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<p>Wind farm development costs remain poorly understood, particularly for manufacturing and transport and installation (T&amp;I) phases. This paper addresses the knowledge gap in understanding how transport and installation (T&amp;I) logistics and wind turbine unit power selection impact the Levelized Cost of Energy (LCOE) of onshore wind farms. Several scenarios for a 12&#xa0;MW wind farm have been formalized, studied and analyzed based on the variation in the unit power of the wind turbines (from 0.8 to 3&#xa0;MW) and the pre-assembly method during the transport and installation phase (T&amp;I). For each pre-assembly method, the number of segments (NL) and the required surface area (At) for transporting the turbine are considered. Results indicate that the minimum LCOE is achieved with 1.5&#xa0;MW wind turbines using pre-assembly method for (At = 3, Nl = 550). This result is supported by the application of the genetic algorithm which confirmed and validated the same choice reducing this cost. In conclusion, the study underscores the critical importance of optimizing pre-assembly methods and turbine powers to minimize T&amp;I costs and thereby reduce the LCOE of onshore wind farms. Additionally, it highlights the efficacy of genetic algorithms in optimizing these parameters within the renewable energy sector.</p>

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LCOE Optimization of a Wind Farm According to the Transport and Installation Phase and Unit Power

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Driss Raouti,
  • Abdelkrim Bouanane,
  • Abdelkarim Tahtah,
  • Rachid Meziane,
  • Lionel Vido,
  • Sedik Touhami

摘要

Wind farm development costs remain poorly understood, particularly for manufacturing and transport and installation (T&I) phases. This paper addresses the knowledge gap in understanding how transport and installation (T&I) logistics and wind turbine unit power selection impact the Levelized Cost of Energy (LCOE) of onshore wind farms. Several scenarios for a 12 MW wind farm have been formalized, studied and analyzed based on the variation in the unit power of the wind turbines (from 0.8 to 3 MW) and the pre-assembly method during the transport and installation phase (T&I). For each pre-assembly method, the number of segments (NL) and the required surface area (At) for transporting the turbine are considered. Results indicate that the minimum LCOE is achieved with 1.5 MW wind turbines using pre-assembly method for (At = 3, Nl = 550). This result is supported by the application of the genetic algorithm which confirmed and validated the same choice reducing this cost. In conclusion, the study underscores the critical importance of optimizing pre-assembly methods and turbine powers to minimize T&I costs and thereby reduce the LCOE of onshore wind farms. Additionally, it highlights the efficacy of genetic algorithms in optimizing these parameters within the renewable energy sector.