Refrigerating system energy consumption optimization control method and device

Abstract

The invention relates to the technical field of refrigeration control, and provides a refrigeration system energy consumption optimization control method and device.The method comprises the steps that based on the total refrigeration demand of a refrigeration system, the refrigeration demand of a single refrigeration unit is calculated when the number of started refrigeration units is n; multiple sets of system operation parameter value sets meeting parameter constraint conditions in the refrigeration system are determined, and the number n of started refrigeration units, the operation mode s and the multiple sets of system operation parameter value sets phi form multiple optimization vectors P (n, s, phi); calculating the power of each device based on the mathematical model of each device in the refrigeration unit and the system operation parameter value, and calculating the total power of the refrigeration system based on the number n of started refrigeration units to obtain a plurality of groups of total power; and in any operation mode, an optimization vector P (n, s, phi) corresponding to the minimum total power is output to carry out optimization control on the refrigeration system. According to the method, the operation mode of the refrigeration system and the number n are taken as optimization dimensions, and the optimal control of the total energy consumption of the equipment is realized by combining the operation parameters of the equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of refrigeration control, and in particular to a method and device for optimizing energy consumption control of a refrigeration system. Background Art

[0002] Figure 1 This is a schematic diagram of a refrigeration system in a data center. The refrigeration system includes at least one refrigeration unit, which includes: a cooling tower, a chiller (water chiller), a cooling pump (cooling water circulation pump), a freezing pump (chilled water circulation pump) and a plate heat exchanger. The refrigeration system of a single data room is generally equipped with 4 refrigeration units, 3 in use and 1 in reserve, with the ability to adjust the number of units. In each refrigeration unit, the chilled water pump is connected to the water collector, and the outlet on the user side of the chiller is connected to the water distributor. The refrigeration system has 3 design operation modes: mechanical refrigeration, partial natural cooling and full natural cooling, which are suitable for summer, transition season and winter respectively (corresponding to three operation modes: summer operation mode, transition season operation mode and winter operation mode). In summer, only the chiller is used for mechanical cooling (V1, V3, V6 and V8 are turned on, and V2, V4, V5 and V7 are turned off). In the transition season, the plate heat exchanger and the chiller are connected in series to achieve partial natural cooling (V2, V4, V6 and V8 are turned on, and V1, V3, V5 and V7 are turned off). In winter, only the plate heat exchanger is used (V2, V4, V5 and V7 are turned on, and V1, V3, V6 and V8 are turned off) to achieve full natural cooling.

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

[0004] In the current energy consumption optimization control scheme, energy consumption optimization control is achieved by establishing a mathematical model combined with an optimization algorithm. However, the mathematical model is too simple and only considers the summer operation mode and some key parameters of the equipment. It does not adapt to the data center's year-round cooling needs. The energy-saving effect of mechanical refrigeration throughout the year is not good, and the mathematical model does not consider the impact of the number of refrigeration unit startups (discrete variables) on refrigeration and energy consumption. Therefore, the current energy consumption optimization control scheme cannot achieve optimal control of the total energy consumption of each device for the whole year based on the refrigeration system operation mode and number of units in operation, combined with the key parameters of each device operation. Summary of the invention

[0005] The present invention provides a refrigeration system energy consumption optimization control method and device, which are used to solve the problem in the prior art that it is impossible to achieve optimal control of the total energy consumption of each device suitable for the whole year according to the refrigeration system operation mode and the number of operating units in combination with the operation parameters of each device.

[0006] The present invention provides a refrigeration system energy consumption optimization control method, comprising the following steps.

[0007] Total cooling demand based on refrigeration system Q req , calculate the number of refrigeration units turned on n When the cooling demand of a single cooling unit is Q’ req .

[0008] Based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, mass conservation principle, energy conservation principle under different operating modes, and number of refrigeration units turned on n Corresponding cooling demand Q’ req and the mathematical model of each device in the refrigeration unit, determine the multiple sets of system operating parameter values ​​that meet the parameter constraints in the refrigeration system, and the number of refrigeration units turned on n , Operation Mode s and multiple sets of system operation parameter value sets φ to form multiple optimization vectors P ( n , s , φ).

[0009] Based on the mathematical model of each device in the refrigeration unit and the corresponding system operation parameter value, 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, and multiple groups of total power are obtained.

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

[0011] According to a refrigeration system energy consumption optimization control method provided by the present invention, based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, mass conservation principle, energy conservation principle under different operation modes, number of refrigeration units turned on, n Corresponding cooling demand Q’ req and mathematical models of each device in the refrigeration unit, and determining multiple sets of system operating parameter value sets that meet parameter constraint conditions in the refrigeration system, including the following steps.

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

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

[0014] In the case where the non-target operating parameter value satisfies the corresponding constraint condition, the non-target operating parameter value and the corresponding target operating parameter value constitute the system operating parameter value to obtain a plurality of sets of the system operating parameter value.

[0015] According to a refrigeration system energy consumption optimization control method provided by the present invention, multiple target operating parameters are screened from a system operating parameter set, and different assignment combinations are used to assign values ​​to the multiple target operating parameters, including the following steps.

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

[0017] Based on the value range in the constraint conditions of the target operating parameters, the assignment step size of the target operating parameters is determined.

[0018] Based on the assignment step, an assignment combination is determined to assign values ​​to the plurality of target operating parameters.

[0019] According to a refrigeration system energy consumption optimization control method provided by the present invention, based on the assignment step, the assignment combination is determined to assign values ​​to multiple target operating parameters, including: at the assignment step of any target operating parameter, the number of assignments of any target operating parameter is determined, and based on the number of target operating parameters and the number of assignments of each target operating parameter, multiple target operating parameters are assigned in an enumerated manner.

[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 condition, the value of the total power is set to a predetermined abnormal value.

[0021] Among them, in any operating mode, the optimization vector corresponding to the minimum total power output is P ( n , s , φ) optimizes and controls the refrigeration system, including: in any operation mode, deleting the optimization vector corresponding to the predetermined abnormal value of the total power P ( n , s , φ), in the remaining optimization vector P (n , s , φ), the optimization vector corresponding to the minimum total output power is P ( n , s , φ) to optimize the control of the refrigeration system.

[0022] According to a refrigeration system energy consumption optimization control method provided by the present invention, based on the total refrigeration demand of the refrigeration system Q req , calculate the number of refrigeration units turned on n When the cooling demand of a single cooling unit is Q’ req , including the following steps.

[0023] Calculate the cooling capacity of a single refrigeration unit under the working condition of the refrigeration system before optimization Q req,i ,in, i =1,2,…, N , N Indicates the total number of cooling units in the refrigeration system.

[0024] Add up the cooling capacity of each refrigeration unit to get the total cooling demand .

[0025] The number of refrigeration units turned on is n Calculate the cooling demand of a single cooling unit when 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] Refrigeration demand calculation module for total refrigeration demand based on refrigeration system Q req , calculate the number of refrigeration units turned on n When the cooling demand of a single cooling unit is Q’ req .

[0028] Parameter value set determination module, 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 turned on n Corresponding cooling demand Q’ reqand the mathematical model of each device in the refrigeration unit, determine the multiple sets of system operating parameter values ​​that meet the parameter constraints in the refrigeration system, and the number of refrigeration units turned on n , Operation Mode s and multiple sets of system operation parameter value sets φ to form multiple optimization vectors 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 operation parameter value, and based on the number of refrigeration units turned on n Calculate the total power of the refrigeration system and obtain multiple groups of total power.

[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 the control of the refrigeration system.

[0031] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for optimizing energy consumption of a refrigeration system as described above is implemented.

[0032] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for optimizing energy consumption control of a refrigeration system as described in any one of the above is implemented.

[0033] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for optimizing energy consumption control of a refrigeration system as described above is implemented.

[0034] The refrigeration system energy consumption optimization control method and device provided by the present invention is based on the outdoor dry-bulb temperature, outdoor wet-bulb temperature, mass conservation principle, energy conservation principle under different operation modes, and the number of refrigeration units turned on. n Corresponding cooling demand Q’ req The mathematical model of each device in the refrigeration unit is used to determine multiple sets of system operation parameter values ​​that meet the parameter constraints in the refrigeration system. Based on the mathematical model of each device in the refrigeration unit and the corresponding system operation parameter values, the power of each device is calculated, and the number of refrigeration units turned on is calculated. n Calculate the total power of the refrigeration system and obtain multiple groups of total power. Take the minimum total power as the goal and output the optimization vector corresponding to the minimum total power. P ( n , s, φ) optimizes the control of the refrigeration system, realizes the optimal control of the total energy consumption of the equipment according to the operating mode and number of operating units of the refrigeration system and combined with the operating parameters of each equipment, which is suitable for the optimal control of the total energy consumption of each equipment throughout the year. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 It is a structural diagram of a refrigeration unit in a refrigeration system.

[0037] Figure 2 It is a flow chart of the refrigeration system energy consumption optimization control method provided by the present invention.

[0038] Figure 3 It is a flow chart of the refrigeration system energy consumption optimization control method provided by the present invention.

[0039] Figure 4 It is a structural schematic diagram of the refrigeration system energy consumption optimization control device provided by the present invention.

[0040] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] In the refrigeration system energy consumption optimization control method of the embodiment of the present invention, the refrigeration system can be Figure 1 Refrigeration system consisting of refrigeration units, but not limited to Figure 1 The refrigeration system in this embodiment. The refrigeration system energy consumption optimization control method of this embodiment, such as Figure 2 As shown, the process includes the following steps S210 to S240.

[0043] Step S210: Based on the total refrigeration demand of the refrigeration system Q req , calculate the number of refrigeration units turned on nWhen the cooling demand of a single cooling unit is Q’ req Total cooling demand Q req It can be determined according to the needs of the actual application scenario, for example: when cooling the data center, the total cooling demand Q req It can be determined based on the optimal operating temperature of the data center and the current temperature in the computer room.

[0044] Step S220: Based on the outdoor dry-bulb temperature, outdoor wet-bulb temperature, the principle of conservation of mass, the principle of conservation of energy under different operating modes, and the number of refrigeration units turned on n Corresponding cooling demand Q’ req and the mathematical model of each device in the refrigeration unit, determine the multiple sets of system operating parameter values ​​that meet the parameter constraints in the refrigeration system, and the number of refrigeration units turned on n , Operation Mode s and multiple sets of system operation parameter value sets φ to form multiple optimization vectors P ( n , s , φ). Among them, the principles of conservation of mass and conservation of energy depend on the structure of the refrigeration unit. Figure 1 Taking the refrigeration unit structure as an example, the principle of conservation of mass is that in the same water cycle, the water flow in and out of each device is equal. The principle of conservation of energy is different in different operating modes. In summer mode, the heat dissipation on the condensing side of the chiller is equal to the heat dissipation of the cooling tower, and the cooling capacity on the evaporation side of the chiller is equal to the actual cooling capacity of the refrigeration unit; in transition season mode, the sum of the heat dissipation on the condensing side of the chiller and the heat exchange of the plate heat exchanger is equal to the heat dissipation of the cooling tower, and the sum of the heat exchange of the plate heat exchanger and the cooling capacity on the evaporation side of the chiller is equal to the actual cooling capacity of the refrigeration unit; in winter mode, the heat dissipation of the cooling tower is equal to the heat exchange of the plate heat exchanger, which is also equal to the actual cooling capacity of the refrigeration unit.

[0045] The mathematical model of each device in the refrigeration unit also depends on the structure of the refrigeration unit. Different refrigeration unit structures have different mathematical models for each device. Figure 1 Taking the refrigeration unit structure as an example, the equipment includes: chiller, water pumps, plate heat exchanger and cooling tower. The mathematical models used for each device are 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 flowG e , Cooling water flow G c And cooling electrical power P chi relationship.

[0047] The mathematical model of the pump is coupled with the chilled water flow G e , Cooling water flow G c and water pump power ( P e , P c ), specifically, the mathematical model of the chilled water pump is coupled to the chilled water flow G e Electric power of refrigeration pump P e The mathematical model of the cooling pump is coupled with the cooling water flow G c With cooling pump electric power P c The relationship between.

[0048] Mathematical model of plate heat exchanger coupled with chilled water flow G e , Cooling water flow 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 relationship.

[0049] The cooling tower mathematical model is coupled with the cooling tower inlet water temperature 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 relationship.

[0050] The mathematical models of the chiller, water pump, plate heat exchanger and cooling tower can adopt mature mathematical models in the existing related technologies, and are not limited in this embodiment.

[0051] The parameter constraints in the refrigeration system depend on the structure of the refrigeration unit and the needs of the user side. The parameter constraints include the upper and lower limits of the system operating parameter value range and the constraints on the relationship between the system operating parameters. Figure 1 Taking the refrigeration unit structure as an example, the parameter constraints include but are not limited to the following constraints.

[0052] The chilled water supply temperature required by the user side process (i.e. the chilled water outlet temperature of the chiller) T e,o ) upper and lower limit constraints (for example: 11~17℃).

[0053] User side process demand chilled water supply and return water temperature difference ( T e,o and T e,i The temperature difference is limited by the upper and lower limits (for example: 3-7℃).

[0054] The upper and lower limits of the cooling capacity of the refrigeration unit are set.

[0055] The upper and lower limits of the flow rate of each pump are set (for example: 50%~100% of the rated flow rate).

[0056] Cooling tower heat dissipation capacity upper limit constraint (for example: cooling tower fan mass flow m a Between 40% and 100% of rated air volume).

[0057] The system operating parameters vary with the refrigeration unit structure and operating mode. Figure 1 As an example, the system operating parameters in different operating modes are as follows: Figure 3 As shown in the table.

[0058] Step S230: Based on the mathematical model of each device in the refrigeration unit and the corresponding system operation parameter value, the power of each device is calculated, and based on the number of refrigeration units turned on n Calculate the total power of the refrigeration system and obtain multiple groups of total power. Since the mathematical model of each device determines the relationship between the power of each device and other related system operating parameters in the system operating parameters, the power of each device can be calculated, so that the power of each device can be calculated according to the number of refrigeration units turned on. n Calculate the total power of the refrigeration system. The total power calculation formula is: P total = nP c + nP e + n P chi + n P tow .

[0059] Step S240: In any operation mode, output the optimization vector corresponding to the minimum total power P ( n , s , φ) to optimize the refrigeration system. Specifically, the minimum total power is taken as the objective function min( P total = nP c + nP e + n P chi + n P tow ) to obtain the optimal vector corresponding to the minimum total power P ( n , s , φ), so as to minimize the total energy consumption of the chiller, water pumps and cooling tower while meeting the total refrigeration demand.

[0060] In the refrigeration system energy consumption optimization control method of this embodiment, based on the outdoor dry-bulb temperature, outdoor wet-bulb temperature, mass conservation principle, energy conservation principle under different operation modes, and the number of refrigeration units turned on, n Corresponding cooling demand Q’ req The mathematical model of each device in the refrigeration unit is used to determine multiple sets of system operation parameter values ​​that meet the parameter constraints in the refrigeration system. Based on the mathematical model of each device in the refrigeration unit and the corresponding system operation parameter values, the power of each device is calculated, and the number of refrigeration units turned on is calculated. n Calculate the total power of the refrigeration system and obtain multiple groups of total power. Take the minimum total power as the goal and output the optimization vector corresponding to the minimum total power. P ( n , s , φ) optimizes the control of the refrigeration system, realizes the optimal control of the total energy consumption of the equipment according to the operating mode and number of operating units of the refrigeration system and combined with the operating parameters of each equipment, which is suitable for 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] Filter multiple target operating parameters from the system operating parameter set, and use different assignment combinations to assign values ​​to the multiple target operating parameters. The target operating parameters can be randomly selected, as long as it is guaranteed 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 multiple target operating parameters, and the target operating parameter values ​​are not the same in different assignment combinations. Of course, the assignment of multiple target operating parameters also needs to meet the upper and lower limits of the target operating parameter value range.

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

[0064] In the case where the non-target operating parameter value satisfies the corresponding constraint condition, the non-target operating parameter value and the corresponding target operating parameter value constitute the system operating parameter value to obtain a plurality of sets of the system operating parameter value.

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

[0066] Specifically, a plurality of target operating parameters are screened from a system operating parameter set, and different assignment combinations are used to assign values ​​to the plurality of target operating parameters, including the following steps.

[0067] Different target operating parameters are 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] Based on the value range in the constraint conditions of the target operating parameters, the assignment step size of the target operating parameters is determined.

[0069] Based on the assignment step, an assignment combination is determined to assign values ​​to the plurality of target operating parameters.

[0070] Further, based on the assignment step length, determining the assignment combination, assigning values ​​to the multiple target operating parameters, includes: determining the number of assignments of any target operating parameter based on the assignment step length of any target operating parameter, and assigning values ​​to the multiple target operating parameters in an enumerated manner based on the number of target operating parameters and the number of assignments of each target operating parameter. For example: there are four target operating parameters, and according to their respective assignment step lengths, their respective assignment numbers can be determined to be 3, 4, 2, and 5, then it can be determined that there are 3×4×2×5=120 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 checked to ensure that the optimization results are within the adjustable performance of the device and can strictly meet the user-side requirements.

[0072] In some embodiments, when the non-target operating parameter value does not satisfy 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 optimization vector corresponding to the predetermined abnormal value of the total power P ( n , s , φ), in the remaining optimization vector P ( n , s , φ), the optimization vector corresponding to the minimum total output power is P ( n , s , φ) to optimize the control of the refrigeration system.

[0074] In this embodiment, when the non-target operating parameter value does not satisfy the corresponding constraint condition, the value of the total power is set to a predetermined abnormal value, and an exclusion mechanism for unreasonable assignment 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 working condition 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. Specifically, in summer mode Q req,i = Ge ( T e,o - T e,i ), in transitional season mode Q req,i = G e ( T 2i - T e,i ), in winter mode Q req,i = G e, exch ( T 2i - T 2o ),in, G e, exch represents the chilled water flow of the plate heat exchanger. If the i-th refrigeration unit is not turned on in the working state of the refrigeration system before optimization, then Q req,i =0.

[0077] Add up the cooling capacity of each refrigeration unit to get the total cooling demand .

[0078] The number of refrigeration units turned on is n Calculate the cooling demand of a single cooling unit when Q’ req = Q req / n ,in, n < N .

[0079] It should be noted that: before executing the refrigeration system energy consumption optimization control method of this embodiment, it is considered that the current working state of the refrigeration system is stable and meets the current refrigeration requirements. Therefore, the refrigeration capacity of the refrigeration unit turned on in the current state can be used to calculate the refrigeration capacity of each refrigeration unit when the number of refrigeration units turned on is n during the optimization process.

[0080] Below 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 described in different operating modes.

[0081] For the summer operation mode, the energy consumption optimization control process of the refrigeration system is as follows.

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

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

[0084] (3) Enumeration: To find the optimal parameters T c,i , G c , T e,o , G e Assignment.

[0085] Cooling water inlet temperature of chiller T c,i Pick( T atm,wet +3, 45), looking for an improvement of 0.1℃.

[0086] Cooling water flow rate of chiller G c Take 50%~100% rated flow and search for the optimal step length of 10 m 3 / h.

[0087] Chiller chilled water outlet temperature T e,o Take (11, 17) and set the optimal step length to 0.1℃.

[0088] Chiller chilled water flow G e Take 50%~100% rated flow and search for the optimal step length of 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 Whether the upper and lower limit constraints of the user-side process requirements are met, if not, then output P total =-1, and jump out of this enumeration.

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

[0091] (6) Calculate the cooling water outlet temperature of the chiller based on the chiller mathematical model T c,o And cooling electrical power P chi Check whether the cooling capacity meets the upper and lower limit constraints of the refrigeration capacity of the refrigeration unit. If not, output P total =-1, and jump out of this enumeration.

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

[0093] (8) Based on the cooling tower mathematical model, water inlet and outlet temperatures, and dry bulb temperature T atm,dry and wet bulb temperature T atm,wet , calculate the cooling tower fan mass flow m a And fan power P tow ,examine m a Whether the cooling tower heat dissipation capacity upper limit constraint is met, if not, then output P total =-1, and jump out of this 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 the series obtained by enumeration P total , delete the result of -1 and output the smallest value. P total , and its corresponding system operating parameters (n , S , ...), as the optimal total power of the cooling system under the current operating mode (season) and the number of cooling units turned on P total and optimal system operating parameters. P total All values ​​are -1), output P total Assign -1 to all optimization parameters, indicating that the optimization failed.

[0096] For the transition season operation mode, the energy consumption optimization control process of the refrigeration system is as follows.

[0097] (1) Calculate the cooling capacity requirement of a single refrigeration unit Q req / n , get the 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 , cooling water flow of chiller G c , chiller chilled water outlet temperature T e,o , chilled water flow rate of chiller G e as optimization parameters.

[0099] (3) Enumeration: To find the optimal parameters T tow, o , G c , T e,o , G e Assignment.

[0100] Cooling tower cooling water outlet temperature T tow, o Pick( T atm,wet +3, 30), the optimal step length is 0.1℃.

[0101] Cooling water flow rate of chiller G c Take 50%~100% rated flow and search for the optimal step length of 10 m 3 / h.

[0102] Chiller chilled water outlet temperature T e,oTake (11, 17) and set the optimal step length to 0.1℃.

[0103] Chiller chilled water flow G e Take 50%~100% rated flow and search for the optimal step length of 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 plate heat exchanger chilled water inlet (secondary side inlet) temperature T 2i .examine T 2i and T 2i - T e,o Whether the upper and lower limit constraints of the user-side process requirements are met, if not, then output P total =-1, and jump out of this enumeration.

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

[0106] (6) Calculate the cooling water outlet temperature of the chiller based on the chiller mathematical model T c,o And cooling electrical power P chi Check whether the cooling capacity meets the upper and lower limit constraints of the refrigeration capacity of the refrigeration unit. If not, output P total =-1, and jump out of this 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 And the plate heat exchanger chilled water (secondary side) outlet water temperature T 2o = T e,i .

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

[0109] (9) Calculate the cooling tower fan mass flow rate based on the cooling tower mathematical model and inlet and outlet water temperatures. m a And fan power P tow ,examine m a Whether the cooling tower heat dissipation capacity upper limit constraint is met, if not, then output P total =-1, and jump out of this 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 the series obtained by enumeration P total In the output, remove the -1 result and output the smallest P total , and its corresponding system operating parameters ( n , TR , ...), as the optimal total power of the cooling system under the current operating mode (season) and the number of cooling units turned on P total and optimal system operating parameters. P total All values ​​are -1), output P total Assign -1 to all optimization parameters.

[0112] For winter operation mode, the energy consumption optimization control process of the refrigeration system is as follows.

[0113] (1) Calculate the cooling capacity requirement of a single refrigeration unit Q req / n , get the 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 G c,exch , Plate heat exchanger chilled water flowG e,exch as optimization parameters.

[0115] (3) Enumeration: To find the optimal parameters T tow,o , G c,exch , G e,exch Assignment.

[0116] Cooling water outlet temperature T tow,o Pick( T atm,wet +3, 30), the optimal step length is 0.1℃.

[0117] Plate heat exchanger cooling water flow G c,exch Take 50%~100% rated flow and search for the optimal step length of 10 m 3 / h.

[0118] Plate heat exchanger chilled water flow G e,exch Take 50%~100% rated flow and search for the optimal step length of 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 cooling tower inlet water temperature T tow,i .

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

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

[0122] (7) Calculate the chilled water (secondary side) inlet temperature of the plate heat exchanger based on the plate heat exchanger mathematical model T 2i And the plate heat exchanger chilled water (secondary side) outlet water temperature T 2o .examine T 2i and T 2i - T2o Whether the upper and lower limit constraints of the user-side process requirements are met, if not, then output P total =-1, and jump out of this enumeration.

[0123] (8) Calculate the cooling tower fan mass flow rate based on the cooling tower mathematical model and inlet and outlet water temperatures m a And fan power P tow ,examine m a Whether the cooling tower heat dissipation capacity upper limit constraint is met, if not, then output P total =-1, and jump out of this 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 the series obtained by enumeration P total In the output, remove the -1 result and output the smallest P total , and its corresponding system operating parameters ( n , W , ...), as the optimal total power of the cooling system under the current operating mode (season) and the number of cooling units turned on P total and optimal system operating parameters. P total All values ​​are -1), output P total Assign -1 to all optimization parameters.

[0126] If an optimization fails, the number of refrigeration units can be changed. n , or change the operation mode (for example, even in summer, it may rain and the temperature may drop. At this time, you can switch to the transition season operation mode for optimization) and continue the optimization. n Optimization is performed in two dimensions: the optimization method and the operation mode.

[0127] The refrigeration system energy consumption optimization control device provided by the present invention is described below. The refrigeration system energy consumption optimization control device described below and the refrigeration system energy consumption optimization control method described above can be referenced to each other.

[0128] The refrigeration system energy consumption optimization control device of the embodiment of the present invention is as follows: 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 based on the total refrigeration demand of the refrigeration system. Q req , calculate the number of refrigeration units turned on n When the cooling demand of a single cooling unit is Q’ req .

[0130] The parameter value set determination module 420 is used to determine the value of the parameter value set based on the outdoor dry bulb temperature, the outdoor wet bulb temperature, the mass conservation principle, the energy conservation principle under different operating modes, and the number of refrigeration units turned on. n Corresponding cooling demand Q’ req and the mathematical model of each device in the refrigeration unit, determine the multiple sets of system operating parameter values ​​that meet the parameter constraints in the refrigeration system, and the number of refrigeration units turned on n , Operation Mode s and multiple sets of system operation parameter value sets φ to form multiple optimization vectors 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 operation parameter value, and based on the number of refrigeration units turned on n Calculate the total power of the refrigeration system and obtain multiple groups of total power.

[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 of this embodiment is based on the outdoor dry-bulb temperature, the outdoor wet-bulb temperature, the mass conservation principle, the energy conservation principle under different operation modes, the number of refrigeration units turned on, n Corresponding cooling demand Q’ reqThe mathematical model of each device in the refrigeration unit is used to determine multiple sets of system operation parameter values ​​that meet the parameter constraints in the refrigeration system. Based on the mathematical model of each device in the refrigeration unit and the corresponding system operation parameter values, the power of each device is calculated, and the number of refrigeration units turned on is calculated. n Calculate the total power of the refrigeration system and obtain multiple groups of total power. Take the minimum total power as the goal and output the optimization vector corresponding to the minimum total power. P ( n , s , φ) optimizes the control of the refrigeration system, realizes the optimal control of the total energy consumption of the equipment according to the operating mode and number of operating units of the refrigeration system and combined with the operating parameters of each equipment, which is suitable for 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 select 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 for each assignment combination based on 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 turned on. n Corresponding cooling demand Q’ req and mathematical models of each device in the refrigeration unit, and calculating non-target operating parameter values ​​corresponding to 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 a system operating parameter value when the non-target operating parameter value meets the corresponding constraint condition, so as to obtain multiple sets of the system operating parameter value sets.

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

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

[0140] The step length determination module is used to determine the assignment step length of the target operating parameter based on the value range in the constraint conditions of the target operating parameter.

[0141] The module for determining a value combination is used to determine a value combination based on the value assignment step and to assign values ​​to the plurality of target operating parameters.

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

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

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

[0145] Optionally, the refrigeration demand calculation module 410 is specifically used to calculate the refrigeration capacity of a single refrigeration unit under the working state 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 refrigeration demand is obtained by adding up the refrigeration capacity of each refrigeration unit ; The number of refrigeration units turned on is n Calculate the cooling demand of a single cooling unit when Q’ req = Q req / n ,in, n < N .

[0146] Figure 5 An example of a physical structure diagram of an electronic device is shown in FIG. 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 through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the refrigeration system energy consumption optimization control method, which includes the following steps.

[0147] Total cooling demand based on refrigeration system Q req , calculate the number of refrigeration units turned on n When the cooling demand of a single cooling unit is Q’ req .

[0148] Based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, mass conservation principle, energy conservation principle under different operating modes, and number of refrigeration units turned on n Corresponding cooling demand Q’ req and the mathematical model of each device in the refrigeration unit, determine the multiple sets of system operating parameter values ​​that meet the parameter constraints in the refrigeration system, and the number of refrigeration units turned on n , Operation Mode s and multiple sets of system operation parameter value sets φ to form multiple optimization vectors P ( n , s , φ).

[0149] Based on the mathematical model of each device in the refrigeration unit and the corresponding system operating parameter values, the power of each device is calculated, and based on the number of refrigeration units turned on n Calculate the total power of the refrigeration system and obtain multiple groups of total power.

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

[0151] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0152] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program 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 refrigeration system energy consumption optimization control method provided by the above methods, which includes the following steps.

[0153] Total cooling demand based on refrigeration system Q req , calculate the number of refrigeration units turned on n When the cooling demand of a single cooling unit is Q’ req .

[0154] Based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, mass conservation principle, energy conservation principle under different operating modes, and number of refrigeration units turned on n Corresponding cooling demand Q’ req and the mathematical model of each device in the refrigeration unit, determine the multiple sets of system operating parameter values ​​that meet the parameter constraints in the refrigeration system, and the number of refrigeration units turned on n , Operation Mode s and multiple sets of system operation parameter value sets φ to form multiple optimization vectors P ( n , s , φ).

[0155] Based on the mathematical model of each device in the refrigeration unit and the corresponding system operating parameter values, the power of each device is calculated, and based on the number of refrigeration units turned on n Calculate the total power of the refrigeration system and obtain multiple groups of total power.

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

[0157] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the refrigeration system energy consumption optimization control method provided by the above methods is implemented, and the method includes the following steps.

[0158] Total cooling demand based on refrigeration system Q req , calculate the number of refrigeration units turned on n When the cooling demand of a single cooling unit is Q’ req .

[0159] Based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, mass conservation principle, energy conservation principle under different operating modes, and number of refrigeration units turned on n Corresponding cooling demand Q’ req and the mathematical model of each device in the refrigeration unit, determine the multiple sets of system operating parameter values ​​that meet the parameter constraints in the refrigeration system, and the number of refrigeration units turned on n , Operation Mode s and multiple sets of system operation parameter value sets φ to form multiple optimization vectors P ( n , s , φ).

[0160] Based on the mathematical model of each device in the refrigeration unit and the corresponding system operating parameter values, the power of each device is calculated, and based on the number of refrigeration units turned on n Calculate the total power of the refrigeration system and obtain multiple groups of total power.

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

[0162] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0163] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment 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, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing energy consumption of a refrigeration system, characterized in that: include: Total cooling demand based on refrigeration system Q req , calculate the number of refrigeration units turned on n When the cooling demand of a single cooling unit is Q’ req ; Based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, mass conservation principle, energy conservation principle under different operating modes, and number of refrigeration units turned on n The corresponding cooling demand Q’ req and the mathematical model of each device in the refrigeration unit, determine the multiple sets of system operating parameter values ​​that meet the parameter constraints in the refrigeration system, and the number of refrigeration units turned on n , Operation Mode s and multiple sets of system operation parameter value sets φ to form multiple optimization vectors P ( n , s , φ); Based on the mathematical model of each device in the refrigeration unit and the corresponding system operating parameter values, the power of each device is calculated, and based on the number of refrigeration units turned on n Calculate the total power of the refrigeration system and obtain multiple groups of total power; In any operating mode, the optimization vector corresponding to the minimum total power output is P ( n , s , φ) to optimize the control of the refrigeration system.

2. The refrigeration system energy consumption optimization control method according to claim 1, characterized in that: Based on outdoor dry-bulb temperature, outdoor wet-bulb temperature, mass conservation principle, energy conservation principle under different operating modes, and number of refrigeration units turned on n The corresponding cooling demand Q’ req and the mathematical models of each device in the refrigeration unit, and determine multiple sets of system operating parameter values ​​that meet the parameter constraints in the refrigeration system, including: Selecting a plurality of target operating parameters from the system operating parameter set, and assigning values ​​to the plurality of target operating parameters using different assignment combinations; For each assignment combination, based on the outdoor dry-bulb temperature, outdoor wet-bulb temperature, the principle of conservation of mass, the principle of conservation of energy under different operating modes, and the number of refrigeration units turned on, n Corresponding cooling demand Q’ req and mathematical models of each device in the refrigeration unit, and calculating non-target operating parameter values ​​corresponding to non-target operating parameters in the system operating parameter set; In the case where the non-target operating parameter value satisfies the corresponding constraint condition, the non-target operating parameter value and the corresponding target operating parameter value constitute the system operating parameter value to obtain a plurality of sets of the system operating parameter value.

3. The refrigeration system energy consumption optimization control method according to claim 2, characterized in that: Selecting multiple target operating parameters from the system operating parameter set and assigning values ​​to the multiple target operating parameters using different assignment combinations includes: Select different target operating parameters based on different operating modes; Determining the step length of the target operating parameter based on the value range in the constraint condition of the target operating parameter; Based on the assignment step, an assignment combination is determined to assign values ​​to the plurality of target operating parameters.

4. The refrigeration system energy consumption optimization control method according to claim 3, characterized in that: Based on the assignment step, determine the assignment combination and assign values ​​to the multiple target operating parameters, including: based on the assignment step of any target operating parameter, determine the number of assignments of any target operating parameter; based on the number of target operating parameters and the number of assignments of each target operating parameter, assign values ​​to the multiple target operating parameters in an enumerated manner.

5. The refrigeration system energy consumption optimization control method according to claim 2, characterized in that: When the non-target operating parameter value does not satisfy the corresponding constraint condition, setting the value of the total power to a predetermined abnormal value; Among them, in any operating mode, the optimization vector corresponding to the minimum total power output is P ( n , s , φ) optimizes the control of the refrigeration system, including: In any operation mode, the optimization vector corresponding to the predetermined abnormal value is deleted. P ( n , s , φ), in the remaining optimization vector P ( n , s , φ), the optimization vector corresponding to the minimum total output power is P ( n , s , φ) to optimize the control of the refrigeration system.

6. The refrigeration system energy consumption optimization control method according to any one of claims 1 to 5, characterized in that: Total cooling demand based on refrigeration system Q req , calculate the number of refrigeration units turned on n When the cooling demand of a single cooling unit is Q’ req ,include: Calculate the cooling capacity of a single refrigeration unit under the working condition 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; Add up the cooling capacity of each refrigeration unit to get the total cooling demand ; The number of refrigeration units turned on is n Calculate the cooling demand of a single cooling unit when Q’ req = Q req / n ,in, n < N .

7. A refrigeration system energy consumption optimization control device, characterized in that: include: Refrigeration demand calculation module for total refrigeration demand based on refrigeration system Q req , calculate the number of refrigeration units turned on n When the cooling demand of a single cooling unit is Q’ req ; Parameter value set determination module, 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 turned on n The corresponding cooling demand Q’ req and the mathematical model of each device in the refrigeration unit, determine the multiple sets of system operating parameter values ​​that meet the parameter constraints in the refrigeration system, and the number of refrigeration units turned on n , Operation Mode s and multiple sets of system operation parameter value sets φ to form multiple optimization vectors P ( n , s , φ); A 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 operation parameter value, and calculate the total power of the refrigeration system based on the number of refrigeration units turned on n, to obtain multiple groups 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 , φ) to optimize the control of the refrigeration system.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the refrigeration system energy consumption optimization control method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the refrigeration system energy consumption optimization control method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the refrigeration system energy consumption optimization control method according to any one of claims 1 to 6 is implemented.

Images