A photovoltaic laying area optimization method based on temperature field distribution

Abstract

The application provides a photovoltaic laying area optimization method based on temperature field distribution, and relates to the technical field of photovoltaic laying.The application adopts heat transfer technology under the influence of internal heat source, evaluates the annual thermal environment influence trend through the photovoltaic surface temperature field distribution characteristics in the winter extreme working condition, and solves the power generation prediction deviation problem caused by the insufficient consideration factors of traditional photovoltaic temperature data; through the isotheral delimitation partition and the optimization gradient high and low temperature area delimitation method, the high temperature area boundary expansion process is quantified as a multi-stage adjustable control, the influence of the thermal environment is systematically evaluated, the precise regulation and control of the heat source interference avoidance degree and the photovoltaic laying area are realized, and the efficiency decay and safety hazards caused by local high temperature are effectively avoided; an evaluation model is constructed, the temperature loss, the flatness degree of electricity cost and the investment recovery period index are evaluated, the best laying scheme is selected in the multiple laying mode, and the overall efficiency of the roof photovoltaic system is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic installation technology, and in particular to a method for optimizing photovoltaic installation area based on temperature field distribution. Background Technology

[0002] With the advancement of the "dual carbon" goals, photovoltaic power generation has become an important way for steel companies to save energy and reduce carbon emissions. Factory rooftops have large areas, making them suitable for large-area photovoltaic installations, which is why factory rooftop photovoltaics have attracted much attention.

[0003] Currently, rooftop photovoltaic (PV) installations, considering factors such as installation angle and shading, primarily focus on geometric parameters (e.g., tilt angle, azimuth angle) and shading analysis, neglecting the temperature rise caused by heat sources within the factory building on the roof and PV panels. This lacks a systematic assessment of the thermal environment's impact. Furthermore, factory rooftop temperature distribution exhibits significant non-uniformity, with high-temperature zones concentrated directly above heat sources or in poorly ventilated areas. Ignoring these temperature field characteristics and directly covering the entire rooftop with PV panels could lead to a sharp drop in power generation efficiency in high-temperature areas, or even trigger localized hot spot effects, impacting the overall efficiency of the rooftop PV system.

[0004] In view of this, the inventors have specifically designed a photovoltaic installation area optimization method based on temperature field distribution, which leads to this invention. Summary of the Invention

[0005] To solve the above problems, the technical solution of the present invention is as follows: A method for optimizing photovoltaic installation area based on temperature field distribution includes the following steps: Temperature field simulation was conducted to construct a thermal environment simulation model of the factory roof, obtain hourly temperature field distribution data of the factory roof, and select the temperature field under extreme low temperature conditions in winter as the benchmark for thermal performance characterization. Isotherm delineation and zoning optimization: Based on the temperature field distribution data of the factory roof, several continuous temperature threshold stepped closed-loop isotherms are generated, and the roof is divided into a high-temperature decay zone and a low-temperature safety zone through the closed-loop isotherms. A gradient avoidance strategy is generated based on the stepped closed-loop isotherm to generate multiple gradient deployment schemes. In this scheme, photovoltaic modules are not deployed in the high-temperature decay zone, and the deployment of photovoltaic modules is maximized in the low-temperature safety zone. The i-th scheme specifies that photovoltaic modules are not deployed in areas where the temperature is higher than the threshold of the i-th closed-loop isotherm, and photovoltaic modules are deployed in areas where the temperature is lower than the threshold of the i-th isotherm, where i is a natural number. A multi-objective evaluation system for levelized cost of electricity (LCOE) and dynamic payback period is established to evaluate the multiple graded laying schemes and select the optimal laying scheme based on the evaluation results.

[0006] Preferably, the temperature field simulation includes the following steps: Create a 3D model of the factory building of the same size, including internal heat sources and building structure; Set core boundary conditions, including heat source surface temperature parameters, wind speed boundary conditions for plant openings, solar radiation parameters, and convective heat transfer parameters inside the plant. Based on the aforementioned model and boundary conditions, the computational domain is meshed to generate a discrete mesh for numerical computation. Hourly photovoltaic temperature field distribution data of the roof were obtained through simulation calculations.

[0007] Preferably, the mesh division specifically involves setting the minimum cell quality value to 0.0015 to 0.0016, performing corner refinement on all computational domains, setting the cell size scaling factor to 0.3 to 0.4, and controlling the total number of cell grids to between 500,000 and 550,000.

[0008] Preferably, the area inside the closed-loop isotherm is the high-temperature attenuation zone, and the area outside the closed-loop isotherm is the low-temperature safety zone.

[0009] Preferably, in the isotherm demarcation and zoning optimization laying step, generating a series of stepped closed-loop isotherms with consecutive temperature thresholds specifically involves: Centered on the highest point in the photovoltaic temperature field distribution, multiple closed-loop isotherms with successively decreasing temperature values ​​are generated by expanding outward at preset temperature change intervals.

[0010] Preferably, the preset temperature change interval is 1°C, and the number of closed-loop isothermal lines is 9.

[0011] Preferably, the number of the gradient laying schemes corresponds to the number of the closed-loop isotherms; For each isotherm, a deployment plan is generated, which requires that photovoltaic modules not be deployed in areas where the temperature is not lower than the isotherm's temperature threshold, and that photovoltaic modules be deployed in areas where the temperature is lower than the isotherm's temperature threshold.

[0012] Preferably, the criterion for selecting the optimal laying scheme is to select the scheme in which both the levelized cost of electricity (LCOE) and the dynamic payback period are minimized.

[0013] The technical solution provided by this invention has the following beneficial effects: This invention employs heat transfer technology influenced by internal heat sources. By assessing the annual thermal environment impact trend through the distribution characteristics of photovoltaic surface temperature field under extreme winter conditions, it overcomes the problem of power generation prediction deviation caused by insufficient consideration of factors in traditional photovoltaic temperature data. Through isotherm demarcation and optimization of gradient high and low temperature zone definition methods, the process of high temperature zone boundary expansion is quantified into multi-level adjustable control, enabling a systematic assessment of the thermal environment impact. This allows for precise control of the degree of heat source interference avoidance and photovoltaic installation area, effectively avoiding efficiency degradation and safety hazards caused by local high temperatures. An evaluation model is constructed to evaluate multiple installation methods based on temperature loss, levelized cost of electricity (LCOE), and investment payback period indicators to select the optimal installation scheme, significantly improving the overall efficiency of rooftop photovoltaic systems. Attached Figure Description

[0014] The accompanying drawings, which are provided to further illustrate the invention and constitute a part of this invention, are illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention.

[0015] in: Figure 1 This is a simulation flowchart of the present invention; Figure 2 This is a temperature distribution diagram of the rooftop photovoltaic panel in this embodiment; Figure 3 The nominal power of photovoltaic modules under different installation methods System cost per kilowatt-hour LCOE and investment payback period Comparison chart; Figure 4 This describes the Mode 6 installation method and photovoltaic panel installation location in this embodiment; Figure 5 This is a comparison chart of the monthly average temperature of the high-temperature attenuation zone and the low-temperature safety zone of mode 6 with that of mode 1 full-coverage mode; Figure 6 This is a comparison chart of temperature loss between mode 1 and mode 6; Figure 7 This is a comparison chart of the first-year efficiency (PR0) of rooftop solar systems of mode 1 and mode 6; Figure 8 This is a comparison chart of the efficiency and economic indicators of photovoltaic systems in Mode 1 and Mode 6. Detailed Implementation

[0016] To make the technical problems, solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of the invention.

[0017] Please see Figures 1 to 8 This is a photovoltaic installation area optimization method based on temperature field distribution, which is a preferred embodiment of the present invention, and includes the following steps: Temperature field simulation was conducted. A simulation model of the factory roof thermal environment was constructed using COMSOL Multiphysics. Hourly temperature field distribution data of the factory roof was obtained through photovoltaic temperature field numerical simulation, and the temperature field under extreme low-temperature conditions in winter was selected as the benchmark for thermal performance characterization. Twelve typical meteorological days were selected from each month of the year, defined as the single day whose temperature and radiation meteorological characteristics are closest to the monthly average. Specifically, for all dates within a month, the Euclidean distances of the daily average temperature and daily average radiation relative to the monthly averages were calculated, and the date with the smallest distance was selected as the typical meteorological day for that month. In this embodiment, the photovoltaic panel surface temperature field at 12:00 on January 3rd, a typical winter meteorological day, was selected as the standard for classification. At this time, the ambient temperature was 10.3℃, and the solar radiation was 580.74 W / m². The main reason for choosing January as the typical month is that the photovoltaic panel surface temperature field data at this time can reflect the thermal characteristics of the factory location under extremely low-temperature conditions. If a local high-temperature area appears at this time, the summer temperature will further increase. The temperature distribution in January has a forward-looking characterization significance for the thermal environment throughout the year.

[0018] A 3D model of the factory building, including its internal heat sources and structural components, was created. The geometric model includes three production workshops and eight doors measuring 6m x 8.3m, six normally open and two normally closed. Each workshop has ventilation openings on its roof. The main heat sources inside the factory include one VD refining furnace (hemispherical, 1.5m radius), one LF refining furnace (rectangular, 3.5m x 3m x 7m), two induction furnaces (cylindrical, 1.5m base radius, 2m height), two ladles (cylindrical, 1.5m base radius, 2m height), seven electroslag furnaces (cylindrical, 1m base radius, 1m height), and five annealing furnaces (rectangular, 4m x 2m x 3m), covering the core heat-generating equipment in the production process. All production workshop roofs have a uniform slope of 9°. The top ventilation openings of workshops ① and ② have the same size: 85m × 2m × 2.16m. The ventilation openings of workshop ③ have the size: 35m × 2m × 2.16m.

[0019] Next, core boundary conditions are set, including surface temperature parameters of the heat source, wind speed boundary conditions for the plant openings, solar radiation parameters, and convective heat transfer parameters inside the plant, among which: Heat source surface temperature parameters: VD refining furnace surface temperature 200℃, LF refining furnace surface temperature 200℃, induction furnace surface temperature 300℃, annealing furnace surface temperature 45℃, electroslag furnace upper surface temperature 1700℃, other surfaces 60℃, ladle tank upper surface temperature 1400℃, other surfaces 300℃. All doors of the plant are the wind speed inlet boundaries, the inlet wind speed is taken as the average wind speed of 2m / s, the outlet is the top ventilation opening, and the relative pressure is set to 0Pa.

[0020] Radiation-related settings: Solar radiation intensity of 580.74 W / m² at noon in winter was selected, date was set to January 3rd, COMSOL model automatically calculated the sun's position, ambient temperature was 10.3℃. Radiation sources on the furnace surface were the upper surface of the ladle and the upper surface of the electroslag furnace.

[0021] Convective heat transfer: Heat transfer inside the plant is calculated based on the law of conservation of energy. Convective heat flux is provided on the external roof. External natural convection is also provided. The sloping wall lengths of the roofs of plants ①②③ are 26.05m, 19.47m, and 15.43m, respectively.

[0022] Mesh Setup: Based on the aforementioned model and boundary conditions, the computational domain is meshed to generate a discrete mesh for numerical calculations. Hourly photovoltaic temperature field distribution data of the roof is obtained through COMSOL Multiphysics simulation. The minimum element mass is 0.001575, and all domains are subjected to corner refinement with an element size scaling factor of 0.35. The total number of elements is 517,839. Mesh generation effectively improves computational speed and accuracy. Excessively coarse meshes lead to inaccurate calculations, while excessively fine meshes result in slow computation speeds and difficulty in model convergence. The mesh in this embodiment satisfies both computational speed and accuracy.

[0023] Isotherm delineation and zoning optimization are implemented. Based on the temperature field distribution data of the factory roof, several stepped closed-loop isotherms with continuous temperature thresholds are generated. The roof is divided into a high-temperature attenuation zone and a low-temperature safety zone by these closed-loop isotherms. The area inside the closed-loop isotherms is the high-temperature attenuation zone, and the area outside is the low-temperature safety zone. Specifically, taking the highest point in the photovoltaic temperature field distribution as the center, multiple closed-loop isotherms with successively decreasing temperature values ​​are generated outward at preset temperature change intervals. The preset temperature change interval is 1℃, and there are 9 closed-loop isotherms. The highest temperature of the roof photovoltaic system at 12:00 on January 3rd in winter is 31.5℃. The corresponding temperatures of the isotherms from high to low are 30.5℃, 29.5℃, 28.5℃, 27.5℃, 26.5℃, 25.5℃, 24.5℃, 23.5℃, and 22.5℃.

[0024] A gradient avoidance strategy is used to generate the factory building. Based on the stepped isotherms, multiple gradient deployment schemes are generated. In this scheme, photovoltaic modules are not deployed in the high-temperature decay zone, and the deployment of photovoltaic modules is maximized in the low-temperature safety zone. The i-th scheme specifies that photovoltaic modules are not deployed in areas where the temperature is higher than the threshold of the i-th closed-loop isotherm, and photovoltaic modules are deployed in areas where the temperature is lower than the threshold of the i-th isotherm, where i is a natural number; in this embodiment, i is 9. Figure 2 (a) shows the roof temperature distribution at 12:00 on January 3rd. The results show that the roof temperature distribution is characterized by higher photovoltaic (PV) temperatures near the heat source, and the PV temperature on the south-facing roof of the factory building is higher than that on the north-facing roof in winter. The lowest temperature of the roof PV panels is 12.9℃, and the highest is 31.5℃. The highest temperature occurs near the top of the ladle tank in Factory Building No. 2, due to the high temperature of the molten steel inside the ladle tank and its strong infrared radiation. This non-uniformity indicates significant differences in the thermal environment of different parts of the roof, and the high temperature loss in high-temperature areas has an adverse effect on the roof PV system. To achieve precise optimization of the PV panel installation area and solve the problem of resource waste and unbalanced benefits in high-temperature areas caused by uniform installation across the entire area, this study, based on the temperature field distribution characteristics of the factory roof, uses isotherms in higher-temperature areas as the dividing line to define the high and low temperature zones of PV installation, providing a standard for comparative analysis of different installation schemes. This embodiment includes 10 installation schemes, where mode 1 is the full-coverage method, and modes 2-10 are isotherm-defined methods, such as... Figure 2 As shown in (b)-(c), isotherms of 30.5℃, 29.5℃, 28.5℃, 27.5℃, 26.5℃, 25.5℃, 24.5℃, 23.5℃, and 22.5℃ were selected as temperature thresholds. The area enclosed by each isotherm was defined as the high-temperature decay zone. Photovoltaic panels were not installed in this zone because the modules experienced high decay due to the heat source. The area outside the isotherms was defined as the low-temperature safety zone (corresponding to the low-decay areas of Plant 3 and Plants 1 and 2, which are far from the heat source). Photovoltaic panels were installed in this zone.

[0025] A multi-objective evaluation system was established, considering temperature loss, levelized cost of electricity (LCOE), and dynamic payback period. This system was used to evaluate multiple graded photovoltaic (PV) installation schemes, and the optimal scheme was selected based on the evaluation results. A comparative study was conducted on the nominal power of PV modules under 10 different installation methods. and LCOE and investment payback period D invest The optimal laying method is determined through the following calculation process: First, calculate the photovoltaic temperature loss and the photovoltaic system's power generation, then calculate the typical daily photovoltaic power generation in the photovoltaic output model. (kWh) is as follows: - h Typical daily power generation in a month - Nominal power of photovoltaic modules (kW). - Typical daily equivalent STC sunshine hours - Typical daily efficiency of photovoltaic systems.

[0026] Annual power generation : D - The total number of days in the month in which a typical day falls.

[0027] Based on the temperature data obtained from the simulation, the typical daily heat loss coefficients of the photovoltaic array are calculated. : -Component temperature coefficient; - Typical daily average temperature of the components.

[0028] Calculate the dust accumulation loss coefficient of the photovoltaic array : - Surface area ash density of photovoltaic array (g / m²) 2 ); PM in the atmosphere 10 mass concentration (μg / m 3 ).

[0029] Daily efficiency of photovoltaic system The calculation is as follows: - Other loss factors, which are fixed values ​​totaling 8%, include 3% for shading, 2% for mismatch loss, 2% for line loss, and 1% for rated loss. Monocrystalline silicon photovoltaic modules with a nominal power of 500W are selected.

[0030] Next, calculate the levelized cost of electricity (LCOE). Calculation formula: - Initial investment in a photovoltaic system; -No. t Annual operating and maintenance costs; r - discount rate available for alternative investments. -No. t Annual power generation n-The entire lifecycle of a photovoltaic system.

[0031] Investment recovery period Calculation formula: - Investment recovery period; , - Cash inflows and outflows in year t; r - Discount rate available for alternative investments.

[0032] Photovoltaic system annual efficiency Calculation formula: -No. t Annual photovoltaic system efficiency; -No. t Annual power generation of the photovoltaic system; - Nominal power of the photovoltaic system; - The annual peak sunshine hours in year t; The final choice is the minimum LCOE. Improving photovoltaic system efficiency while achieving the shortest installation method. .

[0033] Depend on Figure 3 It can be seen that for installation methods 1-10, as the designated high-temperature zone gradually expands, the photovoltaic system... The trend is decreasing; specifically, from method 1 to method 6, The reduction is slow; as the designated high-temperature areas expand further, from method 7 to 10, The temperature decreases rapidly. This is mainly due to the increased temperature range of 22.5℃-25.5℃. Meanwhile, the economic efficiency also exhibits significant stage-specific characteristics depending on the laying method; for laying methods 1 to 6, the system cost per kilowatt-hour... LCOE With investment payback period All gradually decrease; however, as the area of ​​high-temperature zones avoided during photovoltaic module installation gradually expands, for methods 7-10, the levelized cost of electricity (LCOE) of the system... LCOE With investment payback period All showed a clear reversal, exhibiting an upward trend. For deployment methods 1 to 6, the system cost per kilowatt-hour... LCOE The investment payback period decreased continuously from 0.156 CNY / kWh to 0.143 CNY / kWh. The reduction from 3.14 years to 3.08 years reflects a trend of synergistic optimization between system economy and operational efficiency. This is mainly because avoiding high-temperature areas reduces the number of photovoltaic panels, thus lowering both the initial investment cost and the total life-cycle operation and maintenance cost of the photovoltaic system. On the other hand, the inhibitory effect of high-temperature environments on panel power generation efficiency is weakened, resulting in a slight increase in the system's annual operating efficiency from 77.68% to 77.75%. For method 7, with the further reduction in photovoltaic panels, the unit cost of panels tends to rise due to economies of scale in panel procurement. Simultaneously, the decrease in nominal power directly leads to a significant reduction in the system's annual power generation. The combined effect of these two factors means that the reduction in initial investment cost cannot offset the decline in revenue caused by the decrease in power generation, ultimately resulting in... LCOE With investment payback period All are rising, and as the designated high-temperature zones gradually expand, LCOE and Further deterioration. Therefore, it is determined. LCOE With investment payback period When the value is the lowest, Mode 6 corresponds to the optimal installation method for photovoltaic panels.

[0034] Mode 6's high and low temperature zone divisions are as follows: Figure 4 As shown, the red area represents the high-temperature zone (≥26.5℃, area 275.96㎡) without photovoltaic panels. The blue area represents the low-temperature zone (<26.5℃, area 10753㎡) with photovoltaic panels. A total of 5396 photovoltaic modules are installed in the low-temperature zone. The nominal power of the photovoltaic system is... The power is 2698kW. The 12-month average temperature for Mode 1 and Mode 6 (high and low temperature zones) photovoltaic panel installation methods is as follows: Figure 5 As shown, the average temperature of the rooftop photovoltaic panels exhibits a trend of first increasing and then decreasing with the seasons, reaching its highest value in August. Furthermore, for mode 6, the average temperature in the high-temperature zone is consistently higher than that in the low-temperature zone, with the largest temperature difference occurring in August. The peak average temperatures in the high and low-temperature zones reach 46.79℃ and 36.7℃ respectively, with the significant difference primarily due to the concentrated high-temperature zone. It is also evident that for mode 1, the average temperature of the high-temperature zone of the photovoltaic panels is slightly higher than that of the low-temperature zone in mode 6. This is mainly because the high-temperature zone area of ​​mode 6 accounts for only 2.57% of the total roof area of ​​the factory workshop, and the high-temperature zone is more concentrated. Therefore, the average temperature of the photovoltaic panels in mode 1 is slightly higher than that in the low-temperature zone of mode 6.

[0035] Monthly temperature loss in full-area photovoltaic panel installation mode 1 and improved installation mode 6 by avoiding high-temperature areas. k T For example Figure 6As shown, it should be noted that a negative temperature loss indicates that the photovoltaic panel temperature is lower than the temperature under standard test conditions (25℃), and the photovoltaic panel efficiency will be slightly improved. In both full-area installation (mode 1) and improved installation (mode 6) avoiding high-temperature areas, the photovoltaic panel temperature loss shows a trend of first increasing and then decreasing, reaching its peak in August. Meanwhile, the temperature loss in mode 1 is consistently slightly higher than that in mode 6, with the difference Δ... k T The temperature loss remained almost constant at 0.09%. For example, in August, the temperature loss for Mode 1 was 4.3%, while the optimized Mode 6 solution was 4.21%, a relative reduction of 2.1%. The annual average temperature loss for Mode 1 was 0.19%, while for Mode 6 it was 0.1%, a relative reduction of 47.37%. This is mainly because the monthly average temperature of the photovoltaic panels in Mode 1 was higher than that of the photovoltaic panels installed in the low-temperature zone of Mode 6. Meanwhile, although the heat loss base in the high-temperature zone was relatively low in winter, the improved solution directly reduced inefficient power generation units by excluding this area. In summer, due to the overall increase in temperature, the overall heat loss increased. In the fully-installed Mode 1 solution, some modules experienced accelerated efficiency degradation due to the high-temperature environment. In the optimized Mode 6 solution, although the low-temperature zone modules also faced temperature increases in summer, their heat loss increase was lower than that of the fully-installed solution because the initial installation excluded extreme high-temperature areas. Ultimately, this effectively controlled the annual temperature loss of the photovoltaic panels. Through this precise optimization strategy based on temperature thresholds for high and low temperature zones, efficiency degradation caused by heat loss can be reduced, thereby improving the utilization of renewable energy.

[0036] Figure 7 Efficiency of rooftop photovoltaic systems (Mode 1 and Mode 6) for each month of the first year. PR 0. For both Mode 1 and Mode 6, system efficiency showed a continuous decrease from January to May, with the lowest efficiency in May. The minimum system efficiency for Mode 1 and Mode 6 was 0.7542 and 0.7548, respectively. System efficiency improved significantly in June and July, then decreased rapidly in August. Subsequently, system efficiency fluctuated upwards from August to December, reaching its maximum in December. The maximum system efficiency for Mode 1 and Mode 6 was 0.7963 and 0.7970, respectively. This is mainly because temperature loss gradually increased from January to May, dust accumulation loss decreased significantly in June and July (see Fig. 5), and the increase in dust accumulation loss exceeded the decrease in temperature loss from August to December, showing an overall upward trend in system efficiency. Specifically, dust accumulation loss was 14.19% and temperature loss was 2.91% in June, with a system efficiency of 76.65%. In July, the dust accumulation loss was 11.46% and temperature loss was 2.89%, with a system efficiency of... PR The system efficiency was 79.1% (0), compared to June. PRThe efficiency was increased by 3.2%. This demonstrates that to improve the efficiency of the photovoltaic system on the factory roof... PR The level remains high at 0, requiring regular cleaning. In addition, from Mode 1 and Mode 6... PR A month-by-month comparison also reveals that Mode 6, by avoiding high-temperature areas when laying photovoltaics, reduces temperature loss, and therefore its system efficiency is better than Mode 1 for all 12 months.

[0037] To evaluate the overall effectiveness of the method of avoiding high-temperature areas, the annualized efficiency and economic indicators of the photovoltaic systems of Mode 1 and Mode 6 were compared. The results are as follows: Figure 8 As shown. Mode 6 has an initial investment cost. I 0. First-year operation and maintenance costs M 0 and standardized cost of electricity LCOE In all aspects, it is significantly superior to the continuous paving method (Mode 1), and the investment payback period is shorter. The initial investment cost of Mode 1... I The cost is 4.6566 million yuan, representing the first year's operating and maintenance costs. M 0 represents 0.3121 million yuan. Initial investment cost for Mode 6. I The cost was 4.5553 million yuan, a relative decrease of 2.18%, representing the first year's operation and maintenance costs. M The figure was 0.2442 million yuan, a relative decrease of 21.8%. Due to the optimized installation, the system's installed capacity decreased from 2758kW to 2698kW, resulting in a lower first-year photovoltaic power generation. E The efficiency was reduced by 2.08%, but avoiding high-temperature zones improved the system's average efficiency in the first year. PR The cost increased by 0.09%. Meanwhile, the annual maintenance cost over the lifecycle of Mode 6 deployment... M t Below Mode 1, the system levelized cost of electricity (LCOE) is lower. LCOE The indicator decreased from 0.156 CNY / kWh to 0.143 CNY / kWh, a relative decrease of 9.1%. Investment payback period. D invest The deployment time was reduced from 3.14 years to 3.08 years, a relative reduction of 1.91%. These results indicate that the economic indicators and system efficiency of the Mode6 system have been improved, and this deployment method is more economical throughout its entire life cycle.

[0038] In summary, this invention employs heat transfer technology influenced by internal heat sources. By assessing the annual thermal environment impact trend through the distribution characteristics of photovoltaic surface temperature field under extreme winter conditions, it overcomes the problem of power generation prediction deviation caused by insufficient consideration of factors in traditional photovoltaic temperature data. Through isotherm demarcation and optimization of gradient high and low temperature zone definition methods, the process of high-temperature zone boundary expansion is quantified into multi-level adjustable control, enabling a systematic assessment of the thermal environment impact. This allows for precise control of the degree of heat source interference avoidance and photovoltaic installation area, effectively avoiding efficiency degradation and safety hazards caused by local high temperatures. Furthermore, an evaluation model is constructed to select the optimal installation scheme by evaluating multiple installation methods based on temperature loss, levelized cost of electricity (LCOE), and investment payback period indicators, significantly improving the overall efficiency of rooftop photovoltaic systems.

[0039] The present invention has been described above with reference to the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution of the present invention, or the direct application of the inventive concept and technical solution of the present invention to other situations without modification, are all within the protection scope of the present invention.

Claims

1. A method for optimizing photovoltaic installation area based on temperature field distribution, characterized in that, Includes the following steps: Temperature field simulation was conducted to construct a thermal environment simulation model of the factory roof, obtain hourly temperature field distribution data of the factory roof, and select the temperature field under extreme low temperature conditions in winter as the benchmark for thermal performance characterization. Isotherm delineation and zoning optimization: Based on the temperature field distribution data of the factory roof, several continuous temperature threshold stepped closed-loop isotherms are generated, and the roof is divided into a high-temperature decay zone and a low-temperature safety zone through the closed-loop isotherms. A gradient avoidance strategy is generated based on the stepped closed-loop isotherm to generate multiple gradient deployment schemes. In this scheme, photovoltaic modules are not deployed in the high-temperature decay zone, and the deployment of photovoltaic modules is maximized in the low-temperature safety zone. The i-th scheme specifies that photovoltaic modules are not deployed in areas where the temperature is higher than the threshold of the i-th closed-loop isotherm, and photovoltaic modules are deployed in areas where the temperature is lower than the threshold of the i-th isotherm, where i is a natural number. A multi-objective evaluation system for levelized cost of electricity (LCOE) and dynamic payback period is established to evaluate the multiple graded laying schemes and select the optimal laying scheme based on the evaluation results. The temperature field simulation includes the following steps: Establish a 3D geometric model of the factory building of the same size, including internal heat sources and building structure; Set core boundary conditions, including heat source surface temperature parameters, wind speed boundary conditions for plant openings, solar radiation parameters, and convective heat transfer parameters inside the plant. Based on the aforementioned model and boundary conditions, the computational domain is meshed to generate a discrete mesh for numerical computation. Hourly photovoltaic temperature field distribution data of the roof is obtained through simulation calculation; In the isotherm demarcation and zoning optimization laying step, generating a number of continuous temperature threshold stepped closed-loop isotherms specifically involves: taking the highest point in the photovoltaic temperature field distribution as the center, expanding outward with a preset temperature change interval, and generating multiple closed-loop isotherms with successively decreasing temperature values. The preset temperature change interval is 1℃, and the number of closed-loop isotherms is 9. The number of gradient deployment schemes corresponds to the number of closed-loop isotherms; for each isotherm, a deployment scheme is generated, which requires that photovoltaic modules not be deployed in areas not lower than the temperature threshold of the isotherm, and that photovoltaic modules be deployed in areas lower than the temperature threshold of the isotherm. The criterion for selecting the optimal deployment scheme is to choose the scheme that simultaneously minimizes the levelized cost of electricity (LCOE) and the dynamic payback period.

2. The photovoltaic installation area optimization method based on temperature field distribution according to claim 1, characterized in that, The specific meshing is as follows: the minimum cell quality value is set to 0.0015 to 0.0016, all computational domains are subjected to corner refinement and the cell size scaling factor is set to 0.3 to 0.4, and the total number of cell grids is controlled between 500,000 and 550,000.

3. The photovoltaic installation area optimization method based on temperature field distribution according to claim 1, characterized in that, The area inside the closed-loop isotherm is the high-temperature attenuation zone, while the area outside the closed-loop isotherm is the low-temperature safety zone.

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