The fossil-based energy, which is the main source of energy world until today, has been known as one of the most impacts of GHG (greenhouse gas emissions) related to the global warming effect (Al-Ghussain, 2019). Meanwhile, fossil-based energy resources, i.e., oil, gas, and coal, are depleted and limited (Al-Ghussain, 2019; Hansen, Breyer, and Lund, 2019; REN21 Members, 2020). For the long run and energy sustainability, we need to find alternative energy sources that are clean and renewable. Electricity from solar energy using PV (photovoltaic) technology is considered one alternative option. Solar energy has a huge potential in the form of light or radiation. Technologically, it can be converted into electricity using solar cells or PV systems.
In Indonesia, solar electricity is expected to supply power of about 6.5 GW by 2025. This target is a part of 45 GW of total renewable energy power produced in the same year (Aprilia, 2017; Maulidia et al., 2019; Hidayatno et al., 2020). The target is officially stated in the General National Energy Plan (Rencana Umum Energy Nasional, RUEN) published through the Ministry of Energy and Mineral Resources (MEMR). It is also stated in the RUEN that the energy demand in Indonesia will be fulfilled with a renewable energy mix of 23% by 2025 (Tampubolon, 2020).
The amount of PV system energy output is affected mainly by two factors, i.e., internal and external factors. The internal factors include efficiency and material properties, while external factors are related to the operational conditions of the PV panels. For the external factors, one of the most important one is the amount of solar energy or irradiance received by PV panels. Theoretically, solar irradiance consists of three components: direct, diffuse, and reflected components. The sum of the three irradiation components is called global irradiation (Duffie and Beckman, 2013).
The amount of direct component irradiation that falls into a solar panel depends on the angle of incidence. As the Sun moves regularly and continuously, the angle of incidence for a fixed surface change over time throughout the day. Mathematically the angle of incidence is expressed as (Beckman and A., 2013):
where θ = angle of incidence
The amount of solar irradiation received by solar modules will be maximum when the modules are exposed and perpendicularly to the radiation. In terms of the angle of incidence, θ should be 0°. As the position of the sun changes over time, the angle of incidence would change as well. To keep the angle of incidence at 0°, a solar tracker system is required. The solar tracking system, however, would require additional types of equipment, which affects the cost of the PV system. It is important and interesting to figure out the comparison energy output of the PV system between conventionally fixed-mount (without solar tracking) and with a solar tracking system in a particular area.
The potential of solar energy is different for the different sites around the earth’s surface. It is necessary to identify and assess the solar irradiation potential at a particular place before building the real installation. Simulation is commonly done for assessment studies. There were a lot of solar assessment studies and simulations in many different places found in the literature (Marcel S. & Tomáš C., 2012; Singh and Banerjee, 2015; Merrouni et al., 2016; Shukla, Sudhakar and Baredar, 2016; Dondariya et al., 2018; Anang et al., 2021). However, fewer studies had been reported on the Indonesian case, especially in urban areas (Tarigan, Djuwari, and Kartikasari, 2015; Tarigan, 2018).
The Jakarta Capital City Government is pushing for the use of renewable energy to reduce GHG emissions by 30% by 2030. This can be done by building solar power plants. In 2030, Jakarta is estimated to produce 116,910,000 tonnes of CO2. The Jakarta Capital City Government, in accordance with The Paris Agreement, is aiming to reduce this enormous emission by 30%. This amount of CO2 would be equivalent to 43,663,400 MWh in 2030. The government is accelerating green transformation in the city to achieve this goal. One is the acceleration of the implementation of the Regional Climate-Resilient Low Carbon Development Plan.
This work simulates PV systems with and without solar tracking in Jakarta, the largest and most populated city in Indonesia. The simulation studies the comparison of energy output between two systems. The works are conducted using Photovoltaic Geographical Information System (PVGIS) simulation tools(re.jrc.ec.europa.eu, 2022). The aim of the study is to figure out the specific energy output of fixed-mounted and solar-tracking PV Systems in Jakarta. The difference in specific energy between the two systems is compared. The results of the study are expected to be a useful reference for the development, application, and promotion of solar PV systems in Indonesia.
The energy output of PV systems in this work was obtained from simulation works. The simulations were conducted using Photovoltaic Geographical Information System (PVGIS) simulation tools (re.jrc.ec.europa.eu, 2022). The software is free online, and it can be used as a tool to estimate the solar electricity production of a photovoltaic (PV) system. It gives the annual output power of solar photovoltaic panels. As a photovoltaic Geographical Information System, it proposes a google maps application that makes it easy to use. The simulated area in this work was Jakarta, Indonesia. The detail of the geographical data of the location is presented in Table 1.
|PV technology||Crystalline silicon|
|PV installed||1 kWp|
|Terrain Elevation||3 m|
|Inverter type||String inverter|
The PV system simulated is 1000 Wp capacity, with an on-grid connection system. This is to obtain the specific energy output of a simulated PV system. The other input parameters for simulations are shown in Table 1. Two different main mountings of PV systems installation were simulated, i.e., (1) fixed-mount or without a tracking system and, (2) a solar tracking system with a two-axis tracking system. The first installation is fixed–mounted with the optimum tilt of 12° from the horizontal, with a panel azimuth of 0° or facing North. The two different mounting systems are schematically shown in Figure 1.
The PV system-specific energy output is the amount of electricity output in comparison with the input solar irradiation under standard test operating conditions. The value is determined by comparing the energy output Eout, (in kWh) with the maximum power capacity, Pmax (kWp), under standard test conditions. Hence, the unit of the specific energy output is kWh/kWp. It can be mathematically defined as:
The photovoltaic simulation model can be arranged into three steps as Figure 2. These are for the photovoltaic system of the plant dataset. The first is the calculation of the output power of the solar panels using the PVGIS sunlight-to-power algorithms. The second step is to calculate the electricity generated by each photovoltaic panel, taking into account the date of (de)commissioning. The resulting time series for the ensemble is then converted into local time and spatially aggregated and stored in CSV format (Lehneis et al., 2022).
The information on the availability and the potential of solar energy is required to assess PV potential at the particular site. The information includes solar irradiation, sun path, components of radiation, day length, and temperature. Sun path diagrams can provide information about how the sun will impact a site and building throughout the year. It is very useful in determining the period of the year and hours of the day when shading will take place at a particular location. The annual Sun path in Jakarta is shown in Figure 3. The Sun path diagram also shows the corresponding solar elevation, civil time, and solar time, as well as the active area with solar and civil time, terrain horizon, and module horizon over a year. The higher terrain horizon of an object will make a shorter period of the Sun above the horizon than the astronomical day length. The variation of day length and solar zenith angle in Jakarta are shown in Figure 4.
The estimated solar irradiation in Jakarta from 2005 to 2020 is shown in Figure 5. From this figure, it can be seen that the solar irradiation in Jakarta varies between 110.99 kWh/month.m2 to 110.99 kWh/month. m2. Maximum irradiation occurs from July to December, while minimum radiation occurs from December to March. The average monthly irradiation is about 140.0 kWh/month.m2 means that the average daily solar irradiation in Jakarta is around 4.7 kWh/m2. With this number, the annual potential solar energy in Jakarta is found to be 1,716 kWh/m2. This number is quite close to other areas in Indonesia that have been previously reported (Asian Development Bank (ADB), 2016; IESR, 2018).
Global irradiation consists mainly of direct and diffuse components. For some areas with higher surface reflectance, such as snow, ice, and water, the reflected irradiance component might be significant. In Jakarta, the component of diffuse varies from 21% to 55% of the global radiation. On average diffuse radiation is about 0.45% of the global radiation. This number is relatively high in comparison to some areas with clear skies. The diffuse and global irradiation ratio in Jakarta from 2005 to 2020 is shown in Figure 6. (The figure in this section were generated directly from the simulation software). The daily ambient temperature in Jakarta is found from 23°C to 31°C.
The monthly specific energy output of the PV system with optimized-fixed mounted varies from 95.83 kWh/kWp to 137.53 kWh/kWp. The highest electricity energy is generated during the month of September, and the lowest is during February. The monthly specific energy output over a year is shown in Figure 7. In total, the annual specific energy output is found to be about 1,379.05 kWh/kWp.
Figure 8 shows simulation-specific energy output results of the PV system with solar tracking each for the vertical axis, inclined axis, and two-axis, respectively. It is obviously seen that of the three systems, the two-axis system generates the highest energy; however, the difference is relatively small. For all systems, the highest energy is generated in August, and the lowest is in February. For a two-axis system, the monthly specific energy output varies from 110.9 kWh/kWp to 171.4 kWh/kWp, with an annual specific energy output of 1,672.0 kWh/kWp.
A comparison of energy output between fixed-mounted and tracking (two-axis) PV systems over a year is shown in Figure 7 and Figure 8. It is shown that solar tracking PV system generates higher energy than fixed-mounted ones throughout the year.
The difference in specific energy output between fixed-mounted and tracking PV systems over a year ranges from 15%–29%, with an average of 21%, whereas the system with solar tracking always produces higher energy. Figure 9 presents the comparison of energy output in terms of monthly percentage. The total annual specific energy output for five different conditions of installation is presented in Table 2. It is obviously seen that applying a solar tracking system for PV systems in the Jakarta area will generate electricity about 21% higher than with the conventional fixed-mounted system.
|MOUNTING||ANNUAL ENERGY [kWh/m2]||RELATIVE TO FIXED-OPTIMIZED [%]|
Solar tracker’s advantages:
Solar trackers limitations
Solar trackers, particularly for residential solar systems might not be worth the extra investment in almost all cases. Solar trackers aren’t used widely in the residential solar industry. When there is not enough space to install solar trackers, they can be very useful. Installing a solar tracker system might generate more power in a smaller area than installing additional solar panels. Solar trackers can also be useful for large-scale commercial or utility installations.
Simulations study and comparison of PV system energy output between fixed-mount and solar tracking system PV systems in Jakarta, Indonesia, have been conducted. The results of the study showed that the difference in specific energy output between the two systems ranges from 15%–29%, where the PV system with solar tracking always generates more energy than the fixed mount over the year. Annual specific energy with fixed mount installation is about 1379 kWh/kWp, while the system with a two-axis tracking system is about 1672 kWh/kWp. That means energy output by using solar tracking for PV systems in Jakarta would produce energy about 21% higher than with the conventional fixed-mount system. Solar trackers, particularly for residential solar systems might not be worth the extra investment in almost all cases. When there is not enough space to install solar trackers, they can be very useful.
This work was supported by the “Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi” Republic of Indonesia, Grant Number: 073/E5/P6.02.00.PT/2022.
The author has no competing interests to declare.
Al-Ghussain, L. 2019. Global warming: review on driving forces and mitigation. Environmental Progress & Sustainable Energy. John Wiley and Sons Inc, 38(1): 13–21. DOI: https://doi.org/10.1002/ep.13041
Anang, N, et al. 2021. Performance analysis of a grid-connected rooftop solar PV system in Kuala Terengganu, Malaysia. Energy and Buildings, 111–182. Elsevier Ltd. DOI: https://doi.org/10.1016/j.enbuild.2021.111182
Aprilia, A. 2017. Off-Grid Renewable Energy Policies in Indonesia. (June). Available at: https://d2oc0ihd6a5bt.cloudfront.net/wp-content/uploads/sites/837/2017/06/1_Off-Grid-Renewable-Energy-Policies-in-Indonesia-.pdf.
Asian Development Bank (ADB). 2016. Indonesia: Energy Sector Assessment, Strategy, and Road Map. Available at: https://www.adb.org/sites/default/files/institutional-document/189713/ino-energy-asr.pdf.
Dondariya, C, et al. 2018. Performance simulation of grid-connected rooftop solar PV system for small households: A case study of Ujjain, India. Energy Reports, 4: 546–553. DOI: https://doi.org/10.1016/j.egyr.2018.08.002
Duffie, JA and Beckman, WA. 2013. Solar engineering of thermal processes. Wiley. DOI: https://doi.org/10.1002/9781118671603
Hansen, K, Breyer, C and Lund, H. 2019. ‘Status and perspectives on 100% renewable energy systems’. Energy. Elsevier B.V., 175: 471–480. DOI: https://doi.org/10.1016/j.energy.2019.03.092
Hidayatno, A, et al. 2020. Investigating policies on improving household rooftop photovoltaics adoption in Indonesia. Renewable Energy, 156: 731–742. DOI: https://doi.org/10.1016/j.renene.2020.04.106
IESR. 2018. Indonesia Clean Energy Outlook Imprint Indonesia Clean Energy Outlook 2020. IESR, 1–54. Available at: www.iesr.or.id.
Lehneis, R, et al. 2022. Spatiotemporal Modeling of the Electricity Production from Variable Renewable Energies in Germany, ISPRS International Journal of Geo-Information. MDPI, 11(2): 90. DOI: https://doi.org/10.3390/ijgi11020090
Maulidia, M, et al. 2019. Rethinking renewable energy targets and electricity sector reform in Indonesia: A private sector perspective. Renewable and Sustainable Energy Reviews, 231–247. Elsevier Ltd. DOI: https://doi.org/10.1016/j.rser.2018.11.005
re.jrc.ec.europa.eu. 2022. JRC Photovoltaic Geographical Information System (PVGIS) – European Commission. Available at: https://re.jrc.ec.europa.eu/pvg_tools/en/tools.html (Accessed: 2 May 2022).
REN21 Members. 2020. Renewables 2020 Global Status Report, Global Status Report for Buildings and Construction: Towards a Zero-emission, Efficient and Resilient Buildings and Construction Sector. Available at: http://www.ren21.net/resources/publications/%0Ahttps://www.ren21.net/wp-content/uploads/2019/05/gsr_2020_full_report_en.pdf.
Shukla, AK, Sudhakar, K and Baredar, P. 2016. Simulation and performance analysis of 110 kWp grid-connected photovoltaic system for residential building in India: A comparative analysis of various PV technology. Energy Reports. Elsevier Ltd, 2: 82–88. DOI: https://doi.org/10.1016/j.egyr.2016.04.001
Singh, R and Banerjee, R. 2015. Estimation of rooftop solar photovoltaic potential of a city. Solar Energy. Elsevier Ltd, 115: 589–602. DOI: https://doi.org/10.1016/j.solener.2015.03.016
Solarreviews.com. 2022. What Is a Solar Tracker and Is It Worth the Investment? Available at: https://www.solarreviews.com/blog/are-solar-axis-trackers-worth-the-additional-investment (Accessed: 6 December 2022).
Tarigan, E, Djuwari and Kartikasari, FD. 2015. Techno-economic Simulation of a Grid-connected PV System Design as Specifically Applied to Residential in Surabaya. Indonesia. In Energy Procedia, 90–99. Elsevier Ltd. DOI: https://doi.org/10.1016/j.egypro.2015.01.038