Annual simulation and calculation method of central air conditioning system based on coupling of surface cooler and cooling tower heat exchange model

Abstract

The application discloses a kind of based on table cooler and cooling tower heat exchange model coupling central air conditioning system all year round COP simulation measurement method, comprising: constructing table cooler physical heat exchange model, cooling tower physical heat exchange model and chiller part load performance model;Coupling each model, form central air conditioning system hour-by-hour energy calculation model;Input all year round meteorological parameter time series data and building cooling load time series data;In each calculation period, traverse the combination of operating parameters;For each operating parameter combination, calculate the refrigerating capacity of central air conditioning system and the total power consumption of machine room core equipment in the period;Based on the refrigerating capacity and total power consumption of each period, calculate the energy efficiency coefficient of the period, select the optimal operating parameter combination as the optimal operating condition of the period;Statistical all year round energy efficiency coefficient of each period, output all year round energy efficiency prediction value and optimal operating parameter combination.The application realizes economic, accurate central air conditioning system COP simulation measurement.

Description

Technical Field

[0001] This application relates to the technical field of energy system energy efficiency calculation, specifically to a method for simulating and calculating the annual COP of a central air conditioning system based on a coupled heat exchange model of a surface cooler and a cooling tower. Background Technology

[0002] In the field of HVAC and building energy conservation control technology, the energy-saving design and energy efficiency assessment of central air conditioning systems are crucial. With the increasing number of venues equipped with large-scale central air conditioning systems, such as commercial complexes, data centers, clean operating rooms in pharmaceutical facilities, lithium battery and other electronic manufacturing workshops, and industrial high-efficiency refrigeration rooms, the demand for accurate annual energy efficiency calculations of central air conditioning systems is growing. Accurate energy efficiency calculations provide key technical support for the design, energy efficiency assessment, and operational optimization of related projects, helping to reduce energy consumption, improve energy utilization efficiency, and promote the sustainable development of the industry.

[0003] Currently, in the field of central air conditioning system simulation, TRNSYS software is the mainstream tool for calculating the annual energy efficiency of air conditioning systems. As a general-purpose platform for dynamic simulation of energy systems, this software is widely used in the economic analysis of building energy systems, HVAC systems, and renewable energy systems. It adopts a modular modeling approach, abstracting equipment such as chillers, heat exchangers, cooling towers, pumps, and fans into several functional components, and establishing equipment models based on energy balance relationships and empirical performance parameters. Users can connect the components according to the system structure, input meteorological data and load data, and realize dynamic simulation analysis of the central air conditioning system throughout its annual operating cycle. By calculating the operating status and energy consumption of each device hourly, key indicators such as the system's annual energy consumption and operating efficiency are obtained, providing a reference for the energy-saving design, scheme comparison, and operation strategy analysis of central air conditioning systems.

[0004] However, existing technologies have significant drawbacks. While TRNSYS software can meet the technical requirements for year-round energy efficiency simulation of central air conditioning systems, its licensing fees are high, and genuine licenses are expensive, significantly increasing the initial investment cost for energy efficiency calculations. For small and medium-sized design units, energy service companies, and project investors, the procurement cost is too high, hindering widespread adoption. Furthermore, the software is difficult to learn; as a general-purpose simulation software, it is challenging to use, with cumbersome model building processes and complex internal parameter settings. Especially in the field of high-efficiency HVAC computer rooms, simulating key components such as surface coolers, cooling towers, and refrigeration units requires purchasing third-party commercial libraries developed based on TRNSYS. Otherwise, users must independently write relevant component models using Fortran, requiring HVAC professionals to have additional programming skills, further increasing the software's learning curve and resulting in lengthy modeling times, making it difficult to adapt to the high-efficiency calculation needs of actual engineering projects. In addition, existing technologies still have problems in energy-saving design and scheme comparison for refrigeration rooms, such as insufficient accuracy in simulating energy efficiency under annual operating conditions and lack of precise quantitative basis for comparing multiple schemes. They cannot accurately support the calculation of investment and energy-saving benefits throughout the entire life cycle, and it is also difficult to select the best overall configuration scheme. Summary of the Invention

[0005] In order to achieve a convenient, economical, and accurate COP simulation calculation method for central air conditioning systems, this application provides a method for simulating and calculating the annual COP of a central air conditioning system based on the coupling of a heat exchange model of the surface cooler and the cooling tower.

[0006] Firstly, this application provides a method for simulating and calculating the annual COP of a central air conditioning system based on a coupled heat exchange model of the surface cooler and the cooling tower, including: A physical heat transfer model for the surface cooler, a physical heat transfer model for the cooling tower, and a part-load performance model for the chiller unit are constructed. The surface cooler physical heat transfer model is based on a three-layer structure of tube bundle, fins, and heat exchanger, and calculates the heat transfer process between the air side outside the tubes and the water side inside the tubes. The cooling tower physical heat transfer model is based on the Michael enthalpy difference method and calculates the heat transfer process between the cooling water and the air. The part-load performance model for the chiller unit is based on a preset performance data table and reflects the operating energy efficiency at different load rates and cooling water inlet temperatures. The physical heat transfer model of the surface cooler, the physical heat transfer model of the cooling tower, and the partial load performance model of the chiller are coupled to form an hourly energy calculation model for the central air conditioning system. The coupling process includes: using the water-side inlet temperature of the finned tubes calculated by the surface cooler as the chilled water supply temperature input of the chiller, using the cooling water outlet temperature calculated by the cooling tower as the cooling water inlet temperature input of the chiller, and using the current cooling load divided by the rated cooling capacity of the chiller to obtain the main unit load rate input, which together drive the chiller partial load performance model to output the real-time COP value. The system inputs the annual meteorological parameter time series data and building cooling load time series data into the hourly energy calculation model of the central air conditioning system; within each calculation period, it iterates through the combinations of operating parameters of the chilled water pump, cooling water pump, and cooling tower; for each combination of operating parameters, it calculates the cooling capacity of the central air conditioning system and the total power consumption of the core equipment in the computer room within that period; based on the cooling capacity and total power consumption of each period, it calculates the energy efficiency coefficient for that period, and selects the operating parameter combination with the optimal energy efficiency coefficient as the optimal operating condition for that period; Calculate the energy efficiency coefficient for each period of the year, and output the annual energy efficiency forecast and the corresponding optimal combination of operating parameters.

[0007] By adopting the above scheme, with the physical heat exchange model of the surface cooler and cooling tower as the core model, and combined with the partial load characteristics of the chiller unit, the optimal combination of equipment operating parameters under different operating conditions is selected through full-domain traversal optimization. The scheme completes the hourly energy efficiency calculation throughout the year, outputs accurate full COP and sub-item energy consumption data, and improves the accuracy and reliability of the annual COP calculation. It does not rely on professional commercial simulation software and complex programming environment. The calculation can be completed by simply inputting visual parameters. Moreover, all intermediate calculation data are stored in real time, realizing the transparency and traceability of the entire calculation process, reducing the threshold and cost of use.

[0008] Preferred, including: The physical heat transfer model of the finned tube surface cooler adopts the equivalent reference heat exchanger modeling method. The equivalent reference heat exchanger modeling method includes: the equivalent heat transfer area is obtained by weighted average of the heat transfer areas of each type of terminal equipment in the system according to the number of units; the equivalent air volume and equivalent chilled water flow rate are obtained by weighted average of the rated air volume and rated water flow rate of each type of terminal equipment according to the number of units; the geometric structure parameters are the tube bundle arrangement, fin thickness, fin spacing and tube diameter corresponding to the terminal model with the highest proportion of total cooling capacity in the system. The physical heat transfer model of the finned tube surface cooler, after constructing an equivalent reference heat exchanger, uses an iterative algorithm to solve for the air-side and water-side temperature states that meet the system operating conditions. Specifically, it includes: calculating the lateral and longitudinal relative spacing of the tube bundles using the tube bundle layer, introducing tube row correction coefficients and tube bundle shape coefficients; calculating the theoretical efficiency of the fins and the convective heat transfer coefficient of the gas outside the tubes using the fin layer; integrating the heat transfer between the air side outside the tubes and the water side inside the tubes in the heat exchanger layer, calculating the heat capacity ratio, the number of heat transfer units (NTUs), the heat exchanger efficiency, and the overall heat transfer coefficient, and using iterative solutions to ensure that the difference between the calculated heat transfer and the target heat transfer is less than a set error threshold, and the difference between the finned tube return air temperature and the set air conditioning temperature is less than a set temperature error threshold, and obtaining the finned tube outlet air temperature and the finned tube water-side inlet and outlet temperatures output after iterative convergence.

[0009] By adopting the above scheme and using an equivalent benchmark heat exchanger modeling method, both measurement accuracy and computational efficiency are taken into account, avoiding the massive amount of iterative calculations generated in the dynamic simulation scenario of every hour throughout the year, thus achieving a balance between practicality and simulation efficiency. By solving the temperature state of the air side and water side through iterative algorithms, the coupled heat exchange process of the air side outside the tube and the water side inside the tube can be accurately simulated, making full use of the heat exchange capacity of the finned tube heat exchanger, increasing the supply temperature of chilled water to meet the end-user demand, thereby optimizing the operating conditions of the chiller unit and improving the unit's cooling efficiency and actual COP.

[0010] Preferred, including: The cooling towers are defined as open counter-flow cooling towers and open cross-flow cooling towers. Correspondingly, a heat transfer model based on the Michael enthalpy difference method is established for open cooling towers, distinguishing between the two structural forms of counter-flow and cross-flow. The cooling number of the counter-flow cooling tower is calculated based on the Michael enthalpy difference equation, while the cooling number of the cross-flow cooling tower is obtained by multiplying the cooling number of the counter-flow cooling tower by the Tezuka Shunichi correction factor. The thermodynamic characteristic expression of the packing is defined as a power function relationship between the cooling number and the air-to-water ratio. The open cooling tower's McEl enthalpy difference heat transfer model uses a bisection method to iteratively solve for the cooling water outlet temperature. This includes: using the cooling number as the target convergence variable, setting a search range for the cooling water outlet temperature, calculating the cooling number by substituting the air-to-water ratio under the current operating conditions into the thermodynamic characteristic expression of the packing, determining convergence when the absolute value of the difference between the calculated cooling number and the preset target cooling number is less than a set error threshold, and obtaining the converged output cooling water outlet temperature.

[0011] By adopting the above scheme, the cooling capacity of open counter-flow and cross-flow cooling towers can be accurately calculated. Based on the thermodynamic characteristic expression of the packing material, the cooling water outlet temperature can be solved iteratively using the bisection method, which more accurately reflects the actual heat exchange situation of the cooling tower and provides accurate cooling water inlet temperature input for the chiller unit, thereby improving the accuracy of the annual COP calculation and the reliability of the project.

[0012] Preferred, including: For the partial load performance model of the chiller unit, the actual operating energy efficiency of the main unit is set to be obtained by interpolation calculation on a preset performance data table; the energy efficiency value obtained by the interpolation calculation is corrected according to the difference between the actual chilled water supply temperature and the preset reference supply temperature.

[0013] By adopting the above scheme, based on the preset performance data table, the actual operating energy efficiency of the host is obtained through interpolation calculation, and the energy efficiency value is corrected according to the difference between the actual chilled water supply temperature and the preset reference water supply temperature. The actual COP of the host under partial load is accurately calculated, thereby improving the accuracy of the annual COP calculation and the reliability of the project.

[0014] Preferred, including: The traversal process includes: performing a four-level nested traversal of the operating parameters of the chilled water pump, the cooling water pump, and the cooling tower; the four-level nested traversal process sequentially traverses the chilled water pump frequency, the cooling water pump frequency, the number of cooling towers in operation, and the cooling tower fan frequency, and the traversal process is subject to preset operating constraints; the preset operating constraints include: preset minimum operating frequencies of the chilled water pump and the cooling water pump, and preset minimum temperature difference between the chilled water outlet temperature and the cooling water inlet temperature of the main unit.

[0015] By adopting the above solution, the energy consumption coupling contradiction between chilled water pumps, cooling water pumps, cooling tower fans and chiller units is addressed. Through a four-layer nested traversal optimization logic, all feasible combinations of equipment operating parameters are enumerated. Under the premise of meeting safe operation constraints, the operating strategy with the highest system COP in each time period is selected, so as to achieve coordinated energy saving of core equipment in the computer room and maximize the overall energy efficiency of the system.

[0016] Preferably, selecting the optimal combination of operating parameters for energy efficiency also includes: The optimization is achieved by combining decoupled coarse-grained search with local nested traversal. The decoupling coarse search includes: constructing a single-variable power consumption-temperature curve on the chilled water side to obtain the minimum chilled water pump power consumption corresponding to different chilled water supply temperatures; constructing a multi-variable optimal power consumption-temperature mapping on the cooling water side to solve for the minimum total auxiliary power consumption under the coordinated operation of cooling water pump frequency, number of cooling towers, and fan frequency for different cooling water outlet temperatures; and performing two-dimensional joint optimization with chilled water supply temperature and cooling water outlet temperature as continuous decision variables to determine the nominal optimal combination of operating parameters. The local nested traversal includes: within the neighborhood of the nominal optimal combination of operating parameters, performing discrete traversal of the chilled water pump frequency, cooling water pump frequency, number of cooling towers and fan frequency in sequence with a preset step size, calculating the system energy efficiency coefficient according to the hourly energy calculation model, and selecting the one with the best energy efficiency coefficient as a candidate.

[0017] By adopting the above scheme, the decoupling coarse search can determine the minimum chilled water pump power consumption on the chilled water side and the minimum total auxiliary power consumption on the cooling water side, and perform two-dimensional joint optimization to determine the nominal optimal combination of operating parameters, thus initially narrowing the optimization range; local nested traversal is performed discretely in the neighborhood of the nominal optimal combination of operating parameters to further refine the selection, thereby selecting the combination of operating parameters with the best energy efficiency coefficient more efficiently and accurately, realizing coordinated energy saving of the core equipment in the computer room, and maximizing the overall energy efficiency of the system.

[0018] Preferably, calculating the energy efficiency coefficient for this period specifically involves calculating the dynamic average energy efficiency coefficient for this period, including: Based on the first-order inertial time constants of the physical heat transfer models of the surface cooler and the cooling tower, and the difference between the initial and steady-state final values ​​of the state variables caused by the switching of operating parameters during the current period, the duration of the state transition phase is determined; this period is then divided into the state transition phase and the quasi-steady-state phase. During the state transition phase, the average energy efficiency of the transition phase is determined using an hourly energy calculation model based on the phase average temperature, and the cumulative energy consumption of the transition phase is calculated. During the quasi-steady-state phase, the steady-state energy efficiency is determined using the hourly energy calculation model, and the cumulative energy consumption of the quasi-steady-state phase is calculated. The sum of the cumulative energy consumption during the transition phase and the cumulative energy consumption during the quasi-steady-state phase is taken as the dynamic total energy consumption for that period, and the ratio of the cooling load for that period to the dynamic total energy consumption is taken as the dynamic average energy efficiency coefficient.

[0019] By adopting the above scheme and considering the inertial characteristics of the physical heat transfer model of the surface cooler and cooling tower, the time period is divided into a state transition stage and a quasi-steady-state stage to calculate energy consumption separately. This allows for a more accurate calculation of the dynamic average energy efficiency coefficient for that time period, thereby improving the accuracy and reliability of the annual energy efficiency calculation.

[0020] Preferably, acquiring time-series data of meteorological parameters and building cooling load for the whole year includes: using hourly data of a typical meteorological year as a benchmark, generating multiple sets of time-series data of meteorological parameter disturbances containing temperature and humidity fluctuations by superimposing random disturbances, and generating time-series data of building cooling load disturbances by scaling the temperature disturbances proportionally and superimposing random noise; and constructing multiple operating scenarios based on the multiple sets of time-series data of meteorological parameter disturbances and time-series data of building cooling load disturbances. Output the annual energy efficiency forecast and the corresponding optimal operating parameter combination, including: statistically analyzing the simulation results of all operating scenarios to obtain the probability distribution of the annual energy efficiency coefficient, and generating the hourly optimal operating parameter combination for the whole year by aggregating the optimal operating parameter combinations of each operating scenario at the same time.

[0021] By adopting the above scheme, multiple sets of time-series data on meteorological parameter disturbances and building cooling load disturbances are generated based on hourly data of typical meteorological years, forming multiple operating scenarios. This comprehensively considers the fluctuations in meteorology and load, making the simulation closer to actual operation. Statistical analysis of the simulation results of all operating scenarios can obtain the probability distribution of the annual energy efficiency coefficient, more accurately assessing the stability and reliability of the system's energy efficiency. By aggregating the optimal operating parameter combinations of each operating scenario at the same time period, the optimal operating parameter combinations for each hour throughout the year are generated, resulting in a more reasonable and adaptive operating strategy.

[0022] Preferably, the method can be implemented in a spreadsheet software platform, which provides a visual interface for inputting equipment parameters, meteorological parameters and load parameters, and performs model building, coupling and calculation processes, with intermediate calculation data stored in the worksheet of the spreadsheet software platform.

[0023] By adopting the above solution, commercial simulation software is abandoned. Instead, the core calculation logic and visual interface are deeply integrated with the spreadsheet software platform. Users only need to input device and operating condition data to complete the annual energy efficiency calculation. There is no need to master professional programming or complex modeling skills. At the same time, all intermediate calculation processes are traceable, reducing the cost of use.

[0024] In summary, this application has the following beneficial effects: 1. Using the physical heat exchange model of the surface cooler and cooling tower as the core model, combined with the partial load characteristics of the chiller unit, the system performs a full-domain traversal optimization to select the optimal combination of equipment operating parameters under different operating conditions, completes the hourly energy efficiency calculation throughout the year, and outputs accurate full COP and itemized energy consumption data; it reduces the threshold and cost of use, is implemented on a spreadsheet software platform, provides a visual interface, and stores intermediate calculation data in a worksheet, without relying on professional commercial software and complex programming, reducing the initial investment and modeling time; 2. The surface cooler model adopts the equivalent benchmark heat exchanger modeling and iterative algorithm, the cooling tower model uses the McEl enthalpy difference method and the bisection method for iteration, and the chiller unit model is interpolated and its energy efficiency is corrected. By traversing the combination of operating parameters, the system cooling capacity, power consumption and energy efficiency coefficient are accurately calculated, the system operating state is realistically simulated and the measurement accuracy is improved. 3. An optimization approach combining decoupled coarse-grained search and local nested traversal is employed to determine the nominal optimal combination of operating parameters, and the optimal combination is selected through local discrete traversal. The dynamic average energy efficiency coefficient is calculated, taking into account the state transition phase. Multiple sets of meteorological and load disturbance time-series data are also generated to construct operating scenarios. Statistical simulation results are used to obtain the annual probability distribution of the energy efficiency coefficient and the hourly optimal combination of operating parameters, achieving collaborative energy saving of multiple devices and improving the overall energy efficiency of the system. Attached Figure Description

[0025] Figure 1 This is a flowchart of the annual COP simulation calculation method for a central air conditioning system based on the coupling of the surface cooler and cooling tower heat exchange model, as described in a specific embodiment. Figure 2 This is a simplified structural diagram of the air conditioning system in the annual COP simulation calculation method of the central air conditioning system based on the coupling of the surface cooler and cooling tower heat exchange model in a specific embodiment. Figure 3 This is a flowchart of the hourly calculation part of the annual COP simulation calculation method for a central air conditioning system based on the coupling of the surface cooler and cooling tower heat exchange model, as described in a specific embodiment. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0027] like Figure 1 As shown in the embodiment of this application, a method for simulating and calculating the annual COP of a central air conditioning system based on the coupling of a surface cooler and a cooling tower heat exchange model is disclosed. The method includes: constructing a physical heat exchange model of the surface cooler, a physical heat exchange model of the cooling tower, and a partial load performance model of the chiller unit; coupling the above models to form an hourly energy calculation model of the central air conditioning system; inputting annual data; traversing the combination of operating parameters; calculating the energy efficiency coefficient and selecting the optimal one; and finally, statistically analyzing the annual data to output the results, thereby achieving accurate calculation of the annual operating energy efficiency of the central air conditioning system.

[0028] Before detailing the above steps, let's first explain the workflow of the central air conditioning system and its core heat exchange components, such as... Figure 2 As shown. The process of transferring heat from the interior of a building to the exterior air involves four main heat exchange components: (1) Terminal finned tube heat exchanger: heat exchange between the interior air and chilled water, with typical operating conditions of return air 24℃ and outlet air 16℃; (2) Chiller evaporator: heat exchange between low-pressure refrigerant and chilled water, with typical operating conditions of chilled water supply 7℃ and return water 12℃; (3) Chiller condenser: heat exchange between high-pressure refrigerant and cooling water, with typical operating conditions of cooling water supply 32℃ and return water 37℃; (4) Open cooling tower: heat exchange between cooling water and exterior air through evaporation, with typical operating conditions of summer dry bulb 35℃ and wet bulb 28℃.

[0029] Accordingly, the annual COP of a high-efficiency data center is defined as the process of transferring heat from the building's interior to the outside air, involving five main types of rotating energy-consuming components: terminal equipment, chilled water pumps, compressors, cooling water pumps, and cooling tower fans. The annual COP of a high-efficiency data center typically does not include the energy consumption of terminal equipment; only the energy consumption of the core equipment (chilled water pumps, compressors, cooling water pumps, and cooling tower fans) is calculated. The formula is as follows: .

[0030] The following section will mainly introduce the specific steps of this application.

[0031] S1. Construct physical heat transfer models for surface coolers, cooling towers, and chiller units under partial load.

[0032] Specifically, using an object-oriented programming paradigm, the structure and heat transfer process of the finned tube surface cooler are modeled, overcoming the limitations of existing software (such as TRNSYS) that uses simplified empirical formulas, and constructing a refined heat transfer model at the geometric and structural levels.

[0033] First, modeling of finned tube heat exchangers. A three-layer modeling logic of "tube bundle-fins-heat exchanger" is adopted to accurately simulate the coupled heat exchange process between the air side outside the tube and the water side inside the tube.

[0034] First, define the modeling hierarchy. Tube Bundle Layer: Based on actual engineering parameters, construct a tube bundle structure model. The tube bundle layer can use metal pipes, such as copper pipes, arranged in an equilateral triangle pattern, etc. Calculate key structural parameters such as the relative spacing between the tube bundle's lateral and longitudinal sides, minimum flow channel area, and design gas velocity. Simultaneously, introduce tube bundle correction coefficients and tube bundle shape coefficients to adapt to the heat transfer characteristics of different tube bundle arrangements. Fin Layer: For circular finned tubes, calculate the theoretical fin efficiency and the external gas convection heat transfer coefficient (including correction to the baseline heat transfer coefficient of the bare tube), accurately reflecting the influence of fin geometric parameters (thickness, spacing, height) on heat transfer efficiency. Heat Exchanger Layer: Couple the tube bundle and fin models, simultaneously complete the internal fluid side calculations (internal flow channel area, average velocity, Reynolds number, internal convection heat transfer coefficient), and finally integrate to obtain the comprehensive heat transfer parameters of the heat exchanger.

[0035] Secondly, the calculation of core indicators such as finned heat exchange efficiency, number of heat transfer units (NTU), and overall heat transfer coefficient is determined. The calculation process includes: external air side: calculating the relative horizontal / vertical spacing. Minimum flow channel area gas flow rate convective heat transfer coefficient Fin efficiency; Water side inside the tube: Calculate the flow channel area Average flow rate Reynolds number Convection heat transfer coefficient inside the pipe; Overall heat transfer: Calculation of heat capacity ratio Number of heat transfer units (NTU) Heat exchanger efficiency Overall heat transfer coefficient Actual heat exchange and inlet / outlet temperatures.

[0036] Then, based on the determined core indicators, the header file for the finned tube heat exchanger is constructed to assist in the simulation. The header file includes: function definitions, such as: a parameterized constructor to initialize the core parameters of the finned tube heat exchanger; tube bundle structural parameters (including arrangement, number of tubes, tube diameter, etc.); finned tube geometric parameters (fin thickness, spacing, height, etc.); external gas medium parameters (such as air temperature, flow rate, and properties); internal fluid medium parameters (such as chilled water temperature, flow rate, and properties); external air-side heat transfer calculation code text; internal fluid-side heat transfer calculation code text; and overall heat transfer coefficient calculation code text for the heat exchanger.

[0037] Finally, an equivalent reference heat exchanger is constructed and generated. Based on the equivalent reference heat exchanger, the heat exchange process between the air side outside the tube and the water side inside the tube is calculated to obtain the air side and water side temperature states that meet the system operating conditions.

[0038] In actual air conditioning projects, system terminal equipment typically includes multiple models, such as fan coil units FP34, FP51, FP68, FP85, and FP102; the surface coolers in combined air conditioning units are selected based on the actual cooling capacity required. In the HVAC field, although the design parameters such as heat exchange area, air volume, and water flow rate of different models of terminal finned tube heat exchangers vary, their core structural forms are highly similar. They generally adopt copper tube and aluminum fin structures, equilateral triangular tube bundle arrangements, and similar geometric parameters such as fin spacing and tube diameter. Therefore, under the same operating conditions, the heat and mass transfer mechanisms and thermal characteristic curves of various terminals are basically the same. If heat exchange calculations are performed on each individual terminal unit, a massive amount of iterative calculations would be generated in a dynamic simulation scenario of 8760 hours per hour throughout the year, significantly increasing computational complexity and extending the simulation calculation cycle. To balance measurement accuracy and computational efficiency, this embodiment proposes an equivalent benchmark heat exchanger modeling method. By constructing a unified equivalent model with global representativeness, it replaces batch heat exchange calculations for multiple types and numbers of terminal devices, thus achieving a balance between engineering practicality and simulation efficiency.

[0039] Specifically, for multiple types of terminal equipment within the system, an equivalent reference heat exchanger is constructed using a parameter weighting method to serve as a unified representative model for all terminal equipment in the system. The establishment of the equivalent model includes: the equivalent heat transfer area is obtained by weighting the heat transfer area of ​​each type of terminal equipment within the system according to the number of units; the formula is: In the formula, This represents the heat exchange area of ​​a single unit of the i-th model. This represents the number of terminal devices of the i-th type; the equivalent area represents the average heat exchange capacity of a single terminal device in the system. Equivalent air volume. With equivalent chilled water flow rate These are weighted averages of the rated air volume and rated water flow rate of each type of terminal equipment, calculated by the number of units; the formula is: , In the formula, and These represent the rated air volume and rated water flow rate of the i-th type of terminal device, respectively. The geometric parameters are taken from the tube bundle arrangement, fin thickness, fin spacing, and pipe diameter corresponding to the terminal model with the highest proportion of total cooling capacity in the system. For example, in a comfort air conditioning system, assuming that the FP102 fan coil unit has the largest number and the largest proportion of total cooling capacity, FP102 is selected as the representative structure. Based on this, by adjusting the equivalent heat exchange area and flow rate parameters, it can be ensured that the equivalent model can reflect the overall heat exchange characteristics of the system.

[0040] After constructing an equivalent reference heat exchanger, an iterative algorithm is used to solve for the air-side and water-side temperature states that satisfy the system operating conditions. The steps include: inputting boundary conditions, including: the design heat exchange capacity of the equivalent reference heat exchanger, the current actual heat exchange capacity, the current actual water flow rate, the finned tube return air temperature (i.e., the indoor set air conditioning temperature), and iteration error thresholds (heat exchange error, return air temperature error), etc.; by adjusting the air inlet temperature and water-side inlet temperature, the heat exchanger operating state is iteratively calculated to gradually approach the target heat exchange capacity while simultaneously satisfying the return air temperature constraint. The iteration is considered converged when the following conditions are simultaneously met: , The calculation yields the finned tube outlet air temperature and the finned tube water-side inlet and outlet temperatures after iterative convergence. The finned tube water-side inlet temperature is a crucial parameter. Under partial load conditions, fully utilizing the heat exchange capacity of the finned tube heat exchanger by increasing the chilled water supply temperature can meet the terminal usage requirements. Increasing the chilled water supply temperature optimizes the chiller unit's operating conditions, improving its cooling efficiency and actual COP. The calculated finned tube water-side inlet temperature will also serve as a key basis for accurately correcting the actual operating COP of the chiller unit.

[0041] Second, a physical heat transfer model for the cooling tower is modeled. The cooling towers used in the HVAC field are mainly open counterflow cooling towers and open crossflow cooling towers. This embodiment only models these two types of cooling towers.

[0042] Considering that the operation of an open cooling tower involves direct contact heat and mass transfer between air and circulating water, the Michael number method (also known as the Michael enthalpy difference method) is based on the Michael enthalpy difference equation. This equation states that at any location in the cooling tower, the total heat transferred between water and air is proportional to the difference between the saturated air enthalpy corresponding to the water temperature at that location and the local humid air enthalpy. This enthalpy difference is considered the driving force for energy diffusion. The Michael number method is mainly used for cooling tower thermodynamic calculations, evaluating the heat and mass transfer capacity of the cooling tower by considering the enthalpy difference between water and air. Therefore, based on the Michael enthalpy difference method, the heat exchange process between cooling water and air is calculated, and the main steps include: (1) The cooling coefficient of a counter-flow cooling tower is calculated using the following formula: In the formula, N is the cooling number; k is the coefficient of heat carried away by the evaporating water. Indicates the volumetric mass transfer coefficient; This refers to the volume of the water-spraying packing material; This refers to the cooling water flow rate (mass flow rate). , These are the inlet water temperature and the outlet water temperature, respectively. The specific heat of water; For saturated air, Enthalpy of air; In the formula, For the water temperature at the outlet of the tower Enthalpy of saturated air For the water temperature at the outlet of the tower Enthalpy of saturated air Average water temperature Enthalpy of saturated air; Enthalpy of the air exiting the tower; Enthalpy of the air entering the tower; This is the average enthalpy of the air inside the tower.

[0043] (2) Solving the cooling number of a crossflow cooling tower. The thermodynamic model of a crossflow cooling tower is similar to that of a counterflow cooling tower, but the equation solution is more complex. In order to improve the calculation speed, the Tezuka Shunichi formula is used in this embodiment. First, the counterflow cooling tower is calculated according to the above method, and then the correction coefficient is used to correct it to obtain the cooling number of the crossflow cooling tower.

[0044] (3) Expression of the thermodynamic properties of the packing material. The thermodynamic characteristics of the water-spreading packing material are usually expressed as follows: In the formula, , These are the fitting coefficients for different packing materials; This indicates the air-to-water ratio (the ratio of the mass flow rate of dry air entering the tower (kg / h) to the mass flow rate of cooling water entering the tower (kg / h)).

[0045] (4) Iterative Calculation. The calculation steps include: inputting boundary conditions. Based on the cooling tower selection report, the following parameters are obtained: design operating condition air dry-bulb temperature, wet-bulb temperature, inlet water temperature, outlet water temperature, cooling water flow rate, cooling tower type, and thermodynamic characteristic expression of the packing. Input the above parameters into the cooling tower model to calculate the air-to-water ratio and cooling number under the current cooling tower design conditions, i.e., the cooling number Ndesign of the packing, which reflects the comprehensive heat exchange capacity of the cooling tower under the design conditions. If the complete thermodynamic performance curve provided by the packing manufacturer cannot be obtained, a publicly available thermodynamic performance function of the packing with a similar structural form can be selected as a reference model.

[0046] Input operating parameters: Under current operating conditions: dry bulb temperature, wet bulb temperature, cooling water flow rate, cooling airflow rate, cooling water inlet and outlet temperature difference, and target cooling capacity; Under actual operating conditions, the target cooling capacity of the packing should be recalculated by substituting it into the packing thermodynamic characteristic expression based on the air-to-water ratio under operating conditions.

[0047] Iterative control parameters: Set the search range for cooling water outlet temperature [ Define the target cooling number. and error threshold The cooling water outlet temperature is solved iteratively using a bisection method, including: using the cooling number N as the target convergence variable, calculating the cooling number based on the air-to-water ratio under the current operating conditions and substituting it into the thermodynamic characteristic expression of the packing material; determining convergence when the absolute value of the difference between the calculated cooling number and the preset target cooling number is less than a set error threshold, and outputting the converged cooling water outlet temperature. For example, calculating the midpoint temperature. ;when Direct output Otherwise, according to and The size relationship adjustment range, if ,but ;otherwise, .

[0048] Ultimately, the system can output the cooling water inlet and outlet temperatures. The cooling water outlet temperature is the temperature at the bottom of the cooling tower, which is also the inlet temperature of the chiller unit's condenser. This parameter is a core calculation indicator, used to obtain the actual COP of the chiller unit under partial load conditions.

[0049] Third, a partial load performance model for the chiller unit is developed. The partial load performance model for the chiller unit is based on a preset performance data table, reflecting the operating energy efficiency at different load rates and cooling water inlet temperatures.

[0050] The partial load performance model of the chiller unit adopts a 100-point operating condition table. The vertical axis represents the load rate from 10% to 100% in 10% intervals, and the horizontal axis represents the cooling water inlet temperature from 19℃ to 32℃ in 1℃ intervals. The middle data area represents the COP value under the corresponding operating condition. Some values ​​are shown in Table 1 below. In the table, "-" indicates that the host cannot output stably under the operating condition, and the program will automatically avoid calling the operating condition during execution.

[0051] Table 1

[0052] Based on the current operating conditions, including the main unit load rate and cooling water inlet temperature, the corresponding main unit operating COP is calculated using a two-dimensional bilinear interpolation algorithm in the 100-point operating condition table. Considering that the 100-point operating condition data provided by the main unit manufacturer is typically measured under conditions where the chilled water supply temperature is 7℃, and empirical formulas showing that energy efficiency improves by approximately 2.5%-3.0% for every 1℃ increase in water temperature, it is necessary to adjust the COP obtained through interpolation based on the actual chilled water supply temperature to obtain main unit performance parameters that better reflect actual operating conditions. Specifically, the energy efficiency value obtained through interpolation is corrected based on the difference between the actual chilled water supply temperature (e.g., 10℃) and the preset benchmark supply temperature (base supply temperature, e.g., 7℃), as follows: .

[0053] The physical heat exchange model of the surface cooler, the physical heat exchange model of the cooling tower, and the partial load performance model of the chiller unit are coupled to form an hourly energy calculation model for the central air conditioning system.

[0054] Specifically, the coupling process includes: using the water-side inlet temperature of the finned tubes calculated by the surface cooler as the chilled water supply temperature input of the chiller unit; using the cooling water outlet temperature calculated by the cooling tower as the cooling water inlet temperature input of the chiller unit; and using the current cooling load divided by the rated cooling capacity of the chiller unit to obtain the host load rate input, which together drive the chiller unit partial load performance model to output the real-time COP value.

[0055] The real-time COP value of the chiller unit's partial load performance model can be obtained based on the cooling water inlet temperature and the main unit load rate. Then, the COP value is optimized based on the chilled water supply temperature input of the chiller unit, combined with the difference calculation.

[0056] S3. Input the annual meteorological parameter time series data and building cooling load time series data into the hourly energy calculation model of the central air conditioning system. Calculate the energy efficiency coefficient for each calculation period and select the optimal combination of operating parameters with the best energy efficiency coefficient as the optimal operating condition for that period.

[0057] First, the basic parameters to be imported include: meteorological parameters, building cooling load, chiller parameters, water pump parameters, and cooling tower parameters, and limiting conditions are set.

[0058] Meteorological parameters can be obtained by downloading historical data of national meteorological stations in .epw format from the EnergyPlus website. A parsing class can be written using the Excel CAPI, and the city's annual meteorological data can be quickly retrieved and stored in an Excel spreadsheet by selecting the province and city name in the Excel Ribbon interface. Some data is shown in Table 2.

[0059] Table 2

[0060] Among them, the building cooling load is obtained based on the annual cooling load simulation. Specifically, two methods can be used to obtain the cooling load: (1) use other professional simulation software to calculate the hourly cooling load of 8760h throughout the year and copy the calculation results to an Excel spreadsheet; (2) the built-in cooling load calculation function, the calculation algorithm can follow the radiation time series method in 'Non-residential Cooling and Heating Load Calculation' or use a deep learning algorithm for calculation. In addition, considering that different end-use scenarios have different requirements for heat and humidity treatment, the corresponding annual load distribution curves of the air conditioning system are also different. Therefore, the air conditioning system is divided into commonly used types. Users need to classify and select according to the HVAC construction drawings and input the design parameters. Finally, the annual cooling load simulation data of the entire air conditioning system is obtained. Some data are shown in Table 3.

[0061] Table 3

[0062] The chiller unit parameters include: chiller type (usually 5 types: magnetic levitation, fixed-frequency centrifugal, variable-frequency centrifugal, fixed-frequency screw, variable-frequency screw); rated cooling capacity: unit RT (refrigeration tons); chiller operating conditions at 100 points (COP of different load rates and cooling water inlet temperature; this table needs to be provided by the chiller manufacturer, representing the operating status of the chiller under different load rates and conditions; for example, the horizontal axis is the cooling water supply temperature, and the vertical axis is the chiller load rate), input in the form of a two-dimensional table; input water pump parameters: chilled water pump: flow rate, head, energy efficiency rating, suction method, quantity; cooling water pump: flow rate, head, energy efficiency rating, suction method, quantity; suction method of clean water pump used for air conditioning; water pump efficiency calculation based on reference documents; motor energy efficiency calculation based on reference documents.

[0063] The input parameters for the cooling tower include: design operating conditions, dry-bulb temperature, wet-bulb temperature, inlet water temperature, outlet water temperature, cooling water volume, cooling tower type, energy efficiency rating, number of fans, rated power of fans, rated air volume of fans, and the thermodynamic characteristic expression of the packing material.

[0064] Among them, the limiting conditions are set as follows: In order to ensure the safe and stable operation of the system and to ensure the rationality of the subsequent core energy efficiency calculation, the operating parameters of each device in the system need to be set with corresponding limiting conditions to provide reasonable boundary conditions for the subsequent hourly simulation and parameter optimization of the system. Specifically, these include: (1) the minimum operating frequency of the chilled pump, cooling pump, and cooling tower fan; (2) the minimum temperature difference between the chilled water outlet temperature and the cooling water inlet temperature of the host unit; (3) the upper and lower limits of the chilled water temperature; (4) the minimum value of the water temperature at the bottom of the cooling tower; (5) the minimum water supply ratio of the cooling tower; and (6) when multiple hosts are turned on at the same time, the default load rate is the same.

[0065] like Figure 3 As shown, the time-series data of meteorological parameters for the whole year, the time-series data of building cooling load, and other basic parameters (equipment parameters such as chiller, chiller unit, chilled water pump, cooling tower, etc.) are all input into the hourly energy calculation model of the central air conditioning system.

[0066] Secondly, the year is divided into time periods (e.g., 8760 hours). For each time period, the energy efficiency coefficient (COP) of the central air conditioning system is calculated using an hourly energy calculation model. Specifically, the system iterates through all 8760 hours of the year (hourly simulation) with i < 8760, independently calculating the optimal operating condition for each hour. For each time period, all feasible operating combinations of chilled water pumps, cooling pumps, and cooling towers are iterated, and energy efficiency is calculated using a physical model to select the operating condition combination with the highest COP. For the main unit, magnetic levitation is generally activated first, followed by variable frequency centrifugal or variable frequency screw chiller, and finally fixed frequency centrifugal or fixed frequency screw chiller. The specific startup priority order of the main unit can be set in the software. The core steps of the hourly calculation include: Input and initialization: Input hourly meteorological parameters, cooling load, and equipment parameters such as chiller / chilled water pump / cooling pump / cooling tower. Initialize the time period counter i = 0, and enter the hourly loop.

[0067] Load and constraint pre-setting: Calculate the load rate of the host in advance based on the current cooling load; set preset operating constraints, which include: preset minimum operating frequency of chilled water pump and cooling pump, and preset minimum temperature difference between chilled water outlet temperature and cooling water inlet temperature of the host.

[0068] Four-level nested traversal: Traversing the combination of operating parameters of chilled water pumps, cooling water pumps, and cooling towers. This includes: performing global collaborative optimization on four strongly coupled variables: chilled water pump frequency, cooling water pump frequency, number of cooling towers, and fan frequency. The traversal sequentially traverses the chilled water pump frequency, cooling water pump frequency, number of cooling towers in operation, and cooling tower fan frequency, and the traversal process is subject to preset operating constraints.

[0069] Model Coupling and Operating Condition Calculation: Three key physical models are introduced: Surface cooler heat exchange model: calculates chilled water supply and return temperatures and cooling capacity output. Cooling tower heat exchange model: calculates cooling water temperature and fan power consumption (cooling tower fan power consumption is calculated based on its airflow, air pressure, fan efficiency, and motor efficiency). Host Partial Load Energy Efficiency Parameters: Combines load rate calculations to determine host cooling capacity and power consumption (calculated based on the ratio of cooling load to the host's actual COP during that period). Furthermore, chilled water pump power consumption is calculated based on its flow rate, head, pump efficiency, and motor efficiency, and cooling water pump power consumption is calculated based on its flow rate, head, pump efficiency, and motor efficiency.

[0070] For each combination of operating parameters, calculate the cooling capacity of the central air conditioning system (using the cooling load of each time period as the system cooling capacity for that time period) and the total power consumption of the core equipment in the computer room (using the sum of the power consumption of the chilled water pump, cooling water pump, cooling tower fan, and main unit as the total power consumption of the core equipment in the computer room for that time period). Based on the cooling capacity and total power consumption for each time period, calculate the energy efficiency coefficient for that time period. Select the operating parameter combination with the optimal energy efficiency coefficient as the optimal operating condition for that time period.

[0071] S4. Calculate the energy efficiency coefficient for each period of the year, and output the annual energy efficiency forecast value and the corresponding optimal combination of operating parameters.

[0072] Specifically, the final summary includes: total annual cooling capacity of the main unit, total annual power consumption of the main unit / refrigeration pump / cooling pump / cooling tower, and overall system COP for the year. The method described in this embodiment can be implemented in a spreadsheet software platform. This platform provides a visual interface for inputting equipment parameters, meteorological parameters, and load parameters, and performs model building, coupling, and calculation processes. Intermediate calculation data is stored in the worksheet of the spreadsheet software platform, thereby significantly reducing the threshold and cost of simulation calculation.

[0073] By adopting the above scheme, accurate models of the surface cooler, cooling tower, and chiller unit were constructed and coupled together to realistically reproduce the heat exchange mechanism and operating characteristics of the central air conditioning system. Hourly simulations were performed using meteorological and load data from throughout the year, traversing all feasible combinations of operating parameters to select the operating strategy with the highest COP for each time period. This achieved coordinated energy saving of the core equipment in the computer room and maximized the overall energy efficiency of the system.

[0074] A specific embodiment differs from the above embodiment in that: the optimal combination of operating parameters with the best energy efficiency coefficient is selected using a combination of decoupled coarse search and local nested traversal; the decoupled coarse search can quickly narrow down the search range and determine the approximate direction of the optimal operating parameters; and the local nested traversal, based on the coarse search, performs a fine search on the neighborhood of the nominally optimal combination of operating parameters, further improving the accuracy of the search and ensuring that the truly optimal combination of operating parameters is found, thereby improving the energy efficiency performance of the system; the method also includes: Selecting the optimal combination of operating parameters for energy efficiency also includes using a combination of decoupled coarse-grained search and local nested traversal for optimization. The specific optimization process includes the following steps: Specifically, the final summary includes: total annual cooling capacity of the main unit, total annual power consumption of the main unit / refrigeration pump / cooling pump / cooling tower, and overall system COP for the year. The method described in this embodiment can be implemented in a spreadsheet software platform. This platform provides a visual interface for inputting equipment parameters, meteorological parameters, and load parameters, and performs model building, coupling, and calculation processes. Intermediate calculation data is stored in the worksheet of the spreadsheet software platform, thereby significantly reducing the threshold and cost of simulation calculation.

[0075] By adopting the above scheme, accurate models of the surface cooler, cooling tower, and chiller unit were constructed and coupled together to realistically reproduce the heat exchange mechanism and operating characteristics of the central air conditioning system. Hourly simulations were performed using meteorological and load data from throughout the year, traversing all feasible combinations of operating parameters to select the operating strategy with the highest COP for each time period. This achieved coordinated energy saving of the core equipment in the computer room and maximized the overall energy efficiency of the system.

[0076] A specific embodiment differs from the above embodiment in that: the optimal combination of operating parameters with the best energy efficiency coefficient is selected using a combination of decoupled coarse search and local nested traversal; the decoupled coarse search can quickly narrow down the search range and determine the approximate direction of the optimal operating parameters; and the local nested traversal, based on the coarse search, performs a fine search on the neighborhood of the nominally optimal combination of operating parameters, further improving the accuracy of the search and ensuring that the truly optimal combination of operating parameters is found, thereby improving the energy efficiency performance of the system; the method also includes: Selecting the optimal combination of operating parameters for energy efficiency also includes using a combination of decoupled coarse-grained search and local nested traversal for optimization. The specific optimization process includes the following steps: The decoupling coarse search includes: constructing a univariate power consumption-temperature curve on the chilled water side to obtain the minimum chilled water pump power consumption corresponding to different chilled water supply temperatures. Specifically, on the chilled water side, the power consumption is calculated simultaneously as chilled water pump frequency, chilled water flow rate, and chilled water side outlet temperature. For the current period's fixed weather and load, all traversed... For all discrete values, construct a curve: =some Minimum chilled water pump consumption required.

[0077] A multivariate optimal power consumption-temperature mapping is constructed on the cooling water side to solve for the minimum total auxiliary power consumption under the combined effects of cooling water pump frequency, number of cooling towers, and fan frequency for different cooling water outlet temperatures. Specifically, on the cooling water side, the cooling water outlet temperature is calculated using a model that combines cooling water pump frequency, number of cooling towers, and fan frequency with cooling water flow rate and air volume, simultaneously generating pump and fan power consumption. For the current outdoor wet-bulb temperature (from meteorological parameters), a curve is constructed: = The minimum total power consumption of cooling-side auxiliary equipment (water pump) required to achieve a certain cooling water outlet temperature when the cooling tower and cooling water pump work together. Japanese-style fan This is a multivariate optimization problem: ; ; A two-dimensional joint optimization is performed using chilled water supply temperature and cooling water outlet temperature as continuous decision variables to determine the nominal optimal combination of operating parameters. Considering the chiller unit, the input... and fixed load PLR= Obtained by looking up the table Power consumption is This transforms the original multidimensional problem into a two-dimensional optimization problem containing only two temperatures: Solving the above formula, the optimal temperature combination is finally obtained. And the corresponding discrete points of the curve, from which the nearest combination of discrete control variables can be found, called the nominal optimal combination: the closest frequency, closest And the lowest power The combination of .

[0078] The local nested traversal includes: within the neighborhood of the nominal optimal combination of operating parameters, performing discrete traversal of the chilled water pump frequency, cooling water pump frequency, number of cooling towers and fan frequency in sequence with a preset step size, calculating the system energy efficiency coefficient according to the hourly energy calculation model, and selecting the one with the best energy efficiency coefficient as a candidate.

[0079] Here, "within the neighborhood of the nominal optimal combination of operating parameters" means setting a reasonable neighborhood range for each variable centered on the nominal optimal combination of operating parameters: that is, the closest frequency, closest And the lowest power In the combination , Set them separately: , .

[0080] In addition, after nested traversal calculation of the system energy efficiency coefficient, global verification can be performed, and random sampling can be conducted. A small number of combinations outside the window are randomly selected to calculate the COP to ensure that there is no value higher than the current optimal value. If there is, the range of the domain is expanded and the traversal and optimization are performed again.

[0081] A specific embodiment differs from the above embodiment in that: calculating the energy efficiency coefficient for this period specifically involves calculating the dynamic average energy efficiency coefficient for that period; considering the state transition phase when the system switches operating parameters, traditional energy efficiency calculation methods may not accurately reflect the actual energy consumption of the system. By calculating the dynamic average energy efficiency coefficient, dividing the time period into state transition phases and quasi-steady-state phases for separate energy consumption calculations, the energy efficiency performance of the system under different operating states can be more accurately evaluated; the method further includes: Considering that when changing parameters such as chilled water pump frequency and number of cooling towers, it takes time to transition from one steady state to another, during this transition period: the water temperature of the heat exchanger and cooling tower pool is changing, and the inlet and outlet water temperatures of the air conditioning terminal and chiller have not yet reached the new set values, in order to avoid the selected "optimal" parameters from being unstable and inefficient in practice, and to obtain a more accurate energy efficiency coefficient, the energy efficiency coefficient for this period is specifically calculated as the dynamic average energy efficiency coefficient for this period.

[0082] Considering that it is indeed difficult to directly achieve numerical solutions of differential equations at the minute level in a pure spreadsheet formula environment, we abandoned minute-by-minute simulation and adopted state classification analytical integration. When the system operating parameters (such as pump frequency and number of towers) change abruptly at the top of the hour, the key state variables will undergo exponential decay. This transition process is divided into two state stages. In the transition stage, the state variables change from the old steady-state value to near the new steady-state value in an exponential manner, during which the system energy efficiency deviates from the steady-state value. In the quasi-steady-state stage, the state variables have basically stabilized, and the system energy efficiency is equivalent to the steady-state energy efficiency under the new parameters. By integrating these two stages respectively, we obtain the dynamic average total power consumption for that hour.

[0083] Specifically, the calculation steps for the dynamic average energy efficiency coefficient include: First, based on the first-order inertial time constants of the physical heat transfer model of the surface cooler and the physical heat transfer model of the cooling tower, as well as the difference between the initial value and the final steady-state value of the state variables caused by the switching of operating parameters in the current period, the duration of the state transition phase is determined, and this period is divided into the state transition phase and the quasi-steady-state phase.

[0084] Among them, the first-order inertial time constant The first-order inertial constant of the physical heat transfer model for the surface cooler. The total heat capacity (tube wall + fins + stagnant water) and total thermal conductivity (air side + water side) of the equivalent reference heat exchanger are calculated. The total heat capacity is calculated based on the mass and specific heat of the copper tubes, aluminum fins, and stagnant water inside the tubes of the equivalent heat exchanger. The total thermal conductivity (air side + water side) can be directly taken from the steady-state value. The calculated values ​​of the NTU model under rated operating conditions are the air volume and water volume functions, respectively. -NTU relationship conversion is obtained. The time constant is used for the physical heat transfer model of the cooling tower. The thermal conductivity is calculated based on the water holding capacity and heat transfer efficiency of the cooling tower. The total water holding capacity of the cooling tower is calculated from the water volume in the collection basin and the water holding capacity of the packing. The equivalent thermal conductivity of the cooling tower is estimated from the heat transfer efficiency in the steady-state model (calculated by Michael's theory) and the rated heat capacity flow rate of the cooling water.

[0085] The initial value of the state variable corresponding to the switching of operating parameters in the current time period is the chilled water outlet temperature at the end of the previous time period. (chilled water supply temperature) and cooling tower outlet water temperature (Cooling water inlet temperature); The steady-state final value of the corresponding state variable after the current operating parameter switch is: the new steady-state temperature directly calculated from the steady-state surface cooler and cooling tower model for the current candidate operating parameters (chilled water pump frequency, cooling pump frequency, number of cooling towers, fan frequency): surface cooler chilled water outlet temperature. Cooling tower outlet water temperature .

[0086] Determine the duration of the state transition phase: For surface coolers: ; For cooling towers: ; In the formula, Take 0.1, and at the same time use 0- and 60 minutes are used as upper and lower limits, respectively. , The larger of the two values ​​can be used to determine the transition duration.

[0087] Secondly, during the state transition phase, based on the analytical solution or phase average value of the first-order inertial element, the hourly energy calculation model is used to determine the average energy efficiency of the transition phase and calculate the cumulative energy consumption of the transition phase.

[0088] Specifically, this embodiment uses the stage average as an example to calculate the average chilled water supply temperature and average cooling water inlet temperature during the transition stage, as shown in the formula: ; Therefore, based on the current partial load rate, the average chilled water supply temperature during the transition phase, and the average cooling water inlet temperature, the chiller unit performance table can be consulted to obtain... .

[0089] Then, in the quasi-steady-state stage, the steady-state energy efficiency is determined using the hourly energy calculation model, and the cumulative energy consumption in the quasi-steady-state stage is calculated.

[0090] Specifically, based on the current partial load rate and steady-state temperature (chilled water outlet temperature of the surface cooler). Cooling tower outlet water temperature The performance table of the chiller unit was consulted to obtain the following information. .

[0091] Finally, the sum of the cumulative energy consumption during the transition phase and the cumulative energy consumption during the quasi-steady-state phase is taken as the dynamic total energy consumption for that period, and the ratio of the cooling load for that period to the dynamic total energy consumption is taken as the dynamic average energy efficiency coefficient.

[0092] Specifically, the cumulative energy consumption during the transition phase: In the formula, The total power consumption of the water pump and fan corresponding to the current candidate parameters is considered constant because the frequency switching is instantaneous and the power consumption stabilizes momentarily. The cumulative energy consumption in the quasi-steady-state phase is: The dynamic average energy efficiency coefficient for this period: .

[0093] In addition, to avoid the fragmentation of state variables caused by independent hourly simulations, a continuous dynamic simulation is performed once a day, using the final state of the previous hour as the initial state of the next hour, on the finally selected hourly optimal parameter sequence. That is, after selecting the operating parameter combination with the optimal energy efficiency coefficient as the optimal operating condition for that period, a daily continuous verification and fine-tuning step is also included: in the process of determining the optimal operating condition sequentially for each period, the initial state of each period is inherited from the final state of the previous period; after the optimal operating parameters for all periods of the day are selected, starting from the initial state of the first period of the day, the same state division and cumulative calculation are used to perform a continuous recalculation of the parameter sequence selected for each period of the day to obtain the continuous total energy consumption; when the deviation between the continuous total energy consumption and the hourly cumulative total energy consumption exceeds the threshold, the switching period with the largest contribution of the deviation is located, and the original selection is replaced with its second-best parameters and re-verified.

[0094] A specific embodiment, differing from the above embodiment, involves constructing multiple operating scenarios based on multiple sets of meteorological parameter disturbance time-series data and building cooling load disturbance time-series data. Considering the fluctuations in meteorological parameters and building cooling load during actual operation, generating multiple sets of disturbance time-series data to construct multiple operating scenarios for simulation allows for a more comprehensive simulation of the system's operation under different conditions. Statistical analysis is performed on the simulation results of all operating scenarios to obtain the probability distribution of the annual energy efficiency coefficient. Simultaneously, the optimal operating parameter combinations for the same time period in each operating scenario are aggregated to generate more reasonable and adaptive hourly optimal operating parameter combinations for the entire year, improving the system's operational stability and energy efficiency. The method further includes: First, obtaining time-series data of annual meteorological parameters and building cooling load includes: Based on hourly data from a typical meteorological year, multiple sets of time-series meteorological parameter disturbance data, including temperature and humidity fluctuations, are generated by superimposing random disturbances. Building cooling load disturbance time-series data are then generated by scaling the temperature disturbance proportionally and adding random noise. Specifically, hourly dry-bulb temperature and relative humidity (TMY) are used as benchmarks, and first-order autoregressive random errors are superimposed to obtain meteorological parameter disturbance time-series data. Building cooling load is mainly affected by outdoor temperature, solar radiation, and internal heat gain. TMY load can be scaled proportionally and random noise added. Multiple operating scenarios are constructed based on the multiple sets of meteorological parameter disturbance time-series data and building cooling load disturbance time-series data.

[0095] Secondly, the system outputs the annual energy efficiency forecast and the corresponding optimal combination of operating parameters, including: statistically analyzing the simulation results of all operating scenarios to obtain the probability distribution of the annual energy efficiency coefficient, and generating the hourly optimal combination of operating parameters for the whole year by aggregating the optimal combination of operating parameters for the same period of each operating scenario.

[0096] Specifically, for each set of disturbance scenarios, the performance of the hourly energy efficiency coefficient is calculated to obtain the optimal COP and the corresponding hourly parameter sequence for that scenario. This yields N sets of optimal COPs for N scenarios and N sets of hourly parameter sequences, thus obtaining the probability distribution of system energy efficiency, reflecting the energy efficiency fluctuations caused by interannual differences in meteorological load.

[0097] Considering that actual projects require a fixed, write-able, hourly operating parameter table for the entire year, this strategy needs to perform well in most years, not just for a single year. Therefore, it's not feasible to simply take parameters for a specific scenario. Instead, we choose to aggregate the optimal operating strategy for the entire year, thereby achieving optimal performance measurement. The aggregation steps include: for each moment of the year, collecting the optimal parameters for N scenarios at that moment; for each parameter variable, performing statistical aggregation separately, such as: chilled water pump frequency, cooling water pump frequency, and fan frequency: taking the median and rounding it according to control precision; the number of cooling towers in operation: taking the mode (i.e., the number with the highest frequency). If the percentage of the most frequent number is less than 40%, the second most frequent number is taken, and a larger number is selected based on a conservative principle to ensure safety.

[0098] This application also discloses a computer-readable storage medium.

[0099] Specifically, the computer-readable storage medium stores a computer program that can be loaded and executed by a processor, such as the above-mentioned method for simulating and calculating the annual COP of a central air conditioning system based on the coupling of the surface cooler and cooling tower heat exchange model. The computer-readable storage medium includes, for example, various media that can store program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0100] This application also discloses a computer device.

[0101] Specifically, the computer device includes a memory and a processor. The memory stores a computer program that can be loaded by the processor and executed to perform the above-mentioned simulation calculation method for the annual COP of a central air conditioning system based on the coupling of the surface cooler and cooling tower heat exchange model.

[0102] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for simulating and calculating the annual COP of a central air conditioning system based on a coupled heat exchange model of the surface cooler and the cooling tower, characterized in that, include: A physical heat transfer model for the surface cooler, a physical heat transfer model for the cooling tower, and a part-load performance model for the chiller unit are constructed. The surface cooler physical heat transfer model is based on a three-layer structure of tube bundle, fins, and heat exchanger, and calculates the heat transfer process between the air side outside the tubes and the water side inside the tubes. The cooling tower physical heat transfer model is based on the Michael enthalpy difference method and calculates the heat transfer process between the cooling water and the air. The part-load performance model for the chiller unit is based on a preset performance data table and reflects the operating energy efficiency at different load rates and cooling water inlet temperatures. The physical heat transfer model of the surface cooler, the physical heat transfer model of the cooling tower, and the partial load performance model of the chiller are coupled to form an hourly energy calculation model for the central air conditioning system. The coupling process includes: using the water-side inlet temperature of the finned tubes calculated by the surface cooler as the chilled water supply temperature input of the chiller, using the cooling water outlet temperature calculated by the cooling tower as the cooling water inlet temperature input of the chiller, and using the current cooling load divided by the rated cooling capacity of the chiller to obtain the main unit load rate input, which together drive the chiller partial load performance model to output the real-time COP value. The system inputs the annual meteorological parameter time series data and building cooling load time series data into the hourly energy calculation model of the central air conditioning system; within each calculation period, it iterates through the combinations of operating parameters of the chilled water pump, cooling water pump, and cooling tower; for each combination of operating parameters, it calculates the cooling capacity of the central air conditioning system and the total power consumption of the core equipment in the computer room within that period; based on the cooling capacity and total power consumption of each period, it calculates the energy efficiency coefficient for that period, and selects the operating parameter combination with the optimal energy efficiency coefficient as the optimal operating condition for that period; Calculate the energy efficiency coefficient for each period of the year, and output the annual energy efficiency forecast and the corresponding optimal combination of operating parameters.

2. The method for simulating and calculating the annual COP of a central air conditioning system based on the coupling of the surface cooler and cooling tower heat exchange model as described in claim 1, is characterized in that, include: The physical heat transfer model of the finned tube surface cooler adopts the equivalent reference heat exchanger modeling method. The equivalent reference heat exchanger modeling method includes: the equivalent heat transfer area is obtained by weighted average of the heat transfer areas of each type of terminal equipment in the system according to the number of units; the equivalent air volume and equivalent chilled water flow rate are obtained by weighted average of the rated air volume and rated water flow rate of each type of terminal equipment according to the number of units; the geometric structure parameters are the tube bundle arrangement, fin thickness, fin spacing and tube diameter corresponding to the terminal model with the highest proportion of total cooling capacity in the system. The physical heat transfer model of the finned tube surface cooler, after constructing an equivalent reference heat exchanger, uses an iterative algorithm to solve for the air-side and water-side temperature states that meet the system operating conditions. Specifically, it includes: calculating the lateral and longitudinal relative spacing of the tube bundles using the tube bundle layer, introducing tube row correction coefficients and tube bundle shape coefficients; calculating the theoretical efficiency of the fins and the convective heat transfer coefficient of the gas outside the tubes using the fin layer; integrating the heat transfer between the air side outside the tubes and the water side inside the tubes in the heat exchanger layer, calculating the heat capacity ratio, the number of heat transfer units (NTUs), the heat exchanger efficiency, and the overall heat transfer coefficient, and using iterative solutions to ensure that the difference between the calculated heat transfer and the target heat transfer is less than a set error threshold, and the difference between the finned tube return air temperature and the set air conditioning temperature is less than a set temperature error threshold, and obtaining the finned tube outlet air temperature and the finned tube water-side inlet and outlet temperatures output after iterative convergence.

3. The method for simulating and calculating the annual COP of a central air conditioning system based on the coupling of the surface cooler and cooling tower heat exchange model as described in claim 1, is characterized in that, include: The cooling towers are defined as open counter-flow cooling towers and open cross-flow cooling towers. Correspondingly, a heat transfer model based on the Michael enthalpy difference method is established for open cooling towers, distinguishing between the two structural forms of counter-flow and cross-flow. The cooling number of the counter-flow cooling tower is calculated based on the Michael enthalpy difference equation, while the cooling number of the cross-flow cooling tower is obtained by multiplying the cooling number of the counter-flow cooling tower by the Tezuka Shunichi correction factor. The thermodynamic characteristic expression of the packing is defined as a power function relationship between the cooling number and the air-to-water ratio. The open cooling tower's McEl enthalpy difference heat transfer model uses a bisection method to iteratively solve for the cooling water outlet temperature. This includes: using the cooling number as the target convergence variable, setting a search range for the cooling water outlet temperature, calculating the cooling number by substituting the air-to-water ratio under the current operating conditions into the thermodynamic characteristic expression of the packing, determining convergence when the absolute value of the difference between the calculated cooling number and the preset target cooling number is less than a set error threshold, and obtaining the converged output cooling water outlet temperature.

4. The method for simulating and calculating the annual COP of a central air conditioning system based on the coupling of the surface cooler and cooling tower heat exchange model as described in claim 1, is characterized in that, include: For the partial load performance model of the chiller unit, the actual operating energy efficiency of the main unit is set to be obtained by interpolation calculation on a preset performance data table; the energy efficiency value obtained by the interpolation calculation is corrected according to the difference between the actual chilled water supply temperature and the preset reference supply temperature.

5. The method for simulating and calculating the annual COP of a central air conditioning system based on the coupling of the surface cooler and cooling tower heat exchange model as described in claim 4, is characterized in that, include: The traversal process includes: performing a four-level nested traversal of the operating parameters of the chilled water pump, the cooling water pump, and the cooling tower; the four-level nested traversal process sequentially traverses the chilled water pump frequency, the cooling water pump frequency, the number of cooling towers in operation, and the cooling tower fan frequency, and the traversal process is subject to preset operating constraints; the preset operating constraints include: preset minimum operating frequencies of the chilled water pump and the cooling water pump, and preset minimum temperature difference between the chilled water outlet temperature and the cooling water inlet temperature of the main unit.

6. The method for simulating and calculating the annual COP of a central air conditioning system based on the coupling of the surface cooler and cooling tower heat exchange model as described in claim 1, is characterized in that, Selecting the optimal combination of operating parameters for energy efficiency also includes: The optimization is achieved by combining decoupled coarse-grained search with local nested traversal. The decoupling coarse search includes: constructing a single-variable power consumption-temperature curve on the chilled water side to obtain the minimum chilled water pump power consumption corresponding to different chilled water supply temperatures; constructing a multi-variable optimal power consumption-temperature mapping on the cooling water side to solve for the minimum total auxiliary power consumption under the coordinated operation of cooling water pump frequency, number of cooling towers, and fan frequency for different cooling water outlet temperatures; and performing two-dimensional joint optimization with chilled water supply temperature and cooling water outlet temperature as continuous decision variables to determine the nominal optimal combination of operating parameters. The local nested traversal includes: within the neighborhood of the nominal optimal combination of operating parameters, performing discrete traversal of the chilled water pump frequency, cooling water pump frequency, number of cooling towers and fan frequency in sequence with a preset step size, calculating the system energy efficiency coefficient according to the hourly energy calculation model, and selecting the one with the best energy efficiency coefficient as a candidate.

7. The method for simulating and calculating the annual COP of a central air conditioning system based on the coupling of the surface cooler and cooling tower heat exchange model as described in claim 1, is characterized in that, Calculating the energy efficiency coefficient for this period specifically involves calculating the dynamic average energy efficiency coefficient for that period, including: Based on the first-order inertial time constants of the physical heat transfer models of the surface cooler and the cooling tower, and the difference between the initial and steady-state final values ​​of the state variables caused by the switching of operating parameters during the current period, the duration of the state transition phase is determined; this period is then divided into the state transition phase and the quasi-steady-state phase. During the state transition phase, the average energy efficiency of the transition phase is determined using an hourly energy calculation model based on the phase average temperature, and the cumulative energy consumption of the transition phase is calculated. During the quasi-steady-state phase, the steady-state energy efficiency is determined using the hourly energy calculation model, and the cumulative energy consumption of the quasi-steady-state phase is calculated. The sum of the cumulative energy consumption during the transition phase and the cumulative energy consumption during the quasi-steady-state phase is taken as the dynamic total energy consumption for that period, and the ratio of the cooling load for that period to the dynamic total energy consumption is taken as the dynamic average energy efficiency coefficient.

8. The method for simulating and calculating the annual COP of a central air conditioning system based on the coupling of the surface cooler and cooling tower heat exchange model according to claim 1, characterized in that, Acquire time-series data of meteorological parameters and building cooling load throughout the year, including: using hourly data of a typical meteorological year as a benchmark, generating multiple sets of time-series data of meteorological parameter disturbances containing temperature and humidity fluctuations by superimposing random disturbances, and generating time-series data of building cooling load disturbances by scaling the temperature disturbances proportionally and superimposing random noise; constructing multiple operating scenarios based on the multiple sets of time-series data of meteorological parameter disturbances and time-series data of building cooling load disturbances respectively; Output the annual energy efficiency forecast and the corresponding optimal operating parameter combination, including: statistically analyzing the simulation results of all operating scenarios to obtain the probability distribution of the annual energy efficiency coefficient, and generating the hourly optimal operating parameter combination for the whole year by aggregating the optimal operating parameter combinations of each operating scenario at the same time.

9. The method for simulating and calculating the annual COP of a central air conditioning system based on the coupling of the surface cooler and cooling tower heat exchange model according to claim 1, characterized in that, The method can be implemented in a spreadsheet software platform that provides a visual interface for inputting equipment parameters, meteorological parameters, and load parameters, and performs model building, coupling, and calculation processes, with intermediate calculation data stored in the worksheet of the spreadsheet software platform.

Images