Global energy efficiency optimization design method for medium-temperature cold water central air conditioning system

Abstract

The invention discloses a global energy efficiency optimization design method for a medium-temperature cold water central air-conditioning system, which can effectively reduce the modeling workload of a large building on a tail end by considering the form of tail end equipment and the sensible heat load and latent heat load borne by each tail end equipment, and meanwhile, ensures that the tail end operation meets the room heat comfort requirement. The genetic algorithm is adopted to optimize key equipment configuration parameters and system operation parameters of the air conditioning system, the influence of annual load fluctuation of the air conditioning system on system operation energy consumption is considered, and an optimal design scheme with the optimal annual comprehensive energy efficiency is obtained in the system design stage. And the overall operation energy efficiency of the air conditioner is higher while the tail end heat comfort is met, and the system is suitable for a medium-temperature cold water central air-conditioning system.

Description

Technical Field

[0001] The present 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 cold water centralized air-conditioning system. Background Art

[0002] Air conditioning systems are a major component of public building energy consumption, accounting for 30% to 40%. Air conditioning systems primarily handle the heat and humidity loads within rooms. Currently, the mainstream design approach uses a supply / return water temperature of 7 / 12°C, and only considers the cooling load on the design day when selecting system equipment. Based on the operating characteristics of refrigeration equipment, higher supply water temperatures increase the energy efficiency of the cooling unit, while higher supply / return water temperatures reduce system flow rates and pump energy consumption, effectively reducing air conditioning system energy consumption. However, an increase in the temperature difference between the supply and return water temperatures reduces the cooling and dehumidification capacity of the terminal. To meet these cooling and dehumidification requirements, these requirements are often met by increasing the size of the terminal equipment or increasing the air volume, resulting in increased terminal energy consumption. Therefore, during the system design phase, it is important to comprehensively consider the overall operating energy consumption of the air conditioning system and optimize the design of key equipment and operating parameters.

[0003] In the prior art, CN 117053356 A discloses a collaborative optimization method for an air-conditioning system based on minimizing year-round cooling energy consumption. This method considers the coupling and constraints between factors such as the dynamic year-round cooling and heating load variations of a building, the operating temperature of chilled water at different times, and the static design parameters of the system. With the lowest total annual energy consumption of the air-conditioning system as the control objective, collaborative optimization accurately and reliably obtains the design parameters and operating 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 moisture load satisfaction rate of the terminal equipment and cannot be used for the design of medium-temperature cooling systems. Summary of the Invention

[0004] In view of the above problems, the present invention proposes a global energy efficiency optimization design method for a medium-temperature cold water centralized air-conditioning system, which mainly solves the problems of the background technology.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] A method for optimizing the overall energy efficiency of a medium-temperature cold water central air conditioning system includes the following steps:

[0007] Step 1: Calculate the hourly cooling load of the target building model throughout the year according to 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 loads throughout the year, a performance energy efficiency curve dataset of hosts with different rated capacities is set, and a cooling host energy consumption calculation model is established. A genetic algorithm is used to optimize the host configuration parameters in the cooling host energy consumption calculation model with the lowest total annual energy consumption of all cooling hosts in the system as the objective function;

[0009] Step 3: Based on the hourly heat and humidity loads throughout the year, locate the room with the minimum daily heat and humidity ratio. Based on the air supply form of the room with the minimum daily heat and humidity ratio, determine the type of terminal equipment in the room with the minimum daily heat and humidity ratio and the sensible heat load and latent heat load borne by each terminal equipment, and define this 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: setting the system operation parameter range, the terminal heat and humidity load operation constraint, the key equipment configuration parameter range in the system, and the terminal heat and humidity load operation satisfaction rate as the constraint conditions of the air conditioning system model;

[0012] Step 6: Using a genetic algorithm, taking the annual energy consumption of the cooling source equipment and the annual energy consumption of the most unfavorable terminal as the objective function, incorporating the host configuration parameters into the key equipment configuration parameters of the air-conditioning system, and performing multi-objective optimization on the key equipment configuration parameters of the air-conditioning system and the system operating parameters;

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

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

[0015] In some embodiments, in step 2, the host configuration parameters include the rated cooling capacity and number of hosts, wherein 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~2000RT, and the upper limit of the number of computer rooms does not exceed the space limit of the target building model.

[0016] In some embodiments, in step 5, the terminal heat and moisture load operation constraints include:

[0017] It is assumed that the cooling and dehumidification capacity of the terminal equipment in the current room under operating conditions is higher than the terminal heat and humidity load calculated based on the upper limit temperature and humidity of the cold benchmark, and lower than the terminal heat and humidity load calculated based on the lower limit temperature and humidity of the cold benchmark.

[0018] In some embodiments, in step 5, the terminal heat and moisture load operation satisfaction rate is the ratio of the cumulative satisfaction hours of the current terminal equipment under the condition of satisfying the terminal heat and moisture load operation constraints to the annual operation hours of the air-conditioning system model.

[0019] In some embodiments, in step 5, the system operating parameters include the refrigeration unit load rate, the terminal rated sensible cooling load, 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.

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

[0021] In some embodiments, in step 6, the objective function is replaced by the sum of the total energy consumption of the cold source equipment throughout the year 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 embodiments, the air-conditioning system model includes a refrigeration host module, a chilled water pump module, a cooling water pump module, a cooling tower module, a total cooling load module, a most unfavorable terminal module and a control module, wherein the total cooling load module is used to meet the system operation needs, and the most unfavorable terminal module is used to monitor whether the terminal cooling and dehumidification capacity at each moment is in the room heat and humidity load range, and accumulate the number of hours that the most unfavorable terminal meets the requirements. In the control module, the host is operated by adding and subtracting loads to control the host energy efficiency to operate in the high-efficiency range, and based on the current total cooling load, the optimal host load rate distribution plan is calculated, and the host operation load rate is allocated according to the optimal host load rate distribution plan.

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

[0024] The beneficial effects of the present invention are: 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 in large buildings can be effectively reduced, while ensuring that the terminal operation meets the thermal comfort requirements of the room. In addition, a genetic algorithm is used to optimize the configuration parameters of the key equipment and the system operation parameters of the air-conditioning system, and the impact of the load fluctuation of the air-conditioning system throughout the year on the system operation energy consumption is taken into account. The optimal design scheme with the best comprehensive energy efficiency throughout the year is obtained in the system design stage. Compared with the conventional 7°C design, the overall air-conditioning operation energy efficiency is higher while meeting the terminal thermal comfort, and it is suitable for medium-temperature cold water centralized air-conditioning systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1A schematic flow chart of a method for optimizing the overall energy efficiency of a medium-temperature cold water central air-conditioning system 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 disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] To make the objectives, technical solutions, and advantages of the present invention more clear and distinct, the present invention is 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 for the purpose of explaining the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all of the present invention.

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

[0029] Step 1: Calculate the annual hourly cooling load: Calculate the annual hourly cooling load for the target building model based on the preset indoor design conditions to obtain the annual hourly heat and humidity load for each room. In Step 1, the indoor design conditions include a set of upper and lower reference cooling temperature and humidity limits (26°C, 60%) and (24°C, 40%), respectively. Therefore, Step 1 generates two sets of hourly heat and humidity loads for each room.

[0030] Step 2, determine the rated cooling capacity and number of hosts: Based on the hourly heat and humidity load throughout the year, set the performance energy efficiency curve data set of hosts with different rated capacities, establish a cooling host energy consumption calculation model, and use a genetic algorithm to optimize the host configuration parameters in the cooling host energy consumption calculation model with the lowest total annual energy consumption of all hosts in the system cooling as the objective function. In the above step 2, the host configuration parameters at least include the host rated cooling capacity Q ratedi,chiller The sum of the cooling capacity of all the hosts is greater than the design daily air-conditioning load and does not exceed 1.1 times of the design daily air-conditioning load. The cooling capacity range of each host is set to 200RT~2000RT. The upper limit of the number of computer rooms 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 cooling hosts generally does not exceed 5.

[0031] Step 3: Determine the terminal equipment type and the most unfavorable terminal: Based on the hourly heat and humidity load throughout the year (e.g., calculated using the indoor design conditions of [26°C, 60%]), locate the room with the smallest daily heat and humidity ratio. Based on the air supply type of the room with the smallest daily heat and humidity ratio, determine the terminal equipment type in the room with the smallest daily heat and humidity ratio and the sensible heat load q borne by each terminal equipment. sand latent heat load q l , defined as the most unfavorable terminal. Due to the increase in the system's cooling temperature and cooling temperature difference, the terminal's cooling and dehumidification capacity decreases. Therefore, in the process of selecting the most unfavorable terminal, the optional range of its rated cooling capacity should be increased.

[0032] Step 4: Establish an air conditioning system model: Establish an air conditioning system model based on the host configuration parameters and auxiliary equipment. In one example, configure the corresponding chilled water pump and cooling water pump according to the number of hosts m, and calculate the total cooling water volume G of the cooling tower. z,tower , establish a complete air-conditioning system model based on the determined auxiliary equipment configuration.

[0033] In one example, a complete air-conditioning system model is established based on a determined equipment configuration using the transient system simulation program Trnsys (Transient System Simulation Program). The air-conditioning system model mainly includes a refrigeration host module, a chilled water pump module, a cooling water pump module, a cooling tower module, a total cooling load module, a most unfavorable terminal module, and a control module. The total cooling load module is used to meet the system operation needs, and the most unfavorable terminal module is used to monitor whether the terminal cooling and dehumidification capacity at each moment is within the room heat and humidity load range (i.e., the range formed by the two room heat and humidity load end values ​​calculated based on the upper limit temperature and humidity and the lower limit temperature and humidity of the cooling reference), and to accumulate the number of hours that the most unfavorable terminal meets the requirements. The operation control logic of the host load increase and decrease in the control module is to make the host energy efficiency operate in the high-efficiency range, calculate the optimal host load rate distribution plan based on the current total cooling load, and distribute the host operating load rate according to the optimal load rate distribution plan. The water pump, cooling tower, and terminal fan in the air-conditioning system model are all variable frequency controlled.

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

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

[0036] It is assumed that the cooling and dehumidification capacity of the terminal equipment in the current room under operating conditions is higher than the terminal heat and humidity load calculated based on the cold benchmark upper limit temperature and humidity [26°C, 60%], and lower than the terminal heat and humidity load calculated based on the cold benchmark lower limit temperature and humidity [24°C, 40%].

[0037] In step 5, the terminal heat and moisture load operation satisfaction rate is the ratio of the cumulative satisfaction hours of the current terminal equipment under the condition of satisfying the terminal heat and moisture load operation constraints to the annual operation hours of the air-conditioning system model. The above-mentioned terminal heat and moisture 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 during the operation of the air-conditioning system throughout the year, the most unfavorable terminal can meet the terminal cooling and dehumidification capacity. When it is set to 80%, it means that during the operation of the air-conditioning system throughout the year, the most unfavorable terminal cannot meet the terminal cooling or dehumidification capacity 20% of the time.

[0038] In step 5, the system operating parameters include the refrigeration unit load factor, the terminal rated sensible cooling load, the water pump frequency, the rated cooling water flow of each 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. The specific parameter ranges are set as follows:

[0039] The load factor μ of the refrigeration unit is between 40% and 120%; the operating range of the water pump frequency f is between 30Hz and 50Hz; the rated cooling capacity q of the terminal is z,coil The cooling load q borne by the end of the interval between 100% and 200% z ; Rated cooling water volume of a single cooling tower G rated,tower Between 100 and 1000 meters 3 / h, the number of units is 1~1.5(G z,tower / G rated,tower ), the fan frequency f operating range is 30Hz~50Hz; the system water supply temperature operating range is 9~15℃, the supply and return water temperature difference operating range is 5~10℃, and the cooling tower outlet water temperature setting value is 25~35℃.

[0040] Step 6, parameter optimization: Use a genetic algorithm (such as one constructed using Matlab or Python), take the annual energy consumption of the cooling source equipment and the annual energy consumption of the most unfavorable terminal as the objective function, incorporate the host configuration parameters into the key equipment configuration parameters of the air conditioning system, and perform multi-objective optimization on the key equipment configuration parameters and system operating parameters of the air conditioning system. Among them, 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 , terminal rated cooling capacity q z,coil ,The system operating parameters include the supply water temperature T, the supply and return water temperature difference △T, and the cooling tower outlet water temperature setting value t.

[0041] In step 6, the multi-objective optimization process calls upon the performance database of the terminal equipment. To optimize the configuration of key air conditioning system equipment, a performance database must be pre-established. This database primarily includes the performance curves of the refrigeration unit at different cooling and cooling water temperatures, and the cooling and dehumidification performance and energy consumption of the terminal equipment at different water temperatures and air volumes.

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

[0043] For step 6, the genetic algorithm optimization process is as follows: Figure 2 As shown in the figure, the initial values ​​of the parameters to be optimized are set. The parameter ranges and the worst-case scenario satisfaction constraint are then set. Key genetic algorithm parameters such as population size, maximum number of iterations, convergence tolerance, and constraint tolerance are then set. The population is then initialized and the resulting population data is written into the air conditioning system energy consumption simulation model established by Trnsys. The Trnsys simulation model is then used to calculate annual energy consumption. The fitness of the individuals in the population is then calculated, which quantitatively evaluates the objective function calculated for each individual in the population. When multiple objectives are selected, the Pareto frontier method is used for multi-objective optimization. When a single objective is selected, the individual with the lowest energy consumption of the objective function is selected for the next generation of reproduction. If the maximum number of iterations is not reached, a crossover-mutation operation is performed on the individuals. New individuals are generated and written into the air conditioning system energy consumption simulation model established by Trnsys for recalculation 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 operating parameters of the air-conditioning system obtained in steps 2 and 6, and determine the optimal design scheme of the air-conditioning system model.

[0045] The above embodiments are intended only to illustrate the technical concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the contents of the present invention and implement them accordingly. They are not intended to limit the scope of protection of the present invention. Any equivalent changes or modifications made based on the essence of the present invention are intended to be covered by the scope of protection of the present invention.

Claims

1. A global energy efficiency optimization design method for a medium-temperature cold water central air conditioning system, characterized in that: The following steps are involved: Step 1: Calculate the hourly cooling load of the target building model throughout the year according to 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 loads throughout the year, a performance energy efficiency curve dataset of hosts with different rated capacities is set, and a cooling host energy consumption calculation model is established. A genetic algorithm is used to optimize the host configuration parameters in the cooling host energy consumption calculation model with the lowest total annual energy consumption of all cooling hosts in the system as the objective function; Step 3: Based on the hourly heat and humidity loads throughout the year, locate the room with the minimum daily heat and humidity ratio. Based on the air supply form of the room with the minimum daily heat and humidity ratio, determine the type of terminal equipment in the room with the minimum daily heat and humidity ratio and the sensible heat load and latent heat load borne by each terminal equipment, and define this as the most unfavorable terminal. Step 4: Establish an air conditioning system model based on the host configuration parameters and auxiliary equipment; Step 5: setting the system operation parameter range, the terminal heat and humidity load operation constraint, the key equipment configuration parameter range in the system, and the terminal heat and humidity load operation satisfaction rate as the constraint conditions of the air conditioning system model; Step 6: Using a genetic algorithm, taking the annual energy consumption of the cooling source equipment and the annual energy consumption of the most unfavorable terminal as the objective function, incorporating the host configuration parameters into the key equipment configuration parameters of the air-conditioning system, and performing multi-objective optimization on the key equipment configuration parameters of the air-conditioning system and the system operating parameters; Step 7: Summarize the optimization results of the key equipment configuration parameters of the air-conditioning system and the system operating parameters to determine the optimal design solution of the air-conditioning system model.

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

3. The global energy efficiency optimization design method for a medium-temperature cold water centralized air conditioning system according to 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 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 computer rooms does not exceed the space limit of the target building model.

4. The global energy efficiency optimization design method for a medium-temperature cold water centralized air conditioning system according to claim 2, characterized in that: In step 5, the terminal heat and moisture load operation constraints include: It is assumed that the cooling and dehumidification capacity of the terminal equipment in the current room under operating conditions is higher than the terminal heat and humidity load calculated based on the upper limit temperature and humidity of the cold benchmark, and lower than the terminal heat and humidity load calculated based on the lower limit temperature and humidity of the cold benchmark.

5. The global energy efficiency optimization design method for a medium-temperature cold water centralized air conditioning system according to claim 4 is characterized in that: In step 5, the terminal heat and moisture load operation satisfaction rate is the ratio of the cumulative satisfaction hours of the current terminal equipment under the condition of satisfying the terminal heat and moisture load operation constraints to the annual operation hours of the air-conditioning system model.

6. The global energy efficiency optimization design method for a medium-temperature cold water centralized air conditioning system according to claim 1, characterized in that: In step 5, the system operating parameters include the refrigeration unit load rate, the terminal rated sensible cooling load, 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.

7. The global energy efficiency optimization design method for a medium-temperature cold water centralized air conditioning system according to claim 1, characterized in that: In step 6, the multi-objective optimization process calls the performance database of the terminal device.

8. The global energy efficiency optimization design method for a medium-temperature cold water centralized air conditioning system according to claim 1, characterized in that: In step 6, the objective function is replaced by the sum of the total energy consumption of the cold source equipment throughout the year 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 medium-temperature cold water centralized air conditioning system according to claim 1, characterized in that: The air-conditioning system model includes a refrigeration host module, a chilled water pump module, a cooling water pump module, a cooling tower module, a total cooling load module, a most unfavorable terminal module and a control module, wherein the total cooling load module is used to meet the system operation needs, and the most unfavorable terminal module is used to monitor whether the terminal cooling and dehumidification capacity at each moment is in the room heat and humidity load range, and accumulate the number of hours that the most unfavorable terminal meets the requirements. In the control module, the host is operated by adding and reducing load to control the host energy efficiency to operate in the high-efficiency range, and the optimal host load rate distribution plan is calculated based on the current total cooling load, and the host operation load rate is distributed according to the optimal host load rate distribution plan.

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

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