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A STOCHASTIC-OPTIMIZATION MODEL FOR DETERMINING THE OPTIMAL MICRO-SITING OF WIND TURBINES

Year 2016, Volume: 6 Issue: 2, 6 - 14, 23.07.2016

Akiner Tuzuner Issa Almassri Selcuk Goren

Abstract

We propose a general model for the placement of wind turbines in a rectangular grid formation over a flat area. For better realism, we consider stochastic wind speeds and directions, in conjunction with the wake effects that upstream turbines impose on downstream ones. The objective is to pack as many turbines as economically optimal in a given area, i.e. to maximize the expected MW output per dollar of capital investment and O&M costs per meter square. Due to the complex structure of the mathematical model, we apply a hybrid approach of MonteCarlo sampling of wind speeds and directions together with the Nelder-Mead heuristic method to search for the optimal horizontal and vertical spacing of the turbines. Results of a case study based on a real dataset of wind speeds and directions, a selected commercial turbine’s approximated power curve, and industry estimates of costs is discussed

Keywords

wind energy wind power wind investment micro-siting wake effect

References

  • Attias, K. (2011). Optimal Layout for Wind Turbine Farms, Ben-Gurion University.
  • Donovan, S. (2005). 40th Annual Conference: Wind Farm Optimization, Operational Research Society of New Zealand, Wellington, New Zealand.
  • Katic, I., Hojstrup, J., & Jensen N. O. (1986). European wind energy conference and exhibition: A simple model for cluster efficiency. Rome, p. 407-10.
  • Kulunk E. (2011). Fundamental and Advanced Topics in Wind Power: Aerodynamics of Wind Turbines, ISBN: 978-953-307-508-2.
  • Kusiak A., (2010). Design of wind farm layout for maximum wind energy capture, The University of Iowa.
  • Liu, F. and Wang, Z. (2014). Offshore Wind Farm Layout Optimization Using Adapted Genetic Algorithm: A Different Perspective, Virginia Commonwealth university.
  • Renkema, D. J. (2007). Validation of wind turbine wake models using wind farm data and wind tunnel measurements, Delft University of Technology.
  • Samorani, M. (2013) The Wind Farm Layout Optimization Problem, University of Alberta.
  • Schlez, W. New Developments in Precision Wind Farm Modelling, Garrad Hassan and Partners Ltd.
  • Tao, H. (2011). The Assessment of Dynamic Wake Effects on Loading, Delft University of Technology,
  • The Sailhawt Website, https://sites.google.com/site/sailhawt
  • The Windpower Program Website, http://www.wind-power-program.com/turbine_characteristics.htm
  • Zhang, X. & Wang, W. (2009). Wind Farm and Wake Effect Modeling for Simulation of a Studied Power System, Xi’an Jiaotong University

Year 2016, Volume: 6 Issue: 2, 6 - 14, 23.07.2016

Akiner Tuzuner Issa Almassri Selcuk Goren

Abstract

References

  • Attias, K. (2011). Optimal Layout for Wind Turbine Farms, Ben-Gurion University.
  • Donovan, S. (2005). 40th Annual Conference: Wind Farm Optimization, Operational Research Society of New Zealand, Wellington, New Zealand.
  • Katic, I., Hojstrup, J., & Jensen N. O. (1986). European wind energy conference and exhibition: A simple model for cluster efficiency. Rome, p. 407-10.
  • Kulunk E. (2011). Fundamental and Advanced Topics in Wind Power: Aerodynamics of Wind Turbines, ISBN: 978-953-307-508-2.
  • Kusiak A., (2010). Design of wind farm layout for maximum wind energy capture, The University of Iowa.
  • Liu, F. and Wang, Z. (2014). Offshore Wind Farm Layout Optimization Using Adapted Genetic Algorithm: A Different Perspective, Virginia Commonwealth university.
  • Renkema, D. J. (2007). Validation of wind turbine wake models using wind farm data and wind tunnel measurements, Delft University of Technology.
  • Samorani, M. (2013) The Wind Farm Layout Optimization Problem, University of Alberta.
  • Schlez, W. New Developments in Precision Wind Farm Modelling, Garrad Hassan and Partners Ltd.
  • Tao, H. (2011). The Assessment of Dynamic Wake Effects on Loading, Delft University of Technology,
  • The Sailhawt Website, https://sites.google.com/site/sailhawt
  • The Windpower Program Website, http://www.wind-power-program.com/turbine_characteristics.htm
  • Zhang, X. & Wang, W. (2009). Wind Farm and Wake Effect Modeling for Simulation of a Studied Power System, Xi’an Jiaotong University
There are 13 citations in total.

Details

Other ID JA56BF25ZN
Journal Section Articles
Authors

Akiner Tuzuner This is me

Issa Almassri This is me

Selcuk Goren This is me

Publication Date July 23, 2016
Published in Issue Year 2016 Volume: 6 Issue: 2

Cite

APA Tuzuner, A., Almassri, I., & Goren, S. (2016). A STOCHASTIC-OPTIMIZATION MODEL FOR DETERMINING THE OPTIMAL MICRO-SITING OF WIND TURBINES. TOJSAT, 6(2), 6-14.
AMA Tuzuner A, Almassri I, Goren S. A STOCHASTIC-OPTIMIZATION MODEL FOR DETERMINING THE OPTIMAL MICRO-SITING OF WIND TURBINES. TOJSAT. July 2016;6(2):6-14.
Chicago Tuzuner, Akiner, Issa Almassri, and Selcuk Goren. “A STOCHASTIC-OPTIMIZATION MODEL FOR DETERMINING THE OPTIMAL MICRO-SITING OF WIND TURBINES”. TOJSAT 6, no. 2 (July 2016): 6-14.
EndNote Tuzuner A, Almassri I, Goren S (July 1, 2016) A STOCHASTIC-OPTIMIZATION MODEL FOR DETERMINING THE OPTIMAL MICRO-SITING OF WIND TURBINES. TOJSAT 6 2 6–14.
IEEE A. Tuzuner, I. Almassri, and S. Goren, “A STOCHASTIC-OPTIMIZATION MODEL FOR DETERMINING THE OPTIMAL MICRO-SITING OF WIND TURBINES”, TOJSAT, vol. 6, no. 2, pp. 6–14, 2016.
ISNAD Tuzuner, Akiner et al. “A STOCHASTIC-OPTIMIZATION MODEL FOR DETERMINING THE OPTIMAL MICRO-SITING OF WIND TURBINES”. TOJSAT 6/2 (July 2016), 6-14.
JAMA Tuzuner A, Almassri I, Goren S. A STOCHASTIC-OPTIMIZATION MODEL FOR DETERMINING THE OPTIMAL MICRO-SITING OF WIND TURBINES. TOJSAT. 2016;6:6–14.
MLA Tuzuner, Akiner et al. “A STOCHASTIC-OPTIMIZATION MODEL FOR DETERMINING THE OPTIMAL MICRO-SITING OF WIND TURBINES”. TOJSAT, vol. 6, no. 2, 2016, pp. 6-14.
Vancouver Tuzuner A, Almassri I, Goren S. A STOCHASTIC-OPTIMIZATION MODEL FOR DETERMINING THE OPTIMAL MICRO-SITING OF WIND TURBINES. TOJSAT. 2016;6(2):6-14.