Recent research led by Erping Song from the School of Mathematics and Physics at Qinghai University has introduced a novel bi-level optimization model aimed at enhancing the profitability of wind farms. Published in the journal “IET Renewable Power Generation,” this study addresses critical factors that influence wind farm efficiency, such as the power output of turbines and the costs associated with cable installations.
The research highlights the importance of optimizing the micro-locations of wind turbines and the layout of collector cables. The power output from wind turbines can be significantly affected by the wake effect, which occurs when one turbine’s wind shadow impacts another. Additionally, the cost of cables is determined by their length and type, making it essential to minimize both power loss and cable expenses to maximize overall profit.
To tackle these challenges, Song and his team developed a bi-level optimization model that separates the problem into two levels. The upper level focuses on maximizing profit, while the lower level aims to minimize cable costs and power losses. This hierarchical approach allows for a more structured and efficient optimization process.
The researchers implemented an improved algorithm known as IDEDA, which combines differential evolution techniques with Dijkstra’s algorithm. Through simulation experiments, the IDEDA algorithm demonstrated superior performance in maximizing profits and reducing cable costs compared to four other existing algorithms under varying wind conditions.
This innovation presents significant commercial opportunities for the energy sector, particularly as the demand for renewable energy sources continues to grow. By optimizing wind farm layouts, operators can increase their profitability, making investments in wind energy more attractive. As Song notes, “The optimization of micro-locations and cables is crucial for maximizing profit in wind farms.”
The findings of this study not only advance the field of renewable energy generation but also provide actionable insights for energy companies looking to enhance their operational efficiency. As the industry moves towards more sustainable practices, tools like the one developed by Song and his team will be invaluable in shaping the future of wind energy.
