar suppliers, and consultants. These presentations addressed current lidar capabilities, applications of LAC, field experiments, and tool chains for simulating wind turbines with LAC. The invited presentations were followed by small group discussions to “identify models needed to include LAC in wind turbine design.” During the group discussions, participants suggested types of turbine, controller, and lidar models that could be used in the optimization process, as well as cost models, simulation details, and required levels of fidelity.

On the second day, invited presentations were given by researchers and wind turbine manufacturers on the topic of “wind turbine optimization using systems engineering.” The speakers discussed different applications of LAC for reducing LCOE, highlighting challenges in reducing wind turbine capital expenditure (CapEx) costs, but identifying other opportunities, such as lifetime extension. Lastly, small group discussions were held to “identify methods for optimizing wind turbines with LAC,” where participants brainstormed applications of LAC for LCOE reduction and optimization workflows.

Priorities for joint research

Although many applications of LAC for reducing LCOE were proposed at the workshop, the four main applications suggested for further research were:

1. Increasing annual energy production (AEP), primarily through improving yaw alignment using lidar measurements.
2. Reducing turbine CapEx through structural load reduction from LAC.
3. Lifetime extension, by taking advantage of fatigue load reduction from LAC.
4. Wind class upgrades, using the fatigue load reduction from LAC to operate turbines in wind classes with higher wind speeds or turbulence levels.

Main research challenges

The main research challenges identified at the workshop include the following:

1. Extreme load reduction: Methods for reducing extreme loads on wind turbines, addressing finite lidar availability, are needed to reduce CapEx costs for many turbine designs.
2. Cost models: A generic lidar cost model, describing CapEx and operations and maintenance costs, is needed to properly optimize turbines with LAC. Additionally, the economics of lifetime extension should be understood better.
3. Different turbine types and wind classes: The amount of LCOE reduction possible from LAC depends on the type of wind turbine (e.g., rated power/rotor size) and the wind class in which it operates. Therefore, LAC should be investigated for a range of reference wind turbines and wind classes.
4. Formal systems perspective: Holistic design optimization frameworks should be explored to address the complexity of the optimization problem.

Next steps

Proposed next steps following the workshop include submitting a white paper on the state-of-the-art and research challenges of optimizing wind turbines with LAC, developing a generic lidar cost model, and hosting a repository with reference wind turbine, controller, and lidar models to foster additional research.