1. Engineering an Optimal Wind Farm Stjepan Mahulja EWEM Rotor Design : Aerodynamics s132545, 4312600 30th September 2015 SUPERVISORS: Gunner Chr. Larsen (DTU) Ali Elham (TU DELFT) COMMITTEE: Gerard J.W. Van Bussel (TU DELFT) Michiel Zaaijer (TU DELFT)

2. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” WFLO FRAMEWORK The thesis structure 2 Special project –Literature study –Exploration of the topic Thesis –WFLO using surrogate models SURROGATE MODELING OPTIMISATION PLATFORM COST MODEL WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … …

3. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” Plan for today 1.Introduction –Wind farm planning –Wind Farm Layout Optimisation Problem -> How to engineer an optimal wind farm? Consequences of the wake field Design variables, objective function 2.Surrogate modeling –Introduction to surrogate modeling (+ SUMO) –Building a DWM surrogate model 3.Wind Farm Layout Optimisation –Design variables -> optimisation strategies –Example: Middelgrunden 4.Conclusions 3 WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … …

4. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” How to plan a wind farm? 4 WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … …

5. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” Wind Farm PERFORMANCE COSTS Availability or accessibility Component degradation Wind conditions Turbulence characteristics Mean wind speed distribution Wind direction distribution Loads Structure 5 SURROGATE MODELING LAYOUT OPTIMISATION … WFLO … …

6. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” Wind Farm Layout Optimisation Problem (WFLOP) “determining locations of where the turbines should be placed in order to maximize the financial value of the wind farm” Investment Positive cash flow -> Electricity Negative cash flow -> O&M Financial Balance 6 SURROGATE MODELING LAYOUT OPTIMISATION … WFLO … …

7. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” InvestmentPower outputLifetime equivalent loading Turbines + foundationEstimate for AEPFatigue driven degradation of components Cable gridPrice of electricityO&M costs SIMULATIONS SHORTHEST DISTANCE ALGORITHM COST MODEL - design - analysis -grade OPTIMISATION strategy (module) remains... 7 SURROGATE MODELING LAYOUT OPTIMISATION … WFLO … …

8. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” SIMULATIONS SHORTHEST DISTANCE ALGORITHM COST MODEL OPTIMISATION -AEROELASTIC SIMULATIONS OF EACH WT IN THE FARM -SHORTEST CABLING LAYOUT -TRANSLATES AEP, LOADS AND CABLE LENGTH TO MONETARY UNITS (WEIGHTING) - DETERMINES HOW THE TURBINES WILL BE SHIFTED TRADITIONAL APPROACH WIND FARM LAYOUT 8 SURROGATE MODELING LAYOUT OPTIMISATION … WFLO … …

9. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” SIMULATIONS: Dynamic Wake Meandering model In-stationary wind field (as seen from 1 turbine) Part of HAWC2 Simulation of 30min (takes approx. 30min of real time) –For each wind speed (4..25 m/s -> 22) –For each wind direction (0..330° -> 12) –eg. 50 turbines -> 50 x 22 x 12 = 13200 simulations each layout parallel computing (550 CPUs) -> 24runs x 30min = 12h each func. eval –Genetic optimization – O(1000) func. eval. –Gradient based optimization - O(100) func. eval. 13200h = 550 days !!! Optimistic estimate SURROGATE MODEL Need for a 9 SURROGATE MODELING LAYOUT OPTIMISATION … WFLO … …

10. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” SHORTHEST DISTANCE ALGORITHM COST MODEL OPTIMISATION MODIFIED APPROACH SURROGATE MODEL 10 SURROGATE MODELING LAYOUT OPTIMISATION … WIND FARM LAYOUT WFLO … …

11. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” 11 WT SURROGATE MODEL SpacAng sec15D0°0° sec23D30° sec3∞- …….. sect12∞- IN OUT Farm level Turbine level Sector level COST MODEL OPTIMISATION SURROGATE MODELING LAYOUT OPTIMISATION … WFLO … …

12. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” OPTIMISATION: Multi-fidelity strategy GENETIC SEARCH GRADIENT BASED OPTIMISATION on a coarse grid in a continuous domain -Implicitly finds the optimal number of turbines -Primarily deals with balancing the costs -Wake effects are optimised on coarse grid size with accuraccy of 2D -Turbine count is constant -Hardly any effect to the investment -Depends of the initial solution -Minimises the wake effect 12 SURROGATE MODELING LAYOUT OPTIMISATION … WFLO … …

13. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” Surrogate modeling What is a surrogate model? How to build a surrogate? In application to WFLO, what is needed? 13 SURROGATE MODELING LAYOUT OPTIMISATION … WFLO … …

14. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” - Substitution of the true response of some computationally expensive or technically demanding system Needs to be cheap to build and cheap to use Good enough 14 LAYOUT OPTIMISATION … SURROGATE MODELING WFLO … …

15. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” - Approximation based on known function values Locations of sampling points are chosen 15 LAYOUT OPTIMISATION … SURROGATE MODELING WFLO … …

16. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” How is it done? SEQUENTIAL MODELING -Initial samples –Fit a model –Measure the model –Exploit the model / Explore the domain –New samples Fit a model Measure the model Exploit the model / Explore the domain New samples CONVERGENCE 2. PARALLEL COMPUTING 1. EXPERIMENTAL SET-UP 3. COORDINATION 16 LAYOUT OPTIMISATION … SURROGATE MODELING WFLO … …

17. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” 17 LAYOUT OPTIMISATION … SURROGATE MODELING WFLO … …

18. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” results 3 inputs 7 outputs 1051 sampling points (+ 421 for validation) Accuracy of 5% (MRE) RBF gives best results 18 LAYOUT OPTIMISATION … SURROGATE MODELING WFLO … … Coarse ”one-shot” surrogate -> study -> Sequential design surrogate

19. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” Layout optimisation in practice Middelgrunden Wind Farm –ORIGINAL: 20x2MW -> SCALED: 20x5MW 19 LAYOUT OPTIMISATION … SURROGATE MODELING WFLO … …

20. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” 20 LAYOUT OPTIMISATION … SURROGATE MODELING WFLO … … CONVEXITY 2 ND STAGE -53 TURBINES -AEP INCREASE BY 150% -NPV INCREASE BY 100% WAKE LOSS INCREASES, BUT EVENS OUT 1 ST STAGE -CAPACITY FACTOR IS INCREASED BY 4%, BUT TOTALLY REDUCED BY 2% -NPV INCREASES BY 134%

21. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” 21 LAYOUT OPTIMISATION … SURROGATE MODELING WFLO … …

22. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” OPTIMAL design 22 LAYOUT OPTIMISATION … SURROGATE MODELING WFLO … …

23. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” 23 LAYOUT OPTIMISATION … SURROGATE MODELING WFLO … …

24. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” Conclusions Framework makes sense –Surrrogate models are necessary –Multi-fidelity optimisation is prefered Surrogate requires tune-up Financial aspects fo a wind farm need to be taken into account (weighting!) 24 WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … … …

25. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” Thank you 25

26. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” COST MODEL: Objective function Has to consider all parameters describing the wind farm (performance + costs) DVs: –Number of turbines –Turbine positions LCoE (not considering market conditions) -> may not be optimal in from economic POV NPV 26 WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … … …

27. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” SHORTHEST DISTANCE ALGORITHM: Cable grid design Cable accounts for approx. 5% of the total cost Prim’s and Kruskal’s algorithms function written in MATLAB run on layout at the end of each iteration (not considered as DV) -> part of COST MODEL 27 WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … … …

28. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” Turbine mapping in the genetic algorithm 28 WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … … …

29. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” 3. In a form of regression/interpolation 29 WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … … …

30. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” Sequential modeling 30 LAYOUT OPTIMISATION … SURROGATE MODELING WFLO … … SURROGATE MODELING LAYOUT OPTIMISATION … … … …

31. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” RBF Rational Kriging RBF Rational Kriging Quality Domain Surrogate model types: HETEROGENETIC 31 WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … … …

32. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” 32 WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … … …

33. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” Optimisation 33 WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … WFLO SURROGATE MODELING LAYOUT OPTIMISATION … … … …

34. DTU Wind Energy, Technical University of Denmark Add Presentation Title in Footer via ”Insert”; ”Header & Footer” 34