Workshop 15: Optimizing Wind Turbines with Lidar-Assisted Control using Systems Engineering

IEA Wind Task 32 + Task 37 Workshop 15:

Optimizing Wind Turbines with Lidar-Assisted Control using Systems Engineering

17th and 18th October 2019

Workshop leader: Task 32: Eric Simley (NREL), Task 37: Pietro Bortolotti (NREL)

Organization team: Katherine Dykes (DTU Wind Energy), Holger Fürst (SWE), David Schlipf (WETI), Andy Scholbrock (NREL)

Venue: University of Massachusetts Amherst, Amherst, MA, USA

Following the NAWEA/WindTech 2019 Conference

Background to the Workshop

Within the topic of loads and control, Task 32 has been active in mitigating barriers to the use of lidar-assisted wind turbine control. Workshops were held on the topics of optimizing lidars for wind turbine control applications (Workshop 2) and certification of lidar-assisted control applications (Workshop 8).

The purpose of IEA Wind Task 37 (Systems Engineering) is to coordinate international research activities to analyze wind power plants as holistic systems. Through the development of analysis tools and reference models, Task 37 advances systems engineering methods for reducing the levelized cost of energy of wind energy projects.

Lidar-assisted control (LAC) is effective at reducing structural loads on wind turbines, which has been demonstrated through simulation as well as field testing. However, it is difficult to determine the reduction in levelized cost of energy (LCOE) that can be achieved through the use of LAC, especially because of the additional costs associated with integrating lidar hardware into the turbine design. Furthermore, LAC is often analyzed using existing turbine designs. Systems engineering presents an opportunity to directly include the use of LAC during the turbine design process to optimize LCOE. The aim of this workshop was to combine the experience in lidar-assisted controller design and modeling from Task 32 with the knowledge of systems engineering analysis using reference wind turbine models from Task 37 to show how wind turbines can be optimized with LAC.

Summary

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.

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.

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.
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.

All images used with permission. Photos are copyright Eric Simley (NREL) 2019.

Member-only Documents

The minutes, presentations, and other documents from this meeting are only available to logged-in members of IEA Wind Task 32. They include:

  • Introduction to the workshop (Eric Simley and Pietro Bortolotti, NREL)
  • Educational Session
    • Overview of lidar-assisted control for wind turbines (Andy Scholbrock, NREL)
    • Systems engineering for wind turbines (Pietro Bortolotti, NREL)
    • Task 37 overview: Wind turbine systems engineering (Katherine Dykes, DTU Wind Energy)
  • Invited presentations: Understanding the benefits of lidar-assisted control
    • DTU SpinnerLidar for upwind inflow and turbulence measurements (Torben Mikkelsen, DTU Wind Energy)
    • Developing the usage of wind turbine integrated lidars (Matthieu Boquet, Leosphere) (file not available)
    • Benchmark test of LAC using 300kW wind turbine (Hirokazu Kawabata, AIST)
    • Mitsubishi Electric’s nacelle-mounted LiDAR for lidar-assisted control of wind turbines: Recent progress and benefits (Shumpei Kameyama, Mitsubishi Electric)
    • Tools for systems engineering for lidar assisted control (Steffen Raach, sowento)
  • Invited presentations: Wind turbine optimization using systems engineering
    • Towards a systems perspective on LAC: Some preliminary findings on the turbine-level cost/benefit analysis (Carlo Bottasso, TUM)
    • LAC at GE as an experiential overview (Bernie Landa, GE Renewables)
    • Rotor and turbine system trends and tradeoffs – An OEM perspective (Kristian Dixon, Envision Energy)
    • Systems engineering for lidar-assisted control: A sequential approach (David Schlipf, WETI)

 

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