Expected Impacts on Cost of Energy through Lidar Based Wind Turbine Control

1. Expected Impacts on Cost of Energy through Lidar Based Wind Turbine Control Fundedby and in collaboration with EPRI Tony Rogers, DNV Co-authors: Alex Byrne, Tim McCoy, Katy Briggs

2. Introduction • Goal of project: Leverage existing technical research into estimates of cost of energy of nacelle-based light detection and ranging (lidar) turbine control • This presentation • Lidar applications to control • Cost model • Results and sensitivity • Conclusions and recommendations

3. Controls Application of Lidar DTU Tjæreborgexperiment www.vindenergi.dtu.dk • Applications considered: • Nacelle-mounted, forward-looking lidar • Options: load reduction, increased energy • Advantages • Less biased than nacelle anemometry • Advanced knowledge of wind • Challenges • Wind evolves after measurement point • High lidar costs • Technical complexity • Lidar reliability • Turbulence

4. Controls Application of Lidar Typical example: F. Dunne, E. Simley, and L.Y. PaoNREL/SR-5000-52098

5. Cost Model Approach • Benefits based on: • Reported model and test results • Benefits • Increased energy capture • Reduced operations and maintenance (O&M) costs • Costs • Lidar costs • Increased capital or O&M costs • Cost model: equivalent net present value (NPV) method to calculate change in cost of energy • Performed uncertainty and sensitivity analyses using Monte Carlo simulation Wind Iris Prototype at the Alpha VentusOffshore Project, Germany.

6. Benefits Considered and Strategy for Capturing Benefits • Yaw control or gust tracking • Increased power capture • Reduced loads • Reduced O&M and downtime costs • Extended life • Turbine redesign 3. Taller Tower 1. Extended Life 2. Larger Rotor Year 26 Year 20 Year 1

7. Magnitude of Lidar Benefits CTW’s Vindicator atop a Nacelle • Overview • Limited test results • Modelling has many assumptions • Interdependencies often not considered • Load reduction and energy capture estimates transformed into estimates of O&M cost and turbine availability improvements • DNV KEMA’s estimates of lidar benefits from optimized controls for increased energy capture and load reduction:

8. Costs Considered • Capital cost of lidar • Sources: lidar vendors • Considered volume pricing—fairly uncertain • Lidar O&M cost • Sources: lidar vendors—very uncertain • Increased component O&M costs • Yaw motors, pitch motors, etc. • Source: internal DNV KEMA database • Added cost for larger rotor or taller tower • Source: theoretical scaling • Added O&M costs with life extension • Source: internal DNV KEMA database

9. Scenarios and Benefits

10. Cost Benefit Monte Carlo Results

11. Conclusions and Recommendations for Future Work • Conclusions: • Extended life and taller tower scenarios: Noticeable impact on cost of energy (COE) • Larger rotor scenario: increased capital cost of larger rotor outweighs benefits • Biggest factor in COE impact: strategy of capturing loads benefits • Large uncertainty still exists on the loads benefits and some costs • Recommendations for future work: • Offshore considerations • Required to reduce uncertainty: • Prototype tests that include lidar-based pitch control • Firmer volumecapital and O&M costs of lidar • Better understanding of loads reduction effects on O&M costs • Fatigue • Extreme limited designs • Address wind evolution problem • Potential improvements in lidar capabilities (more beams, accuracy, reliability)

12. www.dnvkema.com