Air conditioner energy-saving compound control method and system based on virtual power plant

Abstract

The invention relates to the technical field of building energy management, and discloses an air conditioner energy-saving composite control method and system based on a virtual power plant, and the system comprises an operation parameter monitoring unit and a composite regulation and control unit: the monitoring unit collects fresh air and exhaust air flow, temperature, humidity and power instructions; the regulation and control unit performs flow field component decomposition on the flow to obtain a ventilation circulation component and a differential pressure imbalance component, and identifies a differential pressure wet load gain coefficient according to a real-time latent heat load; and the system converts a power instruction of the virtual power plant into target latent heat cooling capacity, calculates a differential pressure unbalance component required for matching the target cooling capacity by using a gain coefficient on the basis of locking the ventilation circulation component to meet the ventilation reference, and finally generates a unique building static pressure set value and sends the set value to the building automatic control system. According to the method, the problem of load prediction distortion caused by seepage wind in humid climate is solved, and the execution precision of demand response and the building moisture-proof safety are improved.

Description

Technical Field

[0001] This invention relates to the field of building energy management technology, and more specifically, to a composite control method and system for air conditioning energy saving based on a virtual power plant. Background Technology

[0002] With the development of the energy internet, commercial buildings, as flexible loads on the power system, are increasingly being integrated into virtual power plants for demand response, becoming an industry trend. In humid climates such as Miami, Houston, and the southeastern coast of China, commercial buildings typically employ independent fresh air systems combined with variable air volume (VAV) systems to handle high humidity loads. Existing energy-saving control strategies (such as latent heat load ventilation control (LLVC)) often adjust the intake air volume of fresh air units to reduce fan power consumption and the cooling capacity required to process fresh air, thereby responding to peak-shaving commands from virtual power plants. However, in practical engineering applications, the building envelope of commercial buildings is not absolutely airtight, and there are stacking effects and wind pressure effects between floors caused by temperature differences. Existing control logic often treats the building as a pressure-balanced container, focusing only on the adjustment of mechanical ventilation volume while ignoring the significant disturbance to building static pressure caused by mismatch between supply and exhaust air. When the control system unilaterally reduces the intake of fresh air in response to energy-saving commands, if the exhaust and depressurization strategies are not synchronized and precisely matched, a negative pressure field will be established indoors relative to outdoors. In humid climates, this pressure difference will drive a large amount of high-humidity outdoor air to infiltrate into the interior through gaps in the building envelope. This pressure-driven infiltration carries a significant latent heat and moisture load, which is often underestimated by existing energy consumption simulation models. As a result, although the mechanical ventilation system temporarily reduces electrical power, the infiltrated humid air causes a surge in dehumidification energy consumption in the air conditioning system. This not only offsets the expected energy savings and causes deviations in the power response of the virtual power plant, but may also lead to uncontrolled indoor humidity, resulting in the risk of mold growth and deterioration of air quality. Summary of the Invention

[0003] This invention provides a composite control method and system for air conditioning energy saving based on a virtual power plant, which solves the technical problems mentioned in the background art.

[0004] Firstly, an air conditioning energy-saving composite control system based on a virtual power plant includes: The operating parameter monitoring unit is configured to collect fresh air flow, exhaust air flow, air temperature and humidity parameters, and receive power adjustment commands issued by the virtual power plant; The composite control unit is configured to perform flow field component decomposition on the fresh air flow rate and the exhaust air flow rate to obtain the ventilation flow component and the pressure difference imbalance component. The composite control unit calculates the real-time latent heat load based on the air temperature and humidity parameters, and identifies the differential pressure wet load gain coefficient. The differential pressure wet load gain coefficient characterizes the influence weight of the differential pressure imbalance component relative to the ventilation flow component on the real-time latent heat load. The composite control unit converts the power adjustment command into a target latent heat cooling capacity. While maintaining the air exchange flow component to meet the preset ventilation benchmark, it calculates the target differential pressure imbalance component required to match the target latent heat cooling capacity based on the differential pressure wet load gain coefficient, and generates a building static pressure setpoint based on the target differential pressure imbalance component to send to the building automation system.

[0005] Secondly, an air conditioning energy-saving composite control method based on a virtual power plant, realizing an air conditioning energy-saving composite control system based on a virtual power plant as described in any one of the claims, comprising: Collects fresh air flow rate, exhaust air flow rate, air temperature and humidity parameters, and receives power adjustment commands issued by the virtual power plant; The fresh air flow rate and the exhaust air flow rate are decomposed into flow field components to obtain the ventilation flow component and the pressure imbalance component. The real-time latent heat load is calculated based on the air temperature and humidity parameters, and the differential pressure wet load gain coefficient is identified. The differential pressure wet load gain coefficient characterizes the influence weight of the differential pressure imbalance component relative to the ventilation flow component on the real-time latent heat load. The power adjustment command is converted into a target latent heat cooling capacity. Under the condition that the ventilation flow component meets the preset ventilation benchmark, the target differential pressure imbalance component required to match the target latent heat cooling capacity is calculated according to the differential pressure wet load gain coefficient. The building static pressure setpoint is generated according to the target differential pressure imbalance component and sent to the building automation system.

[0006] The beneficial effects of this invention are as follows: This invention can quantitatively identify the gain effect of pressure difference fluctuations on latent heat load under humid climates. By decoupling the flow direction of fresh air and exhaust air into ventilation flow component and pressure difference imbalance component, the power regulation demand of the virtual power plant is converted into the building static pressure setpoint by using the pressure difference wet load gain coefficient while maintaining compliant ventilation benchmarks. This mechanism not only eliminates the interference of humid and hot air infiltration caused by blindly adjusting fresh air and avoids the weakening of grid response accuracy due to dehumidification energy consumption rebound, but also effectively avoids the risk of mold growth on the inside of the building envelope, realizing both energy efficiency optimization and environmental safety considerations in the demand response process of commercial buildings. Attached Figure Description

[0007] Figure 1 This is a flowchart of an air conditioning energy-saving composite control system based on a virtual power plant, according to the present invention. Detailed Implementation

[0008] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0009] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of the present invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in one or more embodiments of the present invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" indicate that the element or object preceding the term encompasses the elements or objects listed following the term and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0010] Example 1: As Figure 1 As shown, an air conditioning energy-saving composite control system based on a virtual power plant includes: The operating parameter monitoring unit is configured to collect fresh air flow, exhaust air flow, air temperature and humidity parameters, and receive power adjustment commands issued by the virtual power plant; The composite control unit is configured to perform flow field component decomposition on the fresh air flow rate and the exhaust air flow rate to obtain the ventilation flow component and the pressure difference imbalance component. The composite control unit calculates the real-time latent heat load based on the air temperature and humidity parameters, and identifies the differential pressure wet load gain coefficient. The differential pressure wet load gain coefficient characterizes the influence weight of the differential pressure imbalance component relative to the ventilation flow component on the real-time latent heat load. The composite control unit converts the power adjustment command into a target latent heat cooling capacity. While maintaining the air exchange flow component to meet the preset ventilation benchmark, it calculates the target differential pressure imbalance component required to match the target latent heat cooling capacity based on the differential pressure wet load gain coefficient, and generates a building static pressure setpoint based on the target differential pressure imbalance component to send to the building automation system.

[0011] Preferably, the process includes collecting fresh air flow rate, exhaust air flow rate, air temperature and humidity parameters, and receiving power adjustment commands from the virtual power plant, including: The operating parameter monitoring unit directly acquires the volumetric flow rate using dedicated airflow measurement stations located at the air inlet and pressure relief outlet, and converts the volumetric flow rate into dry air mass flow rate according to the following formula, which are used as the fresh air flow rate and the exhaust air flow rate, respectively: in, Dry air mass flow rate, For volumetric flow rate, The specific volume of moist air under the current temperature, air pressure, and humidity conditions; The operating parameter monitoring unit acquires building static pressure data through a differential pressure transmitter, wherein the indoor pressure tap of the differential pressure transmitter is arranged in the return air duct or the core public area on the first floor and is far away from the air supply outlet, and the outdoor pressure tap is arranged on the leeward side of the exterior wall. The operating parameter monitoring unit summarizes the power circuits of the cold source station equipment and the terminal fan equipment to establish the metering boundary corresponding to the power adjustment command; The operating parameter monitoring unit performs a first-order low-pass filter on the fresh air flow rate, the exhaust air flow rate, the air temperature and humidity parameters, and the building static pressure data. The formula for the first-order low-pass filter is: in, This is the filtered value at the current moment. This represents the original sampled value at the current moment. This is the filtered value from the previous time step. The sampling period.

[0012] Volumetric flow rate is the volumetric velocity of airflow at the air inlet and pressure relief outlet. It can be directly collected by dedicated airflow measurement stations placed at the air inlet and pressure relief outlet.

[0013] The specific volume of moist air is the volume occupied by a unit mass of dry air and the water vapor it carries.

[0014] Fresh air flow rate is the mass of dry air entering a building per unit time.

[0015] Exhaust air flow rate is the mass of dry air discharged from a building per unit time.

[0016] Building static pressure data is the difference between indoor and outdoor air pressure. It can be collected by a differential pressure transmitter. The indoor pressure tap is located in the return air duct or the core public area on the first floor, away from the air supply outlet, while the outdoor pressure tap is located on the leeward side of the exterior wall.

[0017] The power consumption data of the cold source station equipment refers to the electricity consumed by the relevant equipment during operation. This data can be collected through the electricity meters in the power consumption circuits of the cold source station equipment.

[0018] The power consumption data of the terminal fan equipment refers to the electricity consumed by the terminal fan equipment during operation. It can be collected through the power meter of the terminal fan equipment's power consumption circuit.

[0019] Air temperature and humidity parameters are data related to the temperature and humidity of the air, including dry-bulb temperature and relative humidity. They can be collected by temperature and humidity sensors. Outdoor sensors should be placed on the leeward side, away from direct sunlight, while indoor sensors should be placed in the return air duct where the air is evenly mixed.

[0020] The first-order low-pass filter coefficients are weighting coefficients used to filter high-frequency noise in the data. Preferably, the weighting is 0.2 for the current sampled value and 0.8 for the previous filtered value, to balance data real-time performance and stability, and to avoid interference from instantaneous fluctuations in subsequent calculations.

[0021] The sampling period is the time interval between two consecutive data collections. A 60-second interval is preferred because building thermal and humidity systems have relatively high inertia; a 60-second interval can capture changes in operating conditions without increasing the data processing burden due to overly frequent sampling.

[0022] The filtered fresh air flow rate is the fresh air flow rate data after being processed by a first-order low-pass filter.

[0023] The filtered exhaust air flow rate is the exhaust air flow rate data after being processed by a first-order low-pass filter.

[0024] The filtered air temperature and humidity parameters are the air temperature and humidity data after being processed by a first-order low-pass filter.

[0025] The filtered building static pressure data is the building static pressure data after being processed by a first-order low-pass filter.

[0026] Dedicated airflow measurement stations must be selected with a range that matches the actual flow rate and a measurement accuracy of no less than ±1%. During installation, sufficient straight pipe sections must be maintained before and after the measurement section, for example, 10 times the pipe diameter before and 5 times the pipe diameter after, to avoid airflow disturbance affecting measurement accuracy. If building space is limited, an insertion flow meter can be used, ensuring the probe is inserted into the center of the pipe.

[0027] The differential pressure transmitter should be selected with a range of ±50 Pa, a measurement accuracy of ±0.25 Pa, and temperature drift compensation. The indoor pressure tap should be at least 5 meters away from the air supply outlet and at least 10 meters away from the exterior door to avoid instantaneous pressure fluctuations caused by door opening and closing and airflow. The outdoor pressure tap should be equipped with a rain cap and insect screen, and should be at least 10 meters away from the exhaust and fresh air inlets to prevent interference from exhaust and fresh air flow on the measurement.

[0028] When establishing metering boundaries, all electrical circuits of air conditioning-related equipment at the cold source station, including chillers, cooling towers, chilled water pumps, cooling pumps, and terminal units such as supply fans, return fans, and independent fresh air system fans, must be included. For example, if a commercial building's cold source station contains two chillers and ten supply fans and eight return fans at the terminal units, all electrical circuits of these devices must be included to ensure that power metering is consistent with the load range corresponding to the virtual power plant instructions.

[0029] First-order low-pass filtering must be performed according to a fixed formula: the current filter value equals 0.2 multiplied by the current original sample value plus 0.8 multiplied by the filter value from the previous time step. During initialization, the filter value from the previous time step is directly taken from the initial sampled value.

[0030] Data alignment requires clock synchronization of all data acquisition devices using the Network Time Protocol (NTP), with a synchronization cycle of 30 minutes, ensuring that the timestamp deviation of each device does not exceed 1 second. For example, temperature and humidity sensors, airflow measurement stations, and electricity meters are all connected to the same building automation system, and unified calibration through the system clock ensures that data such as fresh air flow, temperature, humidity, and power are accurately matched at the same time.

[0031] The technical parameters of the dedicated airflow measurement station are a flow range of 0 to 10 cubic meters per second and a measurement accuracy of ±1%. It is made of stainless steel to suit building ventilation environments. If a straight pipe section cannot be accommodated during installation, a curved flow meter with a correction factor can be used. The correction factor should be preset according to the equipment manual to ensure that the measurement accuracy meets the standards.

[0032] The key specifications of the differential pressure transmitter are: range ±50 Pa, measurement accuracy ±0.25 Pa, annual temperature drift not exceeding ±0.1 Pa, and operating temperature range from -20 degrees Celsius to 60 degrees Celsius, adapting to changes in indoor and outdoor building temperatures and avoiding the influence of temperature on measurement accuracy.

[0033] The sampling period is uniformly set to 60 seconds. All parameters are collected according to this period without any additional adjustments, ensuring that the collection frequency of data such as fresh air flow, temperature, humidity, and static pressure is consistent, which facilitates subsequent synchronous processing.

[0034] The specific implementation of data alignment involves using the central clock module of the building automation system to periodically synchronize all connected data acquisition devices. During synchronization, each device receives the central clock signal to calibrate its local time, ensuring that the timestamps of all data are accurate to the second, thus preventing errors in subsequent calculation logic due to timing discrepancies.

[0035] Preferably, the fresh air flow rate and the exhaust air flow rate are decomposed into flow field components to obtain the ventilation flow component and the pressure difference imbalance component, including: The composite control unit constructs a control vector containing the fresh air flow rate and the exhaust air flow rate, and a component vector containing the ventilation flow component and the pressure imbalance component, and performs the following linear transformation formula using a transformation matrix: in, For the component vector, The control vector, The transformation matrix is... The specific calculation formula after expanding the linear transformation formula is as follows: in, The ventilation flow component, This refers to the pressure difference imbalance component. The fresh air flow rate is... The exhaust air flow rate is defined as follows: the ventilation flow component represents the building's ventilation intensity; and the pressure imbalance component represents the flow source driving the building's pressure difference.

[0036] Fresh air flow rate is the mass of dry air entering a building per unit time.

[0037] Exhaust air flow rate is the mass of dry air discharged from a building per unit time.

[0038] The ventilation flow component is a physical quantity that characterizes the ventilation intensity of a building.

[0039] The pressure imbalance component is a physical quantity that characterizes the flow source that drives the pressure difference between the inside and outside of a building.

[0040] The transformation matrix is ​​a two-dimensional matrix that achieves a linear transformation between the fresh air flow rate and the exhaust air flow rate. Preferably, the matrix consists of 1 in the first row and 1 in the first column, 1 in the first row and 1 in the second column, 1 in the second row and -1 in the second column, and multiplied by half. This matrix can decouple the coupled fresh air flow rate and exhaust air flow rate into two orthogonal components.

[0041] The ventilation flow component is defined as half the sum of the fresh air flow rate and the exhaust air flow rate. This is because the sum of the two directly reflects the overall ventilation intensity of the building and is not affected by the imbalance between supply and exhaust. For example, if a building has a fresh air flow rate of 2 kg / s and an exhaust air flow rate of 1.8 kg / s, its ventilation flow component is 1.9 kg / s. This value only reflects the ventilation capacity and is unrelated to the pressure difference.

[0042] The pressure imbalance component is defined as half the difference between the fresh air flow rate and the exhaust air flow rate. This is because the difference between the supply and exhaust flow rates is the direct cause of pressure imbalance inside and outside the building. Dividing by half ensures that the component value is consistent with the flow rate unit. For example, if the fresh air flow rate is 2 kg / s and the exhaust air flow rate is 1.8 kg / s, the pressure imbalance component is 0.1 kg / s, which corresponds to the flow rate intensity driving the pressure difference.

[0043] The core of linear transformation is decoupling through matrices. Orthogonality means that the changes in the two components do not interfere with each other. Adjusting the ventilation flow component will not affect the pressure difference, and adjusting the pressure difference imbalance component will not change the ventilation intensity. For example, when increasing the ventilation flow component, the fresh air and exhaust air flow can be increased simultaneously while keeping the difference constant, thus increasing the ventilation volume without changing the building static pressure.

[0044] During implementation, it is essential to ensure the synchronization of the collected fresh air flow and exhaust air flow data. Component calculations should be based on values ​​from the same timestamp to avoid distortion of the decomposition results due to timing discrepancies. For example, a sampling frequency of once per second can be used to collect fresh air and exhaust air flow data simultaneously each time, and then substitute them into the formula to calculate the two components.

[0045] The orthogonality of a transformation matrix is ​​verified by the condition that the product of the transpose of the matrix and itself is a multiple of the identity matrix. For a transformation matrix, the product of its transpose and itself is a diagonal matrix with all diagonal elements being 1, satisfying the property of orthogonal matrices and ensuring that the two components do not cross-interfere.

[0046] The engineering verification method adopts the data comparison method. When the building supply and exhaust fans are running stably, the ventilation flow component is fixed, the pressure imbalance component is adjusted, and the change in building static pressure is monitored to verify that the static pressure changes only with the pressure imbalance component. The pressure imbalance component is fixed, the ventilation flow component is adjusted, and the indoor air quality index is monitored to verify that the ventilation effect changes only with the ventilation flow component, thus confirming the orthogonality.

[0047] The accuracy assurance measures for component decomposition include: the measurement accuracy of fresh air and exhaust air flow must be controlled within ±1%, and the same brand and model of measuring equipment must be used; when abnormal fluctuations occur in the flow measurement data, the average value of three adjacent sampling points is used to replace the abnormal value before component calculation, so as to avoid abnormal data causing a decrease in decomposition accuracy.

[0048] Preferably, calculating the real-time latent heat load based on the air temperature and humidity parameters includes: The composite control unit uses the air temperature and humidity parameters to calculate the water vapor partial pressure and moisture content according to the following psychohumid formula: in, For water vapor partial pressure, Relative humidity, Based on dry bulb temperature saturated water vapor pressure, Moisture content Atmospheric pressure, This represents the molecular weight ratio of water vapor to dry air. The composite control unit calculates the real-time latent heat load according to the following formula: in, For the real-time latent heat load, The mass flow rate for supplying dry air. Let be the latent heat of vaporization of water, with a value of 2501 kJ / kg. This refers to the moisture content of the return air. This refers to the moisture content of the supplied air.

[0049] Dry-bulb temperature is a physical quantity that characterizes the degree of hotness or coldness of air. It can be collected using a platinum resistance temperature sensor, which needs to be installed in a location with uniform airflow and no direct sunlight.

[0050] Relative humidity is the percentage of actual water vapor pressure in the air to the saturated water vapor pressure at the same temperature. It can be collected by a capacitive humidity sensor, integrated with a temperature sensor to ensure consistent measurement environments.

[0051] Saturated vapor pressure is the pressure corresponding to the maximum amount of water vapor that air can hold at a specific temperature.

[0052] Water vapor partial pressure is the pressure generated by water vapor alone in the air.

[0053] Atmospheric pressure is the force exerted by the atmosphere on a unit area of ​​the Earth's surface. It can be collected by a barometric pressure sensor, installed in the same location as the outdoor temperature and humidity sensor.

[0054] The molecular weight ratio of water vapor to dry air is a constant. It is preferably 0.621945. With a water vapor molecular weight of 18.015 and an average dry air molecular weight of 28.9647, the ratio calculated is applicable to calculations for all humid air conditions.

[0055] Moisture content is the mass of water vapor carried by a unit mass of dry air.

[0056] Supply dry air mass flow rate is the mass of dry air delivered into the room by the air conditioning system per unit time. It can be calculated by collecting volumetric flow rate data from an airflow measurement station inside the supply air duct and combining this data with the current specific volume of moist air.

[0057] The latent heat of vaporization of water is the amount of heat required for a unit mass of water to change from a liquid to a gaseous state at a constant temperature. A value of 2501 kJ per kilogram is preferred, based on the average value under normal temperature and pressure, and is suitable for calculating latent heat in humid climate air conditioning dehumidification scenarios.

[0058] Return air moisture content is the mass of water vapor carried per unit mass of dry air in the return airflow of an air conditioning system.

[0059] Supply air moisture content is the mass of water vapor carried per unit mass of dry air in the supply airflow of an air conditioning system.

[0060] Real-time latent heat load is the amount of cooling energy required by an air conditioning system to remove excess moisture from the air.

[0061] The chain-like calculation logic must be executed in a fixed order. First, the saturated vapor pressure is calculated using the dry-bulb temperature. Then, the partial pressure of water vapor is obtained by combining the relative humidity. Finally, the moisture content is calculated using the formula. For example, at a certain moment, the dry-bulb temperature is 25 degrees Celsius, the relative humidity is 60%, and the atmospheric pressure is 101.325 kPa. First, the saturated vapor pressure corresponding to 25 degrees Celsius is calculated as 3.169 kPa. Then, the partial pressure of water vapor is calculated as 1.901 kPa. Finally, the moisture content is obtained as 11.8 grams per kilogram of dry air.

[0062] Real-time latent heat load calculations must ensure consistent units for all parameters. The supply dry air mass flow rate is in kilograms per second, the latent heat constant is in kilojoules per kilogram, and the moisture content difference is in kilograms per kilogram of dry air. The calculation result is in kilowatts. For example, if the supply dry air mass flow rate is 2 kilograms per second and the moisture content difference is 5 grams per kilogram of dry air, the latent heat load is 2 × 2501 × 0.005 = 25.01 kilowatts.

[0063] The temperature sensor should be a platinum resistance sensor with a measurement range of -20 to 60 degrees Celsius and an accuracy of ±0.1 degrees Celsius; the humidity sensor should be a capacitive sensor with a measurement range of 0 to 100% and an accuracy of ±2%. Both should be kept away from locations with sudden airflow changes, such as air conditioner vents and return vents, to avoid local temperature and humidity fluctuations affecting the measurement.

[0064] Saturated vapor pressure is calculated using the ASHRAE standard formula, which is: 0.61078 multiplied by the natural constant (17.27 × dry-bulb temperature) divided by (dry-bulb temperature + 237.3) raised to the power of 237.3. For example, at a dry-bulb temperature of 20 degrees Celsius, the saturated vapor pressure is 0.61078 × e (17.27×20 / (20+237.3)) ≈2.339 kPa.

[0065] The required accuracy for converting the mass flow rate of dry air to ±1% is specified. The specific volume of humid air must be accurately calculated based on the current temperature, pressure, and moisture content, with the calculation error controlled within ±0.5%. If the straight pipe section at the installation location of the airflow measurement station is insufficient, equipment with flow field correction function must be selected, and the measurement accuracy after correction must meet the requirements.

[0066] Error correction measures for moisture content calculation: When atmospheric pressure fluctuations exceed ±5 kPa, a pressure correction coefficient is activated, which is the ratio of standard atmospheric pressure (101.325 kPa) to actual atmospheric pressure; when the relative humidity measurement is below 10% or above 90%, a piecewise correction formula is used to ensure the accuracy of calculations under low and high humidity environments.

[0067] Preferably, a differential pressure wet load gain coefficient is identified, wherein the differential pressure wet load gain coefficient characterizes the weight of the influence of the differential pressure imbalance component relative to the ventilation flow component on the real-time latent heat load, including: The composite control unit calculates the latent heat driving force according to the following formula: in, Driven by latent heat, Let be the latent heat of vaporization of water. This refers to the outdoor humidity level. This refers to the moisture content of the return air. The composite regulation unit constructs the following linear regression model: in, For the real-time latent heat load, For the intercept term, For the gain of the ventilation flow component, The ventilation flow component, For the gain of the pressure imbalance component, This refers to the pressure difference imbalance component; The composite control unit is based on a preset length. Constructing a data matrix using a sliding time window With the target vector And the ridge regression algorithm is used to solve for the parameter estimate vector. : in, The ridge parameter takes the value of , It is the identity matrix; The composite control unit calculates the differential pressure wet load gain coefficient according to the following formula: in, The differential pressure wet load gain coefficient is... This is an estimated value for the gain of the pressure imbalance component. This is an estimated value for the gain of the ventilation flow component.

[0068] The latent heat of vaporization of water is the amount of heat required for a unit mass of water to change from a liquid to a gaseous state at a constant temperature. A value of 2501 kJ per kilogram is preferred, based on the average value under normal temperature and pressure, and is suitable for calculating latent heat in humid climate air conditioning dehumidification scenarios.

[0069] Outdoor humidity is the mass of water vapor carried per unit mass of dry outdoor air. It can be calculated using the psychohumid formula by collecting data from outdoor temperature and humidity sensors and combining it with atmospheric pressure.

[0070] Return air humidity content is the mass of water vapor carried per unit mass of dry air in the return airflow of an air conditioning system. It can be calculated using a psychohumid formula by collecting data from return air temperature and humidity sensors and combining it with atmospheric pressure.

[0071] Latent heat driving force is a physical quantity that quantifies the driving intensity of outdoor high humidity air on indoor latent heat load.

[0072] Real-time latent heat load is the amount of cooling energy required by an air conditioning system to remove excess moisture from the air.

[0073] The intercept term in a linear regression model is a constant term that offsets the fixed error of the system.

[0074] The gain of the ventilation flow component is a coefficient that characterizes the degree of influence of the ventilation flow component on the latent heat load.

[0075] The gain of the pressure imbalance component is a coefficient that characterizes the degree of influence of the pressure imbalance component on the latent heat load.

[0076] The sliding time window length is the number of historical data sampling points used to construct the regression model. It is preferably 30 to balance the data sample size and real-time performance. 30 sampling points (corresponding to 30 minutes) can cover the inertial changes in building thermal humidity, and will not cause lag in coefficient recognition due to an excessively long window.

[0077] The regression matrix is ​​a two-dimensional matrix constructed from historical latent heat driving force, ventilation flow component, and pressure imbalance component data.

[0078] The target vector is a one-dimensional vector composed of historical real-time latent heat load data.

[0079] The ridge parameter is a regularization parameter used to avoid singularities in the regression matrix. A preferred value is 10 to the power of negative 6, as this value effectively suppresses multicollinearity without affecting the accuracy of coefficient estimation.

[0080] The identity matrix is ​​a square matrix with 1s on the main diagonal and 0s on the rest.

[0081] The differential pressure wet load gain coefficient is the ratio of the weight of the differential pressure imbalance component to the weight of the ventilation flow component on the latent heat load.

[0082] The calculation of latent heat driving force focuses on the difference between the outdoor humidity and the return air humidity, because in humid climates, the outdoor humidity is usually higher than the indoor humidity, and this difference directly determines the amount of latent heat carried by the outdoor air during infiltration. For example, if the outdoor humidity is 18 grams per kilogram of dry air and the return air humidity is 10 grams per kilogram of dry air, the difference is 8 grams per kilogram of dry air, and the driving force is 2501 kJ / kg multiplied by 0.008 kJ / kg of dry air, which equals 20.008 kJ / kg of dry air.

[0083] The explicit intercept term is introduced into the linear regression model to offset fixed system biases such as sensor zero-point error and calculation simplification error. For example, the humidity content calculation may have a fixed bias due to the zero-point offset of the humidity sensor. The intercept term can offset this effect through data fitting, making the gain coefficient estimation more accurate.

[0084] The sliding time window needs to be updated on a fixed length, adding a new sampling point each time while removing the oldest sampling point to ensure the model can adapt to changes in operating conditions. For example, with a window length of 30, on the 31st sampling, the data from the 2nd to the 31st samplings are used to replace the data from the 1st to the 30th samplings to construct the matrix, ensuring the dynamic adaptability of the coefficients.

[0085] Ridge regression was chosen because the ventilation flow component and the pressure imbalance component may have a weak correlation, leading to a near-singular regression matrix. By introducing a small ridge parameter, the matrix can be made invertible. For example, when multicollinearity exists in the data, ordinary least squares may exhibit coefficient fluctuations, while ridge regression can stably output estimated values.

[0086] The ratio definition of the pressure differential wet load gain coefficient is used to quantify the relative amplification effect of pressure differential on latent heat. For example, if the gain of the ventilation flow component is 0.8 and the gain of the pressure differential imbalance component is 2.4, the coefficient is 3, indicating that under the same flow rate change, the impact of the pressure differential imbalance component on latent heat load is 3 times that of the ventilation flow component.

[0087] The sliding time window length is 30, corresponding to a sampling period of 60 seconds, covering 30 minutes of data. During implementation, the matrix must be constructed strictly according to this length to avoid inconsistent coefficient identification results due to arbitrary adjustments to the window size.

[0088] The ridge regression solution includes: using gradient descent for iterative solution, setting the initial value of the iteration to a 0 vector, the iteration step size to 0.001, and the iteration termination condition being that the absolute value of the difference between the coefficients of two iterations is less than 10 to the power of -8, or the number of iterations reaches 100, to ensure solution efficiency and accuracy.

[0089] When the latent heat driving force is negative (outdoor humidity is lower than indoor humidity), the model maintains its original structure, and the coefficient estimation is unaffected. In this case, the effect of the pressure difference imbalance component is to suppress the latent heat load, which is consistent with actual physical laws. For example, after the rainy season ends, the outdoor humidity decreases, the driving force becomes negative, and the positive pressure difference deviation will reduce the indoor moisture discharge, avoiding excessive dehumidification.

[0090] The accuracy criteria for gain coefficient estimates include: the relative error of the coefficient must be controlled within ±5%, and the relative error is calculated by dividing the absolute value of the difference between the estimated value and the true value by the true value. If the error exceeds the threshold, the sliding window length can be appropriately increased to 45, or the sensor data quality can be checked, outliers removed, and the calculation recalculated.

[0091] Preferably, converting the power adjustment command into a target latent heat cooling capacity includes: The composite control unit calculates the total cooling capacity on the air side according to the following formula: in, Enthalpy of moist air Dry bulb temperature, Moisture content This refers to the total cooling capacity on the air side. The mass flow rate for supplying dry air. For the enthalpy of the return air, For air supply enthalpy; The composite control unit calculates the real-time energy efficiency ratio according to the following formula: in, The real-time energy efficiency ratio is... The total cooling capacity on the air side at the current moment. The total electrical power within the metering boundary; The composite control unit calculates the target latent heat cooling capacity according to the following formula: in, The target latent heat or cooling capacity, This refers to the power adjustment command.

[0092] Return air temperature refers to the temperature of the return airflow in an air conditioning system. It can be collected by a platinum resistance temperature sensor installed at a point where the air is evenly mixed in the main return air duct.

[0093] Return air moisture content is the mass of water vapor carried per unit mass of dry air in the return airflow of an air conditioning system.

[0094] Supply air temperature refers to the temperature of the airflow supplied by an air conditioning system. It can be measured by a platinum resistance temperature sensor installed 2 to 5 times the hydraulic diameter after the coil inside the supply air duct.

[0095] Supply air moisture content is the mass of water vapor carried per unit mass of dry air in the supply airflow of an air conditioning system.

[0096] The return air enthalpy is the total energy per unit mass of dry air in the return airflow, including sensible heat and latent heat.

[0097] The specific enthalpy of the supply air is the total energy per unit mass of dry air in the supply airflow, including sensible heat and latent heat.

[0098] Supply dry air mass flow rate is the mass of dry air delivered into the room by the air conditioning system per unit time. It can be calculated by collecting volumetric flow rate data from an airflow measurement station inside the supply air duct and combining this data with the current specific volume of moist air.

[0099] Total cooling capacity on the air side is the total heat removed by the air handling system.

[0100] Total power consumption refers to the total electricity consumed by all air conditioning-related equipment within the metering boundary. It can be collected by aggregating meter data from the power circuits of the cooling station and terminal fan equipment.

[0101] Real-time energy efficiency ratio is the dynamic ratio of total cooling capacity to total electrical power on the air side of an air conditioning system.

[0102] Power regulation commands are power adjustment requirements for the building's air conditioning system issued by the virtual power plant. They can be received through the communication interface between the virtual power plant and the building automation system.

[0103] The target latent heat cooling capacity is the cooling capacity target that the air conditioning system needs to achieve through dehumidification after the power adjustment command is converted.

[0104] The calculation of total air-side cooling capacity must ensure that the units of parameters are consistent. The unit for supply dry air mass flow rate is kilograms per second, the unit for specific enthalpy is kilojoules per kilogram of dry air, and the unit for the calculation result is kilowatts. For example, if the supply dry air mass flow rate is 3 kilograms per second, the return air specific enthalpy is 55 kilojoules per kilogram of dry air, and the supply air specific enthalpy is 35 kilojoules per kilogram of dry air, the total air-side cooling capacity is 3 × (55 - 35) = 60 kilowatts.

[0105] The low-pass filtering of the real-time energy efficiency ratio should follow the first-order low-pass filtering logic. The filtering coefficients are weighted at 0.2 for the current sampled value and 0.8 for the previous filtered value, consistent with the filtering standard in claim 2, to avoid data processing logic conflicts. For example, if the energy efficiency ratio was 3.2 in the previous time step and the current calculated value is 3.5, the real-time energy efficiency ratio after filtering is 0.2×3.5+0.8×3.2=3.26.

[0106] The strategy of projecting power commands to latent heat is based on the fact that latent heat load accounts for a high proportion (56% to 66.4%) in humid climates. By directing all power regulation needs to the latent heat channel, it can directly target core load control. For example, when a virtual power plant issues a command to reduce power by 50 kilowatts, the converted target latent heat is used to adjust the pressure imbalance component, accurately responding to energy-saving needs.

[0107] Specific enthalpy calculations must be performed according to a fixed formula to ensure accurate energy quantification. For example, with a return air temperature of 26 degrees Celsius and a return air moisture content of 12 grams per kilogram of dry air, the specific enthalpy of the return air is 1.006×26 + 0.012×(2501 + 1.86×26) ≈ 54.7 kJ per kilogram of dry air.

[0108] The specific coefficients of the real-time energy efficiency ratio low-pass filter are 0.2 for the current sampled value and 0.8 for the previous filtered value, which are consistent with the filter coefficients in claim 2, ensuring that the data processing standards of the entire system are consistent.

[0109] The metering accuracy requirement for total power is that the meter error class does not exceed ±0.5%. All power circuits of equipment included in the metering boundary must use meters of the same accuracy class to ensure accurate power data.

[0110] The power adjustment command is received every 5 minutes, with a response delay of no more than 60 seconds. That is, the target latent heat and cooling capacity are calculated and transmitted to the next control link within 60 seconds after the command is received.

[0111] The upper limit of the target latent heat cooling capacity is constrained to 80% of the current air conditioning system's maximum dehumidification capacity, and the lower limit is constrained to 0 kilowatts, to avoid control failure or excessive dehumidification due to the target value exceeding the system's capacity. For example, if the system's maximum dehumidification capacity is 100 kilowatts, the upper limit of the target latent heat cooling capacity is 80 kilowatts.

[0112] Preferably, while maintaining the ventilation flow component at a preset ventilation benchmark, the target pressure difference imbalance component required to match the target latent heat cooling capacity is calculated based on the pressure difference wet load gain coefficient, including: The composite control unit calculates the reference mass flow rate corresponding to the preset ventilation reference according to the following formula: in, For the fresh air volume in the breathing zone, For the average fresh air volume per person, For the number of people designed, Fresh air volume per unit area This refers to the net habitable area. For the area's fresh air volume, This is a parameter for the effectiveness of regional air supply distribution. The reference mass flow rate, The specific volume of moist air; The composite control unit locks the ventilation flow component to the reference mass flow rate and calculates the target pressure difference imbalance component according to the following formula: in, The target pressure difference imbalance component, The target latent heat or cooling capacity, This is an estimated value for the gain of the pressure imbalance component. The latent heat driving force is...

[0113] The per capita fresh air volume is the amount of fresh air supplied per person per hour to ensure the health of indoor occupants. A preferred value is 30 cubic meters per person per hour, referencing the ASHRAE 62.1 standard to meet the ventilation needs of commercial building office environments.

[0114] The design capacity is the maximum number of people that a building can accommodate, as determined during the building design phase. Preferred values ​​are calculated according to building design codes; for example, 0.1 people per square meter for office buildings, to ensure that ventilation can meet the needs of full-load use.

[0115] Fresh air volume per unit area is the amount of fresh air supplied per hour for each unit of livable net area. It is preferably 10 cubic meters per hour per square meter to supplement the insufficient fresh air volume per person and ensure ventilation effect in large spaces or when the population density fluctuates.

[0116] The livable net area is the effective area within a building that is actually available for human activity. The value should be based primarily on the figures indicated in the building survey report, ensuring accurate calculation of ventilation volume and avoiding insufficient or wasted ventilation due to discrepancies in area calculation.

[0117] The fresh air volume in the breathing zone is the total fresh air volume that meets the breathing needs of all people in the building.

[0118] The regional air supply distribution effectiveness parameter is a coefficient that corrects for air supply uniformity. A value of 1.0 is preferred, as an air conditioning system with both supply and return airflow exhibits good air supply uniformity and is suitable for most commercial building scenarios.

[0119] The area fresh air volume is the total fresh air volume that the air conditioning system needs to provide after considering the uniformity of air supply.

[0120] The specific volume of moist air is the volume occupied by a unit mass of dry air and the water vapor it carries.

[0121] The reference mass flow rate is the target value of the air exchange flow component that meets the preset ventilation reference.

[0122] The target latent heat cooling capacity is the cooling capacity target that needs to be achieved through dehumidification after the virtual power plant power regulation command is converted.

[0123] The gain of the pressure imbalance component is a coefficient that characterizes the degree of influence of the pressure imbalance component on the latent heat load.

[0124] Latent heat driving force is a physical quantity that quantifies the driving intensity of outdoor high humidity air on indoor latent heat load.

[0125] The target pressure difference imbalance component is the pressure difference-driven modal change value required to match the target latent heat and cold capacity.

[0126] The calculation logic for the fresh air volume in the breathing zone is to combine the per capita demand and the area demand to ensure double protection. For example, if a commercial office building has a net livable area of ​​1,000 square meters and is designed to accommodate 80 people, with a per capita fresh air volume of 30 cubic meters per hour and a unit area fresh air volume of 10 cubic meters per hour per square meter, then the fresh air volume in the breathing zone is 80 × 30 + 1,000 × 10 = 12,400 cubic meters per hour.

[0127] The values ​​for the effectiveness parameters of the zonal air supply distribution need to be adjusted according to the type of air conditioning system. For side-supply and bottom-return systems, a value of 0.9 can be used, and for floor-supply systems, a value of 0.85 can be used to ensure that the corrected zonal fresh air volume can cover the actual breathing zone requirements. For example, if the fresh air volume in the breathing zone is 12,400 cubic meters per hour, the zonal fresh air volume for a side-supply and bottom-return system is approximately 12,400 ÷ 0.9 ≈ 13,778 cubic meters per hour.

[0128] The baseline mass flow rate locking strategy fixes the ventilation flow component at the ventilation baseline value to prevent subsequent latent heat regulation from affecting indoor air quality. For example, if the baseline mass flow rate is calculated to be 3.8 kg / s, the ventilation flow component will remain unchanged regardless of subsequent changes in the target latent heat.

[0129] The calculation of the target pressure imbalance component must ensure unit consistency. The target latent heat is in kilowatts (kW), the pressure imbalance component gain is a dimensionless coefficient, the latent heat driving force is in kilojoules per kilogram of dry air, and the calculation result is in kilograms per second. For example, if the target latent heat is 50 kW, the pressure imbalance component gain is 2.5, and the latent heat driving force is 20 kilojoules per kilogram of dry air, then the target pressure imbalance component is 50 ÷ (2.5 × 20) = 1 kilogram per second.

[0130] The values ​​for per capita fresh air volume and fresh air volume per unit area are based on the ASHRAE 62.1 standard, which stipulates that the per capita fresh air volume for office buildings shall not be less than 25 cubic meters per hour per person and the fresh air volume per unit area shall not be less than 8 cubic meters per hour per square meter. It is preferable to take values ​​higher than the lower limit of the standard to ensure ventilation effect.

[0131] The effective value of the regional air supply distribution parameter ranges from 0.8 to 1.0, and is determined according to the air supply method: 1.0 for top supply and top return, 0.9 for side supply and bottom return, and 0.85 for floor supply. It can be selected according to the actual air conditioning system type during implementation.

[0132] The dynamic adjustment mechanism for the baseline mass flow includes: when the actual number of people using the building is lower than the designed number for a long period of time (e.g., less than 50%), the designed number of people can be recalculated quarterly and the baseline mass flow updated; if there are short-term fluctuations, the original baseline value will be maintained to avoid frequent adjustments affecting system stability.

[0133] The adjustment rate of the target pressure difference imbalance component is limited to no more than 0.1 kg per second. For example, if the calculated target value is 1 kg per second and the current value is 0.5 kg per second, it needs to be gradually adjusted to the target value over 5 seconds to avoid sudden changes in pressure difference that could cause drastic changes in the infiltration air.

[0134] Preferably, generating a building static pressure setpoint based on the target pressure difference imbalance component and sending it to the building automation system includes: The composite control unit constructs the following linear model and uses the ridge regression algorithm to identify the pressure differential permeability coefficient: in, For building static pressure data, The pressure difference permeability coefficient, This refers to the pressure difference imbalance component. For the intercept term; The composite control unit sets a basic moisture-proof pressure difference setting value, which is as follows: in, Set the basic moisture-proof pressure difference value; The composite control unit generates the building static pressure setpoint according to the following formula: in, The static pressure setting value for the building. The pressure differential permeability coefficient identified at the current moment. The target pressure difference imbalance component Building static pressure data is the difference between indoor and outdoor air pressure. It can be collected by differential pressure transmitters placed in return air ducts or the core public area on the ground floor.

[0135] The pressure differential permeability coefficient is a coefficient that characterizes the airtightness of a building and quantifies the relationship between the pressure differential imbalance component and the building's static pressure.

[0136] The pressure imbalance component is a physical quantity that characterizes the flow source that drives the pressure difference between the inside and outside of a building.

[0137] The intercept term in the linear model is a constant term that compensates for the fixed error in building static pressure measurement.

[0138] The basic moisture-proof pressure differential setting value is the benchmark static pressure value for maintaining a slightly positive pressure state in the building during the warm season. A value of ±7 Pa is preferred, as this effectively inhibits the infiltration of high-humidity outdoor air without significantly increasing fan energy consumption, thus meeting the moisture-proofing needs of humid climates.

[0139] The target pressure difference imbalance component is the pressure difference-driven modal change value required to match the target latent heat and cold capacity.

[0140] The differential pressure adjustment offset is a static pressure adjustment that needs to be superimposed on the basic moisture-proof differential pressure in response to the power command of the virtual power plant.

[0141] The building static pressure setpoint is ultimately sent to the building automation system to adjust the target value of the building static pressure.

[0142] The core logic of linear model construction is that the static pressure of a building and the pressure imbalance component have an approximately linear relationship, and the intercept term is used to offset fixed errors such as sensor zero-point offset. For example, when the pressure imbalance component of a building is 0.5 kg / s, the static pressure is 5 Pa; when the component is 1 kg / s, the static pressure is 10 Pa. The linear model can be fitted as static pressure equal to 10 multiplied by the pressure imbalance component, with an intercept term of 0.

[0143] The pressure differential permeability coefficient is identified using ridge regression because building airtightness may vary slightly over time, and a sliding window combined with ridge regression can stably update the coefficient. For example, with a window length of 30, each time a new sampling point is added, the oldest point is removed, and the coefficient obtained through regression can adapt to changes in airtightness in real time.

[0144] The basic moisture-proof pressure differential is set at ±7 Pa because the outdoor humidity is high during the warm season, and a slight positive pressure can prevent humid air from seeping through gaps in the building envelope. For example, during the rainy season, the outdoor humidity can reach 20 grams per kilogram of dry air, and a static pressure of ±7 Pa can reduce infiltration wind by more than 40%, thereby reducing the latent heat load.

[0145] The static pressure setpoint is generated by superimposing the base pressure difference and the adjustment offset, which ensures basic moisture protection requirements while responding to power adjustments. For example, if the base pressure difference is 7 Pa and the adjustment offset is 2 Pa, the final static pressure setpoint is 9 Pa; if the offset is -1 Pa, the setpoint is 6 Pa, always maintaining a slightly positive pressure.

[0146] The sliding time window length for pressure differential permeability coefficient identification is 30, consistent with claim 5, corresponding to 30 minutes of data, ensuring the real-time performance and stability of coefficient identification. If the building's airtightness changes significantly, the window length can be adjusted to 45 to improve coefficient adaptability.

[0147] The applicable differential pressure range for the linear model is 0 to 30 Pa, which covers the typical static pressure control range for commercial buildings. When the pressure exceeds this range, a nonlinear correction term must be enabled. The correction term is the difference between the actual static pressure value and the linearly calculated value to ensure the accuracy of the model.

[0148] Adjustments to the basic moisture-proof pressure differential setting include: in the cold season, it can be adjusted to 0 Pa neutral pressure. In winter, when the outdoor humidity is low, neutral pressure can reduce heat loss and eliminate concerns about humid air infiltration; when the outdoor humidity is lower than the indoor humidity (such as in autumn), it can be adjusted to +3 Pa to balance ventilation and energy consumption.

[0149] The required accuracy for the static pressure setpoint of the building is that the static pressure control error of the building automation system does not exceed ±0.5 Pa, and the fan adjustment response delay does not exceed 10 seconds, ensuring that the static pressure can be quickly stabilized at the setpoint.

[0150] Example 2: A composite control method for air conditioning energy saving based on a virtual power plant, realizing a composite control system for air conditioning energy saving based on a virtual power plant as described in any one of the embodiments, including: Collects fresh air flow rate, exhaust air flow rate, air temperature and humidity parameters, and receives power adjustment commands issued by the virtual power plant; The fresh air flow rate and the exhaust air flow rate are decomposed into flow field components to obtain the ventilation flow component and the pressure imbalance component. The real-time latent heat load is calculated based on the air temperature and humidity parameters, and the differential pressure wet load gain coefficient is identified. The differential pressure wet load gain coefficient characterizes the influence weight of the differential pressure imbalance component relative to the ventilation flow component on the real-time latent heat load. The power adjustment command is converted into a target latent heat cooling capacity. Under the condition that the ventilation flow component meets the preset ventilation benchmark, the target differential pressure imbalance component required to match the target latent heat cooling capacity is calculated according to the differential pressure wet load gain coefficient. The building static pressure setpoint is generated according to the target differential pressure imbalance component and sent to the building automation system.

[0151] It should be noted that the interval and threshold sizes are set for ease of comparison. The size of the threshold depends on the amount of sample data and the base number set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless calculations, and the formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0152] The embodiments of this example have been described above. However, this example is not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of this example, and all of them are within the protection scope of this example.

Claims

1. A composite control system for air conditioning energy saving based on a virtual power plant, characterized in that, include: The operating parameter monitoring unit is configured to collect fresh air flow, exhaust air flow, air temperature and humidity parameters, and receive power adjustment commands issued by the virtual power plant; The composite control unit is configured to perform flow field component decomposition on the fresh air flow rate and the exhaust air flow rate to obtain the ventilation flow component and the pressure difference imbalance component. The composite control unit calculates the real-time latent heat load based on the air temperature and humidity parameters, and identifies the differential pressure wet load gain coefficient. The differential pressure wet load gain coefficient characterizes the influence weight of the differential pressure imbalance component relative to the ventilation flow component on the real-time latent heat load. The composite control unit converts the power adjustment command into a target latent heat cooling capacity. While maintaining the air exchange flow component to meet the preset ventilation benchmark, it calculates the target differential pressure imbalance component required to match the target latent heat cooling capacity based on the differential pressure wet load gain coefficient, and generates a building static pressure setpoint based on the target differential pressure imbalance component to send to the building automation system.

2. The air conditioning energy-saving composite control system based on a virtual power plant according to claim 1, characterized in that, It collects fresh air flow rate, exhaust air flow rate, air temperature and humidity parameters, and receives power adjustment commands from the virtual power plant, including: The operating parameter monitoring unit uses dedicated airflow measurement stations located at the air inlet and pressure relief outlet to obtain volumetric flow rate, and combines the specific volume of moist air under the current condition to convert the volumetric flow rate into dry air mass flow rate, which are used as the fresh air flow rate and the exhaust air flow rate, respectively. The operating parameter monitoring unit acquires building static pressure data through a differential pressure transmitter, wherein the indoor pressure tap of the differential pressure transmitter is arranged in the return air duct, and the outdoor pressure tap is arranged on the leeward side of the exterior wall. The operating parameter monitoring unit summarizes the power circuits of the cold source station equipment and the terminal fan equipment to establish the metering boundary corresponding to the power adjustment command; The operating parameter monitoring unit performs first-order low-pass filtering on the fresh air flow rate, the exhaust air flow rate, the air temperature and humidity parameters, and the building static pressure data, and aligns the data according to a unified timestamp.

3. The air conditioning energy-saving composite control system based on a virtual power plant according to claim 2, characterized in that, The flow field components of the fresh air flow rate and the exhaust air flow rate are decomposed to obtain the ventilation flow component and the pressure difference imbalance component, including: The composite control unit defines the ventilation flow component as the ventilation intensity mode, the value of which is equal to half of the sum of the fresh air flow rate and the exhaust air flow rate; The composite control unit defines the pressure imbalance component as a pressure difference driving mode, the value of which is equal to half of the difference between the fresh air flow rate and the exhaust air flow rate. The composite control unit decouples the fresh air flow rate and the exhaust air flow rate into orthogonal ventilation flow components and pressure imbalance components through linear transformation.

4. The air conditioning energy-saving composite control system based on a virtual power plant according to claim 3, characterized in that, Calculating the real-time latent heat load based on the aforementioned air temperature and humidity parameters includes: The composite control unit determines the saturated vapor pressure based on the dry-bulb temperature in the air temperature and humidity parameters, calculates the partial pressure of water vapor based on the relative humidity, and calculates the moisture content based on the molecular weight ratio of water vapor to dry air and atmospheric pressure. The composite control unit acquires the mass flow rate of the supply dry air and uses the product of the mass flow rate of the supply dry air, the latent heat constant of water, and the difference between the moisture content of the return air and the moisture content of the supply air as the real-time latent heat load.

5. The air conditioning energy-saving composite control system based on a virtual power plant according to claim 4, characterized in that, Identify the differential pressure wet load gain coefficient, which characterizes the weight of the differential pressure imbalance component relative to the ventilation flow component on the real-time latent heat load, including: The composite control unit calculates the latent heat driving force, and the value of the latent heat driving force is equal to the product of the latent heat constant of water vaporization and the difference between the outdoor humidity and the return air humidity. The composite control unit constructs a linear regression model containing an explicit intercept term. The linear regression model characterizes the real-time latent heat load as a linear superposition of the ventilation term, the pressure difference term, and the explicit intercept term. The ventilation term is the product of the ventilation flow component gain, the latent heat driving force, and the ventilation flow component. The pressure difference term is the product of the pressure difference imbalance component gain, the latent heat driving force, and the pressure difference imbalance component. The composite control unit collects historical data based on a sliding time window of a preset length to construct a regression matrix and a target vector, and uses the ridge regression algorithm to solve the regression matrix to obtain the estimated values ​​of the gain of the ventilation flow component and the gain of the pressure imbalance component. The composite control unit uses the ratio of the estimated value of the differential pressure imbalance component gain to the estimated value of the ventilation flow component gain as the differential pressure wet load gain coefficient.

6. The air conditioning energy-saving composite control system based on a virtual power plant according to claim 5, characterized in that, Converting the power regulation command into a target latent heat cooling capacity includes: The composite control unit calculates the return air specific enthalpy and the supply air specific enthalpy based on the return air temperature, return air humidity, supply air temperature, and supply air humidity, and defines the product of the supply air dry air mass flow rate and the difference between the return air specific enthalpy and the supply air specific enthalpy as the total cooling capacity on the air side. The composite control unit acquires the total electrical power within the metering boundary, calculates the ratio of the total cooling capacity on the air side to the total electrical power as the real-time energy efficiency ratio, and performs first-order low-pass filtering on the real-time energy efficiency ratio. The composite control unit directly determines the target latent heat cooling capacity by multiplying the real-time energy efficiency ratio and the power adjustment command, so as to project all the energy changes represented by the power adjustment command to the latent heat regulation channel.

7. The air conditioning energy-saving composite control system based on a virtual power plant according to claim 6, characterized in that, While maintaining the ventilation flow component at a preset ventilation benchmark, the target pressure difference imbalance component required to match the target latent heat cooling capacity is calculated based on the pressure difference wet load gain coefficient, including: The composite control unit calculates the fresh air volume in the breathing zone based on the per capita fresh air volume, the designed number of people, the fresh air volume per unit area, and the livable net area, and uses the regional air supply distribution effectiveness parameters to correct the fresh air volume in the breathing zone to obtain the regional fresh air volume. The composite control unit combines the specific volume of moist air to convert the fresh air volume of the area into a reference mass flow rate, and locks the target value of the ventilation circulation component as the reference mass flow rate. The composite control unit obtains the estimated value of the gain of the pressure difference imbalance component and the latent heat driving force, and divides the target latent heat cooling capacity by the product of the estimated value of the gain of the pressure difference imbalance component and the latent heat driving force to obtain the target pressure difference imbalance component.

8. The air conditioning energy-saving composite control system based on a virtual power plant according to claim 7, characterized in that, The building static pressure setpoint is generated based on the target pressure difference imbalance component and sent to the building automation system, including: The composite control unit constructs a linear model describing the change of building static pressure data with pressure difference imbalance component. The linear model is solved using historical data within a sliding time window and ridge regression algorithm to identify the pressure difference permeability coefficient that characterizes the airtightness of the building. The composite control unit is set to maintain the building in a slightly positive pressure state during the warm season by setting a basic moisture-proof pressure difference; The composite control unit calculates the product of the target pressure difference imbalance component and the pressure difference permeability coefficient as the pressure difference adjustment bias, and adds the pressure difference adjustment bias to the basic moisture-proof pressure difference setting value, which is then sent to the building automation system as the building static pressure setting value to realize virtual power plant power response and building moisture-proof control.

9. A composite control method for air conditioning energy saving based on a virtual power plant, realizing the composite control system for air conditioning energy saving based on a virtual power plant as described in any one of claims 1-8, characterized in that, include: Collects fresh air flow rate, exhaust air flow rate, air temperature and humidity parameters, and receives power adjustment commands issued by the virtual power plant; The fresh air flow rate and the exhaust air flow rate are decomposed into flow field components to obtain the ventilation flow component and the pressure imbalance component. The real-time latent heat load is calculated based on the air temperature and humidity parameters, and the differential pressure wet load gain coefficient is identified. The differential pressure wet load gain coefficient characterizes the influence weight of the differential pressure imbalance component relative to the ventilation flow component on the real-time latent heat load. The power adjustment command is converted into a target latent heat cooling capacity. Under the condition that the ventilation flow component meets the preset ventilation benchmark, the target differential pressure imbalance component required to match the target latent heat cooling capacity is calculated according to the differential pressure wet load gain coefficient. The building static pressure setpoint is generated according to the target differential pressure imbalance component and sent to the building automation system.

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