Building cell energy consumption analysis method considering microclimate

Abstract

The invention discloses a building cell energy consumption analysis method considering microclimate, which analyzes the influence of microclimate on each source load resource model in a building cell by performing modeling research on the building cell comprehensively considering distributed photovoltaic, air conditioners and electric vehicles, and realizes the calculation of the building cell energy consumption and the capability of participating in power demand response. The method provided by the invention can calculate the power energy consumption of the building cell under the influence of microclimate, aims to reveal how the microclimate condition affects the energy consumption behavior and response potential of the building cell, can provide a scientific basis for urban planners and architectural designers, promotes the deep understanding of the building energy consumption under the urban microclimate condition, and improves the economic benefits of the urban planners and architectural designers. And a new view angle and a new method are provided for realizing sustainable utilization of building energy.

Description

Technical Field

[0001] The invention belongs to the technical field of building energy consumption analysis, and specifically is a building cell energy consumption analysis method considering micro-meteorology. Background Art

[0002] Under the current background of accelerated urbanization and climate change, as an important part of urban infrastructure, the energy consumption of buildings is increasingly becoming a key challenge to environmental sustainable development. Current research on building energy consumption analysis has not fully considered the impact of micrometeorology on the comprehensive energy consumption of each power generation or power consumption unit in the building, resulting in poor accuracy in energy consumption calculation of building cells. It is not feasible to propose improvement measures for buildings based on the results of building cell energy consumption analysis. The present invention aims to analyze the concept of micrometeorology and its key factors, further explore the mechanism by which micrometeorological conditions affect building energy consumption and demand response potential, and conduct in-depth research on the quantitative impact of microclimate conditions on building energy consumption. It can provide a scientific basis for urban planning and architectural design, and provide a new perspective and method for accurately calculating building energy consumption and reducing building energy consumption, which is of great significance for achieving sustainable urban development. Summary of the invention

[0003] To solve the above problems, the present invention discloses a building cell energy consumption analysis method taking micrometeorology into consideration, which can formulate a more climate-adaptive building design scheme, effectively reduce energy consumption and improve the overall sustainability of the city.

[0004] The building cell energy consumption analysis method considering microclimate includes the following steps:

[0005] Step 1: Establish a building cellular distributed photovoltaic model affected by micro-meteorology, including:

[0006] Step 1.1, obtaining distributed photovoltaic monitoring data;

[0007] Step 1.2, construct a distributed photovoltaic output model;

[0008] Step 1.3, construct a building cellular distributed photovoltaic model under the influence of micro-meteorology;

[0009] Step 2: Establish a building cellular air conditioning model affected by micro-meteorology, including:

[0010] Step 2.1, obtaining air conditioning power consumption monitoring data;

[0011] Step 2.2, constructing an air conditioning power model;

[0012] Step 2.3, construct a building cellular air conditioning model under the influence of micro-meteorology;

[0013] Step 3: Establish a building cellular electric vehicle model affected by micrometeorology, including:

[0014] Step 3.1, obtain charging pile monitoring data;

[0015] Step 3.2, constructing an electric vehicle charging and discharging model;

[0016] Step 3.3, construct a building cellular electric vehicle model under the influence of micrometeorology;

[0017] Step 4, establish a building cell energy consumption analysis model affected by micro-meteorology;

[0018] Step 5: Establish a building cell electricity demand response potential analysis model affected by micrometeorology.

[0019] Furthermore, in step 1.1, the distributed photovoltaic monitoring data is obtained including U a (t) and T a (t), where U a (t) represents the solar irradiance at time t, T a (t) represents the outside temperature at time t;

[0020] According to claim 1, the method for establishing a distributed photovoltaic model of building cells affected by micrometeorology is characterized in that: in the step 1.2, a distributed photovoltaic output model is constructed, which mainly considers the influence of solar irradiance and external temperature on photovoltaic output, and the specific calculation formula is as follows:

[0021]

[0022] Among them, P PV (t) represents the output power of traditional photovoltaic power generation at time t, in kW; U PV-STC It is the solar irradiance index under standard conditions for photovoltaic power generation, and the unit is W / m 2 ;P PV-N is the rated output power of the photovoltaic system, in kW; α PV-T Indicates the coefficient of the photovoltaic panel affected by temperature, generally -0.4%; T PV-STC It indicates the outside temperature under standard conditions of photovoltaic power generation, and the unit is ℃.

[0023] Furthermore, in step 1.3, a building cellular distributed photovoltaic model under the influence of micrometeorology is constructed. This model is a complex model that needs to consider multi-dimensional meteorological factors. When considering the impact of micrometeorology on photovoltaic power generation, the impact of five micrometeorological factors, namely temperature, humidity, irradiance, wind speed and dust accumulation, on the photovoltaic output model and the key parameter correction method are analyzed. The specific calculation formula is as follows:

[0024]

[0025] in, It represents the output power of the photovoltaic panel at time t after correction according to micro-meteorological factors, in kW; It represents the coefficient after comprehensive correction of the actual solar irradiance at time t and the solar irradiance under standard conditions; It represents the coefficient after comprehensive correction of the actual temperature and the temperature effect under standard conditions at time t; It represents the PV rated output corrected after considering the influence of dust accumulation at time t, in kW. The specific calculation method affected by micro-meteorology is as follows:

[0026]

[0027] Among them, ω PV-U is the solar radiation micrometeorological correction coefficient; is the solar irradiance index under the corrected standard conditions; ω PV-T is the temperature micrometeorological correction coefficient; It is the external temperature index under the corrected standard conditions; D a (t) is the actual dust accumulation degree at time t, It is the dust accumulation degree under standard conditions after micro-meteorological correction, and the unit is g / m 2 ;

[0028] Furthermore, in step 2.1, the air conditioner power consumption monitoring data is obtained, including T a-in (t-1) and T a-in (t), where T a-in (t-1) is the measured external temperature at time t-1, T a-in (t) is the actual measured temperature of the room where the air conditioner is located, and the unit is ℃.

[0029] Furthermore, in step 2.2, an air conditioning power model is constructed, which takes into account the impact of the outside temperature on the air conditioning output and the impact of environmental factors on human energy consumption behavior. The specific calculation formula is as follows:

[0030]

[0031] Among them, L HVAC (t) represents the quantitative coefficient of the influence of temperature on the energy consumption behavior of traditional air conditioners; β HVAC-L Indicates the temperature impact index under HVAC standard conditions; T HVAC-STC is the average temperature under HVAC standard conditions, in °C. The quantization coefficient is mapped to the power prediction model of the air conditioner, and the formula is as follows:

[0032]

[0033] Among them, P HVAC(t) represents the predicted air conditioning power at time t, in kW; α HVAC-P It indicates the heat transfer coefficient between the air conditioner output power and heat, in units of ℃ / kW.

[0034] Furthermore, in step 2.3, a building cellular air conditioning model under the influence of micrometeorology is constructed. This model is a complex model that needs to consider multi-dimensional meteorological factors. When considering the influence of micrometeorology on the air conditioning output power, the influence of three micrometeorological factors, temperature, humidity and atmospheric pressure, on the air conditioning output power model and the key parameter correction method are analyzed. The specific calculation formula is as follows:

[0035]

[0036] in, It represents the output power of the air conditioner at time t after correction based on micro-meteorological factors, in kW; It represents the coefficient of comprehensive correction of the air conditioning energy efficiency heat exchange coefficient under the influence of micro-meteorology at time t; It represents the coefficient of the indoor and outdoor temperature difference corrected by micro-meteorological factors at time t. The specific calculation method affected by micro-meteorological factors is as follows:

[0037]

[0038] Among them, ω HVAC-U (t) is the air conditioning energy efficiency heat exchange micro-meteorological correction coefficient; is the quantitative coefficient of the influence of temperature on the air conditioning energy consumption behavior after micro-meteorological correction; ω HVAC-L (t) is the micro-meteorological correction coefficient for the air conditioning energy consumption behavior affected by temperature.

[0039] Furthermore, in step 3.1, the charging pile monitoring data is obtained, including SOC EV (t) and SOC EV (0), where SOC EV (t) and SOC EV (0) respectively represent the battery capacity of the electric vehicle monitored by the charging pile at time t and the start time of charging completion.

[0040] Furthermore, in step 3.2, a distributed photovoltaic output model is constructed, which mainly considers the impact of solar irradiance and external temperature on photovoltaic output. The specific calculation formula is as follows:

[0041]

[0042] Among them, α EV Indicates the power consumption of electric vehicles per kilometer, in kWh / km; J EV,iis the mileage of the electric vehicle in the i-th segment, in km; Q EV-SOH Indicates the basic capacity of the power battery in electric vehicles; n EV (t) is the number of trips that the electric vehicle travels from the time the electric vehicle is charged to time t; P EV (t) is the prediction result of electric vehicle charging power, in kW; P EV-STC represents the standard power of electric vehicle charging power in the traditional model, in kW; f EV (x EV ) is the probability distribution function of electric vehicle charging, including Gaussian distribution and Poisson distribution probability modes.

[0043] Furthermore, in step 3.3, a building cellular electric vehicle model under the influence of micrometeorology is constructed. This model is a complex model that needs to consider multi-dimensional meteorological factors. When considering the impact of micrometeorology on the charging power of electric vehicles, the impact of micrometeorological factors such as temperature and rainfall on the electric vehicle model and the key parameter correction method are analyzed. The specific calculation formula is as follows:

[0044]

[0045] in, is the battery capacity of the electric vehicle at time t after micro-meteorological correction; ω EV-α The micro-meteorological correction coefficient for the power consumption of electric vehicles per kilometer traveled; The power battery capacity of electric vehicles corrected for the influence of micro-meteorology; is the prediction result of the electric vehicle charging power at time t after micro-meteorological correction, in kW; ω EV-P Represents the micro-meteorological correction factor for electric vehicle charging power.

[0046] Furthermore, in step 4, the energy consumption of the building cells needs to be calculated by comprehensively considering the photovoltaic power generation, air conditioning and electric vehicle charging power models affected by micro-meteorology. The model will consider factors such as the physical characteristics of the building, usage, external climate conditions and efficiency of the energy system. The specific calculation formula is as follows:

[0047]

[0048] in, It represents the power value of building cell energy consumption analysis under the influence of urban microclimate, in kW.

[0049] Furthermore, in step 5, the demand response potential of the building cell refers to the response of the building to the power grid by changing the original electricity consumption behavior. The demand response potential refers to the ability of the building cell to participate in load regulation of power demand response, including both increasing and reducing loads. Considering the time characteristics of demand response, the specific calculation formula is as follows:

[0050]

[0051] in, It is the calculation result of the demand response potential of the building cell under the influence of urban micrometeorology at time t, in kWh.

[0052] Beneficial effects of the present invention:

[0053] The present invention models the energy consumption of building cells considering micrometeorology, studies the influencing factors and influencing paths of micrometeorology on the energy consumption of building cells, and constructs a correction model for the influence of micrometeorology on photovoltaics, air conditioning and electric vehicles in building cells in a targeted manner. An energy consumption analysis model of building cells affected by micrometeorology is constructed, and accurate quantitative calculation of the energy consumption of building cells is achieved. Based on the energy consumption calculation results, valuable data and suggestions can be provided for urban planners and architectural designers, so as to formulate architectural design plans with higher climate adaptability, effectively reduce energy consumption and improve the overall sustainability of the city. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of the building cell energy consumption analysis method considering micro-climate;

[0055] Figure 2 It is the photovoltaic output curve of the building cell under the influence of micro-meteorology;

[0056] Figure 3 It is the electricity consumption curve of building cell air conditioning under the influence of micro-meteorology;

[0057] Figure 4 It is the charging and discharging curve of building cell electric vehicle under the influence of micro-meteorology;

[0058] Figure 5 It is a diagram of building cell energy consumption and demand response potential under the influence of micrometeorology. DETAILED DESCRIPTION

[0059] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. It should be noted that the words "front", "rear", "left", "right", "upper" and "lower" used in the following description refer to directions in the accompanying drawings, and the words "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0060] like Figure 1 As shown, the building cell energy consumption analysis method considering microclimate in this embodiment includes the following steps:

[0061] Step 1: Establish a building cellular distributed photovoltaic model affected by micro-meteorology, including:

[0062] Step 1.1, obtaining distributed photovoltaic monitoring data;

[0063] In step 1.1, obtaining distributed photovoltaic monitoring data includes U a (t) and T a (t), where U a (t) represents the solar irradiance at time t, T a (t) represents the outside temperature at time t;

[0064] In step 1.2, a distributed photovoltaic output model is constructed, and the specific calculation formula is as follows:

[0065]

[0066] Among them, P PV (t) represents the output power of traditional photovoltaic power generation at time t, in kW; U PV-STC It is the solar irradiance index under standard conditions for photovoltaic power generation, and the unit is W / m 2 ;P PV-N is the rated output power of the photovoltaic system, in kW; α PV-T Indicates the coefficient of the photovoltaic panel affected by temperature, generally -0.4%; T PV-STC It indicates the outside temperature under standard conditions of photovoltaic power generation, and the unit is ℃.

[0067] Step 1.2, construct a distributed photovoltaic output model;

[0068] Step 1.3, construct a building cellular distributed photovoltaic model under the influence of micro-meteorology;

[0069] In step 1.3, a building cellular distributed photovoltaic model under the influence of micro-meteorology is constructed, and the influence of five micro-meteorological factors, namely temperature, humidity, irradiance, wind speed and dust accumulation, on the photovoltaic output model and the key parameter correction method are analyzed. The specific calculation formula is as follows:

[0070]

[0071] in, It represents the output power of the photovoltaic panel at time t after correction according to micro-meteorological factors, in kW; It represents the coefficient after comprehensive correction of the actual solar irradiance at time t and the solar irradiance under standard conditions; It represents the coefficient after comprehensive correction of the actual temperature and the temperature effect under standard conditions at time t; It represents the PV rated output corrected after considering the influence of dust accumulation at time t, in kW. The specific calculation method affected by micro-meteorology is as follows:

[0072]

[0073] Among them, ω PV-U is the solar radiation micrometeorological correction coefficient; is the solar irradiance index under the corrected standard conditions; ω PV-T is the temperature micrometeorological correction coefficient; It is the external temperature index under the corrected standard conditions; D a (t) is the actual dust accumulation degree at time t, It is the dust accumulation degree under standard conditions after micro-meteorological correction, and the unit is g / m 2 .

[0074] Step 2: Establish a building cellular air conditioning model affected by micro-meteorology, including:

[0075] Step 2.1, obtaining air conditioning power consumption monitoring data;

[0076] In step 2.1, the air conditioner power consumption monitoring data is obtained, including T a-in (t-1) and T a-in (t), where T a-in (t-1) is the measured external temperature at time t-1, T a-in (t) is the measured temperature in the room where the air conditioner is located, in °C;

[0077] Step 2.2, constructing an air conditioning power model;

[0078] In step 2.2, an air conditioning power model is constructed. The model takes into account the impact of the outside temperature on the air conditioning output and the impact of environmental factors on human energy consumption behavior. The specific calculation formula is as follows:

[0079]

[0080] Among them, L HVAC (t) represents the quantitative coefficient of the influence of temperature on the energy consumption behavior of traditional air conditioners; β HVAC-L Indicates the temperature impact index under HVAC standard conditions; T HVAC-STC is the average temperature under HVAC standard conditions, in °C; the quantization coefficient is mapped to the power prediction model of the air conditioner, and the formula is as follows:

[0081]

[0082] Among them, P HVAC (t) represents the predicted air conditioning power at time t, in kW; α HVAC-P It indicates the heat transfer coefficient between the air conditioner output power and heat, in units of ℃ / kW.

[0083] Step 2.3, construct a building cellular air conditioning model under the influence of micro-meteorology;

[0084] In step 2.3, a building cellular air conditioning model under the influence of micrometeorology is constructed. This model is a complex model that needs to consider multi-dimensional meteorological factors. When considering the influence of micrometeorology on the air conditioning output power, the influence of three micrometeorological factors, temperature, humidity and atmospheric pressure, on the air conditioning output power model and the key parameter correction method are analyzed. The specific calculation formula is as follows:

[0085]

[0086] in, It represents the output power of the air conditioner at time t after correction based on micro-meteorological factors, in kW; It represents the coefficient of comprehensive correction of the air conditioning energy efficiency heat exchange coefficient under the influence of micro-meteorology at time t; It represents the coefficient of the indoor and outdoor temperature difference corrected by micro-meteorological factors at time t. The specific calculation method affected by micro-meteorological factors is as follows:

[0087]

[0088] Among them, ω HVAC-U (t) is the air conditioning energy efficiency heat exchange micro-meteorological correction coefficient; is the quantitative coefficient of the influence of temperature on the air conditioning energy consumption behavior after micro-meteorological correction; ω HVAC-L (t) is the micro-meteorological correction coefficient of air conditioning energy consumption behavior affected by temperature.

[0089] Step 3: Establish a building cellular electric vehicle model affected by micrometeorology, including:

[0090] Step 3.1, obtain charging pile monitoring data;

[0091] In step 3.1, the charging pile monitoring data is obtained, including SOC EV (t) and SOC EV (0), where SOC EV (t) and SOC EV (0) respectively represent the battery capacity of the electric vehicle monitored by the charging pile at time t and the start time of charging completion.

[0092] Step 3.2, constructing an electric vehicle charging and discharging model;

[0093] In step 3.2, a distributed photovoltaic output model is constructed. The model mainly considers the impact of solar irradiance and external temperature on photovoltaic output. The specific calculation formula is as follows:

[0094]

[0095] Among them, α EV Indicates the power consumption of electric vehicles per kilometer, in kWh / km; J EV,i is the mileage of the electric vehicle in the i-th segment, in km; Q EV-SOH Indicates the basic capacity of the power battery in electric vehicles; n EV (t) is the number of trips that the electric vehicle travels from the time the electric vehicle is charged to time t; P EV (t) is the prediction result of electric vehicle charging power, in kW; P EV-STC represents the standard power of electric vehicle charging power in the traditional model, in kW; f EV (x EV ) is the probability distribution function of electric vehicle charging, including Gaussian distribution and Poisson distribution probability modes.

[0096] Step 3.3, construct a building cellular electric vehicle model under the influence of micrometeorology;

[0097] In step 3.3, a building cellular electric vehicle model under the influence of micrometeorology is constructed. This model is a complex model that needs to consider multi-dimensional meteorological factors. When considering the impact of micrometeorology on the charging power of electric vehicles, the impact of micrometeorological factors such as temperature and rainfall on the electric vehicle model and the key parameter correction method are analyzed. The specific calculation formula is as follows:

[0098]

[0099] in, is the battery capacity of the electric vehicle at time t after micro-meteorological correction; ω EV-α The micro-meteorological correction coefficient for the power consumption of electric vehicles per kilometer traveled; The power battery capacity of electric vehicles corrected for the influence of micro-meteorology; is the prediction result of the electric vehicle charging power at time t after micro-meteorological correction, in kW; ω EV-P Represents the micro-meteorological correction factor for electric vehicle charging power.

[0100] Step 4, establish a building cell energy consumption analysis model affected by micro-meteorology;

[0101] In step 4, the energy consumption of building cells is calculated by comprehensively considering the photovoltaic power generation, air conditioning and electric vehicle charging power models affected by micro-meteorology. The model will consider factors such as the physical characteristics of the building, usage, external climate conditions and efficiency of the energy system. The specific calculation formula is as follows:

[0102]

[0103] in, It represents the power value of building cell energy consumption analysis under the influence of urban microclimate, in kW.

[0104] Step 5: Establish a building cell electricity demand response potential analysis model affected by micrometeorology.

[0105] In step 5, the demand response potential of the building cell refers to the response of the building to the power grid by changing the original electricity consumption behavior. The demand response potential refers to the ability of the building cell to participate in load regulation of power demand response, including increasing and reducing loads. Considering the time characteristics of demand response, the specific calculation formula is as follows:

[0106]

[0107] in, It is the calculation result of the demand response potential of the building cell under the influence of urban micrometeorology at time t, in kWh.

[0108] Among them Figure 2 As shown in the figure, the photovoltaic output power curve and the cumulative photovoltaic power generation curve of the building cell under four different micro-meteorological scenarios. It can be seen that the photovoltaic output power and cumulative power of scenario 1 are the smallest, while the photovoltaic output power and cumulative power of scenario 3 are the largest. This is determined by the different micro-meteorological influencing factors of irradiance, temperature and dust accumulation.

[0109] like Figure 3 As shown in the figure, the air conditioning power consumption curve and the outside temperature curve of the building cell under three different micro-meteorological scenarios. It can be seen that the air conditioning power consumption in scenario 1 is the largest and the outside temperature is the highest, while the air conditioning power consumption in scenario 3 is the smallest and the outside temperature is the smallest.

[0110] like Figure 4As shown in the figure, the charging and discharging curves of building cell electric vehicles at different times under three different micro-meteorological scenarios. It can be seen that the charging of electric vehicles in scenario 1 is mainly concentrated between 19:00 and 22:00 in the evening and the charging and discharging amount is relatively large. The charging of electric vehicles in scenario 2 is mainly concentrated between 17:00 and 19:00 in the evening and the charging and discharging amount is relatively small. The charging of electric vehicles in scenario 3 is mainly concentrated between 19:00 and 22:00 in the evening and the charging and discharging amount is also relatively small. This is determined by the different micro-meteorological factors of temperature and rainfall.

[0111] like Figure 5 As shown in the figure, the energy consumption and demand response potential curves of building cells under three different micro-meteorological influence scenarios are shown. The impact of micro-meteorological conditions on distributed photovoltaics, air conditioning and electric vehicles in building cells is comprehensively considered, reflecting that the impact of different micro-meteorological conditions on the demand response potential of building cells ranges from 12 to 28 kW.

[0112] The technical means disclosed in the scheme of the present invention are not limited to the technical means disclosed in the above-mentioned implementation mode, but also include technical schemes composed of any combination of the above technical features.

Claims

1. A building cell energy consumption analysis method considering microclimate, characterized by: The steps include: Step 1: Establish a building cellular distributed photovoltaic model affected by micro-meteorology, including: Step 1.1, obtaining distributed photovoltaic monitoring data; Step 1.2, construct a distributed photovoltaic output model; Step 1.3, construct a building cellular distributed photovoltaic model under the influence of micro-meteorology; Step 2: Establish a building cellular air conditioning model affected by micro-meteorology, including: Step 2.1, obtaining air conditioning power consumption monitoring data; Step 2.2, constructing an air conditioning power model; Step 2.3, construct a building cellular air conditioning model under the influence of micro-meteorology; Step 3: Establish a building cellular electric vehicle model affected by micrometeorology, including: Step 3.1, obtain charging pile monitoring data; Step 3.2, constructing an electric vehicle charging and discharging model; Step 3.3, construct a building cellular electric vehicle model under the influence of micrometeorology; Step 4, establish a building cell energy consumption analysis model affected by micro-meteorology; Step 5: Establish a building cell electricity demand response potential analysis model affected by micrometeorology.

2. The building cell energy consumption analysis method considering microclimate according to claim 1 is characterized by: In step 1.1, obtaining distributed photovoltaic monitoring data includes U a (t) and T a (t), where U a (t) represents the solar irradiance at time t, T a (t) represents the outside temperature at time t; In step 1.2, a distributed photovoltaic output model is constructed, and the specific calculation formula is as follows: Among them, P PV (t) represents the output power of traditional photovoltaic power generation at time t, in kW; U PV-STC It is the solar irradiance index under standard conditions for photovoltaic power generation, and the unit is W / m 2 ;P PV-N is the rated output power of the photovoltaic system, in kW; α PV-T Indicates the coefficient of the photovoltaic panel affected by temperature, generally -0.4%; T PV-STC It indicates the outside temperature under standard conditions of photovoltaic power generation, and the unit is ℃.

3. The building cell energy consumption analysis method considering micro-meteorology according to claim 1 is characterized by: In step 1.3, a building cellular distributed photovoltaic model under the influence of micro-meteorology is constructed, and the influence of five micro-meteorological factors, namely temperature, humidity, irradiance, wind speed and dust accumulation, on the photovoltaic output model and the key parameter correction method are analyzed. The specific calculation formula is as follows: in, It represents the output power of the photovoltaic panel at time t after correction according to micro-meteorological factors, in kW; It represents the coefficient after comprehensive correction of the actual solar irradiance at time t and the solar irradiance under standard conditions; It represents the coefficient after comprehensive correction of the actual temperature and the temperature effect under standard conditions at time t; It represents the PV rated output corrected after considering the influence of dust accumulation at time t, in kW. The specific calculation method affected by micro-meteorology is as follows: Among them, ω PV-U is the solar radiation micrometeorological correction coefficient; is the solar irradiance index under the corrected standard conditions; ω PV-T is the temperature micrometeorological correction coefficient; It is the external temperature index under the corrected standard conditions; D a (t) is the actual dust accumulation degree at time t, It is the dust accumulation degree under standard conditions after micro-meteorological correction, and the unit is g / m 2 .

4. The building cell energy consumption analysis method considering micro-meteorology according to claim 1 is characterized by: In step 2.1, the air conditioner power consumption monitoring data is obtained, including T a-in (t-1) and T a-in (t), where T a-in (t-1) is the measured external temperature at time t-1, T a-in (t) is the measured temperature of the room where the air conditioner is located, and the unit is ℃; in step 2.2, an air conditioner power model is constructed, which takes into account the influence of the external temperature on the air conditioner output, and the influence of environmental factors on human energy consumption behavior. The specific calculation formula is as follows: Among them, L HVAC (t) represents the quantitative coefficient of the influence of temperature on the energy consumption behavior of traditional air conditioners; β HVAC-L Indicates the temperature impact index under HVAC standard conditions; T HVAC-STC is the average temperature under HVAC standard conditions, in °C; the quantization coefficient is mapped to the power prediction model of the air conditioner, and the formula is as follows: Among them, P HVAC (t) represents the predicted air conditioning power at time t, in kW; α HVAC-P It indicates the heat transfer coefficient between the air conditioner output power and heat, in units of ℃ / kW.

5. The building cell energy consumption analysis method considering micro-meteorology according to claim 1 is characterized by: In step 2.3, a building cellular air conditioning model under the influence of micrometeorology is constructed. This model is a complex model that needs to consider multi-dimensional meteorological factors. When considering the influence of micrometeorology on the air conditioning output power, the influence of three micrometeorological factors, temperature, humidity and atmospheric pressure, on the air conditioning output power model and the key parameter correction method are analyzed. The specific calculation formula is as follows: in, It represents the output power of the air conditioner at time t after correction based on micro-meteorological factors, in kW; It represents the coefficient of comprehensive correction of the air conditioning energy efficiency heat exchange coefficient under the influence of micro-meteorology at time t; It represents the coefficient of the indoor and outdoor temperature difference corrected by micro-meteorological factors at time t. The specific calculation method affected by micro-meteorological factors is as follows: Among them, ω HVAC-U (t) is the air conditioning energy efficiency heat exchange micro-meteorological correction coefficient; is the quantitative coefficient of the influence of temperature on the air conditioning energy consumption behavior after micro-meteorological correction; ω HVAC-L (t) is the micro-meteorological correction coefficient for the air conditioning energy consumption behavior affected by temperature.

6. The building cell energy consumption analysis method considering micro-meteorology according to claim 1 is characterized by: In step 3.1, the charging pile monitoring data is obtained, including SOC EV (t) and SOC EV (0), where SOC EV (t) and SOC EV (0) respectively represent the battery capacity of the electric vehicle monitored by the charging pile at time t and the start time of charging completion.

7. The method for establishing a building cellular electric vehicle model affected by microclimate according to claim 1, characterized in that: In step 3.2, a distributed photovoltaic output model is constructed. The model mainly considers the impact of solar irradiance and external temperature on photovoltaic output. The specific calculation formula is as follows: Among them, α EV Indicates the power consumption of electric vehicles per kilometer, in kWh / km; J EV,i is the mileage of the electric vehicle in the i-th segment, in km; Q EV-SOH Indicates the basic capacity of the power battery in electric vehicles; n EV (t) is the number of trips that the electric vehicle travels from the time the electric vehicle is charged to time t; P EV (t) is the prediction result of electric vehicle charging power, in kW; P EV-STC represents the standard power of electric vehicle charging power in the traditional model, in kW; f EV (x EV ) is the probability distribution function of electric vehicle charging, including Gaussian distribution and Poisson distribution probability modes.

8. The method for establishing a building cellular electric vehicle model affected by microclimate according to claim 1, characterized in that: In step 3.3, a building cellular electric vehicle model under the influence of micrometeorology is constructed. This model is a complex model that needs to consider multi-dimensional meteorological factors. When considering the impact of micrometeorology on the charging power of electric vehicles, the impact of micrometeorological factors such as temperature and rainfall on the electric vehicle model and the key parameter correction method are analyzed. The specific calculation formula is as follows: in, is the battery capacity of the electric vehicle at time t after micro-meteorological correction; ω EV-α The micro-meteorological correction coefficient for the power consumption of electric vehicles per kilometer traveled; The power battery capacity of electric vehicles corrected for the influence of micro-meteorology; is the prediction result of the electric vehicle charging power at time t after micro-meteorological correction, in kW; ω EV-P Represents the micro-meteorological correction factor for electric vehicle charging power.

9. The method for establishing a building cellular electric vehicle model affected by microclimate according to claim 1, characterized in that: In step 4, the energy consumption of building cells is calculated by comprehensively considering the photovoltaic power generation, air conditioning and electric vehicle charging power models affected by micro-meteorology. The model will consider factors such as the physical characteristics of the building, usage, external climate conditions and efficiency of the energy system. The specific calculation formula is as follows: in, It represents the power value of building cell energy consumption analysis under the influence of urban microclimate, in kW.

10. The method for establishing a building cellular electric vehicle model affected by microclimate according to claim 1, characterized in that: In step 5, the demand response potential of the building cell refers to the response of the building to the power grid by changing the original electricity consumption behavior. The demand response potential refers to the ability of the building cell to participate in load regulation of power demand response, including increasing and reducing loads. Considering the time characteristics of demand response, the specific calculation formula is as follows: in, It is the calculation result of the demand response potential of the building cell under the influence of urban micrometeorology at time t, in kWh.

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