Quantitative evaluation method for influence of roof photovoltaic deployment on urban dry island effect

Abstract

The invention relates to a quantitative evaluation method for the influence of roof photovoltaic deployment on an urban dry island effect, and the method comprises the following steps: (1) obtaining the land utilization type data of a Chinese region, and then analyzing meteorological grid point data and urban meteorological observation station data; (2) replacing the land utilization type data in the mesoscale climate mode; then, according to the researched city, performing parameterization setting in a mesoscale climate mode; (3) selecting a physical parameterization scheme, and performing a numerical simulation control test; then verifying the simulation capability of the model; (4) setting sensitivity tests of numerical simulation of distributed photovoltaic deployment with different coverage rates, and outputting corresponding simulation results; and (5) performing time and space processing on the air specific humidity variables of the urban area and the suburban area according to results simulated and output by different sensitivity tests, and performing comparative analysis to quantitatively evaluate the influence of the urban scale roof photovoltaic deployment on the urban dry island effect. The method is efficient and accurate, and has relatively low cost and resource consumption.

Description

Technical Field

[0001] The present invention relates to the fields of urban environmental science, renewable energy technology and climate change research, and in particular to a quantitative evaluation method for the impact of rooftop photovoltaic deployment on urban dry island effect. Background Art

[0002] The urban dry island effect refers to the phenomenon that the specific humidity of the air near the ground in the city is lower than that in the surrounding suburbs due to human activities and urbanization in urban areas. This phenomenon has many impacts on the urban environment and residents' lives: for example, the urban dry island effect will change the climate pattern of the city and surrounding areas, which may lead to a decrease in precipitation in the urban area, thereby affecting the regional water cycle; lower humidity may affect the diffusion and deposition of pollutants in the air, sometimes leading to a decrease in air quality; the urban dry island effect may affect the health of urban residents, especially in hot summer; the urban dry island effect may have an impact on urban vegetation and ecosystems, etc.

[0003] Urban building roofs are regarded as an important place for the development of photovoltaic power generation. In 2021, the distributed photovoltaic power generation market, mainly based on building roof photovoltaics, accounted for more than 50% of the total photovoltaic market. It is expected that the installed capacity of distributed photovoltaics will increase 18 times in the next 30 years. The development of rooftop photovoltaics not only contributes to environmental protection and energy sustainability, but also brings economic benefits, improves energy security, and promotes the acceptance and use of green energy in society. It is an important means to achieve green development.

[0004] However, based on current research, there are few studies on the impact of urban-scale rooftop photovoltaic deployment on the urban dry island effect. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a quantitative evaluation method for the impact of rooftop photovoltaic deployment on urban dry island effect with low cost and resource consumption.

[0006] In order to solve the above problems, a quantitative evaluation method of the impact of rooftop photovoltaic deployment on urban dry island effect described in the present invention comprises the following steps:

[0007] ⑴ Obtaining regional land use type data, reanalysis meteorological grid data, and urban meteorological observation station data in China;

[0008] (2) Replace the land use type data in the mesoscale climate model; then collect the city parameters of the city under study and set them parameterized in the mesoscale climate model;

[0009] ⑶ Based on the parameterization settings, select the physical parameterization scheme and conduct a numerical simulation control test; then verify the simulation ability of the model based on the wind speed, temperature and relative humidity data at a height of 2 meters at the urban meteorological observation station;

[0010] (4) Set up sensitivity tests for numerical simulation of distributed photovoltaic deployment with different coverage ratios and output the corresponding simulation results;

[0011] ⑸ According to the simulation output results of different sensitivity tests, the air humidity variables in urban and suburban areas are processed in time and space. After comparative analysis, the impact of urban-scale rooftop photovoltaic deployment on the urban dry island effect is quantitatively evaluated.

[0012] The meteorological grid data reanalyzed in step (1) is NCEP-FNL global analysis data every 6 hours with a resolution of 1°×1°.

[0013] The method for replacing the land use type data in the step (2) is to first create a folder named modis_landuse_17class_500meter_China2020 under the geographic information, and move the binary file and index file generated in step (1) to this folder; then enter the geographic information folder of the weather research and forecast model, modify the geographic information grid table therein, find the corresponding land use data part, and add: landmask_water=China_2020:17; interp_option=China_2020:nearest_neighbor; rel_path=China_2020:modis_landuse_17class_500meter_China2020 / code; then set the code geog_data_res='China_2020' in the configuration file namelist.wps of the weather research and forecast model.

[0014] The urban parameters in step (2) include impervious surface coverage, building height, average building width, street width, air conditioning installation rate, proportion of floor area with air conditioning facilities installed, urban albedo, and urban thermal conductivity.

[0015] The physical parameterization scheme in step (3) refers to the land surface scheme, the urban canopy scheme, the microphysical scheme, the longwave radiation scheme, the shortwave radiation scheme and the boundary layer scheme; the land surface scheme is a unified Noah land surface model; the urban canopy scheme is a coupling scheme of building effect parameterization and building energy model; the microphysical scheme is a five-category cloud microphysical parameterization scheme; the longwave radiation scheme is a rapid radiation transfer model; the shortwave radiation scheme is a Dudhia scheme; and the boundary layer scheme is a Mellor-Yamada-Janjic scheme.

[0016] The sensitivity test of the numerical simulation of distributed photovoltaic deployment with different coverage ratios in step (4) is to set the rooftop photovoltaic coverage ratio parameters in the urban parameter table to 25%, 50%, 75% and 100% respectively, and then simulate each once.

[0017] Compared with the prior art, the present invention has the following advantages:

[0018] 1. The present invention uses the mesoscale climate model WRF4.5 coupled with the building energy model (BEP+BEM) and performs fine parameterization settings according to the study area, so as to achieve the purpose of quantitatively evaluating the impact of urban-scale distributed photovoltaic deployment on the urban dry island effect.

[0019] 2. The present invention can achieve quantitative evaluation of the impact of urban-scale distributed photovoltaic deployment on urban dry island effect at a low cost and resource consumption. The efficiency and accuracy of this method make it an indispensable tool in urban planning and energy policy making, which helps to promote sustainable urban development and address the challenges of global climate change.

[0020] 3. The present invention can not only evaluate the effects of existing rooftop photovoltaic systems, but also conduct preliminary evaluation of planned projects, thereby providing a scientific basis for urban planning and energy deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The specific implementation modes of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0022] Figure 1 A schematic diagram of the mode simulation capability provided by an embodiment of the present invention.

[0023] Figure 2 A schematic diagram of a pattern sensitivity test design provided in an embodiment of the present invention.

[0024] Figure 3 A schematic diagram of the spatial distribution of the impact of rooftop photovoltaics on the urban dry island effect provided by an embodiment of the present invention.

[0025] Figure 4A schematic diagram of daily changes in the impact of rooftop photovoltaics on the urban dry island effect provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] A quantitative evaluation method for the impact of rooftop photovoltaic deployment on urban dry island effect includes the following steps:

[0027] ⑴ Obtain regional land use type data, reanalysis meteorological grid data, and urban meteorological observation station data in China.

[0028] The land use types in WRF are mainly divided into MODIS and USGS. MODIS data is MODIS satellite remote sensing data from 2000, and USGS data is from 1992 to 1993. These data are relatively old. If the latest simulation is performed, there is a difference of 20 years, so they need to be replaced. The land use type data for the Chinese region used in the present invention is MCD12Q1v006 land cover data (2020, 500m horizontal resolution). The processed 2020 Chinese regional land use type data can be downloaded from Zendo (https: / / zenodo.org / records / 6591122#.ZDitz3ZBw2y).

[0029] The reanalysis meteorological grid data are NCEP-FNL (final) global analysis data every 6 hours with a resolution of 1°×1°. They can be downloaded free of charge from the official website (NOAA PSL NCEP / NCAR Reanalysis Data).

[0030] The data of urban meteorological observation stations come from the National Meteorological Information Center-China Meteorological Data Network (https: / / data.cma.cn / ).

[0031] (2) In the mesoscale climate model, replace the land use type data; then collect the urban parameters of the city under study and perform parameterization settings in the mesoscale climate model.

[0032] The method to replace the land use type data is to first create a folder named modis_landuse_17class_500meter_China2020 under geographic information (geog), and move the binary file and index file generated in step ⑴ to this folder; then enter the geographic information (geogrid) folder of the Weather Research and Forecasting Model (WPS), modify the geographic information grid table (GEOGRID.TBL) therein, find the corresponding land use data (LANDUSEF) part, and add: landmask_water = China_2020:17; interp_option = China_2020:nearest_neighbor; rel_path = China_2020:modis_landuse_17class_500meter_China2020 / code; then set the code geog_data_res = 'China_2020' in the configuration file namelist.wps of the Weather Research and Forecasting Model (WPS) to call the new land use type data.

[0033] Urban parameters include impervious surface coverage, building height, average building width, street width, air conditioning installation rate, proportion of floor area with air conditioning facilities, urban albedo, and urban thermal conductivity, which are set according to the study city under URBPARM.TBL of WRF4.5.

[0034] ⑶ Based on the parameterization settings, the physical parameterization scheme is selected and a numerical simulation control test is carried out; then the simulation capability of the model is verified based on the wind speed, temperature and relative humidity data at a height of 2 meters at the urban meteorological observation station.

[0035] The physical parameterization schemes refer to the land surface scheme, urban canopy scheme, microphysics scheme, longwave radiation scheme, shortwave radiation scheme and boundary layer scheme. Among them: the land surface scheme is the unified Noah land surface model; the urban canopy scheme is the building effect parameterization and building energy model coupling scheme (BEP+BEM); the microphysics scheme is the five-type cloud microphysics parameterization scheme (WSM5); the longwave radiation scheme is the rapid radiative transfer model (RRTM); the shortwave radiation scheme is the Dudhia scheme; and the boundary layer scheme is the Mellor-Yamada-Janjic scheme.

[0036] The rooftop photovoltaic panel model was previously developed by Masson et al. (2014), and Zonato et al. (2021) developed the latest parameterization scheme for rooftop photovoltaic panels in BEP-BEM. This scheme assumes that the photovoltaic array is parallel and separated from the roof to avoid unnecessary complexity and retain the details of the individual buildings. The temperature on the rooftop solar panel and its associated heat flow are determined by the derivative equation:

[0037]

[0038] Where: C module is the equivalent heat capacity per unit area; T pv is the temperature of the photovoltaic panel; t is the time; is the net shortwave radiation received by the photovoltaic panel to the upward surface, α PV is the albedo of the photovoltaic panel; is the incident long-wave radiation on the surface of the photovoltaic panel, where is the emissivity of the glass surface; It is the upward long-wave radiation emitted by the photovoltaic panels; It is the long-wave radiation exchanged between the lower surface of the photovoltaic panel cell and the upper surface of the roof, and VF is the viewing angle factor between the lower surface of the photovoltaic panel and the roof; is the energy production of the photovoltaic panels, η PV is the conversion efficiency of photovoltaic panels; SH ↑ -SH ↓ =(h ↑ +h ↓ )(T PV -T air ) is the sensible heat flux above and below the PV panel; The formula depends on empirical fitting, using the EnergyPlus model; is the scattered short-wave and long-wave isotropic radiation reaching the lower surface of the PV, is the incident long-wave radiation, SW Diff Scattered shortwave radiation.

[0039] The simulation capability of the validation model is mainly determined by calculating the root mean square error (RMSE), correlation coefficient (R) and mean bias error (MB) between the observed and simulated values.

[0040] (4) Set up a sensitivity test for the numerical simulation of distributed photovoltaic deployment with different coverage ratios. The test is to set the rooftop photovoltaic coverage ratio (PV_FRAC_ROOF) parameter in the urban parameter table (URBPARM.TBL) to 25%, 50%, 75% and 100%, respectively, and simulate each once.

[0041] ⑸ According to the results of the simulation output of different sensitivity tests, the air humidity variables in urban and suburban areas are processed in time and space, where: the time processing is to separate the day and night for time averaging to evaluate the spatial difference of the impact of urban-scale distributed photovoltaic deployment on the urban dry island effect; the spatial processing is to average the air humidity variables in the city and suburbs hourly to evaluate the difference in the daily cycle of the impact of urban-scale distributed photovoltaic deployment on the urban dry island effect. After comparative analysis, the impact of urban-scale rooftop photovoltaic deployment on the urban dry island effect is quantitatively evaluated.

[0042] The selection of urban areas mainly considers the areas within the administrative boundary of the city with urbanIndex=13. The selection of suburban areas is the upwind direction of the city in the study period, and the urbanIndex excludes other natural underlying surface types of cities, water bodies and wetlands and the areas with an altitude difference of less than 50 meters. If the altitude difference exceeds 50 meters, the air specific humidity needs to be corrected for altitude. In addition, the number of grid points in the suburbs should be equal to the number of grid points in the city.

[0043] Embodiment Lanzhou is selected as the research object, and the impact of urban-scale rooftop photovoltaic deployment on the dry island effect in Lanzhou is quantitatively evaluated.

[0044] The Chinese regional land use type data MCD12Q1v006, the reanalysis meteorological grid data 6-hourly NCEP-FNL Analyses data (1°×1°) and the urban meteorological observation station data (station numbers 52889, W3384, W3471, W3473 and W3476) have been downloaded; in the mesoscale climate model, the land use type data are replaced. Based on the city of Lanzhou, the city's impervious surface coverage, building height, average building width, street width, air conditioning installation rate, floor area ratio with air conditioning facilities installed, urban albedo, urban thermal conductivity, etc. are collected, and parameter settings are performed under URBPARM.TBL of the mesoscale climate model WRF4.5. The specific parameters are shown in Table 1.

[0045] Table 1 Urban morphology and thermal parameters

[0046]

[0047] Based on the above parameterization settings, a physical parameterization scheme is selected to carry out the control test of numerical simulation. The specific mode settings are shown in Table 2.

[0048] Table 2 Mode setting scheme

[0049]

[0050] The simulation capability of the model is verified based on the wind speed, temperature and relative humidity data at a height of 2 meters at the urban meteorological observation station, such as Figure 1 As shown, according to the results of the root mean square error (RMSE), correlation coefficient (R) and mean bias error (MB) between the observed and simulated values, the model has a good simulation ability;

[0051] Set up sensitivity tests for numerical simulations (such as Figure 2 As shown in the figure, it mainly considers the scenarios of distributed photovoltaic deployment with different coverage ratios and outputs the corresponding simulation results. The sensitivity test of rooftop photovoltaic deployment with different coverage ratios is to set the PV_FRAC_ROOF parameter in URBPARM.TBL to 25%, 50%, 75% and 100% respectively, and then simulate them once.

[0052] (5) According to the simulation output results of different sensitivity tests, the air humidity variables in cities and suburbs are processed in time and space, and comparative analysis is performed to quantitatively evaluate the impact of urban-scale rooftop photovoltaic deployment on the urban dry island effect. Figure 3 The figure shows the difference between the rooftop PV deployment (100%) and the control experiment (no PV on the roof) during the daytime under the conditions of high temperature heat waves in summer, which is the specific value of the impact on the urban dry island effect. It shows that the deployment of rooftop PV at the urban scale will increase the specific humidity of the air in the main urban area of ​​Lanzhou and reduce the dry island effect. The specific value varies with space and ranges from 0.1g / kg to 0.4g / kg. That is, it shows that the deployment of rooftop PV has a positive effect on alleviating the urban dry island effect in Lanzhou. Figure 4 This is the difference in daily cycle between rooftop PV deployment (25%, 50%, 75% and 100%) and the control experiment (no PV on the roof) under summer high temperature heat wave weather conditions. The results show that the dry island effect (UDII) is negative in the daily cycle, that is, the dry island effect is reduced, and the effect during the day is more significant than at night, with the highest UDII difference reaching -0.91g / kg.

[0053] The technical solution provided by the present invention is described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, the present invention can also be improved and modified in a number of ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A quantitative evaluation method for the impact of rooftop photovoltaic deployment on urban dry island effect, comprising the following steps: ⑴ Obtaining regional land use type data, reanalysis meteorological grid data, and urban meteorological observation station data in China; (2) Replace land use type data in mesoscale climate models; Then, according to the city under study, the city parameters are collected and parameterized in the mesoscale climate model; ⑶ Based on the parameterization settings, select the physical parameterization scheme and conduct the control test of numerical simulation; Then, the simulation capability of the model was verified based on the wind speed, air temperature and relative humidity data at a height of 2 meters from the urban meteorological observation station; (4) Set up sensitivity tests for numerical simulation of distributed photovoltaic deployment with different coverage ratios and output the corresponding simulation results; ⑸ According to the simulation output results of different sensitivity tests, the air humidity variables in urban and suburban areas are processed in time and space. After comparative analysis, the impact of urban-scale rooftop photovoltaic deployment on the urban dry island effect is quantitatively evaluated.

2. A quantitative evaluation method for the impact of rooftop photovoltaic deployment on urban dry island effect as claimed in claim 1, characterized in that: The meteorological grid data reanalyzed in step (1) is NCEP-FNL global analysis data every 6 hours with a resolution of 1°×1°.

3. A quantitative evaluation method for the impact of rooftop photovoltaic deployment on urban dry island effect as claimed in claim 1, characterized in that: The method for replacing the land use type data in the step (2) is to first create a folder named modis_landuse_17class_500meter_China2020 under the geographic information, and move the binary file and index file generated in step (1) to this folder; then enter the geographic information folder of the weather research and forecast model, modify the geographic information grid table therein, find the corresponding land use data part, and add: landmask_water=China_2020:17; interp_option=China_2020:nearest_neighbor; rel_path=China_2020:modis_landuse_17class_500meter_China2020 / code; then set the code geog_data_res=′China_2020′ in the configuration file namelist.wps of the weather research and forecast model.

4. A quantitative evaluation method for the impact of rooftop photovoltaic deployment on urban dry island effect as claimed in claim 1, characterized in that: The urban parameters in step (2) include impervious surface coverage, building height, average building width, street width, air conditioning installation rate, proportion of floor area with air conditioning facilities installed, urban albedo, and urban thermal conductivity.

5. A quantitative evaluation method for the impact of rooftop photovoltaic deployment on urban dry island effect as claimed in claim 1, characterized in that: The physical parameterization scheme in step (3) refers to the land surface scheme, the urban canopy scheme, the microphysical scheme, the longwave radiation scheme, the shortwave radiation scheme and the boundary layer scheme; the land surface scheme is a unified Noah land surface model; the urban canopy scheme is a coupling scheme of building effect parameterization and building energy model; the microphysical scheme is a five-category cloud microphysical parameterization scheme; the longwave radiation scheme is a rapid radiation transfer model; the shortwave radiation scheme is a Dudhia scheme; and the boundary layer scheme is a Mellor-Yamada-Janjic scheme.

6. A quantitative evaluation method for the impact of rooftop photovoltaic deployment on urban dry island effect as claimed in claim 1, characterized in that: The sensitivity test of the numerical simulation of distributed photovoltaic deployment with different coverage ratios in step (4) is to set the rooftop photovoltaic coverage ratio parameters in the urban parameter table to 25%, 50%, 75% and 100% respectively, and then simulate each once.

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