1. 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

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
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

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

5. 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.

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 1
Year 20
Year 26

7. 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%

8. 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

9. 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; % 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

10. 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

11. 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)

12. 15 April 2017