Module Description
Offshore wind energy offers strong potential for expanding renewable energy production due to higher wind speeds at sea, but it also involves challenges such as bathymetry constraints, turbine wake effects, visual impact on coastal areas, and high infrastructure and maintenance costs.
The module focuses on optimizing offshore wind farm design by improving turbine layout to maximize energy production while minimizing costs and visual intrusion. It adopts a multi-objective optimization approach based on two key metrics: Levelized Cost of Energy (LCOE) and visual impact.
Energy production is evaluated using the FLORIS model, which accounts for wake interactions, while visual impact is measured through specific coastal viewpoint metrics. The optimization is performed using the PyMoo library with the NSGA-II genetic algorithm, enabling analysis of trade-offs between economic efficiency and societal acceptance.
Overall, the work proposes an integrated framework that balances technical, economic, and social factors for more sustainable offshore wind development
Learning Outcomes
- Analyze the trade-offs between maximizing energy production and minimizing visual impacts


















