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Scientific Reports volume 16, Article number: 1000 (2026)
1046
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In this study, a south-facing hill in Pu ‘er City, China was taken as the object, and a mountain photovoltaic model was established based on the topography of the hill. Based on the climate and lighting conditions provided in Meteonorm 8.1 software for the Pu’er Region, PVsyst was used to model the mountain photovoltaic system and study the annual power generation, system losses, and energy efficiency of the photovoltaic system. The results show that the convex terrain in Region A is more conducive to the heat dissipation of the photovoltaic system than the concave terrain in Region B, and the annual loss of a single panel is reduced by 1.4 kWh. Under the same climatic conditions, photovoltaic panels with convex terrain have higher power generation efficiency, with an average annual increase of 13.54 kWh per panel. Convex terrain is significantly better than concave terrain in terms of power generation efficiency, system performance, and reducing shadow loss. The economic benefit analysis of the system indicates that it can yield a profit of approximately 2.57 million yuan over its entire life cycle, with a return on investment of 279.68%. Compared to coal-fired power generation, it can also reduce CO2 emissions by 10,577,809 kg.
Energy serves as the fundamental driving force behind societal development, yet conventional energy production through fossil fuel combustion has led to excessive resource depletion, significant greenhouse gas emissions, and severe environmental degradation1,2. Hence solar energy emerges as a crucial clean and renewable alternative, boasting virtually inexhaustible reserves. Its efficient utilization represents a vital solution to address both the depletion of fossil fuel resources and the ongoing global energy crisis1,2. Photovoltaic technology, which enables the direct conversion of solar radiation into electricity3,4, has become an indispensable approach for harnessing solar power effectively, thereby reducing reliance on fossil fuels and mitigating environmental pollution. This technology plays a pivotal role in facilitating the global transition toward sustainable energy systems.
Solar photovoltaic power generation demonstrates significant development potential5,6,7,8. Extensive research has validated the reliability of PVsyst software for photovoltaic system simulation. Comparative studies by Islam et al.6 evaluating multiple simulation tools (PVsyst, SAM, HOMER, PVHOL) demonstrated PVsyst’s superior accuracy in photovoltaic system modeling. At the engineering application level7,9,10,11,12, integrated simulation approaches combining electrical analysis (ETAP), 3D modeling (AutoCAD), and photovoltaic performance evaluation (PVsyst) enable comprehensive system optimization. A case study of a 100 kW bifacial power plant in Iran9 revealed that coordinated optimization of array configuration and cable losses can enhance the performance ratio by 2.3 percentage points, demonstrating the effectiveness of PVsyst simulation methodology.
In studies on the performance of photovoltaic (PV) systems in complex terrains (particularly mountainous areas, steep slopes, and irregular roof structures), high-precision modeling and simulation technologies have become key methods for quantitatively evaluating power generation efficiency, power output characteristics, and system energy losses13,14,15,16. Supported by such tools, researchers have conducted multidimensional studies on PV system characteristics in mountainous environments, mainly including: optimization analysis of component selection and array configuration17; quantitative modeling of geographical constraints such as slope, orientation, and shadow distribution, along with the construction of regional PV potential assessment systems18,19; and investment feasibility verification based on lifecycle economic models20,21. However, existing research has primarily focused on macro-scale analysis of PV development in mountainous areas (e.g., resource potential assessment, financial forecasting, and climate adaptability). In actual deployment, micro-terrain features (such as local elevation variations and dynamic shading distribution) are often the main factors causing deviations between system performance and expectations. To address this important issue, Tajjour et al.22 used PVsyst to simulate rooftop PV systems in hilly areas of India, showing that terrain complexity and array design flaws could result in power generation losses of up to 18.7%. This conclusion is corroborated by the numerical simulation study of Baben et al.23, whose optimized model demonstrated that adjusting terrain with specific slopes could increase power generation by 13.5%. Furthermore, Xu et al.24 optimized collector tilt angles based on digital elevation model (DEM) data, improving annual solar radiation capture in mountainous areas by 16.24%, which technically confirms the necessity of 3D terrain modeling for research on mountainous PV systems.
In summary, while existing studies have investigated the performance of power generation and thermal collection systems in mountainous terrains, the deployment of PV systems in these regions remains constrained by multiple complex terrain factors. Topographic variations cause heterogeneity in solar radiation distribution, leading to significant differences in array efficiency. Geomorphological depressions and elevations create localized temperature disparities affecting module output power, while complex terrain features induce differential shading effects that result in unpredictable shadow losses for PV arrays. To address the limitations of current detailed simulation studies, this research utilizes real-world elevation data from a south-facing mountain PV system in Pu’er City, Yunnan Province. A 3D terrain model was constructed using Rhino software, and quantitative PV analysis conducted through the PVsyst platform quantifies key operational factors impacting system efficiency—including temperature loss, irradiance loss, and shading loss. These findings provide actionable decision-making support for PV array maintenance and optimization in complex terrain areas.
The research site is situated in Pu’er City (22.785°N, 101.006°E), Yunnan Province, China, with a mean altitude of 1037 m above sea level on a south-oriented slope, and the photovoltaic system employs a flexible mounting structure specifically designed for mountainous terrain adaptation.
Located in southwestern Yunnan Province, Pu’er City exhibits favorable solar energy potential with annual solar radiation levels ranging from 5000 to 5900 MJ/m225, as shown in Fig. 1. The region’s undulating topography facilitates innovative staggered PV array configurations, which effectively mitigate inter-array shading while optimizing land utilization efficiency26. This terrain-adaptive approach significantly reduces the occupation of premium arable land, demonstrating strong alignment with ecological conservation principles and intensive land use policies. Furthermore, the region’s abundant summer precipitation contributes to passive cooling of PV modules, potentially mitigating thermal degradation and consequently extending system operational lifetimes. These conditions make Pu’er City an exemplary case for PV integration in complex terrains.
Solar radiation resources of Pu’er City, China25.
Meteorological data, including mean annual temperature, precipitation, and solar radiation for Pu’er, were obtained from Meteonorm software. (Version: 8.1. URL: https://mn8.meteonorm.com/en/meteonorm-version-8), which contains detailed climate information of 8,325 meteorological stations around the world. The data is in the form of historical annual average values. The relevant meteorological information of Pu’ er region is shown in Fig. 2. Figure 2a indicates significant seasonal temperature variations in the study area, with the lowest monthly mean temperature of 14.3 °C (January) , the highest of 23.4 °C (June) and an average temperature of 19.9 °C . The precipitation data in Fig. 2b demonstrates that January to April constitutes the typical dry season (mean monthly precipitation: 26.6 ± 3.2 mm), with July exhibiting the precipitation peak (324.3 mm/month, representing 21.7% of the annual total). December shows the minimum precipitation (19.6 mm), while the annual cumulative precipitation reaches 1494.1 mm, with 78% of rainfall concentrated during May–October.
Meteorological information of Pu’er City: (a) Monthly average temperature, (b) Annual rainfall and (c) Total horizontal radiation.
By analyzing Fig. 2b and c, it is evident that from January to May, the rainfall is relatively low, while the total horizontal radiation is relatively high. From June to July, as rainfall increases, the total horizontal radiation begins to decline, reaching its lowest point (130.7 kWh/m2) in July. From August to September, the total radiation level slightly increases (136.65 kWh/m2). However, from October to December, despite reduced rainfall, solar radiation continues to decrease, reaching its lowest point (103.7 kWh/m2) in December.
This study employs high-resolution digital elevation model (DEM) data obtained via unmanned aerial vehicle (UAV) photogrammetry to establish a preliminary three-dimensional terrain model (Fig. 3). Through parametric modeling using Rhino’s Grasshopper algorithmic design platform (Version: 8 SR1. URL: https://www.rhino3d.com/cn/), the discrete DEM data points were interpolated into continuous digital terrain surfaces. Photovoltaic module arrays were then precisely arranged within the 3D model based on field-measured coordinates and tilt angles of photovoltaic arrays. The established 3D model was then imported into PVsyst simulation software (Version: 7.2. URL: https://www.pvsyst.com/) to quantitatively assess the impact of terrain-induced shading effects on photovoltaic system performance. As a professional photovoltaic system design and simulation software, PVsyst has been extensively validated through empirical studies, demonstrating high reliability in performance prediction. The software exhibits a prediction error of less than 4.2% for energy yield estimation and maintains an inverter efficiency modeling deviation below 0.2%27, the temperature coefficient error is less than 0.01%28. Comparative studies with alternative simulation tools show consistent results, with inter-software variations typically within 4%, further confirming its accuracy for photovoltaic system performance analysis29.
Establishment of mountain photovoltaic model.
As shown in Fig. 4, the photovoltaic system built in PVsyst consists of 672 modules with a total DC capacity of 386.4 MWp. The PV system is divided into two region, A and B: Region A owns convex terrain, with an average slope of 20.378°, an convex region of 1175 m2, and 456 PV panels are installed; Region B owns concave terrain, with an average slope of 17.703°, an erection area of 561 m2, and 216 PV panels are installed. Four inverters are set up, with each inverter connected to 12 PV string, and each PV string consists of 14 PV panels, forming a complete array design for the entire system (Fig. 5). The photovoltaic arrays in Region A are deployed on an exposed convex terrain, while those in Region B are located in a concave topography surrounded by higher mountains. This geomorphological difference might result in significant terrain-induced shading on Region B arrays during low solar altitude conditions (morning/evening periods).
The establishment of the 3D model in PVsyst: (a) Region A and (b) Region B.
Photovoltaic system string design.
In mountainous photovoltaic system design, array shading is one of the key factors causing power generation loss under complex terrain conditions. To prevent mutual shading between adjacent arrays, it is essential to ensure the minimum shading distance requirement between them, as shown in Eq. 130,31. The corresponding spatial layout diagram of the arrays is illustrated in Fig. 6. Where γ is hill slope angle, D’ is the minimum horizontal spacing between the front and back mounting of PV arrays on the south slope surface, L is the length of the tilted surface of the PV panels, H is the vertical distance from the intersection of the sun rays with the south slope surface to the horizontal, β is the inclination angle of the array, and x is the distance from the slope of the hill surface to the horizontal, since the slope orientation and the orientation of the photovoltaic array are both southward, there is no front and back shading phenomenon between the photovoltaic panels of different heights, so the value calculation of x does not need to consider the factors related to shadow shading.
Schematic diagram of the arrangement of photovoltaic arrays on a slope.
In formula (1) ø represents is the local latitude;γ is the average slope, γ in Region A is 20.378°, and in Region B is 17.703°; L is 2.278 m, β is 0°, and ø is 22.635°. After calculation, the component spacing D’A in region A is 1.498 m and the component spacing D’B in region B is 1.575 m. It is worth noting that the photovoltaic array in this study adopts a soft support structure. Meanwhile, because Pu ‘er is located in a low latitude region (north latitude 22.635°), the annual solar altitude Angle is large, especially in summer, and the solar altitude Angle is close to 90°, so the inclination Angle of photovoltaic panels is set to 0°. In conclusion, the spacing of the PV arrays in this project is greater than D’A and D’B respectively, so the PV arrays do not cause shadow loss to each other.
The photovoltaic system adopted in this study is equipped with high-efficiency N-type monocrystalline silicon modules from JinkoSolar, and the LR5-72HGD-575 M double-glass photovoltaic modules produced by Longi Green Energy are selected as the power generation panels. The external dimensions of this component are 2278 mm × 1134 mm × 30 mm, and the effective light-receiving area is approximately 2.58 m 2. The key performance parameters are shown in Table 1. The default operating parameters of the system include: a temperature coefficient of −0.280%/℃, a first-year power attenuation rate of less than 1%, a line loss of 1.5%, an annual component mass loss rate of 0.1%, and a mismatch loss of 2%. Under standard test conditions (STC, that is, irradiance 1000 W/m2, battery temperature 25 ℃, atmospheric mass AM1.5), the maximum output power of this component varies with the operating temperature in accordance with the temperature coefficient relationship described, that is, for every 1 ℃ increase in operating temperature, its output power decreases by 0.280% relative to the standard value.
The inverter model is SP-100 K-BL, the detailed parameters are shown in Table 2, the maximum conversion efficiency is 99.0%, the system is arranged with a total of four inverters for the maximum input voltage of 1100 V, the maximum input current is 234 A, the voltage range of the MPP is 200–1000 V. In order to make the inverter suitable for the cluster design of photovoltaic array, four SP-100 K-BL inverters were used in simulation.
It is worth mentioning that the following assumptions are made in PVsyst modeling: module performance follows STC and linear irradiance response, neglecting low-light nonlinearity and hotspot effects; string-level parameters are homogenized, ignoring mounting deformations; shading includes only terrain/mounting, excluding clouds and diffuse radiation; inverter efficiency is static with fixed losses; meteorological data is spatially homogenized.
As shown in Fig. 7, the average power generation loss of photovoltaic panels in Region A exceeded that in Region B during May to July, while Region A demonstrating lower losses in the remaining months. Integrated analysis with Fig. 2 reveals that from January to April, rising temperatures combined with low precipitation levels led to increasing photovoltaic module temperatures, peaking in April (35.91 °C for Region A and 35.98 °C for Region B). Correspondingly, the average monthly energy loss per panel reached its maximum in April, recording 4.9 kWh for Region A and 5.0 kWh for Region B. From May to December, increasing rainfall contributed to declining operating temperatures, with panel temperatures decreasing monthly to their lowest levels in December (24.14 °C for Region A and 24.75 °C for Region B). Concurrently, temperature-induced energy losses reached annual minima of 1.1 kWh for Region A and 1.3 kWh for Region B. Based on annual temperature-related energy loss per panel, Region A recorded 38.1 kWh compared to 39.5 kWh for Region B, representing a reduction of 1.4 kWh in Region A.
Loss of system power generation for per panel caused by Photovoltaic panels temperature in region A and B.
As shown in Fig. 8, the monthly power generation of individual photovoltaic (PV) panels in Regions A and B exhibits distinct trends under varying solar irradiance conditions. Integrated analysis with Fig. 2 indicates that from January to April, both regions showed a progressive monthly increase in power generation, reaching peak values in March with average outputs of 84.5 kWh per panel in Region A and 83.3 kWh in Region B. From June to December, however, significantly increased rainfall (as shown in Fig. 2) resulted in reduced horizontal solar irradiance, leading to a gradual decline in PV power generation. The lowest monthly generation occurred in October, with outputs of 55.2 kWh for Region A and 55.1 kWh for Region B. Notably, Region A demonstrated consistently higher generation efficiency throughout the annual cycle, with the most pronounced difference observed in January (66.4 kWh versus Region B’s 64.3 kWh).
Photovoltaic power generation of different months in Region A and B.
In summary, under identical meteorological and geographical conditions, the convex topography in Region A enhanced exposure to the solar apparent trajectory, whereas the concave terrain in Region B exacerbates morning and evening shadow shading. This differential solar radiation distribution, attributable to topographic variations, constitutes the primary factor governing the disparity in monthly power generation per photovoltaic panel between Regions A and B.
Four representative dates (March 20, June 21, September 22, and December 21) corresponding to solar altitude angles of 66°58′, 89°38′, 67°04′, and 43°31′ observed in Pu’er City, Yunnan Province, China during 2023 were selected for shadow loss analysis. The results are presented in Fig. 9. The analysis demonstrates that the maximum sunshine duration occurred at the solar altitude of 89°38′ (summer solstice), while the minimum duration was recorded at 43°31′ (winter solstice). Intermediate and comparable sunshine durations were observed at 66°58′ (spring equinox) and 67°04′ (autumn equinox). These findings indicate distinct seasonal patterns: maximum solar availability during summer, comparable duration during spring and autumn, and minimum duration during winter.
Schematic diagram of the solar altitude angle.
Through the comprehensive analysis of Figs. 9 and 10, it can be seen that the shadow loss of the photovoltaic system is significantly related to the solar altitude Angle and the terrain conditions of the array arrangement, and there is an internal relationship between the system performance difference and the shadow loss. Figure 10a–h shows the direct radiation linear losses and electrical losses of Regions A and B at four representative dates of solar altitude angles (67° 04′, 89° 38′, 66° 58′, and 43° 31’). Among them, when the sun altitude angle is 43° 31′, the difference between the two regions is most significant (region A: 0.1%,0.1%; Region B: 1.0%,6.9%); When the sun altitude Angle is 89° 38’, the loss of region A is slightly higher than that of region B(Region A: 1.3%,3.7%; Region B: 1.1%,3.3%), the reason for this abnormal difference may be that the mountain terrain in Region A causes shading between the photovoltaic arrays; When the sun altitude Angle is 66° 58′, and 67° 04′, the loss characteristics of region A and region B are similar, but the loss of region A is lower than that of region B(region A: Autumn equinox/spring equinox 0.2%/0.2%,0.3%/0.3%; Region B: 0.8%/1.6%,0.8%/1.5%).
Shadow loss analysis for four representative days of solar elevation angles for Region A and Region B.
In conclusion, compared with the concave mountain region B, the direct irradiation loss in the convex mountain Region A is reduced by 65.5% and the electrical loss is reduced by 66.9%. The results show that under the same geographical and meteorological conditions, the open terrain can effectively reduce the Linear loss of direct radiation and electrical loss of photovoltaic system.
Figure 11 shows the energy efficiency of Region A and Region B PV systems, revealing a variety of losses in the conversion process from horizontal total radiation to grid-connected electricity, including near occlusion, IAM coefficient, irradiance intensity and temperature influence. According to the analysis of Fig. 11 a and b, the energy loss in Region A is slightly higher than Region B due to the large number of photovoltaic panels (Region A: −1.5%, 23.5 kWh/m2; Region B: −1.4%, 21.9 kWh/m2). However, the shadow of Region B caused greater electrical losses (Region A: −0.4%, 3.57 kWh/photovoltaic panel; Region B: −2.9%, 26.3 kWh/photovoltaic panel). The annual power generation of Region A is 367,419 kWh, and the average power generation of each photovoltaic panel is 805.7 kWh, while the annual power generation of Region B is 171,125 kWh, and the average power generation of each photovoltaic panel is 792.2 kWh. Compared with Region B, the photovoltaic array power generation efficiency of Region A is better than that of Region B.
Energy efficiency diagram of the photovoltaic system in Regions A and B.
Figure 11 presents the energy efficiency of the photovoltaic (PV) systems in Region A and Region B, revealing a variety of losses in the conversion process from horizontal total radiation to grid-connected electricity, including near occlusion, IAM coefficient, irradiance intensity and temperature influence. As shown in Fig. 11a and b, the energy loss in Region A is slightly higher than that in Region B, attributable to its larger number of PV panels (Region A: −1.5%, 23.5 kWh/m2; Region B: −1.4%, 21.9 kWh/m2). However, shading in Region B resulted in significantly greater electrical losses (Region A: -0.4%, 3.57 kWh per panel; Region B: −2.9%, 26.3 kWh per panel). The annual power generation in Region A reached 367,419 kWh, with an average output of 805.7 kWh per PV panel, whereas Region B generated 171,125 kWh annually, averaging 792.2 kWh per panel. These results indicate that the PV array power generation efficiency in Region A is superior to that of Region B. Although the occlusion loss near Region A is high, its convex terrain provides more light and better energy conversion, making the overall power generation efficiency of the plate and system significantly exceed that of Region B, improving the system performance and economy.
The cost estimation for this project system necessitates a comprehensive analysis of how core parameters—including construction, site selection, and labor costs—affect the actual investment. Key economic parameters, such as PV module unit prices, annual operation and maintenance costs, land rental fees, and regional electricity tariffs, are provided in Table 3. The financial evaluation results are detailed in Table 4. It should be noted that the total cost encompasses only the flexible support system for photovoltaic mounting structures and other consumables, such as cables.
The total expenditure for photovoltaic modules amounts to 393,120 CNY; the supporting bracket system costs 168,970 CNY; and each inverter is priced at 11,210 CNY. The project is designed for an operational lifespan of 20 years, with construction costs estimated on a one-time basis. The annual salaries for engineering technicians and installation personnel total 150,000 CNY, leading to a total initial investment budget of 918,140 CNY. Expenses related to transportation, commissioning, and grid connection are simplified in this calculation and are temporarily excluded from the cost estimation. Ongoing annual maintenance costs—including cleaning, safety management, and routine upkeep—along with land acquisition fees, amount to 15,000 CNY per year.
In accordance with the implementation plan for electricity marketization transactions in Pu’er City, Yunnan Province, the average transaction price for clean energy—including photovoltaic, hydropower, and wind power—was determined to be 0.276 CNY/kWh. The annual power degradation rate of the photovoltaic panel model LR5-72HGD-575 M is 0.38%. The resulting economic benefits of the system are summarized in Table 5.
As shown in the economic benefit schematic diagram over the lifecycle of the photovoltaic system in Fig. 12, the initial revenue of the system is -918,140 CNY (as shown in Table 3). It subsequently generates annual revenue through electricity generation (power generation data can be found in Table 6), but also incurs an annual operation and maintenance cost of 15,000 CNY (as shown in Table 4). The system begins to generate net profit after 6.8 years of operation and accumulates a net profit of 2,567,853.92 CNY by the 20th year (as shown in Table 5).
Schematic diagram of economic benefits in the life cycle of photovoltaic system.
According to the IPCC Guidelines and research by Wang et al.32, the emissions from typical coal-fired power plants in China are 1.018 kg of CO2 per kWh. As summarized in Table 6, the total electricity generation of the PV system over its lifecycle in this study amounts to 10,390,775 kWh. In comparison with conventional coal-fired power generation, the PV system evaluated in this study can achieve a CO₂ emission reduction of 10,577,809 kg.
This study utilizes Rhino software to establish detailed 3D mountain photovoltaic models, focusing on southward slopes in Pu’er City, Yunnan Province. Through PVsyst simulation analysis, the research investigates power generation characteristics of mountain photovoltaic under different terrain conditions, finally, the economic and environmental benefit analysis was evaluated according to the system performance, yielding the following key findings:
The open and well-ventilated terrain in convex mountain Region A demonstrates superior thermal performance, with an annual energy loss of 38.1 kWh per module, compared to 39.5 kWh in the concave mountain Region B. The overall efficiency of the photovoltaic array in Region A is higher than that in Region B, the loss caused by the temperature raise of a single photovoltaic panel was reduced by 3.5%.
The convex Region A achieves 65.5% lower shading coverage and 66.9% reduced electrical losses relative to the concave Region B. The performance gap is most evident in January, with Region A producing 66.4 kWh per panel versus Region B’s 64.3 kWh.
Seasonal analysis indicates that terrain geometry significantly affects shadow losses. During the winter solstice, Region A maintains minimal losses (0.1% for both direct radiation and electrical), while Region B experiences substantially higher losses (1.0% and 6.9% respectively), demonstrating the impact of topographic variations on solar energy capture.
System-level evaluation confirms that the open terrain configuration in Region A not only enhances individual panel performance but also optimizes overall system efficiency. The shading-related electrical loss in Region A (-0.4%, 3.57 kWh per panel) is dramatically lower than in Region B (−2.9%, 26.3 kWh per panel), which revels the importance of considering mountainous terrain in photovoltaic arrays design.
This photovoltaic system research requires an initial investment of 918,140 CNY and has annual operating costs of 15,000 CNY. It achieves payback in 6.8 years and yields a net profit of 2,567,853.92 CNY over its 20-year lifespan. Furthermore, the system generates 10.39 million kWh of electricity, enabling a reduction in carbon emissions by 10.58 million kg compared to conventional coal power.
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Competing interests
The authors declare no competing interests.
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This study was supported by the project “Development and Research of Composite Photovoltaics Based on ‘Tea and Coffee’ Crops in Pu’er City” (KY202311) funded by Yunnan Longyuan New Energy Co., LTD.
Open access funding provided by The Yunnan Longyuan New Energy Co., LTD..
Yunnan Longyuan New Energy Co., LTD., Kunming, 650100, Yunnan, China
Jinxiong Ma, Peng Xie & Tai Gu
Faculty of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming, 650201, Yunnan, China
Xinyu Shen, Hao Zhang & Junwei Huang
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Jinxiong Ma: Writing–original draft, Methodology, Investigation, Formal analysis, Conceptualization, Funding acquisition. Tai Gu: Writing–original draft, Methodology, Investigation, Formal analysis, Conceptualization, Funding acquisition. Peng Xie: Writing–review & editing, Supervision, Project administration, Funding acquisition. Xinyu Shen: Methodology, Investigation. Hao Zhang: Methodology, Investigation. Junwei Huang: Writing–review & editing, Supervision,
Correspondence to Jinxiong Ma.
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Ma, J., Xie, P., Gu, T. et al. Simulation study of a 386.4 MW mountain photovoltaic power plant: a case study. Sci Rep 16, 1000 (2026). https://doi.org/10.1038/s41598-025-30742-7
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DOI: https://doi.org/10.1038/s41598-025-30742-7
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