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Optimizing the Tilt Angle and Direction of Solar Panels in Aceh
with Genetic Algorithm Method
Yassir
1,Teuku Hasannuddin
2,Fauzan
31,2,3Department of Electrical Engineering - Lhokseumawe State Polytechnic, Lhokseumawe Aceh - Indonesia 1 yassirasnawi@gmail.com
Abstract- Performance of photovoltaic (PV) that are installed fixed has strongly influenced the direction and tilt angle of the PV. Changes in the declination angle of the sun within a year Affect the total solar irradiance received by the PV. To Overcome obstacles in the fixed solar cell panel, it is Necessary to optimize the angle of inclination and the direction of the PV placement so that the electrical energy generated will be more optimally. Method Genetic Algorithm (GA) is used to optimize the direction and angle of the PV. The genetic algorithm initializes the PV tilt angle with the specified limits, then calculates to Obtain the total energy value in the inclined plane of PV by crossover, mutation and selection. The algorithm is limited to 50 generations. The results show that the optimization of the tilt angle can Increase the ability of solar panels to receive solar energy Compared to the placement of solar panels with the angle of the tilt without optimization. The angle and direction of the optimum panel with the genetic algorithm in the Aceh area are 4° - 6° with the orient direction
to the south. Installation at an angle after the optimization of the energy received Increased solar panels to 9% Compared with tilt 20°
direction to north without optimization. And an Increase of 8.9% Compared with tilt 30° direction to the south without optimization. As
well as an Increase of 65.5% Compared with the installation of a 90° direction to the south.
Keywords- Photovoltaic, tilt and direction, optimization, renewable energy
I. INTRODUCTION
Indonesia is the country that has a tropical climate, so that the potential energy of the sun is very high. Based on the data on solar radiation in Indonesia as follows, for the Western Region of Indonesia of about 4.5 kWh / m2/ day and in Eastern Indonesia about 5.1 kWh / m2/ day, so the potential of the sun Indonesia's average is equal to 4.8 kWh / m2/ day. This potential is fairly used as a major reason in the development of solar power in Indonesia. Indonesia is a region that has a high solar radiation intensities and stable throughout the year, so the PV modules get optimum power.
The tilt angle has a considerable impact on the solar radiation on the surface of the solar module. The maximum power for a year will be obtained when the angle of the solar module together with the location's latitude. Functioning regulatory system provides settings and security in a solar power generation system such that it can work efficiently and reliably.
PV panels fixed mount (do not follow the movement of the sun) can reduce maintenance costs and power consumption for additional equipment. However, obstacles due to the fixed installation can not capture the maximum sunlight throughout the day and also the change of latitude the sun within one year, so that the electrical energy generated is not optimal. To overcome the constraints on solar cell panels is fixed, it is
necessary to optimize the placement of PV panels that will be obtained electrical energy generated is more optimal.
Changes path of the sun in a year in the direction of latitude (north-south direction) is from 23 ° 0.26 'north latitude to 23 ° 0.26' south latitude [4]. Thus, the area within this range changing path of the sun to the north-south every year, such as in Aceh.
The technique to solve optimization problems can use heuristic global optimization methods. The use of heuristic methods have been widely used to solve optimization problems, such as evolutionary programming (EP), differential evolution (DE), particle swarm optimization (PSO) and Genetic Algorithm (GA).
In this paper, the proposed method of genetic algorithms to optimize the direction and the angle of installation of solar panels in Aceh. Encoding of chromosomes using real coding with the fitness function involving direct radiation (HB), radiation diffuse (HD) and radiation reflected (HR) obtain the total average daily solar radiation on the surface of the incline (HT) which can be accepted solar panels.
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Total average daily solar radiation received by a tilted surface (HT) which can be received by the solar panels is calculated using a mathematical model estimates the potential of solar energy and the ability of solar technology [2] is to consider the direct radiation (HB), radiation diffuse (HD) and the reflected radiation (HR). where,HT = HB + HD + HR (1)
where HB is a direct daily radiation received on an inclined surface (KWH / m2-day) can be expressed as:
𝐻𝐵= (𝐻𝑔− 𝐻 𝑑)𝑐𝑜𝑠(𝜃𝑐𝑜𝑠 (𝜃)
𝑧) (2)
(2)
where:
𝐻𝑔: : Average global radiation Monthly on the horizontal
surface of the earth (kWh / m2/ d)
𝐻𝑑 : Average radiation diffuse monthly on the horizontal
surface of the earth (kWh / m2/ d) 𝜃𝑧: : zenith angle(o)
θ: : angle of incidence(o)
Figure 1 shows the angle of the zenith, the angle of incidence and the angle of solar panel on the surface of the inclined plane [4].
Figure 1. zenith angle, angle of incidence, and the angle of the solar panel [4]
The angle of incidence using the equation:
𝐶𝑜𝑠(𝜃) = 𝑆𝑖𝑛(𝛿)𝑆𝑖𝑛(𝜑)𝐶𝑜𝑠(𝛽) − 𝑆𝑖𝑛(𝛿)𝐶𝑜𝑠(𝜑)𝑆𝑖𝑛(𝛽)𝐶𝑜𝑠(𝛾) + 𝐶𝑜𝑠(𝛿)𝐶𝑜𝑠(𝜑)𝐶𝑜𝑠(𝛽)𝐶𝑜𝑠(𝜔) +
𝐶𝑜𝑠(𝛿)𝑆𝑖𝑛(𝜑)𝑆𝑖𝑛(𝛽)𝐶𝑜𝑠(𝛾)𝐶𝑜𝑠(𝜔) +
𝐶𝑜𝑠(𝛿)𝑆𝑖𝑛(𝛽)𝑆𝑖𝑛(𝛾)𝑆𝑖𝑛(𝜔) (3)
zenith angle using the following equation:
𝑐𝑜𝑠𝜃𝑧= 𝑐𝑜𝑠 (𝜑) 𝑐𝑜𝑠 (𝛿) 𝑐𝑜𝑠 (𝜔) + 𝑠𝑖𝑛(𝜑) 𝑠𝑖𝑛 (𝛿) (4)
(4) where:
𝜑 : latitude (o)
δ : solar declination (o)
ω : hour angle (o)
𝛽 : the angle of the panel (o) 𝛾 : surface azimuth angle (o)
Solar declination based on the following equation.
𝛿 = 23.45𝑜𝑠𝑖𝑛(360𝑜 284 + 𝑛
365
)
(5)(5) HD is calculated by:
𝐻𝐷 = (𝐻𝑔𝜌)(1−𝑐𝑜𝑠2(𝛽)
)
(6)where:
𝜌: ground albedo
HR radiation reflected is calculated by:
𝐻𝑅= 𝐻𝑑𝑅𝑑 (7)
where Rd is a comparison of the daily diffuse radiation at an oblique angle to the horizontal surface.
Sunset angle using the following equation: 𝑐𝑜𝑠𝜔𝑠= −𝑡𝑎𝑛∅ 𝑡𝑎𝑛𝛿 (8)
(8)
B.Genetic Algorithms
1. General
Genetic Algorithms are the computational algorithms for optimization problems inspired by the theory of evolution to find solutions to a problem. There are many variations in the Genetic Algorithm, one of which is the Genetic Algorithm for combinations of optimization problems, which is to obtain an optimal solution to a problem that has many possible solutions.
Genetic Algorithm was first pioneered by John Holland of the University of Michigan in the 1960s, Genetic Algorithm has been widely applied in various fields. Genetic algorithms are widely used to solve optimization problems, despite the fact that also has a good ability to matters other than optimization. John Holland states that any problems in the form of adaptation (natural or artificial) can be formulated in genetic technology.
2. Population Initialization
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several chromosomes that are encoded as a control variable is the angle of the solar panels for 12 months in a year according to the minimum and maximum limits. Figure 2 shows the structure of chromosomes AG representing all control variables. The chromosome encoding scheme used in this research is the real number encoding.xi = xmin + (xmax - xmin).chromosomes (9)
δ 2
... δ
N
Figure 2. Structure of chromosomes AG
3. Value Fitness
An individual is evaluated based on a certain function as a measure of its performance. The function used to measure the value of a match or the optimal degree of a chromosome is called a fitness function. The resulting value of the function indicates how optimal solution is obtained.
In the case discussed in this study, the goal is optimum then fitness is the maximum value. The objective function is to find the total radiation that is about the most optimal PV with a fixed angle with the restrictions met so that if all the restrictions have been met, fitness can be calculated from that. By involving restrictions then the fitness inequality shown in the equation.
(10)
C. Proposed Algorithm
The detail of the proposed algorithm are as follows: 1. Generating initial population.
2. Decodes variable tilt angle of the solar panels (β) with the limits defined.
3. xi = xmin + (xmax - xmin)chromosomes
4. Evaluation of individual to find the best fitness
5. The selection process by the method of selection
tournament, elitism, crossover and mutation. 6. Repeat steps 4-5 until the maximum generation. 7. Show total daily radiation, and the angle of the solar
panel.
In the flow chart diagram shown in Figure 3.
Generation= MaxG?
end Start
no
yes output Chr omosoms decoding xi=xmin+(xmax-xmin).chr omosom
GA process: tournament selection, crossover, mutation and Elitism
Init ial population
Evalua tion of Individuals Fitness : HT = HB + HD + HG
Figure 3. Flowchart of proposed procedure.
III RESULTS AND DISCUSSION A. Results
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Figure 4. Aceh (05 ° N and 97 ° E).1. The tilt angle panel before optimization
Simulations to determine the acceptable solar energy panels on a random tilt angle is done with an angle of 20° to the north, 30° to the south and 90 to the south. Table 1 shows the simulation results prior to optimization of the angle of the solar panel. The simulation was performed to determine the acceptable energy panel with several tilt angle of the solar panel.
TABLE 1.
THE ANGLE OF THE PANEL BEFORE OPTIMIZATION
Month
Solar Radiation Received (kWh / m2-day) Angle of solar panels (β)
β = -20 β = 30 β = 90
2. Monthly angle optimal panel
Simulation to obtain optimal tilt angle panels monthly and yearly period was conducted using a genetic algorithm (AG). Limits used angle of inclination 90° to the south and 90° to the north with the consideration declination of the sun moving in one year amounted to 23.45o to the north and 23.45° to the south. The simulation was performed with 12 variables, 100 population and 50 generations.
Monthly optimal tilt angle of the panel with an acceptable energy panels are shown in Table 2. In the January to March,
the optimal tilt angle of 5° - 23° facing south. April to May and August to September with optimal tilt angle of 2° facing south until 2o facing north. In June and July, the optimal tilt angle of 8 ° facing north. While in November and December, the optimal tilt angle is 10 ° - 22 ° facing south.
TABLE 2
OPTIMAL ANGLE OF THE PANELS MONTHLY PERIODS
Month tilt angle
3. Annual Optimum Tilt angle
Optimalisasi hasil simulasi sudut kemiringan optimal tahunan yang ditunjukkan pada Gambar 5. Panel pemasangan sudut miring tetap 4,42° menghadap ke selatan. Tabel 3 menunjukkan energi yang bisa diterima setelah optimasi sudut kemiringan panel tahunan. Total energi yang diterima panel surya sepanjang tahun sebesar 1,61 MWh /m2/tahun.
Figure 5. The optimal angle of the solar panel annual ACEH
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TABLE 3THE OPTIMAL TILT ANGLE PANEL ANNUAL PERIODS
Month
Energy/year (kWh/m2-year) 1610.000
B. Discussion
Comparison of energy that solar panels can be received average monthly periods optimization is shown in Figure 6. In the tilt angle before optimization with an angle of 20° facing north obtained an average daily energy panel that is acceptable is 4.01 kWh / m2-day with a total annual energy is 1460 kWh / m2. Installation of solar panels with an inclination of 30° facing south will produce an acceptable energy panel is equal to 1550 kWh / m2-year. And installation of panels at an angle of 90° facing south, solar energy panels that can be accepted is equal to 559 kWh / m2-year.
After optimizing the angle of the panels, of the monthly period, the average daily energy received by the solar panel is 4.44 kWh / m2-day, with total energy per year is 1622 kWh / m2-year. The simulation results before and after the optimization of the inclination angle of the solar panels of the monthly period indicate an acceptable solar energy energy increase. For monthly optimization the energy increases of 132 kWh/m2-yr compared to the 200 angle north-facing and the greater energy of 72 kWh/m2-yr compared to the 300 angle facing south.
Figure 6. Comparison of Energy daily average solar panel that can be accepted between the corners without optimization with monthly
optimization angle
Installation with a slope angle after the optimization of the annual period obtained from the optimization, simulation using the genetic algorithm obtained by the angle of 4.42o facing south increased compared to the installation of the slope angle before optimization of 20o facing north of 147 kWh / m2-year or 9% as shown in Figure 7. Figure 8 shows an increase of 144 kWh / m2-year or by 8.9% compared to slope angle installation before optimization 30 degree facing south. And the increase compared to mounting before optimization with a 90 degree angle facing south of 1063 kWh / m2-year or as high as 65.5% as shown in Figure 9.
Figure 7. Comparison of the average daily energy received by PV without optimization 20o to the north at an angle optimization annual
Figure 8. Comparison of the average daily energy received by PV without optimization 30o to the south at an angle optimization annual
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Figure 9. Comparison of the average daily energy received by PV withoutoptimization 90o to the south at an angle optimization annual
The simulation results of Figures 7 through 9 show the installation of solar panels with an annual optimization slope angle can be applied because they are better than mounting with no slope of optimization and also not too much different than the panel's acceptable energy compared to the optimal monthly angle of installation. It can also reduce maintenance costs for changes in inclination angle each month in one year.
IV. CONCLUSION
In this paper genetic algorithm method is proposed to obtain the optimal tilt angle of the solar panels monthly and yearly periods for installation in the Aceh region. Simulation accepted by the panel 8-65%.
2. Monthly optimal tilt angle of the panel in January to March, the optimum tilt angle of 5° - 23° facing south. April to May and August to September with optimal tilt angle of 2° facing south until 2o facing north. In June and July, the optimum tilt angle of 8 ° facing north. While in November and December, the optimum tilt angle is 10 ° - 22 ° facing south.
3. Optimization of the annual period obtained from simulations using genetic algorithms obtained tilt angle of 4.42 degree solar panels facing south.
NOMENCLATURE cooperation so that this research is done well.
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![Figure 1. zenith angle, angle of incidence, and the angle of the solar panel [4]](https://thumb-ap.123doks.com/thumbv2/123dok/4056110.1999530/2.612.85.252.404.581/figure-zenith-angle-angle-incidence-angle-solar-panel.webp)
