Cable Connection Optimization for Heterogeneous Offshore Wind Farms via a Voronoi Diagram Based Adaptive Particle Swarm Optimization with Local Search

Abstract

Offshore wind energy, as one of the featured rich renewable energy sources, is getting more and more attention. The cable connection layout has a significant impact on the economic performance of offshore wind farms. To make better use of the wind resources of a given sea area, a new method for optimal construction of offshore wind farms with different types of wind turbines has emerged in recent years. In such a wind farm, the capacities of wind turbines are not identical which brings new challenges for the cable connection layout optimization. In this work, an optimization model named CCLOP is proposed for such wind farms. The model incorporates both the cable capital cost and the cost of power losses associated with the cables in its objective function. To get an optimized result, a Voronoi diagram based adaptive particle swarm optimization with local search is proposed and applied. The simulation results show that the proposed method can help find a solution that is 12.74% outperformed than a benchmark.

Offshore Electrical Grid Layout Optimization for Floating Wind—A Review

“…Similar to the application of GAs in the inter-array cabling, the metaheuristic information of a PSO can be used in two ways, while works incorporating GAs mainly focus on providing the framework for other heuristics (e.g., OSS or WT positioning) to conduct the inter-array cabling in a nested approach, PSOs are mainly used to optimize the IA cabling ( [51,52]) or OSS position ( [40,53]) or even both simultaneously ( [54][55][56]).…”

“…Related to the Delaunay triangulation mentioned above is the use of a Voronoi diagram which was implemented by Qi et al [51] to prescribe the WT connections. In the K-shaped Voronoi diagram, according to the nearest neighbor rule, each discrete point is assigned to the area of the vertex to which it is nearest so that each discrete point corresponds to only one region as illustrated in Figure 12.…”

Wind farm array cable layout optimisation for complex offshore sites—A decomposition based heuristic approach

“…One study on a no-branching layout demonstrates that the cost of the heuristic designed layouts is only 2% more than the global optimal solution [18]. Another study demonstrated that a Voronoi diagram based adaptive particle swarm optimisation method with additional local search heuristic could produce a 12.74% cost reduction compared to a benchmark case [19]. Clustering-based algorithms, such as quality threshold (QT) clustering, optimise solutions that reduce electrical losses and improve reliability, but increase the capital expenditure.…”

“…An additional constraint equation is included for every pair of crossing cables present in the solution. Equation (19) describes the constraint equation used in the MILP algorithm.…”

Balanced cable routing for offshore wind farms with obstacles

“…The pathfinding includes Steiner points (which they name Fermat points) to route cables around obstacles. Finally Qi et al [21] use a Voronoi tessellation to compute the distances between turbines, which can be modified to handle obstacles.…”