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WFO_DETC2011 Souma


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This is paper presentation from the ASME International Design Engineering Technical Conference (IDETC), 2011 in Washington DC

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WFO_DETC2011 Souma

  1. 1. Characterizing the Influence of Land Configuration on the Optimal Wind Farm Performance Souma Chowdhury*, Jie Zhang*, Achille Messac#, and Luciano Castillo*# Syracuse University, Department of Mechanical and Aerospace Engineering* Rensselaer Polytechnic Institute, Department of Mechanical, Aerospace, and Nuclear Engineering ASME 2011 International Design Engineering Technical Conferences (IDETC) and Computers and Information in Engineering Conference (CIE) August 28 – 31, 2011 Hyatt Regency Washington, DC
  2. 2. Wind Farm Optimization Farm Layout Planning: The power loss in a wind farm due to wake effects can be substantially regained by optimizing the farm layout. Turbine Type Selection: Optimally selecting the turbine-type(s) to be installed can further improve the performance of the wind farm. Planning the Farm Land Configuration: Careful decision making regarding the orientation and the aspect ratio of the farm land is important to provide an effective response to the expected wind distribution. Turbine Rated Rotor Hub Power Power Diameter Height Curve 2
  3. 3. Farm Land ConfigurationFarm Farm Land: Different Aspect Ratio Land: Different N-S-E-W Orientation 3
  4. 4. Motivation Farm Layout Planning: The net power generated by a wind farm is reduced by the wake effects, which can be offset by optimizing the farm layout. Turbine Type Selection: Optimally selecting the turbine-type(s) to be installed can further improve the power generation capacity and the economy of a wind farm. Planning the Farm Land Configuration: Careful decision making regarding the orientation and the aspect ratio of the farm land is important to provide an effective response to the expected wind distribution. An effective wind farm layout optimization method must account for the complex interactions among these three factors 4
  5. 5. Presentation Outline• Existing Farm Optimization Methods• Research Objectives• Characterizing the Role of Land Configuration• Unrestricted Wind Farm Layout Optimization (UWFLO)• Application of the Wind Farm Optimization Framework• Concluding Remarks 5
  6. 6. Existing Wind Farm Optimization Methods Array layout approach Grid based approachComputationally less expensive. Allows the exploration of different farmRestricts turbine locating and introduces configurations.a source of sub-optimality* Results might be undesirably sensitive to the pre-defined grid size#Prevailing Challenges• Simultaneous optimization of the wind turbine selection• Consideration of the farm land configuration• Due consideration of the joint variations of wind speed and direction *Sorenson et al., 2006; Mikkelson et al., 2007; 6 #Grady et al., 2005; Sisbot et al., 2009; Gonzleza et al., 2010
  7. 7. Research Objectives Model the influence of the farm land configuration (land aspect ratio and N-S-E-W orientation) on the optimal farm performance.  Minimize the Cost of Energy (COE) by simultaneously optimizing the farm layout and the selection of the type of turbine to be installed.  Develop response surfaces to represent the COE and the farm efficiency of the optimized wind farm as functions of the land configuration. 7
  8. 8. Modeling the Role of Land Configuration in Wind Farm Optimization Optimization of farm layout and turbine type selection UWFLO Land Configuration 1: Represent the Generate a set of N Minimum COE minimum farm COErandom combinations as a function of land of land aspect ratio Land aspect ratio and and orientation Configuration 2: orientation Sobol’s Algorithm Minimum COE Adaptive Hybrid Functions Land Configuration N: Minimum COE 8
  9. 9. Adaptive Hybrid Functions (AHF)  Cross-Validation: • q-fold strategy 9
  10. 10. Wind Farm Optimization FrameworkWe use the Unrestricted Wind Farm Layout Optimization (UWFLO) framework UWFLO Framework Wind Power Wind Farm Optimization Distribution Generation Cost Model Methodology Model Model 10
  11. 11. Layout based Power Generation Model  Dynamic co-ordinates are assigned to the turbines based on the direction of wind.  Turbine-j is in the influence of the wake of Turbine-i, if and only if Avian Energy, UK Effectiveapproach allows us to consider turbines with differing rotor-  This velocity of wind  Power generated by Turbine-j: approaching Turbine-j: diameters and hub-heights 11
  12. 12. Annual Energy Production and Cost of EnergyAnnual Energy Production (AEP) of a farm is given by:This integral equation can be numerically expressed as:Cost of Energy (COE) is then given by: Cost farm COE AEP Kusiak and Zheng, 2010; Vega, 2008 12
  13. 13. Problem Definition Farm Boundaries Inter-Turbine Spacing 13
  14. 14. Case study• In this paper, we use 10-year wind data for a class 3 site at Baker, ND*.• The optimization framework is applied to design a commercial scale 25MW wind farm at this site. *N. Dakota agricultural weather network: 14
  15. 15. Application of the UWFLO Framework A set of 100 random combinations of land aspect ratio and orientation is generated. For each sample land configuration, we obtain the minimum COE GE 1.5 MW and 2.5 MW turbines were allowed to be selected for this purpose. Best Performing Land Configuration Parameter 1.5 MW Turbines 2.5MW Turbines Overall Power Generation (MW) 16.56 16.74 Overall Farm Efficiency 0.649 0.669 COE ($/kWh) 0.022 0.021 For every sample land configuration, the same turbines were selected during optimization: GE 1.5MW xle and GE 2.5xl 100m/100m. 15
  16. 16. Optimized LayoutsInterestingly, both in the case of 1.5MW and 2.5MW turbines, the best farmperformance was given by the same sample land configuration:Aspect Ratio =2.95 and Orientation =158 (22 W of N) With 1.5MW turbines With 2.5MW turbines 16
  17. 17. Variation of Farm Performance with Land Configuration Similar patterns of variation are observed for COE and Farm Efficiency. Interestingly, particularly linear combinations of aspect ratio and orientation seem to provide better farm performances. COE Farm Efficiency 17
  18. 18. Response Surface Redundancy 18
  19. 19. Concluding Remarks In this paper, the influences of the land aspect ratio (AR) and N-S-E-W orientation on layout optimization was specifically explored. We found that the optimized layout is strongly correlated with the land configuration. However, optimal turbine selection was found to be fairly independent of the land configuration. Periodically varying linear combinations of land AR and orientation were found to provide the optimal farm performance. Future work should include further investigation of the response of land configuration variations to differing wind distributions. 19
  20. 20. Acknowledgement• I would like to acknowledge my research adviser Prof. Achille Messac, and my co-adviser Prof. Luciano Castillo for their immense help and support in this research.• I would also like to thank my friend and colleague Jie Zhang for his valuable contributions to this paper.• Support from the NSF Awards is also acknowledged. 20
  21. 21. Thank you Questions and Comments 21
  22. 22. Mixed-Discrete Particle Swarm Optimization (PSO) This algorithm has the ability to deal with both discrete and continuous design variables, and The mixed-discrete PSO presents an explicit diversity preservation capability to prevent premature stagnation of particles. PSO can appropriately address the non-linearity and the multi- modality of the wind farm model. 22
  23. 23. Annual Energy Production Wind Probability Distribution• Annual Energy Production of a farm is given by:• This integral equation can be numerically expressed as: Wind Farm Power Generation• A careful consideration of the trade-offs between numerical errors and computational expense is important to determine the sample size Np. 23 Kusiak and Zheng, 2010; Vega, 2008
  24. 24. Wind Distribution ModelIn this paper, we use the non-parametric model called the Multivariate andMultimodal Wind Distribution (MMWD).• This model is developed using the multivariate Kernel Density Estimation (KDE) method.• This model is uniquely capable of representing multimodally distributed wind data.• This model can capture the joint variations of wind speed, wind direction and air density.• In this paper, we have only used the bivariate version of this model (for wind speed and direction)24
  25. 25. Wake Model  We implement Frandsen’s velocity deficit model Wake growth Wake velocity – topography dependent wake-spreading constant  Wake merging: Modeled using wake-superposition principle developed by Katic et al.:25 Frandsen et al., 2006; Katic et al.,1986
  26. 26. UWFLO Cost Model • A response surface based cost model is developed using radial basis functions (RBFs). • The cost in $/per kW installed is expressed as a function of (i) the number of turbines (N) in the farm and (ii) the rated power (P) of those turbines. • Data is used from the DOE Wind and Hydropower Technologies program to develop the cost model. Cost farm COE AEP26
  27. 27. Modeling the Land Configuration  Optimum Farm Performance Relationship A set of N random sample land configurations is created using Sobol’s quasirandom sequence generator. For each land configuration, the COE is minimized by optimizing the farm layout and the selection of the type of turbine. A response surface is developed to represent the minimum COE as a function of the land aspect ratio and land orientation. A response surface is developed to represent the farm efficiency of the optimized design as a function of the land aspect ratio and the land orientation. A new hybrid surrogate model is used for developing the two response surfaces.27
  28. 28. Turbine Selection Model • Every turbine is defined in terms of its rotor diameter, hub-height, rated power, and performance characteristics, and represented by an integer code (1 – 66). • The “power generated vs. wind speed” characteristics for GE 1.5 MW xle turbines (ref. turbine) is used to fit a normalized power curve Pn(). • The normalized power curve is scaled back using the rated power and the rated, cut-in and cut-out velocities given for each turbine. U U in Pn if U in <U Ur U r U in P 1 if U out U Ur Pr 0 if U U out • However, if power curve information is available for all the turbines being considered for selection, they can be used directly.28
  29. 29. Wind Energy - Overview Currently wind contributes 2.5% of the global electricity consumption.* The 2010 growth rate of wind energy has been the slowest since 2004.* Large areas of untapped wind potential exist worldwide and in the US. Among the factors that affect the growth of wind energy, the state-of- the-art in wind farm design technologies plays a prominent role. 29 *WWEA, 2011 NREL, 2011