Energy and Wake effects Optimization of Offshore Wind Farm using PSO algorithm

Abstract

As wind farms grow in size, the detrimental effects of wake interactions on energy yields become increasingly pronounced. This leads to the new challenge essential to the production of renewable energy. The two main objectives of offshore wind farm planning are to maximize annual energy production and minimize wake loss. To accomplish the twin goals of reducing wake impacts and maximizing energy production, this research tackles a novel method to investigate trade-offs between competing goals using multi-objective optimization algorithms. We introduce this problem with a sophisticated wake named the Bastankhah-Porté-Agel (BPA) model. To tackle this problem, the research has developed a multi-objective optimization framework in Python that shows the Pareto front, which illustrates the trade-off between wake effects and (AEP) by using a particle swarming optimization (PSO) algorithm. The proposed multi-objective optimization framework offers a disciplined way to balance energy production and wake loss, which advances the offshore wind farm design. The results indicate that the proposed method is robust in finding the optimized layout for improving sustainability and offshore wind energy efficiency. Before carrying out this process, the proposed tool has been validated using data obtained by a wind farm in Georgia.