Optimizing layout of wind farm turbines using genetic algorithms in Tehran province, Iran
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Abstract
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.
Keywords
Wind farm layout Optimization Genetic algorithm Kahrizak TurbineIntroduction
Throughout history, fossil fuels were the primary resources for power production. Despite the efforts in increasing government’s attention to the development and expansion of renewable energy sources, yet fossil fuels are one of the principal sources of power production [1].
However, there is no doubt that some sophisticated circumstances caused striking limitations for fossil fuel consumers, for instances, finite nature of fossil fuels, rising demand for variety of energy sources, sustainable development, awareness of environmental problems from burning fossil fuels, to name a few. Therefore, many countries are paying extra attention to renewable energies.
Recently, the share of renewable energies in the world’s energy portfolio has been constantly increased [2]. Renewable energy resource is often viewed as a good alternative for fossil fuel because of its wide availability and environmentally friendly nature [3]. Although, there are several inexpensive and renewable energy sources in the world, their uses are somewhat low [4]. Among all sources of renewable energies, wind power due to its safety for environment as well as its sustainability, has become more conspicuous in recent years [5]. As the electrical power generated by wind farms is cost-effective, the number of wind farms is noticeably proliferated. Since 1980, from the beginning of wind farms’ development, wind power experienced an unprecedented evolution, increased with almost 1500% within the global wind power installation over the last 15 years, in such a way that at the end of 2015, the total installed capacity reached 432 GW [6].
Wind turbine transforms wind power to electrical power. For a specific large area with high wind speed, to increase power production and decrease costs of installation, wind turbines are grouped within a wind farm. The location of the wind farm, the quality of gearbox, and the shape of wind blades are the factors affecting the conversion efficiency [7]. According to Kenway and Martin [8], changing the blade geometry and structural sizes while maintaining a fixed cost and compatibility with the remainder of turbine system can improve the power output of a wind turbine. Besides, reduced cost of producing wind power can be achieved in different ways, for example, site selection, site layout design, predictive maintenance, and optimal control system design [9]. The installation layout of turbines plays an important role in the design of a wind farm. A non-optimal wind farm layout design can lead to lower than expected wind power capture, increased maintenance costs, and so on [10]. By the ability of turbine’s rotor rotation, wake zone is extracted, which renders negative influence on other turbines [11]. This wake phenomenon, which is generated by upstream turbines, causes speed reduction for downstream ones. Subsequently, the power generated by downstream turbines is lower than upstream ones. Since conventional wind turbines’ layout significantly increases wake effect, optimization of the layout configuration can be a rational strategy to decrease wake effects and also maximize the total power which may be extracted from wind farms. The ‘wind farm layout optimization (WFLO)’ problem is classified under such type of wind turbine layout problem [12].
A lot of research studies have been carried out related to WFLO. There are two major approaches in layout optimization: grid-based approach, and unrestricted layout approach. Grid-based approach is to divide a wind farm into a set of equal-sized cells and place turbines in the center of these cells, while in unrestricted layout approach, turbines are allowed to be placed in any positions within the wind farm [13]. For example, Grady et al. [14] conducted grid-based approach to place wind turbines in a 2 km × 2 km farm and results showed that it was able to place wind turbines in a defined area where the goals of the optimization were maximizing the power output and reducing the cost of implementation. Bazacliu et al. [15] conducted a study on wind farm layout optimization problem (WFLOP). The objective of their study was to determine the optimal placement of wind turbines within the farm. They used a discrete representation of wind farm so that the farm area was decomposed into a set of cells, where each cell can contain only one wind turbine. In that approach, the expected power production was maximized. Chowdhury et al. [16] presented the unrestricted wind farm layout optimization (UWFLO) that addressed critical aspects of optimal wind farm planning using constrained particle swarm optimization (PSO). Reasonable agreement between the wind farm model and the experimental results were obtained. The complex nonlinear optimization problem presented by the wind farm model was effectively solved. They found that an optimal combination of wind turbines with different rotor diameters could improve the wind farm efficiency. Mora et al. [17] applied an evolutionary algorithm (EA) to maximize the profit obtained during the useful life of a wind farm, taking into account a given initial investment. Chen et al. [18] investigated the effect of using wind turbines with different hub height on power output in a small wind farm. Three different wind conditions were analyzed using nested genetic algorithm. Results showed that power output of the wind farm using different hub heights would be increased even when the total number of wind turbines was the same. Moreover, results showed that different hub heights could also improve cost per unit power of a wind farm. Serrano Gonzalez et al. [19] discussed the optimal wind farm configuration problem and proposed EA to optimize the wind farm layout. The algorithm’s optimization process was 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 lifespan. Results showed that the proposed EA was capable of finding the optimum wind farm configuration.
Some other optimization methods have been used for layout optimization of a wind farm. For example, binary particle swarm optimization (BPSO) was used to design optimal wind farm configuration [11]. Ant colony optimization (ACO) algorithm was applied to the design of wind farm layout [20]. Monte Carlo simulation method was used to optimize the layout of a wind farm [21]. Bansal and Farswan [22] presented a solution of WFLO problem using a recent unconventional optimization algorithm, biogeography-based optimization (BBO). Results showed that BBO was capable to outperform conventional methodologies of solving WFLO problem.
- 1.
Developing novel GA-based codes to optimize the wind farm layout as well as reduce the cost per unit power.
- 2.
Implementing the newly developed GA-based codes in a given site under real wind conditions with three scenarios, with different number of generations and population, different effective distances, and different longitudinal and latitudinal distances of turbines from each other.
- 3.
Estimating the annual power production of wind farm and its efficiency.
Materials and methods
Site description and wind condition
Detailed information about Kahrizak region
Parameter | Value |
---|---|
Location | Kahrizak, Tehran, Iran |
Period of record | 2010–2015 |
Longitude | 51°21′36.45″E |
Latitude | 35°31′2.68″N |
Height above mean sea level (m) | 1003 |
Cost model
Turbine parameters and wake model
Technical characteristics of wind turbine AV 928
Parameters | Value |
---|---|
Rated power (kW) | 2500 |
Cut-in wind speed (m/s) | 3 |
Rated wind speed (m/s) | 11.6 |
Cut-out wind speed (m/s) | 25 |
Rotor diameter (m) | 93.2 |
Swept area (m^{2}) | 6822 |
Hub height (m) | 80 |
Optimization method
Overview of genetic algorithm
Wind farm layout optimization (WFLO) problem is a discrete problem that, due to its complexity, for example, non-uniform wind speeds with variable directions, cannot be solved using classical methods and have to be solved by intelligent algorithms. Among all intelligent algorithms that have been used in WFLO problem, GA is the best one. It is actually the most famous type of evolutionary algorithms due to its specific properties to yield a set of preliminary solutions, search for optimal solution from amongst the proper ones and repeat this process to achieve the optimum solution [27]. GA is a heuristic solution-search or optimization technique, originally motivated by the Darwinian principle of natural genetic and evolution mechanisms observed in nature. GA was first proposed by John Holland [28] as a means of finding good solutions to problems that were otherwise computationally intractable. Holland’s Schema Theorem and the related building block hypothesis provided a theoretical and conceptual basis for the design of efficient GA that was successfully applied to a wide range of practical problems in science, engineering and industry [29, 30, 31, 32, 33, 34]. This algorithm is based on artificial intelligence and is a population-based method which improves the solutions during different iterations and contributes to the evolutionary process of solutions. GA includes following stages:
In the first stage, a random set of solutions is created. Each solution is called a chromosome and each chromosome is made of smaller parts named as gene. In GA, these set of solutions are called population. After the formation of each generation, it must be specified to what extent the chromosomes of the next generation are close to optimal solution. This is known as fitness function. The best solutions for each generation could be defined using fitness function. In the next stage, we create a new generation using a series of GA operators. These operators include selection, crossover and mutation. Creation of a new generation is accomplished through reproduction, for which we need parents. The parents are determined from current population by a selection operator. This operator selects the best cases from the chromosomes existing in a population to create a new generation. With the fusion of two chromosomes, a new member will be created. These two genetic chromosomes play the role of the parents and the new member plays the role of the child. The most common types of crossovers contain: one-point crossover, two-point crossover, and uniform crossover. Mutation operator uses only one parent to make a child. This is realized through slight changes in initial strings of chromosomes. Finally, after the creation of a new generation, we select the best solution (chromosome) of this generation and compare it with best present solution. This process (making generation) continues till the algorithm reaches its final position, e.g., the optimal solution is found or the stopping criterion for the algorithm is brought to an end.
Process of optimization
GA coding is implemented in MATLAB to search the optimal layout of a given wind farm. The coding is sub-divided into several sections as follows:
Insert data: in this section, data are introduced as input to the algorithm, e.g., terrain dimensions, wind turbine characteristics, wind characteristics, and the number of turbines.
Parameter setting: in this section, parameters related to reproduction, such as population size, max_iter, percentages of children produced by crossover and by mutation are determined.
Initialization: in this section, an empty matrix for individuals (chromosomes) is defined. In this study, as initial population, GA makes a m × n binary matrix including different individuals representing wind farm layouts, where m is the total number of individuals in one generation and n is the length of each individual representing potential positions of wind turbines. The values of m and n are defined at the beginning of optimization. In each individual, 1 means there is a turbine in the cell and 0 means there is no turbine in it. After that, a random solution should be created for the total population. Members of the population are then sorted and ultimately the best solutions are found and saved.
GA main loop: in the main loop of the algorithm, main computations of the algorithm are performed to determine the optimal solution for the problem. In this section, the algorithm generates new children based on crossover and mutation operations. The two operators of crossover and mutation are written as a function in this section.
Crossover: this section includes properties related to crossover operator. The present paper applies the single-point crossover method and the crossover rate is considered 0.9.
Mutation: this part includes properties related to mutation operator. The present paper applies the mutation method in chromosomes with real variable and the mutation rate is considered 0.1.
Results: results of the algorithm, including the best solution, the best fitness, computation time, figures representing the number of iteration, fitness, etc., are coded in this section.
Results and discussion
Optimal turbine layout of the studied region is presented using three scenarios: (1) different population and generations, (2) different effective distances, and (3) different longitudinal and latitudinal distance of the turbines from one another.
Optimization results with different numbers of population and generations
Optimization results of Kahrizak region using different generations and population
Parameter | Generation = 500 population = 250 | Generation = 500 population = 500 | Generation = 1000 population = 250 |
---|---|---|---|
Fitness value | 0.0036 | 0.0036 | 0.0036 |
Total power (kW/year) | 9228.2 | 9418.6 | 9422.4 |
Normalized cost | 33.5 | 34.18 | 34.18 |
Efficiency (%) | 88.63 | 88.69 | 88.72 |
Number of turbines | 50 | 51 | 51 |
Optimization results with different effective distances
Comparison of results with different effective distance in Kahrizak region
Parameter | \({{ED}} = 6 \times \left( {\frac{{{{x}} + {{y}}}}{2}} \right)\) | \({{ED}} = 20 \times \left( {\frac{{{{x}} + {{y}}}}{2}} \right)\) |
---|---|---|
Fitness value | 0.0036 | 0.0036 |
Total power (kW/year) | 9422.4 | 9205.2 |
Normalized cost | 34.18 | 33.54 |
Efficiency (%) | 88.72 | 88.41 |
Number of turbines | 51 | 50 |
Optimization results with different longitudinal and latitudinal distance of turbines from one another
Optimization results with different longitudinal and latitudinal distance of turbines from one another in Kahrizak region
Parameter | X = 372.8 Y = 186.4 | X = 186.4 Y = 372.8 |
---|---|---|
Fitness value | 0.0036 | 0.0036 |
Total power (kW/year) | 10,441 | 8834.9 |
Normalized cost | 37.41 | 32.29 |
Efficiency (%) | 89.54 | 88.39 |
Number of turbines | 56 | 48 |
According to results from this study, it can be concluded that GA is capable of optimizing the turbine layout so that the energy cost is reduced compared to that of the traditional layout. These results can be confirmed by some similar studies. For example, Pillai et al. [35] conducted a case study wherein they explored the application of a wind farm layout evaluation function and layout optimization framework to Middelgrunden wind farm in Denmark using genetic algorithm. Results showed that this optimization algorithm was capable of identifying layouts with reduced levelized energy cost compared to the existing layout while still considering the specific conditions and constraints of this site and those typical of future projects. In another study, Rajper and Amin [36] conducted a GA-based study to find out the optimal solution for the wind turbine micro-sightings. The site that was under consideration for greener solutions of the problem was Gharo-Sindh, Pakistan. The basic objective of the study was to find the most optimal solution for cost per unit power. Results showed that GA was able to optimize the value of fitness function. Parada et al. [37] proposed an approach to solve the WFLO problem based on a Gaussian wake model using GA. The proposed approach minimized the annual cost of energy of a wind farm. The application of the proposed approach yielded higher annual generation and a lower computational time for all wind scenarios under study.
Conclusions
Given the relatively low cost of electrical power generated by wind farms, the number of installed wind farms is increasing every year. The installation layout of turbines plays an important role in the design of a wind farm. Thus, wind farm layout optimization (WFLO) problem is proposed so as to maximize the total power output with minimum cost. This study introduces innovative GA-based codes considering the rotor’s 360° rotation ability against the wind direction to make the optimization of wind turbine layout closer to reality. In this study, by selecting the Kahrizak region as a windy region and choosing a turbine named “AV980”, three different scenarios, including different numbers of generations and population, different effective distances, and different longitudinal and latitudinal distance of turbines from one another are studied. Among three scenarios, the best result is obtained for a scenario wherein the longitudinal distance is greater than the latitudinal distance. By observing the wind rose of Kahrizak region, it is found that the dominant wind direction of the region is toward the east and south–east. Therefore, increasing the longitudinal distance of the turbines from one another will improve the efficiency and also make the turbine layout more realistic in the terrain. In this case, the efficiency and normalized cost of turbines are 89.5% and 37.4, respectively, and also 56 turbines are needed. Total power is computed annually over 10.1 MW. These amounts of efficiency and power output are very suitable based on real data of a region.
Notes
Acknowledgements
The authors would like to thank the University of Tehran for funding the project numbered 05-6-324030.
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