Computer room energy efficiency optimization simulation system and method based on digital twinning

Abstract

The invention discloses a machine room energy efficiency optimization simulation system and method based on digital twinning, and relates to the technical field of machine room energy efficiency optimization. The system comprises a model construction and load analysis module, an equipment combination design module, an energy efficiency simulation calculation module and an optimal scheme adaptation and output module. According to the method, related parameters of a machine room are collected to construct a digital twinborn simulation model, different levels of load intervals are divided, a differentiated combination scheme composed of a high-voltage fixed-frequency large cold source unit and a low-voltage variable-frequency small cold source unit is designed for each interval, an energy efficiency optimal scheme is matched through simulation, a correlation database is established, and a result is output. Dynamic adaptation of the cold source equipment and the load interval is achieved, low-load energy efficiency attenuation is avoided, the full-working-condition energy efficiency of a machine room is remarkably improved, and reliable support is provided for optimal configuration of a cold source system.

Description

Technical Field

[0001] This invention relates to the field of data center energy efficiency optimization technology, specifically to a data center energy efficiency optimization simulation system and method based on digital twins. Background Technology

[0002] Currently, in the fields of energy efficiency optimization simulation and cold source equipment configuration for refrigeration rooms, a static design mode based on peak load is commonly used. This mode determines the installed capacity and combination of equipment solely based on the peak value of the room's cooling load, ignoring the actual operating conditions where the room spends most of the year in the low-to-medium load range. This leads to a significant decrease in equipment efficiency under partial load conditions, becoming a key issue restricting the overall energy efficiency improvement of the room. Existing equipment combination simulation technology can only verify the energy efficiency performance of a single capacity combination, failing to design differentiated equipment combination schemes for different load ranges, and neglecting the collaborative operation strategy of fixed-frequency and variable-frequency units. Consequently, it cannot adapt to the dynamic changes in the room's load.

[0003] Meanwhile, traditional data center load simulation uses a general unit area cooling load index, failing to incorporate detailed analysis of actual operating conditions, such as variations in business loads and hourly changes in personnel. This results in significant deviations in load simulation results, directly impacting the accuracy of equipment combination simulation and hindering precise optimization of cooling source equipment configuration. Furthermore, existing technologies lack the means to deeply integrate load range characteristics with equipment combination energy efficiency, making dynamic matching of cooling source equipment to different load ranges impossible. Additionally, a reusable data system linking load ranges, equipment combinations, and operational energy efficiency has not been established, leading to redundant investment in similar data center designs, low optimization efficiency, and an inability to meet the actual needs of efficient and stable operation of refrigeration data centers. Summary of the Invention

[0004] The purpose of this invention is to provide a data center energy efficiency optimization simulation system and method based on digital twins to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A simulation method for optimizing data center energy efficiency based on digital twins, characterized by the following steps: S1. Construct a digital twin simulation model of the data center, collect the building thermal parameters, cold source equipment basic parameters, data center operating condition parameters, and load impact correlation parameters; based on the digital twin simulation model, conduct hourly load simulation calculations of the data center throughout the year, analyze the interval distribution characteristics of the hourly load of the data center throughout the year, and divide the data center into different load intervals. S2. Based on the different load ranges of the divided computer room, a suitable combination scheme of cooling source equipment is designed for each load range; the combination scheme of cooling source equipment is based on high-voltage fixed-frequency large cooling source unit and low-voltage variable-frequency small cooling source unit as the basic combination unit, and the capacity ratio and number of operating units of the cooling source equipment combination scheme corresponding to different load ranges are different; S3. Import each cooling source equipment combination scheme into the computer room digital twin simulation model. Under the corresponding load range conditions, simulate the actual operating energy efficiency of each cooling source equipment combination scheme in the computer room and obtain the energy efficiency matching result between each load range and the corresponding cooling source equipment combination scheme. The energy efficiency matching result includes the computer room operating energy efficiency value of the corresponding cooling source equipment combination scheme under each load range. S4. Based on the energy efficiency matching results, select the optimal cooling source equipment combination scheme for the computer room operation energy efficiency value under each load level range, so as to realize the dynamic adaptation of the cooling source equipment combination with the computer room load levels range; establish and store the association database of load range, cooling source equipment combination scheme, and computer room operation energy efficiency value, and output the computer room energy efficiency optimization simulation results based on the association database. The simulation results include the optimal cooling source equipment combination scheme corresponding to each load level range and the computer room energy efficiency parameters under the scheme.

[0006] Furthermore, S1 includes the following: The thermal parameters of the computer room building are the thermal characteristic parameters of the building envelope, the basic parameters of the cold source equipment are the rated operating characteristic parameters of the unit, the operating condition parameters of the computer room are the indoor and outdoor environment and system operating parameters of the computer room, and the load impact correlation parameters are the on-site operating condition parameters that affect the load fluctuation of the computer room. Using existing digital twin simulation platforms, and combining the collected parameters, a simulation model is constructed that includes a thermal model of the computer room building, a performance model of the cooling source equipment, and a load influence factor model. Each sub-model achieves parameter association and real-time data transmission through the platform's built-in interface specifications, thus completing joint simulation calculations. The hourly load calculation method is adopted. By integrating various cooling load components and deducting heat recovery, the hourly cooling load of the computer room throughout the year is obtained. The hourly cooling load data is statistically analyzed to extract the frequency distribution, duration distribution and fluctuation characteristics of the load value. Multiple load intervals are divided according to the magnitude of the cooling load value. The load range of each load interval does not overlap and completely covers all hourly cooling loads throughout the year.

[0007] Furthermore, S2 includes the following: Extract the core load parameters for each load level range, including the peak cooling load, average cooling load, and load duration of that range; combine the rated cooling capacity of a single high-voltage fixed-frequency large-source chiller unit and a low-voltage variable-frequency small-source chiller unit, and determine the total cooling capacity requirement for that load range based on the peak cooling load and the cooling capacity redundancy coefficient. The cooling capacity redundancy coefficient is a conventional design coefficient for the chiller configuration of the computer room and can be selected according to the actual operating conditions of the computer room. Based on two types of units as the basic combination unit, the number of units in operation for each type of unit is set so that the total cooling capacity output of the unit combination meets the total configuration cooling capacity requirements. Among them, the low-voltage variable frequency small cold source unit needs to be adapted by considering the ratio of its actual cooling capacity output to its rated cooling capacity. By combining the average cooling load and the duration of the load, the energy efficiency of the two types of units is matched and verified, so that the units can operate in a load rate range that can maintain a stable state and avoid energy efficiency degradation at low loads. Based on the calculation and verification results, the capacity ratio and number of units of the two types of units are determined, forming a unique combination scheme of cooling source equipment that is compatible with this load range.

[0008] By adopting a basic combination unit that combines high-voltage fixed-frequency large-source chiller units with low-voltage variable-frequency small-source chiller units, cooling demand calculations, unit number configurations, and operating load rate verifications are performed for different load ranges to form a chiller equipment combination scheme that uniquely corresponds to each load range. This allows for differentiated settings of chiller equipment capacity ratios and operating numbers according to load levels, ensuring that the units operate stably and reasonably in each load range from the design stage, effectively avoiding energy efficiency degradation caused by low-load operation.

[0009] Furthermore, S3 includes the following: Set corresponding simulation conditions for each load level range. These conditions include the average cooling load, cooling load fluctuation range, environmental parameters, and unit operation mode for that range. Input the number of operating units and capacity ratio of each cold source equipment combination scheme into the digital twin simulation model of the computer room; carry out simulation operation under the corresponding load range, calculate the real-time operating power consumption and real-time cooling capacity of the computer room under the cold source equipment combination scheme, and calculate the real-time energy efficiency ratio based on the ratio of real-time cooling capacity to real-time operating power consumption. The real-time energy efficiency ratios at all simulation times within the load range are statistically averaged to obtain the range-average energy efficiency ratio of the cooling source equipment combination scheme within the load range. Based on the above simulation data, an energy efficiency matching result is generated, which includes the actual cooling capacity, total system operating power consumption, real-time energy efficiency ratio sequence, and interval average energy efficiency ratio. The above simulation and calculation are performed sequentially on all load ranges and corresponding cold source equipment combination schemes to obtain all energy efficiency matching results.

[0010] Furthermore, S4 includes the following: Using the average energy efficiency ratio of the interval as the evaluation index, different combinations of cooling source equipment within the same load level interval are ranked, and the cooling source equipment combination scheme with the best energy efficiency within the interval is selected. The above selection process is performed for all load levels intervals to obtain the optimal cooling source equipment combination scheme for each interval, forming an optimal scheme set. Establish and store a relational database containing a one-to-one correspondence between load range level, cooling load range, optimal cooling source equipment combination scheme, number of operating units of the two types of units, capacity ratio, and average energy efficiency ratio of the range. Based on the correlation database, the simulation results of the data center energy efficiency optimization are output. The results include not only the optimal combination scheme of cold source equipment, number of operating units, capacity ratio and average energy efficiency ratio of each load range, but also the overall expected energy efficiency improvement in the entire load range of the data center. This improvement is obtained by comparing the weighted average energy efficiency ratio of the entire range after optimization with the annual average energy efficiency ratio of the traditional single configuration scheme. Based on the optimal solution set, the combination of cooling source equipment is dynamically adapted to different load ranges of the computer room to ensure that the computer room operates in the optimal energy efficiency state under each load level.

[0011] A data center energy efficiency optimization simulation system based on digital twins includes: a model building and load analysis module, an equipment combination design module, an energy efficiency simulation calculation module, and an optimal solution adaptation and output module; The model building and load analysis module collects relevant parameters of the computer room, builds a digital twin simulation model, and completes the hourly load simulation throughout the year and the division of load ranges of different levels based on the model; The equipment combination design module designs cold source equipment combination schemes based on high-voltage fixed-frequency large cold source units and low-voltage variable-frequency small cold source units according to the load range of each level, so as to realize different capacity ratios and operating numbers for different load ranges. The energy efficiency simulation calculation module imports the combination schemes of various cold source equipment into the digital twin simulation model, performs energy efficiency simulation under the corresponding load range conditions, and obtains the energy efficiency matching results of each scheme. The optimal solution adaptation and output module filters the most energy-efficient combination of cooling source equipment in each load range, establishes a correlation database, and outputs the optimized simulation results to achieve dynamic adaptation of cooling source equipment combinations to different load ranges.

[0012] Furthermore, the model building and load analysis module includes a parameter acquisition unit and a load interval division unit; The parameter acquisition unit collects the building thermal parameters of the computer room, the basic parameters of the cold source equipment, the operating condition parameters of the computer room, and the load-related parameters. The load interval division unit uses the existing digital twin simulation platform to build a joint simulation model to realize the calculation of the cooling load hourly throughout the year, and divides multiple load intervals of non-overlapping and full coverage according to the load distribution characteristics.

[0013] Furthermore, the equipment combination design module includes a cooling demand calculation unit and an energy efficiency adaptation verification unit; The cooling demand calculation unit extracts the core load parameters of each load range, combines the rated cooling capacity of the unit with the cooling capacity redundancy coefficient to determine the total configuration cooling demand, and sets the number of high-voltage fixed-frequency large cooling source units and low-voltage variable-frequency small cooling source units in operation. The energy efficiency adaptation verification unit combines the average cooling load and load duration to verify the number of operating units and determine the unique capacity ratio and equipment combination scheme that is compatible with each load range.

[0014] Furthermore, the energy efficiency simulation calculation module includes an operating condition configuration unit and an energy efficiency calculation unit; The operating condition configuration unit configures simulation operating conditions for each load level range, including the average cooling load, cooling load fluctuation range, environmental parameters, and unit operating mode. The energy efficiency calculation unit simulates operation under corresponding working conditions, calculates real-time cooling capacity and real-time operating power consumption to obtain real-time energy efficiency ratio, and obtains interval average energy efficiency ratio by statistical averaging, forming an energy efficiency matching result containing multiple energy efficiency data.

[0015] Furthermore, the optimal solution adaptation and output module includes a solution selection unit, a database construction unit, and a result output unit; The scheme selection unit uses the average energy efficiency ratio of the interval as an indicator to select the optimal combination of cooling source equipment for each load interval, forming an optimal scheme set; The database construction unit establishes and stores a related database of load range levels, cooling load ranges, optimal equipment combination schemes, number of operating units, capacity ratios and average energy efficiency ratios of the ranges; The output unit outputs optimized simulation results including the optimal solution, energy efficiency data, and overall expected energy efficiency improvement, and dynamically adapts the combination of cold source equipment and load range based on the set of optimal solutions.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention realizes the simulation of the cooling load of the computer room throughout the year and the division of the load range through the existing digital twin simulation platform. For different load ranges, it adopts a combination of high-voltage fixed-frequency large cooling source units and low-voltage variable-frequency small cooling source units, and designs a differentiated cooling source equipment combination scheme to adapt to different load ranges. It also performs energy efficiency adaptation verification on the unit capacity ratio and the number of operating units, and then obtains the average energy efficiency ratio of each scheme in the range through simulation to select the optimal combination. It establishes a correlation database of load range, equipment combination and operating energy efficiency, and finally realizes the dynamic adaptation of cooling source equipment combination with different load levels. It enables the units to be in a stable and efficient operating range in each load range, avoids the energy efficiency degradation problem caused by low load operation from the design source, significantly improves the operating energy efficiency of the computer room in the entire operating range, and provides reliable data support and scheme guidance for the optimized configuration and efficient operation of the computer room cooling source system. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of a computer room energy efficiency optimization simulation system based on digital twins according to the present invention. Detailed Implementation

[0018] Please see Figure 1 The present invention provides the following technical solution: A data center energy efficiency optimization simulation system based on digital twins includes: a model building and load analysis module, an equipment combination design module, an energy efficiency simulation calculation module, and an optimal solution adaptation and output module; The model building and load analysis module collects relevant parameters of the computer room, builds a digital twin simulation model, and completes the hourly load simulation throughout the year and the division of load ranges of different levels based on the model; The equipment combination design module designs cold source equipment combination schemes based on high-voltage fixed-frequency large cold source units and low-voltage variable-frequency small cold source units according to the load range of each level, so as to realize different capacity ratios and operating numbers for different load ranges. The energy efficiency simulation calculation module imports the combination schemes of various cold source equipment into the digital twin simulation model, performs energy efficiency simulation under the corresponding load range conditions, and obtains the energy efficiency matching results of each scheme. The optimal solution adaptation and output module filters the most energy-efficient combination of cooling source equipment in each load range, establishes a correlation database, and outputs the optimized simulation results to achieve dynamic adaptation of cooling source equipment combinations to different load ranges.

[0019] The model building and load analysis module includes a parameter acquisition unit and a load interval division unit; The parameter acquisition unit collects the building thermal parameters of the computer room, the basic parameters of the cold source equipment, the operating condition parameters of the computer room, and the load-related parameters. The load interval division unit uses the existing digital twin simulation platform to build a joint simulation model to realize the calculation of the cooling load hourly throughout the year, and divides multiple load intervals of non-overlapping and full coverage according to the load distribution characteristics.

[0020] The equipment combination design module includes a cooling demand calculation unit and an energy efficiency adaptation verification unit; The cooling demand calculation unit extracts the core load parameters of each load range, combines the rated cooling capacity of the unit with the cooling capacity redundancy coefficient to determine the total configuration cooling demand, and sets the number of high-voltage fixed-frequency large cooling source units and low-voltage variable-frequency small cooling source units in operation. The energy efficiency adaptation verification unit combines the average cooling load and load duration to verify the number of operating units and determine the unique capacity ratio and equipment combination scheme that is compatible with each load range.

[0021] The energy efficiency simulation calculation module includes an operating condition configuration unit and an energy efficiency calculation unit; The operating condition configuration unit configures simulation operating conditions for each load level range, including the average cooling load, cooling load fluctuation range, environmental parameters, and unit operating mode. The energy efficiency calculation unit simulates operation under corresponding working conditions, calculates real-time cooling capacity and real-time operating power consumption to obtain real-time energy efficiency ratio, and obtains interval average energy efficiency ratio by statistical averaging, forming an energy efficiency matching result containing multiple energy efficiency data.

[0022] The optimal solution adaptation and output module includes a solution selection unit, a database construction unit, and a result output unit; The scheme selection unit uses the average energy efficiency ratio of the interval as an indicator to select the optimal combination of cooling source equipment for each load interval, forming an optimal scheme set; The database construction unit establishes and stores a related database of load range levels, cooling load ranges, optimal equipment combination schemes, number of operating units, capacity ratios and average energy efficiency ratios of the ranges; The output unit outputs optimized simulation results including the optimal solution, energy efficiency data, and overall expected energy efficiency improvement, and dynamically adapts the combination of cold source equipment and load range based on the set of optimal solutions.

[0023] A simulation method for optimizing data center energy efficiency based on digital twins, characterized by the following steps: S1. Construct a digital twin simulation model of the data center, collect the building thermal parameters, cold source equipment basic parameters, data center operating condition parameters, and load impact correlation parameters; based on the digital twin simulation model, conduct hourly load simulation calculations of the data center throughout the year, analyze the interval distribution characteristics of the hourly load of the data center throughout the year, and divide the data center into different load intervals. S2. Based on the different load ranges of the divided computer room, a suitable combination scheme of cooling source equipment is designed for each load range; the combination scheme of cooling source equipment is based on high-voltage fixed-frequency large cooling source unit and low-voltage variable-frequency small cooling source unit as the basic combination unit, and the capacity ratio and number of operating units of the cooling source equipment combination scheme corresponding to different load ranges are different; S3. Import each cooling source equipment combination scheme into the computer room digital twin simulation model. Under the corresponding load range conditions, simulate the actual operating energy efficiency of each cooling source equipment combination scheme in the computer room and obtain the energy efficiency matching result between each load range and the corresponding cooling source equipment combination scheme. The energy efficiency matching result includes the computer room operating energy efficiency value of the corresponding cooling source equipment combination scheme under each load range. S4. Based on the energy efficiency matching results, select the optimal cooling source equipment combination scheme for the computer room operation energy efficiency value under each load level range, so as to realize the dynamic adaptation of the cooling source equipment combination with the computer room load levels range; establish and store the association database of load range, cooling source equipment combination scheme, and computer room operation energy efficiency value, and output the computer room energy efficiency optimization simulation results based on the association database. The simulation results include the optimal cooling source equipment combination scheme corresponding to each load level range and the computer room energy efficiency parameters under the scheme.

[0024] S1 includes the following: The thermal parameters of the computer room building are the thermal characteristic parameters of the building envelope, the basic parameters of the cold source equipment are the rated operating characteristic parameters of the unit, the operating condition parameters of the computer room are the indoor and outdoor environment and system operating parameters of the computer room, and the load impact correlation parameters are the on-site operating condition parameters that affect the load fluctuation of the computer room. Using existing digital twin simulation platforms, and combining the collected parameters, a simulation model is constructed that includes a thermal model of the computer room building, a performance model of the cooling source equipment, and a load influence factor model. Each sub-model achieves parameter association and real-time data transmission through the platform's built-in interface specifications, thus completing joint simulation calculations. The hourly load calculation method is adopted. By integrating various cooling load components and deducting heat recovery, the hourly cooling load of the computer room throughout the year is obtained. The hourly cooling load data is statistically analyzed to extract the frequency distribution, duration distribution and fluctuation characteristics of the load value. Multiple load intervals are divided according to the magnitude of the cooling load value. The load range of each load interval does not overlap and completely covers all hourly cooling loads throughout the year.

[0025] In this embodiment, relevant parameters of the computer room are collected. The thermal parameters of the computer room building are the thermal characteristic parameters of the building envelope, specifically: heat transfer coefficient of the envelope, window-to-wall ratio, shading coefficient, and roof thermal parameters; the basic parameters of the cooling source equipment are the rated operating characteristic parameters of the unit, specifically: rated cooling capacity, rated power, rated energy efficiency, and equipment performance curve; the operating condition parameters of the computer room are the indoor and outdoor environmental and system operating parameters, specifically: indoor design temperature and humidity, outdoor meteorological parameters, fresh air volume, and equipment heat dissipation power; the load impact correlation parameters are the on-site operating condition parameters that affect the load fluctuation of the computer room, specifically: hourly heat generation of IT equipment, personnel density and hourly variation coefficient, and outdoor temperature and humidity variation patterns; Based on the collected parameters, a simulation model is constructed using a mature digital twin simulation platform in the field, which includes a thermal model of the computer room building, a performance model of the cooling source equipment, and a load influence factor model. The parameters of each sub-model are associated with each other using the platform's built-in interface specifications to achieve real-time data transmission and joint simulation calculation. Based on the digital twin simulation model of the computer room, the hourly load calculation method is used to calculate the hourly cooling load of the computer room throughout the year. The hourly cooling load of the computer room in a single time period is the comprehensive calculation value of various cooling load components in that time period. The calculation formula is: Qt=∑Qit-Qr, where Qt is the hourly cooling load of the computer room in time period t, Qit is the i-th type of cooling load component in time period t, and Qr is the heat recovery amount of the computer room in time period t. Statistical analysis is performed on the calculated hourly cooling load data to obtain the interval distribution characteristics of the hourly load of the computer room throughout the year. The interval distribution characteristics include the frequency distribution, duration distribution and fluctuation characteristics of the load values. Based on the interval distribution characteristics of the hourly load throughout the year, the computer room is divided into different load intervals according to the magnitude of the cooling load values. The load value range of each load interval does not overlap and completely covers all the calculated hourly cooling load values ​​throughout the year.

[0026] S2 includes the following: Extract the core load parameters for each load level range, including the peak cooling load, average cooling load, and load duration of that range; combine the rated cooling capacity of a single high-voltage fixed-frequency large-source chiller unit and a low-voltage variable-frequency small-source chiller unit, and determine the total cooling capacity requirement for that load range based on the peak cooling load and the cooling capacity redundancy coefficient. The cooling capacity redundancy coefficient is a conventional design coefficient for the chiller configuration of the computer room and can be selected according to the actual operating conditions of the computer room. Based on two types of units as the basic combination unit, the number of units in operation for each type of unit is set so that the total cooling capacity output of the unit combination meets the total configuration cooling capacity requirements. Among them, the low-voltage variable frequency small cold source unit needs to be adapted by considering the ratio of its actual cooling capacity output to its rated cooling capacity. By combining the average cooling load and the duration of the load, the energy efficiency of the two types of units is matched and verified, so that the units can operate in a load rate range that can maintain a stable state and avoid energy efficiency degradation at low loads. Based on the calculation and verification results, the capacity ratio and number of units of the two types of units are determined, forming a unique combination scheme of cooling source equipment that is compatible with this load range.

[0027] In this embodiment, the core load parameters of each load level range are extracted. The core load parameters include the peak cooling load Qfn, the average cooling load Qan, and the load duration Tn of each load level range, where n is the load range level number and n=1,2,3...N, and N is the total number of load range levels. Obtain the rated cooling capacity QG of a single high-voltage fixed-frequency large-scale cold source unit and the rated cooling capacity QS of a single low-voltage variable-frequency small-scale cold source unit. Based on the peak cooling load of each load level range, calculate the total configuration cooling capacity requirement of the cold source equipment combination. The calculation formula is: Qn=k×Qfn, where Qn is the total configuration cooling capacity requirement of the nth load level range, k is the cooling capacity redundancy coefficient, which is the conventional design coefficient for the configuration of the cold source in the computer room. It is selected according to the type of computer room and safety redundancy requirements, for example, the value range is 1.0 to 1.2. Using high-voltage fixed-frequency large-source chiller units and low-voltage variable-frequency small-source chiller units as basic combination units, the number of operating high-voltage fixed-frequency large-source chiller units in the nth load level range is set as Xn, and the number of operating low-voltage variable-frequency small-source chiller units is set as Yn. The matching relationship between the total configured cooling capacity and the number of units and the rated cooling capacity of a single unit is established. The calculation formula is: Qn≤Xn×QG+Yn×QS×η, where η is the frequency conversion adjustment coefficient of the low-voltage variable-frequency small-source chiller unit, which represents the ratio of the actual cooling capacity output capacity of the low-voltage variable-frequency small-source chiller unit to the rated cooling capacity. It is continuously adjustable with the operating load rate, for example, the value range is 0.2~1.0; Xn and Yn are both positive integers, and the values ​​of Xn and Yn are different in different load levels. Based on the average cooling load Qan and load duration Tn of each load level range, energy efficiency adaptation verification is performed on Xn and Yn. The verification formula is: Qan / (Xn×QG+Yn×QS×η)∈R, where R is the operating load rate range of the chiller unit. For example, the value range is 0.6 to 0.9. This range is determined according to the operating characteristics of the chiller unit, which can keep the unit in a stable operating state and avoid energy efficiency degradation caused by low load operation. Based on the calculation and verification results, the capacity ratio of high-voltage fixed-frequency large cold source units and low-voltage variable-frequency small cold source units corresponding to each load level range is determined, and the capacity ratio is expressed as (Xn×QG) / (Yn×QS) and the number of operating units Xn and Yn, forming a cold source equipment combination scheme that uniquely corresponds to each load level range. The capacity ratio and the number of operating units are different for different load levels range.

[0028] S3 includes the following: Set corresponding simulation conditions for each load level range. These conditions include the average cooling load, cooling load fluctuation range, environmental parameters, and unit operation mode for that range. Input the number of operating units and capacity ratio of each cold source equipment combination scheme into the digital twin simulation model of the computer room; carry out simulation operation under the corresponding load range, calculate the real-time operating power consumption and real-time cooling capacity of the computer room under the cold source equipment combination scheme, and calculate the real-time energy efficiency ratio based on the ratio of real-time cooling capacity to real-time operating power consumption. The real-time energy efficiency ratios at all simulation times within the load range are statistically averaged to obtain the range-average energy efficiency ratio of the cooling source equipment combination scheme within the load range. Based on the above simulation data, an energy efficiency matching result is generated, which includes the actual cooling capacity, total system operating power consumption, real-time energy efficiency ratio sequence, and interval average energy efficiency ratio. The above simulation and calculation are performed sequentially on all load ranges and corresponding cold source equipment combination schemes to obtain all energy efficiency matching results.

[0029] In this embodiment, a corresponding hourly dynamic simulation condition is set for each load level range. The hourly dynamic simulation condition includes the average cooling load, cooling load fluctuation range, environmental parameters, and unit operation mode of the load level range. The number of high-voltage fixed-frequency large-source cold source units Xn and the number of low-voltage variable-frequency small-source cold source units Yn, as determined in S2, and their capacity ratio (Xn×QG) / (Yn×QS) are input into the digital twin simulation model. Based on the digital twin simulation model, simulation operation is carried out under the corresponding load range. According to the performance curve of the cold source equipment and the operating load rate, the real-time operating power consumption Pt of the computer room is calculated, and the real-time cooling capacity Qt of the computer room is calculated according to the load demand and the unit output capacity. The real-time energy efficiency ratio of the computer room is: COPt=Qt / Pt, where COPt is the real-time energy efficiency ratio of the computer room at time t, Qt is the actual cooling capacity of the computer room at time t, and Pt is the total operating power consumption of the cold source system of the computer room at time t. The real-time energy efficiency ratios at all simulation moments within the nth load level range are statistically averaged to obtain the range-average energy efficiency ratio of the current cooling source equipment combination scheme under this load range. The calculation formula is: COPan=(1 / M)∑ t∈[1,M] COPt, where COPan is the average energy efficiency ratio of the nth load level range, M is the total number of simulation calculation times within that load range, ∑ t∈[1,M] COPt is the cumulative value of the real-time energy efficiency ratio at all times within this interval; Based on the simulation results, the energy efficiency matching results between the nth load level range and the current cooling source equipment combination scheme are formed. The energy efficiency matching results include: the actual cooling capacity of the corresponding cooling source equipment combination scheme in the corresponding load range, the total operating power consumption of the system, the real-time energy efficiency ratio sequence, and the average energy efficiency ratio of the range. The above simulation and calculation are performed on the cooling source equipment combination schemes corresponding to all load levels in sequence to obtain the energy efficiency matching results between all load ranges and the corresponding cooling source equipment combination schemes.

[0030] S4 includes the following: Using the average energy efficiency ratio of the interval as the evaluation index, different combinations of cooling source equipment within the same load level interval are ranked, and the cooling source equipment combination scheme with the best energy efficiency within the interval is selected. The above selection process is performed for all load levels intervals to obtain the optimal cooling source equipment combination scheme for each interval, forming an optimal scheme set. Establish and store a relational database containing a one-to-one correspondence between load range level, cooling load range, optimal cooling source equipment combination scheme, number of operating units of the two types of units, capacity ratio, and average energy efficiency ratio of the range. Based on the correlation database, the simulation results of the data center energy efficiency optimization are output. The results include not only the optimal combination scheme of cold source equipment, number of operating units, capacity ratio and average energy efficiency ratio of each load range, but also the overall expected energy efficiency improvement in the entire load range of the data center. This improvement is obtained by comparing the weighted average energy efficiency ratio of the entire range after optimization with the annual average energy efficiency ratio of the traditional single configuration scheme. Based on the optimal solution set, the combination of cooling source equipment is dynamically adapted to different load ranges of the computer room to ensure that the computer room operates in the optimal energy efficiency state under each load level.

[0031] In this embodiment, the average energy efficiency ratio (COPan) of each cold source equipment combination scheme within the nth load level range obtained in S3 is read, and the average energy efficiency ratio of the range is used as the evaluation index of the scheme. For different combinations of cooling source equipment within the same load level, sort them from high to low according to the average energy efficiency ratio of the interval, and select the combination of cooling source equipment with the highest average energy efficiency ratio within the load interval as the optimal combination of cooling source equipment for the load interval. Each load range is screened to obtain the optimal combination of cooling source equipment for each load range, forming a set of optimal solutions that match different load ranges. Establish and store a relational database, which stores a one-to-one mapping relationship, specifically including: load range level, cooling load range, optimal cold source equipment combination scheme, number of high-voltage fixed frequency large cold source units Xn in operation, number of low-voltage variable frequency small cold source units Yn in operation, capacity ratio (Xn×QG) / (Yn×QS), and average energy efficiency ratio COPa of the range. Based on the aforementioned associated database, the simulation results of data center energy efficiency optimization are output. The simulation results include: the optimal combination scheme of cooling source equipment for each load range, the number of operating units, capacity ratio, average energy efficiency ratio of the range, and the overall expected energy efficiency improvement of the data center in the full load range. The overall expected energy efficiency improvement is calculated according to the following formula: H=(COPp-COPr) / COPr×100%, where H is the overall expected energy efficiency improvement rate of the data center, COPp is the weighted average energy efficiency ratio of the entire range after optimization, and COPr is the annual average energy efficiency ratio of the traditional single configuration scheme. Based on the optimal solution set, the combination of cold source equipment is dynamically adapted to the different load ranges of the computer room, so that the computer room operates in the optimal energy efficiency state under each load level.

Claims

1. A simulation method for optimizing data center energy efficiency based on digital twins, characterized in that: The method includes the following steps: S1. Collect the thermal parameters of the computer room building, the basic parameters of the cold source equipment, the operating parameters of the computer room, and the load impact correlation parameters to construct a digital twin simulation model; based on the digital twin simulation model, conduct hourly load simulation calculations of the computer room throughout the year, analyze the interval distribution characteristics of the hourly load of the computer room throughout the year, and divide the computer room into different load intervals of different levels. S2. Based on the different load ranges of the divided computer room, a suitable combination scheme of cooling source equipment is designed for each load range; the combination scheme of cooling source equipment is based on high-voltage fixed-frequency large cooling source unit and low-voltage variable-frequency small cooling source unit as the basic combination unit, and the capacity ratio and number of operating units of the cooling source equipment combination scheme corresponding to different load ranges are different; S3. Import each cooling source equipment combination scheme into the computer room digital twin simulation model. Under the corresponding load range conditions, simulate the actual operating energy efficiency of each cooling source equipment combination scheme in the computer room and obtain the energy efficiency matching result between each load range and the corresponding cooling source equipment combination scheme. The energy efficiency matching result includes the computer room operating energy efficiency value of the corresponding cooling source equipment combination scheme under each load range. S4. Based on the energy efficiency matching results, select the optimal cooling source equipment combination scheme for the computer room operation energy efficiency value under each load level range, so as to realize the dynamic adaptation of the cooling source equipment combination with the computer room load levels range; establish and store the association database of load range, cooling source equipment combination scheme, and computer room operation energy efficiency value, and output the computer room energy efficiency optimization simulation results based on the association database. The simulation results include the optimal cooling source equipment combination scheme corresponding to each load level range and the computer room energy efficiency parameters under the scheme.

2. The data center energy efficiency optimization simulation method based on digital twins according to claim 1, characterized in that: S1 includes the following: The thermal parameters of the computer room building are the thermal characteristic parameters of the building envelope, the basic parameters of the cold source equipment are the rated operating characteristic parameters of the unit, the operating condition parameters of the computer room are the indoor and outdoor environment and system operating parameters of the computer room, and the load impact correlation parameters are the on-site operating condition parameters that affect the load fluctuation of the computer room. Using existing digital twin simulation platforms, and combining the collected parameters, a simulation model is constructed that includes a thermal model of the computer room building, a performance model of the cooling source equipment, and a load influence factor model. Each sub-model achieves parameter association and real-time data transmission through the platform's built-in interface specifications, thus completing joint simulation calculations. The hourly load calculation method is adopted. By integrating various cooling load components and deducting heat recovery, the hourly cooling load of the computer room throughout the year is obtained. The hourly cooling load data is statistically analyzed to extract the frequency distribution, duration distribution and fluctuation characteristics of the load value. Multiple load intervals are divided according to the magnitude of the cooling load value. The load range of each load interval does not overlap and completely covers all hourly cooling loads throughout the year.

3. The data center energy efficiency optimization simulation method based on digital twins according to claim 2, characterized in that: S2 includes the following: Extract the core load parameters for each load level range, including the peak cooling load, average cooling load, and load duration of that range; combine the rated cooling capacity of a single high-voltage fixed-frequency large-source chiller unit and a low-voltage variable-frequency small-source chiller unit, and determine the total cooling capacity requirement for that load range based on the peak cooling load and the cooling capacity redundancy coefficient. The cooling capacity redundancy coefficient is a conventional design coefficient for the chiller configuration of the computer room and can be selected according to the actual operating conditions of the computer room. Based on two types of units as the basic combination unit, the number of units in operation for each type of unit is set so that the total cooling capacity output of the unit combination meets the total configuration cooling capacity requirements. Among them, the low-voltage variable frequency small cold source unit needs to be adapted by considering the ratio of its actual cooling capacity output to its rated cooling capacity. By combining the average cooling load and the duration of the load, the energy efficiency of the two types of units is matched and verified, so that the units can operate in a load rate range that can maintain a stable state and avoid energy efficiency degradation at low loads. Based on the calculation and verification results, the capacity ratio and number of units of the two types of units are determined, forming a unique combination scheme of cooling source equipment that is compatible with this load range.

4. The data center energy efficiency optimization simulation method based on digital twins according to claim 3, characterized in that: S3 includes the following: Set corresponding simulation conditions for each load level range. These conditions include the average cooling load, cooling load fluctuation range, environmental parameters, and unit operation mode for that range. Input the number of operating units and capacity ratio of each cold source equipment combination scheme into the digital twin simulation model of the computer room; carry out simulation operation under the corresponding load range, calculate the real-time operating power consumption and real-time cooling capacity of the computer room under the cold source equipment combination scheme, and calculate the real-time energy efficiency ratio based on the ratio of real-time cooling capacity to real-time operating power consumption. The real-time energy efficiency ratios at all simulation times within the load range are statistically averaged to obtain the range-average energy efficiency ratio of the cooling source equipment combination scheme within the load range. Based on the above simulation data, an energy efficiency matching result is generated, which includes the actual cooling capacity, total system operating power consumption, real-time energy efficiency ratio sequence, and interval average energy efficiency ratio. The above simulation and calculation are performed sequentially on all load ranges and corresponding cold source equipment combination schemes to obtain all energy efficiency matching results.

5. The data center energy efficiency optimization simulation method based on digital twins according to claim 4, characterized in that: S4 includes the following: Using the average energy efficiency ratio of the interval as the evaluation index, different combinations of cooling source equipment within the same load level interval are ranked, and the cooling source equipment combination scheme with the best energy efficiency within the interval is selected. The above selection process is performed for all load levels intervals to obtain the optimal cooling source equipment combination scheme for each interval, forming an optimal scheme set. Establish and store a relational database containing a one-to-one correspondence between load range level, cooling load range, optimal cooling source equipment combination scheme, number of operating units of the two types of units, capacity ratio, and average energy efficiency ratio of the range. Based on the correlation database, the simulation results of the data center energy efficiency optimization are output. The results include not only the optimal combination scheme of cold source equipment, number of operating units, capacity ratio and average energy efficiency ratio of each load range, but also the overall expected energy efficiency improvement in the entire load range of the data center. This improvement is obtained by comparing the weighted average energy efficiency ratio of the entire range after optimization with the annual average energy efficiency ratio of the traditional single configuration scheme. Based on the optimal solution set, the combination of cooling source equipment is dynamically adapted to different load ranges of the computer room to ensure that the computer room operates in the optimal energy efficiency state under each load level.

6. A data center energy efficiency optimization simulation system based on digital twins, characterized in that: The system includes: a model building and load analysis module, an equipment combination design module, an energy efficiency simulation calculation module, and an optimal solution adaptation and output module; The model building and load analysis module collects relevant parameters of the computer room, builds a digital twin simulation model, and completes the hourly load simulation throughout the year and the division of load ranges of different levels based on the model; The equipment combination design module designs cold source equipment combination schemes based on high-voltage fixed-frequency large cold source units and low-voltage variable-frequency small cold source units according to each load range, so as to realize different capacity ratios and operating numbers for different load ranges. The energy efficiency simulation calculation module imports the combination schemes of each cold source equipment into the digital twin simulation model, performs energy efficiency simulation under the corresponding load range conditions, and obtains the energy efficiency matching results corresponding to each scheme. The optimal solution adaptation and output module filters the most energy-efficient combination of cooling source equipment in each load range, establishes a correlation database, and outputs the optimization simulation results to achieve dynamic adaptation of cooling source equipment combinations to different load ranges.

7. The data center energy efficiency optimization simulation system based on digital twins according to claim 6, characterized in that: The model building and load analysis module includes a parameter acquisition unit and a load interval division unit; The parameter acquisition unit collects the building thermal parameters of the computer room, the basic parameters of the cold source equipment, the operating condition parameters of the computer room, and the load-related parameters. The load interval division unit uses the existing digital twin simulation platform to build a joint simulation model to realize the calculation of the hourly cooling load throughout the year, and divides multiple load intervals of non-overlapping and full coverage according to the load distribution characteristics.

8. The data center energy efficiency optimization simulation system based on digital twins according to claim 6, characterized in that: The equipment combination design module includes a cooling demand calculation unit and an energy efficiency adaptation verification unit. The cooling demand calculation unit extracts the core load parameters of each load range, combines the rated cooling capacity of the unit with the cooling capacity redundancy coefficient to determine the total configuration cooling demand, and sets the number of high-voltage fixed-frequency large cooling source units and low-voltage variable-frequency small cooling source units in operation. The energy efficiency adaptation verification unit combines the average cooling load and load duration to verify the number of operating units and determine the unique capacity ratio and equipment combination scheme that is compatible with each load range.

9. A data center energy efficiency optimization simulation system based on digital twins according to claim 6, characterized in that: The energy efficiency simulation calculation module includes an operating condition configuration unit and an energy efficiency calculation unit; The operating condition configuration unit configures simulated operating conditions for each load level range, including the average cooling load, cooling load fluctuation range, environmental parameters, and unit operating mode. The energy efficiency calculation unit simulates operation under corresponding working conditions, calculates real-time cooling capacity and real-time operating power consumption to obtain real-time energy efficiency ratio, and obtains interval average energy efficiency ratio by statistical averaging, forming an energy efficiency matching result containing multiple energy efficiency data.

10. A data center energy efficiency optimization simulation system based on digital twins according to claim 6, characterized in that: The optimal solution adaptation and output module includes a solution filtering unit, a database construction unit, and a result output unit. The scheme selection unit uses the average energy efficiency ratio of the interval as an indicator to select the optimal combination scheme of cold source equipment for each load interval, forming an optimal scheme set; The database construction unit establishes and stores a related database of load range levels, cooling load range, optimal equipment combination scheme, number of operating units, capacity ratio and average energy efficiency ratio of the range; The result output unit outputs optimized simulation results including the optimal solution, energy efficiency data, and overall expected energy efficiency improvement, and dynamically adapts the combination of cold source equipment and load range based on the set of optimal solutions.

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