A global energy efficiency optimization design method for a medium-temperature cold water centralized air conditioning system

Abstract

The application discloses a kind of medium-temperature cold water centralized air conditioning system global energy efficiency optimization design method, by considering the form of terminal equipment and the sensible heat load and latent heat load that each terminal equipment bears, can effectively reduce the modeling workload of large building to terminal, while guaranteeing that terminal operation meets room thermal comfort requirements, further, genetic algorithm is used to optimize key equipment configuration parameters and system operation parameters of air conditioning system, and the influence of annual air conditioning system load fluctuation on system operation energy consumption is considered, to obtain the optimal design scheme of optimal annual comprehensive energy efficiency in system design stage, compared with conventional 7 ℃ design, while meeting the terminal thermal comfort, air conditioning system operation energy efficiency is higher, suitable for medium-temperature cold water centralized air conditioning system.

Description

Technical Field

[0001] This invention relates to the field of air conditioning system design, and in particular to a global energy efficiency optimization design method for a medium-temperature chilled water centralized air conditioning system. Background Technology

[0002] In public building energy consumption, air conditioning system energy consumption is a major component, accounting for 30% to 40%. Air conditioning systems primarily handle the heat and humidity loads within rooms. Currently, the mainstream approach is to design systems with supply and return water temperatures of 7 / 12℃, and equipment selection only considers the cooling load of the design day. According to the operating characteristics of refrigeration equipment, higher supply water temperatures result in higher energy efficiency of the refrigeration unit, while higher supply and return water temperatures lead to lower system flow rates and lower pump energy consumption, effectively reducing the overall energy consumption of the air conditioning system. However, increasing the supply water temperature and the temperature difference between supply and return water can reduce the cooling and dehumidification capacity of the terminal units. To meet these requirements, the size of the terminal equipment or the airflow rate is often increased, leading to increased terminal energy consumption. Therefore, during the system design phase, it is necessary to comprehensively consider the overall energy consumption of the air conditioning system and optimize the design parameters of key equipment and operating parameters.

[0003] In the prior art, CN 117053356 A discloses an overall collaborative optimization method for air conditioning systems based on minimizing annual cooling energy consumption. This method considers the coupling and constraints between factors such as the building's annual dynamic cooling and heating load variations, chilled water supply temperatures at different times, and the system's static design parameters. It uses minimizing the total annual energy consumption of the air conditioning system as the control objective and, through collaborative optimization, accurately and reliably obtains the design parameters and operation control parameters of the air conditioning system based on the overall high energy consumption of the entire system throughout the year. However, this method does not consider the heat and humidity load satisfaction rate of the terminal equipment and cannot be used for the design of medium-temperature cooling systems. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes a global energy efficiency optimization design method for a medium-temperature chilled water centralized air conditioning system, which mainly solves the problems in the background technology.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] A global energy efficiency optimization design method for a medium-temperature chilled water centralized air conditioning system includes the following steps:

[0007] Step 1: Calculate the hourly cooling load of the target building model throughout the year based on the preset indoor design conditions to obtain the hourly heat and humidity load of each room throughout the year.

[0008] Step 2: Based on the hourly heat and humidity load throughout the year, set up a dataset of performance and energy efficiency curves of main units with different rated capacities, establish a calculation model for the energy consumption of the main unit, and use a genetic algorithm to optimize the main unit configuration parameters in the calculation model for the energy consumption of the main unit with the objective function of minimizing the total annual energy consumption of all main units in the system.

[0009] Step 3: Based on the hourly heat and humidity load throughout the year, locate the room with the lowest daily heat and humidity ratio. According to the air supply mode of the room with the lowest daily heat and humidity ratio, determine the form of the terminal equipment in the room with the lowest daily heat and humidity ratio and the sensible heat load and latent heat load borne by each terminal equipment, and define it as the most unfavorable terminal.

[0010] Step 4: Establish an air conditioning system model based on the host configuration parameters and auxiliary equipment;

[0011] Step 5: Set the system operating parameter range, terminal heat and humidity load operating constraints, key equipment configuration parameter range within the system, and terminal heat and humidity load operation satisfaction rate as constraints for the air conditioning system model;

[0012] Step 6: Using a genetic algorithm, with the annual energy consumption of the cold source equipment and the annual energy consumption of the most unfavorable terminal as the objective function, the host configuration parameters are incorporated into the configuration parameters of the key equipment of the air conditioning system, and multi-objective optimization is performed on the configuration parameters of the key equipment of the air conditioning system and the system operation parameters.

[0013] Step 7: Summarize the optimization results of the key equipment configuration parameters and system operating parameters of the air conditioning system, and determine the optimal design scheme of the air conditioning system model.

[0014] In some implementations, in step 1, the indoor design conditions include a set of upper limit temperature and humidity for cooling and lower limit temperature and humidity for cooling, which are [26°C, 60%] and [24°C, 40%], respectively.

[0015] In some implementations, in step 2, the host configuration parameters include the host's rated cooling capacity and number of units. The sum of the cooling capacities of all hosts is greater than the design daily air conditioning load and does not exceed 1.1 times the design daily air conditioning load. The cooling capacity range of each host is set to 200RT to 2000RT, and the upper limit of the number of units in the computer room does not exceed the space limit of the target building model.

[0016] In some implementations, step 5, the terminal heat and humidity load operating constraints include:

[0017] The cooling and dehumidification capacity of the terminal equipment in the current room is set to be higher than the heat and humidity load borne by the terminal calculated based on the upper limit temperature and humidity of the cold reference, and lower than the heat and humidity load borne by the terminal calculated based on the lower limit temperature and humidity of the cold reference.

[0018] In some implementations, in step 5, the terminal heat and humidity load operation satisfaction rate is the ratio of the cumulative number of hours that the current terminal equipment meets the terminal heat and humidity load operation constraints to the annual operating hours of the air conditioning system model.

[0019] In some implementations, in step 5, the system operating parameters include the chiller unit load rate, the rated sensible cooling load at the terminal, the water pump frequency, the rated cooling water volume of a single cooling tower, the cooling tower fan frequency, the system water supply temperature, the supply and return water temperature difference, and / or the cooling tower outlet water temperature.

[0020] In some implementations, in step 6, the multi-objective optimization process invokes the performance database of the end device.

[0021] In some implementations, in step 6, the objective function is replaced by the sum of the total annual energy consumption of the cold source equipment and the product of the most unfavorable terminal and the total number of terminals, and a single-objective genetic algorithm is used for optimization.

[0022] In some implementations, the air conditioning system model includes a refrigeration unit module, a chilled water pump module, a cooling water pump module, a cooling tower module, a total cooling load module, a worst-case terminal module, and a control module. The total cooling load module is used to meet the system's operational needs. The worst-case terminal module is used to monitor whether the terminal's cooling and dehumidification capacity is within the room's heat and humidity load range at each moment and to accumulate the number of hours that the worst-case terminal meets the requirements. In the control module, the refrigeration unit operates by adding or removing loads to control its energy efficiency within the high-efficiency range. Based on the current total cooling load, the optimal refrigeration unit load rate allocation scheme is calculated, and the refrigeration unit's operating load rate is allocated according to the optimal refrigeration unit load rate allocation scheme.

[0023] In some implementations, the water pump, cooling tower, and terminal fan in the air conditioning system model are all frequency converter controlled.

[0024] The beneficial effects of this invention are as follows: by considering the form of the terminal equipment and the sensible heat load and latent heat load borne by each terminal equipment, the modeling workload of the terminal equipment in large buildings can be effectively reduced, while ensuring that the terminal operation meets the room thermal comfort requirements. Furthermore, by using a genetic algorithm to optimize the configuration parameters of key equipment and system operation parameters of the air conditioning system, and considering the impact of the annual air conditioning system load fluctuation on the system's energy consumption, the optimal design scheme with the best overall energy efficiency throughout the year is obtained during the system design stage. Compared with the conventional 7°C design, the overall energy efficiency of the air conditioning system is higher while meeting the thermal comfort requirements of the terminal equipment, and it is suitable for medium-temperature chilled water centralized air conditioning systems. Attached Figure Description

[0025] Figure 1This is a flowchart illustrating the global energy efficiency optimization design method for a centralized air conditioning system with medium-temperature chilled water disclosed in an embodiment of the present invention.

[0026] Figure 2 This is a schematic diagram of the genetic algorithm optimization process in step 6 of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the content of this invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to this invention are shown in the accompanying drawings, not all of them.

[0028] This embodiment proposes a global energy efficiency optimization design method for a medium-temperature chilled water centralized air conditioning system, such as... Figure 1 As shown, it includes the following steps:

[0029] Step 1, Hourly Cooling Load Calculation: Calculate the hourly cooling load of the target building model throughout the year based on the preset indoor design conditions to obtain the hourly heat and humidity load for each room. In Step 1, the indoor design conditions include a set of upper and lower limits for cooling baseline temperature and humidity, which are [26℃, 60%] and [24℃, 40%], respectively. Therefore, Step 1 will obtain two sets of hourly heat and humidity loads for each room.

[0030] Step 2: Determine the rated cooling capacity and number of chillers: Based on the hourly heat and humidity load throughout the year, a dataset of performance and energy efficiency curves for chillers with different rated capacities is created. An energy consumption calculation model for the chillers is established, and a genetic algorithm is used to optimize the chiller configuration parameters in the energy consumption calculation model, with the objective function being the minimum total annual energy consumption of all chillers within the system. In Step 2 above, the chiller configuration parameters must include at least the rated cooling capacity Q of the chiller. ratedi,chiller The total number of units is m, where the sum of the cooling capacity of all main units is greater than the design daily air conditioning load and does not exceed 1.1 times the design daily air conditioning load. The cooling capacity range of each main unit is set to 200RT to 2000RT. The upper limit of the number of main units in the computer room does not exceed the space limit of the target building model. Considering the limited building space, for general small and medium-sized buildings, the number of main units is generally no more than 5.

[0031] Step 3: Determine the type of terminal equipment and the most unfavorable terminal: Based on the hourly heat and humidity load throughout the year (e.g., calculated using indoor design conditions of [26℃, 60%]), locate the room with the lowest daily heat and humidity ratio. According to the air supply mode of the room with the lowest daily heat and humidity ratio, determine the type of terminal equipment in the room with the lowest daily heat and humidity ratio and the sensible heat load q borne by each terminal equipment. sand latent heat load q l The most unfavorable terminal is defined as the terminal whose cooling and dehumidification capacity decreases due to the increase in system cooling temperature and cooling temperature difference. Therefore, the range of rated cooling capacity that can be selected should be increased during the selection of the most unfavorable terminal.

[0032] Step 4: Establish the air conditioning system model: Establish the air conditioning system model based on the main unit configuration parameters and auxiliary equipment. In one example, configure the corresponding chilled water pumps and cooling water pumps according to the number of main units m, and calculate the total cooling water volume G of the cooling tower. z,tower A complete air conditioning system model is established based on the determined auxiliary equipment configuration.

[0033] In one example, the Transient System Simulation Program (Trnsys) is used to build a complete air conditioning system model based on a given equipment configuration. This model mainly includes a chiller module, a chilled water pump module, a cooling water pump module, a cooling tower module, a total cooling load module, a worst-case terminal module, and a control module. The total cooling load module is used to meet the system's operational needs. The worst-case terminal module monitors whether the terminal's cooling and dehumidification capacity is within the room's heat and humidity load range at each moment (i.e., the range formed by the two room heat and humidity load values ​​calculated based on the upper limit temperature and humidity and the lower limit temperature and humidity of the cooling baseline), and accumulates the number of hours the worst-case terminal meets the requirements. In the control module, the chiller's load control logic ensures that the chiller operates within its high-efficiency range. Based on the current total cooling load, the optimal chiller load rate allocation scheme is calculated, and the chiller's operating load rate is allocated according to this optimal scheme. In this air conditioning system model, the water pumps, cooling tower, and terminal fans are all frequency converters.

[0034] Step 5: Set the system operating parameter range, terminal heat and humidity load operating constraints, key equipment configuration parameter range within the system, and terminal heat and humidity load operating satisfaction rate as constraints for the air conditioning system model.

[0035] In step 5, the terminal heat and humidity load operation constraints include:

[0036] The cooling and dehumidification capacity of the terminal equipment in the current room is set to be higher than the heat and humidity load of the terminal calculated based on the upper limit of the cold reference temperature and humidity [26℃, 60%], and lower than the heat and humidity load of the terminal calculated based on the lower limit of the cold reference temperature and humidity [24℃, 40%].

[0037] In step 5, the terminal heat and humidity load operation satisfaction rate is the ratio of the cumulative number of hours that the current terminal equipment meets the terminal heat and humidity load operation constraints to the annual operating hours of the air conditioning system model. The above-mentioned terminal heat and humidity load operation satisfaction rate can be set according to user needs. The satisfaction rate can be set between 100% and 80%. When the satisfaction rate is set to 100%, it means that the most unfavorable terminal can meet the terminal cooling and dehumidification capabilities during the annual operation of the air conditioning system. When it is set to 80%, it means that the most unfavorable terminal cannot meet the terminal cooling or dehumidification capabilities for 20% of the time during the annual operation of the air conditioning system.

[0038] In step 5, the system operating parameters include the chiller unit load rate, the rated sensible cooling load at the terminal, the water pump frequency, the rated cooling water volume of a single cooling tower, the cooling tower fan frequency, the system supply water temperature, the supply and return water temperature difference, and / or the cooling tower outlet water temperature, and the specific parameter ranges are set as follows:

[0039] The refrigeration unit load rate μ ranges from 40% to 120%; the water pump frequency f operates from 30Hz to 50Hz; and the rated cooling capacity q at the terminal is... z,coil The cooling load q borne by the range at the end of 100% to 200% z Rated cooling water capacity per unit of cooling tower (G) rated,tower Located between 100 and 1000m 3 / h, the number of units is between 1 and 1.5 (G) z,tower / G rated,tower The fan frequency f operates in the range of 30Hz to 50Hz; the system water supply temperature operates in the range of 9 to 15℃; the supply and return water temperature difference operates in the range of 5 to 10℃; and the cooling tower outlet water temperature is set at 25 to 35℃.

[0040] Step 6, Parameter Optimization: Using a genetic algorithm (e.g., constructed using Matlab or Python), with the annual energy consumption of the cooling source equipment and the annual energy consumption of the most unfavorable terminal unit as the objective function, the host configuration parameters are incorporated into the configuration parameters of the key equipment of the air conditioning system. Multi-objective optimization is then performed on the configuration parameters of the key equipment and the system operating parameters. The key equipment configuration parameters of the air conditioning system include the number of cooling towers n and the rated cooling capacity Q. rated,tower Rated cooling capacity at the terminal q z,coil The system operating parameters include the water supply temperature T, the temperature difference between the supply and return water ΔT, and the cooling tower outlet water temperature setpoint t.

[0041] In step 6, the multi-objective optimization process calls the performance database of the terminal devices. In order to optimize the configuration of key equipment in the air conditioning system, it is necessary to set up the performance database of the equipment in advance, which mainly includes the performance curves of the chiller at different cooling temperatures and cooling water temperatures, and the cooling and dehumidification performance and energy consumption values ​​of the terminal devices at different water supply temperatures and air supply volumes.

[0042] In step 6, to simplify the objective function, the calculation of the total energy consumption of the air conditioning system can also be simplified. The objective function can be replaced by the sum of the total annual operating energy consumption of the cold source equipment and the product of the most unfavorable terminal and the total number of terminals, and a single-objective genetic algorithm can be used for optimization.

[0043] For step 6, the genetic algorithm optimization process is as follows: Figure 2 As shown, first, the initial values ​​of the parameters to be optimized are set. Then, the range of parameter variables and the worst-case terminal satisfaction rate constraint are set. Next, key parameters of the genetic algorithm are set, such as population size, maximum number of iterations, convergence tolerance, and constraint tolerance. Then, the population is initialized, and the initialized population data is written into the air conditioning system energy consumption simulation model built by Trnsys. The Trnsys simulation model is then called to calculate the annual energy consumption, calculating the fitness of individual populations, i.e., quantifying and evaluating the objective function calculated for each individual in the population. When multiple objectives are selected, the Pareto front method is used for multi-objective optimization. When it is a single objective, the individual with the lowest energy consumption of the objective function is selected for the next generation. When the number of iterations does not reach the maximum, crossover-mutation operations are performed on the individuals, and new individuals are generated and written into the air conditioning system energy consumption simulation model built by Trnsys for recalculation of energy consumption until the maximum number of iterations is reached. The individual output at this point is the optimal individual.

[0044] Step 7, Determine the optimal design scheme: Summarize the optimization results of the key equipment configuration parameters and system operation parameters of the air conditioning system obtained in Step 2 and Step 6, and determine the optimal design scheme of the air conditioning system model.

[0045] The above embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made based on the essence of the content of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A global energy efficiency optimization design method for a medium-temperature chilled water centralized air conditioning system, characterized in that, Includes the following steps: Step 1: Calculate the hourly cooling load of the target building model throughout the year based on the preset indoor design conditions to obtain the hourly heat and humidity load of each room throughout the year. Step 2: Based on the hourly heat and humidity load throughout the year, set up a dataset of performance and energy efficiency curves of main units with different rated capacities, establish a calculation model for the energy consumption of the main unit, and use a genetic algorithm to optimize the main unit configuration parameters in the calculation model for the energy consumption of the main unit with the objective function of minimizing the total annual energy consumption of all main units in the system. Step 3: Based on the hourly heat and humidity load throughout the year, locate the room with the lowest daily heat and humidity ratio. According to the air supply mode of the room with the lowest daily heat and humidity ratio, determine the form of the terminal equipment in the room with the lowest daily heat and humidity ratio and the sensible heat load and latent heat load borne by each terminal equipment, and define it as the most unfavorable terminal. Step 4: Establish an air conditioning system model based on the host configuration parameters and auxiliary equipment; Step 5: Set the system operating parameter range, terminal heat and humidity load operating constraints, key equipment configuration parameter range within the system, and terminal heat and humidity load operation satisfaction rate as constraints for the air conditioning system model; Step 6: Using a genetic algorithm, with the annual energy consumption of the cold source equipment and the annual energy consumption of the most unfavorable terminal as the objective function, the host configuration parameters are incorporated into the configuration parameters of the key equipment of the air conditioning system, and multi-objective optimization is performed on the configuration parameters of the key equipment of the air conditioning system and the system operation parameters. Step 7: Summarize the optimization results of the key equipment configuration parameters and system operating parameters of the air conditioning system, and determine the optimal design scheme of the air conditioning system model.

2. The global energy efficiency optimization design method for a medium-temperature chilled water centralized air conditioning system as described in claim 1, characterized in that, In step 1, the indoor design conditions include a set of upper limit temperature and humidity for cooling and lower limit temperature and humidity for cooling, which are [26℃, 60%] and [24℃, 40%], respectively.

3. The global energy efficiency optimization design method for a centralized chilled water air conditioning system as described in claim 1, characterized in that, In step 2, the host configuration parameters include the rated cooling capacity and number of hosts. The sum of the cooling capacity of all hosts is greater than the design daily air conditioning load and does not exceed 1.1 times the design daily air conditioning load. The cooling capacity of each host is set to be between 200RT and 2000RT. The upper limit of the number of hosts in the computer room does not exceed the space limit of the target building model.

4. The global energy efficiency optimization design method for a centralized chilled water air conditioning system as described in claim 2, characterized in that, In step 5, the terminal heat and humidity load operation constraints include: The cooling and dehumidification capacity of the terminal equipment in the current room is set to be higher than the heat and humidity load borne by the terminal calculated based on the upper limit temperature and humidity of the cold reference, and lower than the heat and humidity load borne by the terminal calculated based on the lower limit temperature and humidity of the cold reference.

5. The global energy efficiency optimization design method for a centralized chilled water air conditioning system as described in claim 4, characterized in that, In step 5, the terminal heat and humidity load operation satisfaction rate is the ratio of the cumulative number of hours that the current terminal equipment meets the terminal heat and humidity load operation constraints to the annual operating hours of the air conditioning system model.

6. The global energy efficiency optimization design method for a centralized chilled water air conditioning system as described in claim 1, characterized in that, In step 5, the system operating parameters include the refrigeration unit load rate, the rated sensible cooling load at the terminal, the water pump frequency, the rated cooling water volume of a single cooling tower, the cooling tower fan frequency, the system water supply temperature, the supply and return water temperature difference, and / or the cooling tower outlet water temperature.

7. The global energy efficiency optimization design method for a centralized chilled water air conditioning system as described in claim 1, characterized in that, In step 6, the multi-objective optimization process calls the performance database of the end device.

8. The global energy efficiency optimization design method for a centralized chilled water air conditioning system as described in claim 1, characterized in that, In step 6, the objective function is replaced by the sum of the total annual energy consumption of the cold source equipment and the product of the most unfavorable terminal and the total number of terminals, and a single-objective genetic algorithm is used for optimization.

9. The global energy efficiency optimization design method for a centralized chilled water air conditioning system as described in claim 1, characterized in that, The air conditioning system model includes a refrigeration unit module, a chilled water pump module, a cooling water pump module, a cooling tower module, a total cooling load module, a worst-case terminal module, and a control module. The total cooling load module is used to meet the system's operational needs. The worst-case terminal module is used to monitor whether the terminal's cooling and dehumidification capacity is within the room's heat and humidity load range at each moment and to accumulate the number of hours that the worst-case terminal meets the requirements. In the control module, the refrigeration unit is operated by adding or removing loads to control its energy efficiency to operate within the high-efficiency range. Based on the current total cooling load, the optimal refrigeration unit load rate allocation scheme is calculated, and the refrigeration unit's operating load rate is allocated according to the optimal refrigeration unit load rate allocation scheme.

10. The global energy efficiency optimization design method for a medium-temperature chilled water centralized air conditioning system as described in claim 1, characterized in that, In the air conditioning system model, the water pump, cooling tower, and terminal fan are all controlled by frequency converters.

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