Central air conditioning system collaborative optimization control method based on master-slave game

Abstract

The invention relates to a central air conditioning system collaborative optimization control method based on a master-slave game. The method is suitable for the technical field of building energy saving and intelligent building control. According to the technical scheme, on the basis of the system structure of the central air conditioning system, an overall optimization control task is set and comprises an overall objective function and energy conservation constraints of a central air conditioning water system and a central air conditioning air system, and the overall objective function aims at minimizing the total operation energy consumption of the central air conditioning system while maintaining the thermal comfort degree of end users; an overall optimization control task of the central air conditioning system is decomposed into a series of subsystem-level main optimization tasks; according to the equipment composition of each subsystem, the subsystem-level master optimization task is decomposed into a series of equipment-level slave optimization tasks; and based on the main optimization task of the subsystem level and the slave optimization task of the equipment level, performing game optimization of the main-slave level by using a multi-level alternating direction multiplier method.

Description

Technical Field

[0001] The invention relates to a collaborative optimization control method for a central air-conditioning system based on master-slave game, which is applicable to the technical fields of building energy conservation and intelligent building control. Background Art

[0002] During the operation of public buildings, the energy consumption of central air-conditioning systems accounts for about 50%-60%, exceeding the total energy consumption of lighting, elevators and other systems, and is the largest energy consumer in the operation stage of public buildings. Regulating the air-conditioning cooling water system through reasonable optimization control methods, thereby reducing the system operation energy consumption while meeting the requirements of each terminal load, is a powerful entry point for achieving low-carbon energy conservation in public buildings.

[0003] The actual central air-conditioning system is extremely complex, with strong coupling between different devices and subsystems, and the energy consumption of the equipment usually shows the characteristics of "one increases while the other decreases". Most of the existing central air-conditioning system optimization control methods are concentrated at the device group level or subsystem level. When applied to the system-level optimization control, there are problems with insufficient optimization accuracy and calculation efficiency, which will not only reduce the overall operating efficiency of the central air-conditioning system, but also affect the real-time control performance of the automatic control system, making it difficult to meet the actual large-scale and complex central air-conditioning system operation and control needs. Summary of the invention

[0004] The technical problem to be solved by the present invention is: in view of the above-mentioned problems, a collaborative optimization control method for a central air-conditioning system based on master-slave game is provided.

[0005] The technical solution adopted by the present invention is: a collaborative optimization control method for a central air-conditioning system based on master-slave game, characterized in that it includes:

[0006] S1. Based on the system structure of the central air-conditioning system, set the overall optimization control task, including the overall objective function, the energy conservation constraints of the central air-conditioning water system and the wind system. The overall objective function aims to minimize the total operating energy consumption of the central air-conditioning system while maintaining the thermal comfort of the end users;

[0007] S2. Decompose the overall optimization control task of the central air-conditioning system into a series of subsystem-level main optimization tasks, including the main optimization task of the water system and the main optimization task of the wind system, so as to coordinate the energy balance between different subsystems to achieve the regulation of the entire central air-conditioning system;

[0008] S3. According to the equipment composition of each subsystem, the main optimization task at the subsystem level is decomposed into a series of sub-optimization tasks at the equipment level, so as to optimize and regulate the operating parameters of each device in the subsystem according to the energy supply requirements of different subsystems;

[0009] S4. Based on the master optimization task at the subsystem level and the slave optimization task at the equipment level, a multi-level alternating direction multiplier method is used to perform master-slave hierarchical game optimization to minimize the total operating energy consumption of the central air-conditioning system while maintaining the thermal comfort of end users.

[0010] The main optimization task of the water system includes the main optimization task of the water system and the energy conservation constraint of the central air-conditioning water system, and the main optimization task of the water system aims to minimize the operating energy consumption of the central air-conditioning water system;

[0011] The main optimization task of the wind system includes the main optimization task of the wind system and the energy conservation constraint of the central air conditioning wind system. The main optimization task of the wind system takes minimizing the operating energy consumption of the central air conditioning wind system while maintaining the thermal comfort of the end users as the optimization goal.

[0012] The main optimization tasks of the water system include:

[0013]

[0014] The main optimization tasks of the wind system include:

[0015]

[0016] The main optimization task of the water system is decomposed into the sub-optimization tasks of the chiller, the chilled water pump, the cooling water pump and the cooling tower;

[0017] Among them, the optimization tasks of the chiller include:

[0018]

[0019] The optimization tasks of the chilled water pump include:

[0020]

[0021] Cooling water pump optimization tasks include:

[0022]

[0023] Cooling tower optimization tasks include:

[0024]

[0025] The main optimization task of the wind system is decomposed into the sub-optimization tasks of the air handling unit and the terminal room; wherein the sub-optimization task of the air handling unit includes:

[0026]

[0027] The optimization tasks of the terminal room include:

[0028]

[0029] The method of using the multi-level alternating direction multiplier method to perform master-slave level game optimization includes:

[0030] S4-1. Initialize the parameters to be optimized and and the Lagrange multipliers and

[0031] S4-2. Set the penalty coefficient ρ at each level m , s,w and ρ s,a , number of iterations k m =0, k s,w = 0 and k s,a,i = 0, and the upper limit of the number of iterations and

[0032] S4-3, each chiller, chilled water pump, cooling water pump and cooling tower of the water system solves its own optimization task in parallel and obtains the kth s,w +1 round of optimal calculation results;

[0033] At the same time, the air handling units of each wind system and each terminal room solve their own optimization tasks in parallel to obtain the kth s,a,i +1 round of optimal calculation results;

[0034] S4-4, respectively update the Lagrange multipliers of the water system equipment and wind system equipment from the hierarchy, as shown in the following formula:

[0035]

[0036] S4-5, judging whether the hierarchical optimization process converges, if the convergence condition is met, the hierarchical optimization process ends and enters step S4-6; otherwise, set k s,w and k s,a,i The value increases by 1, and steps S4-3 to S4-5 are repeated;

[0037] S4-6. Update the main-level Lagrange multiplier as shown in the following formula:

[0038]

[0039] S4-7, determine whether the main level optimization process converges. If the convergence condition is met, the main level optimization process ends and enters step S4-8; otherwise, set k mThe value increases by 1, and steps S4-3 to S4-7 are repeated; S4-8, the calculation results of the parameters to be optimized are output to achieve the optimization control of the central air-conditioning system.

[0040] The convergence condition of the hierarchical optimization process is to satisfy the following equation or and

[0041]

[0042] Among them, ε s,w and ε s,a,i They are the judgment thresholds of the hierarchical optimization process for the water system and the wind system, respectively. Formula 1 represents the absolute value of the energy balance constraint deviation between the various devices in the water system, which measures the degree to which the optimization results of the various devices in the water system satisfy the coupling constraints. Formula 2 represents the absolute value of the mass conservation constraint deviation between the air handling unit of the wind system and each terminal room, which measures the degree to which the optimization results of the air handling unit in the wind system and each terminal room satisfy the coupling constraints.

[0043] The convergence condition of the main level optimization process is to satisfy the following equation or

[0044]

[0045] Among them, ε m,1 and ε m,2 They are the primary residual threshold and dual residual threshold of the optimization process of the main level, respectively. Formula 1 represents the absolute value of the energy balance constraint deviation between the water system and the wind system, which measures the satisfaction degree of the optimization results of the water system and the wind system for the coupling constraints. Formula 2 represents the absolute value of the difference between the variables to be optimized before and after the two iterations of the main level, which measures the stability of the optimization process of the main level.

[0046] A collaborative optimization control device for a central air-conditioning system based on a master-slave game, characterized by comprising:

[0047] The overall task setting module is used to set the overall optimization control task based on the system structure of the central air-conditioning system, including the overall objective function, the energy conservation constraints of the central air-conditioning water system and the wind system. The overall objective function aims to minimize the total operating energy consumption of the central air-conditioning system while maintaining the thermal comfort of the end users;

[0048] The overall task decomposition module is used to decompose the overall optimization control task of the central air-conditioning system into a series of subsystem-level main optimization tasks, including the main optimization task of the water system and the main optimization task of the wind system, so as to coordinate the energy balance between different subsystems to achieve the regulation of the entire central air-conditioning system;

[0049] The main task decomposition module is used to decompose the main optimization task at the subsystem level into a series of sub-optimization tasks at the equipment level according to the equipment composition of each subsystem, so as to optimize and regulate the operating parameters of each device in the subsystem according to the energy supply requirements of different subsystems;

[0050] The game optimization module is used to perform master-slave level game optimization based on the master optimization task at the subsystem level and the slave optimization task at the equipment level, using a multi-level alternating direction multiplier method to minimize the total operating energy consumption of the central air-conditioning system while maintaining the thermal comfort of end users.

[0051] A storage medium stores a computer program that can be executed by a processor, characterized in that the steps of the collaborative optimization control method are implemented when the computer program is executed.

[0052] A data processing device comprises a memory and a processor, wherein the memory stores a computer program executable by the processor, and wherein the computer program implements the steps of the collaborative optimization control method when executed.

[0053] The beneficial effects of the present invention are as follows: the present invention uses the recursive divide-and-conquer theory to establish a master-slave collaborative optimization framework for the central air-conditioning system, logically decomposes the system-level optimization and control tasks into the master optimization tasks between subsystems and the slave optimization tasks between a series of subsystem devices, and further uses the multi-level alternating direction multiplier method to perform alternating optimization at the master-slave level to achieve the overall optimal energy consumption of the entire central air-conditioning system. The present invention decomposes the complex system-level optimization and control tasks into a series of simple subtasks that are easy to solve through the master-slave game method, has high optimization accuracy and computational efficiency, and can be applied to the optimization control scenarios of actual large-scale complex central air-conditioning systems.

[0054] The collaborative optimization control method for a central air-conditioning system based on master-slave game proposed in the present invention has high optimization accuracy. The method establishes a master-slave collaborative optimization architecture for the central air-conditioning system. The master level is responsible for coordinating the energy balance between different subsystems to achieve the regulation of the entire central air-conditioning system. The slave level further optimizes and regulates the operating parameters of each device in the subsystem according to the energy supply requirements of different subsystems, thereby achieving optimal global operating energy consumption of the central air-conditioning system.

[0055] The collaborative optimization control method for a central air-conditioning system based on master-slave game proposed in the present invention has high computational efficiency. The method decomposes complex optimization tasks at the system level into a series of master optimization tasks at the subsystem level and slave optimization tasks at the equipment level, and performs alternating optimization on the master-slave level through a multi-level alternating direction multiplier method, which can achieve global optimization of large-scale complex central air-conditioning systems in a relatively short time. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a calculation flow chart of the collaborative optimization control method in an embodiment of the present invention.

[0057] Figure 2 Schematic diagram of a central air conditioning system in an embodiment of the present invention.

[0058] Figure 3 It is the terminal cooling load demand on a typical summer day in the embodiment of the present invention.

[0059] Figure 4 The following is a comparison of the system operating energy consumption results under different control methods in the embodiments of the present invention.

[0060] Figure 5 The results of the terminal room temperature distribution under different control methods in the embodiments of the present invention are compared.

[0061] Figure 6 The figure is a comparison of the calculation time required for optimizing different control methods in the embodiments of the present invention. DETAILED DESCRIPTION

[0062] Example 1: Figure 1 As shown, this embodiment is a collaborative optimization control method for a central air-conditioning system based on a master-slave game, which specifically includes the following steps:

[0063] S1. The central air conditioning system structure in this embodiment is as follows Figure 2 As shown, based on the system structure of the central air-conditioning system, the overall optimization control task is set, including the overall objective function, the energy conservation constraints of the central air-conditioning water system and the wind system.

[0064] The overall objective function in this embodiment is to minimize the total operating energy consumption of the central air-conditioning system while maintaining the thermal comfort of the end users. The specific formula is as follows:

[0065]

[0066] Among them, N chi 、N chwp 、N cwp 、N ct and N ahu N represents the number of chillers, chilled water pumps, cooling water pumps, cooling towers and air handling units respectively. In this embodiment, the number of each device is 3. room,i Indicates the number of terminal rooms that the i-th air handling unit is responsible for. In this embodiment, N room,1 =10, N room,2 =5, N room,3 =5,P chi , P chwp , P cwp , Pct and P ahu Respectively represent the operating energy consumption of chiller, chilled water pump, cooling water pump, cooling tower and air handling unit, C room,i,j It represents the thermal comfort of the user in the jth room that the i-th air handling unit is responsible for, and β represents the thermal comfort weight coefficient.

[0067] The energy conservation constraint of the central air conditioning water system in this example is that the total heat rejection of the cooling tower cooling water loop should be equal to the sum of the total operating energy consumption of the chiller and the cooling capacity of the chiller chilled water loop. The specific formula is as follows:

[0068]

[0069] Among them, c p,w represents the specific heat capacity of water at constant pressure, m chw,i represents the chilled water loop flow rate of the i-th chiller, T chws,i and T chwr,i Respectively represent the supply water temperature and return water temperature of the chilled water loop of the i-th chiller, m cw,i represents the cooling water loop flow rate of the i-th cooling tower, T cwi,i and T cwo,i They represent the inlet temperature and outlet temperature of the cooling water loop of the i-th cooling tower respectively.

[0070] The energy conservation constraint of the central air conditioning system in this embodiment is that the air supply volume of each air handling unit should be equal to the sum of the air supply volumes of the terminal rooms it is responsible for. The specific formula is as follows:

[0071]

[0072] Among them, m ahu,i represents the air supply volume of the i-th air handling unit, m room,i,j It represents the air volume delivered to the jth terminal room by the i-th air handling unit.

[0073] S2. According to the composition of subsystems, the overall optimization task at the system level in S1 is decomposed into a series of main optimization tasks at the subsystem level, including the main optimization task of the water system and the main optimization task of the wind system. Its purpose is to coordinate the energy balance between different subsystems to achieve the regulation of the entire central air-conditioning system.

[0074] The main optimization task of the water system in this embodiment includes the main optimization task of the water system and the energy conservation constraint of the central air-conditioning water system. The main optimization task of the water system aims to minimize the operating energy consumption of the central air-conditioning water system. The specific formula is as follows:

[0075]

[0076] The main optimization task of the water system consists of two parts. The first three items are the first part, which represent the total operating energy consumption of the chiller, the chilled water pump, and the cooling water pump; the fourth item is the second part, which represents the augmented Lagrangian term used to deal with the coupling constraint between the water system and the wind system. The coupling constraint means that the total cooling capacity of the chiller in the water system should be equal to the total cooling capacity of the air handling unit in the wind system. The augmented Lagrangian term transforms the constraint condition into part of the objective function and guides the optimization of the water system in the direction of satisfying the coupling constraint by updating the Lagrangian multiplier.

[0077] Among them, Q chi,i represents the cooling capacity of the i-th chiller, ρ m represents the penalty coefficient of the main level, k m represents the number of iterations of the main level, λ m represents the Lagrange multiplier at the principal level, Indicates the kth m The optimal value of cooling capacity of air handling unit i in the wind system during the sub-main level iteration process.

[0078] The main optimization task of the wind system in this example includes the main optimization task of the wind system and the energy conservation constraint of the central air conditioning wind system. The main optimization task of the wind system aims to minimize the operating energy consumption of the central air conditioning wind system while maintaining the thermal comfort of the end users. The specific formula is as follows:

[0079]

[0080] The main optimization task of the wind system consists of three parts. The first part represents the total operating energy consumption of the air handling unit; the second part represents the sum of the thermal comfort of each terminal room; the third part represents the augmented Lagrangian term used to deal with the coupling constraints between the water system, which guides the wind system to optimize in the direction of satisfying the coupling constraints by updating the Lagrangian multiplier.

[0081] in, Indicates the kth m The optimal value of the cooling capacity of chiller i in the water system during the sub-primary level iteration process, Indicates the kth m The optimal value of cooling capacity of air handling unit k in the wind system during the sub-main level iteration process.

[0082] S3. According to the equipment composition of each subsystem, the main optimization task at the subsystem level in S2 is further decomposed into a series of sub-optimization tasks at the equipment level. Its purpose is to optimize and regulate the operating parameters of each device in the subsystem according to the energy supply requirements of different subsystems.

[0083] S3-1: The sub-optimization tasks of the water system include the sub-optimization tasks of the chiller, chilled water pump, cooling water pump and cooling tower.

[0084] In this embodiment, the optimization task of the chiller is as follows:

[0085]

[0086] The sub-optimization task of the chiller consists of three parts. The first part represents the operating energy consumption of a single chiller; the second part represents the augmented Lagrangian term used to deal with the coupling constraints between the main optimization task and the wind system; the third part represents the augmented Lagrangian term used to deal with the energy balance constraints between the chiller and other equipment in the water system.

[0087] Among them, ρ s,w represents the penalty coefficient of water system equipment from the hierarchy, k s,w represents the number of iterations of water system equipment from the hierarchy, λ s,w represents the Lagrange multiplier of the water system equipment from the hierarchy, and Respectively represent the kth s,w The secondary water system obtains the optimal values ​​of cooling capacity, operating energy consumption and water supply temperature of other chillers from hierarchical iteration. and Respectively represent the kth s,w The optimal value of the water flow of other chilled water pumps and cooling water pump loops in the secondary water system from the hierarchical iteration, Indicates the kth s,w The secondary water system obtains the optimal value of the cooling water outlet temperature of other cooling towers from the hierarchical iteration.

[0088] The optimization task of the chilled water pump is as follows:

[0089]

[0090] The optimization task of the chilled water pump consists of two parts. The first part represents the operating energy consumption of a single chilled water pump; the second part represents the augmented Lagrangian term used to deal with the energy balance constraint between the chilled water pump and other equipment in the water system.

[0091] The optimization task of the cooling water pump is as follows:

[0092]

[0093] The optimization task of the cooling tower is as follows:

[0094]

[0095] The optimization task of cooling tower consists of two parts. The first part represents the operating energy consumption of a single cooling tower; the second part represents the augmented Lagrangian term used to deal with the energy balance constraints between the cooling tower and other equipment in the water system.

[0096] S3-2: The sub-optimization tasks of the wind system include the sub-optimization tasks of the air handling units and terminal rooms.

[0097] In this example, the optimization task of the air handling unit is as follows:

[0098]

[0099] The secondary optimization task of the air handling unit consists of three parts. The first part represents the operating energy consumption of a single air handling unit; the second part represents the augmented Lagrangian term used to deal with the coupling constraints between the main optimization task and the water system; the third part represents the augmented Lagrangian term used to deal with the mass conservation constraints between the air handling unit and each terminal room.

[0100] Among them, ρ s,a represents the penalty coefficient of the stroke system device from the hierarchy, k s,a,i represents the number of iterations of the i device from the hierarchical stroke system, λ s,a,i represents the Lagrange multiplier of the i-th wind system in the hierarchy.

[0101] The optimization task of the terminal room is as follows:

[0102]

[0103] The optimization task of the terminal room consists of two parts. The first part represents the thermal comfort of a single terminal user; the second part represents the augmented Lagrangian term used to deal with the mass conservation constraints between the terminal room and the air handling unit and other terminal rooms.

[0104] S4. Based on the master optimization task at the subsystem level and the slave optimization task at the equipment level, a multi-level alternating direction multiplier method is used to perform master-slave hierarchical game optimization to minimize the total operating energy consumption of the central air-conditioning system while maintaining the thermal comfort of end users.

[0105] S4-1: Set the parameters to be optimized and Initialize to 0, and set each Lagrange multiplier and Initialized to 0;

[0106] S4-2: Set the penalty coefficient ρ for each level m =1,ρ s,w =10 and ρ s,a =10, set the number of iterations k m =0, k s,w = 0 and k s,a,i =0, set the upper limit of the number of iterations and

[0107]

[0108] S4-3: Each chiller, chilled water pump, cooling water pump and cooling tower of the water system solves its own optimization task in parallel and obtains the kth s,w At the same time, the air handling units of each wind system and each terminal room solve their own optimization tasks in parallel to obtain the kth s,a,i +1 round of optimal calculation results.

[0109] S4-4: Update the Lagrange multipliers of the water system equipment and wind system equipment from the hierarchy respectively, as shown in the following formula:

[0110]

[0111]

[0112] The value of the Lagrangian multiplier in the slave level depends on the coupling constraint deviation between the water system equipment or the wind system equipment. When the constraint deviation becomes larger, the value of the Lagrangian multiplier in the slave level increases, thereby guiding the next round of slave task iteration to optimize in the direction of reducing the constraint deviation.

[0113] S4-5: Determine whether the hierarchical optimization process converges, as shown in the following formula:

[0114]

[0115] Among them, ε s,w and ε s,a,i are the judgment thresholds of the hierarchical optimization process for the water system and the wind system, respectively, and are both set to 0.01 in this embodiment. and The hierarchical optimization process ends and goes to step S4-6. Otherwise, set k s,w and k s,a,i The value increases by 1, and steps S4-3 to S4-5 are repeated.

[0116] S4-6: Update the main-level Lagrange multiplier as shown below:

[0117]

[0118] The value of the Lagrange multiplier in the main level depends on the coupling constraint deviation between the water system and the wind system. When the constraint deviation becomes larger, the value of the Lagrange multiplier in the main level increases, thereby guiding the next round of main task iteration to optimize in the direction of reducing the constraint deviation.

[0119] S4-7: Determine whether the main level optimization process converges, as shown in the following formula:

[0120]

[0121] Among them, ε m,1 and ε m,2 are the primary residual threshold and the dual residual threshold of the main level optimization process, respectively, and are both set to 0.01 in this embodiment. The main level optimization process ends and goes to step S4-8. Otherwise, set k m The value increases by 1, and steps S4-3 to S4-7 are repeated.

[0122] S4-8: Output the calculation results of the parameters to be optimized to achieve optimal control of the central air-conditioning system.

[0123] The central air conditioning system in this embodiment is mainly configured as follows: the rated cooling capacity of each chiller is 2300kW, and the rated COP is 5.93; the rated power of each chilled water pump is 37kW, and the rated flow rate is 414m 3 / h; the rated power of each cooling water pump is 55kW and the rated flow rate is 468m 3 / h; the rated heat exchange of each cooling tower is 2835kW, the rated power of the fan is 22kW, and the rated flow rate of the fan is 3470m 3 / min; the rated cooling capacity of each air handling unit is 2150kW, the rated power of the blower is 100kW, and the rated flow rate is 9280m 3 / min.

[0124] A typical summer day was selected as the test condition for performance evaluation. The cooling load demand of each terminal room on that day was as follows: Figure 3 As shown. The indoor temperature setting value of each terminal room is uniformly set to 26°C. Two commonly used control methods in two fields are set for performance comparison, including the rule control method and the traditional optimization control method without master-slave game architecture. The rule control method does not use any optimization means. The chilled water outlet temperature of the chiller is constantly set to 7°C. The chilled water pump, cooling water pump, cooling tower fan, and air handling unit fan all maintain industrial frequency operation. The valve opening of each terminal variable air volume air conditioning box is always kept fully open. The traditional optimization control method does not adopt a master-slave game architecture. The optimal control result is searched through the alternating direction multiplier method. Its penalty coefficient, convergence threshold and other parameters are consistent with the settings in the method of the present invention. The control interval of the three methods is set to 1h, and the simulation time is from 8:00 to 19:00.

[0125] Comparison of the energy consumption of central air conditioning system under different control methods Figure 4As shown. Because the water pump and fan always run at rated frequency under the rule control method, there is a large energy waste under partial load conditions, and the system operating energy consumption is the highest among the three. In contrast, the method of the present invention optimizes the control parameters through a master-slave game architecture, and the system operating energy consumption drops from 17311.0kWh to 13661.5kWh, with an energy saving rate of 21.1%. In addition, compared with the traditional optimization control method without a master-slave game architecture, the method of the present invention has higher optimization accuracy, and the operating energy consumption deviation between the two is 0.4%.

[0126] Comparison of indoor temperature distribution results of each terminal room under different control methods Figure 5 As shown. The temperature control effect of the rule control method is the worst, the temperature distribution is uneven, the range of the indoor temperature is as high as 7.3°C, and the standard deviation is 2.2°C. The temperature control effect of the traditional optimization control method is better than the rule control method, with a range of 2.1°C and a standard deviation of 0.4°C. However, due to the limited optimization accuracy of this method, its temperature control effect in some time periods is still insufficient. In contrast, under the method of the present invention, the indoor temperature of each terminal room is always maintained at 25.5°C-26.0°C, the range of the indoor temperature is only 0.5°C, and the standard deviation is only 0.1°C. It has good temperature control capabilities throughout the test conditions.

[0127] Comparison of the computation time required for optimization between the traditional optimization control method and the method of the present invention Figure 6 As shown in the figure, it can be seen that the average calculation time required for the traditional optimization control method in the entire test condition is 137.8s. In contrast, the method of the present invention adopts the master-slave game optimization architecture, and the average calculation time is reduced to 48.6s, and the calculation efficiency is improved by 64.7%.

[0128] It can be seen that compared with the rule control method and optimization control method commonly used in the current field, the method of the present invention has better optimization accuracy and calculation efficiency, can be applied to the optimization and control tasks of large-scale complex central air-conditioning systems, and can improve the operating efficiency of the system while ensuring the thermal comfort of end users.

[0129] Embodiment 2: This embodiment is a collaborative optimization control device for a central air-conditioning system based on master-slave game, including: a general task setting module, a general task decomposition module, a main task decomposition module and a game optimization module, etc.

[0130] In this example, the overall task setting module is used to set the overall optimization control task based on the system structure of the central air-conditioning system, including the overall objective function, the energy conservation constraints of the central air-conditioning water system and the wind system. The overall objective function aims to minimize the total operating energy consumption of the central air-conditioning system while maintaining the thermal comfort of the end users.

[0131] In this embodiment, the overall task decomposition module is used to decompose the overall optimization control task of the central air-conditioning system into a series of subsystem-level main optimization tasks, including the main optimization task of the water system and the main optimization task of the wind system, so as to coordinate the energy balance between different subsystems to achieve the regulation of the entire central air-conditioning system.

[0132] In this example, the main task decomposition module is used to decompose the main optimization task at the subsystem level into a series of equipment-level sub-optimization tasks according to the equipment composition of each subsystem, so as to optimize and regulate the operating parameters of each device in the subsystem according to the energy supply requirements of different subsystems.

[0133] In this embodiment, the game optimization module is used to perform master-slave level game optimization based on the main optimization task at the subsystem level and the slave optimization task at the equipment level, using a multi-level alternating direction multiplier method to achieve minimization of the total operating energy consumption of the central air-conditioning system while maintaining the thermal comfort of end users.

[0134] Embodiment 3: This embodiment is a storage medium on which a computer program that can be executed by a processor is stored. When the computer program is executed, the steps of the collaborative optimization control method in Embodiment 1 are implemented.

[0135] Embodiment 4: A data processing device comprises a memory and a processor, wherein the memory stores a computer program executable by the processor, and the computer program implements the steps of the collaborative optimization control method in Embodiment 1 when executed.

Claims

1. A collaborative optimization control method for a central air-conditioning system based on master-slave game, characterized in that: include: S1. Based on the system structure of the central air-conditioning system, set the overall optimization control task, including the overall objective function, the energy conservation constraints of the central air-conditioning water system and the wind system. The overall objective function aims to minimize the total operating energy consumption of the central air-conditioning system while maintaining the thermal comfort of the end users; S2. Decompose the overall optimization control task of the central air-conditioning system into a series of subsystem-level main optimization tasks, including the main optimization task of the water system and the main optimization task of the wind system, so as to coordinate the energy balance between different subsystems to achieve the regulation of the entire central air-conditioning system; S3. According to the equipment composition of each subsystem, the main optimization task at the subsystem level is decomposed into a series of sub-optimization tasks at the equipment level, so as to optimize and regulate the operating parameters of each device in the subsystem according to the energy supply requirements of different subsystems; S4. Based on the master optimization task at the subsystem level and the slave optimization task at the equipment level, a multi-level alternating direction multiplier method is used to perform master-slave hierarchical game optimization to minimize the total operating energy consumption of the central air-conditioning system while maintaining the thermal comfort of end users.

2. The collaborative optimization control method for a central air-conditioning system based on master-slave game according to claim 1 is characterized in that: The main optimization task of the water system includes the subsystem objective function of the water system and the energy conservation constraint of the central air-conditioning water system. The main optimization task of the water system takes minimizing the operating energy consumption of the central air-conditioning water system as the optimization goal; The main optimization task of the wind system includes the subsystem objective function of the wind system and the energy conservation constraint of the central air conditioning wind system. The main optimization task of the wind system is to minimize the operating energy consumption of the central air conditioning wind system while maintaining the thermal comfort of the end users.

3. The collaborative optimization control method for a central air-conditioning system based on master-slave game according to claim 2 is characterized in that: The main optimization tasks of the water system include: The main optimization tasks of the wind system include:

4. The collaborative optimization control method for a central air-conditioning system based on master-slave game according to claim 1 is characterized in that: The main optimization task of the water system is decomposed into the sub-optimization tasks of the chiller, the chilled water pump, the cooling water pump and the cooling tower; Among them, the optimization tasks of the chiller include: The optimization tasks of the chilled water pump include: Cooling water pump optimization tasks include: Cooling tower optimization tasks include:

5. The collaborative optimization control method for a central air-conditioning system based on master-slave game according to claim 1 is characterized in that: The main optimization task of the wind system is decomposed into the sub-optimization tasks of the air handling units and the terminal rooms; Among them, the optimization tasks of air handling units include: The optimization tasks of the terminal room include:

6. The collaborative optimization control method for a central air-conditioning system based on master-slave game according to claim 1 is characterized in that: The method of using the multi-level alternating direction multiplier method to perform master-slave level game optimization includes: S4-1. Initialize the parameters to be optimized and and the Lagrange multipliers and S4-2. Set the penalty coefficient ρ at each level m , s,w and ρ s,a , number of iterations k m =0, k s,w = 0 and k s,a,i = 0, and the upper limit of the number of iterations and S4-3, each chiller, chilled water pump, cooling water pump and cooling tower of the water system solves its own optimization task in parallel and obtains the kth s,w +1 round of optimal calculation results; At the same time, the air handling units of each wind system and each terminal room solve their own optimization tasks in parallel to obtain the kth s,a,i +1 round of optimal calculation results; S4-4, respectively update the Lagrange multipliers of the water system equipment and wind system equipment from the hierarchy, as shown in the following formula: S4-5, judging whether the hierarchical optimization process converges, if the convergence condition is met, the hierarchical optimization process ends and enters step S4-6; otherwise, set k s,w and k s,a,i The value increases by 1, and steps S4-3 to S4-5 are repeated; S4-6. Update the main-level Lagrange multiplier as shown in the following formula: S4-7, determine whether the main level optimization process converges. If the convergence condition is met, the main level optimization process ends and enters step S4-8; otherwise, set k m The value increases by 1, and steps S4-3 to S4-7 are repeated; S4-8, output the calculation results of the parameters to be optimized to achieve optimal control of the central air-conditioning system.

7. The collaborative optimization control method of the central air-conditioning system based on master-slave game according to claim 6 is characterized in that: The convergence condition of the hierarchical optimization process is to satisfy the following equation or and Among them, ε s,w and ε s,a,i They are the judgment thresholds of the hierarchical optimization process for the water system and the wind system respectively.

8. The collaborative optimization control method for a central air-conditioning system based on master-slave game according to claim 6 is characterized in that: The convergence condition of the main level optimization process is to satisfy the following equation or Among them, ε m,1 and ε m,2 They are the primary residual threshold and dual residual threshold of the main level optimization process respectively.

9. A collaborative optimization control device for a central air-conditioning system based on master-slave game, characterized in that: include: The overall task setting module is used to set the overall optimization control task based on the system structure of the central air-conditioning system, including the overall objective function, the energy conservation constraints of the central air-conditioning water system and the wind system. The overall objective function aims to minimize the total operating energy consumption of the central air-conditioning system while maintaining the thermal comfort of the end users; The overall task decomposition module is used to decompose the overall optimization control task of the central air-conditioning system into a series of subsystem-level main optimization tasks, including the main optimization task of the water system and the main optimization task of the wind system, so as to coordinate the energy balance between different subsystems to achieve the regulation of the entire central air-conditioning system; The main task decomposition module is used to decompose the main optimization task at the subsystem level into a series of sub-optimization tasks at the equipment level according to the equipment composition of each subsystem, so as to optimize and regulate the operating parameters of each device in the subsystem according to the energy supply requirements of different subsystems; The game optimization module is used to perform master-slave level game optimization based on the master optimization task at the subsystem level and the slave optimization task at the equipment level, using a multi-level alternating direction multiplier method to minimize the total operating energy consumption of the central air-conditioning system while maintaining the thermal comfort of end users.

10. A storage medium having stored thereon a computer program executable by a processor, characterized in that: When the computer program is executed, the steps of the collaborative optimization control method according to any one of claims 1 to 8 are implemented.

11. A data processing device comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, wherein: When the computer program is executed, the steps of the collaborative optimization control method according to any one of claims 1 to 8 are implemented.

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