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An economic assessment of near-shore wind farm development using a weather research forecast-based genetic algorithm model

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  • Dhunny, A.Z.
  • Timmons, D.S.
  • Allam, Z.
  • Lollchund, M.R.
  • Cunden, T.S.M.

Abstract

Faced with the impacts of climate change, countries around the world are striving to adopt renewable sources of energy in order to reduce their emission of CO2 gases. There are several planned onshore wind farms at advanced stages that are soon to be made operational, but there is an increasing issue in the form of land area scarcity, hence highlighting the importance of alternatives scenarios offshore. In this paper, a methodology is proposed for assessing and optimizing wind turbines placement in offshore wind farms. In this research, the design method considers the optimal wind farm location and wind turbine layout, developed using an approach that couples a Weather Research and Forecasting (WRF) mesoscale model with a Genetic algorithm (GA). Even though increased numbers of turbines result in greater energy production, wake effects are seen to cause steeply increasing marginal costs. A comparative cost study is further performed for the different arrangements; GA model, linear line and block arrangement. It is first demonstrated that optimizing wind farm layout with the GA results in somewhat lower installation cost (4.4%) than a standard linear wind farm arrangement. The GA is then used to optimize layouts for increasing numbers of wind turbines and generate a full marginal cost (or supply) functions for wind energy at the case-study site. Turbine density is thus shown to be a critical consideration for wind farm economics at the study site. The methods presented in this study are easily adaptable to offshore wind farm design and economic assessment in other locations.

Suggested Citation

  • Dhunny, A.Z. & Timmons, D.S. & Allam, Z. & Lollchund, M.R. & Cunden, T.S.M., 2020. "An economic assessment of near-shore wind farm development using a weather research forecast-based genetic algorithm model," Energy, Elsevier, vol. 201(C).
    • Handle: RePEc:eee:energy:v:201:y:2020:i:c:s0360544220306484
    DOI: 10.1016/j.energy.2020.117541
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