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Scientific Reports volume 15, Article number: 43578 (2025)
1360
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The paper presents a comprehensive technical evaluation of grid-connected rooftop solar photovoltaic (PV) systems installed at two public sector buildings located in climatically diverse regions. The primary objective is to maximize the energy output of each PV system, which has been designed and optimized based on the locally-constrained conditions and the resource availability. Proposed location (PL-1) situated in a semi-arid region, was simulated using Aurora Solar, while PL-2, situated in a hilly region, was modeled with PVSYST. At PL-1, a 417.96 kWp system has been installed, estimated to generate around 594.743 MWh of energy annually, offsetting 95.52% of the building’s annual energy consumption with a performance ratio (PR) of 82.6%. Similarly, PL-2 has a 63.2 kWp system, which is projected to produce 93.76 MWh, with a predicted PR of 80.1%. Under a degradation rate of 1%, the anticipated lifetime energy output is 13.214 GWh and 2.081 GWh, respectively. Environmental impact research indicates that each system achieves a carbon payback time of just 42 days, with cumulative CO₂ offsets of approximately 6606.96 metric tons (PL-1) and 1040.367 metric tons (PL-2) throughout their operational lifespan. A sensitivity analysis of degradation rates, energy output variations, and emission factor of grid emphasises the systems’ resilience across diverse operational conditions. The economic evaluations show that PV systems installed at PL-1 and PL-2 have LCOE of 9.54 PKR/kWh and 9.37 PKR/kWh and payback period of around 3.27 and 3.39 years respectively, hence confirming their long-term economic sustainability. The findings validate that the implementation of rooftop photovoltaic systems in public sector buildings is both technically and economically feasible, with a combination of tailored modeling, solar resource assessment in specific locations, and a net metering policy being crucial for optimal renewable energy integration within national energy policy framework.
The industrial revolution and the increase in world population in recent decades have led to a rise in worldwide energy consumption1. By 2050, the global population is expected to reach approximately 9.8 billion people, according to UN world population prospects. With such a large population and increasing industrialization, greater energy will be required to maintain a standard and sustainable lifestyle. Global energy consumption is projected to exceed 0.740 billion Tera Joules by 20402, requiring approximately 22.70 billion tons of fossil fuel, predominantly anthracite coal, to fulfill this energy demand3. Modern infrastructure, encompassing both commercial and residential buildings, consumes significantly more energy than it used a decade ago due to growing industrialization. Although this growing industrialization has provided social advantages and economic assistance to the global population, it has contributed to a significant migration of people from rural to metropolitan areas. Currently, 30–40% of global energy produced through various methods is utilized by buildings and construction infrastructures4,5,6. By 2050, two-thirds of the global population is anticipated to reside urban areas, resulting in a significant energy demand to facilitate economic and social development7.
The environmental hazards and global warming caused by non-renewable fossil fuel energy sources encourage contemporary architects to incorporate climate change implications and on-site production of clean energy into their building designs8. Alongside climate change mitigation, utilizing sustainable renewable energy technologies is essential in order to prevent reliance on non-renewable energy sources (coal, oil, and gas), which contribute to severe environmental issues, including the emission of greenhouse gases, atmospheric pollutants (such as NOx, SO2, trace metals, and particulates), contaminated water from coal pollution, and excessive ash waste9,10. Innovation in hybrid renewable energy systems, incorporating solar PV, wind, and storage technologies, is essential for sustainable electrification and energy availability11,12. Recent advancements in decentralised energy systems, incorporating cogeneration, renewable energy, and battery energy storage, have received considerable attention13. The integration of storage system with renewable energy system has strengthened grid applications14.
PV is a highly promising renewable energy technology, distinguished by two key attributes: (i) simplicity and cost-effectiveness, and (ii) minimal maintenance requirements. PV modules do not have moving components and transform solar energy into electric energy in an efficient way relative to other conversion methods15,16. In urban areas, PV system is among the most accessible renewable energy sources for distributed energy generation17,18. Multiple factors encourage this extensive adoption, including as the relatively low technical complexity of photovoltaic systems, their demonstrated economic feasibility, and the growing recognition of their beneficial environmental impacts19. In addition to enhancing the efficiency of urban buildings, PV system has assisted cities in achieving their climate-neutral objectives. Similarly, the creative integration of PV systems within building envelopes facilitates its quick adoption20,21,22. Artificial intelligence and optimization methodologies are progressively employed for monitoring PV systems, enhancing the reliability of the grid and energy distribution23. Hybrid PV systems are increasingly recognised for their durability and efficiency, especially in developing nations24. Moreover, solar PV systems represent a promising category of distributed energy systems that mitigate transmission losses, reduce peak demand, and enhance energy resilience25,26.
In the context of photovoltaic, a specific focus has been placed on Rooftop PV (RPV) systems, which indicate decentralised renewable energy generation that is consumed on-site. This type of system could become crucial in the transition to renewable energy within the building sector due to its viability in energy production27,28. Furthermore, this approach provides numerous benefits for overall building performance, especially in moderate and warm climates, and its installation might significantly reduce a city’s energy consumption29. Roofs are typically unimpeded by obstacles, allowing for unrestricted solar irradiance access. In this regard, RPV explicitly serves as a promising option for substantial renewable energy output and meeting the energy requirements of both existing and new buildings30. Annual installations of RPV systems have significantly increased, as predicted by the IEA, from 0.0092 TW in 2010 to 0.076 TW in 202131. This type of system contributes a significant portion of final energy consumption. The percentage varies by country, ranging from 20% to 30% in Spain, France and Germany, and from 30% to 40% in Greece and Italy, and more than 40% in Portugal, Cyprus and Malta32. The flexible nature of these RPV systems allows their size to vary from several kW in residential buildings to hundreds of kW for offices, industries and shopping centres33,34,35.
Many research studies have evaluated the techno-economic viability of RPV systems for residential energy generation in various geographic locations. In Brownsville, Texas, RPV systems have capacity to fulfil around 11.0% of city’s overall electricity consumption, indicating their feasibility in improving local utility supplies36. Research in Gaza Strip examined the installation of RPV systems in order to fulfil a critical demand of 552,000 MWh per year. Their findings indicated a requirement for a 555 MWp PV system, to be installed over an area of 652,500 m² spanning over 32,625 building rooftops, with an approximate cost of $800 million37. A techno-economic examination of 6-kW domestic RPV systems in Queensland, Australia, indicated that they might offset 55–60% of average residential energy consumptions (23 kWh/day) while considerably decreasing carbon emissions38. The analysis of Nepal’s RPV system indicated a cumulative capacity of 970-MW, with the capability of producing 1,310,000 MWh/year, which accounted for 24% of the electricity supplied by the electric grid in 2017-1839.
A number of researchers have examined PV systems integrated into buildings through various configurations, including RPV systems, building-integrated PV systems (commonly with walls), PV modules serving as shading components, PV modules functioning as windows, and analysing their impact on energy consumption and CO2 emissions. The research by Najafi et al.40 analyses the technical and economic efficiency of an on-grid RPV system with an estimated capacity of 8456 kW. The analysis incorporates a case study of an industrial plant, examining both the time-of-use and feed-in tariff pricing model. A three-stage methodology has been implemented, incorporating MATLAB software, HOMER Grid and PVSYST. The simulated RPV system attained an average PR of 80.40%. The system can potentially decrease CO2 emissions by 188,611.560 tonnes throughout its lifespan. Singh et al.41 conducted an operational analysis of a 10-kW RPV system in India over a period of 5 years. The actual energy output over five years was 58.911 MWh, in contrast to the estimated 77.769 MWh. The average daily energy output was 3.2 kWh/kWp, in contrast to estimated 4.2 kWh/kWp. A substantial discrepancy exists between the actual and estimated values, potentially attributable to local environmental factors, system downtime, and the expected deterioration of modules over time. The average PR of RPV system was 70.7%, and average CUF was 13.36%. Shahriar et al.42 designed A 35.5 kWp RPV system for KUET Medical Center building. The multiple string connected system demonstrated optimal performance in electricity output, cooling load reduction, and CO2 reduction, achieving an annual energy output of 65.6 MWh, cooling load reduction of 16.6%, and reducing CO2 emissions by 32.55 tonnes. Digital twins and sophisticated simulations enhance photovoltaic system efficiency, hence decreasing operational expenses43.
Several researchers have employed various software to evaluate the prospective benefits of grid-connected RPV systems. Phap and Nga44 used PVSYST software to design and evaluate a 56.70-kW RPV system installed on research facility in Vietnam. Their findings demonstrated financial benefits of solar PV energy as well as showed the potential to offset an annual average of 59.3 tonnes of CO2 emissions. Similarly, in45, the research examined the techno-economic viability of installing a 3 kWp grid-connected RPV system in Nepal. Their evaluation of energy provision for a residence resulted in a system efficiency of 17%, a PR of 84%, and a reduction of 0.33 tonnes of CO2 emissions throughout its lifespan. Hanni et al.46 designed a grid-connected RPV system with a capacity of 581 kWp for an Indian academic institution. Their findings indicated a PR of 84.14%, and annual energy savings of $95,000. Habib et al.47 examine the viability of RPV systems for commercial buildings in Pakistan by employing PVSYST software. The simulation results demonstrate that the RPV system generates 371.6 MWh of energy, with a specific yield of 1508.0 kWh/kWp and a PR of 82.1%. The payback period for the RPV system is 4.2 years, and the LCOE is $0.0229 per kWh.
A research work on 8.2 kW grid-connected RPV system has been performed by Vidal et al.48 in Chilean Patagonia region for supplying electricity to a public institution and to feed surplus energy into the utility grid. Their findings revealed an average annual CF of 15.1%, with the system capable of accommodating on-site demand and supplying to the utility grid. Another research49 employs HOMER software to design of optimal grid-connected RPV systems of 2 kW and 3 kW for residential applications, incorporating various incentive schemes based on real-time data from 50 residential buildings. The efficacy of each scheme is evaluated based on the designated solar energy production capacities of 2 kW, yielding 3257 kWh/year, and 3 kW, yielding 4885 kWh/year, against a home energy consumption total of 3248 kWh/year, with the lowest energy cost being $0.26. A research work has been conducted to compare the PVSYST simulated findings with theoretical outcomes for evaluating the performance of grid-connected RPV system for modest residential properties in India50. Their findings demonstrated that the PVSYST outcomes surpassed all critical criteria of energy output, energy storage, and CO2 emissions by substantial margins. Government subsidies and policy frameworks are essential for the economic feasibility of utility-scale PV systems51,52.
This research presents an analysis of RPV systems installed on two public sector buildings located in diverse climatic regions of Pakistan. This research utilizes the Aurora Solar and PVSYST modeling tools to evaluate the effects of local solar resources, load profiles, and environmental factors on system performance. Both systems have been evaluated based on their energy output, PR, and financial viability of the installations, considering current grid tariffs and net metering setups. The objective is to provide information-driven, location-specific findings on the successful integration of RPV systems into public buildings, focusing on scalability, economic benefits, and contributions to national renewable energy targets.
Researchers conducted a site visit to investigate and evaluate the potential for solar irradiation. Data regarding area availability, site suitability for solarisation, and current electricity load requirements was obtained through consultations with relevant officials and staff. The designated suitable areas for solarisation at PL-1 have been shown in Fig. 1, while those at PL-2 have been shown in Fig. 2.
Designated areas for RPV system at PL-153.
Designated areas for RPV system at PL-254.
The rooftops of PL-1 and PL-2 were designated for the installation of a PV System. The total area allocated for solarisation for PL-1 and PL-2 is presented in Tables 1 and 2 respectively.
The primary load of PL-1 occurs in the office sector rather than the hostel area. Therefore, the office roofs are delineated as illustrated in Fig. 1. The usable area for PL-1 is less than the overall rooftop area. Around 10547.21 square-feet of roof-top area is used for the installation of a RPV System at PL-2. As well as around 5,800 square-feet of roof-top area is used for the installation of a RPV System at PL-2.
An evaluation of sanctioned load, average demand, peak demand, and energy consumptions profile has been conducted to determine the energy requirements of both sites. PL-1 has a sanctioned load of 367 kW, with an approximate consumption of 622,578 kWh calculated for the year 2024. The primary energy usage derives from air conditioning systems, water pumping mechanisms, and lighting equipment. In November 2024, the load was reduced by 50% as the load of hostels and residential complexes was separated from the main load. The estimated annual energy consumption of the PL-1 is demonstrated in Table 3. The analysis of the utility bill indicates that the energy has been used at an average rate of 46.0 PKR/kWh throughout year, resulting in an annual electricity cost of 51.42 million (PKR). The tariff and energy consumption values in this research have been based on the electricity bills provided by Lahore Electric Supply Company (LESCO)55.
PL-2 has a sanctioned load of 56 kW, with an approximate consumption of 76,599 kWh calculated for the year 2024. The primary energy use derives from HVAC systems, lighting, and ancillary power usage in diverse workplaces. The estimated annual energy consumption of the PL-2 is demonstrated in Table 4. The analysis of the utility bill indicates that the energy has been used at an average rate of 41.0 PKR/kWh throughout year, resulting in an annual electricity cost of 43.8 million (PKR).
To determine the appropriate sizing specifications for the RPV system at the PL-1, solar irradiance potential data was acquired from the Aurora database, which includes both satellite and ground-based meteorological measurements. Analysis of this data and hourly simulations performed with Aurora, a PV system simulation software, demonstrate that energy demands can be almost totally met by the installation of RPV system, due to the substantial roof space available at the proposed PL-1. For PL-2, solar irradiance potential data was acquired from the METEONORM database. Analysis of this data and hourly simulations performed with PVSYST, demonstrate that energy demands can be partially reduced by the installation of RPV system, due to the limited roof space available at the proposed PL-2.
The coordinates of the selected location-1 are 31.48° N latitude and 74.36° E longitude. The availability of solar energy in a particular place depends on several factors. The resource depends on various factors, including longitude, latitude, precipitation, cloud cover, and topography. The size of PV Systems are determined by modelling solar energy resources and PV module orientation with specialised softwares, such as Aurora and PVSYST, specifically designed for this purpose. Long-term data from weather stations and satellites has been obtained for a TMY utilising ‘PVSYST’ to precisely determine the output of photovoltaic systems. Furthermore, probabilistic modeling has been performed to ascertain the possibility of output energy utilizing Aurora software for PL-1 and PVSYST for PL-2. Aurora Solar has been utilized for PL-1 due to its high resolution solar irradiation date and optimizing features as well as suitability in modelling RPV system in semi-arid regions. PVSYST has been utilized for PL-1 due to its precision in modelling sophisticated terrain and shading effect making it appropriate for hilly regions. The solar irradiance and mean temperature of the location, according to METEONORM’S database are presented in Tables 5 and 6 for PL-1 and PL-2, respectively.
Tables 5 and 6 indicates that average annual and monthly temperatures at both locations have been suitable for RPV system that has module lifespans of 25 years and inverters exceeding 15 years in durability. The annual GHI is 1637.6 and 1717.6 kWh/m2, whereas average annual ambient temperature is 24.31 and 22 °C for PL-1 and PL-2 respectively.
Considering the site’s physical constraints, it has been decided that the optimal efficiency has to be selected to optimise the sizes of the RPV systems within given area. Mono-PERC (Half-cut) modules having a peak efficiency of 21.50% have been accessible in the local market. They are being offered due to their outstanding performance. The technical specification of selected module is presented in Table 7 below.
The feasible solar PV capacity for installation on the rooftop of PL-1, based on the available area, is 417.96 kW, consisting of a total of 774 photovoltaic modules. The designed photovoltaic system has four arrays, as presented in Table 8.
The feasible solar PV capacity for installation on the rooftop of PL-2, based on the available area, is 63.2 kW, consisting of a total of 117 modules. The photovoltaic system consists of a single array, as presented in in Table 9.
A solar inverter functions as the intermediary that transforms the DC output from a photovoltaic array into AC, assuring compatibility with the AC grid. The string inverter approach enables more precise adjustments to irradiance conditions, particularly in shaded situations. A standard system could demonstrate many sub-array configurations or probabilities of partial shading. Employing a string inverter design, sub-arrays with analogous azimuth, tilt, and shading characteristics have been coupled to a common inverter. Table 10 indicates the inverters utilized for each PV array (PA) at PL-1 and Table 11 indicates the inverters utilized for each PV array PL-2.
Without tracking or alteration, the angle remains consistently constant; hence, an optimal tilt must be determined for this research. A tilt angle of 15 degrees is recommended for the PV arrays at the both proposed locations, taking into account architectural limitations and the optimization of the Ground-Coverage ratio of selected areas. Azimuth angle of solar modules is recommended to be oriented towards the South direction. The research proposes a portrait orientation to minimize losses under partial shadowing situations, with a recommended pitch of 86.6 cm for location 1 and 3.5 m for location 2.
All array configurations in the PV system have to be properly grounded. The earthing system needs to be electrically and mechanically connected to ensure an independent return path to the earth. For photovoltaic arrays, a conductor size of at least 6 mm² has been utilized; however, for the lightning protection system, a conductor size of 16 mm² has been used.
The design of the roof mounting system for this research considers that structure must withstand a maximum gust wind speed of 150 km/h. All elements of mounting system and associated materials have been made from corrosion-resistant substances. The structural components comprise anodized aluminium, galvanised steel according to ASTM A123/A 123 M, or materials exhibiting equal or superior qualities. The mounting structures have been adequately grounded to ensure optimal lightning and short-circuit protections. A racking solution that eliminates the necessity for grounding between PV modules by utilizing common rail/PV module connectors for grounding has been implemented.
Solar DC cables possess dual insulation made of cross-linked polyolefin (XLPO). This cable has demonstrated an enhanced nominal temp range of -40 to 120 °C, along with exceptional UV resistance, considerable chemical resistance, outstanding flame retardancy, and excellent durability. Furthermore, XLPO has no content of halogens, indicating that it would not release harmful gasses during combustion. The cables have been routed through UPVC conduits and cable trays. The configuration of DC cable both locations is seen in the Figs. 3 and 4. The measurements were taken from inverters room where DC cables have been connected.
Cable configuration for location 153.
Cable configuration for location 254.
Tables 12 and 13 describes the primary component configurations for the proposed PV system utilizing mono-PERC technology.
This research study initiates with an assessment of the geographical location and climatic conditions, leading to the simulation of the proposed configuration and subsequent analysis of the results. This research work presents innovative approach by conducting a comprehensive, location-specific assessment of RPV systems across two diverse climate regions in Pakistan, even though well-known simulation tool like Aurora and PVSYST have been used. This specific research offers findings that are immediately applicable to buildings in public sector under various environmental circumstances. The comprehensive methodology of this research has been shown in the flowchart presented in Fig. 5 and is discussed sequentially in the subsequent sub-sections.
Methodology Flowchart.
A simulation of the RPV system has been performed utilizing Aurora software, considering site’s energy resource potential and proposed design, during the first year post-installation. The 417.96 kW PV system is projected to produce 594.743 MWh per year, with a performance ratio of 82.6%. The maximum monthly energy production is 72,041 kWh in month of May and minimum is 32,834 in month of December as shown in Table 14. The monthly PR of RPV for PL-1 is maximum in winter season (85.8% in February) and minimum in summer (78.9% in June) season as shown in Fig. 6. The PR of RPV for PL-1 is similar to the average of 76.60%56 for similar size PV system in Pakistan, demonstrating optimal system performance under local conditions.
Monthly PR of RPV for PL-1.
The energy produced by the 417.96 kW PV System has been measured in comparison to the energy consumption profile of proposed location 1. The overall analysis is presented in Table 15; Fig. 7. As shown in the Figure, in the month of January, 34.859 MWh energy is produced and 48.156 MWh energy is consumed, remaining 13.297 MWh energy is received form the utility grid. In month of May, 72.041 MWh energy is produced and 23.538 MWh energy is consumed, resulting in a surplus of 48.503 MWh supplied (net-metered) to the utility grid.
Energy supply and consumption profile for PL-1.
The public building located in plain industrial areas primarily utilizes energy during daylight, creating a solar PV system exceptionally suitable to its energy generation requirements. The PL-1 anticipates fulfilling about 95.52% of its annual energy demands through the implementation of a 417.96 kWp PV system, as shown in Fig. 7.
For PL-2, the 63.2 kW RPV system is projected to produce 93.76 MWh per year, with a performance ratio of 80.1%. The maximum monthly energy production is 10,060 kWh in month of May and minimum is 4,960 in month of February as shown in Table 16. The monthly PR of RPV for PL-2 is maximum in winter season (84.1% in February) and minimum in summer (77.7% in May) season as shown in Fig. 8.
Monthly PR of RPV for PL-1.
The energy produced by the 63.2 kW PV system has been measured in comparison to the energy consumption profile of proposed location 2. The overall analysis is presented in Table 17; Fig. 9. As shown in the Figure, in the month of January, 5.38 MWh energy is produced and 9.8 MWh energy is consumed, remaining 4.42 MWh energy is received form the utility grid. In month of April, 7.92 MWh energy is produced, and 2.4 MWh energy is consumed, resulting in a surplus of 5.52 MWh supplied (net-metered) to the utility grid.
Energy supply and consumption profile for PL-2.
The proposed public building located in hilly areas primarily utilizes energy during daylight, creating a solar PV system exceptionally suitable to its energy generation requirements. The PL-2 anticipates fulfilling about 100% of its annual energy demands through the implementation of a 63.2 kWp PV system, as shown in Fig. 9.
The Aurora Solar system loss diagram as shown in Fig. 10 demonstrates the sequence of energy losses from irradiance to the final AC energy output of a PV system, represented using a Sankey-style flow. The incident radiation measures 1,888 kWh/m² at the optimal tilt and orientation. However, after accounting for efficiency-related losses (-2.4%) due to tilt and orientation, shading (-2.2%), soiling (-2.0%), and incidence angle modifier losses (-2.5%), the effective irradiance is reduced to 1,722 kWh/m². The resultant DC energy through PV conversion is 719,802 kWh further decreased by environmental causes (-8.0%), light-induced degradation (-1.5%), connections (-0.5%), mismatches (-2.0%), DC wiring (-2.0%), and additional minor losses. A 1.6% loss is due to DC to AC conversion, while inverter clipping accounts for a 0.0% loss, leading to 617,137 kWh of energy output in the AC. In the end, the output is affected by system availability by an additional 3.0%, resulting in a net deliverable energy of 594,743 kWh. This comprehensive analysis indicates the complicated relationship between PV system inefficiencies and the need to provide an optimal efficiency of each component to improve the total energy output.
The PVSYST simulation system losses as shown in Fig. 11 presents a comprehensive visual representation of multiple factors involved in total energy losses in a photovoltaic (PV) system. Temperature effects contribute to the majority of the energy losses (6.1% of total energy), module mismatch to 3.6% and reflection losses to 3.1%. The inverter’s efficiency contributes an additional 2.5%, while shade and soiling diminish system performance by 1.2 and 2.0%, respectively. Minor but considerable energy losses have been attributed to irradiance fluctuations (0.4%), inefficiencies within the AC system (0.5%), DC/AC wiring losses (0.5%), and clipping (0.0%). This analysis demonstrates the multidimensional aspect of the performance degradation in PV systems, showing the significance of thermal and electrical balancing of inverters, as well as the size and configuration of the array, in relation to system yield and stability.
System Losses for PL-1.
System Losses for PL-2.
The present degradation rate (DR) for newly manufactured PV modules ranges from 0.5% to 1% per annum. This indicates that the PV modules output will decrease by this percentage annually. In this research, annual degradation rate of 1% have been assumed. This have caused an impact on overall energy output and influenced financial analysis (LCOE and payback period). The energy output degradation of RPV system over 25 years for PL-1 shown in Fig. 12.
Energy output degradation of RPV system over 25 years for PL-1.
The Fig. 12 demonstrates the impact of degradation on the energy output of the 417.96 kW RPV system installed at PL-1, assuming a degradation rate of 1% per annum over the system’s lifespan. Initially, during the first year of the operation of the RPV system, it generates 594.743 MWh of energy. However, due to the degradation of PV modules, the energy output has been diminished every year. It is calculated that by the end of Year-25, the system has been generating 467.277 MWh/year of energy, indicating a reduction of approximately 21.43% from the initial energy output at the system’s inception. This degradation has been attributed to environmental factors (such as temperature fluctuations and UV irradiation), material fatigue and accumulation of dust. The energy output curve decreases smoothly and uniformly, reflecting the linear degradation characteristic commonly seen within modern PV systems. The energy output degradation of RPV system over 25 years for PL-2 shown in Fig. 13.
Energy output degradation of RPV system over 25 years for PL-2.
The Fig. 13 demonstrates the impact of degradation on the energy output of the 63.2 kW RPV system installed at PL-2, assuming a degradation rate of 1% per annum over the system’s lifespan. Initially, during the first year of the operation of the RPV system, it generates 93.76 MWh of energy. However, due to the degradation of PV modules, the energy output have been diminished every year. It is calculated that by the end of Year-25, the system has been generating 73.486 MWh/year of energy, indicating a reduction of approximately 21.62% from the initial energy output at the system’s inception.
Although the simulation results demonstrate substantial energy generation at all PLs, predictions can be highly inaccurate in different seasons and weather conditions. PL-1 (semi-arid) demonstrated comparatively higher accuracy during the summer months when solar irradiation is more consistent, but PL-2 (hilly-area) shown significant prediction errors in the winter months due to the influence of snow and cloud cover on irradiation levels. The PR decreased in winter particularly in PL-2 where there were changes in temperature and shadowing during snowmelt cycles which influenced the efficiency of the PV modules. By taking this seasonal and climatic effects into consideration, biases can be identified and removed by improving the interpretability of the model in a wider range of environments.
Carbon emissions offset, and payback time have significance as environmental indicators utilised for determining the ecological benefits of PV systems, including the reduction of greenhouse gas (GHG) emissions achieved by replacing conventional grid-based electricity.
PV systems are an eco-friendly energy source that reduce carbon footprints by replacing fossil fuel-based electrical power from the grid. Following Eq. (1) have been used to calculate the CO2 emission offset of RPV system.
Where.
Energy generated by PV systems refers to the annual output of the PV system.
Emission Factor of Grid (EFG) indicates the quantity of CO₂ released per unit of electricity utilised from electric grid, commonly quantified in kg CO₂/kWh.
Considering the EFG is around 0.5 kg CO2/kWh (a number contingent upon the local energy mix), the annual emissions offset has to be determined for both sites.
The annual output energy of RPV system installed at PL-1 in Year-1 is 594.743 MWh.
In the Year-1 of operation at PL-1, 297.37 metric tonnes of CO2 emissions have been offset. This has been reduced further in later years as the level of energy generation declines because of module degradation. Despite this, the system will effectively mitigate significant annual emissions.
The annual output energy of RPV system installed at PL-2 in Year-1 is 93.76 MWh.
In the first year of operation at PL-2, 46.88 metric tonnes of CO2 emissions have been offset. Similar like PL-1, this will be gradually reduced further in later years as the level of energy generation declines because of module degradation.
Carbon Payback Time (CPT) indicates the duration in which the solar PV system offsets the carbon emissions generated from its manufacture, installation, and operating procedures. The duration required for the system to offset the carbon emissions incurred during its production, using the carbon savings from energy it generates. To calculate CPT, following Eq. (6) has been used.
The total carbon footprint of the RPV system installed at PL-1 have been computed (Eq. 7) using the assumption of 60 kg CO₂/kWh.
Now, using the CO₂ offset for first year (297,371.5 kg CO₂), the CPT for PL-1 is (:text{C}text{P}text{T}:text{f}text{o}text{r}:text{P}text{L}-1=frac{text{35,684.58}:}{text{297,371.5}:})
The total carbon footprint of the RPV system installed at PL-2 have been computed using the assumption of 60 kg CO₂/kWh.
Now, using the CO₂ offset for first year (357,427 kg CO₂), the CPT for PL-2 is
It is estimated that both RPV system installed at PL-1 and PL-2 substantially mitigate CO2 emissions during their operational lifespans. PL-1 is anticipated to mitigate approximately 297.37 metric tonnes of CO2 in the first year, while PL-2 is going to mitigate 46.88 metric tonnes of CO2. Both systems demonstrate an estimated 0.12 year (≅ 44 days) CPT, indicating their high efficiency concerning carbon offset during the second month of operation. The CPT determined in this research is comparable to that calculated by Zhang, S. et al.57, which is 0.37 months, and Nain, P. et al.58, which ranges from 7 to 13 months.
Figure 14 demonstrates the energy output (in MWh) and carbon dioxide offset (in metric tonnes) for RPV system installed at PL-1 over a 25-year period, assuming for a 1% degradation per year.
Energy output and CO₂ offset for PL-1 over a 25-year period.
The Red line indicates the annual energy generation (MWh) from the 417.96 kW RPV system installed at PL-1. Energy generation is associated with an annual DR of 1%, resulting in a decline in output over time. The RPV system have been generated 594.743 MWh in Year-1. At the end of Year-25, energy generation declines to 467.277 MWh, reflecting a 21.43% decrease throughout the system’s 25-year lifespan. The blue line represents the CO2 emissions offset (in metric tonnes) in comparison to the energy having been generated. The offset is determined using an EFG of 500 kg CO2/MWh, which is a feasible output for grid power. PL-1 has offset 297.37 metric tonnes of CO2 in Year-1. However, the released CO2 is also diminished as energy output decreases. Despite a lower energy output, PL-1 still offsets approximately 233.64 metric tonnes of CO2 by Year-25. This indicates that, although the system’s energy diminishing over time, it nevertheless generates a substantial environmental impact by contributing to the reduction of CO2 emissions over its 25 years of operation. Figure 15 demonstrate the energy output and CO₂ offset of RPV system installed at PL-2 over a 25-year period.
Energy output and CO₂ offset for PL-2 over a 25-year period.
The Red line indicates the annual energy generation (MWh) from the 63.2 kW RPV system installed at PL-2. Energy generation is associated with an annual DR of 1%, resulting in a decline in output over time. The PV system have been generated 93.76 MWh in Year-1. At the end of Year-25, energy generation declines to 73.66 MWh, reflecting a 21.62% decrease throughout the system’s 25-year lifespan. The blue line represents the CO2 emissions offset (in metric tonnes) in comparison to the energy having been generated. PL-1 has offset 46.88 metric tonnes of CO2 in Year-1. However, the released CO2 is also diminished as energy output decreases. Despite a lower energy output, PL-2 still offsets approximately 36.74 metric tonnes of CO2 by Year-25.
A sensitivity analysis analyses the influence of various input variables (such as first-year energy production, DR, and EFG) on the energy output and reduction in CO2 emission of PV system. This analysis is crucial for assessing the system’s reliability and efficiency under varying conditions. Following different scenarios have been considered for sensitivity analysis.
Scenario 1: Energy Output Year-1 = Base Value, DR = 1%, EFG = 500 kg CO₂/kWh.
Scenario 2: Energy Output Year-1 = + 10%, DR = 0.5%, EFG = 400 kg CO₂/kWh.
Scenario 3: Energy Output Year-1 = -10%, DR = 1.5%, EFG = 600 kg CO₂/kWh.
Figure 16 demonstrates the energy otput (MWh) over a 25-year for the three scenarios at PL-1. The green line with square symbol denotes Scenario 1. The blue dashed line with circle symbol denotes Scenario 2, whereas the red dotted line with triangle symbol denotes Scenario 3. At PL-1, the energy generated in Scenario 1 is 594.743 MWh in the Year-1, thereafter diminishing over time due to a 1% annual DR. At Year-25, the energy declines to around 467.276 MWh, representing a 21.43% reduction over the system’s complete lifespan. Energy generation is initially higher in Scenario 2 (654.217 MWh, due to a + 10% increase in the first year’s output) and DR is lower (0.5%). Even in Year-25, Scenario 2 continually generates a higher output energy than Scenario 1, with an approximate total of 580.064 MWh. Scenario 3 has the highest degradation rate of 1.5% and an initial energy output of -10%, resulting in 535.269 MWh for Year-1 and consistent decline that reaches in a total of 372.427 MWh by the end of the Year-25.
Sensitivity of energy output to fluctuating DR and initial energy production for PL-1.
Sensitivity of energy output to fluctuating DR and initial energy production for PL-2.
Figure 17 demonstrates the energy output (MWh) over a 25-year for the three scenarios at PL-2. At PL-2, the energy generated in Scenario 1 is 93.76 MWh in the Year-1, thereafter diminishing over time due to a 1% annual DR. At Year-25, the energy declines to around 73.486 MWh, representing a 21.62% reduction over the system’s complete lifespan. Energy generation is initially higher in Scenario 2 (103.136 MWh, due to a + 10% increase in the first year’s output) and DR is lower (0.5%). Even in Year-25, Scenario 2 continually generates a higher output energy than Scenario 1, with an approximate total of 91.225 MWh. Scenario 3 has the highest degradation rate of 1.5% and an initial energy output of -10%, resulting in 84.384 MWh for Year-1 and consistent decline that reaches in a total of 58.569 MWh by the end of the Year-25.
The offset of CO2 emissions in metric tonnes for the same three scenarios over a 25-year period for PL-1 is demonstrated in Fig. 18. The orange line with diamond symbol denotes Scenario 1.
Sensitivity of CO₂ offset to varying DR and initial energy output for PL-1.
The purple dash-dot line with star symbol denotes Scenario 2, whereas the dark-gray dotted line with circle symbol denotes Scenario 3. The total amount of CO2 offset is determined in accordance with the energy generation and the EFG (500 kg CO2/MWh). In Scenario 1, a steady CO2 offset has been observed, with a starting quantity of 297.372 metric tonnes for Year-1, and a steadily diminishing over time due to the decline in energy output. In Year-25, the CO2 offset for Scenario 1 decreases to 233.638 metric tonnes. In Scenario 2, due to a 10% increase in Year-1 energy output and a reduced DR of 5%, the CO2 offset is optimised in the initial years, reaching 327.109 metric tonnes in Year-1. In Year-25, the offset in Scenario 2 is approximately 290.032 metric tonnes, which is greater than what is obtained through Scenario 1. Scenario 3 demonstrates the minimal CO2 offset resulting from a -10% decrease in Year-1 energy output and a higher DR of 5%, commencing with 267.634 metric tonnes in Year-1, and it rapidly diminished due to a high DR. Scenario 3 has achieved a CO2 offset of 186.214 metric tonnes by Year-25, representing a significant reduction in CO2 emissions.
Sensitivity of CO₂ offset to varying DR and initial energy output for PL-2.
The offset of CO2 emissions in metric tonnes for the same three scenarios over a 25-year period for PL-2 is demonstrated in Fig. 19. In Scenario 1, a steady CO2 offset has been observed, with a starting quantity of 46.88 metric tonnes for Year-1, and a steadily diminishing over time due to the decline in energy output. In Year-25, the CO2 offset for Scenario 1 decreases to 36.743 metric tonnes. In Scenario 2, due to a 10% increase in Year-1 energy output and a reduced DR of 5%, the CO2 offset is optimised in the initial years, reaching 51.568 metric tonnes in Year-1. In Year-25, the offset in Scenario 2 is approximately 45.613 metric tonnes, which is greater than what is obtained through Scenario 1. Scenario 3 demonstrates the minimal CO2 offset resulting from a -10% decrease in Year-1 energy output and a higher DR of 5%, commencing with 42.192 metric tonnes in Year-1, and it rapidly diminished due to a high DR. Scenario 3 has achieved a CO2 offset of 29.285 metric tonnes by Year-25, representing a significant reduction in CO2 emissions.
Data regarding capital expenditure (CAPEX) and operational expenditure (OPEX) is extremely important to conducting a financial assessment of the PV system. The CAPEX and OPEX of photovoltaic systems fluctuate significantly based on parameters like plant design, module technology, inverter, and mounting structures.
Capital expenditures for photovoltaic systems generally incorporate the following elements:
Procurement of PV Modules.
Procurement of plant components, encompassing inverter, cabling, and panel mounting structures.
Site Preparation.
Civil Infrastructure work.
In addition to capital costs, operational and maintenance (O&M) expenses are second major expenditure for photovoltaic systems. The following components comprise these:
Salaries for labour.
PV Module cleanness.
Replacement of the inverter.
Other components Replacement.
For proposed location 1, a comprehensive analysis has been performed to evaluate the economic feasibility of investing in a PV system, based on the following criteria:
Capital expenditure: 84.04 million PKR (Based on an exchange rate of 270 USD to PKR59,60.
Operational expenditure: 1.68 million PKR (2% of Capital Expenditures61.
Lifespan of photovoltaic system: 25 Years.
Inverter replacement: after to a period of 12 years.
Annual Energy Output: 594.742 MWh.
Unit cost: 46 PKR.
The longer-term inflations rate for electricity and operational maintenance expenses: 5% per annum.
Additional losses due to unrecorded shading and unforeseen circumstances: 3% annually.
Annual energy output decline: 1% (photovoltaic module DR).
The LCOE is a fundamental economic indicator utilised to assess the efficiency of PV systems. The LCOE is calculated by using Eq. (13).
Where:
Total system costs encompass CAPEX and OPEX during its lifespan of the system.
Total energy output of the PV system throughout its life of 25 years, by taking into consideration energy degradation.
Total System Costs for PL-1
For proposed location 2, a comprehensive analysis has been performed to evaluate the economic feasibility of investing in a PV system, based on the following criteria:
Capital expenditure: 12 million PKR (Based on an exchange rate of 270 USD to PKR59,60.
Operational expenditure: 0.3 million PKR (2% of Capital Expenditures61.
Lifespan of photovoltaic system: 25 Years.
Inverter replacement: after to a period of 12 years.
Annual Energy Output: 93.76 MWh.
Unit cost: 41 PKR.
The longer-term inflations rate for electricity and operational maintenance expenses: 5% per annum.
Additional losses due to unrecorded shading and unforeseen circumstances: 3% annually.
Annual energy output decline: 1% (photovoltaic module DR).
Total System Costs for PL-2
The financial study indicates that the LCOE and payback period for PL-1 is around 9.54 PKR/kWh (0.034 $/kWh) and 3.27 years, whereas for PL-2, it is around 9.37 PKR/kWh (0.033 $/kWh) and 3.39 years. The LCOE of RPV at PL-1 and PL-2 have been estimated 0.034 $/kWh and 0.033 $/kWh is competitive compared to RPV system installed in Pakistan having LCOE range from 0.026 $/kWh to 0.049 $/kWh56,62. The projected lifespan of 25 years for the installed solar photovoltaic system demonstrates it the most efficient strategy for reducing future electricity costs.
This research demonstrates the proposed model development using METEONORM database and Aurora/PVSYST simulations for two distinct meteorological locations. Nonetheless, this could potentially be scaled to larger datasets through the use of batch processing algorithms and use of parallel computations in these simulation systems. Furthermore, real-time grid applications can be interconnected with smart meters and monitoring systems to make use of these grid models to perform dynamic forecasting, demand-side management and efficient net-metering operations. The improvements would enable the methodology to extend beyond building-scales analysis, and provide benefits in a wider regional or national sustainable energy planning and grid integration scenarios.
In order to validate the research findings, we have compared the system performance metrics with the recent results of the studies that used deep learning and hybrid physics-ML models for PV systems. The mean absolute percentage errors of CNN or LSTM-based forecasting models in hourly and daily solar energy prediction range from 3 to 7%63,64,65. Furthermore, physics28 based hybrid ML models demonstrated significantly better accuracy in comparison with conventional statistical models, with PR values approaching 70–85% across distinct meteorological conditions66,67. The PR achieved this research work (82.6% for PL-1 and 80.1% for PL-2) are comparable to or better than those determined by these complex models. Additionally, methodology of this research provides techno-economic parameters (payback period, LCOE), CPT and CO2 offsets, which have rarely been directly addressed in studies employing only ML models. This benchmarking demonstrates that the suggested methodology is competitive with existing state-of-the-art models, and it seems to have the advantage of being transparent and interpretable to be used in policy-oriented applications.
The installation of RPV systems on public buildings across diverse climatic regions in Pakistan indicates the technical feasibility and financial desirability of distributed renewable energy. This research employed two simulation systems, Aurora solar for PL-1 and PVSYST for PL-2, to assess annual and monthly energy output, PR, financial viability, and grid integration capacity of each system. At PL-1, a 417.96 kWp system has been installed, estimated to generate around 594.743 MWh of energy annually, with a performance ratio (PR) of 82.6%, offsetting 95.52% of the annual energy consumption at the building. The average electricity rate at this PL-1 has been 46 PKR/kWh, resulting in an annual savings of around PKR 25.68 million. Similarly, PL-2 has a 63.2 kWp system, which is projected to produce 93.76 MWh, with a predicted PR of 80.1%. This system is expected to fulfill 100% of the building’s energy requirements and yield yearly cost savings of 3.544 million PKR, based on an average unit rate of 41 PKR/kWh. The two installations adhere to current net metering regulations and demonstrate significant financial benefits, with a payback period of around 3.27 and 3.39 years respectively and substantial savings throughout the 25-year lifespan of the photovoltaic modules.
In addition to technical and economic feasibility, the systems ensure energy security and environmental sustainability by reducing reliance on grid energy generated by fossil fuels. Despite differing climatic conditions and system sizes, these two systems demonstrated the viability of RPV technology as a robust and scalable energy option for public sector buildings. Appropriate simulation software have been employed to enhance performance and optimize system design. The results of the present study endorse the concept that the expansion of RPV installations in public sector buildings should be enhanced, with particular emphasis on the significance of focused policy support, thorough site assessment, and performance modeling as critical components for effective solar PV integration. This research demonstrates the importance of a regulatory framework that facilitates utility-scale installation of RPV systems in public buildings, integrating incentives for customized system designs and efficient net-metering regulations. Future research works should concentrate on optimizing the performance of RPV systems for various climatic conditions, evaluating long-term financial implications, and investigating the scalability of these systems over extensive public sector infrastructures.
The data-sets used and/or analysed during the current study is available from the corresponding author on reasonable request. All of data-set used in the study has been either provided or cited in the article.
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The Researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support (QU-APC-2025).
Department of Control & Instrumentation Engineering, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
Salman Habib & Muhammad Majid Gulzar
Department of Electrical and Energy Engineering, University of Rasul, Mandi Bahauddin, 50400, Pakistan
Muhammad Tamoor
Center for Smart Mobility and Logistics (IRC-SML), King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
Salman Habib
Center for Sustainable Energy Systems (IRC-SES), King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
Muhammad Majid Gulzar
Government College University Faisalabad, Faisalabad, 38000, Pakistan
Paris ZakaUllah
Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah, 52571, Saudi Arabia
Ali Faisal Murtaza & Talal Alharbi
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Dr. Salman Habib: Writing—original draft, Conceptualization, Software, Methodology, Writing – review & editing, Investigation, validation. Dr. Muhammad Tamoor: Writing – review & editing, conceptualization, validation. Dr. Muhammad Majid Gulzar: Visualization, Software, Writing– review & editing. Mr. Paris ZakaUllah: Writing– review & editing, Data Curation. Dr. Ali Faisal Murtaza: Data curation, Formal analysis, Validation, Project administration, Funding acquisition. Dr. Roobaea Alroobaea: Data curation, Formal analysis, Validation, Project administration, Funding acquisition.
Correspondence to Salman Habib or Talal Alharbi.
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Habib, S., Tamoor, M., Gulzar, M.M. et al. Modeling and integration of rooftop photovoltaic systems for sustainable energy access in public sector buildings in diverse climates. Sci Rep 15, 43578 (2025). https://doi.org/10.1038/s41598-025-28396-6
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