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Analysis of 1-year energy data of a 5 kW and a 122 kW rooftop photovoltaic installation in Dhaka

  • Mohammad Abul Hossion EMAIL logo
Published/Copyright: April 25, 2024
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Energy Harvesting and Systems
From the journal Energy Harvesting and Systems Volume 11 Issue 1

Abstract

Since 2010, rooftop photovoltaic (PV) systems have been extensively used in Bangladesh. This PV system contributes 2–3% to the country's energy demand. In recent years (2020–2024), at least ten large-scale 20–100 MW PV power plants are coming into operation. However, the growth rate of the new PV system is limited by a few factors, such as sustainable energy output over a long time, financial return on investment, and reliability of the energy. To maintain a steady growth of the electrical energy produced from the PV system, research on the performance of the older installations is required. This study analyzes the various data (generated dc power from sunlight, transmitted ac power to the grid) of a 5 kW (March 2021–February 2022) and a 122.4 kW (January 2022–December 2022) rooftop grid-connected solar installation for 12 months. The polycrystalline silicon PV systems are 8 and 4 years old, respectively. The yearly average performance ratio of the 5 and 122.4 kW systems is 17% and 79%, respectively. The results of the study will encourage the investors and community to achieve a 10% share in the national energy demand in the context of Bangladesh.

Keywords: photovoltaic; performance ratio; polysilicon; rooftop PV; clean energy

1 Introduction

The renewable and clean energy resources are competing with the conventional energy resources. This has become possible because of the global awareness against fossil fuel-related pollution. In recent years, with post-COVID-19 and global economic uncertainties, the supply of fossil fuels such as oil and coal has become unpredictable. To maintain energy security, many countries are now emphasizing toward increasing the proportion of renewable energy share in the national energy mix. Figure 1 shows the renewable energy mix for the national electrical energy generation of 62 countries with a maximum share of 87% to a minimum of 3% (Renewable Energy by Country 2023). Renewable energy resources, such as wind, solar, hydro, ocean wave, tidal current, etc., are the main source of large-scale electricity generation. Among these renewable resources, 12% of the global electricity is produced from photovoltaic (PV) and wind (Wiatros-Motyka 2023).

Figure 1 Renewable energy mix to the national electrical energy generation of 62 countries with a maximum share of 87% to lowest 3%.
Figure 1

Renewable energy mix to the national electrical energy generation of 62 countries with a maximum share of 87% to lowest 3%.

Bangladesh is no exception, with its abundance of sunlight and wind energy. However, resources are not being used to their limit, especially in Bangladesh, where there is a 3-month monsoon season. The recent global economic hardship and post-COVID-19 placed Bangladesh in a difficult situation with regard to the fuel for its large-scale electrical power plants, which rely on coal and oil. This has reminded us to increase the investment in renewable resources, mainly in PV and wind, which are the two most promising renewable energy sources for Bangladesh (Tachev 2022). The long coastline of the Bay of Bengal (BoB) is ideal for wind energy generation. The country’s coastal regions have an average wind speed of 5–8 m/s. This falls into the lower end of the ideal range for wind turbines. Bangladesh has 20,000 km2 of viable land with a potential wind generation capacity of 30,000 MW (Babu et al. 2022). However, BoB is a cyclone-prone area where flash flood and ghastly wind is a yearly phenomenon. Thus, the growth of wind plants is limited. Bangladesh receives an average of 4–6.5 kW h/m2 of solar energy per day, which is sufficient to generate the required portion of electricity for the national grid (Abdullah-Al-Mahbub and Islam 2023). The Power Development Board of Bangladesh maintains an up-to-date record of all current and upcoming renewable energy power plants through the Sustainable Renewable Energy Development Authority (SREDA, National Database of Renewable Energy 2023) (RE Generation mix, 2023). At present, the PV provides 80% (PV 960 MW, hydro 230 MW, and wind 2.9 MW) of the total 1,193 MW renewable energy share to the national electrical energy installed capacity of 24,263 MW. Considering the growth, Figure 2 plots the PV and wind power plants that are operational, under construction, and in planning using the data from SREDA. It shows 438.6 MW of PV and 62 MW of wind power plants, which are under construction. These power plants are scheduled to be active in 1–2 years. Under the planning phase, there are 1,311 MW of PV and 295 MW of wind power plants, which are expected to be active in 3–4 years’ time. At this rate, renewable energy resources will contribute 6–8% to the national electrical energy demand in 3–5 years.

Figure 2 Installed capacities versus PV and wind power plants bar chart, which are active, under construction, and under planning by the Ministry of Power, Energy and Mineral Resources, Bangladesh.
Figure 2

Installed capacities versus PV and wind power plants bar chart, which are active, under construction, and under planning by the Ministry of Power, Energy and Mineral Resources, Bangladesh.

It appears that the growth of PV power generation will exceed all other renewable resources in Bangladesh (Talut et al. 2022). Hence, it will be crucial to maintain and use the PV systems with maximum capacity. In recent times, performance evaluation, degradation (Hossion 2020), and aging have become significant regarding the electrical energy contribution from the PV installation (Hossion et al. 2023). This is used to forecast the actual return on investment period (Abas et al. 2022, Alashqar et al. 2022), reliability (Sun et al. 2019), and energy cost per unit (Aziz et al. 2023) using suitable software tools (Khan and Minai 2023) which in turn attracts more investment from the private sector. In the context of Bangladesh, there are two kinds of PV systems that are widely available in the field; one is 5–20 kW capacity installations, which are suitable for home, office, irrigation, and rural community spaces. The other PV systems are 100 kW and above, which are suitable for industrial rooftop as cluster systems capable of producing 1–4 MW electrical power (Joshi et al. 2021). This study acquires energy data of a 5 kW (March 2021–February 2022) and a 122.4 kW (January 2022–December 2022) rooftop grid-connected solar installation for 12 months. The energy data are processed to evaluate the performance of the PV system in terms of indexes such as specific yields (Schardt and te Heesen 2021), electrical degradation (Al Mansur et al. 2023, Pascual et al. 2021), overall system efficiency (Anang et al. 2021), performance ratio (PR) (Yadav and Bajpai 2020), potential induced degradation (Dhimish and Tyrrell 2022), and payback period (Alazazmeh et al. 2022). This study analyzed the 1-year energy data from the two PV systems for the calculation of PR (Syahindra et al. 2021) using a generalized method (Navothna and Thotakura 2022), which will be useful for the field-level study.

2 Experimental sites and setup

The two polycrystalline silicon PV installations were considered as the experimental sites. The installed capacity of the PV system in site 1 is 5 kW and in site 2 is 122.4 kW. Both the PV systems use polycrystalline silicon PV modules facing south. The PV system in site 1 is mounted on a metal frame with an angle of 22 degrees to the roof surface on the rooftop of a commercial building. The ground clearance was maintained between 2 and 5 ft. The PV modules in site 2 are installed on the export processing zone industrial building fitted with the curved industrial tin roof with no ground clearance. Hence, the modules are fitted at a variable inclination ranging from 5 degrees to 19 degrees along the roof to maintain the slope. The details of the sites are given in Table 1.

Table 1

PV installation sites and system details data

PV array Site-1 Thirty-three modules in each string, 3 strings are connected in one inverter. Site-2 Seventeen modules in each string, 4 strings in each inverter, 5 inverters.
Location Section-12, Mirpur, Dhaka, Bangladesh EPZ, Savar, Dhaka, Bangladesh
Latitude 23.82720168664972, longitude 90.36371646641032 Latitude 23.94700099969477, longitude 90.27506736317311
Installed capacity 4,950 W 122,400 W
PV modules Ninety-nine modules, each 50 W Three hundred forty modules, each 360 W
PV modules type Poly-crystalline silicon Poly-crystalline silicon
Installation year 2014 2018
Type of installation On-grid On-grid
Capacity-to-area ratio 112.5 W/m2 180 W/m2

The PV modules and their technology are of utmost importance to the performance of the system. The PV modules in sites 1 and 2 are each 50 and 360 W, respectively. The 50 W modules are 14.53% efficient, and 360 W modules are 18.15% efficient. The array designed for the 5 kW PV system uses one string with 33 modules in series. The three strings are connected to the inverter in parallel, which gives a maximum DC voltage of 570.9 V, a current of 8.82, and a power of 5 kW. The array designed for the 122-kW PV system uses one string with 17 modules in series. The four strings are connected in parallel to one inverter, which gives a maximum DC voltage of 673.2 V, a current of 36.4 A, and a power of 24.5 kW. Thus, five inverters give 122 kW power. The nameplate data of each type of PV modules are given in Table 2.

Table 2

PV module characteristics (nameplate) data

PV module parameters Poly silicon 50 W Poly silicon 360 W
Maximum power (P max) (Wp) 50 360
Open circuit voltage (V oc) (V) 21.6 47.0
Short circuit current (I sc) (A) 3.29 9.67
Voltage at maximum power (V mpp) (V) 17.3 39.6
Current at maximum power (I mpp) (A) 2.94 9.10
Fill factor (FF) (%) 71.6 79.28
Efficiency (E ff) (%) 14.53 18.15
Nominal operational cell temperature (°C) (−)40 to +85 (−)40 to +85
Cell area (cm2) 6.4 × 15.4 15.67 × 7.838
Cell number 36 (6 × 24) = 144
Module area (cm2) 66 × 67 200 × 100
Total solar installation area (m2) (66 × 67 × 99 × 10−4) = 44 (200 × 100 × 340 × 10−4) = 680
Series resistance (ohm) 1.46 0.81
Shunt resistance (ohm) 49.42 69.47

The inverters are an integral part of a PV installation. These inverters have multiple roles, such as DC to AC conversion, battery charger, maximum power point tracker for optimum performance, intelligent grid connectivity, frequency matching, etc. The inverter also acts as a communication and data acquisition device. The inverters are fitted with communication ports such as serial and ethernet connectors. The connectors can be used to link the inverter to a network using a computer. The data such as DC voltage, current, power, AC voltage, current, power, working duration, and inverter temperature can be recorded using suitable software and a computer. The detailed specifications of the inverters are given in Table 3.

Table 3

Grid-connected inverter (nameplate) data

Inverter details Kaco-5500 Sunny Tripower, SMA Solar Tech.
PC interface RS232 RJ 45 Ethernet
Max input voltage (d.c) 800 V 1,000 V
Max input current (d.c) 15.2 A 36.2 A
Max input power (d.c) 6,000 W 25,000 W
Rated frequency (a.c) 50 Hz 50 Hz

3 Data acquisition procedure

The daily energy production data are acquired from the PV systems using specific software provided by the inverter. The data are recorded every 1 min for the day duration. The schematics of the data acquisition process from sites 1 and 2 are shown in Figures 3 and 4, respectively. The photographic image of the PV modules on the rooftop is given in Figure 5 for sites 1 and 2.

Figure 3 Schematics of electrical connection and data acquisition process for 5 kW PV installation in site-1.
Figure 3

Schematics of electrical connection and data acquisition process for 5 kW PV installation in site-1.

Figure 4 Schematics of electrical connection and data acquisition process for 122.4 kW PV installation in site-2.
Figure 4

Schematics of electrical connection and data acquisition process for 122.4 kW PV installation in site-2.

Figure 5 Image of the 5 kW PV installation on a metal frame with ground clearance at an angle of 22 degrees (left). Image of the 122.4 kW PV installation on the roof at 5–19 degree slope facing south (right).
Figure 5

Image of the 5 kW PV installation on a metal frame with ground clearance at an angle of 22 degrees (left). Image of the 122.4 kW PV installation on the roof at 5–19 degree slope facing south (right).

4 Results and discussion

4.1 Yearly data analysis

Yearly data of a PV system provide insights regarding the performance and productivity (Jamil et al. 2023). The output of a PV system is electrical energy in kilo-watt-hour (kW h). The energy data can be extracted from the inverter in real-time and stored for further analysis. Table 4 shows the 1-year electrical energy data of the two PV systems used in this study where the capacity of site 1 is 5 kW and site 2 is 122.4 kW. The data from site 1 are recorded for the months of March 2021–February 2022. The data from site 2 are recorded for the months of January 2022–December 2022. The data are tabulated according to the three seasons of Bangladesh for over 365 days and total 4,350 working hours. The annual average energy output from 5 and 122.4 kW system is 3.23 and 369.36 kW h/day, respectively. The maximum productive month is March and minimum is December.

Table 4

Total electrical energy data of 1 year for 5 kW and 122.4 kW PV system

Season Month Days (A) Working duration (hours/month) Monthly PV energy output to the grid (5 kW system) (kW h/month) (B) Average PV energy output to the grid (5 kW system) (kW h)/day (B/A) Monthly PV energy output to the grid (122.4 kW system) (kW h/month) (C) Average PV energy output to the grid (122.4 kW system) (kW h)/day (C/A)
Summer March 31 365 136 4.4 12983.5 418.82
April 30 380 144 4.8 12462.99 415.43
May 31 406 126 4.0 11424.3 368.52
June 30 380 81 2.7 11020.27 367.34
Monsoon July 31 403 91 2.9 12660.69 408.40
August 31 379 84 2.7 12557.35 405.07
September 30 360 94 3.1 10126.12 337.53
October 31 353 106 3.4 11753.92 379.15
Winter November 30 327 85 2.8 10737.43 357.91
December 31 327 64 2.0 9113.83 293.99
January 31 334 70 2.3 9206.7 296.99
February 28 336 98 3.5 10,772 384.72
365 4,350 1,179 3.23 134819.4 369.36

4.2 Daily data analysis

The inverter provides electrical voltage and current data, which is used to calculate the total electrical energy generation over a day. For analysis, the daily data are presented in Figure 6 for March 2021 of the 5 kW PV system installed in site 1. Figure 6 is a bar chart that shows the 31 days of total electrical energy (red) and total working hours (blue). The PV system was active for an average of 12 h daily over March 2021. The maximum and minimum electricity generations were 6.9 kW h on 14 March 2021 and 2.1 kW h on 9 March 2021, respectively. For further analysis, Figures 7 and 8 show the voltage and power data of the days 9 March 2021 and 14 March 2021, respectively. These two days are chosen as the minimum and maximum productive days of the month, respectively. The data plot in Figure 7 shows the PV system voltage and power from morning 6 am to afternoon 6 pm on 9 March 2021. The rise in PV system voltage shows that the energy is being produced but not transmitted to the grid. After sunrise, the voltage increases gradually and reaches a peak of 500 V at around 8 am. At this point, the voltage decreases to 350 V, and power is transferred to the grid. The plot shows many spikes in voltage for which the power becomes zero. Hence, the system is not transmitting the energy to the grid. These voltage spikes are caused by a sudden fall in the generated current and grid failure. The sudden fall in the generated current may be due to cloud shading. The grid failure can be caused by frequency fluctuation between the grid and the inverter. The mains electricity cut is also a reason for the grid failure. For each spike in the voltage, the inverter takes at least 3 min to restore the power to the main grid. Figure 7 shows at least six spikes, some of which are 1 h long. Besides the interruptions, the series resistances of 50 W PV modules have increased, and shunt resistance has decreased over the aging period of 8 years. The measured series and shunt resistance of 50 W PV module are 2.01 ohm and 42.60 ohm, respectively (Hossion 2020). This shows an increase in the series resistance and a decrease in the shunt resistance value shown in Table 2 as nameplate data. The open circuit voltage for the 5 kW system is 21.6 V × 33 modules = 712 V, as calculated from Table 2. From Figures 7 and 8, the open circuit voltage (V oc) is 540 V, which is 24% less than the name plate value. The reduction in V oc causes the series resistance to increase and the decrease in the maximum current of the system.

Figure 6 Energy and working hours versus day duration bar chart of the 5 kW rooftop PV system installed in Dhaka, Bangladesh.
Figure 6

Energy and working hours versus day duration bar chart of the 5 kW rooftop PV system installed in Dhaka, Bangladesh.

Figure 7 DC voltage and DC power versus day duration plot of the 5 kW PV system in site 1 using the electrical data dated 9 March 2021.
Figure 7

DC voltage and DC power versus day duration plot of the 5 kW PV system in site 1 using the electrical data dated 9 March 2021.

Figure 8 DC voltage and DC power versus day duration plot of 5 kW PV system in site 1 using the electrical data dated 14 March 2021.
Figure 8

DC voltage and DC power versus day duration plot of 5 kW PV system in site 1 using the electrical data dated 14 March 2021.

The data plot in Figure 8 shows the PV system voltage and power from morning 6 am to afternoon 6 pm on 14 March 2021. The plot shows that the peak sunlight hour is 10 am to 3 pm, which is 5 h. The plot shows a uniform PV system voltage over the day duration 7 am–5 pm. Thus, the system transferred the electrical energy to the grid uninterrupted over the day without any grid failure. The feature of the power curve shows a gradual rise in the power with the sun’s inclination, which becomes maximum at 11:30 am. This has caused a maximum generation of electrical energy from sunlight in a day by the 5 kW PV system.

4.3 Performance data analysis

Since the time PV plants became operational, evaluating and predicting their actual performance has been of great interest to researchers, scientists, PV manufacturers, and PV plant developers. The PR of a PV plant is one of the most important parameters used by the industry today to evaluate the performance of PV plants (Jed et al. 2021). PR is a globally accepted PV plant performance parameter in evaluating different technologies through simulated feasibility studies and economic analysis (Singh et al. 2022) even before the implementation stage (Jamil et al. 2017). The PR represents the actual energy generated by the PV plant to its expected energy with reference to its nameplate rating. In other words, the PR is an indicator of losses resulting from invertor problems, wiring, shading, cell mismatch, reflection, outages, module temperatures, etc. The PR of the plant is usually independent of the site location and system size but it has strong dependence upon weather variability. The performance of a PV system can be quantified using the following equation (Khalid et al. 2016):

(1) PR = Measured energy output Estimated nominal output × 100 .

This study evaluates the PR of the two PV systems of capacity 5 and 122.4 kW. Table 5 shows the brief calculation and results of the PR estimated using equation (1). The nominal solar irradiation to the useful solar irradiation is taken according to the PV technology and location given in columns A and B (Global Solar Atlas 2023). The simulated energy output from the 5 kW system is 6,993 kW h/year, and from the 122.4 kW system is 171,364 kW h/year as estimated by the National Renewable Energy Laboratory (NREL), given in column C (PVWatts Calculator 2023). The measured electrical energy produced from both the PV system in sites 1 and 2 is given in columns D and E. Column F shows the calculated PR for 5 and 122.4 kW systems, which is 10 and 58%/m2 year, respectively. The installed capacity to the area ratio is 112.5 and 180 W/m2 for sites 1 and 2, respectively. Thus, the PV systems were not designed to utilize the space efficiently. Column G shows the PR for 5 and 122.4 kW systems, which is (17 and 79)%/year, respectively. The lower PR value for the 5 kW system can be explained as (i) the system is 8 years old with no cleaning and maintenance in this period. It is the seasonal rain and regular wind that keeps the system working; (ii) the system uses 33 50 W PV modules connected in series; thus, the increased series resistance in the bus bar and the solder-bond for each module contributes to the loss in electrical energy (Asadpour et al. 2022) to thermal energy; (iii) the increased series resistance causes a decrease in the shunt resistance and in turn the short circuit current is reduced; (iv) the PV modules operate at high operating temperature (55–60°C); and (v) the PV system has 24% lower V oc as compared to the nameplate data. To understand the lower PR value for 5 kW system, the single-day PR is also calculated. The calculated PR for data presented in Figure 7 (minimum) and 8 (maximum) is 11 and 36%, respectively. The average PR calculated in other literature using real-time data ranges from 77 to 87%, where the global standard is 80% (Khalid et al. 2016). Yearly average PR calculated for PV systems such as a 98.1 kW monocrystalline PV system with a 327 W module in Seoul, Korea is found 83–87% (Singh et al. 2023), 11.2 kW polycrystalline silicon with a 280 W module is found 78% in India (Sharma and Goel 2017), and for 5 kW PV system, it is 77% in India (Yadav and Bajpai 2018).

Table 5

Polycrystalline silicon PV installations PR data as calculated in this study in comparison with the articles published in literature

PV system/location/age Nominal solar irradiance (kW h/m2 year) By NREL, USA. (A) Useful solar irradiance to electricity (kW h/m2 year) (B) Estimated output energy from the PV system by NREL, USA (C) Measured output energy from the PV system (D) Calculated output energy from the PV system (kW h/m2 year) (E) = D/Area PR (%/m2.year) (F) = E/B PR (%/year) (G) = D/C Remarks
5 kWp (50 Wp module) poly c-Si, 44 m2 area Dhaka, Bangladesh Eight (8) years aged 1,887 283@15% of A 6,993 kW h/year 1,179 kW h/year 1,179 44 = 27 = 27/283 17 Yearly average (Mar-2021–Feb-2022)
19 kW h/day 6.9 kW h/day = 10 36 14 March 2021
2.1 kW h/day 11 9 March 2021
122.4 kWp (360 Wp module) poly c-Si, 680 m2 Savar EPZ, Dhaka, Bangladesh Four (4) years aged 340@18% of A 171,364 kW h/year 134,819 kW h/year 134,819 680 = 198.3 = 198.3/340 79 Yearly average (2022)
469.5 kW h/day = 58

5 Conclusion

The PR of two PV systems installed in Dhaka, Bangladesh, over 12-month period is estimated. From the yearly analysis, the PR of the 5 kW PV system in site 1 is 17%, and the 122.4 kW system in site 2 is 79%. The yearly PR per square meter is 10% in site 1 and 58% in site 2. The capacity-to-area ratio in site 1 is 112.5 W/m2 and in site 2 is 180 W/m2. The PR is estimated by taking the NREL database as standard for the nominal solar radiation, useful electrical energy, and estimated output of the particular PV systems. Thus, this study shows a simplified procedure to evaluate the expected PR to the measured PR, which, in turn effective for the long-term monitoring and using the system efficiently. This will also reduce the gap between the installed capacities and to production of electrical energy from PV systems. It appears that for Bangladesh to achieve the SDG-7 and increase renewable energy share in the national electrical energy mix, large-scale cluster PV systems on the rooftop are more feasible in terms of maintenance, monitoring, and productivity.

Acknowledgments

The author would like to thank the Post Graduate Research Management and Technology Transfer Center, Bangabandhu Sheikh Mujibur Rahman Maritime University, Dhaka, Bangladesh, for the research facility.

  1. Funding information: None declared.

  2. Author contributions: The author contributed in the conceptualization, designing methodology, data collection, analysis, interpretation and editing. The author has also prepared all the figures, tables and images.

  3. Conflict of interest: The author states no conflict of interest.

  4. Research ethics: Not applicable.

  5. Data availability statement: The raw data can be obtained on request from the corresponding author.

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Received: 2023-07-22
Revised: 2023-09-06
Accepted: 2023-12-17
Published Online: 2024-04-25

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/EHS-2023-0089
Keywords for this article
photovoltaic; performance ratio; polysilicon; rooftop PV; clean energy
Creative Commons
BY 4.0

Articles in the same Issue

  1. Solar photovoltaic-integrated energy storage system with a power electronic interface for operating a brushless DC drive-coupled agricultural load
  2. Analysis of 1-year energy data of a 5 kW and a 122 kW rooftop photovoltaic installation in Dhaka
  3. Reviews
  4. Real yields and PVSYST simulations: comparative analysis based on four photovoltaic installations at Ibn Tofail University
  5. A comprehensive approach of evolving electric vehicles (EVs) to attribute “green self-generation” – a review
  6. Exploring the piezoelectric porous polymers for energy harvesting: a review
  7. A strategic review: the role of commercially available tools for planning, modelling, optimization, and performance measurement of photovoltaic systems
  8. Comparative assessment of high gain boost converters for renewable energy sources and electrical vehicle applications
  9. A review of green hydrogen production based on solar energy; techniques and methods
  10. A review of green hydrogen production by renewable resources
  11. A review of hydrogen production from bio-energy, technologies and assessments
  12. A systematic review of recent developments in IoT-based demand side management for PV power generation
  13. Research Articles
  14. Hybrid optimization strategy for water cooling system: enhancement of photovoltaic panels performance
  15. Solar energy harvesting-based built-in backpack charger
  16. A power source for E-devices based on green energy
  17. Theoretical and experimental investigation of electricity generation through footstep tiles
  18. Experimental investigations on heat transfer enhancement in a double pipe heat exchanger using hybrid nanofluids
  19. Comparative energy and exergy analysis of a CPV/T system based on linear Fresnel reflectors
  20. Investigating the effect of green composite back sheet materials on solar panel output voltage harvesting for better sustainable energy performance
  21. Electrical and thermal modeling of battery cell grouping for analyzing battery pack efficiency and temperature
  22. Intelligent techno-economical optimization with demand side management in microgrid using improved sandpiper optimization algorithm
  23. Investigation of KAPTON–PDMS triboelectric nanogenerator considering the edge-effect capacitor
  24. Design of a novel hybrid soft computing model for passive components selection in multiple load Zeta converter topologies of solar PV energy system
  25. A novel mechatronic absorber of vibration energy in the chimney
  26. An IoT-based intelligent smart energy monitoring system for solar PV power generation
  27. Large-scale green hydrogen production using alkaline water electrolysis based on seasonal solar radiation
  28. Evaluation of performances in DI Diesel engine with different split injection timings
  29. Optimized power flow management based on Harris Hawks optimization for an islanded DC microgrid
  30. Experimental investigation of heat transfer characteristics for a shell and tube heat exchanger
  31. Fuzzy induced controller for optimal power quality improvement with PVA connected UPQC
  32. Impact of using a predictive neural network of multi-term zenith angle function on energy management of solar-harvesting sensor nodes
  33. An analytical study of wireless power transmission system with metamaterials
  34. Hydrogen energy horizon: balancing opportunities and challenges
  35. Development of renewable energy-based power system for the irrigation support: case studies
  36. Maximum power point tracking techniques using improved incremental conductance and particle swarm optimizer for solar power generation systems
  37. Experimental and numerical study on energy harvesting performance thermoelectric generator applied to a screw compressor
  38. Study on the effectiveness of a solar cell with a holographic concentrator
  39. Non-transient optimum design of nonlinear electromagnetic vibration-based energy harvester using homotopy perturbation method
  40. Industrial gas turbine performance prediction and improvement – a case study
  41. An electric-field high energy harvester from medium or high voltage power line with parallel line
  42. FPGA based telecommand system for balloon-borne scientific payloads
  43. Improved design of advanced controller for a step up converter used in photovoltaic system
  44. Techno-economic assessment of battery storage with photovoltaics for maximum self-consumption
  45. Analysis of 1-year energy data of a 5 kW and a 122 kW rooftop photovoltaic installation in Dhaka
  46. Shading impact on the electricity generated by a photovoltaic installation using “Solar Shadow-Mask”
  47. Investigations on the performance of bottle blade overshot water wheel in very low head resources for pico hydropower
  48. Solar photovoltaic-integrated energy storage system with a power electronic interface for operating a brushless DC drive-coupled agricultural load
  49. Numerical investigation of smart material-based structures for vibration energy-harvesting applications
  50. A system-level study of indoor light energy harvesting integrating commercially available power management circuitry
  51. Enhancing the wireless power transfer system performance and output voltage of electric scooters
  52. Harvesting energy from a soldier's gait using the piezoelectric effect
  53. Study of technical means for heat generation, its application, flow control, and conversion of other types of energy into thermal energy
  54. Theoretical analysis of piezoceramic ultrasonic energy harvester applicable in biomedical implanted devices
  55. Corrigendum
  56. Corrigendum to: A numerical investigation of optimum angles for solar energy receivers in the eastern part of Algeria
  57. Special Issue: Recent Trends in Renewable Energy Conversion and Storage Materials for Hybrid Transportation Systems
  58. Typical fault prediction method for wind turbines based on an improved stacked autoencoder network
  59. Power data integrity verification method based on chameleon authentication tree algorithm and missing tendency value
  60. Fault diagnosis of automobile drive based on a novel deep neural network
  61. Research on the development and intelligent application of power environmental protection platform based on big data
  62. Diffusion induced thermal effect and stress in layered Li(Ni0.6Mn0.2Co0.2)O2 cathode materials for button lithium-ion battery electrode plates
  63. Improving power plant technology to increase energy efficiency of autonomous consumers using geothermal sources
  64. Energy-saving analysis of desalination equipment based on a machine-learning sequence modeling
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