CN113553718A - Method for configuring equipment capacity of comprehensive energy supply system of green data center - Google Patents

Method for configuring equipment capacity of comprehensive energy supply system of green data center Download PDF

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CN113553718A
CN113553718A CN202110854835.3A CN202110854835A CN113553718A CN 113553718 A CN113553718 A CN 113553718A CN 202110854835 A CN202110854835 A CN 202110854835A CN 113553718 A CN113553718 A CN 113553718A
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data center
energy supply
power
equipment
supply system
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郭明星
傅晨
王晓晖
刘盼盼
莫阮清
吕征宇
王梦薇
宣伟博
梅飞
刘皓明
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Hohai University HHU
State Grid Shanghai Electric Power Co Ltd
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Hohai University HHU
State Grid Shanghai Electric Power Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract

The invention discloses a method for configuring the equipment capacity of a comprehensive energy supply system of a green data center, which comprises the following specific steps: establishing an energy flow system of a green data center comprehensive energy supply system; establishing a mathematical model of energy supply equipment in an energy flow system of a green data center comprehensive energy supply system; establishing an objective function considering the lowest annual total cost and the lowest carbon emission of a green data center comprehensive energy supply system at the same time; establishing constraint conditions for optimal configuration of a comprehensive energy supply system of a green data center; establishing a configuration optimization model of the capacity of the green data center comprehensive energy supply system equipment according to the energy flow system, the mathematical model, the objective function and the constraint condition; and solving the configuration optimization model to obtain a system configuration result and outputting various economic cost, carbon emission and PUE values of the comprehensive energy supply system of the green data center. The invention realizes the optimized configuration of the capacity of the comprehensive energy supply system of the green data center, ensures the green and economic operation of the data center and has wide market application prospect.

Description

Method for configuring equipment capacity of comprehensive energy supply system of green data center
Technical Field
The invention belongs to the technical field of comprehensive energy systems, and particularly relates to a method for configuring the equipment capacity of a comprehensive energy supply system of a green data center.
Background
With the rapid development of technologies such as artificial intelligence, big data, smart cities, internet and the like, massive data need to be stored, encrypted, calculated and transmitted, and the number of data centers will show explosive growth. And for the operator of the data center, the annual energy consumption cost of a single data center can be up to several millions of yuan.
From the energy supply side, the energy supply form of the current data center is single, the commercial power is taken as the main power, the green power supply proportion and the primary energy utilization ratio are low, a large amount of carbon emission is caused, and the use proportion of clean energy is still required to be improved. Furthermore, how to reduce the energy consumption cost of the data center, improve the energy utilization rate, and realize sustainable development becomes a problem to be solved urgently at present by the data center.
In recent years, with the continuous development of distributed energy technology, the comprehensive energy system gradually becomes a research hotspot of the energy industry. The comprehensive energy system utilizes energy in a cascade manner through combined cooling, heating and power, and has the characteristics of environmental protection, flexible energy configuration and the like. Therefore, the deep integration of the integrated energy system and the traditional power technology has become a necessary trend for the development of green data centers. The design of integrated energy systems, including the selection of the types of system equipment, such as prime movers, accumulators, and refrigeration units, and the determination of the capacity of the equipment to optimize the capacity of the equipment is a complex task, and the variation in cooling, heating, and power loads, as well as the price of the equipment and fuel, are all factors that need to be considered in the design.
Disclosure of Invention
The invention aims to provide a method for configuring the equipment capacity of a green data center comprehensive energy supply system, which perfects a mathematical optimization model of the system on the basis of providing an energy supply mode of the green data center comprehensive energy supply system and provides an optimized configuration result of the equipment capacity.
The invention aims to solve the problems by the following technical scheme:
a method for configuring the equipment capacity of a comprehensive energy supply system of a green data center is characterized by comprising the following steps: the data center and the comprehensive energy supply system are combined together, a mathematical model of energy supply equipment of the data center is established, and the optimal configuration result of the capacity of the energy supply equipment and the electric energy utilization efficiency value PUE of the data center are obtained by taking the system economy and the environmental protection as objective functions and combining constraint conditions; the method comprises the following specific steps:
the method comprises the following steps: establishing an energy flow system of a green data center comprehensive energy supply system;
step two: establishing a mathematical model of energy supply equipment in an energy flow system of a green data center comprehensive energy supply system;
step three: establishing an objective function considering the lowest annual total cost and the lowest carbon emission of a green data center comprehensive energy supply system at the same time;
step four: establishing constraint conditions for optimal configuration of a comprehensive energy supply system of a green data center;
step five: establishing a configuration optimization model of the capacity of the green data center comprehensive energy supply system equipment according to the energy flow system, the mathematical model, the objective function and the constraint condition;
step six: and solving the configuration optimization model to obtain a system configuration result, and outputting various economic cost, carbon emission and PUE values of the comprehensive energy supply system of the green data center.
In the energy flow system in the first step, a gas internal combustion engine, commercial power provided by a power grid and photovoltaic power generation meet the electrical load demand of a data center; during the operation of the gas internal combustion engine, a large amount of high-temperature and high-pressure flue gas and cylinder jacket water are generated and are respectively supplied to the absorption refrigerator and the heat exchanger; the centrifugal refrigerator consumes electric energy to generate cold energy, and the cold energy and the absorption refrigerator meet the refrigeration requirement of the data center; if the heat generated by the heat exchanger cannot meet the requirements of the office area, adding a gas boiler for supplement.
The mathematical model of the energy supply equipment in the second step comprises a photovoltaic power generation model, a gas engine model, a centrifugal refrigerator model, an absorption refrigerator model, a heat exchanger model and a gas boiler model, specifically,
(21) photovoltaic power generation model:
Figure BDA0003183743800000021
wherein, PPVThe unit is kW which is the generating power of the photovoltaic power generation equipment; f. ofPVFor the energy conversion efficiency of photovoltaic power output, 0.9 is usually taken;
Figure BDA0003183743800000022
rated output power of the photovoltaic power generation equipment under a standard condition; i is the actual radiation intensity; i iseIs the standard radiation intensity; etaPIs the temperature power coefficient; t is tPVIs the actual temperature of the photovoltaic module;
Figure BDA0003183743800000023
is the rated temperature of the photovoltaic module;
(22) gas internal combustion engine model: pGE＝ηGEVGEqgWherein P isGEPower output by the internal combustion engine; etaGEThe power generation efficiency of the internal combustion engine; vGEIs the natural gas consumption rate; q. q.sgIs the heat value of natural gas;
(23) centrifugal refrigerator model: cCC＝ηCCPCCWherein, CCCThe refrigeration power of the centrifugal refrigerator; etaCCIs the conversion efficiency coefficient of the centrifugal refrigerator; pCCElectrical input power for the centrifugal chiller;
(24) absorption chiller model: cAC＝ηACHREWherein, CACThe refrigeration power of the absorption refrigerator; etaACIs the conversion efficiency coefficient of the absorption refrigerator; hREHeat power for waste heat recovery;
(25) a heat exchanger model: hGB＝ηGBVGBqgWherein H isGBThe heating power of the gas boiler is provided; etaGBThe heat efficiency of the gas boiler; vGBIs the natural gas consumption rate m3/h；qgIs the heat value kWh/m of natural gas3；
(26) A heat exchanger model:
Figure BDA0003183743800000024
wherein the content of the first and second substances,
Figure BDA0003183743800000025
the heat output power of the heat exchanger; etaEXThe heat exchange efficiency of the heat exchanger;
Figure BDA0003183743800000026
the heat input power of the heat exchanger.
The establishment in the third step considers the objective functions of the lowest annual total cost and the lowest carbon emission of the comprehensive energy supply system of the green data center at the same time as follows:
(31) total annual cost FATCThe lowest, namely: minFATC＝FInv+FGas+FM+FGridWherein F isInvEqual investment cost for each year; fGasAnnual fuel costs; fMAnnual maintenance costs; fGridThe electricity purchase cost for each year;
(32) annual carbon emission FACEThe lowest, namely:
Figure BDA0003183743800000031
wherein, PGrid,s,hElectric power output by the power grid at h hour of a typical day of s season; pGE,s,hThe output power of the gas internal combustion engine; pGB,s,hThe output power of the gas boiler; sigmaGridA power grid carbon emission factor; sigmaGasIs a natural gas carbon emission factor.
The cost functions involved in the total annual cost minimum are:
(311) annual equal investment cost FInvThe expression of (a) is:
Figure BDA0003183743800000032
wherein r is the discount rate; y is the service life of the equipment; j is the total number of devices; qjOptimizing installed capacity for the equipment; c. CInv,jInvestment cost per unit volume for equipment j;
(312) annual fuel cost FGasThe expression of (a) is:
Figure BDA0003183743800000033
wherein, cGasIs the gas value (yuan/m)3) (ii) a S is the total number of typical season classification such as summer, winter and transition season; dsDays of operation of the equipment for a typical season; h is the number of hours of operation of the equipment in a typical day; pGE,hAnd PGB,hRespectively the output power of the gas combustion engine and the gas boiler at the h hour of a typical day of a season s;
(313) annual maintenance cost FMThe expression of (a) is:
Figure BDA0003183743800000034
wherein, Pj,s,hPower for device j at h hour of typical day of season s; c. CM,jUnit maintenance costs for the equipment;
(314) annual electricity purchase fee FGridThe expression of (a) is:
Figure BDA0003183743800000035
wherein, PGrid,s,hThe power supply power of the power grid is the h hour of a typical day in the season s; cE,hThe time-sharing price for purchasing electricity.
The constraints in the fourth step include:
(41) electric load balance constraint: pL,s,h＝PPV,s,h+PGE,s,h+PGrid,s,h-PCC,s,hWherein P isLElectrical load at hour h of a typical day of a data center season s; pPV,s,hThe photovoltaic power generation power of the photovoltaic at the h hour of a typical day of a season s; pGE,s,hThe generated power is the h hour of a typical day of a season s of the gas combustion engine; pGrid,s,hThe power supply power of the power grid is h hour of a typical day of a season s; pCC,s,hElectrical input power for a centrifugal chiller unit at hour h of a typical day of season s;
(42) cold load balancing constraint: cL,s,h＝CAC,s,h+CCC,s,hWherein, CL,s,hTypical day hour cooling load of s of the data center season; cAC,s,hThe refrigerating output power of the absorption refrigerator at h hour of typical day of season s; cCC,s,hThe refrigerating output power of the centrifugal refrigerator at h hour of typical day of season s;
(43) thermal load balancing constraints:
Figure BDA0003183743800000041
wherein HL,s,hA thermal load for the data center at a typical h hour of a typical day in a typical season s; hGB,s,hHeat output power of the gas boiler at h hour of typical day of typical season s; hEX,s,hIs the heat output power of the heat exchanger at the h hour of a typical day in a typical season s;
Figure BDA0003183743800000042
is the heat-to-electricity ratio of a gas internal combustion engine, which is usually 1.05;
(44) and (3) equipment installed capacity constraint:
Figure BDA0003183743800000043
wherein the content of the first and second substances,
Figure BDA0003183743800000044
and
Figure BDA0003183743800000045
respectively, an upper limit and a lower limit of the installed capacity of the device j.
The configuration optimization model in the fifth step is as follows: the method takes the lowest annual total cost and the lowest annual carbon emission as objective functions and converts the double objective functions into a single objective function F ═ lambda-1FATC+λ2FACEWherein 0 is not more than lambda1≤1、0≤λ2Less than or equal to 1; based on a mathematical model of the energy supply equipment, an optimal configuration model of the equipment capacity of the green data center comprehensive energy supply system is established by taking an electric load balance constraint, a cold load balance constraint, a heat load balance constraint and an equipment installed capacity constraint as constraint conditions of a configuration optimization model.
The PUE value in the sixth step can measure the energy consumption level of the data center, and the specific calculation formula of the PUE value is as follows:
Figure BDA0003183743800000046
wherein, PIT,s,hThe h hour of a typical day of the s season, the electrical power consumed by data center IT equipment.
Compared with the prior art, the invention has the following advantages:
the configuration method of the invention considers the economic performance and environmental index of the system at the same time, takes the minimum annual total cost and the minimum carbon emission as the objective function, considers various load balance constraints and equipment operation constraints, formulates the operation strategy of each equipment in the system, establishes the configuration optimization model of the equipment capacity of the comprehensive energy supply system of the green data center, improves the optimization efficiency of the system model, ensures the green economic operation of the data center and has larger market application prospect.
Drawings
FIG. 1 is a schematic diagram of an energy flow system of a green data center integrated energy supply system according to the present invention;
FIG. 2 is a flow chart of a method for configuring the equipment capacity of the comprehensive energy supply system of the green data center according to the invention;
FIG. 3 is a summer typical daily load graph for a green data center used in an embodiment of the present invention;
FIG. 4 is a typical daily load graph for the transition season for a green data center used in an embodiment of the present invention;
FIG. 5 is a graph of typical daily winter loading for a green data center used in an embodiment of the present invention;
fig. 6 is a composite annual total cost diagram for a green data center in an embodiment of the invention.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
A method for configuring the capacity of a comprehensive energy supply system of a green data center is characterized in that the data center and the comprehensive energy supply system are combined together, a mathematical model of energy supply equipment of the data center is established, and an optimized configuration result of the capacity of the energy supply equipment and an electric energy utilization efficiency value PUE of the data center are obtained by taking the system economy and the environmental protection as objective functions and combining constraint conditions; as shown in fig. 2, the specific steps are as follows:
the method comprises the following steps: establishing an energy flow system (as shown in fig. 1) of a green data center comprehensive energy supply system, wherein in the energy flow system, a gas internal combustion engine, commercial power provided by a power grid and photovoltaic power generation meet the electric load demand of the data center; during the operation of the gas internal combustion engine, a large amount of high-temperature and high-pressure flue gas and cylinder jacket water are generated and are respectively supplied to the absorption refrigerator and the heat exchanger; the centrifugal refrigerator consumes electric energy to generate cold energy, and the cold energy and the absorption refrigerator meet the refrigeration requirement of the data center; if the heat generated by the heat exchanger cannot meet the requirements of the office area, adding a gas boiler for supplement.
Step two: establishing a mathematical model of energy supply equipment in an energy flow system of a green data center comprehensive energy supply system; the mathematical models comprise a photovoltaic power generation model, a gas engine model, a centrifugal refrigerator model, an absorption refrigerator model, a heat exchanger model and a gas boiler model, and comprise,
(21) photovoltaic power generation model:
Figure BDA0003183743800000051
wherein, PPVThe unit is kW which is the generating power of the photovoltaic power generation equipment; f. ofPVFor the energy conversion efficiency of photovoltaic power output, 0.9 is usually taken;
Figure BDA0003183743800000052
rated output power of the photovoltaic power generation equipment under a standard condition; i is the actual radiation intensity; i iseIs the standard radiation intensity; etaPIs the temperature power coefficient; t is tPVIs the actual temperature of the photovoltaic module;
Figure BDA0003183743800000053
is the rated temperature of the photovoltaic module;
(22) gas internal combustion engine model: pGE＝ηGEVGEqgWherein P isGEPower output by the internal combustion engine; etaGEThe power generation efficiency of the internal combustion engine; vGEIs the natural gas consumption rate; q. q.sgIs the heat value of natural gas;
(23) centrifugal refrigerator model: cCC＝ηCCPCCWherein, CCCThe refrigeration power of the centrifugal refrigerator; etaCCIs the conversion efficiency coefficient of the centrifugal refrigerator; pCCElectrical input power for the centrifugal chiller;
(24) absorption chiller model: cAC＝ηACHREWherein, CACThe refrigeration power of the absorption refrigerator; etaACIs the conversion efficiency coefficient of the absorption refrigerator; hREHeat power for waste heat recovery;
(25) a heat exchanger model: hGB＝ηGBVGBqgWherein H isGBThe heating power of the gas boiler is provided; etaGBThe heat efficiency of the gas boiler; vGBIs the natural gas consumption rate m3/h；qgIs the heat value kWh/m of natural gas3；
(26) A heat exchanger model:
Figure BDA0003183743800000061
wherein the content of the first and second substances,
Figure BDA0003183743800000062
the heat output power of the heat exchanger; etaEXThe heat exchange efficiency of the heat exchanger;
Figure BDA0003183743800000063
the heat input power of the heat exchanger.
Step three: establishing an objective function considering the lowest annual total cost and the lowest carbon emission of a green data center comprehensive energy supply system at the same time; comprises the steps of (a) preparing a mixture of a plurality of raw materials,
(31) total annual cost FATCThe lowest, namely: min FATC＝FInv+FGas+FM+FGridWherein F isInvEqual investment cost for each year; fGasAnnual fuel costs; fMAnnual maintenance costs; fGridThe electricity purchase cost for each year; wherein the content of the first and second substances,
(311) annual equal investment cost FInvThe expression of (a) is:
Figure BDA0003183743800000064
wherein r is the discount rate; y is the service life of the equipment; j is the total number of devices; qjOptimizing installed capacity for the equipment; c. CInv,jIs a device jInvestment cost per unit volume of (a);
(312) annual fuel cost FGasThe expression of (a) is:
Figure BDA0003183743800000065
wherein, cGasIs the gas value (yuan/m)3) (ii) a S is the total number of typical season classification such as summer, winter and transition season; dsDays of operation of the equipment for a typical season; h is the number of hours of operation of the equipment in a typical day; pGE,hAnd PGB,hRespectively the output power of the gas combustion engine and the gas boiler at the h hour of a typical day of a season s;
(313) annual maintenance cost FMThe expression of (a) is:
Figure BDA0003183743800000066
wherein, Pj,s,hPower for device j at h hour of typical day of season s; c. CM,jUnit maintenance costs for the equipment;
(314) annual electricity purchase fee FGridThe expression of (a) is:
Figure BDA0003183743800000067
wherein, PGrid,s,hThe power supply power of the power grid is the h hour of a typical day in the season s; cE,hTime-sharing price for purchasing electricity;
(32) annual carbon emission FACEThe lowest, namely:
Figure BDA0003183743800000068
wherein, PGrid,s,hElectric power output by the power grid at h hour of a typical day of s season; pGE,s,hThe output power of the gas internal combustion engine; pGB,s,hThe output power of the gas boiler; sigmaGridA power grid carbon emission factor; sigmaGasIs a natural gas carbon emission factor.
Step four: establishing constraint conditions for optimal configuration of a comprehensive energy supply system of a green data center; comprises the steps of (a) preparing a mixture of a plurality of raw materials,
(41) electric load balance constraint: pL,s,h＝PPV,s,h+PGE,s,h+PGrid,s,h-PCC,s,hWherein P isLElectrical load at hour h of a typical day of a data center season s; pPV,s,hThe photovoltaic power generation power of the photovoltaic at the h hour of a typical day of a season s; pGE,s,hThe generated power is the h hour of a typical day of a season s of the gas combustion engine; pGrid,s,hThe power supply power of the power grid is h hour of a typical day of a season s; pCC,s,hElectrical input power for a centrifugal chiller unit at hour h of a typical day of season s;
(42) cold load balancing constraint: cL,s,h＝CAC,s,h+CCC,s,hWherein, CL,s,hTypical day hour cooling load of s of the data center season; cAC,s,hThe refrigerating output power of the absorption refrigerator at h hour of typical day of season s; cCC,s,hThe refrigerating output power of the centrifugal refrigerator at h hour of typical day of season s;
(43) thermal load balancing constraints:
Figure BDA0003183743800000071
wherein HL,s,hA thermal load for the data center at a typical h hour of a typical day in a typical season s; hGB,s,hHeat output power of the gas boiler at h hour of typical day of typical season s; hEX,s,hIs the heat output power of the heat exchanger at the h hour of a typical day in a typical season s;
Figure BDA0003183743800000072
is the heat-to-electricity ratio of a gas internal combustion engine, which is usually 1.05;
(44) and (3) equipment installed capacity constraint:
Figure BDA0003183743800000073
wherein the content of the first and second substances,
Figure BDA0003183743800000074
and
Figure BDA0003183743800000075
respectively, an upper limit and a lower limit of the installed capacity of the device j.
In the green data center integrated energy supply system, the economic and technical parameters of each energy supply device are shown in tables 1 and 2, and the data center load conditions are shown in fig. 3, 4 and 5.
TABLE 1 technical parameters of the energy supply device
Figure BDA0003183743800000076
TABLE 2 Economy parameters of energy supply devices
Device name Investment cost (Yuan/kW) Maintenance costs (Yuan/kWh)
Gas internal combustion engine 2526 0.094
Centrifugal refrigerator 980 0.008
Absorption refrigerator 1056 0.014
Heat exchanger 205 0.006
Gas boiler 628 0.003
Photovoltaic system 7535 0.012
Step five: establishing a configuration optimization model of the capacity of the green data center comprehensive energy supply system equipment according to the energy flow system, the mathematical model, the objective function and the constraint condition; wherein, the lowest annual total cost and the lowest annual carbon emission are used as objective functions, and the double objective functions are converted into a single objective function F ═ lambda-1FATC+λ2FACEWherein 0 is not more than lambda1≤1、0≤λ2Less than or equal to 1; based on a mathematical model of the energy supply equipment, an optimal configuration model of the equipment capacity of the green data center comprehensive energy supply system is established by taking an electric load balance constraint, a cold load balance constraint, a heat load balance constraint and an equipment installed capacity constraint as constraint conditions of a configuration optimization model.
Step six: and solving the configuration optimization model by using MATLAB/CPLEX, solving the linear programming problem, obtaining a system configuration result, and outputting various economic cost, carbon emission and PUE values of the comprehensive energy supply system of the green data center.
The concrete calculation formula of the PUE value is as follows:
Figure BDA0003183743800000081
wherein, PIT,s,hThe h hour of a typical day of the s season, the electrical power consumed by data center IT equipment; PUE values can measure energy consumption levels of a data center.
In this embodiment, the discount rate of the design is 6%, the service life of the data center is 10 years, and the natural gas price is 3 yuan/cubic meter temporarily. The results of obtaining the optimum configuration of the equipment capacity are shown in tables 3 and 4, and the total annual cost composition is shown in fig. 6.
TABLE 3 configuration results of energy supply device capacities
Energy supply device Configuring results
Gas internal combustion engine (kW) 1692
Centrifugal type refrigerator (kW) 305
Absorption refrigerator (kW) 1396
Heat exchanger (kW) 423
Gas boiler (kW) 272
Photovoltaic (kW) 285
TABLE 4 indexes of energy supply equipment configuration results
Figure BDA0003183743800000082
Figure BDA0003183743800000091
According to the embodiment, the optimal configuration scheme of the capacity of the comprehensive energy supply system equipment of the green data center is successfully obtained, various economic expenses are successfully obtained, and the PUE value for measuring the energy consumption level of the data center and the annual carbon emission of the system are calculated. As can be seen from this embodiment: the green data center comprehensive source supply system effectively reduces the dependence of the system on commercial power by cascade utilization of energy, increases the use of natural gas, and ensures the economical and environment-friendly operation of the green data center. The PUE value of the green data center is less than 1.5, and the requirement of the country on the PUE of the newly-built data center is met.
Although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the invention; the technology not related to the invention can be realized by the prior art.

Claims (8)

1. A method for configuring the equipment capacity of a comprehensive energy supply system of a green data center is characterized by comprising the following steps: the data center and the comprehensive energy supply system are combined together, a mathematical model of energy supply equipment of the data center is established, and the optimal configuration result of the capacity of the energy supply equipment and the electric energy utilization efficiency value PUE of the data center are obtained by taking the system economy and the environmental protection as objective functions and combining constraint conditions; the method comprises the following specific steps:
the method comprises the following steps: establishing an energy flow system of a green data center comprehensive energy supply system;
step two: establishing a mathematical model of energy supply equipment in an energy flow system of a green data center comprehensive energy supply system;
step three: establishing an objective function considering the lowest annual total cost and the lowest carbon emission of a green data center comprehensive energy supply system at the same time;
step four: establishing constraint conditions for optimal configuration of a comprehensive energy supply system of a green data center;
step five: establishing a configuration optimization model of the capacity of the green data center comprehensive energy supply system equipment according to the energy flow system, the mathematical model, the objective function and the constraint condition;
step six: and solving the configuration optimization model to obtain a system configuration result, and outputting various economic cost, carbon emission and PUE values of the comprehensive energy supply system of the green data center.
2. The method for configuring the equipment capacity of the comprehensive energy supply system of the green data center according to claim 1, wherein the method comprises the following steps: in the energy flow system in the first step, a gas internal combustion engine, commercial power provided by a power grid and photovoltaic power generation meet the electrical load demand of a data center; during the operation of the gas internal combustion engine, a large amount of high-temperature and high-pressure flue gas and cylinder jacket water are generated and are respectively supplied to the absorption refrigerator and the heat exchanger; the centrifugal refrigerator consumes electric energy to generate cold energy, and the cold energy and the absorption refrigerator meet the refrigeration requirement of the data center; if the heat generated by the heat exchanger cannot meet the requirements of the office area, adding a gas boiler for supplement.
3. The method for configuring the equipment capacity of the comprehensive energy supply system of the green data center according to claim 1, wherein the method comprises the following steps: the mathematical model of the energy supply equipment in the second step comprises a photovoltaic power generation model, a gas engine model, a centrifugal refrigerator model, an absorption refrigerator model, a heat exchanger model and a gas boiler model, specifically,
(21) photovoltaic power generation model:
Figure FDA0003183743790000011
wherein, PPVThe unit is kW which is the generating power of the photovoltaic power generation equipment; f. ofPVFor the energy conversion efficiency of photovoltaic power output, 0.9 is usually taken;
Figure FDA0003183743790000012
rated output power of the photovoltaic power generation equipment under a standard condition; i is the actual radiation intensity; i iseIs the standard radiation intensity; etaPAs temperature powerA coefficient; t is tPVIs the actual temperature of the photovoltaic module;
Figure FDA0003183743790000013
is the rated temperature of the photovoltaic module;
(22) gas internal combustion engine model: pGE＝ηGEVGEqgWherein P isGEPower output by the internal combustion engine; etaGEThe power generation efficiency of the internal combustion engine; vGEIs the natural gas consumption rate; q. q.sgIs the heat value of natural gas;
(23) centrifugal refrigerator model: cCC＝ηCCPCCWherein, CCCThe refrigeration power of the centrifugal refrigerator; etaCCIs the conversion efficiency coefficient of the centrifugal refrigerator; pCCElectrical input power for the centrifugal chiller;
(24) absorption chiller model: cAC＝ηACHREWherein, CACThe refrigeration power of the absorption refrigerator; etaACIs the conversion efficiency coefficient of the absorption refrigerator; hREHeat power for waste heat recovery;
(25) a heat exchanger model: hGB＝ηGBVGBqgWherein H isGBThe heating power of the gas boiler is provided; etaGBThe heat efficiency of the gas boiler; vGBThe natural gas consumption rate is m 3/h; q. q.sgIs the heat value kWh/m of natural gas3；
(26) A heat exchanger model:
Figure FDA0003183743790000021
wherein the content of the first and second substances,
Figure FDA0003183743790000022
the heat output power of the heat exchanger; etaEXThe heat exchange efficiency of the heat exchanger;
Figure FDA0003183743790000023
the heat input power of the heat exchanger.
4. The method for configuring the equipment capacity of the comprehensive energy supply system of the green data center according to claim 1, wherein the method comprises the following steps: the establishment in the third step considers the objective functions of the lowest annual total cost and the lowest carbon emission of the comprehensive energy supply system of the green data center at the same time as follows:
(31) total annual cost FATCThe lowest, namely: minFATC＝FInv+FGas+FM+FGridWherein F isInvEqual investment cost for each year; fGasAnnual fuel costs; fMAnnual maintenance costs; fGridThe electricity purchase cost for each year;
(32) annual carbon emission FACEThe lowest, namely:
Figure FDA0003183743790000024
wherein, PGrid,s,hElectric power output by the power grid at h hour of a typical day of s season; pGE,s,hThe output power of the gas internal combustion engine; pGB,s,hThe output power of the gas boiler; sigmaGridA power grid carbon emission factor; sigmaGasIs a natural gas carbon emission factor.
5. The method for configuring the equipment capacity of the comprehensive energy supply system of the green data center according to claim 4, wherein the method comprises the following steps: the cost functions involved in the total annual cost minimum are:
(311) annual equal investment cost FInvThe expression of (a) is:
Figure FDA0003183743790000025
wherein r is the discount rate; y is the service life of the equipment; j is the total number of devices; qjOptimizing installed capacity for the equipment; c. CInv,jInvestment cost per unit volume for equipment j;
(312) annual fuel cost FGasThe expression of (a) is:
Figure FDA0003183743790000026
wherein, cGasIs the gas value (yuan/m)3) (ii) a S is the total number of typical season classification such as summer, winter and transition season; dsDays of operation of the equipment for a typical season; h is the number of hours of operation of the equipment in a typical day; pGE,s,hAnd PGB,s,hRespectively the output power of the gas combustion engine and the gas boiler at the h hour of a typical day of a season s;
(313) annual maintenance cost FMThe expression of (a) is:
Figure FDA0003183743790000031
wherein, Pj,s,hPower for device j at h hour of typical day of season s; c. CM,jUnit maintenance costs for the equipment;
(314) annual electricity purchase fee FGridThe expression of (a) is:
Figure FDA0003183743790000032
wherein, PGrid,s,hThe power supply power of the power grid is the h hour of a typical day in the season s; cE,hThe time-sharing price for purchasing electricity.
6. The method for configuring the equipment capacity of the comprehensive energy supply system of the green data center according to claim 1, wherein the method comprises the following steps: the constraints in the fourth step include:
(41) electric load balance constraint: pL,s,h＝PPV,s,h+PGE,s,h+PGrid,s,h-PCC,s,hWherein P isLElectrical load at hour h of a typical day of a data center season s; pPV,s,hThe photovoltaic power generation power of the photovoltaic at the h hour of a typical day of a season s; pGE,s,hThe generated power is the h hour of a typical day of a season s of the gas combustion engine; pGrid,s,hThe power supply power of the power grid is h hour of a typical day of a season s; pCC,s,hElectrical input power for a centrifugal chiller unit at hour h of a typical day of season s;
(42) cold load balancing constraint: cL,s,h＝CAC,s,h+CCC,s,hWherein, CL,s,hData centerCooling load at h hours of a typical day of s of the season; cAC,s,hThe refrigerating output power of the absorption refrigerator at h hour of typical day of season s; cCC,s,hThe refrigerating output power of the centrifugal refrigerator at h hour of typical day of season s;
(43) thermal load balancing constraints:
Figure FDA0003183743790000033
wherein HL,s,hA thermal load for the data center at a typical h hour of a typical day in a typical season s; hGB,s,hHeat output power of the gas boiler at h hour of typical day of typical season s; hEX,s,hIs the heat output power of the heat exchanger at the h hour of a typical day in a typical season s;
Figure FDA0003183743790000034
is the heat-to-electricity ratio of a gas internal combustion engine, which is usually 1.05;
(44) and (3) equipment installed capacity constraint:
Figure FDA0003183743790000035
wherein the content of the first and second substances,
Figure FDA0003183743790000036
and
Figure FDA0003183743790000037
respectively, an upper limit and a lower limit of the installed capacity of the device j.
7. The method for configuring the equipment capacity of the comprehensive energy supply system of the green data center according to claim 4, wherein the method comprises the following steps: the configuration optimization model in the fifth step is as follows: the method takes the lowest annual total cost and the lowest annual carbon emission as objective functions and converts the double objective functions into a single objective function F ═ lambda-1FATC+λ2FACEWherein 0 is not more than lambda1≤1、0≤λ2Less than or equal to 1; based on mathematical model of energy supply equipment, with electric load balance constraint, cold load balance constraint, heat load balance constraint and equipmentAnd (4) taking the machine capacity constraint as a constraint condition for configuring an optimization model, and establishing an optimization configuration model of the green data center comprehensive energy supply system equipment capacity.
8. The method for configuring the equipment capacity of the comprehensive energy supply system of the green data center according to claim 1, wherein the method comprises the following steps: the PUE value in the sixth step can measure the energy consumption level of the data center, and the specific calculation formula of the PUE value is as follows:
Figure FDA0003183743790000041
wherein, PIT,s,hThe h hour of a typical day of the s season, the electrical power consumed by data center IT equipment.
CN202110854835.3A 2021-07-28 2021-07-28 Method for configuring equipment capacity of comprehensive energy supply system of green data center Pending CN113553718A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114396822A (en) * 2022-01-17 2022-04-26 中煤科工(天津)清洁能源研究院有限公司 Energy comprehensive utilization configuration and operation method
CN114936810A (en) * 2022-07-25 2022-08-23 东南大学溧阳研究院 Day-ahead scheduling method based on data center space-time transfer characteristics

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114396822A (en) * 2022-01-17 2022-04-26 中煤科工(天津)清洁能源研究院有限公司 Energy comprehensive utilization configuration and operation method
CN114936810A (en) * 2022-07-25 2022-08-23 东南大学溧阳研究院 Day-ahead scheduling method based on data center space-time transfer characteristics
CN114936810B (en) * 2022-07-25 2022-10-18 东南大学溧阳研究院 Day-ahead scheduling method based on data center space-time transfer characteristics

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