Optimization of Large Wind Farms Using a Genetic Algorithm

Abstract

Optimizing the placement of the wind turbines in a wind farm to achieve optimal performance is an active area of research, with numerous research studies being published every year. Typically, the area available for the wind farm is divided into cells (a cell may/may not contain a wind turbine) and an optimization algorithm is used. In this study, the effect of the cell size on the optimal layout is being investigated by reducing it from five rotor diameter (previous studies) to 1/40 rotor diameter (present study). A code is developed for optimizing the placement of wind turbines in large wind farms. The objective is to minimize the cost per unit power produced from the wind farm. A genetic algorithm is employed for the optimization. The velocity deficits in the wake of the wind turbines are estimated using a simple wake model. The code is verified using the results from the previous studies. Results are obtained for three different wind regimes: (1) Constant wind speed and fixed wind direction, (2) constant wind speed and variable wind direction, and (3) variable wind speed and variable wind direction. Cost per unit power is reduced by 11.7% for Case 1, 11.8% for Case 2, and 15.9% for Case 3 for results obtained in this study. The advantages/benefits of a refined grid spacing of 1/40 rotor diameter (1 m) are evident and are discussed. To get an understanding of the sensitivity of the power produced to the wake model, optimized layout is obtained for the Case 1 using a different wake model.

Wind farm layout optimization by using Definite Point selection and genetic algorithm

“…Afterwards, the efficiency of wind farm was inversely proportional with the increased number of wind turbines. In this section, the obtained results from present research for constant wind speed 12 m/s with uniform direction are presented and compared to the results from earlier studies [12,13,15]. Fig.…”

“…They used the Monte Carlo method for optimization instead of using genetic algorithm; however, description on their method was not given. Mittal [15] employed the same fitness function as used by [12,13]. He proposed reduction of size of the wind farm by reducing the spacing.…”

“…Fig. 6 shows the optimal layouts proposed by [12][13][14][15]. Black boxes indicate the positions of wind turbines.…”

Wind farm layout optimization on complex terrains – Integrating a CFD wake model with mixed-integer programming

“…The WFLO problem can be modeled by two approaches, discrete and continuous. In discrete models [4,5,26], the wind farm domain is divided into a number of possible turbine locations, while for continuous models [27][28][29][30][31][32][33], the turbine location is represented by two-dimensional continuous coordinates. Continuous models are typically solved using evolutionary metaheuristic algorithms [31,[34][35][36][37]32,[38][39][40] and nonlinear optimization methods [41,42].…”

Offshore wind farm layout optimization using particle swarm optimization

“…More recent work in the field of offshore wind farm layout optimization has explored the applicability of different optimization algorithms as well as the inclusion of additional constraints and more detailed cost functions that a developer may face. The most frequent optimization algorithm applied to the wind farm layout optimization problem has been the genetic algorithm with several studies exploring its applicability to the problem as posed by Mosetti et al (1994) and to more complex extensions (Chen et al 2013;Couto et al 2013;Elkinton 2007;Elkinton et al 2008;Geem and Hong 2013;Grady et al 2005;Huang 2009;Mittal 2010;Shakoor et al 2016;Zhang et al 2014). In a similar vein, recent studies have also explored optimization algorithms such as viral based optimization (Ituarte-Villarreal and Espiritu 2011), pattern search (DuPont and Cagan 2012), mixed-integer linear programming (Fagerfjäll 2010), Monte Carlo method (Marmidis et al 2008), and random search (Feng and Shen 2015) applied to the wind farm layout optimization problem.…”