Energy-saving optimization method and system for hybrid refrigeration system of data center

Abstract

The invention provides an energy-saving optimization method and device for a hybrid refrigeration system of a data center, a storage medium and electronic equipment, and relates to the technical field of energy conservation and consumption reduction. According to the method, the starting number of the water side evaporative cooling units is determined based on the real-time cooling load requirement of the data center, and the operation modes of the water side evaporative cooling units in different time periods are determined based on the real-time environment wet bulb temperature; then combining the corresponding real-time cold storage capacity, and constructing an energy-saving optimization model by taking the lowest energy consumption of the hybrid refrigeration system of the data center as a target; and continuously updating the energy-saving optimization model by adopting a rolling horizon optimization RHC method, and solving and obtaining the optimal operation parameters of the system in the current time period until the whole scheduling cycle is traversed. And real-time optimization is carried out by utilizing the updated environment and system state parameters, so that the optimal operation parameters are determined, the system is dynamically adjusted, and efficient, stable and energy-saving operation under different working conditions can be ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of energy conservation and consumption reduction, and specifically relates to an energy-saving optimization method, device, storage medium and electronic device for a hybrid refrigeration system in a data center. Background Art

[0002] With the rapid development of information technology, as the core infrastructure for information processing and storage in modern society, the scale and quantity of data centers are constantly expanding. A large number of IT devices are installed in data centers, and a large amount of energy is consumed to maintain their normal operation. Among them, the energy consumption of the refrigeration system usually accounts for about 40% of the total energy consumption of the data center and is an important part of the operating energy consumption of the data center. Therefore, how to optimize the operation of the data center refrigeration system and improve the energy efficiency of the refrigeration system has become the key to energy conservation and consumption reduction in data centers.

[0003] The refrigeration system of a data center usually consists of multiple key components, including chillers, cooling towers, water pumps, etc. The chiller is the core equipment of the refrigeration system, and its energy consumption accounts for 50% to 70% of the total energy consumption of the entire refrigeration system. The operating efficiency of the chiller is affected by various factors, such as the partial load rate (PLR), cooling water temperature, etc. The energy consumption of auxiliary equipment such as cooling towers and water pumps cannot be ignored, and their energy consumption accounts for 20% to 30% of the total energy consumption of the refrigeration system. The energy consumption of the cooling tower is mainly related to the cooling water flow rate and the wet bulb temperature, while the energy consumption of the water pump is closely related to the water flow rate and the head.

[0004] In related technologies, some energy-saving optimizations of data center refrigeration systems focus on the application of new cooling technologies, such as high-efficiency heat dissipation solutions like liquid cooling, indirect evaporative cooling, and phase change material cooling. Although this method can significantly reduce the energy consumption of the data center refrigeration system, this energy-saving optimization method is only applicable to newly built data centers, and for existing data centers, they need to be rebuilt. On the one hand, it requires additional construction costs, and on the other hand, it may cause the data center to stop operating, affecting the normal operation of the data center.

[0005] Therefore, some energy-saving optimizations of data center refrigeration systems focus more on the optimization of the control strategy of the data center refrigeration system. A common optimization method is to improve the overall energy efficiency by adjusting the load distribution of the chillers. For example, the optimal chiller loading (OCL) method is used to minimize the total energy consumption by calculating the load distribution of multiple chillers. This method mainly focuses on the operating efficiency of the chillers but does not consider the synergistic effect of the water energy storage unit and the utilization of natural cold sources.

[0006] In addition, some studies have considered the collaborative optimization of water-cooled energy storage equipment and chillers to further reduce energy consumption. For example, by storing cold energy using water-cooled energy storage equipment when natural cold sources are abundant and releasing cold energy when natural cold sources are scarce, the operating energy consumption can be reduced. This method achieves better energy-saving effects by adjusting the charging and discharging strategies of water-cooled energy storage equipment and combining with the operation mode switching of chillers. However, most of these methods are based on static optimization models and lack dynamic prediction and real-time adjustment capabilities. Summary of the Invention

[0007] (1) Technical problems to be solved

[0008] Aiming at the deficiencies of the prior art, the present invention provides an energy-saving optimization method, device, storage medium and electronic device for a data center hybrid refrigeration system, which solves the technical problem of lacking dynamic prediction and real-time adjustment capabilities.

[0009] (2) Technical solutions

[0010] To achieve the above object, the present invention is realized through the following technical solutions:

[0011] An energy-saving optimization method for a data center hybrid refrigeration system, the data center hybrid refrigeration system includes a water-side evaporative cooling unit and a water-cooled energy storage unit; including:

[0012] During different time periods of the scheduling cycle, obtain the real-time cooling load demand of the data center and the real-time ambient wet-bulb temperature of the area where it is located; and obtain the initial stored cold energy of the water-cooled energy storage unit;

[0013] Based on the real-time cooling load demand, determine the number of enabled water-side evaporative cooling units during the scheduling cycle; and based on the real-time ambient wet-bulb temperature, determine the operation mode of the water-side evaporative cooling unit during different time periods;

[0014] Based on the number of enabled water-side evaporative cooling units and the operation mode of the water-side evaporative cooling unit during the current time period, combined with the corresponding real-time stored cold energy, with the goal of minimizing the energy consumption of the data center hybrid refrigeration system, construct an energy-saving optimization model;

[0015] Use the rolling horizon control (RHC) method to continuously update the energy-saving optimization model, and solve to obtain the optimal partial load ratio of the chiller host, chilled water flow rate, cooling water flow rate, water-cooled energy storage operation mode, and stored cold energy and released cold energy of the water-cooled energy storage during the current time period until the entire scheduling cycle is traversed.

[0016] Preferably, the data center hybrid refrigeration system consists of several cooling towers, plate heat exchangers, cooling water pumps, variable frequency pumps, chilled water pumps, chillers, and a cold storage tank. Among them, the water-side evaporative cooling unit is jointly composed of a chiller and a plate heat exchanger, and the water cold storage unit consists of a cold storage tank;

[0017] The operating modes of the water-side evaporative cooling unit include mechanical refrigeration, hybrid refrigeration, and natural refrigeration. Among them,

[0018] When the ambient wet bulb temperature is greater than the first preset temperature, the water-side evaporative cooling unit adopts mechanical refrigeration, and the chilled water return is cooled separately by the chiller;

[0019] When the ambient wet bulb temperature is between the second preset temperature and the first preset temperature, the water-side evaporative cooling unit adopts hybrid refrigeration. The chilled water return is first precooled by the plate heat exchanger and then cooled by the chiller;

[0020] When the ambient wet bulb temperature is less than the second preset temperature, the water-side evaporative cooling unit adopts natural refrigeration, and the chilled water return is cooled separately by the plate heat exchanger;

[0021] The water cold storage operating modes include cold storage, cold release, and cold standby. Among them,

[0022] When the cold supply in the system is greater than the cold demand, the water cold storage unit switches to the cold storage mode, and the chilled water cooled by the hybrid cooling system is supplied to both the cold storage tank and the computer room at the same time;

[0023] When the cold supply in the system is less than the cold demand, the water cold storage unit switches to the cold release mode, and the cooling water supplied to the computer room is jointly replenished by the hybrid cooling system and the cold storage tank;

[0024] When the cold supply in the system is equal to the cold demand, the water cold storage unit switches to the cold standby mode, and the cooling water supplied by the hybrid cooling system is directly supplied to the computer room without flowing through the cold storage tank.

[0025] Preferably, before constructing the energy-saving optimization model, construct the chiller energy consumption model:

[0026]

[0027] Among them, P chiller,t is the power consumption of the chiller at time t; Q chiller,t is the cooling capacity provided by the chiller at time t; COP t is the energy efficiency ratio of the chiller at time t; PLR t is the partial load ratio of the chiller host at time t; They are the return water temperature of the cooling water and the supply water temperature of the chilled water of the chiller in period t; a0, a1, a2, a3, a4, and a5 are all the first regression coefficients.

[0028] Preferably, before constructing the energy-saving optimization model, a variable-frequency pump energy consumption model is constructed:

[0029]

[0030] Among them, P pump,t is the power of the variable-frequency pump in period t; H P,t is the head of the variable-frequency pump in period t; η P,t is the efficiency of the variable-frequency pump in period t; G P,t is the flow rate of the variable-frequency pump in period t; w t is the speed ratio of the variable-frequency pump in period t, that is, the ratio between the current speed and the rated speed of the pump; η M,t and η VFD,t are the motor efficiency and the inverter efficiency in period t respectively, and their values are related to w t ; b1, b2, b3, c1, c2, and c3 are all variable-frequency pump performance constants.

[0031] Based on the variable-frequency pump energy consumption model, a cooling water pump energy consumption model is constructed:

[0032]

[0033] Among them, is the power of the cooling water pump in period t; is the head of the cooling water pump in period t; is the efficiency of the cooling water pump in period t; is the flow rate of the cooling water pump in period t; is the speed ratio of the cooling water pump in period t; are the motor efficiency and the inverter efficiency of the cooling water pump in period t respectively, and their values are related to ; Q cw,t is the heat transferred by the cooling water pump in period t; ρ cw is the density of the cooling water; c cw is the specific heat capacity of the cooling water; T cw_hot,t is the return water temperature of the cooling water in period t; T cw_cold,t is the supply water temperature of the cooling water in period t;

[0034] And based on the variable-frequency pump energy consumption model, a chilled water pump energy consumption model is constructed:

[0035]

[0036] Among them, is the power of the chilled water pump in period t; is the head of the chilled water pump during period t; is the efficiency of the chilled water pump during period t; is the flow rate of the chilled water pump during period t; is the speed ratio of the chilled water pump during period t; are respectively the motor efficiency and the frequency converter efficiency of the chilled water pump during period t, and their values are related to Q chw,t is the heat transferred by the chilled water pump during period t; ρ chw is the density of the chilled water; c chw is the specific heat capacity of the chilled water; T chw_hot,t is the return water temperature of the chilled water during period t; T chw_cold,t is the supply water temperature of the chilled water during period t.

[0037] Preferably, before constructing the energy-saving optimization model, a cooling tower energy consumption model is also constructed:

[0038]

[0039] wherein, P tower,t is the power of the cooling tower fan during period t; is the normalized value of the cooling water flow rate during period t; T wet,nor,t is the normalized value of the ambient wet bulb temperature during period t; d0, d1, d2, d3, d4, d5, d6, d7, d8, d9 are all second regression coefficients;

[0040] Construct a heat transfer model of the plate heat exchanger:

[0041]

[0042] wherein, is the heat transferred from the cooling water to the chilled water during period t; is the heat absorbed by the chilled water from the cooling water during period t; is the mass flow rate of the cooling water flowing through the heat exchanger during period t; is the mass flow rate of the chilled water flowing through the heat exchanger during period t; are respectively the return water and supply water temperatures of the cooling water of the plate heat exchanger during period t; are respectively the return water and supply water temperatures of the chilled water of the plate heat exchanger during period t;

[0043] And construct a cold storage and cold release model of the cold storage tank:

[0044]

[0045] wherein, Q cs,t is the cold storage capacity of the cold storage tank during period t; Q cr,t is the cold release amount of the cold storage tank at the current moment during period t; is the inlet water temperature of the cold storage tank during period t; is the outlet water temperature of the cold storage tank during period t; is the mass flow rate of the chilled water in the cold storage tank during period t.

[0046] Preferably, the energy-saving optimization model includes:

[0047] Objective function:

[0048]

[0049] where N is the number of enabled water-side evaporative cooling units;

[0050] Constraint conditions:

[0051] A. Cooling and heating load balance constraint:

[0052] Q demand,t = NQ chw,t -γ1Q cs,t +(1 - γ1)Q cr,t

[0053]

[0054] where γ1 represents a binary variable of the operation mode of the water-cooled energy storage unit. When it is 0, it means the ice storage unit is in the cold release state; when it is 1, it means the water-cooled energy storage unit is in the cold storage state;

[0055] γ2 represents a binary variable of the operation state of the plate heat exchanger. When it is 0, it means the plate heat exchanger is not operating and the water-side evaporative cooling unit is in the mechanical refrigeration mode; when it is 1, it means the heat exchanger is operating and the water-side evaporative cooling unit is in the hybrid refrigeration mode or the natural refrigeration mode;

[0056] B. Chiller operation constraint:

[0057] PLR min ≤ PLR t ≤ PLR max

[0058] where PLR min 、PLR max are the minimum and maximum partial load ratios of the main chiller unit respectively;

[0059] C. Variable-frequency water pump operation constraint:

[0060]

[0061] where, are the minimum and maximum water flow rates of the cooling water pump respectively; are the minimum and maximum water flow rates of the chilled water pump respectively;

[0062] D. Operating constraints of the water thermal energy storage unit:

[0063] When t = t0,

[0064] 0 ≤ Q cs,t ≤ Q cs,max -Q cs,start

[0065] 0 ≤ Q cr,t ≤ Q cs,start

[0066] When t0 < t ≤ t max time,

[0067]

[0068] wherein, t0 and t max are the start time period and the maximum time period of the scheduling cycle respectively, and Q cs,start is the initial chilled water storage capacity of the water thermal energy storage unit; Q cs,max is the maximum chilled water storage capacity of the water thermal energy storage unit.

[0069] Preferably, MATLAB is used to solve and obtain the optimal partial load ratio of the main unit of the chiller, the chilled water flow rate, the cooling water flow rate, the operating mode of the water thermal energy storage, and the chilled water storage capacity and chilled water release capacity during the current time period.

[0070] An energy-saving optimization device for a data center hybrid refrigeration system, the data center hybrid refrigeration system includes a water-side evaporative cooling unit and a water thermal energy storage unit; including:

[0071] A data acquisition module, configured to obtain the real-time cooling load demand of the data center and the real-time ambient wet bulb temperature of its location area during different time periods of the scheduling cycle; and obtain the initial chilled water storage capacity of the water thermal energy storage unit;

[0072] A parameter determination module, configured to determine the number of enabled water-side evaporative cooling units during the scheduling cycle based on the real-time cooling load demand; and determine the operating mode of the water-side evaporative cooling unit during different time periods based on the real-time ambient wet bulb temperature;

[0073] A model construction module, configured to construct an energy-saving optimization model with the lowest energy consumption of the data center hybrid refrigeration system as the goal based on the number of enabled water-side evaporative cooling units and the operating mode of the water-side evaporative cooling unit during the current time period, in combination with the corresponding real-time chilled water storage capacity;

[0074] The model solving module is used to continuously update the energy-saving optimization model by using the rolling horizon optimization RHC method, and solve to obtain the optimal partial load ratio of the main chiller, chilled water flow rate, cooling water flow rate, water-cooled energy storage operation mode, and chilled water storage and chilled water release amount within the current time period until the entire scheduling cycle is traversed.

[0075] A storage medium stores a computer program for energy-saving optimization of a data center hybrid refrigeration system, wherein the computer program causes a computer to execute the energy-saving optimization method of the data center hybrid refrigeration system as described above.

[0076] An electronic device includes:

[0077] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the programs include those for executing the energy-saving optimization method of the data center hybrid refrigeration system as described above.

[0078] (III) Beneficial effects

[0079] The present invention provides an energy-saving optimization method, device, storage medium, and electronic device for a data center hybrid refrigeration system. Compared with the prior art, it has the following beneficial effects:

[0080] In the present invention, the number of enabled water-side evaporative cooling units is determined based on the real-time cooling load demand of the data center, and the operation mode of the water-side evaporative cooling units in different time periods is determined based on the real-time ambient wet-bulb temperature; then, in combination with the corresponding real-time chilled water storage amount, an energy-saving optimization model is constructed with the lowest energy consumption of the data center hybrid refrigeration system as the goal; the rolling horizon optimization RHC method is used to continuously update the energy-saving optimization model, and the optimal operation parameters of the system within the current time period are solved until the entire scheduling cycle is traversed. Real-time optimization is carried out using the updated environmental and system state parameters to determine the optimal operation parameters and dynamically adjust the system to ensure efficient, stable, and energy-saving operation under different working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0082] Figure 1 It is a block diagram of an energy-saving optimization method for a data center hybrid refrigeration system provided by an embodiment of the present invention;

[0083] Figure 2 A flowchart of an energy-saving optimization method for a data center hybrid refrigeration system provided by an embodiment of the present invention;

[0084] Figure 3 A structural block diagram of an energy-saving optimization device for a data center hybrid refrigeration system provided by an embodiment of the present invention. Detailed implementation manners

[0085] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0086] By providing an energy-saving optimization method, device, storage medium, and electronic device for a data center hybrid refrigeration system, embodiments of the present application solve the technical problem of lacking dynamic prediction and real-time adjustment capabilities.

[0087] Embodiments of the present application aim to propose an energy-saving optimization method for a data center hybrid refrigeration system based on Receding-Horizon Control (RHC). The general idea is as follows:

[0088] First, determine the optimal number of enabled water-side evaporative cooling units based on the real-time cooling load demand of the data center, and determine the operating mode (mechanical refrigeration, hybrid refrigeration, natural refrigeration) of the evaporative cooling unit based on the current ambient wet-bulb temperature.

[0089] Second, obtain the real-time cooling load demand of the data center and the initial chilled water storage capacity of the chilled water storage unit. Taking the minimum energy consumption of the data center hybrid refrigeration system as the goal, perform energy-saving optimization on the data center refrigeration system to obtain the optimal partial load ratio of the main unit of the chiller, chilled water flow rate, cooling water flow rate, operating mode of the chilled water storage unit, and chilled water storage / discharge capacity of the chilled water storage unit at the current moment.

[0090] Finally, use the Receding-Horizon Control (RHC) method. Through the rolling optimization method, continuously update the prediction model to ensure that in each future time step, the system can meet the cooling load demand with the lowest energy consumption. Specifically, the Receding-Horizon Control (RHC) method will dynamically adjust the partial load ratio of the main unit of the chiller, chilled water flow rate, cooling water flow rate, operating mode of the chilled water storage unit, and its chilled water storage / discharge capacity according to the current system state and predicted future cooling load, so as to achieve efficient energy-saving control of the data center refrigeration system.

[0091] To better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0092] Example 1:

[0093] As Figure 1 shown, an energy-saving optimization method for a data center hybrid refrigeration system is provided in an embodiment of the present invention. The data center hybrid refrigeration system includes a water-side evaporative cooling unit and a water thermal energy storage unit, and includes:

[0094] S1. During different time periods of a scheduling cycle, obtain the real-time cooling load demand of the data center and the real-time ambient wet-bulb temperature in the area where it is located; and obtain the initial cooling storage capacity of the water thermal energy storage unit;

[0095] S2. Based on the real-time cooling load demand, determine the number of enabled water-side evaporative cooling units during the scheduling cycle; and based on the real-time ambient wet-bulb temperature, determine the operating modes of the water-side evaporative cooling units during different time periods;

[0096] S3. Based on the number of enabled water-side evaporative cooling units and the operating modes of the water-side evaporative cooling units during the current time period, in combination with the corresponding real-time cooling storage capacity, with the goal of minimizing the energy consumption of the data center hybrid refrigeration system, construct an energy-saving optimization model;

[0097] S4. Continuously update the energy-saving optimization model by using the rolling horizon control (RHC) method, and solve to obtain the optimal partial load ratio of the main unit of the chiller, chilled water flow rate, cooling water flow rate, water thermal energy storage operation mode, and cooling storage capacity and cooling release amount of the water thermal energy storage during the current time period until the entire scheduling cycle is traversed.

[0098] In the embodiment of the present invention, by optimizing the control strategy of the data center hybrid refrigeration system, the operating efficiency of the refrigeration system is improved and the energy consumption is reduced. Different from applying new cooling technologies, the proposed method is applicable to existing data centers without the need for large-scale equipment transformation, thus avoiding additional construction costs and downtime risks.

[0099] Moreover, the embodiment of the present invention performs real-time optimization by using updated environmental and system state parameters to determine the optimal operating parameters and dynamically adjust the system to ensure efficient, stable, and energy-saving operation under different working conditions.

[0100] It should be noted that the following provides an example of a feasible framework of a data center hybrid refrigeration system in the embodiment of the present invention:

[0101] The data center hybrid refrigeration system consists of several cooling towers, plate heat exchangers, cooling water pumps, variable frequency pumps, chilled water pumps, chillers, and a cold storage tank. Among them, the water-side evaporative cooling unit is jointly composed of a chiller and a plate heat exchanger, and the water cold storage unit consists of a cold storage tank.

[0102] At the same time, an exemplary operation strategy of the data center hybrid refrigeration system is given:

[0103] In the embodiment of the present invention, it is set that the water-side evaporative cooling unit is the main cold source in the data center hybrid refrigeration system. Before the optimization starts, based on the data center cooling load prediction data and the equipment parameters of the water-side evaporative cooling unit, the number of initial water-side evaporative cooling units to be started can be determined to ensure that the water-side evaporative cooling unit is in an efficient operation range. In addition, according to the ambient wet bulb temperature condition, the water-side evaporative cooling unit can flexibly switch among three modes: mechanical refrigeration, hybrid refrigeration, and natural refrigeration, so as to make the most of natural cold sources and reduce the power consumption of the refrigeration system. Among them:

[0104] A. Mechanical refrigeration mode of the water-side evaporative cooling unit

[0105] When the ambient wet bulb temperature is greater than the first preset temperature (for example, 20°C), the water-side evaporative cooling unit adopts mechanical refrigeration, and the chilled water return is cooled separately by the chiller. In this mode, the operating energy consumption of the water-side evaporative cooling unit is the highest.

[0106] B. Hybrid refrigeration mode of the water-side evaporative cooling unit

[0107] When the ambient wet bulb temperature is between the second preset temperature and the first preset temperature (for example, 14 - 20°C), the water-side evaporative cooling unit adopts hybrid refrigeration, and the chilled water return is precooled by the plate heat exchanger first and then cooled by the chiller. In this mode, the operating energy consumption of the water-side evaporative cooling unit is the second highest.

[0108] C. Natural refrigeration mode of the water-side evaporative cooling unit

[0109] When the ambient wet bulb temperature is less than the second preset temperature (for example, 14°C), the water-side evaporative cooling unit adopts natural refrigeration, and the chilled water return is cooled separately by the plate heat exchanger. In this mode, the operating energy consumption of the water-side evaporative cooling unit is the lowest.

[0110] Furthermore, the water cold storage unit can switch among three operating modes: cold storage, cold release, and cold standby according to the overall cooling demand of the system; among them:

[0111] A. When the cold supply in the system is greater than the cold demand, the water cold storage unit switches to the cold storage mode, and the chilled water cooled by the hybrid cooling system is supplied to both the cold storage tank and the computer room at the same time.

[0112] B. When the internal cold supply in the system is less than the cold demand, the water thermal energy storage unit switches to the cold release mode, and the cooling water supplied to the computer room is jointly replenished by the hybrid cooling system and the thermal energy storage tank.

[0113] C. When the internal cold supply in the system is equal to the cold demand, the water thermal energy storage unit switches to the cold standby mode, and the cooling water supplied by the hybrid cooling system is directly supplied to the computer room without flowing through the thermal energy storage tank.

[0114] As Figure 2 shown, Figure 2 a flowchart of an energy-saving optimization method for a data center hybrid cooling system is given.

[0115] Next, in combination with the relevant information of the data center hybrid cooling system and Figure 2 , each step of the above solution will be introduced in detail:

[0116] In step S1, within different time periods of the scheduling cycle, obtain the real-time cold load demand of the data center and the real-time ambient wet-bulb temperature of its location area; and obtain the initial cold storage capacity of the water thermal energy storage unit.

[0117] In step S2, based on the real-time cold load demand, determine the number of enabled water-side evaporative cooling units within the scheduling cycle; and based on the real-time ambient wet-bulb temperature, determine the operating modes of the water-side evaporative cooling units in different time periods.

[0118] In this step: The number of enabled water-side evaporative cooling units within the scheduling cycle can be determined by dividing the real-time cold load demand by the cooling capacity of a single water-side evaporative cooling unit; and based on the real-time ambient wet-bulb temperature, according to the switching principles of the three modes of mechanical refrigeration, hybrid refrigeration, and natural refrigeration introduced above, the operating modes of the water-side evaporative cooling units in different time periods are determined.

[0119] In step S3, based on the number of enabled water-side evaporative cooling units and the operating mode of the water-side evaporative cooling units in the current time period, combined with the corresponding real-time cold storage capacity, with the goal of minimizing the energy consumption of the data center hybrid cooling system, an energy-saving optimization model is constructed.

[0120] In this step, first, in combination with the data center hybrid cooling system framework, according to the physical characteristics of each refrigeration device, construct its corresponding energy consumption and heat transfer models, and then construct the energy-saving optimization model. Among them,

[0121] First, in the embodiments of the present invention, in combination with the data center hybrid cooling system framework, according to the physical characteristics of each refrigeration device, construct its corresponding energy consumption and heat transfer models as follows:

[0122] (1) Chiller energy consumption model:

[0123]

[0124] Among them, P chiller,t is the power consumption (kW) of the chiller during the t period; Q chiller,t is the cooling capacity (kW) provided by the chiller during the t period; COP t is the energy efficiency ratio of the chiller during the t period; PLR t is the part-load ratio of the main unit of the chiller during the t period; are respectively the return water temperature of the cooling water and the supply water temperature of the chilled water of the chiller during the t period (°C); a0, a1, a2, a3, a4, and a5 are all the first regression coefficients, which can be determined from the performance curve of the chiller main unit.

[0125] (2) Variable-frequency pump energy consumption model:

[0126]

[0127] Among them, P pump,t is the power of the variable-frequency pump (kW) during the t period; H P,t is the head (m) of the variable-frequency pump during the t period; η P,t is the efficiency (%) of the variable-frequency pump during the t period; G P,t is the flow rate (m3 / s) of the variable-frequency pump during the t period; w t is the speed ratio of the variable-frequency pump during the t period, that is, the ratio between the current speed and the rated speed of the pump; η M,t and η VFD,t are respectively the motor efficiency and the frequency converter efficiency (%) during the t period, and their values are related to w t ; b1, b2, b3, c1, c2, and c3 are all performance constants of the variable-frequency pump, which can be obtained by fitting sample data or measured data.

[0128] On this basis, combined with the specific heat capacity characteristics of different conveying media, the embodiments of the present invention continue to construct the energy consumption models of the cooling water pump and the chilled water pump.

[0129] (3) Cooling water pump energy consumption model:

[0130]

[0131] Among them, is the power of the cooling water pump (kW) during the t period; is the head (m) of the cooling water pump during the t period; is the efficiency (%) of the cooling water pump during the t period; is the flow rate of the cooling water pump during the t period (m 3 / s); is the speed ratio of the cooling water pump during the t period; They are the cooling water pump motor efficiency and the frequency converter efficiency (%) during period t, and their values are related to Q cw,t is the heat transferred by the cooling water pump during period t (kW); ρ cw is the cooling water density (kg / m 3 ); c cw is the specific heat capacity of the cooling water [kJ / (kg·°C)]; T cw_hot,t is the return water temperature of the cooling water during period t (°C); T cw_cold,t is the supply water temperature of the cooling water during period t (°C).

[0132] (4) Chilled water pump energy consumption model:

[0133]

[0134] Among them, is the power of the chilled water pump during period t (kW); is the head of the chilled water pump during period t (m); is the efficiency of the chilled water pump during period t (%); is the flow rate of the chilled water pump during period t (m 3 / s); is the speed ratio of the chilled water pump during period t; They are the chilled water pump motor efficiency and the frequency converter efficiency (%) during period t, and their values are related to Q chw,t is the heat transferred by the chilled water pump during period t (kW); ρ chw is the chilled water density (kg / m 3 ); c chw is the specific heat capacity of the chilled water [kJ / (kg·°C)]; T chw_hot,t is the return water temperature of the chilled water during period t (°C); T chw_cold,t is the supply water temperature of the chilled water during period t (°C).

[0135] Furthermore, before constructing the energy-saving optimization model, the embodiment of the present invention also constructs

[0136] (5) Cooling tower energy consumption model:

[0137]

[0138] Among them, P tower,t is the power of the cooling tower fan during period t (kW); is the normalized value of the cooling water flow rate during period t; T wet,nor,t is the normalized value of the ambient wet bulb temperature during period t; d0, d1, d2, d3, d4, d5, d6, d7, d8, d9 are regression coefficients, and their values can be obtained from samples or measured data and change with the change of the refrigeration mode.

[0139] (6) Plate heat exchanger heat transfer model:

[0140]

[0141] Among them, is the heat transferred from the cooling water to the chilled water in the t period; is the heat absorbed by the chilled water from the cooling water in the t period; is the mass flow rate of the cooling water flowing through the heat exchanger in the t period (kg / s); is the mass flow rate of the chilled water flowing through the heat exchanger in the t period (kg / s); are the return water and supply water temperatures of the cooling water of the plate heat exchanger in the t period (°C) respectively; are the return water and supply water temperatures of the chilled water of the plate heat exchanger in the t period (°C) respectively.

[0142] (7) Cold storage tank cold storage and cold release model:

[0143]

[0144] Among them, Q cs,t is the cold storage capacity of the cold storage tank in the t period; Q cr,t is the cold release amount of the cold storage tank at the current moment in the t period; is the inlet water temperature of the cold storage tank in the t period; is the outlet water temperature of the cold storage tank in the t period; is the mass flow rate of the chilled water in the cold storage tank in the t period.

[0145] On the second hand, considering the environmental conditions and the cold load demand comprehensively, and combining with the operation strategy of the data center hybrid refrigeration system introduced above, an energy-saving optimization model is constructed, specifically including:

[0146] Objective function:

[0147]

[0148] Among them, N is the number of enabled water-side evaporative cooling units.

[0149] Constraint conditions:

[0150] A. Cooling and heating load balance constraint:

[0151] Q demand,t = NQ chw,t -γ1Q cs,t +(1 - γ1)Q cr,t

[0152]

[0153] Among them, γ1 represents a binary variable of the operation mode of the water thermal energy storage unit. When it is 0, it means that the ice thermal energy storage unit is in the cold release state; when it is 1, it means that the water thermal energy storage unit is in the cold storage state.

[0154] The embodiment of the present invention considers the collaborative optimization of the water-side evaporative cooling unit and the water thermal energy storage unit. While determining the optimal operation plan of the water-side evaporative cooling unit, the energy-saving optimization model of the data center refrigeration system dynamically adjusts the cold storage / cold release mode and the cold storage / cold release capacity of the water thermal energy storage unit according to the surplus degree of the natural cold source, and specifically quantifies and expresses it through the above binary variable γ1.

[0155] γ2 represents a binary variable of the operation state of the plate heat exchanger. When it is 0, it means that the plate heat exchanger is not operating, and the water-side evaporative cooling unit is in the mechanical refrigeration mode; when it is 1, it means that the heat exchanger is operating, and the water-side evaporative cooling unit is in the hybrid refrigeration mode or the natural refrigeration mode.

[0156] The embodiment of the present invention also takes into account the full utilization of the natural cold source, and proposes a differential optimization model of the water-side evaporative cooling unit based on the ambient wet-bulb temperature under different ambient wet-bulb temperatures for the switching schemes of mechanical refrigeration, hybrid refrigeration and natural refrigeration, and specifically quantifies and expresses it through the above binary variable γ2.

[0157] B. Chiller operation constraints:

[0158] PLR min ≤PLR t ≤PLR max

[0159] Among them, PLR min 、PLR max are respectively the minimum and maximum values of the partial load ratio of the main chiller.

[0160] C. Variable-frequency water pump operation constraints:

[0161]

[0162] Among them, are respectively the minimum and maximum water flow rates of the cooling water pump; are respectively the minimum and maximum water flow rates of the chilled water pump.

[0163] D. Water thermal energy storage unit operation constraints:

[0164] When t = t0,

[0165] 0 ≤ Q cs,t ≤ Q cs,max -Q cs,start

[0166] 0 ≤ Qcr,t ≤Q cs,start

[0167] When t0 < t ≤ t max At this time

[0168]

[0169] wherein, t0 and t max are respectively the start time period and the maximum time period of the scheduling cycle, and Q cs,start is the initial cooling storage capacity of the water-cooled energy storage unit; Q cs,max is the maximum cooling storage capacity of the water-cooled energy storage unit.

[0170] In summary, the energy-saving optimization model of the data center hybrid refrigeration system established in the embodiments of the present invention takes the minimum energy consumption of the data center hybrid refrigeration system as the objective function, and at the same time considers constraint conditions such as cold load balance, chiller operation, cooling water pump operation, and water-cooled energy storage unit operation, so as to realize the efficient and energy-saving operation of the refrigeration system.

[0171] In step S4, the rolling horizon control (RHC) method is used to continuously update the energy-saving optimization model, and the optimal partial load ratio of the main chiller, chilled water flow rate, cooling water flow rate, water-cooled energy storage operation mode, and cooling storage capacity and cooling release amount of the water-cooled energy storage are obtained by solving until the entire scheduling cycle is traversed.

[0172] Rolling Horizon Control (RHC) is an optimization strategy widely used in dynamic systems. It determines the current input by optimizing a finite time window in the future at each moment, then executes this input and immediately updates the optimization problem for the next time step. The advantage of this method is that it considers the future behavior of the system, thus enabling better performance and robustness.

[0173] In this step, the rolling horizon control (RHC) method is adopted. In each time period, real-time optimization is carried out according to the updated environmental and system state parameters to determine the optimal operating parameters of the equipment and dynamically adjust them to ensure that the system can operate efficiently and energy-savingly under different working conditions. The specific steps are as Figure 2 shown:

[0174] In Figure 2 , exemplarily, let the time step be 1 h and an optimization period be 24 h. The optimization starts at t = 1 and 24 rounds of iteration are carried out within an optimization period. Whenever a round of optimization ends, the result of the first moment of this round of optimization is output for actual control. At 1 h after the start of the optimization, t = 2, and the optimization time domain becomes 23 h, and so on.

[0175] Moreover, in this step, based on the obtained data center environmental data, load demand, and equipment parameters, solvers such as the software MATLAB can also be used in this step to solve the above energy-saving optimization model. Finally, the optimal partial load ratio of the chiller host, chilled water flow rate, cooling water flow rate, water storage cooling operation mode, and water storage cooling capacity and cold release amount within the current time period are obtained. The relevant operations are repeatedly executed until the entire scheduling cycle is traversed.

[0176] Continuing with the above example, starting from t = 1, every 1 hour, solve to obtain the optimal partial load ratio PLR of the chiller host within the corresponding time period. t Chilled water flow rate Cooling water flow rate The variable γ1 related to the water storage cooling operation mode and the water storage cooling capacity Q cs,t Cold release amount Q cr,t until t = 24.

[0177] So far, the embodiment of the present invention has completed all the processes of the energy-saving optimization method for the data center hybrid refrigeration system.

[0178] Embodiment 2:

[0179] As Figure 3 shown, the embodiment of the present invention provides an energy-saving optimization device for a data center hybrid refrigeration system. The data center hybrid refrigeration system includes a water-side evaporative cooling unit and a water storage cooling unit, and includes:

[0180] A data acquisition module, configured to obtain the real-time cooling load demand of the data center and the real-time ambient wet-bulb temperature of its location area during different time periods of the scheduling cycle; and obtain the initial cooling storage amount of the water storage cooling unit.

[0181] A parameter determination module, configured to determine the number of enabled water-side evaporative cooling units within the scheduling cycle based on the real-time cooling load demand; and determine the operation mode of the water-side evaporative cooling unit during different time periods based on the real-time ambient wet-bulb temperature.

[0182] A model construction module, configured to construct an energy-saving optimization model with the lowest energy consumption of the data center hybrid refrigeration system as the goal, based on the number of enabled water-side evaporative cooling units and the operation mode of the water-side evaporative cooling unit within the current time period, in combination with the corresponding real-time cooling storage amount.

[0183] A model solution module, configured to continuously update the energy-saving optimization model using the rolling horizon control (RHC) method, and solve to obtain the optimal partial load ratio of the chiller host, chilled water flow rate, cooling water flow rate, water storage cooling operation mode, and water storage cooling capacity and cold release amount within the current time period until the entire scheduling cycle is traversed.

[0184] Example 3:

[0185] An embodiment of the present invention provides a storage medium storing a computer program for energy - saving optimization of a hybrid refrigeration system in a data center. Among them, the computer program enables a computer to execute the energy - saving optimization method for the hybrid refrigeration system in the data center as described in Example 1.

[0186] Example 4:

[0187] An embodiment of the present invention provides an electronic device, including:

[0188] One or more processors; a memory; and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the programs include a method for executing the energy - saving optimization of the hybrid refrigeration system in the data center as described in Example 1.

[0189] It can be understood that the energy - saving optimization device, storage medium, and electronic device for the hybrid refrigeration system in the data center provided by the embodiments of the present invention correspond to the energy - saving optimization method for the hybrid refrigeration system in the data center provided by the embodiments of the present invention. For the explanations, examples, beneficial effects, etc. of the relevant content, reference can be made to the corresponding parts in the energy - saving optimization method for the hybrid refrigeration system in the data center, and details are not repeated here.

[0190] In summary, compared with the prior art, the following beneficial effects are achieved:

[0191] 1. By optimizing the control strategy of the hybrid refrigeration system in the data center, the embodiment of the present invention improves the operating efficiency of the refrigeration system and reduces energy consumption. Different from applying new cooling technologies, the method of the present invention is applicable to existing data centers without the need for large - scale equipment transformation, thus avoiding additional construction costs and downtime risks.

[0192] 2. By using the receding - horizon control (RHC) method for optimization, the embodiment of the present invention enables the flexible switching of the water - side evaporative cooling unit between mechanical refrigeration, hybrid refrigeration, and natural refrigeration modes, and dynamically adjusts the operating mode of the water - cooled chiller according to real - time load demands and environmental conditions, so as to make full use of natural cold sources and reduce energy consumption.

[0193] 3. By using the receding - horizon control (RHC) method for optimization, the embodiment of the present invention can dynamically adjust the charging and discharging cold strategies of the water - cooled energy storage unit, realizing the coordinated optimization of the water - cooled energy storage unit and the water - cooled chiller. When natural cold sources are sufficient, the water - cooled energy storage device is used to store cold; when natural cold sources are scarce, cold is released, thereby further reducing the operating energy consumption.

[0194] 4. The rolling horizon optimization RHC method according to the embodiments of the present invention can adjust the operating parameters of the refrigeration equipment in advance based on real-time data and prediction models, avoiding system instability caused by load fluctuations or environmental changes.

[0195] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0196] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data center hybrid cooling system energy-saving optimization method, characterized in that: The data center hybrid cooling system includes a water-side evaporative cooling unit and a water cold storage unit; including: During different periods of the scheduling cycle, the real-time cooling load demand of the data center and the real-time ambient wet-bulb temperature of the area where the data center is located are obtained; and the initial cooling capacity of the water cooling unit is obtained; Based on the real-time cooling load demand, determining the number of water-side evaporative cooling units to be enabled within the scheduling period; and based on the real-time ambient wet-bulb temperature, determining the operation mode of the water-side evaporative cooling units within different time periods; Based on the number of activated water-side evaporative cooling units and the operation mode of the water-side evaporative cooling units in the current period, combined with the corresponding real-time cold storage capacity, an energy-saving optimization model is constructed with the goal of minimizing the energy consumption of the data center hybrid cooling system; The rolling time domain optimization RHC method is used to continuously update the energy-saving optimization model, and solve and obtain the optimal partial load rate of the chiller host, chilled water flow, cooling water flow, water storage operation mode, and water storage cold storage capacity and cold release capacity in the current period until the entire scheduling cycle is traversed.

2. The energy-saving optimization method for a data center hybrid cooling system according to claim 1, characterized in that: The data center hybrid refrigeration system is composed of several cooling towers, plate heat exchangers, cooling water pumps, variable frequency water pumps, chilled water pumps, chillers, and a cold storage tank, wherein the water-side evaporative cooling unit is composed of a chiller and a plate heat exchanger, and the water cold storage unit is composed of a cold storage tank; The operation modes of the water-side evaporative cooling unit include mechanical refrigeration, mixed refrigeration and natural refrigeration; wherein, When the ambient wet-bulb temperature is greater than the first preset temperature, the water-side evaporative cooling unit adopts mechanical refrigeration, and the chilled water return is cooled separately by the chiller; When the ambient wet-bulb temperature is between the second preset temperature and the first preset temperature, the water-side evaporative cooling unit adopts mixed refrigeration, and the chilled water return water is first precooled by the plate heat exchanger and then cooled by the chiller; When the ambient wet-bulb temperature is less than the second preset temperature, the water-side evaporative cooling unit adopts natural cooling, and the chilled water return water is cooled separately by the plate heat exchanger; The water storage operation mode includes cold storage, cold release and cold standby; wherein, When the cold supply in the system is greater than the cold demand, the water cold storage unit switches to cold storage mode, and the chilled water cooled by the mixed cooling system is supplied to the cold storage tank and the machine room at the same time; When the cold supply in the system is less than the cold demand, the water cold storage unit switches to the cold release mode, and the cooling water supplied to the machine room is supplied by the mixed cooling system and the cold storage tank; When the cold supply in the system is equal to the cold demand, the water cold storage unit switches to the cold standby mode, and the cooling water supplied by the mixed cooling system is directly supplied to the machine room without flowing through the cold storage tank.

3. The energy-saving optimization method for a data center hybrid cooling system according to claim 1, characterized in that: Before building the energy-saving optimization model, build the chiller energy consumption model: Among them, P chiller,t is the power consumption of the chiller during period t; Q chiller,t The cooling capacity provided by the chiller during period t; COP t PLR is the energy efficiency ratio of the chiller during period t; t is the partial load rate of the main unit of the chiller during period t; are the cooling water return temperature and chilled water supply temperature of the chiller during period t respectively; a0, a1, a2, a3, a4, and a5 are the first regression coefficients.

4. The energy-saving optimization method for a data center hybrid cooling system according to claim 3, characterized in that: Before building the energy-saving optimization model, build the variable frequency water pump energy consumption model: Among them, P pump,t H is the power of the variable frequency water pump during period t; P,t is the head of the variable frequency pump during period t; η P,t G is the efficiency of the variable frequency water pump during period t; P,t is the flow rate of the variable frequency water pump during period t; w t is the speed ratio of the variable frequency water pump during period t, that is, the ratio between the current speed of the water pump and the rated speed; η M,t , η VFD,t They are the motor efficiency and inverter efficiency in period t, and their values ​​are related to w t Related; b1, b2, b3, c1, c2, c3 are all performance constants of variable frequency water pumps; Based on the variable frequency water pump energy consumption model, a cooling water pump energy consumption model is constructed: in, is the cooling water pump power during period t; is the cooling water pump head during period t; is the efficiency of the cooling water pump during period t; is the flow rate of the cooling water pump during period t; is the cooling water pump speed ratio during period t; They are respectively the cooling water pump motor efficiency and the inverter efficiency in period t, and their values ​​are Related; Q cw,t is the heat transferred by the cooling water pump during period t; cw is the cooling water density; c cw is the specific heat capacity of cooling water; T cw_hot,t is the cooling water return temperature during period t; T cw_cold,t is the cooling water supply temperature during period t; And based on the variable frequency water pump energy consumption model, a chilled water pump energy consumption model is constructed: in, is the chilled water pump power during period t; is the chilled water pump head during period t; is the efficiency of the chilled water pump during period t; is the flow rate of the chilled water pump during period t; is the speed ratio of the chilled water pump during period t; are the chilled water pump motor efficiency and inverter efficiency in period t, and their values ​​are Related; Q chw,t is the heat transferred by the freezing water pump during period t; chw is the density of frozen water; c chw is the specific heat capacity of chilled water; T chw_hot,t is the chilled water return temperature during period t; T chw_cold,t is the chilled water supply temperature during period t.

5. The data center hybrid cooling system energy-saving optimization method according to claim 4, characterized in that: Before building the energy-saving optimization model, a cooling tower energy consumption model is also built: Among them, P tower,t is the cooling tower fan power during period t; is the normalized value of cooling water flow rate during period t; T wet,nor,t is the normalized value of the ambient wet-bulb temperature during period t; d0, d1, d2, d3, d4, d5, d6, d7, d8, and d9 are the second regression coefficients; Build a plate heat exchanger heat transfer model: in, is the heat transferred from cooling water to chilled water during period t; The heat absorbed by the chilled water from the cooling water during period t; is the mass flow rate of cooling water flowing through the heat exchanger during period t; is the mass flow rate of chilled water flowing through the heat exchanger during period t; They are the cooling water return and supply water temperatures of the plate heat exchanger during period t respectively; They are the return and supply temperatures of chilled water in the plate heat exchanger during period t; And build the cold storage and release model of the cold storage tank: Among them, Q cs,t Q is the cold storage capacity of the cold storage tank during period t; cr,t is the cooling capacity of the cold storage tank at the current moment in period t; is the water inlet temperature of the cold storage tank during period t; is the outlet water temperature of the cold storage tank during period t; is the mass flow rate of chilled water in the cold storage tank during period t.

6. The energy-saving optimization method for a data center hybrid cooling system according to claim 5, characterized in that: The energy-saving optimization model includes: Objective function: Wherein, N is the number of activated water-side evaporative cooling units; Constraints: A. Cold and hot load balance constraints: Q demand,t =NQ chw,t -γ1Q cs,t +(1-γ1)Q cr,t Among them, γ1 represents the zero-one variable of the operation mode of the water cooling unit. When it is 0, it means that the ice cooling unit is in the cold release state, and when it is 1, it means that the water cooling unit is in the cold storage state; γ2 represents a zero-one variable of the operating state of the plate heat exchanger. When it is 0, it means that the plate heat exchanger is not operating and the water-side evaporative cooling unit is in the mechanical cooling mode. When it is 1, it means that the heat exchanger is operating and the water-side evaporative cooling unit is in the mixed cooling mode or the natural cooling mode. B. Chiller operation constraints: PLR min ≤PLR t ≤PLR max Among them, PLR min ,PLR max They are the minimum and maximum values ​​of the partial load rate of the main unit of the chiller respectively; C. Frequency conversion pump operation constraints: in, are the minimum and maximum water flow rates of the cooling water pump respectively; are the minimum and maximum water flow rates of the chilled water pump respectively; D. Water storage unit operation constraints: When t = t0, 0≤Q cs,t ≤Q cs,max -Q cs,start 0≤Q cr,t ≤Q cs,start When t0 <t≤t max hour, Among them, t0, t max are the starting time and maximum time of the scheduling cycle, Q cs,start is the initial cooling capacity of the water cooling unit; Q cs,max It is the maximum cooling capacity of the water cooling unit.

7. The energy-saving optimization method for a data center hybrid cooling system according to claim 1, characterized in that: MATLAB is used to solve and obtain the optimal partial load rate of the chiller host, chilled water flow, cooling water flow, water storage operation mode, and water storage cold storage capacity and cold release capacity in the current period.

8. A data center hybrid refrigeration system energy-saving optimization device, characterized in that: The data center hybrid cooling system includes a water-side evaporative cooling unit and a water cold storage unit; including: A data acquisition module is used to obtain the real-time cooling load demand of the data center and the real-time ambient wet-bulb temperature of the area where the data center is located during different periods of the scheduling cycle; and to obtain the initial cooling capacity of the water cooling unit; A parameter determination module, configured to determine the number of water-side evaporative cooling units to be enabled within the scheduling period based on the real-time cooling load demand; and to determine the operation mode of the water-side evaporative cooling units within different time periods based on the real-time ambient wet-bulb temperature; A model building module, for building an energy-saving optimization model based on the number of activated water-side evaporative cooling units and the operation mode of the water-side evaporative cooling units in the current period, combined with the corresponding real-time cold storage capacity, with the goal of minimizing the energy consumption of the data center hybrid cooling system; The model solving module is used to continuously update the energy-saving optimization model using the rolling time domain optimization RHC method, and solve to obtain the optimal partial load rate of the chiller host, the chilled water flow rate, the cooling water flow rate, the water storage operation mode, and the water storage cold storage capacity and cold release capacity in the current period until the entire scheduling cycle is traversed.

9. A storage medium, characterized in that: It stores a computer program for energy-saving optimization of a hybrid cooling system in a data center, wherein the computer program enables a computer to execute the energy-saving optimization method for a hybrid cooling system in a data center as claimed in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: one or more processors; Memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, the programs including a method for executing the energy-saving optimization method for a data center hybrid cooling system as described in any one of claims 1 to 7.

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