ELKHA : Jurnal Teknik Elektro.
Vol.
17 No.
April 2025, pp.
15 - 23
ISSN: 1858-1463 .
, 2580-6807 .
Analysis of 3 kW Solar Power Plant Production Optimization by Improving Energy Production Efficiency Using 3d Pvsyst Simulation Method Teguh Mohammad Fiqri1*).
Redi Ratiandi Yacoub.
Rudi Kurnianto.
Rusman.
, and Hasan.
1,4,.
1,2,.
Department of Electrical Engineering.
Politeknik Negeri Pontianak.
Indonesia Department of Master Electrical Engineering.
Tanjungpura University.
Indonesia Corresponding Email : *) teguhmohammad112@gmail.
Abstract AeThis study aims to analyze the effect of optimizing the position of solar panels on improving energy production efficiency in a 3 kW solar power plant (PLTS) system.
The 3D PVsyst simulation method was used to model the system and predict performance before and after optimization.
Simulation results indicate that repositioning the solar panels can increase energy production by 2% .
99 kWh to 01 kW.
A comparison between simulation results and actual data shows reasonably good agreement, although some differences require further investigation.
Discrepancies between simulation and actual data may be attributed to several factors, such as weather conditions, component efficiency, and other environmental factors.
This study concludes that optimizing the position of solar panels is an effective step to enhance the performance of PLTS systems.
However, further research is needed to consider additional factors affecting system performance and to develop more accurate simulation models.
Keywords: Solar Power Plant (PLTS), optimization.
PVsyst, simulation, energy efficiency INTRODUCTION Solar energy utilization generally takes two forms:
converting sunlight into thermal energy and directly converting it into electricity using solar cells.
Solar thermal systems collect heat by concentrating sunlight through mirrors or lenses to generate temperatures of up to 300AC / 20 bar, which are used to drive turbines.
Meanwhile, solar cells convert electromagnetic waves from sunlight directly into electrical energy.
This approach is commonly used in Solar Power Plants (PLTS).
Currently, the utilization of solar energy in Indonesia only accounts for 0.
05% of its potential, and the installed capacity of Solar Power Plants (PLTS) is only 100 MW.
This capacity needs to increase by 900 MW to meet the targets set in the National Energy Plan (RUEN).
The Government's goal of building 6.
5 GW of PLTS by 2025 must also be pursued.
PLTS is part of the energy solutions for the future, contributing to better air quality.
In the National Energy Plan, as outlined in Presidential Regulation No.
79 of 2014, the Indonesian government has set a policy to increase renewable energy in the national energy mix to 23% by 2025 .
To support this effort, particularly in solar energy utilization, the government has Manuscript received 2025-01-16 .
revised 2025-03-02.
accepted 2025-03-03 issued several technical regulations as implementation One such regulation is the Minister of Energy and Mineral Resources Regulation No.
49 of 2018, amended by Regulations No.
13 of 2019 and No.
16 of 2019, regarding the use of rooftop solar power systems by customers of PT Perusahaan Listrik Negara (PLN) Persero .
This regulation aims to create opportunities for all PLN customers, including households, businesses, government institutions, social organizations, and industries, to participate in utilizing and managing renewable energy for achieving energy security and independence, especially solar energy (EBTKE, 2.
To promote widespread implementation, the government issued Circular Letter No.
363/22/MEM.
L/2019 to Cabinet Ministers.
Governors, and Regents/Mayors, encouraging the installation of rooftop PLTS systems in buildings, including offices, official residences, warehouses, parking areas, and public facilities .
This research focuses on analyzing the energy production of a 3 kW PLTS installed at Polnep.
The goal is to improve the efficiency and performance of the existing system.
To achieve this objective, the author will use PVsyst software and data from the Sunny Boy SMA inverter already in place at the PLTS.
PVsyst is software used for photovoltaic (PV) system simulation and analysis.
Using PVsyst, the author can simulate PLTS performance based on various parameters such as geographical location, panel orientation, and weather conditions.
PVsyst also enables the evaluation of different design and operational scenarios to identify the most efficient configuration for the PLTS system.
The PVsyst simulation is configured to match the geographic and site-specific conditions of the existing PLTS, providing optimal data for simulations and enabling effective system planning.
Additionally, the author will focus on data collection from the Sunny Boy SMA, an inverter used in the 3 kW PLTS at Polnep.
The Sunny Boy SMA can monitor and record energy production data in real time, providing accurate information about system performance.
Data from the Sunny Boy SMA will be used for an in-depth analysis of energy production, identifying factors affecting - 15 - This work is licensed under a Creative Commons Attribution 4.
0 License For more information, see https://creativecommons.
org/licenses/by-nc-sa/4.
Analysis of 3 kW Solar Power Plant Production Optimization (T.
M Fiqri, et al.
efficiency, and finding solutions to improve system The primary focus of this research is to analyze the energy production of the 3 kW PLTS installed at Polnep.
By integrating PVsyst simulations and real-time data from the Sunny Boy SMA, the study aims to provide a comprehensive understanding of system performance and effective optimization strategies.
This research is expected to offer practical benefits in PLTS management, such as reducing operational costs, increasing system reliability, and contributing to greater carbon emission reductions.
The results of this study will not only benefit Polnep in managing its PLTS system but also serve as a valuable reference for implementing PLTS in other locations.
improving energy production efficiency in PLTS, we can support the transition to broader and more sustainable renewable energy use.
In conclusion, this research aims to optimize the performance of the 3 kW PLTS at Polnep through energy production analysis and simulation using PVsyst.
It also conducts simulations to assess the existing PLTS needs for building energy requirements through 3D modeling in PVsyst software.
This approach is expected to make a positive contribution to renewable energy development and environmental preservation.
The author also seeks to explore and study the potential of the installed PLTS in the context of West Kalimantan, especially in Pontianak City.
3 kWp Solar Power Plant Sunny Boy Inverter Battery Sunny Island Inverter AC Coupling PLN (State Power Plan.
Selektor Switch Polnep Electronics Laboratory Building Figure 2.
Existing Scheme This quantitative analysis will produce optimization strategies that can be implemented to achieve improved performance and energy production efficiency in the Solar Power Plant system.
Figure 3.
Existing Solar Power Plant System II.
METHODOLOGY
Start Methods of Research This study employs a quantitative method to analyze and optimize energy production in a 3 kW Solar Power Plant (PLTS) installed in a building at Politeknik Negeri Pontianak (Polne.
Utilizing PVsyst software with 3Dbased simulations combined with SketchUp software for 3D visualization and real-time data from the existing Sunny Boy SMA inverter.
Field Study Literature Study Date Collection Primary Data Secondary Data Calculation :
Production Efficiency 3 kWp Solar Power Plant Battery Testing Sunny Boy Inverter Production Data Focus AC Coupling Sunny Island Inverter Analysis Polnep Electronics Laboratory Building Conclusions and Recommendations Figure 1.
Analysis Focus Scheme Finish His research aims to examine and analyze improvements in energy production efficiency through the identification and evaluation of factors affecting system Figure 4.
Flow Diagram of Research Primary Data Data was collected directly from the first source.
For the primary data in this research, the author will conduct field measurements, such as temperature data and voltage data recorded during the related study.
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Analysis of 3 kW Solar Power Plant Production Optimization (T.
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Calculation of Solar Panel Production Plan Calculating Area Array.
In the construction of a solar power plant (PLTS), an area is required to arrange photovoltaic modules into a specific array.
The required array area can be calculated using the following formula:
ycyyceycayco ycyycuycyceyc Array Area = ycyycaycuyceyco yceyceyceycnycaycnyceycuycaycy ycycayccycnycaycycnycuycu ycnycuycyceycuycycnycyc a yaya yaycuyceycyciyc yaycuycuycycycoyceycc PV Efficiency = .
aycuyceycyciyc yaycuycyyceycaycyceycc ycycu ycayce ycyycycuyccycycayceyc.
y 100 %.
Calculating Power (Watt Pea.
In the construction of a solar power plant (PLTS), an area is required to arrange photovoltaic modules into a specific array.
The required array area can be calculated using the following formula:
Figure 5.
Azimuth & Tilt Configuration PVsyst PVsyst is a highly useful tool for designing and analyzing PV systems.
With features such as climate and component databases, performance simulations, and economic analysis.
PVsyst enables users to optimize the design and performance of PV systems.
In the context of research at Politeknik Negeri Pontianak.
PVsyst can be used to simulate the performance of a solar power system (PLTS) with an SMA inverter and analyze the factors affecting system efficiency in a tropical environment.
P watt peak = PV area x PSI x pv .
PSI = Peak Solar Insolation, which is 1000 W/m2 yuC pv = Solar Panel Efficiency Next, based on the amount of power to be generated, the number of solar panels required can be determined as follows.
ycNycuycycayco ycEycO = P watt peak
PMPP
ycEycOycaycyc ycEyceycayco ycEycAycyycy .
RESULTS AND DISCUSSION
= Power generated (W.
= Maximum output power of the solar panel (W).
Evaluation and Dimensions of the Existing Location The elevation installed on the existing 3 kW solar power plant (PLTS) corresponds to the actual building structure and is applied using technical drawing language.
Figure 6, specifically dimensional drawings of the building to be analyzed.
These dimensional measurements provide elevation data for the placement of the existing PLTS on the building.
This dimensional data is critical for providing elevation information from the ground level (Elevation 0.
of the Electronics Laboratory building at Polnep.
The building's dimensions and the 0.
elevation position are shown in the diagram below.
Secondary Data Data obtained from existing sources, such as data provided by BMKG related to solar radiation, temperature in the Pontianak City area, and annual wind speed data, are used as references for utilizing PVsyst software.
PVsyst.
PVsyst is a simulation software used to design.
Figure 5 simulate, and optimize Solar Power Plant (PLTS) systems.
This software was developed by the University of Geneva and is widely used by engineers, designers, and researchers in the field of solar energy.
PVsyst is widely used in various applications:
Project Planning: Designing and optimizing PV systems for commercial, industrial, and residential Feasibility Studies: Assessing the technical and economic feasibility of PV projects.
Academic Research: Utilizing simulations for research and development of PV technologies.
Monitoring and Evaluation: Comparing actual system performance with simulation results for monitoring and evaluation.
Figure 6.
Dimension of elevation Building The dimensional measurements in the drawing are based on observations and manual measurements conducted in the field.
This is done to ensure that the simulation results align with the actual physical structure.
Database The production database for the related research includes both secondary and primary data, where the author establishes correlations between the related data to identify the basis of the analysis to be conducted and tested.
The production database is structured to achieve more optimal research results, as outlined below.
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Analysis of 3 kW Solar Power Plant Production Optimization (T.
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Sunny Boy Production Data Collection.
The illustration of the Sunny Boy inverter is shown in the image below, along with an explanation of how to retrieve production data from the Sunny Boy SMA inverter.
Figure 7.
Table 2.
Solar Radiations Data in 2019 Figure 7.
Time Series Data Energy Secondary Data.
The secondary data collected as a reference for optimizing the PLTS, which will be input into the PVsyst 7.
4 software, includes previous research data such as journals that serve as a foundation for the study.
The secondary data collected is as Table 1.
Secondary Data on Weather Anomalies Table 3.
Comparison of Solar Radiation Data for 2013 & 2019 Calculation of the Solar Power System.
Energy Calculation Based on PV Specifications The complete solar radiation data provided directly by BMKG consists of daily data for the year 2019, as follows.
Based on the data presented and the comparison made, the data obtained from the research journal Sagian, 2013 and the data from BMKG Pontianak show the comparison of solar radiation intensity .
n units of Wh/mA) in Pontianak City for each month between 2013 and 2019.
Data PV STC = 1000w/m Temp = 25 oC mpp = 25.
11 Volt mpp = 8.
Air masses AM 1.
Table 2 and tabel 3, the 2013 data comes from the research journal, while the 2019 data is sourced from BMKG Pontianak.
This data has been discussed with BMKG Pontianak for the validation of solar radiation data in Pontianak City.
Figure 8.
Brief Specification of Installed Solar Panel Figure 8 is calculation for existing 3 kW solar power system is as follows.
It is known that the existing solar panels have a capacity of 210 Wp, with 14 pieces.
Therefore, the calculation is as follows.
210 y 14 = 2940 ycycy ycE=ycO y ya 11 ycO y 8.
54 ya 439 ycycaycyc
- 18 -
Effective Energy Absorption = 4 Hours ycE = 2940 ycycy y 4 760 ycEa yaycu 1 yccycayc Analysis of 3 kW Solar Power Plant Production Optimization (T.
M Fiqri, et al.
34,536 ycoycEa A System Efficiency = ( 69,36 ycoycEa ) y 100 % = 49,77% The losses used as reference factors affecting the energy generated by the PV system are as follows, where the impact of these losses will be used as a reference for energy calculations.
ycEyceycayco ycEycuycyceyc Array area = .
cEycO yayceyceycnycaycnyceycuycayc ycu yaycuycyceycuycycnycy.
Data : Peak Power Regeneration Factor = 20 % = 0.
Loss Factor = 20% = 0.
Backup Energy = 15 % = 0.
Thus.
When calculated: 20% y 20 y 15% = 0.
PV Efficiency = 49.
yc Intensity= 121.
218 AEyco2 (Secondary Dat.
Thus:
Battery data at location.
Battery Type NSAG 100 yaEa 12ycO = 8 ycEycayc Total = 100 ycu 8 = 800 yaEa Based on the energy specifications obtained:
yaycycycayc yaycyceyca O Simulation With PVsyst.
To achieve optimal results in analyzing the 3 kW solar power system installed in the Polnep Laboratory building, software with the best qualifications is utilized to obtain results that closely match actual conditions.
In this analysis, the author uses the PVsyst software.
The display and identity details of the related software are shown Total Energy Generated After Loss Factors Are Applied A A A 2940 ycycy 49 y 77 % y 121.
218 yc AEyco2 = 48.
77 yco2 ycE=ycOycuya ycO = 12 Volt ya = 800 Ah ycNEaycyc, ycE = ycOycuya = 12 ycu 800 = 9600 ycEa ycE = 11760 ycEa Loss = 0,544 Thus = ycE ycu .
Energy Effected 6397,44 ycEaPV Array Area = 2940 Wp loss= 11760 ycu 0,544 = Linier Power = 2.
94 kwp (Installed PV) Effective Irradiation = 4 Hours (Assumed from ESDM Panel Efficiency = 20 % (Assumed from ESDM Nominal DC Energy :
Linear Power ycu Effective Irradiation x Number of Days x Panel Efficiency Nominal DC Energy = 94 ycoycOycy 4 yaycuycycycAEyccycayc y 30 yccycayc y 20% Figure 10.
Initial PVsyst Selecting the Standalone Menu = 69.
36 kWh After performing the calculations based on the basic formula used, the nominal energy that should be produced is 69.
36 kWh.
However, in one month, the monitoring system produced 34.
kWh (Monitoring results from the Sunny Boy Inverte.
Based on the observations, the sample data has been processed in the form of a graph as follows.
Figure 11.
The Menu Entering the Coordinate of Existing Location After accessing the menu (Geographical Site Parameter.
New Sit.
as shown in Figure 12, enter the desired location's coordinates in the (Latitude & Longitud.
Coordinate data can be directly checked Figure 9.
Energy Data for The Last 1 Month yaya yaycuyceycyciyc ycEycycuyccycycayceycc A System Efficiency = ( yaycuycyyceycaycyceycc yaycuyceycyciyc ) y 100% - 19 - Analysis of 3 kW Solar Power Plant Production Optimization (T.
M Fiqri, et al.
on Google Earth or Google Maps, which will direct you to the exact location, as displayed in Figure 12.
using SketchUp software.
This 3D model is then exported into a format compatible with PVsyst and imported into the 'Construction/Perspective' menu for further shading Figure 12.
Interactive Map of the Existing PV Location In Figure 11, after entering the coordinates of the location to be analyzed, input the relevant data that affects the actual location, such as Global Horizontal Irradiation (GHI/Solar Radiatio.
Horizontal Diffuse Irradiation (HDI).
Temperature, and Wind Velocity, as shown in Figure 13.
Figure 15.
PVsyst Software Simulation Run Process In Figure 15, the process of running the simulation data after all the data has been input into PVsyst, as shown in Figure 10.
Figure 13.
Display of Monthly Input Data for the Existing System The next step is to analyze the potential for shading on the solar panel modules.
Using the "Construction/ Perspective" feature in PVsyst, the researcher can create a three-dimensional model of the solar panel installation This model is then used to identify areas that are prone to shading, whether from nearby buildings, trees, or other objects.
Figure 16.
Normalized Productions Figure 14.
Synchronization of the Existing 3 kW Solar Power System Based on 3D In Figure 14, to obtain a more detailed and accurate 3D model, the geometry of the project location, including surrounding buildings and topography, is first modeled Figure 17.
Loss Diagram Graph Before Optimization By Pvsyst
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Analysis of 3 kW Solar Power Plant Production Optimization (T.
M Fiqri, et al.
Figure 21.
Values for Tilt and Azimuth Before Optimization In Figure 20 & Figure 21, the change in the solar panel orientation before and after optimization is shown, where:
Tilt: The tilt angle of the panel relative to the horizontal plane.
The same tilt value .
A) in both conditions indicates that the tilt angle of the panel was not changed during optimization.
Azimuth: The orientation angle of the panel relative to the south .
n azimuth of 0A means the panel faces A significant change occurred in the azimuth Before Optimization: Azimuth -135A, meaning the panel is tilted towards the southwest.
After Optimization: Azimuth 180A, meaning the panel faces directly south.
The change in panel orientation from southwest to south has significant implications for the performance of the solar power system, as it affects the reception of solar Before Optimization: With a southwest orientation, the panel will receive solar radiation indirectly, especially during the day.
This reduces the amount of radiation that can be converted into electrical energy.
After Optimization: A southward orientation allows the panel to receive solar radiation directly and optimally throughout the day, especially at midday when the sunAos intensity is highest.
This increases the amount of radiation captured and boosts energy Figure 18.
Loss Diagram Graph After Optimization By PVsyst Based on Figure 17 and Figure 18, which are the loss diagrams, the PVsyst simulation results show that the optimization process successfully improved the efficiency of the solar power system by reducing the percentage of unused energy and increasing the amount of energy that was effectively used.
In conducting the simulation analysis based on the 3D simulation, an evaluation was also carried out on the 3D Several experimental analyses of the solar panel tilt radius showed no significant changes.
The changes observed in the analysis were due to the repositioning of the solar panels to align with the direction of sunrise and sunset, as shown in Figure 19.
Table 4.
Effective Energy from The Array Figure 19.
Simulation design after optimization Figure 20.
Values for Tilt and Azimuth After Optimization - 21 - Categories After Optomization Before Optomization PV loss due to irradiance PV loss due to Module quality loss Mismatch loss, modules and strings Ohmic wiring loss Unused energy .
attery Efecetive Energy From Array 1204 kWh 1149 kWh Analysis of 3 kW Solar Power Plant Production Optimization (T.
M Fiqri, et al.
1,085.
This indicates that the optimization successfully improved the amount of usable energy.
Changes in Energy Usage:
Direct Use: The percentage of energy used directly increased, indicating that more energy is being used in real-time.
Stored: The percentage of energy stored in the batteries decreased, showing that the batteries are more efficient in supplying energy as Losses: Most types of energy losses decreased, indicating that the systemAos overall efficiency Figure 22.
Loss Diagram of effective energy from the array Based on the table and graph above, the results of the PVsyst simulation, particularly to determine the effective energy of the array, are as follows:
Increased Effective Energy: After optimization, the effective energy of the array increased from 1,149 kWh to 1,204 kWh.
This indicates that the optimization has successfully improved energy Reduction in Losses: Most types of energy losses experienced a reduction after optimization.
This means the optimization efforts successfully minimized various factors causing energy loss.
Greatest Impact: The most significant energy loss occurs due to radiation levels and temperature.
The reduction in losses in these components greatly contributes to the increase in effective energy.
Figure 23.
Loss Diagram of Energy Supplied to Users After Optimization Before Optimization Direct use Figure 23, the loss diagram graph provides a clear overview of the changes in the percentage of energy losses across various system components before and after Based on the loss diagram graph, which focuses on the factors causing energy loss before and after optimization, the following can be outlined: General Reduction in Losses: Overall, the percentage of energy losses in most components decreased after optimization.
This indicates that the optimization efforts successfully reduced various types of energy loss.
Stored Battery Stored Energy balance Battery efficiency loss Charge/Disch.
Current Efficiency Loss Battery Selfdischarge Current Energy to users Table 5.
Total Energy Supplied to The Users Categories Table 5, which is explained based on the loss diagram produced from the 3D PVsyst simulation of the existing position change to achieve the best azimuth value, can be outlined as follows:
Increase in Final Energy: After optimization, the final energy available for users increased from 1,061 to - 22 - Significant Reduction in Losses Due to Radiation and Temperature: The largest reduction in energy losses occurred in the components "PV loss due to irradiance level" and "PV loss due to temperature.
This shows that the optimization successfully improved the solar panelAos efficiency in converting sunlight into electricity, even though radiation and temperature conditions vary.
Small Increase in Module Quality Loss: Although a reduction occurred in most components, there was a slight increase in the "Module quality loss" This may be due to other factors unrelated to optimization, such as the age of the modules or minor damage to the panels.
Significant Reduction in Unused Energy: The significant decrease in "Unused energy .
attery ful.
" indicates that the energy generated is more effectively utilized, both for direct consumption and storage in the batteries.
Analysis of 3 kW Solar Power Plant Production Optimization (T.
M Fiqri, et al.
After Optimization Before Optimization .
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Short-term solar irradiance forecasting based on a hybrid deep learning methodology.
Information (Switzerlan.
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Figure 24.
Graph Eergy to Users For Energy Production System .
ystem Productio.
Energy production before optimization: 1,061 kWh Energy production after optimization: 1,085 kWh Calculation Method:
Calculate Production Increase:
Production increase = Production after optimization Production before optimization A Production increase = 1085 kWh - 1061 kWh Production increase = 24 kWh .
Calculate the percentage increase :
A Percentage Increase .
Hidayat.
, et al.
The Impact of Tropical Climate on Solar Panel Efficiency.
Journal of Renewable Energy Research, 7.
, 123-130.
ycEycycuyccycycaycycnycuycu ycnycuycaycyceycaycyce =.
cEycycuyccycycaycycnycuycu ycayceyceycuycyce ycCycyycycnycoycnycycaycycnycuyc.
ycu 100% .
Ramli.
, et al.
Effects of Ambient Temperature on the Performance of Photovoltaic Systems in Tropical Regions.
Renewable Energy Journal, 21.
, 245-251.
24 ycoycOEa Percentage Increase = .
,061 ycoycOE.
ycu100% A Percentage Increase = 2.
Ahmed.
, et al.
Real-Time Monitoring and Optimization of PV Systems Using IoT.
International Journal of Smart Grid and Clean Energy, 6.
, 324-332.
Based on the calculation results, after optimization, the energy production of the solar power system (PLTS) increased by approximately 2.
This indicates that the optimization efforts successfully improved the energy production efficiency of the PLTS.
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Advanced Inverter Technologies for Solar PV Applications.
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, 57-65.
IV.
CONCLUSION
Optimization Increases Production: Adjustments to the solar panel positioning as part of the optimization successfully increased energy production by 2% .
rom 99 kWh to 1.
01 kW.
This indicates that the optimization process effectively improved system Changing the azimuth angle to 180A with a tilt angle of 15A increased energy production to 1.
01 kWh with an efficiency of 18.
Kalogirou.
Optimization of Solar PV Systems.
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