Simultaneous Optimisation of Cable Connection Schemes and Capacity for Offshore Wind Farms via a Modified Bat Algorithm

Abstract

Offshore wind energy has attracted worldwide attention and investments in the last decade due to the stability and abundance of wind resources. As one of the main components of this, internal array cables have a great impact on the levelised cost of energy of offshore wind farms, and thus their connection layout is a matter of concern. In this paper, a classical mathematical problem—the traveling salesman problem, which belongs to the field of graph theory—is applied to solve the offshore wind farm cable connection layout optimization problem. Both the capital investment on cables, cable laying, and the cost of power losses associated with array cables are considered in the proposed model. A modified bat algorithm is presented to resolve the problem. Furthermore, a cable crossing detection method is also adopted to avoid obtaining crossed cable connection layouts. The effectiveness was verified through a case study.

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

“…In Figure 1b, the WTs transmitting energies to cables (1,2) include two type 1 WTs and two type 2 WTs. Therefore, the actual power reached at the cable (1,2) in Figure 1b is In Figure 1a, there are four WTs transmitting their energies to cable (1,2), which has the same type. Therefore, the actual carrying capacity of cable (1,2) in Figure 1a is 4 × 2 = 8 MW.…”

“…Compared with onshore wind farms, offshore wind farms (OWFs) possess superiorities involving higher wind speed, rich wind energy resources, less turbulence intensity, good visualization, noise-free to residents, etc. [2]. However, the immense investment, including capital expenditure (CAPEX) and the operational cost of OWFs, is much higher than that of onshore wind farms, which seriously hinders their commission and development [3].…”

A Framework for Profit Maximization in a Grid-Connected Microgrid With Hybrid Resources Using a Novel Rule Base-BAT Algorithm

“…Nevertheless, this work did not upgrade velocity update of bats during the optimization process. Reference [41] reformed the frequency equation only to improve the BAT algorithm performance. However, this may only increase velocity of bats during the optimization process.…”

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.…”