Windmill Farm Pattern Optimization using Evolutionary Algorithms

Renewable Energy, 2018

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.

Open Computer Science, 2011

This paper presents a mini-review of the main works recently published about optimal wind turbines layout in wind farms. Specifically, we focus on discussing articles where evolutionary computation techniques have been applied, since this computational framework has obtained very good results in different formulations of the problem. A summary of the main concepts needed to face the problem are also included in the article, such as a basic wake model and several cost models and objective functions previously used in the literature. This review includes works published in the most significant journals and international conferences, and it gives a brief remark of the optimization models proposed and the implemented algorithms, so it can be useful for readers who want to be quickly introduced in this research area.

International Journal of Advances in Applied Sciences (IJAAS) , 2019

The usage of fossil fuels is actually not good for living nature and in future, this limited source of energy will vanish. Therefore, we need to go with the clean and renewable source of energy such as wind power, solar energy etc. In this paper, we are concentrating in wind power through optimizing the wind turbine placement in wind farm. The area-of-convex hull, maximize ‘output power’ and minimum spanning tree distance are our main objective topics, due to their effect in wind farm design. An implementation of modified version of the wind turbine (WT) placement model is uses to estimate the yields of the (wind farm) WF layouts and for simplifying the behavior of wind field, in this paper we use a simple wake approach. Moreover, to resolve the multi-objective problem here we proposed (Modified Genetic Algorithm) MGA, which is considerably better than the (Genetic Algorithm) GA and for evaluate the performance of MGA we use the multi-objective (EA) evolutionary algorithms such as; Genetic algorithm (GA) and SPEA2 and, produce different number of WT layouts. These methodologies are considered with various ‘problematic specific operators’ that are present in this paper.

Clean Technologies and Environmental Policy, 2017

The placement of wind turbines is crucial for a wind farm because the power generation of wind turbine is greatly affected by the wake effect produced by the upstream turbines. The optimum placement of wind turbines in a wind farm will give the maximum total power output. The effective methodology is imperative to place turbines in such a way that effect of the wake is minimum on a performance of turbines and has a proper utilization of the wind farm area. The present study proposes a novel approach based on the geometrical pattern-inspired placement methodology to locate wind turbines and maximize the power output of a wind farm. In the proposed approach, various geometrical patterns made from circle, hexagon, pentagon and triangle are considered for the numerical investigation. Further, wind behavior is modeled for uniform and variable wind speed from all directions. The present approach is also experimented numerically with and without land availability constraints. Heat transfer search algorithm is used to solve the wind farm layout optimization problem with the proposed approach and the results are compared with other approaches available in the literature. Results show that the proposed geometrical pattern-based approach produces the higher power generation (*4-8%) compared to other approaches for the variable wind speed scenario. In the case of land availability constraints, about 4% higher power generation compared to the grid-based approach is achieved. In order to place turbines with an optimum performance of a wind farm, the present approach can be helpful to wind farm developers. Keywords Heat transfer search algorithm Á Land availability constraint Á Wake effect Á Wind farm layout Á Wind farm modeling

International Journal of Energy and Environmental Engineering

Seeking for an appropriate design of wind farm (WF) layout constitutes a complex task in a wind energy project. An optimization approach is seriously needed to deal with this complexity, especially with current trend of large WFs area with important number of wind turbines (WTs). The present paper investigates optimization study of realistic offshore WF design layout (horns-rev1). The main objective of the current study is to design WF area that maximizes the extraction of wind power with low cost. In the first step, an optimization model using genetic algorithm with continuous layout representation is developed to look for the optimal design as a function of WTs placement. The effectiveness of such a methodology is validated and compared with the reference and irregular layout of hors-rev1 offshore WF. With the aim to analyze the impact of WTs types on WF objectives, four commercial WTs are considered in the second step. The results showed that designing WF with big WTs gives best design layout. In addition, it demonstrated that selecting WTs based uniquely on rotor diameter size is not always a good idea. It should includes as well the number of WTs that influence significantly the power production and WF cost.

FME Transactions

This paper presents a method for determination of optimum positions of single wind turbines within the wind farms installed on arbitrary configured terrains, in order to achieve their maximum production effectiveness. This method is based on use of the genetic algorithm as optimization technique. The wind turbine aerodynamic calculation is unsteady, based on the blade modeled as a vortex lattice and a free-wake type airflow behind the blade. Optimization method is developed for two different fitness functions. Both functions use the total energy obtained from the farm as one of the key variables. The second also involves the total investments in a single wind turbine, so the optimization process can also include the total number of turbines as an additional variable. The method has been tested on several different terrain configurations, with special attention paid to the overall algorithm performance improvements by selecting certain genetic algorithm parameters.

Renewable Energy, 2010

The optimum wind farm configuration problem is discussed in this paper and an evolutive algorithm to optimize the wind farm layout is proposed. The algorithm's optimization process is based on a global wind farm cost model using the initial investment and the present value of the yearly net cash flow during the entire wind-farm life span. The proposed algorithm calculates the yearly income due to the sale of the net generated energy taking into account the individual wind turbine loss of production due to wake decay effects and it can deal with areas or terrains with non-uniform load-bearing capacity soil and different roughness length for every wind direction or restrictions such as forbidden areas or limitations in the number of wind turbines or the investment. The results are first favorably compared with those previously published and a second collection of test cases is used to proof the performance and suitability of the proposed evolutive algorithm to find the optimum wind farm configuration.

E3S Web of Conferences, 2021

The optimization of the size of wind farms is little studied in the literature. The objective of this study is to renew the existing wind farms by inserting new wind turbines with different characteristics. To evaluate our approach, a genetic algorithm was chosen to optimize our objective function, which aims to maximize the power of the wind farm studied at a reasonable cost, the Jensen wake model was chosen for the power calculation of the park. The results obtained from the simulation on the Horns-rev wind farm showed a significant increase in energy and a relatively reasonable cost of energy.

TELKOMNIKA Indonesian Journal of Electrical Engineering, 2014

In the present study, genetic algorithm has been used to resolve the placement of wind turbines in a wind park giving maximum power and efficiency with minimum number of turbines. Unlike past approaches where each plot was subdivided into smaller square grids at the centre of which a turbine can be placed, the present study does not require division of the plot. Thus, a turbine now has more flexibility to be placed anywhere outside a radius of 200m of each other yielding better results. The case of unidirectional uniform wind is considered and 600 individuals evolve 3000 generations. Along with the optimal layout, fitness value, total power output, efficiency and number of turbines have also been reported. Comparison with results of earlier study and possible explanation is also provided.

International Journal of Energy and Environmental Engineering , 2018

Installation layout of wind turbines plays a prominent role in the design of every wind farm. Thus, the wind farm layout optimization problem is proposed to maximize the total power output with the minimum cost. In this research, Kahrizak region in Tehran province of Iran is selected as a windy region and its real wind speed data are gleaned. Three different scenarios are also considered, with various number of generations and populations for GA parameters, effective distances, and longitude and latitude distances of turbines from each other. Among these scenarios, the best result is obtained for the one in which the longitudinal distance between turbines is greater than the latitudinal distance. By observing the wind rose of Kahrizak region, it is observed that the dominant wind direction of the region is toward the east and south–east. Therefore, by increasing the longitudinal distance of the turbines from each other, the efficiency can be improved and the turbine layout becomes more realistic. In this case, the efficiency rate and normalized cost of turbines are 89.5% and 37.4, respectively, and also 56 turbines are needed. The amounts of efficiency and power output are very convenient for real wind speed data of a region.