Refrigeration system energy consumption optimization control method and device

Abstract

This invention relates to the field of refrigeration control technology, and provides a method and apparatus for optimizing the energy consumption control of a refrigeration system. The method includes: calculating the number of refrigeration units to be activated based on the total refrigeration demand of the refrigeration system. n At that time, the cooling demand of a single refrigeration unit; determine the set of multiple sets of system operating parameter values ​​that satisfy the parameter constraints in the refrigeration system, and the number of refrigeration units to be turned on. n Operating Mode s Multiple optimization vectors are formed from multiple sets of system operating parameter values ​​φ. P ( n , s Based on the mathematical models of each device in the refrigeration unit and the system operating parameter values, the power of each device is calculated, and the power is determined based on the number of refrigeration units in operation. n Calculate the total power of the refrigeration system to obtain multiple sets of total power values; under any operating mode, output the optimization vector corresponding to the minimum total power. P ( n , s The refrigeration system is optimized and controlled based on the operating mode and number of units in the refrigeration system. n To find the optimal dimension, the total energy consumption of the equipment was controlled optimally by combining the operating parameters of each device.

Description

Technical Field

[0001] This invention relates to the field of refrigeration control technology, and in particular to a method and apparatus for optimizing energy consumption control of a refrigeration system. Background Technology

[0002] Figure 1 This is a schematic diagram of a cooling system for a data center. The cooling system includes at least one cooling unit, which comprises: a cooling tower, a chiller (water chiller unit), a cooling pump (cooling water circulation pump), a chilled water pump (chilled water circulation pump), and a plate heat exchanger. A typical data center cooling system is configured with four cooling units, three in operation and one on standby, with adjustable unit count. In each cooling unit, the chilled water pump is connected to a water collector, and the chiller's outlet on the user side is connected to a water distributor. The cooling system has three design operating modes: mechanical cooling, partial natural cooling, and full natural cooling, suitable for summer, transitional season, and winter respectively (corresponding to three operating modes: summer operation mode, transitional season operation mode, and winter operation mode). In summer, mechanical refrigeration is achieved using only chillers (V1, V3, V6, and V8 are on, while V2, V4, V5, and V7 are off). During the transitional season, partial natural cooling is achieved by connecting plate heat exchangers and chillers in series (V2, V4, V6, and V8 are on, while V1, V3, V5, and V7 are off). In winter, complete natural cooling is achieved by using only plate heat exchangers (V2, V4, V5, and V7 are on, while V1, V3, V6, and V8 are off).

[0003] In practical applications, energy consumption of equipment often exhibits a trade-off phenomenon. For example, increasing the chilled water temperature can reduce the energy consumption of the chiller unit, but it will lead to an increase in the energy consumption of the chilled water circulation pump (in order to provide sufficient cooling capacity to the terminal). Therefore, it is necessary to introduce energy consumption optimization and control technology to determine the optimal operating parameters to ensure that the total energy consumption of the chiller unit, cooling tower, and each water pump is minimized under the same cooling load demand and ambient humidity.

[0004] Current energy consumption optimization control schemes employ mathematical models combined with optimization algorithms. However, these models are overly simplistic, only considering summer operating modes and some key equipment parameters. They fail to meet the year-round cooling needs of data centers, resulting in poor energy efficiency with mechanical refrigeration throughout the year. Furthermore, the mathematical models do not account for the impact of the number of refrigeration units in operation (a discrete variable) on cooling and energy consumption. Therefore, current energy consumption optimization control schemes cannot achieve optimal total energy consumption control for all equipment throughout the year based on the refrigeration system's operating mode and the number of units in operation, combined with the key operating parameters of each device. Summary of the Invention

[0005] This invention provides a method and apparatus for optimizing energy consumption control of a refrigeration system, which solves the problem in the prior art that it is impossible to achieve optimal control of the total energy consumption of each device throughout the year based on the operating mode and number of operating units of the refrigeration system, combined with the operating parameters of each device.

[0006] This invention provides a method for optimizing and controlling the energy consumption of a refrigeration system, comprising the following steps.

[0007] Total cooling demand based on the refrigeration system Q req The number of refrigeration units to be turned on is calculated as follows: n At that time, the cooling demand of a single refrigeration unit Q’ req .

[0008] Based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, the principle of mass conservation, the principle of energy conservation under different operating modes, and the number of refrigeration units in operation. n Corresponding cooling demand Q’ req And the mathematical models of each device in the refrigeration unit, determine the set of multiple sets of system operating parameter values ​​that satisfy the parameter constraints in the refrigeration system, and the number of refrigeration units to be turned on. n Operating Mode s Multiple optimization vectors are formed from multiple sets of system operating parameter values ​​φ. P ( n , s , φ).

[0009] Based on the mathematical models of each device in the refrigeration unit and the corresponding system operating parameter values, the power of each device is calculated, and the total power of the refrigeration system is calculated based on the number of refrigeration units turned on (n), resulting in multiple sets of total power values.

[0010] In any operating mode, output the optimization vector corresponding to the minimum total power. P ( n , s , φ) to optimize the control of the refrigeration system.

[0011] The present invention provides a method for optimizing and controlling the energy consumption of a refrigeration system, based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, the principle of mass conservation, the principle of energy conservation under different operating modes, and the number of refrigeration units in operation. n Corresponding cooling demand Q’ req The mathematical models of each device in the refrigeration unit are used to determine a set of multiple system operating parameter values ​​that satisfy the parameter constraints in the refrigeration system, including the following steps.

[0012] Multiple target operating parameters are selected from the system operating parameter set, and different assignment combinations are used to assign values ​​to the multiple target operating parameters.

[0013] For each assignment combination, based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, the principle of mass conservation, the principle of energy conservation under different operating modes, and the number of refrigeration units in operation... n Corresponding cooling demand Q’ req The mathematical models of each device in the refrigeration unit are used to calculate the non-target operating parameter values ​​corresponding to the non-target operating parameters in the system operating parameter set.

[0014] When the non-target operating parameter values ​​satisfy the corresponding constraints, the non-target operating parameter values ​​and the corresponding target operating parameter values ​​constitute the system operating parameter values, so as to obtain multiple sets of the system operating parameter values.

[0015] According to the present invention, a method for optimizing and controlling the energy consumption of a refrigeration system includes the following steps: selecting multiple target operating parameters from a set of system operating parameters and assigning values ​​to the multiple target operating parameters using different combinations of values.

[0016] Different target operating parameters are selected based on different operating modes.

[0017] The step size for assigning values ​​to the target operating parameters is determined based on the range of values ​​in the constraints of the target operating parameters.

[0018] Based on the assignment step size, an assignment combination is determined, and values ​​are assigned to multiple target operating parameters.

[0019] According to the present invention, a method for optimizing and controlling the energy consumption of a refrigeration system is provided, which determines the assignment combination based on the assignment step size and assigns values ​​to multiple target operating parameters, including: determining the number of assignments for any target operating parameter based on the assignment step size of any target operating parameter, and assigning values ​​to multiple target operating parameters by enumeration based on the number of target operating parameters and the number of assignments for each target operating parameter.

[0020] According to a refrigeration system energy consumption optimization control method provided by the present invention, when the non-target operating parameter value does not meet the corresponding constraint conditions, the total power value is set to a predetermined abnormal value.

[0021] In any operating mode, the optimization vector corresponding to the minimum total power is output. P ( n , s , φ) Optimize the control of the refrigeration system, including: in any operating mode, deleting the value of the total power as the optimization vector corresponding to a predetermined outlier. P ( n , s , φ), in the remaining optimization vector P (n , s In φ), the optimization vector corresponding to the minimum total power is output. P ( n , s , φ) to optimize the control of the refrigeration system.

[0022] According to the present invention, a method for optimizing and controlling the energy consumption of a refrigeration system is provided, based on the total refrigeration demand of the refrigeration system. Q req The number of refrigeration units to be turned on is calculated as follows: n At that time, the cooling demand of a single refrigeration unit Q’ req This includes the following steps.

[0023] Calculate the cooling capacity of a single refrigeration unit under the original operating conditions of the refrigeration system before optimization. Q req,i ,in, i =1,2,…, N , N This indicates the total number of refrigeration units in the refrigeration system.

[0024] The total cooling demand is obtained by summing the cooling capacity of each individual refrigeration unit. .

[0025] The number of refrigeration units in operation is n At that time, calculate the cooling demand of a single refrigeration unit. Q’ req = Q req / n ,in, n < N .

[0026] The present invention also provides a refrigeration system energy consumption optimization control device, comprising the following modules.

[0027] The cooling demand calculation module is used to calculate the total cooling demand of the refrigeration system. Q req The number of refrigeration units to be turned on is calculated as follows: n At that time, the cooling demand of a single refrigeration unit Q’ req .

[0028] The parameter value set determination module is used to determine the outdoor dry-bulb temperature, outdoor wet-bulb temperature, mass conservation principle, energy conservation principle under different operating modes, and the number of refrigeration units in operation. n Corresponding cooling demand Q’ reqAnd the mathematical models of each device in the refrigeration unit, determine the set of multiple sets of system operating parameter values ​​that satisfy the parameter constraints in the refrigeration system, and the number of refrigeration units to be turned on. n Operating Mode s Multiple optimization vectors are formed from multiple sets of system operating parameter values ​​φ. P ( n , s , φ).

[0029] The total power calculation module is used to calculate the power of each device based on the mathematical model of each device in the refrigeration unit and the corresponding system operating parameter values, and to calculate the power of each device based on the number of refrigeration units in operation. n Calculate the total power of the refrigeration system and obtain multiple sets of total power values.

[0030] The optimization control module is used to output the optimization vector corresponding to the minimum total power in any operating mode. P ( n , s ,φ) to optimize and control the refrigeration system.

[0031] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the energy consumption optimization control method for the refrigeration system as described above.

[0032] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the energy consumption optimization control method for a refrigeration system as described above.

[0033] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the energy consumption optimization control method for a refrigeration system as described above.

[0034] The energy consumption optimization control method and device for a refrigeration system provided by this invention optimizes energy consumption based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, the principle of mass conservation, the principle of energy conservation under different operating modes, and the number of refrigeration units in operation. n Corresponding cooling demand Q’ req Based on the mathematical models of each device in the refrigeration unit, a set of multiple system operating parameter values ​​satisfying the parameter constraints of the refrigeration system is determined. Then, based on the mathematical models of each device in the refrigeration unit and the corresponding system operating parameter values, the power of each device is calculated, and the power is determined based on the number of refrigeration units in operation. n Calculate the total power of the refrigeration system, obtain multiple sets of total power values, and output the optimization vector corresponding to the minimum total power value as the objective. P ( n , sThe φ) optimizes the control of the refrigeration system, realizing optimal control of the total energy consumption of the equipment based on the operating mode and number of operating units of the refrigeration system, combined with the operating parameters of each equipment. It is applicable to the optimal control of the total energy consumption of each equipment throughout the year. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0036] Figure 1 This is a schematic diagram of the structure of a refrigeration unit in a refrigeration system.

[0037] Figure 2 This is a flowchart illustrating the energy consumption optimization and control method for a refrigeration system provided by the present invention.

[0038] Figure 3 This is a flowchart illustrating the energy consumption optimization and control method for a refrigeration system provided by the present invention.

[0039] Figure 4 This is a schematic diagram of the structure of the energy consumption optimization and control device for the refrigeration system provided by the present invention.

[0040] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0042] In the energy consumption optimization control method for a refrigeration system according to embodiments of the present invention, the refrigeration system can be Figure 1 A refrigeration system consisting of a refrigeration unit, but not limited to Figure 1 The refrigeration system in this embodiment. The energy consumption optimization and control method for the refrigeration system in this embodiment is as follows: Figure 2 As shown, the procedure includes steps S210 to S240.

[0043] Step S210: Based on the total cooling demand of the refrigeration system Q req The number of refrigeration units to be turned on is calculated as follows: nAt that time, the cooling demand of a single refrigeration unit Q’ req Total cooling demand Q req It can be determined based on the needs of the actual application scenario, such as the total cooling demand when dissipating heat from a data center. Q req It can be determined based on the optimal operating temperature of the data center and the current temperature inside the server room.

[0044] Step S220: Based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, the principle of mass conservation, the principle of energy conservation under different operating modes, and the number of refrigeration units in operation. n Corresponding cooling demand Q’ req And the mathematical models of each device in the refrigeration unit, determine the set of multiple sets of system operating parameter values ​​that satisfy the parameter constraints in the refrigeration system, and the number of refrigeration units to be turned on. n Operating Mode s Multiple optimization vectors are formed from multiple sets of system operating parameter values ​​φ. P ( n , s , φ). Among them, the principles of mass conservation and energy conservation depend on the structure of the refrigeration unit, in order to Figure 1 Taking the refrigeration unit structure as an example, the principle of mass conservation states that in the same water cycle, the water flow rate entering and exiting each device is equal. The principle of energy conservation varies under different operating modes. In summer mode, the heat dissipation on the condenser side of the chiller equals the heat dissipation on the cooling tower, and the cooling capacity on the evaporator side of the chiller equals the actual cooling capacity of the refrigeration unit. In transitional season mode, the sum of the heat dissipation on the condenser side of the chiller and the heat exchange of the plate heat exchanger equals the heat dissipation on the cooling tower, and the sum of the heat exchange of the plate heat exchanger and the cooling capacity on the evaporator side of the chiller equals the actual cooling capacity of the refrigeration unit. In winter mode, the heat dissipation on the cooling tower equals the heat exchange of the plate heat exchanger, which is also equal to the actual cooling capacity of the refrigeration unit.

[0045] The mathematical models of each component in a refrigeration unit also depend on the structure of the refrigeration unit; different refrigeration unit structures result in different mathematical models for each component. Figure 1 Taking the refrigeration unit structure as an example, the equipment includes: chiller, various water pumps, plate heat exchanger and cooling tower. The mathematical model used for each piece of equipment is as follows.

[0046] The mathematical model of the chiller is coupled with the chilled water inlet temperature. T e,i chilled water outlet temperature T e,o Cooling water inlet temperature T c,i Cooling water outlet temperature T c,o chilled water flow rateG e Cooling water flow rate G c and refrigeration power P chi The relationship.

[0047] The mathematical model of the water pump is coupled with the chilled water flow rate. G e Cooling water flow rate G c and water pump power ( P e , P c Specifically, the mathematical model of the chilled water pump is coupled with the chilled water flow rate. G e Electric power of refrigeration pump P e The relationship between the cooling pump and the cooling water flow rate is reflected in the mathematical model of the cooling pump. G c With cooling pump power P c The relationship between them.

[0048] The mathematical model of the plate heat exchanger is coupled with the chilled water flow rate. G e Cooling water flow rate G c Primary side inlet water temperature T 1i Primary side outlet water temperature T 1o Secondary side inlet water temperature T 2i and secondary side outlet water temperature T 2o The relationship.

[0049] The mathematical model of the cooling tower is coupled with the inlet water temperature of the cooling tower. T tow,i Cooling tower outlet water temperature T tow,o Outdoor dry bulb temperature T atm,dry Outdoor wet-bulb temperature T atm,wet ), air mass flow m a and cooling tower fan power P tow The relationship.

[0050] The mathematical models for the chiller, water pump, plate heat exchanger and cooling tower mentioned above can be any mature mathematical models from existing related technologies, and this embodiment does not impose any limitations.

[0051] The parameter constraints in a refrigeration system depend on the structure of the refrigeration unit and the user's requirements. These constraints include upper and lower limits on the range of system operating parameters, as well as constraints on the relationships between these parameters. Figure 1 Taking the refrigeration unit structure as an example, the parameter constraints include, but are not limited to, the following constraints.

[0052] User-side process requirements for chilled water supply temperature (i.e., chilled water outlet temperature of the chiller) T e,o Upper and lower limits (e.g., 11~17℃).

[0053] User-side process requirements for chilled water supply and return water temperature difference ( T e,o and T e,i Temperature difference) upper and lower limit constraints (e.g., 3-7℃).

[0054] Upper and lower limits of cooling capacity constraints for refrigeration systems.

[0055] Upper and lower limits of flow rate for each water pump (e.g., take 50%~100% of the rated flow rate).

[0056] Cooling tower heat dissipation capacity upper limit constraint (e.g., cooling tower fan mass flow rate) m a Between 40% and 100% of the rated air volume.

[0057] System operating parameters vary depending on the structure of the refrigeration unit and the operating mode. In this embodiment, the parameters are as follows: Figure 1 Taking the refrigeration unit structure as an example, the system operating parameters in different operating modes are as follows: Figure 3 As shown in the table below.

[0058] Step S230: Based on the mathematical models of each device in the refrigeration unit and the corresponding system operating parameter values, calculate the power of each device, and determine the number of refrigeration units to be turned on. n The total power of the refrigeration system is calculated, yielding multiple sets of total power values. Since the mathematical models of each device determine the relationship between the power of each device and other relevant system operating parameters, the power of each device can be calculated, allowing the determination of the number of refrigeration units to be activated. n Calculate the total power of the refrigeration system. The formula for calculating the total power is: P total = nP c + nP e + n P chi + n P tow .

[0059] Step S240: In any operating mode, output the optimization vector corresponding to the minimum total power. P ( n , s The refrigeration system is optimized and controlled by the function min(φ), specifically, the objective function is to minimize the total power. P total = nP c + nP e + n P chi + n P tow ) Obtain the optimization vector corresponding to the minimum total power P ( n , s This allows for minimizing the total energy consumption of the chiller, water pumps, and cooling tower while meeting the total cooling demand.

[0060] In the energy consumption optimization control method of the refrigeration system in this embodiment, the following factors are considered: outdoor dry-bulb temperature, outdoor wet-bulb temperature, mass conservation principle, energy conservation principle under different operating modes, and the number of refrigeration units in operation. n Corresponding cooling demand Q’ req Based on the mathematical models of each device in the refrigeration unit, a set of multiple system operating parameter values ​​satisfying the parameter constraints of the refrigeration system is determined. Then, based on the mathematical models of each device in the refrigeration unit and the corresponding system operating parameter values, the power of each device is calculated, and the power is determined based on the number of refrigeration units in operation. n Calculate the total power of the refrigeration system, obtain multiple sets of total power values, and output the optimization vector corresponding to the minimum total power value as the objective. P ( n , s The φ) optimizes the control of the refrigeration system, realizing optimal control of the total energy consumption of the equipment based on the operating mode and number of operating units of the refrigeration system, combined with the operating parameters of each equipment. It is applicable to the optimal control of the total energy consumption of each equipment throughout the year.

[0061] In some embodiments, step S220 specifically includes the following steps.

[0062] Multiple target operating parameters are selected from the system operating parameter set, and different assignment combinations are used to assign values ​​to these target operating parameters. The target operating parameters can be randomly selected, as long as it ensures that the remaining non-target operating parameters can be calculated based on the selected target operating parameters, combined with the mass conservation principle, the energy conservation principle under different operating modes, and the mathematical model. Each assignment combination includes target operating parameter values ​​corresponding to the multiple target operating parameters; the target operating parameter values ​​will differ in different assignment combinations. Of course, the assignment of values ​​to the multiple target operating parameters must also satisfy the upper and lower limits of the target operating parameter value range.

[0063] For each assignment combination, based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, the principle of mass conservation, the principle of energy conservation under different operating modes, and the number of refrigeration units in operation... n Corresponding cooling demand Q’ req The mathematical models of each device in the refrigeration unit are used to calculate the non-target operating parameter values ​​corresponding to the non-target operating parameters in the system operating parameter set.

[0064] When the non-target operating parameter values ​​satisfy the corresponding constraints, the non-target operating parameter values ​​and the corresponding target operating parameter values ​​constitute the system operating parameter values, so as to obtain multiple sets of the system operating parameter values.

[0065] In this embodiment, by selecting multiple target operating parameters from the system operating parameter set, assigning values ​​to the multiple target operating parameters, calculating the values ​​of non-target operating parameters, and then determining whether the non-target operating parameter values ​​meet the corresponding constraint conditions, multiple sets of system operating parameter values ​​that satisfy the parameter constraint conditions in the refrigeration system can be quickly obtained.

[0066] Specifically, the process involves selecting multiple target operating parameters from a set of system operating parameters and assigning values ​​to these multiple target operating parameters using different combinations of values, including the following steps.

[0067] Different target operating parameters can be selected based on different operating modes. Since the equipment involved in refrigeration is different in different operating modes, different target operating parameters can be selected in different operating modes.

[0068] The step size for assigning values ​​to the target operating parameters is determined based on the range of values ​​in the constraints of the target operating parameters.

[0069] Based on the assignment step size, an assignment combination is determined, and values ​​are assigned to multiple target operating parameters.

[0070] Furthermore, based on the assignment step size, assignment combinations are determined to assign values ​​to multiple target operating parameters. This includes: determining the number of assignments for any target operating parameter based on its assignment step size; and assigning values ​​to multiple target operating parameters using an enumeration method based on the number of target operating parameters and the number of assignments for each target operating parameter. For example, if there are four target operating parameters, and their respective assignment steps can be determined to have 3, 4, 2, and 5 assignments, then there are 3 × 4 × 2 × 5 = 120 possible assignment combinations.

[0071] In this embodiment, an enumeration method is used to assign values ​​to multiple target operating parameters. In each enumeration to find the optimal optimization vector, the above constraints are verified to ensure that the optimization results are within the adjustable performance of the device and can strictly meet the user's requirements.

[0072] In some embodiments, if the non-target operating parameter value does not meet the corresponding constraint condition, the total power value is set to a predetermined abnormal value, for example: P total =-1.

[0073] Based on this, step S240 specifically includes: in any operating mode, deleting the value of the total power as the optimization vector corresponding to a predetermined outlier. P ( n , s , φ), in the remaining optimization vector P ( n , s In φ), the optimization vector corresponding to the minimum total power is output. P ( n , s , φ) to optimize the control of the refrigeration system.

[0074] In this embodiment, when the non-target operating parameter values ​​do not meet the corresponding constraints, the total power value is set to a predetermined abnormal value. An exclusion mechanism for unreasonable value combinations is set, thereby improving the safety of system operation.

[0075] In some embodiments, step S210 specifically includes the following steps.

[0076] Calculate the cooling capacity of a single refrigeration unit under the original operating conditions of the refrigeration system before optimization. Q req,i ,in, i =1,2,…, N , N This indicates the total number of refrigeration units in the refrigeration system. Specifically, in summer mode... Q req,i = Ge ( T e,o - T e,i ), Transitional Season Mode Q req,i = G e ( T 2i - T e,i Winter mode Q req,i = G e, exch ( T 2i - T 2o ),in, G e, exch This represents the chilled water flow rate of the plate heat exchanger. If, under the original operating conditions of the refrigeration system, the i-th refrigeration unit is not turned on, then... Q req,i =0.

[0077] The total cooling demand is obtained by summing the cooling capacity of each individual refrigeration unit. .

[0078] The number of refrigeration units in operation is n At that time, calculate the cooling demand of a single refrigeration unit. Q’ req = Q req / n ,in, n < N .

[0079] It should be noted that before implementing the energy consumption optimization control method of the refrigeration system in this embodiment, it is assumed that the current working state of the refrigeration system is a stable state and meets the current refrigeration requirements. Therefore, the refrigeration capacity of each refrigeration unit when the number of refrigeration units turned on during the optimization process can be calculated based on the refrigeration capacity of the refrigeration units turned on in the current state.

[0080] The following is based on Figure 1 Taking a refrigeration system composed of refrigeration units as an example, the energy consumption optimization control process of the refrigeration system of the present invention is specifically explained under different operating modes.

[0081] The energy consumption optimization control process for the refrigeration system under summer operation mode is as follows.

[0082] (1) Calculate the cooling capacity requirement of a single refrigeration unit Q req / n Obtain outdoor dry bulb temperature Tatm,dry Outdoor wet-bulb temperature T atm,wet .

[0083] (2) Selection: Cooling water inlet temperature of the chiller T c,i Chiller cooling water flow rate G c chilled water outlet temperature T e,o chiller chilled water flow rate G e As an optimization parameter.

[0084] (3) Enumeration: for finding optimal parameters T c,i , G c , T e,o , G e Assign a value.

[0085] Chiller cooling water inlet temperature T c,i Pick( T atm,wet +3, 45), optimization step size 0.1℃.

[0086] Chiller cooling water flow rate G c Use 50%~100% of the rated flow rate, and optimize the step size by 10 m. 3 / h.

[0087] chilled water outlet temperature T e,o We take (11, 17) and the optimization step size is 0.1℃.

[0088] chiller chilled water flow rate G e Use 50%~100% of the rated flow rate, and optimize the step size by 10 m. 3 / h.

[0089] (4) According to the law of conservation of energy G e ( T e,i - T e,o ) = Q req / n Calculate the chiller chilled water inlet temperature T e,i .examine T e,i and Te,i - T e,o Does it meet the upper and lower limits of the user-side process requirements? If not, output... P total =-1, and exit the current enumeration.

[0090] (5) Calculate the power of the chilled water pump based on the mathematical model of the chilled water pump. P e .

[0091] (6) Calculate the outlet temperature of the chiller cooling water based on the chiller mathematical model. T c,o and refrigeration power P chi Check if the cooling capacity meets the upper and lower limits of the refrigeration unit's cooling capacity constraints. If not, output... P total =-1, and exit the current enumeration.

[0092] (7) Calculate the power of the cooling pump based on the mathematical model of the cooling water pump. P c .

[0093] (8) Based on the cooling tower mathematical model, the inlet and outlet water temperatures, and the dry bulb temperature T atm,dry and wet-bulb temperature T atm,wet Calculate the mass flow rate of the cooling tower fan. m a and fan power P tow ,examine m a Does the cooling tower's heat dissipation capacity limit constraint meet? If not, output... P total =-1, and exit the current enumeration.

[0094] (9) Calculate the total electrical power of the refrigeration system. P total = nP c + nP e + n P chi + n P tow .

[0095] (10) In a series of enumerations P total In the middle, remove the results with -1 and output the smallest one. P total and its corresponding system operating parameters (n , S (, ...), representing the optimal total electrical power of the refrigeration system under the current operating mode (season) and the combination of the number of refrigeration units in operation. P total and optimal system operating parameters. (If the search fails...) P total (All values ​​are -1) Output P total Assigning a value of -1 to all optimization parameters results in an optimization failure message.

[0096] The energy consumption optimization control process for the refrigeration system under the transitional season operation mode is as follows.

[0097] (1) Calculate the cooling capacity requirement of a single refrigeration unit Q req / n Obtain outdoor dry bulb temperature T atm,dry Outdoor wet-bulb temperature T atm,wet .

[0098] (2) Selection: Cooling tower cooling water outlet temperature T tow,o Chiller cooling water flow rate G c chilled water outlet temperature T e,o chiller chilled water flow rate G e As an optimization parameter.

[0099] (3) Enumeration: for finding optimal parameters T tow, o , G c , T e,o , G e Assign a value.

[0100] Cooling tower cooling water outlet temperature T tow, o Pick( T atm,wet +3, 30), with an optimization step size of 0.1℃.

[0101] Chiller cooling water flow rate G c Use 50%~100% of the rated flow rate, and optimize the step size by 10 m. 3 / h.

[0102] chilled water outlet temperature T e,oWe take (11, 17) and the optimization step size is 0.1℃.

[0103] chiller chilled water flow rate G e Use 50%~100% of the rated flow rate, and optimize the step size by 10 m. 3 / h.

[0104] (4) According to the law of conservation of energy G e ( T 2i - T e,o ) = Q req / n Calculate the chilled water inlet (secondary side inlet) temperature of the plate heat exchanger. T 2i .examine T 2i and T 2i - T e,o Does it meet the upper and lower limits of the user-side process requirements? If not, output... P total =-1, and exit the current enumeration.

[0105] (5) Calculate the power of the chilled water pump based on the mathematical model of the chilled water pump. P e .

[0106] (6) Calculate the outlet temperature of the chiller cooling water based on the chiller mathematical model. T c,o and refrigeration power P chi Check if the cooling capacity meets the upper and lower limits of the refrigeration unit's cooling capacity constraints. If not, output... P total =-1, and exit the current enumeration.

[0107] (7) Calculate the outlet temperature of the cooling water (primary side) of the plate heat exchanger based on the mathematical model of the plate heat exchanger. T 1o = T c,i Chilled water (secondary side) outlet temperature of plate heat exchanger T 2o = T e,i .

[0108] (8) Calculate the power of the cooling pump based on the mathematical model of the cooling water pump. P c .

[0109] (9) Calculate the mass flow rate of the cooling tower fan based on the cooling tower mathematical model and the inlet and outlet water temperatures. m a and fan power P tow ,examine m a Does the cooling tower's heat dissipation capacity limit constraint meet? If not, output... P total =-1, and exit the current enumeration.

[0110] (9) Calculate the total electrical power of the refrigeration system. P total = nP c + nP e + n P chi + n P tow .

[0111] (10) In a series of enumerations P total In the middle, remove the results with -1, and output the smallest one. P total and its corresponding system operating parameters ( n , TR (, ...), representing the optimal total electrical power of the refrigeration system under the current operating mode (season) and the combination of the number of refrigeration units in operation. P total and optimal system operating parameters. (If the search fails...) P total (All values ​​are -1) Output P total Assign a value of -1, and assign a value of -1 to all optimization parameters.

[0112] The energy consumption optimization control process for the refrigeration system under winter operation mode is as follows.

[0113] (1) Calculate the cooling capacity requirement of a single refrigeration unit Q req / n Obtain outdoor dry bulb temperature T atm,dry Outdoor wet-bulb temperature T atm,wet .

[0114] (2) Selection: Cooling tower cooling water outlet temperature T tow,o Plate heat exchanger cooling water flow rate G c,exch Plate heat exchanger chilled water flow rateG e,exch As an optimization parameter.

[0115] (3) Enumeration: for finding optimal parameters T tow,o , G c,exch , G e,exch Assign a value.

[0116] Cooling water outlet temperature T tow,o Pick( T atm,wet +3, 30), with an optimization step size of 0.1℃.

[0117] Plate heat exchanger cooling water flow rate G c,exch Use 50%~100% of the rated flow rate, and optimize the step size by 10 m. 3 / h.

[0118] Plate heat exchanger chilled water flow rate G e,exch Use 50%~100% of the rated flow rate, and optimize the step size by 10 m. 3 / h.

[0119] (4) According to the law of conservation of energy G c,exch ( T tow,i - T tow,o ) = Q req / n Calculate the inlet water temperature of the cooling tower. T tow,i .

[0120] (5) Calculate the power of the chilled water pump based on the mathematical model of the chilled water pump. P e .

[0121] (6) Calculate the electrical power of the cooling pump based on the mathematical model of the cooling water pump. P c .

[0122] (7) Calculate the inlet temperature of chilled water (secondary side) of the plate heat exchanger based on the mathematical model of the plate heat exchanger. T 2i Chilled water (secondary side) outlet temperature of plate heat exchanger T 2o .examine T 2i and T 2i - T2o Does it meet the upper and lower limits of the user-side process requirements? If not, output... P total =-1, and exit the current enumeration.

[0123] (8) Calculate the mass flow rate of the cooling tower fan based on the cooling tower mathematical model and the inlet and outlet water temperatures. m a and fan power P tow ,examine m a Does the cooling tower's heat dissipation capacity limit constraint meet? If not, output... P total =-1, and exit the current enumeration.

[0124] (9) Calculate the total electrical power of the refrigeration system. P total = nP c + nP e + n P chi + n P tow .

[0125] (10) In a series of enumerations P total In the middle, remove the results with -1, and output the smallest one. P total and its corresponding system operating parameters ( n , W (, ...), representing the optimal total electrical power of the refrigeration system under the current operating mode (season) and the combination of the number of refrigeration units in operation. P total and optimal system operating parameters. (If the search fails...) P total (All values ​​are -1) Output P total Assign a value of -1, and assign a value of -1 to all optimization parameters.

[0126] If the optimization fails once, the number of refrigeration units activated can be changed. n Alternatively, the operating mode can be changed (e.g., even in summer, there may be rainy and cooler weather; in this case, a transitional season operating mode can be switched for optimization) and optimization can continue. The number of cooling units activated can be used as a starting point. n Optimization is performed in two dimensions: operation mode and operating mode.

[0127] The energy consumption optimization control device for a refrigeration system provided by the present invention is described below. The energy consumption optimization control device for a refrigeration system described below can be referred to in correspondence with the energy consumption optimization control method for a refrigeration system described above.

[0128] The energy consumption optimization and control device for the refrigeration system according to embodiments of the present invention, such as Figure 4 As shown, it includes the following modules.

[0129] Refrigeration demand calculation module 410 is used to calculate the total refrigeration demand of the refrigeration system. Q req The number of refrigeration units to be turned on is calculated as follows: n At that time, the cooling demand of a single refrigeration unit Q’ req .

[0130] The parameter value set determination module 420 is used to determine the outdoor dry-bulb temperature, outdoor wet-bulb temperature, mass conservation principle, energy conservation principle under different operating modes, and the number of refrigeration units in operation. n Corresponding cooling demand Q’ req And the mathematical models of each device in the refrigeration unit, determine the set of multiple sets of system operating parameter values ​​that satisfy the parameter constraints in the refrigeration system, and the number of refrigeration units to be turned on. n Operating Mode s Multiple optimization vectors are formed from multiple sets of system operating parameter values ​​φ. P ( n , s ,φ).

[0131] The total power calculation module 430 is used to calculate the power of each device based on the mathematical model of each device in the refrigeration unit and the corresponding system operating parameter values, and to calculate the power of each device based on the number of refrigeration units in operation. n Calculate the total power of the refrigeration system and obtain multiple sets of total power values.

[0132] The optimization control module 440 is used to output the optimization vector corresponding to the minimum total power in any operating mode. P ( n , s , φ) to optimize the control of the refrigeration system.

[0133] The refrigeration system energy consumption optimization control device in this embodiment optimizes energy consumption based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, the law of mass conservation, the energy conservation principle under different operating modes, and the number of refrigeration units in operation. n Corresponding cooling demand Q’ reqBased on the mathematical models of each device in the refrigeration unit, a set of multiple system operating parameter values ​​satisfying the parameter constraints of the refrigeration system is determined. Then, based on the mathematical models of each device in the refrigeration unit and the corresponding system operating parameter values, the power of each device is calculated, and the power is determined based on the number of refrigeration units in operation. n Calculate the total power of the refrigeration system, obtain multiple sets of total power values, and output the optimization vector corresponding to the minimum total power value as the objective. P ( n , s The φ) optimizes the control of the refrigeration system, realizing optimal control of the total energy consumption of the equipment based on the operating mode and number of operating units of the refrigeration system, combined with the operating parameters of each equipment. It is applicable to the optimal control of the total energy consumption of each equipment throughout the year.

[0134] Optionally, the parameter value set determination module 420 includes the following modules.

[0135] The parameter assignment module is used to filter multiple target operating parameters from the system operating parameter set and assign values ​​to the multiple target operating parameters using different assignment combinations.

[0136] The non-target parameter calculation module is used to calculate each assigned parameter combination based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, the mass conservation principle, the energy conservation principle under different operating modes, and the number of refrigeration units in operation. n Corresponding cooling demand Q’ req The mathematical models of each device in the refrigeration unit are used to calculate the non-target operating parameter values ​​corresponding to the non-target operating parameters in the system operating parameter set.

[0137] The parameter value combination module is used to combine the non-target operating parameter value and the corresponding target operating parameter value to form system operating parameter values, so as to obtain multiple sets of system operating parameter values, when the non-target operating parameter value meets the corresponding constraint conditions.

[0138] Optionally, the parameter assignment module includes the following modules.

[0139] The target operating parameter selection module is used to select different target operating parameters based on different operating modes.

[0140] The step size determination module is used to determine the assignment step size of the target operating parameters based on the range of values ​​in the constraints of the target operating parameters.

[0141] The assignment combination determination module is used to determine the assignment combination based on the assignment step size and assign values ​​to multiple target operating parameters.

[0142] Optionally, the assignment combination determination module is specifically used to determine the number of assignments for any target operating parameter based on the assignment step size of any target operating parameter, and to assign values ​​to multiple target operating parameters by enumeration based on the number of target operating parameters and the number of assignments for each target operating parameter.

[0143] Optionally, the energy consumption optimization control device for the refrigeration system further includes: an anomaly setting module, used to set the total power value to a predetermined anomaly value when the non-target operating parameter value does not meet the corresponding constraint conditions.

[0144] Specifically, the optimization control module 440 is used to delete the optimization vector corresponding to a predetermined abnormal value of the total power in any operating mode. P ( n , s , φ), in the remaining optimization vector P ( n , s In φ), the optimization vector corresponding to the minimum total power is output. P ( n , s , φ) to optimize the control of the refrigeration system.

[0145] Optionally, the cooling demand calculation module 410 is specifically used to calculate the cooling capacity of a single cooling unit under the operating state of the cooling system before optimization. Q req,i ,in, i =1,2,…, N , N This represents the total number of refrigeration units in the refrigeration system; by summing the cooling capacity of each individual refrigeration unit, the total refrigeration demand is obtained. The number of refrigeration units in operation is n At that time, calculate the cooling demand of a single refrigeration unit. Q’ req = Q req / n ,in, n < N .

[0146] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5As shown, the electronic device may include a processor 510, a communications interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communications interface 520, and the memory 530 communicate with each other via the communication bus 540. The processor 510 can call logic instructions in the memory 530 to execute a cooling system energy consumption optimization control method, which includes the following steps.

[0147] Total cooling demand based on the refrigeration system Q req The number of refrigeration units to be turned on is calculated as follows: n At that time, the cooling demand of a single refrigeration unit Q’ req .

[0148] Based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, the principle of mass conservation, the principle of energy conservation under different operating modes, and the number of refrigeration units in operation. n Corresponding cooling demand Q’ req And the mathematical models of each device in the refrigeration unit, determine the set of multiple sets of system operating parameter values ​​that satisfy the parameter constraints in the refrigeration system, and the number of refrigeration units to be turned on. n Operating Mode s Multiple optimization vectors are formed from multiple sets of system operating parameter values ​​φ. P ( n , s , φ).

[0149] Based on the mathematical models of each device in the refrigeration unit and the corresponding system operating parameter values, the power of each device is calculated, and the number of refrigeration units in operation is determined accordingly. n Calculate the total power of the refrigeration system and obtain multiple sets of total power values.

[0150] In any operating mode, output the optimization vector corresponding to the minimum total power. P ( n , s , φ) to optimize the control of the refrigeration system.

[0151] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0152] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the energy consumption optimization control method for the refrigeration system provided by the above methods, which includes the following steps.

[0153] Total cooling demand based on the refrigeration system Q req The number of refrigeration units to be turned on is calculated as follows: n At that time, the cooling demand of a single refrigeration unit Q’ req .

[0154] Based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, the principle of mass conservation, the principle of energy conservation under different operating modes, and the number of refrigeration units in operation. n Corresponding cooling demand Q’ req And the mathematical models of each device in the refrigeration unit, determine the set of multiple sets of system operating parameter values ​​that satisfy the parameter constraints in the refrigeration system, and the number of refrigeration units to be turned on. n Operating Mode s Multiple optimization vectors are formed from multiple sets of system operating parameter values ​​φ. P ( n , s , φ).

[0155] Based on the mathematical models of each device in the refrigeration unit and the corresponding system operating parameter values, the power of each device is calculated, and the number of refrigeration units in operation is determined accordingly. n Calculate the total power of the refrigeration system and obtain multiple sets of total power values.

[0156] In any operating mode, output the optimization vector corresponding to the minimum total power. P ( n , s , φ) to optimize the control of the refrigeration system.

[0157] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the energy consumption optimization control method for the refrigeration system provided by the above methods, the method comprising the following steps.

[0158] Total cooling demand based on the refrigeration system Q req The number of refrigeration units to be turned on is calculated as follows: n At that time, the cooling demand of a single refrigeration unit Q’ req .

[0159] Based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, the principle of mass conservation, the principle of energy conservation under different operating modes, and the number of refrigeration units in operation. n Corresponding cooling demand Q’ req And the mathematical models of each device in the refrigeration unit, determine the set of multiple sets of system operating parameter values ​​that satisfy the parameter constraints in the refrigeration system, and the number of refrigeration units to be turned on. n Operating Mode s Multiple optimization vectors are formed from multiple sets of system operating parameter values ​​φ. P ( n , s , φ).

[0160] Based on the mathematical models of each device in the refrigeration unit and the corresponding system operating parameter values, the power of each device is calculated, and the number of refrigeration units in operation is determined accordingly. n Calculate the total power of the refrigeration system and obtain multiple sets of total power values.

[0161] In any operating mode, output the optimization vector corresponding to the minimum total power. P ( n , s , φ) to optimize the control of the refrigeration system.

[0162] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0163] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing and controlling the energy consumption of a refrigeration system, characterized in that, include: Total cooling demand based on the refrigeration system Q req The number of refrigeration units to be turned on is calculated as follows: n At that time, the cooling demand of a single refrigeration unit Q’ req ; Based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, the principle of mass conservation, the principle of energy conservation under different operating modes, and the number of refrigeration units in operation. n The corresponding cooling demand Q’ req And the mathematical models of each device in the refrigeration unit, determine the set of multiple sets of system operating parameter values ​​that satisfy the parameter constraints in the refrigeration system, and the number of refrigeration units to be turned on. n Operating Mode s and multiple sets of system operating parameter values Form multiple optimization vectors P ( n , s , ); Based on the mathematical models of each device in the refrigeration unit and the corresponding system operating parameter values, the power of each device is calculated, and the number of refrigeration units in operation is determined accordingly. n Calculate the total power of the refrigeration system and obtain multiple sets of total power values; In any operating mode, output the optimization vector corresponding to the minimum total power. P ( n , s , Optimize and control the refrigeration system; Based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, the principle of mass conservation, the principle of energy conservation under different operating modes, and the number of refrigeration units in operation. n The corresponding cooling demand Q’ req And the mathematical models of each device in the refrigeration unit, determining a set of multiple system operating parameter values ​​that satisfy the parameter constraints in the refrigeration system, including: Multiple target operating parameters are selected from the system operating parameter set, and different assignment combinations are used to assign values ​​to the multiple target operating parameters; For each assignment combination, based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, the principle of mass conservation, the principle of energy conservation under different operating modes, and the number of refrigeration units in operation... n Corresponding cooling demand Q’ req And mathematical models of each device in the refrigeration unit, and calculate the non-target operating parameter values ​​corresponding to the non-target operating parameters in the system operating parameter set; When the non-target operating parameter values ​​satisfy the corresponding constraints, the non-target operating parameter values ​​and the corresponding target operating parameter values ​​constitute the system operating parameter values, so as to obtain multiple sets of the system operating parameter values; Multiple target operating parameters are selected from the system operating parameter set, and different assignment combinations are used to assign values ​​to the multiple target operating parameters, including: Different target operating parameters are selected based on different operating modes; Based on the range of values ​​in the constraints of the target operating parameters, determine the step size for assigning values ​​to the target operating parameters; Based on the assignment step size, an assignment combination is determined, and values ​​are assigned to multiple target operating parameters.

2. The energy consumption optimization and control method for a refrigeration system according to claim 1, characterized in that, Based on the assignment step size, an assignment combination is determined, and values ​​are assigned to multiple target operating parameters, including: based on the assignment step size of any target operating parameter, determining the number of assignments for any target operating parameter; based on the number of target operating parameters and the number of assignments for each target operating parameter, assigning values ​​to multiple target operating parameters by enumeration.

3. The energy consumption optimization and control method for a refrigeration system according to claim 1, characterized in that, If the non-target operating parameter value does not meet the corresponding constraint condition, the total power value is set to a predetermined abnormal value; In any operating mode, the optimization vector corresponding to the minimum total power is output. P ( n , s , Optimize and control the refrigeration system, including: In any operating mode, the value of the total power to be deleted is the optimization vector corresponding to the predetermined outlier. P ( n , s , ), in the remaining optimization vector P ( n , s , In the output, the optimization vector corresponding to the minimum total power is calculated. P ( n , s , ) Optimize and control the refrigeration system.

4. The energy consumption optimization and control method for a refrigeration system according to any one of claims 1 to 3, characterized in that, Total cooling demand based on the refrigeration system Q req The number of refrigeration units to be turned on is calculated as follows: n At that time, the cooling demand of a single refrigeration unit Q’ req ,include: Calculate the cooling capacity of a single refrigeration unit under the original operating conditions of the refrigeration system before optimization. Q req,i ,in, i =1,2,…, N , N Indicates the total number of refrigeration units in the refrigeration system; The total cooling demand is obtained by summing the cooling capacity of each individual refrigeration unit. ; The number of refrigeration units in operation is n At that time, calculate the cooling demand of a single refrigeration unit. Q’ req = Q req / n ,in, n < N .

5. A refrigeration system energy consumption optimization and control device, characterized in that, include: The cooling demand calculation module is used to calculate the total cooling demand of the refrigeration system. Q req The number of refrigeration units to be turned on is calculated as follows: n At that time, the cooling demand of a single refrigeration unit Q’ req ; The parameter value set determination module is used to determine the outdoor dry-bulb temperature, outdoor wet-bulb temperature, mass conservation principle, energy conservation principle under different operating modes, and the number of refrigeration units in operation. n The corresponding cooling demand Q’ req And the mathematical models of each device in the refrigeration unit, determine the set of multiple sets of system operating parameter values ​​that satisfy the parameter constraints in the refrigeration system, and the number of refrigeration units to be turned on. n Operating Mode s and multiple sets of system operating parameter values Form multiple optimization vectors P ( n , s , ); The total power calculation module is used to calculate the power of each device based on the mathematical model of each device in the refrigeration unit and the corresponding system operating parameter values, and to calculate the total power of the refrigeration system based on the number of refrigeration units n that are turned on, thus obtaining multiple sets of total power. The optimization control module is used to output the optimization vector corresponding to the minimum total power in any operating mode. P ( n , s , Optimize and control the refrigeration system; The parameter value set determination module includes the following modules: The parameter assignment module is used to filter multiple target operating parameters from the system operating parameter set and assign values ​​to the multiple target operating parameters using different assignment combinations; The non-target parameter calculation module is used to calculate each assigned parameter combination based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, the mass conservation principle, the energy conservation principle under different operating modes, and the number of refrigeration units in operation. n Corresponding cooling demand Q’ req And mathematical models of each device in the refrigeration unit, and calculate the non-target operating parameter values ​​corresponding to the non-target operating parameters in the system operating parameter set; The parameter value combination module is used to combine the non-target operating parameter value and the corresponding target operating parameter value to form system operating parameter values, so as to obtain multiple sets of system operating parameter values, when the non-target operating parameter value satisfies the corresponding constraint conditions. The parameter assignment module includes the following modules: The target operating parameter selection module is used to select different target operating parameters based on different operating modes. The step size determination module is used to determine the assignment step size of the target operating parameters based on the value range of the constraints of the target operating parameters; The assignment combination determination module is used to determine the assignment combination based on the assignment step size and assign values ​​to multiple target operating parameters.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the energy consumption optimization control method for the refrigeration system as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the energy consumption optimization control method for the refrigeration system as described in any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the energy consumption optimization control method for the refrigeration system as described in any one of claims 1 to 4.

Images