In a significant stride towards optimizing offshore wind farm development, a recent study published in the journal ‘Results in Engineering’ (translated from French) has introduced an integrated approach to evaluate offshore wind energy potential. The research, led by Badr El Kihel from the Laboratory of Engineering Sciences (LES) at Sidi Mohamed Ben Abdellah University in Morocco, combines statistical modeling with multi-criteria decision analysis to provide a comprehensive site evaluation framework.
Offshore wind farms are a complex beast, with a whole different set of parameters to consider compared to onshore sites. We’re talking maritime conditions, coastal distance, water depth, and seabed stability—all of which can significantly impact the feasibility and efficiency of a project. El Kihel and his team tackled this complexity by evaluating 25 selected locations, including five operational sites across China, Denmark, the USA, England, and France.
The study uses the Weibull distribution for wind modeling, a common approach in wind energy assessment. Among the nine methods evaluated for estimating the Weibull parameters, the Maximum Likelihood Method, the Least Squares Method, and the WAsP Method stood out for their accuracy. These derived parameters were then fed into a TOPSIS-based multi-criteria analysis, which considered indicators like wind speed, power density, capacity factor, water depth, and proximity to shore.
The results were promising, with strong alignment between predictions and operational values for sites like Denmark’s P6 and France’s P19. However, sites P1 (China) and P11 (England) showed substantial positive deviations, indicating underutilized wind resources. El Kihel suggests that these sites could benefit from advanced turbine technology to optimize their potential.
From a commercial perspective, the study reveals economic disparities among sites, with production costs ranging between 0.008 and 0.028 $/kWh. Site P4 in Canada demonstrated top-tier performance across five sensitivity scenarios, highlighting its potential as a prime location for offshore wind development.
El Kihel’s integrated approach, combining NREL classification with TOPSIS, offers a high-precision assessment of offshore wind sites. This method enhances sustainable energy planning by balancing technical and economic indicators, providing a robust tool for investors and developers in the maritime sector.
As El Kihel puts it, “The integration of NREL classification with TOPSIS proves effective in offshore wind prioritization. This system enhances sustainable energy planning through high-precision assessment and balanced evaluation of technical and economic indicators.”
For maritime professionals, this research opens up new opportunities for optimizing offshore wind farm development. By providing a clear, data-driven approach to site evaluation, it can help stakeholders make informed decisions, ultimately driving the growth of the offshore wind industry and contributing to a more sustainable energy future.