CN117241561B - Data center distributed precise air conditioning unit optimized operation parameter estimation method - Google Patents
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Abstract
The invention discloses a data center distributed precise air conditioning unit optimizing operation parameter estimation method, which belongs to the technical field of air conditioning and energy source crossing, and comprises the following steps: determining an air supply temperature range of a precise air conditioning unit in a distributed precise air conditioning unit of which the operation parameters are optimized to be estimated, and selecting a plurality of different temperature values as candidate air supply temperatures in the temperature range; calculating server air supply parameters corresponding to each server at each candidate air supply temperature; calculating working condition parameters of each precise air conditioning unit at each candidate air supply temperature based on the server air supply parameters corresponding to each server; based on the working condition parameters, the energy consumption of the precise air conditioner unit under each candidate air supply temperature is calculated, the candidate air supply temperature with the lowest energy consumption is selected as the optimal air supply temperature, and the corresponding optimal air supply quantity is calculated. By adopting the technical scheme of the invention, the energy-saving operation of the air conditioning system with matched supply and demand can be realized on the premise of meeting the safe operation of the server.
Description
Technical Field
The invention relates to the technical field of air conditioning and energy intersection, in particular to a data center distributed precise air conditioning unit optimizing operation parameter estimation method suitable for safe and energy-saving regulation of a data center air conditioning system.
Background
With the development of modern informatization technology, the market of data centers (Internet Data Center, IDC) is continuously enlarged, the consumed energy is also continuously enlarged, the power consumption of the data centers of the modern large data society is 5% of the total power consumption of all industries of the society, and meanwhile, about 30% of the power consumption is generated by a cooling and heat exhausting system. The data center adopts energy consumption indexes (Power Usage Effectiveness, PUE) to measure the utilization efficiency of the electric energy of the IT equipment in the data machine room, and the closer the PUE value is to 1, the more energy-saving the data center is.
At present, one of the reasons for large energy consumption of a refrigeration system of a data center is that the operation regulation and control of a heat extraction system of the data center is mostly dependent on manual experience, so that the safe operation requirement of a server is met, the actual requirements of the server and a cabinet on the air supply quantity and the air supply temperature are not considered from the energy saving perspective, the problem of serious excessive cooling exists, the air supply temperature regulation and control and the air supply quantity optimal distribution are not carried out according to the end requirements and the difference of the requirements among different ends, the natural cold source is not effectively utilized, and the energy efficiency of an active cold source is reduced.
Disclosure of Invention
The invention provides a data center distributed precise air conditioning unit optimizing operation parameter estimation method, which aims to solve the technical problems that in order to meet the safe operation requirement of a server by means of the operation parameter regulation scheme of the conventional precise air conditioning unit relying on manual experience, the actual requirements of the server and a cabinet on the air supply quantity and the air supply temperature are not considered from the energy saving perspective, the serious excessive cooling problem exists, the air supply temperature regulation and the air supply quantity optimization distribution are not carried out according to the end requirements and the difference of the requirements among different ends, the natural cooling source is not effectively utilized, and the energy efficiency of an active cooling source is reduced.
In order to solve the technical problems, the invention provides the following technical scheme:
in one aspect, the invention provides a data center distributed precise air conditioning unit optimizing operation parameter estimation method, which comprises the following steps:
determining an air supply temperature range of a precise air conditioning unit in a distributed precise air conditioning unit with optimized operation parameters to be estimated, and selecting a plurality of different temperature values as candidate air supply temperatures in the air supply temperature range;
calculating server air supply parameters corresponding to each server at each candidate air supply temperature;
calculating working condition parameters of each precise air conditioning unit at each candidate air supply temperature based on the server air supply parameters corresponding to each server at each candidate air supply temperature; calculating the energy consumption of the precise air conditioning unit at each candidate air supply temperature based on the working condition parameters of the precise air conditioning unit;
and selecting the candidate air supply temperature with the lowest energy consumption as the optimal air supply temperature, and calculating the corresponding optimal air supply quantity to realize the optimal operation parameter estimation of the data center distributed precise air conditioning unit.
Further, the server air supply parameters include the air supply quantity and the return air temperature of the server.
Further, the return air temperature of the server is calculated by the following formula:
；
wherein,indicating the return air temperature of the ith server; />The temperature of the chip of the ith server to be maintained is represented, and the value range is 60-80 ℃; />The heating value of the i-th server is represented; />Representing the thermal resistance of the ith server; />The air supply temperature of the precision air conditioning unit is shown.
Further, the air supply amount of the server is calculated by the following formula:
；
wherein,representing the air supply quantity of the ith server; />The heating value of the i-th server is represented; />The temperature of the chip of the ith server to be maintained is represented, and the value range is 60-80 ℃;representing the ith serviceThe heating value of the device; />Representing the thermal resistance of the ith server; />The air supply temperature of the precise air conditioning unit is shown; />Represents the specific heat capacity of air under constant pressure.
Further, the operating parameters of the precision air conditioning unit include: the return air temperature of the precise air conditioning unit and the air supply quantity of the precise air conditioning unit.
Further, the return air temperature of the precision air conditioning unit is calculated by the following formula:
；
wherein,representing the return air temperature of the precision air conditioning unit; />Representing the number of servers within the control area;indicating the return air temperature of the ith server; />The air supply amount of the i-th server is shown.
Further, the air supply amount of the precision air conditioning unit is calculated by the following formula:
；
wherein,representing a precision air conditioning unitIs set in the air supply amount; />Representing the number of servers within the control area; />Representing the air supply quantity of the ith server; />Indicating the number of precision air conditioning units within the control area.
Further, the energy consumption of the precision air conditioning unit is calculated by the following formula:
；
wherein,represents the energy consumption of the precision air conditioning unit; />Representing compressor energy consumption; />Representing the energy consumption of the wind turbine; />And->The calculation formulas of (a) are respectively as follows:
；
；
wherein,indicating the wet bulb temperature of the outdoor ambient airA degree; />The air supply temperature of the precise air conditioning unit is shown;representing the return air temperature of the precision air conditioning unit; />The air supply amount of the precise air conditioning unit is shown; />The heat exchange temperature difference of the natural cold source is represented; />Indicating evaporator end difference; />Indicating condenser end difference; />The total pressure from the inlet to the outlet of the fan; />Representing a compressor energy consumption prediction model; />Represents a wind turbine energy consumption prediction model.
Further, the optimal air supply amount at the optimal air supply temperature is not higher than the rated air supply amount of the precise air conditioning unit, and the server chip temperature can be maintained in a preset range at the optimal air supply temperature and the optimal air supply amount.
In yet another aspect, the present invention also provides an electronic device including a processor and a memory; wherein the memory stores at least one instruction that is loaded and executed by the processor to implement the above-described method.
In yet another aspect, the present invention also provides a computer readable storage medium having at least one instruction stored therein, the instruction being loaded and executed by a processor to implement the above method.
The technical scheme provided by the invention has the beneficial effects that at least:
the invention provides an air conditioning system optimizing operation parameter estimation method meeting the requirements of safe and energy-saving operation aiming at a data center air conditioning system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram of a data center heat removal system;
FIG. 2 is a schematic diagram of a server heat rejection process;
fig. 3 is a schematic diagram of execution logic of a method for estimating optimized operation parameters of a distributed precise air conditioning unit in a data center according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings.
First embodiment
Aiming at a data center air conditioning system, the embodiment provides an optimized operation parameter estimation method of a data center distributed precise air conditioning unit capable of meeting safe and energy-saving operation, namely, based on server thermal resistance, on the premise that the server thermal resistance is constant, the heating value of a server, the safe operation temperature of a chip and the operation energy consumption of a cold source system are considered, and the air supply parameters, including the air supply temperature and the air quantity, required by the precise air conditioner are dynamically estimated. The method is suitable for an air conditioning system of a data center, and is used for providing D (room-level cooling when D=1 is adopted, otherwise, line-level cooling is adopted, the room-level cooling and line-level cooling processes are shown in fig. 1) independent control areas, each control area is provided with a plurality of cabinets, each cabinet is provided with a plurality of servers, and a plurality of precision air conditioning units (CRAC) are adopted for cooling the control areas. The method is used for dynamically estimating the air supply parameters in the control area under the condition that the energy-saving safety target is met aiming at a certain control area.
The method may be implemented by an electronic device. The execution flow of the method comprises the following steps:
s1, determining an air supply temperature range of a precise air conditioning unit in a distributed precise air conditioning unit with optimized operation parameters to be estimated, and selecting a plurality of different temperature values as candidate air supply temperatures in the air supply temperature range;
s2, calculating server air supply parameters corresponding to the servers at each candidate air supply temperature;
the heat removal process of the server is shown in fig. 2. Assuming that there is a control areaThe ith server extracts the temperature from the cold aisle as +.>The cold air exchanges heat with the chip in the air, and the heat taken away from the chip is +.>The temperature is increased to +.>Sending into a thermal channel to maintain the chip temperature at +.>. Assume that the heat transfer resistance of a single server chip is +.>The heat transfer process can be represented by formula (1):
（1）
at this time, the air volume for cooling the server isConsidering the conservation of energy on the cold air side, the air side heat exchange amount can be calculated using equation (2):
（2）
the server air supply parameter calculation module is adopted to calculate the air supply parameters of the server according to the steps (1) - (2)The air supply and return temperatures under the conditions are calculated as shown in the formulas (3) - (4):
（3）
（4）
wherein,the return air temperature of the ith server is expressed in degrees celsius; />The temperature of the chip of the ith server to be maintained is expressed in degrees celsius; />The heating value of the ith server is represented by kW;representing the thermal resistance of the i-th server,the unit is kW/. Degree.C; />The supply air temperature of the precise air conditioning unit is expressed in the unit of DEG C; />The air supply quantity of the ith server is expressed in kg/s; />Represents the specific heat capacity of air under constant pressure, and the unit is kJ/(kg) o C）。
As can be seen from the formulas (3) and (4), if calculation is to be performedAnd->Need to know +.>This value can be obtained by testing, and the results of the existing tests show that +.>Is stable and can be processed according to a fixed value.It is generally necessary to control the temperature between 0℃and 70℃and not more than 90℃at the maximum, as specified. In this regard, values can be obtained in the range of 60℃to 80℃as desired.
S3, calculating working condition parameters of each precise air conditioning unit at each candidate air supply temperature based on the server air supply parameters corresponding to each server at each candidate air supply temperature; calculating the energy consumption of the precise air conditioning unit at each candidate air supply temperature based on the working condition parameters of the precise air conditioning unit;
specifically, in this embodiment, the CRAC energy consumption prediction module calculates the energy consumption of CRAC at each candidate air supply temperature, and the specific implementation process is as follows:
assuming M CARC's in the control area, at each serverOn the premise of meeting the requirements, the return air temperature and the air supply quantity of each server can be calculated, and then the operation condition parameters of each CRAC, namely the return air temperature and the air supply quantity, can be calculated by adopting formulas (5) - (6):
（5）
（6）
wherein,the return air temperature of the precise air conditioning unit is expressed in DEG C; />Representing the number of servers within the control area; />The return air temperature of the ith server is expressed in degrees celsius; />The air supply quantity of the ith server is expressed in kg/s; />The air supply amount of the precise air conditioning unit is expressed in kg/s; />Indicating the number of precision air conditioning units within the control area.
The energy consumption of the compressor can be known according to the energy consumption mechanism formula of the compressorWith outdoor ambient air conditions, CRAC operating parameters, and CRAC thermal characteristicsThe sexual parameters are related. Wherein, outdoor ambient air state parameters include: outdoor ambient air wet bulb temperature +.>Can be obtained by sensor test; CRAC operating parameters include: supply air amount of CRAC->Supply air temperature of CRAC->Return air temperature of CRAC->Can be calculated from formulas (1) - (6); the CRAC thermal characterization parameters include: natural cold source heat exchange temperature difference->Evaporator end difference->And condenser end difference->Can be obtained by sensor test. In the running process of the air conditioning system of the data machine room, a training data set can be constructed by testing and recording running data of the parameters and compressor energy consumption data corresponding to the running data in real time, and a preset neural network model can be trained by using the constructed training data set to obtain a compressor energy consumption prediction model, so that the energy consumption of the compressor can be predicted in a data driving mode. Wherein, the thermal characteristic parameters of the CRAC can also comprise thermodynamic perfection +.>Fan efficiency->And the total pressure from the inlet to the outlet of the fan +.>And the like.
Similarly, the energy consumption of the fan can be known according to the energy consumption mechanism formula of the fanAir supply quantity with CRAC fanAnd a pressure difference from the inlet to the outlet of the fan +.>Related to the following. In the CRAC operation process, operation data of the parameters and fan energy consumption data corresponding to the parameters can be tested and recorded in real time to construct a training data set, and a preset neural network model is trained by utilizing the constructed training data set to obtain a fan energy consumption prediction model, so that the fan energy consumption is predicted in a data-driven mode.
Thus, the functional relation equation between the compressor energy and the fan energy and the respective key parameters is obtained in the embodiment. In the later operation process, as more and more operation data are obtained, the two function relation equations can be corrected continuously, so that self-adaption is realized.
The relation formula of the prediction model of the energy consumption of the energy and the energy consumption of the fan is shown in formulas (7) - (8):
（7）
（8）
wherein,indicating the outdoor ambient air wet bulb temperature; />The air supply temperature of the precise air conditioning unit is shown;the heat exchange temperature difference of the natural cold source is represented; />Indicating evaporator end difference; />Indicating condenser end difference;the air supply amount of the precise air conditioning unit is shown; />Representing the pressure difference from the inlet to the outlet of the fan; />The compressor energy consumption prediction model is used for predicting the compressor energy consumption according to the outdoor ambient air wet bulb temperature, the air supply temperature of the precise air conditioning unit, the return air temperature of the precise air conditioning unit, the air supply quantity of the precise air conditioning unit, the natural cold source heat exchange temperature difference, the evaporator end difference and the condenser end difference; />The air blower energy consumption prediction model is used for predicting air blower energy consumption according to the air supply quantity of the precise air conditioning unit and the pressure difference between the inlet and the outlet of the air blower.
Based on the above, the energy consumption of the CRACThe method comprises the following steps:
（9）
and S4, selecting the candidate air supply temperature with the lowest energy consumption as the optimal air supply temperature, and calculating the corresponding optimal air supply quantity to realize the optimal operation parameter estimation of the data center distributed precise air conditioning unit.
Wherein, in the process of passing through the meterAfter calculation, the method can optimize the air supply parameter calculation module toThe lowest target is to determine the optimal supply air temperature. Wherein the air supply temperature range is [ t ] as,min , t as,max ]Wherein t is as,min Indicating the minimum value of the set air supply temperature, t as,max A maximum value indicating a set air supply temperature; the K supply air temperatures are selected in this range, namely: t is t as,1 , t as,2 , …, t as,K Wherein t is as,i I=1, 2, …, K, which represents the i-th temperature value selected from the supply air temperature range; calculating the corresponding +.>The output air supply parameters and energy consumption are K +.>Matrix of 4:
wherein,the air supply amount of the precision air conditioning unit at the ith temperature value; />The return air temperature of the precision air conditioning unit at the ith temperature value; />CRAC energy consumption at the ith temperature value;
selecting the minimum energy consumptionThe corresponding air supply temperature is the optimal air supply temperature>The method comprises the steps of carrying out a first treatment on the surface of the Thereby obtaining the corresponding optimal air supply quantity +.>The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the optimal air supply quantity at the optimal air supply temperatureCannot be higher than rated air quantity of CRAC>And under the optimal air supply temperature and the corresponding optimal air supply quantity, the chip temperature of the server can be ensured to be maintained in a safe range.
The logical relationship of the above three modules is shown in fig. 3.
In summary, the embodiment provides a method for estimating the optimized operation parameters of a distributed air conditioning system according to the thermal resistance of a data center server, which can meet the energy-saving operation parameter estimation of a precise air conditioning system for safely operating a plurality of servers with different heating values and thermal resistances, namely, the total air quantity and the average return air temperature required by all the servers calculated under the same air supply temperature condition, then calculate the energy consumption of the precise air conditioning system, select the air supply temperature and the air supply quantity with the lowest energy consumption as the optimal operation parameters, and realize the lowest energy consumption of the air conditioning system under the supply and demand matching condition.
Second embodiment
The embodiment provides an electronic device, which comprises a processor and a memory; wherein the memory stores at least one instruction that is loaded and executed by the processor to implement the method of the first embodiment.
The electronic device may vary considerably in configuration or performance and may include one or more processors (central processing units, CPU) and one or more memories having at least one instruction stored therein that is loaded by the processors and performs the methods described above.
Third embodiment
The present embodiment provides a computer-readable storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement the method of the first embodiment described above. The computer readable storage medium may be, among other things, ROM, random access memory, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. The instructions stored therein may be loaded by a processor in the terminal and perform the methods described above.
Furthermore, it should be noted that the present invention can be provided as a method, an apparatus, or a computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the invention may take the form of a computer program product on one or more computer-usable storage media having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
It is finally pointed out that the above description of the preferred embodiments of the invention, it being understood that although preferred embodiments of the invention have been described, it will be obvious to those skilled in the art that, once the basic inventive concepts of the invention are known, several modifications and adaptations can be made without departing from the principles of the invention, and these modifications and adaptations are intended to be within the scope of the invention. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Claims (1)
1. The data center distributed precise air conditioning unit optimizing operation parameter estimation method is characterized by comprising the following steps of:
determining an air supply temperature range of a precise air conditioning unit in a distributed precise air conditioning unit with optimized operation parameters to be estimated, and selecting a plurality of different temperature values as candidate air supply temperatures in the air supply temperature range;
calculating server air supply parameters corresponding to each server at each candidate air supply temperature;
calculating working condition parameters of each precise air conditioning unit at each candidate air supply temperature based on the server air supply parameters corresponding to each server at each candidate air supply temperature; calculating the energy consumption of the precise air conditioning unit at each candidate air supply temperature based on the working condition parameters of the precise air conditioning unit;
selecting the candidate air supply temperature with the lowest energy consumption as the optimal air supply temperature, and calculating the corresponding optimal air supply quantity to realize the optimal operation parameter estimation of the data center distributed precise air conditioning unit;
the air supply parameters of the server comprise the air supply quantity and the return air temperature of the server;
the return air temperature of the server is calculated by the following formula:
；
wherein,represent the firstiThe return air temperature of the individual servers; />Represent the firstiThe temperature of the chip of each server needs to be maintained, and the value range is 70 ℃; />Represent the firstiThe heating value of the individual servers; />Represent the firstiThermal resistance of the individual servers; />The air supply temperature of the precise air conditioning unit is shown;
the air supply amount of the server is calculated by the following formula:
；
wherein,represent the firstiThe air supply quantity of each server; />Represent the firstiThe heating value of the individual servers;represent the firstiThe temperature of the chip of each server needs to be maintained, and the value range is 70 ℃; />Represent the firstiThermal resistance of the individual servers; />The air supply temperature of the precise air conditioning unit is shown; />Representing the specific heat capacity of air under constant pressure;
the working condition parameters of the precise air conditioning unit comprise: the return air temperature of the precise air conditioning unit and the air supply quantity of the precise air conditioning unit;
the return air temperature of the precision air conditioning unit is calculated by the following formula:
；
wherein,representing the return air temperature of the precision air conditioning unit; />Representing the number of servers within the control area; />Represent the firstiThe return air temperature of the individual servers; />Represent the firstiThe air supply quantity of each server;
the air supply amount of the precise air conditioning unit is calculated by the following formula:
；
wherein,the air supply amount of the precise air conditioning unit is shown; />Representing the number of servers within the control area;represent the firstiThe air supply quantity of each server; />Representing the number of precision air conditioning units in the control area;
the energy consumption of the precision air conditioning unit is calculated by the following formula:
；
wherein,represents the energy consumption of the precision air conditioning unit; />Representing compressor energy consumption; />Representing the energy consumption of the wind turbine;and->The calculation formulas of (a) are respectively as follows:
；
；
wherein,indicating the outdoor ambient air wet bulb temperature; />The air supply temperature of the precise air conditioning unit is shown; />Representing the return air temperature of the precision air conditioning unit; />The air supply amount of the precise air conditioning unit is shown; />The heat exchange temperature difference of the natural cold source is represented; />Indicating evaporator end difference; />Indicating condenser end difference; />The total pressure from the inlet to the outlet of the fan; />Representing a compressor energy consumption prediction model; />Represents a wind turbine energy consumption prediction model.
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| CN112539529A (en) * | 2020-11-27 | 2021-03-23 | 珠海格力电器股份有限公司 | Control method and control device of air conditioning system and machine room air conditioning system |
| WO2023010556A1 (en) * | 2021-08-06 | 2023-02-09 | 西门子瑞士有限公司 | Dynamic prediction control method, apparatus and system for precision air conditioner |
| CN116261300A (en) * | 2023-01-03 | 2023-06-13 | 中国电力科学研究院有限公司 | Combined optimization method and device for refrigerating equipment and airflow organization of data center |
| CN117062419A (en) * | 2023-10-11 | 2023-11-14 | 北京科技大学 | Multi-terminal supply-demand matched data center cold source system parameter optimization method and device |
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