Optimized design of collector topology for offshore wind farm based on ant colony optimization with multiple travelling salesman problem

Abstract

A layout of the offshore wind farm (OSWF) plays a vital role in its capital cost of installation. One of the major contributions in the installation cost is electrical collector system (ECS). ECS includes: submarine cables, number of wind turbines (WTs), offshore platforms etc. By considering the above mentioned problem having an optimized design of OSWF provides the better feasibility in terms of economic considerations. This paper explains the methodology for optimized designing of ECS. The proposed methodology is based on combined elitist ant colony optimization and multiple travelling salesman problem. The objective is to minimize the length of submarine cable connected between WTs and to minimize the wake loss in the wind farm in order to reduce the cost of cable and cable power loss. The methodology is applied on North Hoyle and Horns Rev OSWFs connected with 30 and 80 WTs respectively and the results are presented.

GA based algorithms for offshore wind farm collector cable optimization

“…𝑃 √3 * 𝑉 (13), where P is the power rating of WT 𝑁 = 𝐼 𝐶 𝐼 𝑊𝑇 (14), where the N is the maximum number of WTs inter-connection the current carrying capacity of 630 mm2 cross-sectional cable is 1200A as show in table 1, from (14) 1200/88.3 ≈13 indicated the maximum allowable current is the sum of current from 13 WTs with eight GW capacity. In the HV cables, the process of calculation the maximum allowable WTs inter connection number is similar with the MV cable; when the total WTs current flow large than the HV cable rated current capacity, the collection design may require more OSSs to distribute the power, therefore, the magnitude of cross-section cable selection could determine the number of OSSs.…”

“…Hence, heuristic algorithms have been widely applied in solving WTs collection system. Well acknowledged methods are Genetic Algorithm (GA) [9][10], Particle Swarm Optimization (PSO) [11], Simulated Annealing (SA) [12], Ant Colony Optimization (ACO) [13][14], The authors in [14] first determined the location of WT with minimize the wake loss, then proposed an ACO multiple traveling salesman model to minimize the length of cabling.…”

HVAC Optimal Control with the Multistep-Actor Critic Algorithm in Large Action Spaces

“…e second one includes the heuristic methods, such as genetic algorithm (GA) [24], ant colony optimization [25], and particle swarm optimization [26]. ese methods are suitable for almost all optimization problems and have good performance, especially for nonconvex optimization.…”

Key Issues on the Design of an Offshore Wind Farm Layout and Its Equivalent Model

“…In the early stage, genetic algorithms have been frequently utilized to solve the optimal wind farm layout problem [29]. However, many other algorithms have recently been proposed for the purpose [30][31][32][33][34]; they include the binary artificial algae algorithm (AAA), the bionic method, the ant colony algorithm, the minimum spanning tree algorithm, particle swarm optimization, Gaussian particle swarm optimization with a local search strategy, the turbine distribution algorithm, the extended pattern search method, the neural network algorithm, the evolutionary strategy algorithm, and quality threshold clustering. For instance, the study in reference [30] applied different binary algorithms with various transfer functions of AAA to solve the problem of wind turbine locations; the result demonstrates that the proposed algorithm obtains an effective placement of wind turbines with a larger number of grids.…”