The escalating demand for electricity driven by population growth has strained Algeria's power stations to boost their output. With the significant decline in solar photovoltaic (SPV) electricity prices and the escalating tariffs on conventional electricity, there has been a growing interest in solar photovoltaic plants for electricity generation. Consequently, accurately evaluating the annual and monthly yield of SPV plants becomes crucial for designing and installing new facilities. This paper presents an assessment of a 12 MW grid-connected photovoltaic power plant situated in the Dhaya region of Sidi Bel Abbes, Algeria. Real-time observational data was collected from January 1st, 2023, to December 2023. Evaluated parameters include energy output, final yield, performance ratio (PR), among others. The power plant supplied 20780.67 MWh to the grid in 2023. The final yield (Yf) ranged from 3.25 to 5.88 kWh/kWp, the performance ratio (PR) varied from 79% to 89.71%, and the annual capacity factor was determined to be 19.44%. These parameters are compared with those obtained from simulations using PVsyst software and solar GIS-PV planner.
Research Article
Performance Analysis of Grid-Connected Photovoltaic Systems in Dhaya Algeria
https://doi.org/10.21203/rs.3.rs-4208894/v1
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Performance evaluation
Grid connected
SCADA system
PVsyst software
SolarGIS PV planner
Algeria.
Energy is a key component of socioeconomic development, and electricity in particular has become essential to industrial and social processes. The need for energy has increased dramatically over the last few decades; it is expected to reach 73,000 TWh by 2050 and rise at a 3.1% yearly pace [1]. Historically, fossil fuels like coal, oil, and natural gas have been the main source of power. These fossil fuel reserves are limited, though [2], and generating power from them emits a significant amount of greenhouse gases (GHGs) [3], endangering the ecosystems and the surrounding environment. The transition to cleaner and renewable energy sources is required for the sustainable production and consumption of energy.
In order to meet future energy demands and reduce environmental impact, alternative and renewable energy sources such as solar, wind, biomass, and others are essential [4]. As a result, a number of nations are working hard to raise the share of renewable energy in their mix of power generation. When it comes to renewable energy, solar photovoltaics (PV) is leading the way due to its increased solar cell efficiency, less production costs [5], and ease of installation in comparison to other renewables. Once placed, PV panels begin producing electricity instantly. Furthermore, the globe receives around \(\text{3,8}\times {10}^{14}\) kW of solar energy each second, indicating the free and plentiful availability of solar energy [6]. Because solar PV has a short payback period, it has proven to be a lucrative industry that has drastically lowered the LCOE [7]. The International Renewable Energy Agency (IRENA) estimates that at the end of 2018, the installed capacity of solar PV worldwide had reached 480.3 GW, and by 2025, it is expected to climb to 969 GW [8]. With 746.46 GW of installed capacity, Asia now has the highest share of PV installations.
The Algerian government, like the majority of developing nations, has developed the "National Program for Renewable Energies and Energy Efficiency" as a doable first step in the direction of sustainability. By 2030, 40% of Algeria's electricity demands are expected to be supplied by renewable energy, according to the country's national program [9]. Due to its considerable sun exposure, which covers 90% of the nation across an area of 2,382 million k\({m}^{2}\), it has committed to obtaining the bulk of its renewable energy supply from solar photovoltaics (PV) [10]. According to [11], the expected amount of sunshine is 3000 hours per year, and the daily energy can reach up to 5 kWh/\({m}^{2}\).
Examining the effectiveness of photovoltaic (PV) systems in diverse settings while taking into account many influences has been a subject of research. For example, [12] used real performance metrics to track a photovoltaic system in Tangier over the course of a year. The PV system's efficient operation was shown by the capacity factor of 14.83% and the yearly performance ratio of 79%. In a similar vein, [13] examined the operation of a 50 MW PV project in southern India using SCADA data from April 2018 to March 2019. The capacity utilization factor and yearly average performance ratio yielded figures of 24.65% and 79.94%, respectively. In order to evaluate the quality of substantial amounts of data for a more precise research of PV system performance, the authors of [14] carried out the first performance analysis of time series databases of PV systems deployed in Europe. Comparative research between the simulated and measured approaches is therefore required. [15] in Malaysia concentrated on utilizing a straightforward model and measurements to determine the desired performance. The target-oriented model's dependability was proven by the earlier investigation. After 12 years of operation, a grid-connected photovoltaic facility in central Spain was evaluated by [16] utilizing on-site data for 2020 and two additional modeling approaches the physical model and the statistical model based on the random forest method. When metric parameters were included, the random forest algorithm technique produced good results using simply ambient temperature and sun radiation as inputs. Using both measured data and the PVGIS database, the research [17] assessed the Zagtouli Grid-Connected Solar Power Station (ZGCSPS)'s performance.
However, research on evaluating large-scale PV systems in Algeria by comparing measured and simulated performances is limited. This study aims to compare the measured performance values of the Dhaya photovoltaic plant in Sidi Bel Abbess for the year 2023 with values simulated by both PVSyst and Solar GIS-PV Planner. This comparison will allow us to assess the reliability of sizing software in the region to predict production and performance parameters, thereby facilitating the installation of other photovoltaic plants in Algeria. The comparison focuses on various performance indicators such as performance ratio, capacity factor, energy production, temperature, and irradiation.
2.1 Geographical location of the site
The Dhaya Photovoltaic Power Plant in in Sidi bel Abbes (Algeria), with a capacity of 12 MW(Fig. 1), is located at a longitude of -0.6′′ N, a latitude of 34.69′′ E, and an altitude of 1329 m[18]
2.2 Plant layout
The power plant covers 33.59 hectares and has a total output of 12 MW. The facility is separated into six skids in this section. The entire aggregate output capacity of all six skids is 12 MW, with each skid having a production capacity of about 2 MWp. There are around 7,868 solar panels on each skid. These enormous quantities of solar panels are further split up into 329 parallel threads, each of which has 24 panels connected in series. These strings are linked to two inverters (Sunny Central 800–900 CP XT) (Fig. 2).
2.3 Specification of solar panel
Polycrystalline silicon solar panels (HSL60-PB-1-250) are used in the Dhaya solar power facility. These are fixed type panels, which means they don't follow the path of the sun, and have an efficiency of 15%. Both the short-circuit current (I_sc) and open circuit voltage (V_co) are measured at 37.7 V and 8.79 A respectively. Temperatures between − 40°C and + 80°C are their operational range. The panels are cleaned twice a month in order to keep their maximum effectiveness.
2.4 Power conditioning units
An inverter is made to change direct current (DC) into alternating current (AC). This procedure is crucial for a number of applications, particularly those involving systems like solar panels, batteries, or rectifiers where the power source produces direct current [19]The inverter has an 880-kW power rating. The input of the inverter receives a DC input current of 1270.9 A and a PV voltage of 1000 V. The inverter's output alternating voltage is 360 V, and its output current is 1411.
Real-time information on wind, temperature, and sun radiation is available from a weather station close to the facility (Fig. 3). In the control room, a specialized server with SCADA software records the voltage, current, and inverter power output minute by minute. The weather station's ambient temperature and sun irradiance are also recorded by it. This technology facilitates data flow across a bus and guarantees efficient monitoring and control. Through the assessment of electrical and environmental parameters, the SCADA software optimizes plant performance by simplifying retrieval and analysis (Shiva Kumar & Sudhakar, 2015).
This article divides the performance evaluation of the grid-connected solar photovoltaic power plant into three stages.
➢ Using a SCADA system, manually extract the power generating parameters.
➢ Evaluate the results using the PVSYST program.
➢ Evaluate the results with the Solar GIS PV planner.
Performance criteria IEC 61724 have been set by the International Energy Agency (IEA) [21] to evaluate grid-connected solar photovoltaic (PV) systems. These metrics include energy output, solar resource use, and the effect of total system losses, among other elements that together characterize the system's performance. The reference yield, final PV system yield, and performance ratio are important variables.
5.1 Array yield:
It is equivalent to the amount of time needed for the PV plant to produce array DC energy Ea when operating at nominal solar generator power Po. According to[22], its units are kWh/d∗kWp.
$${\text{Y}}_{\text{A}}=\frac{{\text{E}}_{\text{D}\text{C}}}{{\text{P}}_{0}}$$
1
5.2 Reference yield:
The reference yield is calculated by dividing the total in-plane irradiation (H) by the PV's reference irradiance (G). It represents the energy that may be attained under ideal conditions. If G = 1 kW/m^2, then Y_R represents the number of peak sun hours or the solar radiation in kWh/m^2. The Y_R indicates the solar radiation resource for the PV system. The PV array's orientation, location, and whether or not its variability changes from month to month and year to year are all factors.[22, 23].
$${\text{Y}}_{\text{R}}={\text{H}}_{\text{T}}/{\text{G}}_{0}$$
2
5.3 Final yield
The final yield is calculated using standard test conditions (STC) of 1000 W/m^2 solar irradiation and 25°C cell temperature. It is expressed as the yearly, monthly, or daily net AC energy production of the system divided by the peak power of the installed PV array. kW h/d∗kWp are its units [22]
$${\text{Y}}_{\text{F}}={\text{E}}_{\text{A}\text{C}}/{\text{P}}_{\text{M}\text{a}\text{x},\text{S}\text{T}\text{C}}$$
3
5.4 Performance ratio
By dividing a system's actual energy production by its theoretical maximum output, one may get the performance ratio (PR). When taking into account variables like irradiation, panel temperature, grid availability, aperture area size, nominal power output, and temperature adjustment values, it acts as a gauge for how well a plant converts sunlight into energy. Essentially, the PR compares the output that is actually produced with the maximum output that can be produced, giving information on the effectiveness and performance of the system in different environmental circumstances [22]
$${\text{P}}_{\text{R}}={\text{Y}}_{\text{F}}/{\text{Y}}_{\text{R}}$$
4
5.5 Capacity utilization
The actual production of a plant in relation to its highest potential output is measured by the Capacity Utilization Factor, or CUF. It is computed by multiplying the installed capacity of the plant by 365 days, 24 hours, and the total measured energy production (in kWh).
One of the simulation programs designed to gauge a solar power plant's efficiency is called PVsyst. It may import both personal data and weather data from several sources. Using a defined module selection, this program may assess the performance of pumping, off-grid, and grid-connected systems. The program computes the system yields using comprehensive hourly simulation data, and it does so with accuracy[24].
According to [25], solar GIS is a geographic information system designed specifically to meet the needs of the solar energy industry. This platform combines meteorological and solar resource data into a web-based application system, providing all-encompassing assistance for the design, development, and administration of solar energy systems.
7.1 Peak power output
Solar panels transform the solar radiation they collect into usable power. The power production is dependent on both the ambient temperature and sun insolation. Figure 4 shows the average result of the Dhaya power plant for the longest and shortest day of the year, which helps to illustrate how irradiance and ambient temperature affect the power production of the system.
The Fig. 4. shows that electricity production is higher in June and more concentrated around noon, following the solar irradiation curve. The higher temperature during the day appears to have a secondary effect on production. In December, production is lower, correlated with lower and more variable irradiation, as seen at 11 a.m. when irradiation decreased due to cloudy winter weather, leading to a drop in production. The lower and more stable temperature seems to have a less pronounced impact. These observations highlight the major influence of solar irradiation on photovoltaic energy production and also confirm the intermittent nature of this energy source, which is more productive in summer than in winter.
7.2 Performance Ratio (PR)
The Performance Ratio (PR) is almost 85% on a yearly average. The PR for the month of August was 79.1%, while the PR for the month of December was the highest at 89.71%. Lower PR levels indicate problems like improper system functioning and inverter faults. The PR values work as indications of system malfunction (Fig. 5).
7.3 Capacity utilization factor
The Capacity Utilization Factor (CUF) ranges from 13.1–24.5%, with an average of 19.44% every year. Comparatively, throughout the course of a year of operation, Algerian PV plants usually display CUF values ranging from 12.29–18.8%. The fluctuations in CUF are ascribed to losses in the system that are impacted by regional weather patterns. It is noteworthy that this plant's average CUF of 19.44% closely matches the values published for comparable solar photovoltaic facilities in Algeria [26]. Lower energy generating costs are implied by a greater CUF (Fig. 5).
7.4 Energy generation
The data is gathered manually through SCADA software. The Table 1 offers insights into the monthly and yearly production of photovoltaic energy, measured in megawatt-hours (MWh). There is notable variation in production levels across different months, peaking during the summer and dipping in winter. Particularly, August emerges as the most productive month, recording a monthly total of 2181.5 MWh. Conversely, December demonstrates the lowest production, with a monthly total of 1193.27 MWh. The overall annual output tallied to 20780.67 MWh.
| Months | Average daily energy generation (m w h) | Monthly energy / p (MWh) |
|---|---|---|
| January February March April May June July August September October November December | 38.980 44.325 63.348 70.576 59.18 70.626 67.203 70.370 58.243 53.461 37.8 38.492 | 1208.4 1241.1 1963.8 2117.3 1834.6 2118.8 2083.3 2181.5 1747.3 1657.3 1434 1193.27 |
The highest energy generation occurs in April with a total of 2002 MWh, while the lowest energy generation is observed in December with only 1604 MWh. Throughout the entire year, a total of 21774 MWh is injected into the grid.
8.1 Balances and main results
The worldwide incident energy on the collector plane reaches 2260.9 kWh/m^2 annually, whereas the global horizontal irradiation is recorded at 1959.9 kWh/year. 22164 MWh is the total energy obtained from the PV array's output (Table 2).
| Mounth | GlobHor kWh/m² | T_Amb °C | GlobInc kWh/m² | Energie into Array kWh | Energie into Grid kWh | PR |
|---|---|---|---|---|---|---|
| Januray February March April May June July August September October November December | 94.6 108.9 161.3 194.5 213 231.9 233.7 215.8 172.1 141.4 103.7 88.5 | 7.17 8.73 12.99 16.44 21.11 26.38 31.11 29.7 23.97 19.09 11.43 7.90 | 152.3 155.6 197.1 204.9 202.2 207.7 215.6 219.5 199.8 192.6 162.6 151 | 1636304 1657284 2020962 2036264 1976264 1939514 1867584 1903322 1852613 1917382 1720421 1636270 | 1604752 1627079 1985493 2002165 1939913 1908226 1835810 1871415 1821567 1884608 1689518 1604114 | 89 88.3 85.1 82.5 81 77.6 71.9 72 77 82.6 87.7 89.7 |
| Total | 1959.5 | 18.06 | 2260.9 | 22164183 | 21774659 | 81.3 |
8.2 Normalized productions
The climate-related losses are represented by the LC value, which is 1.07 kWh/kWp/day. Likewise, the losses resulting from system problems are represented by the LS value, which is 0.09 kWh/kWp/day. Furthermore, as seen in Fig. 6, the yield factor, or YF value, is 5.04 kWh/kWp/day.
8.3 Performance ratio
The performance ratio on a yearly average is 81.3%. Minimal variations are detected between the performance ratio generated from PVsyst findings and the actual performance ratio seen utilizing the SCADA system for the solar facility.
8.4 Energy conservation step
The unit of measurement for global horizontal irradiance is 1959 kWh/m. On the collection plane, the effective irradiation is 2247 kWh/m. Electrical energy is produced by the solar panels using incident sun energy. The nominal array energy reaches 26633 MWh after the PV conversion. The PV array's efficiency, measured under standard test conditions (STC), is 15.48%. 22988 MWh of virtual energy were collected from the array. The available energy received at the inverter output is 21774 MWh after accounting for the inverter losses (Table 3).
| Energy conservation step | Energy (MWh) | Energy Loss (MWh) | Energy loss (%) |
|---|---|---|---|
| Array nominal energy Array virtual energy at MPP Available Energy at Inverter Output | 26633 22988 21774 | 4145 3645 1214 | 15.57 13.69 5.28 |
9.1 Direct normal and diffuse horizontal irradiation
The highest direct normal irradiation at the plant occurs in June (201.6 kWh/m²), while the lowest is observed in November (136.7 kWh/m²). Regarding diffuse horizontal irradiation, the highest values are recorded in July (84.3 kWh/m²), whereas the lowest occur in December (27.4 kWh/m²) (Fig. 7).
9.2 PV electricity production
The electricity generation at the plant peaked in July at 1957 MWh, resulting in a higher daily sum of specific electricity produced. Conversely, December witnessed the lowest generation at 1408 MWh. March and April showed a comparable energy generation, both reaching 1843 MWh. (Table 4 and Table 5).
9.3 Performance ration
The performance ratio on a yearly average is 80.7%. Table 4 and Table 5 demonstrate that there are not many discrepancies between the performance ratio obtained from the solar GIS-PV planner and the real performance ratio recorded by the SCADA system.
| Energie specific monthly sum kWh/kWp | Energie specific daily sum Wh/kWp | Energie total Monthly sum MWh | Energie total share | PR ratio (%) | |
|---|---|---|---|---|---|
| Januray February March April May June July August September October November December | 123.7 127.6 153.6 153.6 159.8 158.6 163.1 159.6 151.8 147.2 121.2 117.3 | 3991.2 4555.8 4954.4 5118.7 5154.0 5287.3 5260.2 5149 5058.6 4748.3 4040.8 3784.6 | 1485 1531 1843 1843 1917 1903 1957 1915 1821 1766 1455 1408 | 7.1 7.35 8.84 8.84 9.2 9.13 9.39 9.19 8.74 8.47 6.98 6.75 | 85.9 84.8 82.8 81.1 79.3 77.4 75.8 76.3 79 81.6 84.6 85.4 |
| Total | 1737 | 4758.6 | 20844 | 100 | 80.7 |
| Energy conservation step | Energy output (kW h/kW p) | Energy loss (kW h/kW p) | Energy loss (%) | PR (%) |
|---|---|---|---|---|
| 1. Global in-plane irradiation (input) 2. Global irradiation reduced by terrain shading 3. Global irradiation reduced by reflectivity 4. Conversion to DC in the modules 5. Other DC losses 6. Inverters (DC/AC conversion) 7. Transformer and AC cabling losses 8. Reduced availability | 2153 2077.6 2030.6 1891.8 1823.8 1770.5 1745.7 1737 | 75.4 47 138.8 63 53.3 24.8 8.7 | -3.5 -2.3 -6.8 -3.6 -2.9 -1.4 -0.5 | 100 96.5 94.3 87.9 84.7 82.2 81.1 80.7 |
| Total system performance | 1737 | 411 | 21 | 80.7 |
The conversion of solar energy through various stages involves a series of losses and adaptations that gradually diminish its initial potential as shown in Table V. Initially, the global solar irradiation at the incidence plane is assessed at 2153 kWh/kWp, representing the energy captured by the solar panels over a year. However, this value is reduced to 2077.6 kWh/kWp due to terrain shading, resulting in a loss of 3.5%. Furthermore, the global irradiation is further reduced to 2030.6 kWh/kWp due to reflectivity, which corresponds to an additional loss of 2.3% due to light reflection on the solar panels. This remaining solar energy is then converted into direct current (DC) in the modules, with a conversion efficiency of 1891.8 kWh/kWp, resulting in a loss of 6.8%. After this conversion, there are further losses in direct current, evaluated at 1823.8 kWh/kWp, representing a loss of 3.6% due to connections and cables. Next, the step of converting direct current to alternating current (AC) by inverters incurs an additional loss of 2.9%, reducing the available energy to 1770.5 kWh/kWp. These losses continue with reduced availability at 1745.7 kWh/kWp, where 1.4% is lost due to outages and maintenance. Finally, transformers and AC wiring losses add an additional loss of 0.5%, bringing the final available solar energy to 1737 kWh/kWp. Thus, through these different conversion stages, solar energy undergoes adjustments and losses that diminish its initial yield.
The outcomes of the SCADA data system monitoring are contrasted with the PVSYST software and Solar GIS PV planner's simulations. The results, as illustrated in Fig. 8, indicate a near-perfect match between the real performance and the simulated performance forecast by PVSYST and Solar GIS for the whole year.
Figure 8 shows that the actual energy production closely aligns with the estimations from PVsyst and Solar GIS simulations, although it tends to be slightly lower, especially during winter and slightly higher in summer. This seasonal variation is attributed to the correlation between energy production and sunlight, which is more abundant in summer, as well as temperature, which is lower in winter. However, the monitor's performance ratio is higher than the performance ratio of the two simulations. These results lead to interpretations suggesting that simulations tend to slightly overestimate photovoltaic energy production, perhaps due to slightly inaccurate meteorological data.
An annual performance evaluation was carried out on a grid-connected 12 MW peak power solar photovoltaic plant situated in Sidi Bel Abbess, Dhaya area. The study came to numerous important conclusions. The plant demonstrated a maximum peak power of 10249.86 KW and a low power of 47.102 KW during the course of the operating year. A total of 20780.67 MWh of power were sent to the grid throughout the course of the year. Remarkably, August saw the highest total energy output of 2181.5 MWh, while December saw the lowest total energy generation of 1193.27 MWh. The facility had a capacity factor of 14.83% and an annual average performance ratio of 79%. Actual energy generation and predicted energy generation from models were found to be well aligned when compared to simulation results from PVSyst and solar GIS-PV. As a result, the 12 MW solar power plant demonstrated excellent capacity utilization and energy efficiency, guaranteeing operational effectiveness with a roughly 99% availability rate.
Author Contribution
Author 1 carried out the majority of the work, being significantly involved in study conception, data collection and analysis, as well as manuscript drafting.Authors 2 and 3 supervised the work, providing expertise and guidance throughout the research and writing process.The other authors contributed by offering ideas, implementing figures and tables, and participating in the general discussion of the work.All authors have read and approved the final version of the manuscript, and they agree to be accountable for its content, ensuring its accuracy and integrity.
Acknowledgement
The authors would like to thank the director of the Dhaya power plant for providing us with the information about the plant.
- Global Energy Interconnection. Elsevier
- Mahesh A, Sandhu KS (2015) Hybrid wind/photovoltaic energy system developments: Critical review and findings. Renewable and Sustainable Energy Reviews 52:1135–1147. https://doi.org/10.1016/j.rser.2015.08.008
- Akella AK, Saini RP, Sharma MP (2009) Social, economical and environmental impacts of renewable energy systems. Renew Energy 34:390–396. https://doi.org/10.1016/j.renene.2008.05.002
- Paulinus Ugwuoke, John O. Agwunobi, Aliyu A. Babadoko (2012) Renewable energy as a climate change mitigation strategy in Nigeria. Aliyu A Babadoko 3:11–19
- Martín-Martínez S, Cañas-Carretón M, Honrubia-Escribano A, Gómez-Lázaro E (2019) Performance evaluation of large solar photovoltaic power plants in Spain. Energy Convers Manag 183:515–528. https://doi.org/10.1016/j.enconman.2018.12.116
- Arora R, Arora R, Sridhara SN (2022) Performance assessment of 186 kWp grid interactive solar photovoltaic plant in Northern India. International Journal of Ambient Energy 43:128–141. https://doi.org/10.1080/01430750.2019.1630312
- Wang K, Herrando M, Pantaleo AM, Markides CN (2019) Technoeconomic assessments of hybrid photovoltaic-thermal vs. conventional solar-energy systems: Case studies in heat and power provision to sports centres. Appl Energy 254:113657. https://doi.org/10.1016/j.apenergy.2019.113657
- Power Technology. The Projected Expansion of Global Solar PV Capacity by 2025.
- Ghezloun A, Saidane A, Oucher N, Merabet H (2015) Actual Case of Energy Strategy In Algeria and Tunisia. Energy Procedia 74:1561–1570. https://doi.org/10.1016/j.egypro.2015.07.720
- Razagui A, Bachari NI, Bouchouicha K, Hadj Arab A (2017) Modeling the Global Solar Radiation Under Cloudy Sky Using Meteosat Second Generation High Resolution Visible Raw Data. Journal of the Indian Society of Remote Sensing 45:725–732. https://doi.org/10.1007/s12524-016-0628-8
- Abdeladim K, Bouchakour S, Hadj Arab A, et al (2014) RENEWABLE ENERGIES IN ALGERIA: CURRENT SITUATION AND PERSPECTIVES
- Attari K, Elyaakoubi A, Asselman A (2016) Performance analysis and investigation of a grid-connected photovoltaic installation in Morocco. Energy Reports 2:261–266. https://doi.org/10.1016/j.egyr.2016.10.004
- Boddapati V, Nandikatti ASR, Daniel SA (2021) Techno-economic performance assessment and the effect of power evacuation curtailment of a 50 MWp grid-interactive solar power park. Energy for Sustainable Development 62:16–28. https://doi.org/10.1016/j.esd.2021.03.005
- Lindig S, Ascencio-Vasquez J, Leloux J, et al (2021) Performance Analysis and Degradation of a Large Fleet of PV Systems. IEEE J Photovolt 11:1312–1318. https://doi.org/10.1109/JPHOTOV.2021.3093049
- Saleheen MZ, Salema AA, Mominul Islam SM, et al (2021) A target-oriented performance assessment and model development of a grid-connected solar PV (GCPV) system for a commercial building in Malaysia. Renew Energy 171:371–382. https://doi.org/10.1016/j.renene.2021.02.108
- Fuster-Palop E, Vargas-Salgado C, Ferri-Revert JC, Payá J (2022) Performance analysis and modelling of a 50 MW grid-connected photovoltaic plant in Spain after 12 years of operation. Renewable and Sustainable Energy Reviews 170:112968. https://doi.org/10.1016/j.rser.2022.112968
- Palm SF, Youssef L, Waita S, et al (2023) Performance Evaluation of Burkina Faso’s 33 MW Largest Grid-Connected PV Power Plant. Energies (Basel) 16:6177. https://doi.org/10.3390/en16176177
- Bouzid Z, Ghellai N, Mezghiche T (2015) Overview of solar potential, state of the art and future of photovoltaic installations in Algeria. International Journal of Renewable Energy Research 5:427–434
- Mokhtaria J, Brahami MN, Nemmich S, et al (2018) Ozone food storage supplied by photovoltaic energy. Carpathian Journal of Food Science and Technology 10:129–136
- Shiva Kumar B, Sudhakar K (2015) Performance evaluation of 10 MW grid connected solar photovoltaic power plant in India. Energy Reports 1:184–192. https://doi.org/10.1016/j.egyr.2015.10.001
- IEC (International Electro-technical Commission) standard 61724 (1998). Photovoltaic system performance monitoring-guidelines for measurement, data exchange and analysis. (Accessed 14 March 2017).
- Ayompe LM, Duffy A, McCormack SJ, Conlon M (2011) Measured performance of a 1.72kW rooftop grid connected photovoltaic system in Ireland. Energy Convers Manag 52:816–825. https://doi.org/10.1016/j.enconman.2010.08.007
- Marion B, Adelstein J, Boyle K, et al Performance parameters for grid-connected PV systems. In: Conference Record of the Thirty-first IEEE Photovoltaic Specialists Conference, 2005. IEEE, pp 1601–1606
- Yadav P, Kumar N, Chandel SS (2015) Simulation and performance analysis of a 1kWp photovoltaic system using PVsyst. In: 2015 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC). IEEE, pp 0358–0363
- Tarigan E, Djuwari, Purba L (2014) Assessment of PV Power Generation for Household in Surabaya Using SolarGIS–pvPlanner Simulation. Energy Procedia 47:85–93. https://doi.org/10.1016/j.egypro.2014.01.200
- Bouacha S, Malek A, Benkraouda O, et al (2020) Performance analysis of the first photovoltaic grid-connected system in Algeria. Energy for Sustainable Development 57:1–11. https://doi.org/10.1016/j.esd.2020.04.002
No competing interests reported.