Data center regulation and control method and system for combined cooling heating and power supply and surplus power demand response

Abstract

The invention discloses a data center regulation and control method and system for combined cooling heating and power supply and residual power demand response, and relates to the technical field of energy management, and the method comprises the steps: obtaining basic parameters of a data center in which a CCHP and a residual heat recycling device are introduced; constructing a basic model of each type of equipment; a power load tracking strategy model through waste heat reutilization and an operation cost awareness energy management strategy model through waste heat reutilization are constructed respectively; the two strategies are evaluated according to actual requirements, and a low-carbon operation strategy is selected; and comprehensively considering the operation states of various energy devices, and dynamically adjusting the operation plans of various devices. The CCHP is introduced to replace power generation, heat production and refrigeration equipment of an original data center, mutual conversion between energy is utilized, the carbon emission of the data center is reduced, and low-carbon operation is maintained. By introducing waste heat recycling equipment, redundant heat generated by an original data center is consumed, and the operation cost of the data center is effectively reduced while efficient operation of the system is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular to a data center control method and system for combined cooling, heating and power supply and surplus power demand response. Background Art

[0002] With the rapid development of science and technology and population, more and more data centers are deployed around the world. However, the network equipment, air conditioning equipment, servers, storage equipment and other energy-consuming equipment contained in the data center cause the data center to consume a lot of energy, posing a serious threat to the environment. Therefore, the rational allocation of various production capacities and energy-consuming equipment in the data center is the key factor in achieving the low-carbon long-term operation of the data center.

[0003] Due to its unique energy consumption characteristics, data centers involve the comprehensive utilization of electrical energy and thermal energy. At the same time, in order to ensure the efficient operation of core equipment in the data center, some refrigeration equipment is also required to reduce the temperature of the core equipment. For this reason, traditional data centers need to be equipped with power generation, heat generation and refrigeration equipment to maintain their normal operation. CCHP, as a widely used type of production capacity equipment, can be used in data centers to replace the original power generation, heat generation and refrigeration equipment due to its unique cogeneration characteristics. At the same time, compared with traditional equipment, CCHP uses the waste heat generated by power generation for cooling and heating, which can effectively reduce the carbon emissions of the data center's production capacity.

[0004] When a data center is operating normally, the operation of its internal equipment will generate a lot of heat, which will cause the internal temperature to be too high and damage the equipment. Waste heat recycling equipment is a device that can recycle waste heat generated in the industrial production process, which can effectively improve energy efficiency, reduce production costs, and reduce environmental pollution. To this end, waste heat recycling equipment can be introduced to reuse the heat generated during the operation of the data center, protecting the safety of equipment operation while reducing carbon emissions. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: how to introduce combined cooling, heating and power (CCHP) and waste heat recycling equipment in the data center, maximize the energy efficiency and minimize the operating costs of the data center through optimized scheduling, while meeting demand response requirements, reducing carbon emissions and achieving low-carbon operation.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a data center control method for combined cooling, heating and power supply and surplus power demand response, which comprises the following steps:

[0008] Obtain the basic parameters of various equipment in the data center that has introduced CCHP and waste heat recovery equipment;

[0009] Build basic models of various devices in the data center based on the acquired device parameters;

[0010] The power load tracking strategy model through waste heat reuse and the operational cost-aware energy management strategy model through waste heat reuse are constructed respectively;

[0011] Based on the two constructed energy management strategies, the two strategies are evaluated according to the actual needs of the data center, and the strategy suitable for the low-carbon operation of the data center is selected according to the evaluation results;

[0012] Through the selected data center low-carbon operation strategy, comprehensive consideration of the operating status of various energy equipment, dynamic adjustment of the operation plan of various equipment in the data center, the energy efficiency of the data center is maximized and the operating cost is minimized.

[0013] As a preferred solution of the data center control method for combined cooling, heating and power and surplus power demand response described in the present invention, the basic parameters include the power generation efficiency ηPGU of the PGU when the data center is regulated, the efficiency ε of the heat recovery device HRU , PGU heat recovery efficiency ε PGU 、Energy consumption ratio of AC equipment in CCHP Cop AC , EC equipment energy consumption ratio Cop EC and the heat recovery efficiency of IT equipment in the data center ε IT .

[0014] As a preferred solution of the data center control method for combined cooling, heating and power and surplus power demand response described in the present invention, wherein: based on the acquired equipment parameters, basic models of various equipment in the data center are constructed, including a generator unit model, an absorption chiller model, an electric chiller model and an IT system thermal model;

[0015] The generator unit model is expressed as:

[0016] P PGU =F PGU η PGU

[0017]

[0018] H PGU =P PGU ε HRU

[0019] Among them, F PGU is the natural gas power injected into PGU; P PGU is the power generation capacity of PGU; is the maximum power generation of PGU, H PGU is the heat recovered from the PGU;

[0020] The absorption refrigerator model is expressed as:

[0021] C AC =H AC Cop AC

[0022] Among them, C AC is the cooling capacity generated by the AC system; H AC Heat recovered from PGU and IT systems for AC;

[0023] The electric chiller model is expressed as:

[0024] C EC =P EC Cop EC

[0025] Among them, C EC is the cooling capacity generated by the EC system; P EC is the cooling power of the EC system;

[0026] The IT system thermal model is expressed as:

[0027] H IT =P IT ε IT

[0028] Among them, H IT P is the heat recovered from the IT system; IT The electrical power used to run IT systems.

[0029] As a preferred solution of the data center control method for combined cooling, heating and power and surplus power demand response described in the present invention, the power load tracking strategy model through waste heat reuse is to construct an objective function based on the unit price of electricity purchased from the data center and the amount of energy purchased from the data center as the minimum, and the expression is:

[0030] minp GD P GD

[0031] P GD +P PGU -P IT =P EC

[0032] C EC +C AC ≥P IT

[0033]

[0034] Among them, p GDP is the unit price of electricity purchased by the data center; GD The amount of electricity purchased from outside.

[0035] As a preferred solution of the data center control method for combined cooling, heating and power and surplus power demand response described in the present invention, the energy management strategy model for operating cost awareness through waste heat reuse is expressed as follows:

[0036]

[0037] P PGU +P GD -P IT ≥P EC

[0038] C EC +C AC ≥P IT

[0039]

[0040] P EC ,P GD ≥0

[0041] Among them, p b The energy price of natural gas.

[0042] As a preferred solution of the data center control method for combined cooling, heating and power and surplus power demand response described in the present invention, wherein: the two energy management strategies constructed include evaluating the effectiveness of the two strategies in reducing the operating costs of the data center and reducing the carbon emissions of the data center according to the actual needs of the data center and the characteristics of various internal equipment, and selecting a strategy suitable for the low-carbon operation of the data center;

[0043] The economic evaluation indexes for data center operation and the carbon emission evaluation indexes for data center are established respectively. According to these two evaluation indexes and in combination with the actual operation conditions of multiple data centers, a strategy evaluation is conducted on the strategy model for tracking power load through waste heat reuse and the strategy model for energy management with operational cost awareness through waste heat reuse. Based on the evaluation results, the appropriate low-carbon operation strategy for the data center is selected for optimal scheduling. By dynamically adjusting the operation plans of various equipment in the data center, the energy efficiency of the data center is maximized and the operating cost is minimized.

[0044] As a preferred solution of the data center control method for combined cooling, heating and power and surplus power demand response described in the present invention, the operation economic evaluation index is the product of the sum of the data center energy purchase cost and the operation cost and the unit cost economic conversion coefficient ψS, and the expression is:

[0045]

[0046] Among them, S E is the economic evaluation index of data center operation; E is the economic conversion coefficient of unit cost of data center; is the sum of the energy purchase cost and operation cost of the data center; GD The unit price of electricity purchased by the data center; b is the energy unit price of natural gas; η PGU is the power generation efficiency of the generator unit; P t GD,0 P is the amount of electricity purchased from the data center in one scheduling cycle after the model is solved. t PGU,0 is the power generation of PGU in one dispatching cycle after the model is solved; T is one dispatching cycle;

[0047] The carbon emission evaluation index of the data center is the difference between the power of various power-consuming equipment in the system and the power of waste heat recycling equipment and the unit power carbon emission coefficient ζ C The product of is expressed as:

[0048] S C =ζ C (P t IT +P t EC -P t AC )

[0049] Among them, S C It is the carbon emission evaluation index of data center; C is the carbon emission coefficient per unit power of the data center; (P t IT,0 +P t EC,0 -P t AC,0 ) is the power difference between various power-consuming equipment and waste heat recycling equipment in the data center; P t IT，0 is the power consumption of the IT system in one scheduling cycle after the model is solved, P t EC，0 is the cooling power of the EC system in one scheduling cycle after the model is solved, P t AC，0 It is the power consumption of the AC system in one scheduling cycle after the model is solved.

[0050] Another object of the present invention is to provide a data center control system for combined cooling, heating and power and surplus power demand response, which can solve the problems of low energy efficiency, high operating costs and large carbon emissions in existing data centers by real-time collection and analysis of equipment operation data, dynamic adjustment of equipment operation strategies, and optimization of energy utilization efficiency.

[0051] In order to solve the above technical problems, the present invention provides the following technical solutions: a data center control system for combined cooling, heating and power and surplus power demand response, comprising: an equipment parameter acquisition module, an equipment model construction module, a strategy optimization module, a scheduling execution module, an evaluation analysis module and a learning optimization module;

[0052] The equipment parameter acquisition module is used to obtain the basic parameters of various equipment in the data center that introduces CCHP and waste heat recycling equipment;

[0053] The device model constructs basic models of various types of devices in the data center according to the acquired device parameters;

[0054] The strategy optimization module respectively constructs a strategy model for tracking power load through waste heat reuse and a strategy model for operating cost-aware energy management through waste heat reuse, and selects a strategy suitable for low-carbon operation of the data center;

[0055] The scheduling execution module dynamically adjusts the operation strategy of the equipment according to the results of the strategy optimization module, sends control instructions to each new energy equipment, and realizes the collaborative work between the equipment;

[0056] The evaluation analysis establishes the economic evaluation index of data center operation and the carbon emission evaluation index of data center, and completes the strategy evaluation based on the current two evaluation indexes and the actual operation conditions of multiple data centers;

[0057] The learning optimization module uses a machine learning algorithm to learn and optimize the control strategy of the equipment from historical data, thereby improving the equipment operation efficiency and the effect of grid voltage support.

[0058] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the data center control method for combined cooling, heating and power and surplus power demand response as described above are implemented.

[0059] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the data center control method for combined cooling, heating and power and surplus power demand response as described above.

[0060] Beneficial effects of the invention: The invention introduces CCHP to replace the power generation, heat generation and refrigeration equipment of the original data center, and utilizes the mutual conversion between energies to effectively reduce the carbon emissions of the data center and maintain the low-carbon operation of the data center. By introducing waste heat recycling equipment in the data center, the excess heat generated by the original data center is consumed, effectively reducing the operating cost of the data center while ensuring the efficient operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0062] Figure 1 An overall flow chart of a data center control method for combined cooling, heating and power generation and surplus power demand response provided by the first embodiment of the present invention;

[0063] Figure 2 A comparison chart of load balancing rates of various algorithms under the same task request in the data center control method for combined cooling, heating and power and surplus power demand response provided by the second embodiment of the present invention;

[0064] Figure 3 A data center framework diagram of a data center control method for combined cooling, heating and power and surplus power demand response provided by a third embodiment of the present invention;

[0065] Figure 4 A comparison chart of average energy consumption of IT equipment and electric chillers in four cases in the data center control method for combined cooling, heating and power and surplus power demand response provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0067] Example 1, reference Figure 1 According to an embodiment of the present invention, a data center control method for combined cooling, heating and power generation and surplus power demand response is provided, comprising:

[0068] Step S1: Obtaining equipment parameters: Obtaining basic parameters of various equipment in a data center that has introduced CCHP and waste heat recovery equipment.

[0069] Step S2: Equipment model establishment: According to the equipment parameters obtained in step S1, basic models of various equipment in the data center are constructed.

[0070] Step S3: Strategy model construction: Based on steps S1 and S2, two energy management strategy models, namely, tracking power load through waste heat reuse (FEL-WHR) and operational cost-aware energy management through waste heat reuse (OCM-WHR), are constructed respectively.

[0071] Step S4: Strategy selection: Based on the two energy management strategies proposed in step S3, considering the actual needs of the data center, select a strategy suitable for the low-carbon operation of the data center.

[0072] First, obtain the basic parameters of various equipment used to build the data center. The data center contains a large number of energy-consuming and energy-supplying equipment such as servers, network equipment, storage equipment, power equipment, and air conditioning equipment to maintain the normal operation of the data center. The data center that has introduced CCHP and WHR has a more complex internal equipment structure. In order to achieve the goal of low-carbon operation of the data center, the basic parameters of various equipment inside the data center must be obtained before regulating the data center. The parameters that need to be obtained for each type of equipment are as follows:

[0073] The CCHP of a data center consists of three parts: a power generation unit (PGU), an absorption chiller (AC), and an electrical chiller (EC). Therefore, when regulating the data center, it is necessary to obtain the power generation efficiency η of the PGU. PGU 、Efficiency of heat recovery device ε HRU And the PGU heat recovery efficiency ε calculated from the first two items PGU .

[0074] At the same time, the energy consumption ratio Cop of the AC equipment in CCHP also needs to be obtained AC And the energy consumption ratio Cop of EC equipment EC .

[0075] Finally, IT equipment in the data center is the main energy-consuming and heat-generating equipment. However, the accumulated heat is not conducive to the reliability of system operation and the maintenance of performance. Traditional data centers usually use air conditioning systems to discharge heat to the outside. In the data center constructed by the present invention, the waste heat is reused through WHR technology. Therefore, it is necessary to obtain the heat recovery efficiency ε of the IT system IT .

[0076] Then, the basic models of various devices in the data center are constructed. The data center proposed in this invention includes the modeling of the flow of electrical energy and thermal energy. Therefore, the equipment models that need to be modeled include: PGU, AC, EC and IT system thermal models, which are as follows:

[0077] Generator unit model

[0078] P PGU =F PGU η PGU

[0079] Among them, F PGU is the natural gas power injected into PGU; P PGU is the power generation power of PGU. At the same time, the power generation power of PGU must also meet the following constraints:

[0080]

[0081] in, is the maximum power generation of PGU. The heat recovered from PGU is as follows:

[0082] H PGU =P PGU ε HRU

[0083] Among them, H PGU is the heat recovered from the PGU.

[0084] Absorption Refrigerator Model

[0085] The absorption chiller is driven by heat recovered from the PGU and IT systems, for which the cooling capacity generated by the AC is:

[0086] C AC =H AC Cop AC

[0087] Among them, C AC is the cooling capacity generated by the AC system; H AC Heat recovered from PGU and IT systems for AC.

[0088] Electric Chiller Model

[0089] Sometimes, a single AC is not enough to meet the cooling needs of the entire data center, so EC is needed to maintain the stability of the data center's cooling. The cooling capacity provided by EC can be expressed as:

[0090] C EC =P EC Cop EC

[0091] Among them, C ECis the cooling capacity generated by the EC system; P EC is the cooling power of the EC system.

[0092] IT System Thermal Model

[0093] In order to ensure the safe and stable operation of the data center and avoid the degradation of operating performance due to excessive temperature, waste heat recovery equipment is introduced to reuse the heat generated by the IT system. Therefore, the heat recovered from the IT system can be expressed as:

[0094] H IT =P IT ε IT

[0095] Among them, H IT P is the heat recovered from the IT system; IT The electrical power used to run IT systems.

[0096] Secondly, the energy management strategy model of the equipment center is constructed. Since the PGU operation strategy has a significant impact on the performance of the entire system, a key issue for system operators is to develop an efficient energy management strategy. There are two widely used energy management strategies for CCHP systems, namely follow the electric load (FEL) and follow the thermal load (FTL). FEL encourages PGU to meet the electric load first. If there is excess power, the energy storage is charged first. During the peak hours of the real-time electricity price in the power market, the energy storage is discharged to sell electricity to the grid and participate in the peak response of the grid. Electric energy storage reserve can also participate in the frequency regulation demand response of the grid as a fast response resource. The heat generated by the PGU is regarded as a by-product and is used to provide thermal energy demand. If there is excess heat, it can be stored through thermal storage facilities (such as water tanks). However, if there is a shortage, the boiler can be started to offset the demand. On the other hand, FTL gives priority to thermal loads. Both strategies and their variants have been extensively studied, and the results show that FEL performs better than FTL in terms of primary energy consumption and carbon dioxide emissions. Since the thermal load in the data center is negligible and the energy consumption of air conditioning is relatively low, the FTL strategy is ignored. To this end, the present invention proposes to design two candidate energy management strategies for the system, namely, tracking power load through waste heat reuse (FEL-WHR) and operating cost-aware energy management through waste heat reuse (OCM-WHR). The model construction of the two strategies is as follows:

[0097] FEL-WHR Strategy Model

[0098] Objective function: minp GD P GD

[0099] Constraints:

[0100] Among them, p GD P is the unit price of electricity purchased by the data center; GD The amount of electricity purchased from outside.

[0101] OCM-WHR Strategy Model

[0102] Objective function:

[0103] Constraints:

[0104] Among them, p b The energy price of natural gas.

[0105] It should be further explained that the basic models of various types of equipment in the data center are constructed based on the basic parameters of various types of equipment. The two strategy models constructed in the third step, namely the waste heat reuse tracking power load strategy model and the waste heat reuse operating cost awareness energy management strategy model, are further developed on the basis of the basic model in the second step. (Specifically, the waste heat reuse tracking power load strategy model relies on the generator unit model and absorption chiller model in the basic model to minimize the cost of purchasing electricity for the data center by optimizing waste heat recovery and utilization; the waste heat reuse operating cost awareness energy management strategy model combines various equipment models in the basic model to optimize overall energy management and minimize operating costs. This strategy takes into account a variety of factors including the energy price of natural gas)

[0106] Finally, based on the two data center energy management strategies and the characteristics of various internal equipment during the actual operation of the data center, the effectiveness of the two strategies in reducing the operating costs of the data center and reducing the carbon emissions of the data center are evaluated.

[0107] To this end, we established the economic evaluation index of data center operation and the carbon emission evaluation index of data center respectively. Then, based on the two types of evaluation indicators, combined with the actual operation of multiple data centers, we evaluated the effectiveness of the two energy management strategies. Among them, the economic evaluation index of operation is the sum of the energy purchase cost and operation cost of the data center and the unit cost economic conversion coefficient ψ S The carbon emission evaluation index is the difference between the power of various power-consuming equipment in the system and the power of waste heat recycling equipment and the unit power carbon emission coefficient ζ C The product of .

[0108] It is further explained that the operating economy evaluation index S E It is the sum of the data center energy purchase cost and operating cost and the unit cost economic conversion coefficient ψ E The product of is as follows.

[0109]

[0110] Among them, S E is the economic evaluation index of data center operation; E is the economic conversion coefficient of unit cost of data center; is the sum of the energy purchase cost and operation cost of the data center; GD The unit price of electricity purchased by the data center; b is the energy unit price of natural gas; η PGU is the power generation efficiency of the generator unit; P t GD,0 P is the amount of electricity purchased from the data center in one scheduling cycle after the model is solved. t PGU,0 is the power generation of PGU in one scheduling cycle after the model is solved; T is one scheduling cycle.

[0111] Data Center Carbon Emission Evaluation Index S C It is the difference between the power of various power-consuming equipment and waste heat recycling equipment in the data center and the carbon emission coefficient per unit power ζ C The product of is as follows.

[0112] S C =ζ C (P t IT,0 +P t EC,0 -P t AC,0 )

[0113] Among them, S C It is the carbon emission evaluation index of data center; C is the carbon emission coefficient per unit power of the data center; (P t IT,0 +P t EC,0 -P t AC,0 ) is the power difference between various power-consuming equipment and waste heat recycling equipment in the data center; P t IT，0 is the power consumption of the IT system in one scheduling cycle after the model is solved, P t EC，0 is the cooling power of the EC system in one scheduling cycle after the model is solved, P t AC，0 It is the power consumption of the AC system in one scheduling cycle after the model is solved.

[0114] It is further explained that:

[0115] According to the actual needs of the data center and the characteristics of various internal equipment, the effectiveness of the two strategies in reducing the operating costs of the data center and reducing the carbon emissions of the data center is evaluated respectively, and based on this, a strategy suitable for the low-carbon operation of the data center is selected;

[0116] Establish the economic evaluation index S of data center operation respectively E And the data center carbon emission evaluation index S C ,According to these two evaluation indicators and combined with the actual operation ,of multiple data centers, a strategic evaluation is conducted on the ,strategy model of tracking power load through waste heat reuse and the ,strategy model of operating cost awareness through waste heat reuse. ,Based on these evaluation results, the appropriate ,data center low-carbon operation strategy is selected for optimal ,scheduling. By dynamically adjusting the operation plans of various ,equipment in the data center, the energy efficiency of the data center is maximized and ,the operation cost is minimized.

[0117] Among them, the proposed strategy evaluation method is: operation economy and low-carbon operation are important indicators for considering the operation characteristics of data centers. For data centers, the emphasis on the two is somewhat different; for this reason, different weights are given to the above two indicators to construct an operation comprehensive evaluation index S; finally, the size of the operation comprehensive evaluation index S of the two energy management strategies is comprehensively compared, and the energy management strategy with a smaller operation comprehensive evaluation index S is more suitable for the low-carbon operation of the data center; the comprehensive evaluation index S can be calculated by the following formula.

[0118] S=a1S E +a2S C

[0119] Among them, a1 and a2 are weights, and their sum is equal to 1.

[0120] Among them, the proposed optimization scheduling method is: after selecting the strategy model suitable for the low-carbon operation of the data center from the "strategy model for tracking power load through waste heat reuse" and the "strategy model for operating cost awareness energy management through waste heat reuse", the model can be solved to obtain the operation plan of various equipment in the data center within a scheduling cycle after the model is solved, including: the power generation power P of PGU within a scheduling cycle t PGU，0 , AC system power consumption in one dispatching cycle P t AC，0 and the cooling capacity C t AC，0 , the cooling power P of the EC system in one scheduling cycle t EC，0 and the amount of cooling produced The power consumption P of the IT system in one dispatching cycle t IT，0 and recovered heat The amount of electricity purchased from the data center within a scheduling cycle P t GD，0 According to the series of operation plans obtained from the above solution, the data center issues corresponding operation plans to various types of equipment within it; after considering the actual operation conditions, various types of equipment are allowed to perform equipment operation within a range of 3% above or below the issued operation plan, so as to maximize the energy efficiency of the data center and minimize the operating costs.

[0121] Example 2

[0122] Reference Figure 2 , which is an embodiment of the present invention, provides a system for a data center control method for combined cooling, heating and power and surplus power demand response, including: an equipment parameter acquisition module, an equipment model construction module, a strategy optimization module, a scheduling execution module, an evaluation analysis module and a learning optimization module;

[0123] The equipment parameter acquisition module is used to obtain the basic parameters of various equipment in the data center that has introduced CCHP and waste heat recycling equipment;

[0124] The device model builds basic models of various devices in the data center based on the acquired device parameters;

[0125] The strategy optimization module constructs a strategy model for tracking power load through waste heat reuse and a strategy model for energy management with operational cost awareness through waste heat reuse. Based on the strategy evaluation results, it selects a strategy suitable for low-carbon operation of the data center.

[0126] The scheduling execution module dynamically adjusts the operation plans of various devices in the data center according to the results of the strategy optimization module, sends control instructions to various devices, and realizes collaborative work among devices;

[0127] Evaluation and analysis: Establish data center operation economic evaluation indicators and data center carbon emission evaluation indicators, and complete the strategy evaluation based on the current two evaluation indicators and the actual operation of multiple data centers;

[0128] The learning optimization module uses machine learning algorithms to learn and optimize the control strategy of equipment from historical data, thereby improving equipment operating efficiency and grid voltage support effects.

[0129] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0130] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0131] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0132] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0133] Example 3

[0134] In this embodiment, in order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments. This embodiment experiments the existing traditional method and the method of this embodiment respectively.

[0135] Use Figure 3 The data center framework shown in the figure is simulated and verified. Among them, the energy consumption ratio of the electric chiller is 4; the energy consumption ratio of the absorption chiller is 1.2; the installed capacity of the power generation unit in the cogeneration unit is 0.5kW, the power generation efficiency is 0.3, and the heat recovery efficiency is 1.87; the heat recovery efficiency of the waste heat recycling equipment is 0.8; and the heat recovery efficiency of the IT system equipment is 0.2. At the same time, the energy prices used by the system are shown in Table 1.

[0136] Table 1 Energy prices

[0137]

[0138] In order to verify the superiority of the method proposed in this invention, different cases were set for simulation and the simulation results were compared as follows:

[0139] Case 1: The data center has neither a combined cooling, heating and power unit nor waste heat recovery equipment. It can only be cooled by electric chillers, and the electricity is purchased entirely from the external power grid.

[0140] Case 2: Case 2 adds a waste heat recovery device WHR based on Case 1;

[0141] Case 3: The FEL-WHR strategy in step S3 of the present invention includes a combined cooling, heating and power unit and waste heat recycling equipment. This case tracks the power load through waste heat recycling;

[0142] Case 4: The OCM-WHR strategy in step S3 of the present invention includes a combined cooling, heating and power unit and waste heat recycling equipment. This case implements operation cost-conscious energy management through waste heat recycling.

[0143] The average energy consumption of IT equipment and electric chillers after the simulation of the four cases is as follows: Figure 4 As shown in Table 2, the comparison of the four cases in terms of operating cost, primary energy consumption, carbon dioxide emissions and power efficiency is shown in Table 2.

[0144] Table 24 Comparison of indicators of cases

[0145]

[0146]

[0147] Depend on Figure 4 It can be seen that the method proposed in the present invention has a significant effect in reducing the energy consumption of the data center. At the same time, Table 2 shows the effectiveness of the method proposed in the present invention in reducing the operating costs of the data center, primary energy consumption, etc. At the same time, compared with cases 1 and 2, the carbon dioxide emissions and power efficiency of the method proposed in the present invention are not significantly different, and it has a certain degree of environmental protection.

[0148] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A data center control method for combined cooling, heating and power supply and surplus power demand response, characterized in that: include: Obtain the basic parameters of various equipment in the data center that has introduced CCHP and waste heat recovery equipment; Build basic models of various devices in the data center based on the acquired device parameters; The power load tracking strategy model through waste heat reuse and the operational cost-aware energy management strategy model through waste heat reuse are constructed respectively; Based on the two constructed energy management strategies, the two strategies are evaluated according to the actual needs of the data center, and the strategy suitable for the low-carbon operation of the data center is selected according to the evaluation results; Through the selected data center low-carbon operation strategy, comprehensive consideration of the operating status of various energy equipment, dynamic adjustment of the operation plan of various equipment in the data center, the energy efficiency of the data center is maximized and the operating cost is minimized.

2. The data center control method for combined cooling, heating and power and surplus power demand response according to claim 1, characterized in that: The basic parameters include the power generation efficiency η of the PGU when the data center is regulated PGU 、Efficiency of heat recovery device ε HRU , PGU heat recovery efficiency ε PGU 、Energy consumption ratio of AC equipment in CCHP Cop AC , EC equipment energy consumption ratio Cop EC and the heat recovery efficiency of IT equipment in the data center ε IT .

3. The data center control method for combined cooling, heating and power and surplus power demand response according to claim 2, characterized in that: Based on the acquired equipment parameters, basic models of various equipment in the data center are constructed, including generator unit model, absorption chiller model, electric chiller model, and IT system thermal model; The generator unit model is expressed as: P PGU =F PGU the PGU H PGU =P PGU e HRU Among them, F PGU is the natural gas power injected into PGU; P PGU is the power generation capacity of PGU; is the maximum power generation of PGU, H PGU is the heat recovered from the PGU; The absorption refrigerator model is expressed as: C AC =H AC Cop AC Among them, C AC is the cooling capacity generated by the AC system; H AC Heat recovered from PGU and IT systems for AC; The electric chiller model is expressed as: C EC =P EC Cop EC Among them, C EC is the cooling capacity generated by the EC system; P EC is the cooling power of the EC system; The IT system thermal model is expressed as: H IT =P IT e IT Among them, H IT P is the heat recovered from the IT system; IT The electrical power used to run IT systems.

4. The data center control method for combined cooling, heating and power and surplus power demand response according to claim 3, characterized in that: The power load tracking strategy model through waste heat reuse is to construct an objective function based on the unit price of electricity purchased from the data center and the amount of energy purchased from the data center as the minimum, and the expression is: minp GD P GD P GD +P PGU -P IT =P EC C EC +C AC ≥P IT Among them, p GD P is the unit price of electricity purchased by the data center; GD The amount of electricity purchased from outside.

5. The data center control method for combined cooling, heating and power and surplus power demand response according to claim 4, characterized in that: The operational cost-conscious energy management strategy model through waste heat reuse is expressed as: P PGU +P GD -P IT ≥P EC C EC +C AC ≥P IT P EC ,P GD ≥0 Among them, p b The energy price of natural gas.

6. The data center control method for combined cooling, heating and power and surplus power demand response according to claim 5, characterized in that: The two energy management strategies constructed include evaluating the effectiveness of the two strategies in reducing the operating costs of the data center and reducing the carbon emissions of the data center according to the actual needs of the data center and the characteristics of various internal equipment, and selecting a strategy suitable for the low-carbon operation of the data center; The economic evaluation indexes for data center operation and the carbon emission evaluation indexes for data center are established respectively. According to these two evaluation indexes and in combination with the actual operation conditions of multiple data centers, a strategy evaluation is conducted on the strategy model for tracking power load through waste heat reuse and the strategy model for energy management with operational cost awareness through waste heat reuse. Based on the evaluation results, the appropriate low-carbon operation strategy for the data center is selected for optimal scheduling. By dynamically adjusting the operation plans of various equipment in the data center, the energy efficiency of the data center is maximized and the operating cost is minimized.

7. The data center control method for combined cooling, heating and power and surplus power demand response according to claim 6, characterized in that: The operation economic evaluation index is the sum of the data center's energy purchase cost and operation cost and the unit cost economic conversion coefficient ψ S The product of is expressed as: Among them, S E is the economic evaluation index of data center operation; E is the economic conversion coefficient of unit cost of data center; is the sum of the energy purchase cost and operation cost of the data center; GD The unit price of electricity purchased by the data center; b is the energy unit price of natural gas; η PGU is the power generation efficiency of the generator unit; P t GD,0 P is the amount of electricity purchased from the data center in one scheduling cycle after the model is solved. t PGU,0 is the power generation of PGU in one dispatching cycle after the model is solved; T is one dispatching cycle; The carbon emission evaluation index of the data center is the difference between the power of various power-consuming equipment in the system and the power of waste heat recycling equipment and the unit power carbon emission coefficient ζ C The product of is expressed as: S C =ζ C (P t IT +P t EC -P t AC ) Among them, S C It is the carbon emission evaluation index of data center; C is the carbon emission coefficient per unit power of the data center; (P t IT,0 +P t EC ,0 -P t AC,0 ) is the power difference between various power-consuming equipment and waste heat recycling equipment in the data center; P t IT，0 is the power consumption of the IT system in one scheduling cycle after the model is solved, P t EC，0 is the cooling power of the EC system in one scheduling cycle after the model is solved, P t AC，0 It is the power consumption of the AC system in one scheduling cycle after the model is solved.

8. A system using the data center control method for combined cooling, heating and power and surplus power demand response as claimed in any one of claims 1 to 7, characterized in that: It includes equipment parameter acquisition module, equipment model building module, strategy optimization module, scheduling execution module, evaluation and analysis module and learning optimization module; The equipment parameter acquisition module is used to obtain the basic parameters of various equipment in the data center that introduces CCHP and waste heat recycling equipment; The device model constructs basic models of various types of devices in the data center according to the acquired device parameters; The strategy optimization module respectively constructs a strategy model for tracking power load through waste heat reuse and a strategy model for operating cost-aware energy management through waste heat reuse, and selects a strategy suitable for low-carbon operation of the data center; The scheduling execution module dynamically adjusts the operation strategy of the equipment according to the results of the strategy optimization module, sends control instructions to each new energy equipment, and realizes the collaborative work between the equipment; The evaluation analysis establishes the economic evaluation index of data center operation and the carbon emission evaluation index of data center, and completes the strategy evaluation based on the current two evaluation indexes and the actual operation conditions of multiple data centers; The learning optimization module uses a machine learning algorithm to learn and optimize the control strategy of the equipment from historical data, thereby improving the equipment operation efficiency and the effect of grid voltage support.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the data center control method for combined cooling, heating and power and surplus power demand response according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the data center control method for combined cooling, heating and power and surplus power demand response as described in any one of claims 1 to 7 are implemented.

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