Building and air conditioning system coupling cold storage-based power peak shaving duration calculation method and system

Abstract

The application relates to a power peak regulation time length calculation method and system based on building and air conditioning system coupling cold storage, and belongs to the technical field of power grid peak regulation. The method comprises the following steps: constructing a first heat balance equation of the building and the air conditioning system; constructing a fan coil heat dissipation model in the air conditioning system; constructing a temperature difference change attenuation expression in a shutdown state of a refrigeration host of the air conditioning system; obtaining a fan coil heat dissipation expression after the refrigeration host is shut down according to the attenuation expression and the fan coil heat dissipation model, so as to obtain a dynamic change model of indoor air temperature in a building cold storage release process; calculating corresponding indoor air temperature values at each time according to the dynamic change model, so as to obtain a whole-time indoor temperature time sequence data set; drawing an indoor temperature rise change curve after shutdown according to the data set, and obtaining a time node corresponding to a temperature upper limit value from the curve, and then determining the power peak regulation time length of the building and air conditioning system coupling cold storage capacity. The application improves the power peak regulation accuracy of the air conditioning system.

Description

Technical Field

[0001] This invention relates to the field of power grid peak shaving technology, and in particular to a method and system for calculating power peak shaving duration based on the coupling of cold storage between buildings and air conditioning systems. Background Technology

[0002] With the rapid development of the new energy power generation industry, the scale of intermittent new energy units such as wind power and photovoltaic power connected to the grid continues to expand. The inherent randomness, volatility and output uncertainty of new energy power generation significantly exacerbate grid load fluctuations, continuously consume the grid's limited frequency regulation and peak shaving resources, and pose a severe challenge to the safe and stable operation of the power system.

[0003] In the existing energy storage technology system, chemical energy storage suffers from problems such as high construction costs, limited service life, insufficient economic viability for large-scale promotion, and high subsequent operation and maintenance costs, making it difficult to achieve large-scale popularization and application in the short term, and unable to fully adapt to the grid's normalized peak-shaving needs. Against this backdrop, deeply exploring demand-side flexible regulation resources has become a core means to fill the grid's peak-shaving capacity gap, improve the absorption capacity of new energy sources, and ensure the stable operation of the grid. Air conditioning systems are the core load of building electricity consumption in summer, accounting for a high proportion of electricity consumption and possessing great load regulation potential. Buildings themselves possess natural cold storage characteristics. Utilizing the cold storage capacity of the building envelope for grid peak-shaving and regulation is an important technical path for low-cost, high-efficiency improvement of the grid's demand-side regulation capacity, possessing extremely high engineering application value. Currently, the industry has widely focused on the development and application of various artificial cold storage technologies, but the development and engineering application of peak-shaving based on the building's native cold storage resources still have significant shortcomings, failing to fully explore the passive cold storage peak-shaving potential of the building envelope. Research has found that the inability to accurately and efficiently grasp the dynamic laws of building cold storage peak-shaving and accurately quantify the building's cold storage peak-shaving potential is the core technical bottleneck restricting the large-scale implementation of this technology.

[0004] Currently, existing technologies for calculating the cold storage and peak-shaving potential of buildings mainly fall into two categories: numerical simulation and analytical methods. Both methods have inherent technical limitations, making it difficult to meet the needs of large-scale and widespread engineering applications. Numerical simulation primarily utilizes professional simulation software such as EnergyPlus and TRNSYS. Based on the actual dimensions of the building and heat transfer parameters such as the thermal conductivity and specific heat capacity of the building envelope (exterior walls, doors, windows, roof, and ground), a refined building heat transfer model is built. Simulation calculations simulate the indoor temperature rise characteristics after the building stops cooling, thereby calculating the building's cold storage and peak-shaving potential. While this method offers relatively high accuracy, it suffers from drawbacks such as high modeling difficulty, long development cycles, poor versatility, and high application costs. Specifically, for buildings with different structures and parameters, it is necessary to re-collect building parameters and build a dedicated simulation model. The modeling cycle for a single building typically lasts up to a week, resulting in a massive workload. Furthermore, this method requires the purchase of specialized commercial simulation software and demands high levels of expertise from operators, requiring extensive specialized training to become proficient. This high barrier to entry makes it unsuitable for the rapid, batch calculation needs of a large number of buildings. Analytical methods represent another mainstream approach. This method constructs a mathematical model of heat transfer within the building envelope, generating a multi-dimensional, multi-variable set of partial differential equations. By solving these equations analytically, it deduces the temperature rise pattern after the building's cooling system is deactivated, thereby quantifying its potential for cold storage and peak shaving. While this method also boasts high theoretical accuracy, it suffers from complex modeling logic, high solution difficulty, high parameter dependence, and extremely poor practicality. On one hand, the differential equations governing building heat transfer are logically complex, and solving high-order partial differential equations is extremely difficult. In most complex building conditions, effective analytical solutions cannot be obtained, resulting in insufficient calculation stability. On the other hand, this method relies on comprehensive and accurate detailed heat transfer physical parameters of the building envelope. Obtaining these parameters is challenging, and the method demands a high level of expertise in mathematical modeling and equation solving from operators, making it difficult for ordinary engineering technicians to master. This significantly limits the method's widespread adoption and large-scale application. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the problems in the prior art that the calculation and modeling of the building's cold storage and peak-shaving potential is complicated, has a long cycle, poor universality, is difficult to quickly calculate in batches, and cannot accurately assess the peak-shaving time of the building combined with the air conditioning pipes to store water for cold storage.

[0006] Firstly, to solve the aforementioned technical problems, this invention provides a method for calculating the peak-shaving duration of electricity based on coupled cold storage of building and air conditioning systems, comprising: S1. Based on the building's indoor parameters and the heat dissipation of the fan coil units in the air conditioning system, construct the first heat balance equation; based on the flow rate of the water in the pipes of the air conditioning system, the heat transfer coefficient and heat transfer area of ​​the fan coil units, construct the fan coil unit heat dissipation model. S2. Based on the attenuation law of the temperature difference between indoor air and the water stored in the pipe, construct an expression for the attenuation of temperature difference change when the air conditioning system's refrigeration unit is stopped; based on the expression for the attenuation of temperature difference change and the heat dissipation model of the fan coil unit, obtain an expression for the heat dissipation of the fan coil unit after the refrigeration unit is stopped. S3. Based on the expression for the heat dissipation of the fan coil unit after the refrigeration unit stops operating and the first heat balance equation, the second heat balance equation is obtained; the second heat balance equation is solved to obtain the dynamic change model of indoor air temperature during the building's cold storage and release process. S4. Based on the dynamic change model and according to the preset interval, calculate the indoor air temperature value corresponding to each discrete moment to obtain the full-time indoor temperature time series dataset; draw the indoor temperature rise change curve after the refrigeration unit stops based on the time series dataset. S5. Preset an upper limit threshold for indoor temperature, and locate the time node corresponding to the curve value reaching the upper limit threshold in the indoor temperature rise change curve; use the duration corresponding to the time node as the power peak shaving duration for the coupled cold storage capacity of the building and air conditioning system.

[0007] In one embodiment of the present invention, step S3, in which the second heat balance equation is solved to obtain a dynamic change model of indoor air temperature during the building's cold storage and release process, further includes fitting key parameters of the dynamic change model using measured data; wherein, the step of fitting key parameters of the dynamic change model using measured data is as follows: Stop the refrigeration unit from running, but keep the chilled water pump and fan coil units of the air conditioning system running; install indoor temperature measuring points in each room of the building; According to the preset sampling time interval, the real-time temperature data of the indoor temperature measuring points in each room are obtained to obtain the temperature dataset of each room at different times; Based on the temperature dataset, the arithmetic mean of the measured temperature data of all rooms at the same collection time is calculated to obtain the overall indoor average temperature of the building at the current collection time. Based on the overall average indoor temperature of the building at each time point, calculate the indoor temperature rise relative to the initial time of shutdown of the chiller unit at each time point; Calculate the temperature decay coefficient; based on the indoor temperature rise value and the temperature decay coefficient, and using the least squares method to linearly fit the dynamic change model, obtain the key parameters of the dynamic change model.

[0008] In one embodiment of the present invention, the expression for the average indoor temperature of the building at the current data collection time is: ; in, Indicates the first Average indoor temperature of the building at the time of data collection. This indicates the total number of indoor temperature measuring points installed in the room. Indicates the first Collection time number Real-time temperature data for each room.

[0009] In one embodiment of the present invention, the expression of the first heat balance equation is: ; in, Indicates the equivalent heat capacity of the room. Indicates indoor air temperature. Indicates time, Indicates the heat gained indoors. This indicates the heat dissipation of the fan coil unit.

[0010] In one embodiment of the present invention, the mathematical expression of the fan coil unit heat dissipation model is: ; in, This indicates the heat carried away by the fan coil unit. Indicates the water flow rate in the air conditioning duct system. This represents the specific heat capacity of water at constant pressure. This indicates the average temperature of the water stored in the pipe. This indicates the heat transfer coefficient of the fan coil unit. This indicates the heat exchange area of ​​the fan coil unit. This indicates the indoor air temperature.

[0011] In one embodiment of the present invention, the expression for the attenuation of the temperature difference change is: ; ; in, Indicates indoor air temperature. This indicates the average temperature of the water stored in the pipe. This indicates the temperature of the indoor air at the moment the refrigeration unit stops. This indicates the average temperature of the water in the pipes at the moment the refrigeration unit stops. This indicates the heat transfer coefficient of the fan coil unit. This indicates the heat exchange area of ​​the fan coil unit. Indicates the water flow rate in the air conditioning duct system. This represents the specific heat capacity of water at constant pressure.

[0012] In one embodiment of the present invention, the mathematical expression of the dynamic change model is: ; in, Indicates indoor air temperature. This indicates the temperature of the indoor air at the moment the refrigeration unit stops. Indicates the temperature decay coefficient. Indicates time, and These represent the key parameters of the model.

[0013] In one embodiment of the present invention, the mathematical expression of the second heat balance equation is: ; in, Indicates the equivalent heat capacity of the room. Indicates indoor air temperature. Indicates time, Indicates the heat gained indoors. This indicates the amount of heat removed by the fan coil unit when the refrigeration unit stops running. This represents the temperature decay coefficient.

[0014] In one embodiment of the present invention, in step S4, the indoor temperature rise curve after the refrigeration unit stops is plotted with the duration after the refrigeration unit stops as the horizontal axis and the temperature rise difference between the indoor air temperature at each moment and the initial temperature at the time of shutdown as the vertical axis.

[0015] Secondly, to solve the above-mentioned technical problems, the present invention provides a power peak shaving duration calculation system based on building and air conditioning system coupled cold storage, used to implement the above-mentioned power peak shaving duration calculation method based on building and air conditioning system coupled cold storage, including: The first construction module is used to construct the first heat balance equation based on the building's indoor parameters and the heat dissipation of the fan coil units in the air conditioning system; and to construct the fan coil unit heat dissipation model based on the flow rate of the water in the pipes of the air conditioning system, the heat transfer coefficient and heat transfer area of ​​the fan coil units. The second construction module is used to construct an expression for the attenuation of temperature difference change when the air conditioning unit is shut down, based on the attenuation law of the temperature difference between indoor air and water in the pipe; and to obtain an expression for the heat dissipation of the fan coil unit after the air conditioning unit is shut down, based on the expression for the attenuation of temperature difference change and the heat dissipation model of the fan coil unit. The dynamic change model construction module is used to obtain the second heat balance equation based on the expression of the heat dissipation of the fan coil unit after the refrigeration unit stops and the first heat balance equation; and to solve the second heat balance equation to obtain the dynamic change model of indoor air temperature during the building's cold storage and release process. The curve plotting module is used to calculate the indoor air temperature values ​​corresponding to each discrete moment according to the dynamic change model and at preset intervals, so as to obtain the indoor temperature time series dataset for the whole time period; and to plot the indoor temperature rise curve after the refrigeration unit stops based on the time series dataset. The power peak shaving duration output module is used to preset the upper limit threshold of indoor temperature, locate the time node corresponding to the curve value reaching the upper limit threshold in the indoor temperature rise change curve, and use the duration corresponding to the time node as the power peak shaving duration of the building and air conditioning system's coupled cold storage capacity.

[0016] Compared with the prior art, the above-described technical solution of the present invention has the following advantages: (1) The present invention provides a method and system for calculating peak power shaving duration based on coupled cold storage of building and air conditioning system. It adopts a step-by-step modeling and layered decoupling design approach, which gets rid of the redundant modeling of the entire building structure and irrelevant equipment in traditional technology. It only focuses on the core thermal parameters of the indoor environment, the water storage characteristics of the air conditioning pipes and the heat exchange parameters of the fan coil units to build the heat balance equation and heat dissipation model. It accurately anchors the core heat transfer mechanism of the building cold storage release after the refrigeration unit stops. At the same time, based on the objective law of the natural decay of the temperature difference between the cold and hot media, it fits the real working condition of no active cold source replenishment after the shutdown, and derives the dynamic temperature difference change expression and the fan coil heat dissipation formula. It realizes the accurate decoupling of cold storage of building envelope, cold storage of pipe water and heat dissipation of terminal equipment, effectively avoids the problems of fuzzy mechanism, redundant parameters and poor adaptability of working conditions in traditional simulation model, fundamentally improves the accuracy of the physical logic of the model, and provides a theoretical basis for subsequent dynamic temperature calculation and peak shaving duration calculation.

[0017] (2) This invention uses heat transfer mechanism to ensure the accuracy of basic calculations. By adapting to working conditions and dynamically correcting theoretical parameter deviations, the calculation accuracy is stably controlled within the allowable range of engineering while greatly reducing modeling difficulty, computing power consumption and time cost. It perfectly balances the three core requirements of measurement accuracy, calculation efficiency and engineering adaptability, and solves the industry problem that traditional simulation technology cannot achieve both high accuracy and high efficiency.

[0018] (3) This invention has good reusability. It does not require repeated modeling for the same building. It can quickly generate temperature rise curves and adapt to the calculation of peak shaving duration under multiple working conditions, effectively reducing the time and economic costs of engineering calculations. It has stronger versatility and applicability. Attached Figure Description

[0019] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0020] Figure 1This is a flowchart of a method for calculating the peak power shaving duration based on the coupled cold storage of building and air conditioning systems in a preferred embodiment of the present invention. Figure 2 This is a schematic diagram of the heat transfer model of a building and an air conditioning terminal in a preferred embodiment of the present invention; Figure 3 This is a graph showing the change in indoor temperature rise after the refrigeration unit stops in a preferred embodiment of the present invention. Figure 4 This is a structural diagram of a power peak shaving duration calculation system based on the coupled cold storage of building and air conditioning systems in a preferred embodiment of the present invention. Detailed Implementation

[0021] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0022] Example 1: Reference Figure 1 As shown, this embodiment of the invention provides a method for calculating the peak power shaving duration based on the coupled cold storage of building and air conditioning systems, including but not limited to the following steps: S1. Based on the building's indoor parameters and the heat dissipation of the fan coil units in the air conditioning system, construct the first heat balance equation; based on the flow rate of water in the pipes of the air conditioning system, the heat transfer coefficient and heat transfer area of ​​the fan coil units, construct the fan coil unit heat dissipation model. S2. Based on the attenuation law of the temperature difference between indoor air and water in the pipe, construct an expression for the attenuation of temperature difference change when the air conditioning unit is stopped; based on the expression for the attenuation of temperature difference change and the fan coil heat dissipation model, obtain an expression for the heat dissipation of the fan coil after the air conditioning unit is stopped. S3. Based on the expression for the heat dissipation of the fan coil unit after the refrigeration unit stops operating and the first heat balance equation, the second heat balance equation is obtained; the second heat balance equation is solved to obtain the dynamic change model of indoor air temperature during the building's cold storage and release process. S4. Based on the dynamic change model and according to the preset interval, calculate the indoor air temperature value corresponding to each discrete moment to obtain the indoor temperature time series dataset for the whole time period; draw the indoor temperature rise curve after the refrigeration unit stops based on the time series dataset. S5. Preset the upper limit threshold of indoor temperature. In the indoor temperature rise change curve, locate the time node corresponding to the curve value reaching the upper limit threshold of temperature. Use the duration corresponding to the time node as the power peak shaving duration of the building and air conditioning system's coupled cold storage capacity.

[0023] The present invention provides a method for calculating the peak power shaving duration based on the coupled cold storage of building and air conditioning systems. This method solves the problems of cumbersome procedures and time-consuming and labor-intensive traditional calculation methods, and provides a simple and accurate calculation method. It provides a calculation method for formulating the operation strategy of air conditioning system participating in power demand response, and improves the peak power shaving capability of air conditioning system. This invention adopts a step-by-step modeling and layered decoupling design approach, breaking away from the redundant modeling of the entire building structure and irrelevant equipment in traditional technologies. It constructs heat balance equations and heat dissipation models only around the core indoor thermal parameters, the water storage characteristics of air conditioning pipes, and the heat exchange parameters of fan coil units. It accurately anchors the core heat transfer mechanism of the building's cold storage and release after the chiller is shut down. At the same time, based on the objective law of the natural decay of the temperature difference between the hot and cold media, it closely matches the real working condition of no active cold source replenishment after shutdown, and derives the dynamic temperature difference change expression and the fan coil unit heat dissipation formula. It achieves precise decoupling of building envelope cold storage, pipe water cold storage, and terminal equipment heat dissipation, effectively avoiding the problems of fuzzy mechanism, redundant parameters, and poor adaptability to working conditions in traditional simulation models. It fundamentally improves the accuracy of the model's physical logic and provides a theoretical basis for subsequent dynamic temperature calculation and peak-shaving duration calculation. Step S3 of this invention uses the core model and equations constructed in the early stage to iteratively derive the second heat balance equation adapted to the shutdown condition, and solves the dynamic change model of indoor temperature. Unlike the static calculation mode of traditional simulation with fixed parameters, this model can accurately adapt to the variable operating condition characteristics of temperature difference decay and dynamic change of heat dissipation power after shutdown, and completely restore the entire process of cold release of building and air conditioning pipe coupling and storage. At the same time, it effectively gets rid of the high dependence of simulation calculation on the thermal properties of building materials and equipment calibration parameters. Dynamic deduction can be completed without massive precise parameter input, which greatly reduces the calculation error caused by parameter matching deviation, and takes into account the model calculation accuracy and engineering condition adaptability. Based on the model solution, step S4 uses a discretization calculation method with a preset time interval to obtain the full-time indoor temperature time series dataset and plot a visualized indoor temperature rise curve. This breaks through the limitation of traditional technology that can only output single-point temperature data, fully covers the entire cycle of temperature rise after the refrigeration unit stops, accurately captures the subtle changes in indoor temperature at different times, realizes the fine, continuous and quantitative analysis of the entire process of cold storage and energy release, and clearly presents the cold release rate and cooling capacity change law of building-air conditioning coupled cold storage. This not only solves the problems of fragmented and poor intuitiveness of traditional calculation results, but also provides complete and reliable data support for the accurate determination of peak shaving duration.Step S5 establishes a standardized and quantitative mechanism for determining the temperature constraint matching time by setting a preset upper limit threshold for indoor temperature. This accurately locates the critical time node corresponding to the temperature rise curve, thereby defining the effective power peak-shaving time corresponding to the coupled cold storage of the building and air conditioning system. This determination method strictly conforms to the building indoor comfort design specifications and the actual needs of power grid peak-shaving projects. It is standardized, and the results are objective and quantifiable, avoiding the problems of strong subjectivity, vague judgment basis, and large deviation in results of traditional experience calculations. It can accurately quantify the cold storage and peak-shaving capacity of the building body and air conditioning pipes, providing reliable data support for power grid peak shifting, air conditioning off-peak operation, and building energy-saving scheduling. In addition, the method described in this invention avoids the complex mode of traditional full-dimensional simulation modeling and massive computing power iterative calculation. It adopts a lightweight technical path of mechanism modeling, equation solving, time-series discretization, and quantitative determination. It does not require professional simulation software and high-end computing power equipment, greatly simplifying the modeling and calculation process and significantly improving the calculation efficiency. It can complete on-site testing in half a day and output complete calculation formulas and peak-shaving calculation results in a single day, which is a significant improvement in efficiency compared to the traditional modeling and simulation cycle of more than a week. Meanwhile, the model of this invention possesses excellent reusability, eliminating the need for repeated modeling of the same building. It can quickly generate temperature rise curves and adapt to peak-shaving duration calculations under multiple operating conditions, effectively reducing the time and economic costs of engineering calculations, and offering greater versatility and applicability. This method uses heat transfer mechanisms to ensure the accuracy of basic calculations, and weakens theoretical parameter deviations through operating condition adaptation and dynamic correction. While significantly reducing modeling difficulty, computing power consumption, and time costs, it stably controls calculation accuracy within the allowable range for engineering applications, perfectly balancing the three core requirements of calculation accuracy, computational efficiency, and engineering adaptability. It solves the industry problem of traditional simulation technologies being unable to simultaneously achieve high accuracy and high efficiency. This method combines theoretical and engineering testing approaches, providing a convenient calculation method that significantly reduces computational costs while ensuring the required calculation accuracy.

[0024] Specifically, in this embodiment of the invention, to simplify the calculation model, the following assumptions are made: the indoor temperature of a building has spatial distribution characteristics, and there are small differences in air temperature in different areas of the building; to simplify the model calculation, the spatial distribution effect of indoor temperature is ignored, and it is assumed that the overall indoor temperature is uniform; the cold energy stored in the building envelope, indoor furniture, and indoor air is integrated and uniformly equivalent to the total heat capacity; and various indoor comprehensive heat gains, such as heat dissipation from people, heat dissipation from equipment, solar radiation heat gain, and heat transfer from the building envelope, are equivalent to a constant heat flux. ,Right now The value is constant; the heat transfer coefficient between the fan coil unit and the indoor air remains constant, and the chilled water temperature rises slowly with the running time; after the chiller unit is shut down, the entire chilled water system relies solely on the cold storage capacity stored in the chilled water inside the pipes and fan coil units to continuously supply cooling to the room.

[0025] Specifically, in step S1, refer to Figure 2 As shown, taking indoor air as the research object, the first heat balance equation is constructed. Wherein, Figure 2 In the middle, symbol and Let represent the indoor temperature and the outdoor temperature, respectively. In this embodiment, the total heat transferred into the indoor air from both inside and outside the room, minus the cooling energy removed by the fan coil unit, equals the change in the internal energy of the indoor air. Therefore, the expression for the first heat balance equation is: (1) in, It represents the equivalent heat capacity of the room, specifically the equivalent heat capacity of the air, walls, water pipes and furniture as a whole, and the unit is kJ / ℃; Indicates indoor air temperature, in °C; Indicates time, in seconds; It represents the indoor heat gain, including heat gain from the building envelope and heat generated by people and equipment inside the room. It is a constant value and the unit is kW. This indicates the heat dissipation of the fan coil unit, i.e., the heat carried away by the fan coil unit, measured in kW, and varies over time. The building's indoor parameters include the indoor equivalent heat capacity, indoor air temperature, and indoor heat gain.

[0026] Specifically, based on the flow rate of water in the pipes of the air conditioning system, the heat transfer coefficient and heat transfer area of ​​the fan coil unit, a fan coil unit heat dissipation model is obtained. This model characterizes the change in heat carried away by the fan coil unit over time, and its mathematical expression is as follows: (2) in, This indicates the heat carried away by the fan coil unit. This indicates the water flow rate in the air conditioning piping system, expressed in kg / s. This represents the specific heat capacity of water at constant pressure, expressed in kJ / (°C). kg); This indicates the average temperature of the water stored in the pipe, expressed in °C. This indicates the heat transfer coefficient of the fan coil unit, with units of kW / (m²). 2 ℃); This indicates the heat exchange area of ​​the fan coil unit, in square meters (m²). 2 ; This indicates the indoor air temperature.

[0027] Specifically, in step S2, based on the attenuation law of the temperature difference between indoor air and water in the pipes, an expression for the attenuation of temperature difference change in the air conditioning system's refrigeration unit under shutdown conditions is constructed. The specific expression is as follows: (3) (4) in, Indicates indoor air temperature. This indicates the average temperature of the water stored in the pipe; This indicates the temperature of the indoor air at the moment the refrigeration unit stops, in °C. This indicates the average temperature of the water in the pipes at the moment the refrigeration unit stops, in °C. Indicates the temperature decay coefficient. This indicates the heat transfer coefficient of the fan coil unit. This indicates the heat exchange area of ​​the fan coil unit. Indicates the water flow rate in the air conditioning duct system. This represents the specific heat capacity of water at constant pressure. Furthermore, the expression for the decay of temperature difference (3) can be used to represent the solution of differential equation (2).

[0028] Furthermore, substituting the temperature difference change attenuation expression (3) into the fan coil heat dissipation model (2), we obtain the expression for the fan coil heat dissipation after the refrigeration unit stops operating, specifically: (5) in, This represents the heat carried away by the fan coil unit when the refrigeration unit stops operating, expressed in kW. According to the expression (5) for the heat dissipation of the fan coil unit after the refrigeration unit stops operating, it can be concluded that the heat carried away by the fan coil unit decreases exponentially after the refrigeration unit stops operating.

[0029] Specifically, in step S3, the expression for the heat dissipation of the fan coil unit after the refrigeration unit is shut down (5) is substituted into the first heat balance equation (1) to obtain the second heat balance equation, the specific mathematical expression of which is: (6) in, Indicates the equivalent heat capacity of the room. Indicates indoor air temperature. Indicates time, Indicates the heat gained indoors. This represents the temperature decay coefficient.

[0030] Furthermore, initially, the heat gained in the room is equal to the heat removed by the fan coil unit, thus we obtain: .

[0031] Therefore, the second heat balance equation (6) can be transformed into: .

[0032] Furthermore, the modified second heat balance equation is solved, and the resulting expression can be used to characterize the dynamic change model of indoor air temperature during the building's cold storage and release process. Therefore, the mathematical expression of the dynamic change model is: (7) in, Indicates indoor air temperature. This indicates the temperature of the indoor air at the moment the refrigeration unit stops. Indicates the temperature decay coefficient. Indicates time, and These represent the key parameters of the model.

[0033] The mathematical model theoretically yields the mathematical form of the indoor temperature change law during the release of the building's stored cold capacity. Since the relevant parameters are difficult to obtain, the next step in this embodiment of the invention is to use the experimental method to determine the key parameters of the mathematical model (i.e., the dynamic change model).

[0034] Furthermore, solving the second heat balance equation to obtain the dynamic change model of indoor air temperature during the building's cold storage and release process also includes fitting the key parameters of the dynamic change model using measured data; the steps for fitting the key parameters of the dynamic change model using measured data are as follows: S310, operating condition setting: Stop the chiller unit, but keep the chilled water pump and fan coil units of the air conditioning system running. Indoor temperature measuring points are installed in each room of the building. .

[0035] S320. According to the preset sampling time interval, acquire the real-time temperature data of each indoor temperature measuring point in each room. This yields temperature datasets for each room at different times. In this embodiment of the invention, the preset sampling time interval is preferably 60 seconds, meaning that indoor temperature data for each room is recorded every 60 seconds. .

[0036] S330. Based on the temperature dataset, calculate the arithmetic mean of the measured temperature data of all rooms at the same acquisition time to obtain the overall indoor average temperature of the building at the current acquisition time; wherein, the expression for the overall indoor average temperature of the building at the current acquisition time is: ; in, Indicates the first Average indoor temperature of the building at the time of data collection. This indicates the total number of indoor temperature measuring points installed in the room. Indicates the first Collection time number Real-time temperature data for each room.

[0037] S340, based on the overall average indoor temperature of the building at each time. Calculate the indoor temperature rise at each time point relative to the initial time of the cooling unit's shutdown. Its expression is: ; in, This indicates the temperature of the indoor air at the initial moment when the refrigeration unit stops.

[0038] S350, Calculate the temperature decay coefficient using formula (4) Based on the indoor temperature rise value and temperature decay coefficient, and by using the least squares method to linearly fit the dynamic change model (7), the key parameters of the dynamic change model are obtained. and .

[0039] Specifically, in step S4, the steps for plotting the indoor temperature rise curve after the refrigeration unit stops are as follows: S410. Based on the dynamic change model (7) and according to the preset interval (e.g., every 1 minute), calculate the indoor air temperature value corresponding to each discrete moment to obtain the full-time indoor temperature time series dataset.

[0040] S420. Plot the indoor temperature rise curve after the refrigeration unit stops, with the duration after the refrigeration unit stops as the horizontal axis and the temperature rise difference between the indoor air temperature at each moment and the initial temperature at the time of shutdown as the vertical axis.

[0041] Specifically, in step S5, the upper limit threshold for indoor temperature is determined. (For example, 29℃), find the temperature reached on the indoor temperature change curve. The corresponding time is the peak-shaving duration that can be achieved by utilizing the cooling capacity of water stored in the building and air conditioning pipes.

[0042] To clearly illustrate the methods described in the embodiments of the present invention, the following description is provided in conjunction with specific examples.

[0043] Example scenario parameters are as follows: Room dimensions: 6m long, 6m wide, 4.5m high; Fan coil unit water side flow The value is 0.12 kg / s. Specific heat capacity of water at constant pressure It is: 4.2 kJ / (kg) K); Fan coil unit convective heat transfer coefficient The value is: 0.03kW / (m 2 K); Fan coil unit heat exchange area It is: 5.09m 2 ; The temperature decay coefficient was calculated. The value is: 0.3044; The indoor ambient temperature at the moment the refrigeration unit stopped was 22℃.

[0044] Furthermore, based on the above scenario parameters, the indoor temperature rise data after the refrigeration unit stopped was obtained, as shown in Table 1. Table 1 is a table of indoor temperature rise data after the refrigeration unit stopped.

[0045] Table 1:

[0046] Furthermore, based on the temperature data in Table 1, the key parameters of the dynamic change model (7) are calculated, thus obtaining: .

[0047] Furthermore, based on the aforementioned dynamic change model, and according to a preset interval (60s), the indoor temperature rise curve after the cooling unit stops is generated, as shown below. Figure 3 As shown in the curve, after 20 minutes, the temperature rise reaches 7°C, and the room temperature reaches 29°C, which is the upper limit of the room temperature. This shows that utilizing the building's internal cooling capacity has a peak-shaving potential of about 20 minutes.

[0048] Regarding the calculation method for extending peak-shaving time by utilizing the residual cooling capacity of water stored in the pipes of buildings and their air conditioning systems, existing technologies employ TRNSYS software to build a full-area heat transfer simulation model of the building. The modeling process requires sequentially completing the modeling of the building's geometric outline, the thermal properties of the building envelope, chillers, fan coil units, and other equipment. After the chiller is shut down, the dynamic evolution of the temperature field in each room is output through numerical simulation. This simulation method has the advantage of high simulation accuracy; under normal operating conditions, the temperature simulation error can be controlled within ±0.3℃. However, it still has the following technical problems: First, the process of building a complete building model is cumbersome and the modeling cycle is long, typically taking up to a week for a single building. Second, the simulation accuracy is highly dependent on the accuracy of the input thermal property parameters; the software's built-in standard property parameters generally deviate from the actual thermal properties of materials on-site, directly reducing the reliability of the simulation. Third, the computational load for dynamically solving the temperature field of all rooms is large, requiring significant hardware computing resources. Compared with the above-mentioned traditional simulation methods, the calculation method proposed in this invention has the following advantages: First, this invention employs a dual-drive architecture, combining mechanistic models and measured data, to ensure computational accuracy. It first derives a mathematical model of indoor temperature rise changes through heat transfer mechanisms, locking in the standard paradigm for temperature rise calculation formulas, thus guaranteeing the physical logic accuracy of the calculation formulas from the model architecture level. Verification has shown that the calculated values ​​deviate from the actual measured temperatures within ±0.5℃, fully adapting to power peak-shaving engineering calculation scenarios.

[0049] Secondly, by analyzing heat transfer theory, the coefficients of the nonlinear terms in the calculation formula can be directly determined. This allows the entire formula to be transformed into a linear fitting form, avoiding the problems of divergence and fitting distortion that are prone to occur in nonlinear fitting, and significantly improving the accuracy of parameter fitting.

[0050] Third, by using on-site temperature test data to fit and solve the linear term coefficients of the formula, it is not necessary to obtain the thermal properties of various building envelopes and media in advance, effectively overcoming the industry problem that it is difficult to accurately measure and assign values ​​to physical properties in traditional simulations.

[0051] Fourth, the calculation process cycle of this invention is greatly reduced. On-site temperature testing can be completed in just half a day, and a complete temperature rise calculation formula can be output in a single day. For the same building, indoor temperature rise curves can be drawn in batches based on this formula, and the power peak shaving duration that the building's cooling storage can support can be quickly and intuitively queried, adapting to the rapid calculation needs of power grid peak shifting and valley filling.

[0052] Example 2: Based on the same inventive concept, this embodiment provides a power peak shaving duration calculation system based on the coupled cold storage of building and air conditioning system. The principle of solving the problem is similar to the power peak shaving duration calculation method based on the coupled cold storage of building and air conditioning system provided in Embodiment 1, and the repeated parts will not be described again.

[0053] Reference Figure 4 As shown, this embodiment provides a power peak shaving duration calculation system based on building and air conditioning system coupled cold storage, used to implement the power peak shaving duration calculation method based on building and air conditioning system coupled cold storage described in Embodiment 1, including: The first construction module is used to construct the first heat balance equation based on the building's indoor parameters and the heat dissipation of the fan coil units in the air conditioning system; and to construct the fan coil unit heat dissipation model based on the flow rate of water in the pipes of the air conditioning system, the heat transfer coefficient and heat transfer area of ​​the fan coil units. The second construction module is used to construct an expression for the attenuation of temperature difference change when the air conditioning unit is shut down, based on the attenuation law of temperature difference between indoor air and water in the pipe; and to obtain an expression for the heat dissipation of the fan coil unit after the air conditioning unit is shut down, based on the expression for the attenuation of temperature difference change and the heat dissipation model of the fan coil unit. The dynamic change model construction module is used to obtain the second heat balance equation based on the expression of the heat dissipation of the fan coil unit after the refrigeration unit stops and the first heat balance equation; the second heat balance equation is solved to obtain the dynamic change model of indoor air temperature during the building's cold storage and release process. The curve plotting module is used to calculate the indoor air temperature values ​​at each discrete moment according to the dynamic change model and preset intervals, so as to obtain the indoor temperature time series dataset for the whole time period; and plot the indoor temperature rise curve after the refrigeration unit stops based on the time series dataset. The power peak shaving duration output module is used to preset the upper limit threshold of indoor temperature and locate the time node corresponding to the curve value reaching the upper limit threshold in the indoor temperature rise change curve; the duration corresponding to the time node is used as the power peak shaving duration of the building and air conditioning system's coupled cold storage capacity.

[0054] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0055] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0056] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0057] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0058] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for calculating peak-shaving duration of electricity based on coupled cold storage of building and air conditioning systems, characterized in that, include: S1. Based on the building's indoor parameters and the heat dissipation of the fan coil units in the air conditioning system, construct the first heat balance equation; based on the flow rate of the water stored in the pipes of the air conditioning system, the heat transfer coefficient and heat transfer area of ​​the fan coil units, construct the fan coil unit heat dissipation model. S2. Based on the attenuation law of the temperature difference between indoor air and water stored in the pipe, construct an expression for the attenuation of temperature difference change when the air conditioning system's refrigeration unit is off. Based on the temperature difference change attenuation expression and the fan coil heat dissipation model, the expression for the fan coil heat dissipation after the refrigeration unit stops working is obtained. S3. Based on the expression for the heat dissipation of the fan coil unit after the refrigeration unit stops operating and the first heat balance equation, the second heat balance equation is obtained. Solving the second heat balance equation yields a dynamic change model of indoor air temperature during the building's cold storage and release process; S4. Based on the dynamic change model and according to the preset interval, calculate the indoor air temperature value corresponding to each discrete moment to obtain the full-time indoor temperature time series dataset; draw the indoor temperature rise change curve after the refrigeration unit stops based on the time series dataset. S5. Preset an upper limit threshold for indoor temperature, and locate the time node corresponding to the curve value reaching the upper limit threshold in the indoor temperature rise change curve; use the duration corresponding to the time node as the power peak shaving duration for the coupled cold storage capacity of the building and air conditioning system.

2. The method for calculating peak power shaving duration based on coupled cold storage of building and air conditioning systems according to claim 1, characterized in that, In step S3, solving the second heat balance equation to obtain the dynamic change model of indoor air temperature during the building's cold storage and release process also includes fitting the key parameters of the dynamic change model using measured data; wherein, the step of fitting the key parameters of the dynamic change model using measured data is as follows: Stop the refrigeration unit from running, but keep the chilled water pump and fan coil units of the air conditioning system running; install indoor temperature measuring points in each room of the building; According to the preset sampling time interval, the real-time temperature data of the indoor temperature measuring points in each room are obtained to obtain the temperature dataset of each room at different times; Based on the temperature dataset, the arithmetic mean of the measured temperature data of all rooms at the same collection time is calculated to obtain the overall indoor average temperature of the building at the current collection time. Based on the overall average indoor temperature of the building at each time point, calculate the indoor temperature rise relative to the initial time of shutdown of the chiller unit at each time point; Calculate the temperature decay coefficient; based on the indoor temperature rise value and the temperature decay coefficient, and using the least squares method to linearly fit the dynamic change model, obtain the key parameters of the dynamic change model.

3. The method for calculating peak power shaving duration based on coupled cold storage of building and air conditioning systems according to claim 2, characterized in that, The expression for the average indoor temperature of the building at the current data collection time is: ； in, Indicates the first Average indoor temperature of the building at the time of data collection. This indicates the total number of indoor temperature measuring points installed in the room. Indicates the first Collection time number Real-time temperature data for each room.

4. The method for calculating peak power shaving duration based on coupled cold storage of building and air conditioning systems according to claim 1, characterized in that, The expression for the first heat balance equation is: ； in, Indicates the equivalent heat capacity of the room. Indicates indoor air temperature. Indicates time, Indicates the heat gained indoors. This indicates the heat dissipation of the fan coil unit.

5. The method for calculating peak power shaving duration based on coupled cold storage of building and air conditioning systems according to claim 1, characterized in that, The mathematical expression for the fan coil unit heat dissipation model is: ； in, This indicates the heat removed by the fan coil unit. Indicates the water flow rate in the air conditioning duct system. This represents the specific heat capacity of water at constant pressure. This indicates the average temperature of the water stored in the pipe. This indicates the heat transfer coefficient of the fan coil unit. This indicates the heat exchange area of ​​the fan coil unit. This indicates the indoor air temperature.

6. The method for calculating peak power shaving duration based on coupled cold storage of building and air conditioning systems according to claim 1, characterized in that, The expression for the attenuation of the temperature difference change is: ； ； in, Indicates indoor air temperature. This indicates the average temperature of the water stored in the pipe. This indicates the temperature of the indoor air at the moment the refrigeration unit stops. This indicates the average temperature of the water in the pipes at the moment the refrigeration unit stops. This indicates the heat transfer coefficient of the fan coil unit. This indicates the heat exchange area of ​​the fan coil unit. Indicates the water flow rate in the air conditioning duct system. This represents the specific heat capacity of water at constant pressure.

7. The method for calculating peak power shaving duration based on coupled cold storage of building and air conditioning systems according to claim 1, characterized in that, The mathematical expression for the dynamic change model is: ； in, Indicates indoor air temperature. This indicates the temperature of the indoor air at the moment the refrigeration unit stops. Indicates the temperature decay coefficient. Indicates time, and These represent the key parameters of the model.

8. The method for calculating peak power shaving duration based on coupled cold storage of building and air conditioning systems according to claim 1, characterized in that, The mathematical expression for the second heat balance equation is: ； in, Indicates the equivalent heat capacity of the room. Indicates indoor air temperature. Indicates time, Indicates the heat gained indoors. This indicates the amount of heat removed by the fan coil unit when the refrigeration unit stops running. This represents the temperature decay coefficient.

9. The method for calculating peak power shaving duration based on coupled cold storage of building and air conditioning systems according to claim 1, characterized in that, In S4, the indoor temperature rise curve after shutdown is plotted with the duration after the refrigeration unit stops as the horizontal axis and the temperature rise difference between the indoor air temperature at each moment and the initial temperature at the time of shutdown as the vertical axis.

10. A power peak shaving duration calculation system based on building-air conditioning system coupled cold storage, used to implement the power peak shaving duration calculation method based on building-air conditioning system coupled cold storage as described in any one of claims 1 to 9, characterized in that, include: The first building module is used to construct the first heat balance equation based on the building's indoor parameters and the heat dissipation of the fan coil units in the air conditioning system. Based on the flow rate of water in the pipes of the air conditioning system, the heat transfer coefficient and heat transfer area of ​​the fan coil unit, a heat dissipation model of the fan coil unit is constructed. The second construction module is used to construct an expression for the temperature difference change attenuation when the air conditioning system's refrigeration unit is off, based on the attenuation law of the temperature difference between indoor air and the water stored in the pipe. Based on the temperature difference change attenuation expression and the fan coil heat dissipation model, the expression for the fan coil heat dissipation after the refrigeration unit stops working is obtained. The dynamic change model construction module is used to obtain the second heat balance equation based on the expression of the heat dissipation of the fan coil unit after the refrigeration unit stops and the first heat balance equation. Solving the second heat balance equation yields a dynamic change model of indoor air temperature during the building's cold storage and release process; The curve plotting module is used to calculate the indoor air temperature values ​​corresponding to each discrete moment according to the dynamic change model and at preset intervals, so as to obtain the indoor temperature time series dataset for the whole time period; and to plot the indoor temperature rise curve after the refrigeration unit stops based on the time series dataset. The power peak shaving duration output module is used to preset the upper limit threshold of indoor temperature, locate the time node corresponding to the curve value reaching the upper limit threshold in the indoor temperature rise change curve, and use the duration corresponding to the time node as the power peak shaving duration of the building and air conditioning system's coupled cold storage capacity.

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