Evolutionary computation for wind farm layout optimization

Abstract

This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of five generated wind farms based on a simplified cost of energy evaluation function of the wind farm layouts. Online and offline APIs were implemented in Cþþ, Java, Matlab and Python for this competition to offer a common framework for the competitors. The top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation, the research field of the conference at which the competition was held. Competitors were able to downscale the optimization problem size (number of parameters) by casting the wind farm layout problem as a geometric optimization problem. This strongly reduces the number of evaluations (limited in the scope of this competition) with extremely promising results.

Realistic wind farm design layout optimization with different wind turbines types

“…These algorithms are based on different optimization techniques in which heuristic methods widely used. These methods includes genetic algorithm [5,6], evolutionary algorithm [7], particle swarm optimization algorithm [8] and among others methods that can found in detail in these references [2,9,10].…”

Topology Design of an Offshore Wind Farm with Multiple Types of Wind Turbines in a Circular Layout

“…WF mathematical modeling involves the consideration of multiple wind directions [6], complex terrains [7], electrical layout [8,9], multiple WT types [10], energy benefit models [5], and novel wake models [11,12]. Searching for more efficient optimization algorithms includes the study and application of modified GA [10], the particle swarm optimization (PSO) algorithm [6], evolutionary algorithm [13], artificial intelligence (AI) technology [14], and so forth.…”

“…However, there are some limitations regarding the existing OWF optimization problems. Firstly, in the majority of the operational OWFs, the WTs are usually laid out in a symmetrical and grid-like layout, and the shape of the whole WF generally appears as a square [3,4,6,7,[10][11][12]14,15], a rectangle [8,13], or a parallelogram [5,16]. The aforementioned patterns are convenient for WF planning and construction.…”

Late Acceptance Selection Hyper-heuristic for Wind Farm Layout Optimisation Problem

“…• Turbine type: Different types of turbines have different diameters, heights, power ratings and speed. Previous studies do not usually take this into account, but they are of great importance and highly significant to build the wind farm [5]. • Wake model: This represents a challenge in the farm layout problem as it is very difficult to accurately depict the wake equations in a mathematical model due to their stochastic behaviour.…”

“…• Wake model: This represents a challenge in the farm layout problem as it is very difficult to accurately depict the wake equations in a mathematical model due to their stochastic behaviour. Wind farm layout optimisation problem is considered an N P-hard problem as it requires computation time that scales exponentially with the problem size [5]. In addition, introducing other dimensions such as different heights of turbines, construction and maintenance costs of each turbine, electrical grid infrastructure and other constraints increases the search space exponentially.…”