Offshore wind farm layout optimization using particle swarm optimization

Abstract

This article explores the application of a wind farm layout optimization framework using a particle swarm optimizer to three benchmark test cases. The developed framework introduces an increased level of detail characterizing the impact that the wind farm layout can have on the levelized cost of energy by modelling the wind farm's electrical infrastructure, annual energy production, and cost as functions of the wind farm layout. Using this framework, this paper explores the application of a particle swarm optimizer to the wind farm layout optimization problem considering three different levels of wind farm constraint faced by modern wind farm developers. The particle swarm optimizer is found to yield improvements in the layout with respect to the levelized cost of energy for the three benchmark cases when compared to two past studies. This highlights both applicability of the particle swarm optimizer to the problem and the ways in which a wind farm developer could make use of the present framework in the development and design of future wind farms.

Optimization of reliable cyclic cable layouts in offshore wind farms

“…Since the frequently cited work by Mosetti, Poloni, and Diviacco (1994), a large number of research articles on optimization of turbine locations have emerged. Approaches that have been applied include various metaheuristic methods, such as genetic algorithms (Grady, Hussaini, and Abdullah 2005;Emami and Noghreh 2010;Chen et al 2013;Pillai et al 2016Pillai et al , 2017, particle swarm optimization (Chowdhury et al 2013;Pillai et al 2018), simulation of viral life (Ituarte-Villarreal and Espiritu 2011) and evolutionary algorithms (González et al 2010;Rodrigues, Bauer, and Bosman 2016). Optimized turbine location has also been approached by pattern search (Du Pont and Cagan 2012), simulation (Marmidis, Lazarou, and Pyrgioti 2008), and mathematical programming techniques, such as linear (Fagerfjäll 2010) and quadratic (Quan and Kim 2019) integer programming, and constraint programming (Zhang et al 2014).…”

“…The model presented by Lindahl et al (2013) is the basis for Ørsted's cabling methodology. Metaheuristic methods, especially genetic algorithms (Lingling, Yang, and Xiaoming 2009;Gonzalez-Longatt et al 2012;Pillai et al 2016;Shin and Kim 2017), and, more recently, particle swarm methods (Pillai et al 2018) have also been applied for optimizing turbine locations and cable routes connecting the turbines.…”

“…Recent scientific publications (Lindahl et al 2013;Bauer and Lysgaard 2015;Klein et al 2015;Pillai et al 2015Pillai et al , 2016Pillai et al , 2018Wędzik, Siewierski, and Szypowski 2016;Hertz et al 2017;Fischetti and Pisinger 2018a, 2018b, 2018c, 2019Klein and Haugland 2019) assume that all turbines are connected to substations in a tree with the substation as the root node. From each turbine therefore, there exists a unique cable path to the substation.…”

Wind farm layout optimization using pseudo-gradients

“…They used a genetic algorithm for a discretized solution space. The problems they analyze have been used as a benchmark by many others (e.g., Grady et al, 2005;Pookpunt and Ongsakul, 2013;Turner et al, 2014;Feng and Shen, 2015b;Pillai et al, 2018). Here, their multiple wind direction and multiple wind speed problem (Mosetti et al, 1994, Sec.…”

“…Pseudo-gradients find a natural application in gradient-descent-type approaches to layout optimization, but they can be used in other approaches as well. Because of the limited computational impact of calculating them, they can be used to replace (some of) the random turbine displacement steps used in the many heuristic layout optimization approaches (e.g., Mosetti et al, 1994;Grady et al, 2005;Pookpunt and Ongsakul, 2013;Feng and Shen, 2015b;Pillai et al, 2018). This should improve the convergence speed to local optima, while the remaining random-search aspects of these approaches can preserve their exploratory power.…”