Optimization of floating wind farm power collection system using a novel two-layer hybrid method

Deep-sea floating wind farms have gained significant attention in recent years, however, their high construction and operation cost remains a notable challenge. In the floating wind farm, floating wind turbines (WTs) have a certain range of movement, and dynamic cables are used, thus the collection system needs a careful design that relies heavily on iterative optimization. In this study, a life cycle cost model for the collection system of floating wind farms is firstly established considering the floating characteristics. Due to the complexity of the optimization model, the two-layer optimization framework is presented. The upper layer uses Binary Particle Swarm Optimization (BPSO) to determine the optimal connection between the WTs and offshore substations (OSs), while the lower layer optimizes the cable topology between WTs via Improved Monte Carlo Tree Search (IMCTS). Simultaneously, the cable selection scheme corresponding to the obtained topology is optimized by the lower level. Simulations are performed to validate the proposed method. The results demonstrate that the proposed algorithm achieves a minimum optimization cost of 45.277 million euros, and attains significant reductions of 2.31, 0.486 and 1.667 million euros, respectively, compared to Genetic Algorithm (GA), BPSO-Sweep-IMCTS and BPSO-AA-GA. Further, this study evaluates the differences in the optimized structures of the collection system between floating and fixed wind farms, and analyzes the effects of the OS position and environmental factors. The analysis results show that locating OSs at the central position and upstream side reduces the cost of the collection system, and the wind condition has a considerable influence on the design of the collection system.