Optimized Placement of Wind Turbines in Large-Scale Offshore Wind Farm Using Particle Swarm Optimization Algorithm

Abstract

With the increasing size of wind farms, the impact of the wake effect on wind farm energy yields become more and more evident. The arrangement of locations of the wind turbines (WTs) will influence the capital investment and contribute to the wake losses, which incur the reduction of energy production. As a consequence, the optimized placement of the WTs may be done by considering the wake effect as well as the components cost within the wind farm. In this paper, a mathematical model which includes the variation of both wind direction and wake deficit is proposed. The problem is formulated by using levelized production cost (LPC) as the objective function. The optimization procedure is performed by a particle swarm optimization (PSO) algorithm with the purpose of maximizing the energy yields while minimizing the total investment. The simulation results indicate that the proposed method is effective to find the optimized layout, which minimizes the LPC. The optimization procedure is applicable for optimized placement of WTs within wind farms and extendible for different wind conditions and capacity of wind farms.Index Terms-Energy yields, levelized production cost (LPC), optimized placement, particle swarm optimization (PSO), wake effect, wake model.

Integrated offshore wind farm design: Optimizing micro‐siting and cable layout simultaneously

“…The tool of this paper is designed to refine micro‐siting and cable layouts and not to address other important but broader questions, such as the optimal turbine size and optimal number of turbines to place in a given area. When making these decisions at an earlier design stage, heuristic optimization algorithms like particle swarm or genetic algorithms might produce general approximate results in a computationally efficient manner . If different turbine layouts were suggested by different algorithms and for different numbers of turbines, then it would be appropriate to use our more computationally expensive classical optimization tool to very precisely determine and compare the optimal arrangement for each layout suggested.…”

“…21 Classical mixed-integer programming has been employed to solve this problem up to optimality. 22,23 However, a large range of metaheuristic techniques have also been employed, including multiobjective evolutionary algorithms, 16,24 gradient search, 17,24 greedy heuristics, 17,24 genetic algorithms, 17,21,[24][25][26] simulated annealing, 17,20,24 particle swarm optimization, 19 and pattern-search algorithms. 17,24 Only a very reduced subset of these works considers nonconventional layouts.…”

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

“…2. The amount of wake effect is minimized, if: À the WT 12 is placed with an angle h 1 with respect to WT 11 ; the WT 13 is placed with an angle h 2 and greater than that with respect to WT 11 . This pattern continues for next columns and it is indicated in Fig.…”

“…The particle swarm optimization (PSO) [11,12] and mixed integer PSO [13] techniques are adopted to optimize the wind farm layout in terms of optimal placement allocation of WTs. Clarke and Wright savings heuristic method with vehicle routing [14], ant colony optimization (ACO) with GA [15] and capacitated MST [16] were implemented for the optimization of inter-array cable routing between WTs in OSWFs.…”

Distributed Complementary Control Research of Wind Turbines in Two Offshore Wind Farms

“…In eastern China, the load requirements are relatively high for the developed regions along the coastlines. Thus, offshore wind power has been rapidly developed [1][2][3][4]. Unlike onshore wind farms, offshore wind farms are located far away from the land, which makes maintenance fairly difficult.…”