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

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

Controls Application of Lidar 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 DTU Tjæreborg experiment www.vindenergi.dtu.dk

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

Cost Model Approach Benefits based on: Benefits Costs 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 Ventus Offshore Project, Germany.

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 1 Year 20 Year 26

Magnitude of Lidar Benefits 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: CTW’s Vindicator atop a Nacelle Increased energy due to optimized controls 0.6% Reduced turbine O&M costs (life-time average) 6% Increased turbine availability, reduced O&M downtime 0.4%

Costs Considered Capital cost of lidar Lidar O&M cost 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

Scenarios and Benefits 2.5 MW Turbines, Retrofitted Lidar, Extended Life 5 MW Turbines, Integrated Lidar, Larger Rotor 5 MW Turbines, Integrated Lidar, Taller Tower Increased energy/revenue due to extended project life 6-year extension; 30% energy increase N/A  N/A Increased energy due to larger rotor  N/A  6% increase in rotor area; 4% energy increase N/A   Increased energy due to taller tower (assumed wind shear exp: 0.2)  N/A 8% increase in tower height; 3% energy increase

Cost Benefit Monte Carlo Results 15 April 2017 Cost Benefit Monte Carlo Results Box plots: Whiskers: max and min Box: mean+/- one standard deviation Bar: mean

Conclusions and Recommendations for Future Work 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 volume capital 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)

15 April 2017 www.dnvkema.com