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Heuristic and probabilistic wind power availability estimation procedures: Improved tools for technology and site selection

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  • Nigim, K.A.
  • Parker, Paul

Abstract

The paper describes two investigative procedures to estimate wind power from measured wind velocities. Wind velocity data are manipulated to visualize the site potential by investigating the probable wind power availability and its capacity to meet a targeted demand. The first procedure is an availability procedure that looks at the wind characteristics and its probable energy capturing profile. This profile of wind enables the probable maximum operating wind velocity profile for a selected wind turbine design to be predicted. The structured procedures allow for a consequent adjustment, sorting and grouping of the measured wind velocity data taken at different time intervals and hub heights. The second procedure is the adequacy procedure that investigates the probable degree of availability and the application consequences. Both procedures are programmed using MathCAD symbolic mathematical software. The math tool is used to generate a visual interpolation of the data as well as numerical results from extensive data sets that exceed the capacity of conventional spreadsheet tools. Two sites located in Southern Ontario, Canada are investigated using the procedures. Successful implementation of the procedures supports informed decision making where a hill site is shown to have much higher wind potential than that measured at the local airport. The process is suitable for a wide spectrum of users who are considering the energy potential for either a grid-tied or off-grid wind energy system.

Suggested Citation

  • Nigim, K.A. & Parker, Paul, 2007. "Heuristic and probabilistic wind power availability estimation procedures: Improved tools for technology and site selection," Renewable Energy, Elsevier, vol. 32(4), pages 638-648.
    • Handle: RePEc:eee:renene:v:32:y:2007:i:4:p:638-648
    DOI: 10.1016/j.renene.2006.03.001
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    Cited by:

    1. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    2. Yin, Peng-Yeng & Wu, Tsai-Hung & Hsu, Ping-Yi, 2017. "Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D," Energy, Elsevier, vol. 141(C), pages 579-597.
    3. Ayodele, T.R. & Ogunjuyigbe, A.S.O., 2016. "Wind energy potential of Vesleskarvet and the feasibility of meeting the South African׳s SANAE IV energy demand," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 226-234.
    4. Esmeralda Mukoni & Karen S. Garner, 2022. "Multi-Objective Non-Dominated Sorting Genetic Algorithm Optimization for Optimal Hybrid (Wind and Grid)-Hydrogen Energy System Modelling," Energies, MDPI, vol. 15(19), pages 1-18, September.
    5. Hung, Tzu-Chieh & Chong, John & Chan, Kuei-Yuan, 2017. "Reducing uncertainty accumulation in wind-integrated electrical grid," Energy, Elsevier, vol. 141(C), pages 1072-1083.

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