Layout optimization for renovation of operational offshore wind farm based on machine learning wake model

In the past, offshore wind farms commonly had a large wind turbine distance to avoid power loss induced by the wake effects. For this reason, the operational offshore wind farms with such a large wind turbine distance are relatively low in capacity density and have a great potential to include more wind turbines. This paper aims to provide a suitable renovation plan for those operational wind farms. Different from traditional wind farm layout optimization, the purpose of the renovation is to install new wind turbines while maintaining the status quo, which is an optimization problem with the constraint of initial wind turbines. Therefore, the renovation plan can be applied flexibly without waiting for the decommissioning of initial wind turbines. In this paper, the renovation plan is provided by a robust wind farm layout optimization framework based on a machine learning wake model, which can evaluate overall power production more accurately than traditional analytical models. Both heuristic and gradient-based optimization algorithms are applied and compared. Horns Rev wind farm is analyzed for demon-stration. The differences between the two algorithms are limited to 1%. Furthermore, the renovation and freeform optimizations are successfully applied to the cases with 12.5%-50% more wind turbines. According to the results, the freeform optimizations without the constraint of initial wind turbines have similar performance to renovation plans with an error smaller than 0.3%. When the number of wind turbines is added from 80 to 120, the normalized AEP (annual energy production) slightly decreases from 96.6% to 93.4%, which means the power loss increases by 3%-4%. In the meantime, the capacity density increases significantly by 50% as the area remains the same. In conclusion, the renovation plan is recommended for aging wind farms with a low capacity density like Horns Rev wind farm.