Optimal scheduling method of data center energy supply considering comprehensive utilization of natural gas pressure energy

Abstract

The application discloses a data center energy supply optimization scheduling method considering comprehensive utilization of natural gas pressure energy, and relates to the technical field of data center energy management. The application constructs a data center energy supply system, including a natural gas pressure energy comprehensive utilization unit, a gas turbine, an absorption refrigeration machine, an electric refrigeration machine, energy storage equipment and a power distribution network connected with the data center respectively; an expansion power generation potential model and an expansion refrigeration potential model of the natural gas pressure energy comprehensive utilization unit are respectively constructed by using an analysis method; an electric power demand model and a cold power demand model of the data center are established, an optimization scheduling model of the data center energy supply system is constructed and solved, and the comprehensive utilization rate of the natural gas pressure energy is improved; meanwhile, through optimization of power supply and refrigeration of the data center, power supply and refrigeration demands of the data center are more optimized, and the data center is operated in a more energy-saving and low-carbon mode.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data center energy management, and more particularly to a data center energy supply optimization scheduling method considering comprehensive utilization of natural gas pressure energy. BACKGROUND

[0002] Natural gas, as a clean energy, is abundant in the southwest region and accounts for a large share in the energy market. With the development of the natural gas industry, the construction of natural gas pipelines has also rapidly expanded, forming a nationwide natural gas pipeline network based on the West-East Gas Pipeline and the Sichuan-East Gas Pipeline. In the future, the natural gas pipeline system will develop rapidly, and the southwest region, as an important natural gas production area, contains huge pressure energy in the process of natural gas extraction to users.

[0003] Currently, natural gas pressure energy is mainly used for power generation and refrigeration. However, due to the fact that pressure regulating stations are usually located in suburban areas, there are difficulties in consuming the generated electric energy and cold energy, resulting in energy waste. At the same time, with the full start of the "East Data West Calculation" project, more data centers will be built in the west in the future. In the past, data centers in China have relied on fossil fuels, and the utilization rate of clean energy is very low.

[0004] Therefore, how to improve the comprehensive utilization rate of natural gas pressure energy and provide a data center energy supply optimization scheduling method considering comprehensive utilization of natural gas pressure energy are problems that need to be solved by those skilled in the art. SUMMARY

[0005] Therefore, the present application provides a data center energy supply optimization scheduling method considering comprehensive utilization of natural gas pressure energy to solve the technical problems existing in the background art.

[0006] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0007] A data center energy supply optimization scheduling method considering comprehensive utilization of natural gas pressure energy, comprising:

[0008] Step 1, constructing a data center energy supply system; the data center energy supply system comprises a natural gas pressure energy comprehensive utilization unit, and a gas turbine, an absorption chiller, an electric chiller, an energy storage device and a power distribution network connected with the data center, respectively;

[0009] The natural gas pressure energy comprehensive utilization unit comprises a preheating device, an expander and a heat exchanger unit, the input end of the preheating device is connected with the output end of the natural gas high-pressure pipeline network, the output end of the preheating device is connected with the input end of the expander, the first output end of the expander is connected with the data center, and the second output end of the expander is connected with the heat exchanger unit and the data center in sequence;

[0010] Step 2, using The analytical method was used to construct expansion power generation potential models and expansion cooling potential models for the natural gas pressure energy integrated utilization unit, respectively, to obtain the pressure of the natural gas pressure energy integrated utilization unit. Power generation and cooling capacity;

[0011] Step 3: Establish the power demand model and cooling power demand model of the data center, and obtain the power and cooling power of the data center during operation;

[0012] Step 4: Construct an optimized scheduling model for the data center power supply system; to balance economic efficiency, the objective function is to minimize daily operating costs, including electricity purchase costs, energy purchase costs, and equipment maintenance costs; constraints are considered for power balance constraints, cooling power balance constraints, equipment power constraints, data center temperature constraints, and energy storage constraints.

[0013] Step 5: Use the Matlab-Yalmip-CPLEX toolkit to solve the optimized scheduling model and obtain the optimized objective function value.

[0014] Preferably, in the data center power supply system of step 1, the gas turbine provides electricity to the data center by burning depressurized natural gas, while the waste heat generated by the combustion of the gas turbine is used to drive the absorption chiller to provide cooling energy to the data center.

[0015] Preferably, in the data center power supply system of step 1, the electric chiller provides cooling energy to the data center through the power supplied by energy storage equipment or the power distribution network.

[0016] Preferably, in step 2, using The analytical method for constructing an expansion power generation potential model for a natural gas pressure energy integrated utilization unit specifically includes:

[0017]

[0018] In the formula, P r Expressing natural gas pressure Power generation capacity; η e This is a natural gas pressure energy integrated utilization unit that can generate electricity. Ratio; T0 is the temperature of natural gas after pressure regulation; R g q is the gas constant for natural gas; v P is the volumetric flow rate of natural gas through the expander under standard conditions; P is the pressure of natural gas before pressure regulation; P0 is the pressure of natural gas after pressure regulation; ρ is the density of natural gas in the pipeline network under standard conditions.

[0019] In step 2, using The analytical method for constructing an expansion and refrigeration potential model for a natural gas pressure energy integrated utilization unit specifically includes:

[0020]

[0021] In the formula, P t Indicates the temperature of natural gas cooling capacity; e x,t This represents the temperature of the natural gas in the natural gas pressure energy utilization unit at time t. q v ρ is the volumetric flow rate of natural gas passing through the expander under standard conditions; ρ is the density of natural gas in the pipeline network under standard conditions.

[0022] Preferably, step 3 involves establishing the power demand model and cooling power demand model for the data center, specifically including:

[0023] Establish a power demand model for the data center:

[0024] p d,t =(kf CPU +bN)+p server N

[0025] In the formula: p d,t Let be the power consumption of the data center at time t, N be the number of servers in the data center, k be the chip utilization rate of the servers in the data center, b be the positive correlation coefficient of the real-time power loss of the chips in the servers in the data center, and p be the power consumption of the servers in the data center. server This represents the base power consumption of the server cluster.

[0026] Establish a cooling power requirement model for the data center:

[0027]

[0028] In the formula: p d,t Q is the electrical power of the data center at time t. d，t Let t be the cooling power required for the data center to operate at time t, and LF be the load factor of the data center.

[0029] Preferably, the objective function for optimizing the scheduling model in step 4 specifically includes:

[0030]

[0031] In the formula p ex,t For the power exchange between the data center's power supply system and the power grid at time t, C ph,t C represents the grid purchase price of electricity. set Indicates the price of electricity sold to the grid; C g P represents the price per cubic meter of natural gas. g p represents the rate at which natural gas is consumed. r,t p b,tp e,t p m,t These represent the natural gas pressure at time t in the expander. The power generation at time t, the battery charging and discharging power at time t, the power of the electric chiller at time t, and the power generation of the gas turbine at time t, where η represents the heat exchange efficiency of the gas turbine, and c r c b c e c m c a These represent the operating and maintenance costs per unit power of the expander, the operating and maintenance costs per unit power of the battery, the operating and maintenance costs per unit power of the electric chiller, the operating and maintenance costs per unit power of the gas turbine, and the operating and maintenance costs per unit power of the absorption chiller, respectively.

[0032] Preferably, in step 4, the constraints consider electrical power balance constraints, cooling power balance constraints, equipment power constraints, data center temperature constraints, and energy storage constraints, specifically including:

[0033] Electric power balance constraints:

[0034] pr,t+pex,t+pm,t+pb,t=pe,t+pd,t,

[0035] In the formula, p r,t This indicates the natural gas pressure at time t in the expander. Power generation capacity; p ex,t The data center's power supply system exchanges power with the power grid at time t; p m,t p represents the power output of the gas turbine at time t. b,t p represents the battery charging and discharging power at time t. e，t p is the power of the electric chiller at time t; d,t The electrical power of the data center at time t during operation;

[0036] Cooling power balance constraints:

[0037] η HE (Q r +Q a +Q e )≥Q d

[0038] In the formula: η HE For cooling efficiency, Q r Q is the refrigeration power of the expansion mechanism. a For the refrigeration power of an absorption refrigeration system, Q e Q represents the refrigeration power of an electric refrigeration unit. d This refers to the cooling power required by the data center to maintain normal operating temperature.

[0039] Equipment power constraints:

[0040]

[0041]

[0042]

[0043] in, p r,t , These represent the natural gas pressure at time t in the expander. Upper and lower limits of power generation constraints; p ex,t , These represent the upper and lower limits of the power exchanged between the data center's power supply system and the power grid at time t, respectively. p b,t , These represent the upper and lower limits of the battery charging and discharging power at time t, respectively.

[0044] Data center temperature constraints:

[0045]

[0046] In the formula, This represents the ambient temperature value of the servers in the data center at time t. These represent the upper and lower limits of the ambient temperature for the servers in the data center at time t, respectively.

[0047] Energy storage constraints:

[0048]

[0049] In the formula, W b This refers to the energy stored by the energy storage device during charging and discharging. W b , These represent the upper and lower limits of the energy storage device's charging and discharging energy storage, respectively.

[0050] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a data center power supply optimization scheduling method that considers the comprehensive utilization of natural gas pressure energy, which has the following beneficial effects:

[0051] This invention utilizes natural gas pressure energy for power generation and cooling, while simultaneously optimizing the power supply from the distribution network, gas turbine, and battery. By coordinating the gas turbine, distribution network, battery, and natural gas pressure energy generation to provide electrical power to the data center, and the absorption chiller, electric chiller, and heat exchanger group to provide cooling energy, this invention improves the overall utilization rate of natural gas pressure energy and optimizes the power supply and cooling needs of the data center. This allows the cooling center to operate at a lower cost and in a more energy-efficient and low-carbon manner. Attached Figure Description

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below only constitute a part of the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of the provided drawings.

[0053] Figure 1 The method flowchart provided by the present application is shown in the figure.

[0054] Figure 2 The data center power supply system structure provided by the present application is shown in the figure.

[0055] Figure 3 The three scene annual carbon emission comparison graphs provided by the embodiments of the present application are shown in the figures. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0057] As Figure 1 , the embodiments of the present application disclose a data center power supply optimization scheduling method considering comprehensive utilization of natural gas pressure energy, comprising:

[0058] Step 1, constructing a data center power supply system; the data center power supply system comprises a natural gas pressure energy comprehensive utilization unit, and a gas turbine, an absorption chiller, an electric chiller, an energy storage device and a power distribution network connected with the data center respectively;

[0059] The natural gas pressure energy comprehensive utilization unit comprises a preheating device, an expander and a heat exchanger unit, the input end of the preheating device is connected with the output end of the natural gas high-pressure pipe network, the output end of the preheating device is connected with the input end of the expander, the first output end of the expander is connected with the data center, and the second output end of the expander is connected with the heat exchanger unit and the data center in sequence;

[0060] Step 2, using analysis method to construct an expansion power generation potential model and an expansion refrigeration potential model of the natural gas pressure energy comprehensive utilization unit respectively, to obtain the pressure power generation power and refrigeration power of the natural gas pressure energy comprehensive utilization unit;

[0061] Step 3, establish the electric power demand model and the cold power demand model of the data center, and obtain the electric power and refrigeration power of the data center during operation;

[0062] Step 4, build an optimal scheduling model of the energy supply system of the data center; in order to take into account the economy, the daily operation cost is taken as the objective function, including the purchase cost of electricity, the purchase cost of energy and the equipment maintenance cost; the constraint conditions include the electric power balance constraint, the refrigeration power balance constraint, the equipment power constraint, the data center temperature constraint and the energy storage constraint;

[0063] Step 5, use the Matlab-Yalmip-CPLEX tool package to solve the optimal scheduling model, and obtain the optimal objective function value.

[0064] The following will further detail each step of the embodiment:

[0065] First, step 1, build the energy supply system of the data center, and the data center energy supply architecture considering the comprehensive utilization of natural gas pressure energy in the embodiment is as shown in Figure 2 , which includes a natural gas pressure energy comprehensive utilization unit, and a gas turbine, an absorption chiller, an electric chiller, an energy storage device and a power distribution network connected with the data center respectively;

[0066] The natural gas pressure energy comprehensive utilization unit mainly consists of a preheating device, an expander and a heat exchanger set, the input end of the preheating device is connected with the output end of the high-pressure pipe network of natural gas, the output end of the preheating device is connected with the input end of the expander, the first output end of the expander is connected with the data center, and the second output end of the expander is connected with the heat exchanger set and the data center in sequence.

[0067] In the natural gas pressure energy comprehensive utilization unit, the natural gas in the high-pressure pipe network is first preheated by the preheating device, and then enters the natural gas expander to expand and do work to drive the generator to generate electricity. The expander in the embodiment can adopt a turbine expander. The electric energy generated by the expander is connected to the data center power supply network through a line. As the temperature of the natural gas decreases, the cold energy generated is connected with the heat dissipation components of the data center through the heat exchanger set, and then the low-pressure natural gas enters the downstream low-pressure pipe network.

[0068] The power supply system of the data center includes the electric energy generated by the natural gas comprehensive utilization unit, the power distribution network connected through the tie line, the gas turbine generating electricity by burning natural gas and the battery as an energy storage element.

[0069] The cooling system of the data center includes the cold energy generated by the natural gas pressure energy comprehensive utilization system, the electric chiller consuming system electric energy for heat dissipation of the data center, and the absorption chiller driven by the waste heat of the gas turbine, which are used together for the heat dissipation system of the data center.

[0070] Secondly, in step 2, through An analytical model was used to establish the power generation and cooling potential of the natural gas pressure energy integrated utilization unit.

[0071] This embodiment uses The analytical method assesses the pressure energy of a natural gas pressure regulating station. In this station, the high-pressure natural gas undergoes pressure reduction via an expander, resulting in a decrease in both pressure and temperature. Treating the natural gas during the pressure regulating process as an open system, the specific enthalpy of the natural gas is then analyzed. Including specific pressure caused by pressure changes during the pressure regulation process and the specific temperature caused by temperature changes Composition. Natural gas pressure energy generates electricity through specific pressure. The formula for calculating the generated energy is as follows:

[0072]

[0073] In the formula, P r Expressing natural gas pressure Power generation capacity; η e This is a natural gas pressure energy integrated utilization unit that can generate electricity. Ratio; T0 is the temperature of natural gas after pressure regulation; R g q is the gas constant for natural gas; v P is the volumetric flow rate of natural gas through the expander under standard conditions; P is the pressure of natural gas before pressure regulation; P0 is the pressure of natural gas after pressure regulation; ρ is the density of natural gas in the pipeline network under standard conditions.

[0074] The cold energy generated during the comprehensive utilization of natural gas pressure energy can be used to... Analysis and calculations yielded the enthalpy of natural gas as an ideal gas. This indicates the enthalpy of natural gas. Divided into two parts: temperature e x,t and pressure e x,p The temperature of natural gas e x,t It can be represented as:

[0075]

[0076] In the formula, C p This represents the specific heat capacity of natural gas, and T and T0 represent the temperature values ​​of natural gas before and after pressure regulation, respectively.

[0077] To calculate the cold energy generated during the expansion of natural gas, we must first calculate the gas temperature after expansion. Since the expansion process of natural gas is highly variable, the outlet temperature after expansion can be expressed as:

[0078]

[0079] Wherein, n is the non-isentropic index of the expander, P2 is the pressure value of the natural gas before expansion, P2 is the pressure value of the natural gas after expansion, the non-isentropic index of the expander can be obtained by the following formula,

[0080]

[0081] ξ represents the energy loss rate of the expander, and the embodiment takes 0.085; m is the isentropic expansion index of the high-pressure pipe network natural gas through the expander, and the embodiment takes 1.29; therefore, the non-isentropic index n of the turbine expander can be calculated to be about 1.26;

[0082] The expansion refrigeration potential model of the natural gas pressure energy comprehensive utilization unit can be expressed by the following formula:

[0083]

[0084] In the formula, P t represents the refrigeration power of the natural gas temperature e x,t represents the temperature of the natural gas in the natural gas pressure energy comprehensive utilization unit at time t q v is the volume flow of the natural gas through the expander under standard state; and ρ is the density of the natural gas in the pipe network under standard state.

[0085] Step 3, establish the electric power demand model and the cold power demand model of the data center, and obtain the electric power and the refrigeration power when the data center is running.

[0086] The data center needs to consume a large amount of electric energy when performing user storage, operation and other tasks, and the required electric power is shown in the formula:

[0087] p d,t =(kf CPU +bN)+p server N

[0088] In the formula, p d,t is the electric power when the data center is running at time t, N is the number of servers of the data center, k represents the utilization rate of the chip in the server of the data center, b represents the positive correlation coefficient of the real-time power loss of the chip in the server of the data center, and p server is the basic power consumption of the server cluster.

[0089] The actual operation of the data center consumes much more electric energy than the electric energy generated by the expander of the natural gas pressure energy comprehensive utilization unit. In order to meet the actual demand for electric power of the data center, the power grid, the battery and the gas turbine generator are jointly used as the power supply source of the data center.

[0090] Meanwhile, the data center generates a large amount of heat during operation, which will endanger the normal operation of the data center if not dissipated in time. Therefore, the refrigeration equipment is needed to dissipate heat for the data center. The refrigeration power required by the data center is shown in the following formula:

[0091]

[0092] In the formula, p d,t is the electric power of the data center at time t, Q d，t is the refrigeration power required by the data center at time t, and LF is the load factor of the data center.

[0093] Similarly, the actual operation of the data center requires much more cold energy than the cold energy generated by the expander and the heat exchange unit of the natural gas pressure energy comprehensive utilization unit. In order to meet the actual refrigeration power of the data center, the absorption refrigeration machine and the electric refrigeration machine are jointly used as the power supply source of the data center.

[0094] Step 4, an optimal scheduling model of the data center energy supply system is constructed. In this embodiment, the power supply constraints of the power grid, the power constraints of the gas turbine and the absorption refrigeration machine and the charge-discharge power constraints of the battery are comprehensively considered. An optimal scheduling model is constructed with the minimum daily operation cost as the objective function.

[0095] Objective function:

[0096] The natural gas pressure energy comprehensive utilization system is integrated into the optimal scheduling model of the data center energy supply, and the power exchange power of the power grid, the output electric power of the gas turbine, the charge-discharge power of the battery, the refrigeration power of the electric refrigeration machine and the refrigeration power of the absorption refrigeration machine are comprehensively considered. An economic optimal scheduling model of the data center energy supply is constructed. The main goal of the economic optimal scheduling of the energy supply is to minimize the daily operation cost. The objective function is composed of three parts, that is, the purchase cost of the power grid, the purchase cost of the gas and the use and maintenance cost of the equipment. Therefore, the objective function of the optimal scheduling model of the data center energy supply considering the natural gas pressure energy comprehensive utilization is:

[0097]

[0098] In the formula, p ex,t is the power exchange power of the data center energy supply system and the power grid at time t, C ph,t represents the purchase price of the power grid, C set represents the sale price of the power grid, C g represents the price of each cubic meter of natural gas, and Pg p represents the rate at which natural gas is consumed. r,t p b,t p e,t p m,t These represent the natural gas pressure at time t in the expander. The power generation at time t, the battery charging and discharging power at time t, the power of the electric chiller at time t, and the power generation of the gas turbine at time t, where η represents the heat exchange efficiency of the gas turbine, and c r c b c e c m c a These represent the operating and maintenance costs per unit power of the expander, the operating and maintenance costs per unit power of the battery, the operating and maintenance costs per unit power of the electric chiller, the operating and maintenance costs per unit power of the gas turbine, and the operating and maintenance costs per unit power of the absorption chiller, respectively.

[0099] Constraints:

[0100] The constraints consider power balance constraints, cooling power balance constraints, equipment power constraints, data center temperature constraints, and energy storage constraints, specifically including:

[0101] Electric power balance constraints:

[0102] pr,t+pex,t+pm,t+pb,t=pe,t+pd,t,

[0103] In the formula, p r,t This indicates the natural gas pressure at time t in the expander. Power generation capacity; p ex,t The data center's power supply system exchanges power with the power grid at time t; p m,t p represents the power output of the gas turbine at time t. b,t p represents the battery charging and discharging power at time t. e，t p is the power of the electric chiller at time t; d,t The electrical power of the data center at time t during operation;

[0104] Cooling power balance constraints:

[0105] η HE (Q r +Q a +Q e )≥Q d

[0106] In the formula: η HE For cooling efficiency, Q r Q is the refrigeration power of the expansion mechanism. a For the refrigeration power of an absorption refrigeration system, Q e Q represents the refrigeration power of an electric refrigeration unit.d the cooling power required by the data center to maintain normal working temperature;

[0107] Device power constraints:

[0108]

[0109]

[0110]

[0111] wherein, p r,t , respectively represent the upper limit constraint and the lower limit constraint of the power exchanged between the data center and the power grid at time t; respectively represent the upper limit constraint and the lower limit constraint of the power exchanged between the data center and the power grid at time t; p ex,t respectively represent the upper limit constraint and the lower limit constraint of the power exchanged between the data center and the power grid at time t; p b,t respectively represent the upper limit constraint and the lower limit constraint of the power exchanged between the data center and the power grid at time t;

[0112] Data center temperature constraints:

[0113]

[0114] wherein, represents the ambient temperature value of the server of the data center at time t; respectively represent the upper limit constraint value and the lower limit constraint value of the ambient temperature value of the server of the data center at time t;

[0115] Energy storage constraints:

[0116]

[0117] wherein, W b represents the energy storage of the energy storage device during charging and discharging, Wb, respectively represent the upper limit constraint and the lower limit constraint of the energy storage of the energy storage device during charging and discharging.

[0118] Step 5, for the above optimization scheduling model, an optimization scheduling program is written by using a Matlab-Yalmip-CPLEX tool package to call CPLEX to solve the optimization scheduling model, so that an optimized target function value is obtained.

[0119] In combination with examples, the application has the following specific applications:

[0120] ​​A natural gas pressure station and a class A data center in the southwest region were used to set up an example to verify the feasibility of the scheme. The load demand data of the data center on a typical day were selected, and the specific data of the data center's demand for electric energy and cold energy were simulated by an energy consumption simulation software. The price of the data center's power supply system purchased from the power distribution network at different time periods was obtained according to the electricity price curve constructed according to the actual electricity price in the southwest region. The selling price of the system was the electricity price multiplied by the selling coefficient 0.8. The natural gas price was 2.5 yuan / Nm3, the natural gas heat value was 36.22 MJ / kg, and the exchange power limit between the system and the power distribution network was ±2000 kW.

[0121] An optimization scheduling model of a data center power supply system containing natural gas pressure energy was constructed, which involved the upper and lower limits of the power of each device, the efficiency of the heat exchange device, the energy efficiency ratio of the absorption refrigerator, and the use and maintenance cost of each device as shown in the following table:

[0122]

[0123]

[0124] The energy efficiency level and carbon emissions of the data center were calculated under three different scenarios:

[0125] 1. The data center power supply optimization scheduling without natural gas pressure energy was used as the control group to obtain the data center power supply operation strategy without natural gas pressure energy.

[0126] 2. The data center power supply optimization scheduling with natural gas pressure energy was used as the control group without energy storage.

[0127] 3. The data center power supply optimization scheduling with natural gas pressure energy and energy storage elements was used as the experimental group to verify the low-carbon nature of the scheme.

[0128] The optimization operation cost of the data center under the three different schemes and the energy efficiency level of the data center are shown in the following table.

[0129]

[0130] The total cost and the energy efficiency level of the data center in a scheduling period under the three scenarios were compared. The gas cost of the data center power supply system in the first scenario was relatively high, accounting for 79.4% of the total operation cost, while the total cost of the power supply system in the second and third scenarios decreased by 33% and 36.9% respectively compared with the first scenario, indicating that the introduction of the natural gas pressure energy comprehensive utilization system has great energy-saving potential. At the same time, it can be found that after the introduction of the natural gas pressure energy comprehensive utilization system, the power supply efficiency PUE of the data center is improved from the secondary energy efficiency level (1.6 < PUE ≤ 1.8) to the primary energy efficiency level (1 < PUE ≤ 1.6).

[0131] In this embodiment, the data center PUE expression is:

[0132]

[0133] In the formula: P ex P represents the electricity purchased by the data center from the power distribution network. r Expressing natural gas pressure Power generation; P m P represents the power generation from the gas turbine. d This represents the total power consumption of the data center.

[0134] Annual carbon emissions of data centers in three different scenarios, as follows Figure 3 As shown, from Figure 3 It can be seen that the annual carbon emissions of the data center in the third scenario, which includes natural pressure relief energy and increases energy storage, are lower than those of the data center in the second and third scenarios.

[0135] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0136] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data center power supply optimization scheduling method considering the comprehensive utilization of natural gas pressure energy, characterized in that, The method includes the following steps: Step 1: Construct a data center power supply system; the data center power supply system includes a natural gas pressure energy comprehensive utilization unit, as well as a gas turbine, absorption chiller, electric chiller, energy storage equipment and power distribution network respectively connected to the data center; The natural gas pressure energy comprehensive utilization unit includes a preheating device, an expander, and a heat exchanger unit. The input end of the preheating device is connected to the output end of the natural gas high-pressure pipeline network, the output end of the preheating device is connected to the input end of the expander, the first output end of the expander is connected to the data center, and the second output end of the expander is connected to the heat exchanger unit and the data center in sequence. Step 2: Using the expansion analysis method, construct the expansion power generation potential model and expansion cooling potential model of the natural gas pressure energy integrated utilization unit respectively, and obtain the pressure expansion power generation and cooling power of the natural gas pressure energy integrated utilization unit; Specifically, constructing the expansion power generation potential model of the natural gas pressure energy integrated utilization unit using the expansion analysis method includes: ； In the formula, P r The power generation capacity expressed by natural gas pressure η; e The ratio of electricity generated by the natural gas pressure energy integrated utilization unit; T0 is the temperature of the natural gas after pressure regulation; R g q is the gas constant for natural gas; v P is the volumetric flow rate of natural gas through the expander under standard conditions; P is the pressure of natural gas before pressure regulation; P0 is the pressure of natural gas after pressure regulation; ρ is the density of natural gas in the pipeline network under standard conditions. Step 3: Establish the power demand model and cooling power demand model of the data center, and obtain the power and cooling power of the data center during operation; Step 4: Construct an optimized scheduling model for the data center power supply system; to balance economic efficiency, the objective function is to minimize daily operating costs, including electricity purchase costs, energy purchase costs, and equipment maintenance costs; constraints are considered for power balance constraints, cooling power balance constraints, equipment power constraints, data center temperature constraints, and energy storage constraints. Step 5: Use the Matlab-Yalmip-CPLEX toolkit to solve the optimized scheduling model and obtain the optimized objective function value.

2. The data center power supply optimization scheduling method according to claim 1, characterized in that, In the data center power supply system of step 1, the gas turbine provides electricity to the data center by burning depressurized natural gas, while the waste heat generated by the gas turbine combustion is used to drive the absorption chiller to provide cooling energy to the data center.

3. The data center power supply optimization scheduling method according to claim 1, characterized in that, In the data center power supply system of step 1, the electric chiller provides cooling energy to the data center through the power supplied by energy storage equipment or the power distribution network.

4. The data center power supply optimization scheduling method according to claim 1, characterized in that, Step 2, which involves constructing an expansion and refrigeration potential model for the natural gas pressure energy integrated utilization unit using the tandem analysis method, specifically includes: ； In the formula, P t The refrigeration capacity indicates the temperature of the natural gas; e x,t The temperature of the natural gas in the natural gas pressure energy utilization unit at time t is represented by q. v ρ is the volumetric flow rate of natural gas passing through the expander under standard conditions; ρ is the density of natural gas in the pipeline network under standard conditions.

5. The data center power supply optimization scheduling method according to claim 1, characterized in that, Step 3 involves establishing the power demand model and cooling power demand model for the data center, specifically including: Establish a power demand model for the data center: ； In the formula: p d,t Let be the power consumption of the data center at time t, N be the number of servers in the data center, k be the chip utilization rate of the servers in the data center, b be the positive correlation coefficient of the real-time power loss of the chips in the servers in the data center, and p be the power consumption of the servers in the data center. server This represents the base power consumption of the server cluster. Establish a cooling power requirement model for the data center: ； In the formula: p d,t Q is the electrical power of the data center at time t. d，t Let t be the cooling power required for the data center to operate at time t, and LF be the load factor of the data center.

6. The data center power supply optimization scheduling method according to claim 1, characterized in that, The objective function for optimizing the scheduling model in step 4 specifically includes: ； In the formula p ex,t For the power exchange between the data center's power supply system and the power grid at time t, C ph,t C represents the grid purchase price of electricity. set Indicates the price of electricity sold to the grid; C g P represents the price per cubic meter of natural gas. g p represents the rate at which natural gas is consumed. r,t p b,t p e,t p m,t Let represent the power generation of the expander at time t (natural gas pressure σ), the battery charging / discharging power at time t, the power of the electric chiller at time t, and the power generation of the gas turbine at time t, respectively. Let η represent the heat exchange efficiency of the gas turbine, and c represent the power generation of the gas turbine at time t. r c b c e c m c a These represent the operating and maintenance costs per unit power of the expander, the operating and maintenance costs per unit power of the battery, the operating and maintenance costs per unit power of the electric chiller, the operating and maintenance costs per unit power of the gas turbine, and the operating and maintenance costs per unit power of the absorption chiller, respectively.

7. The data center power supply optimization scheduling method according to claim 6, characterized in that, In step 4, the constraints consider electrical power balance constraints, cooling power balance constraints, equipment power constraints, data center temperature constraints, and energy storage constraints, specifically including: Electric power balance constraints: ， In the formula, p r,t p represents the power generation capacity based on the natural gas pressure at time t in the expander; ex,t The data center's power supply system exchanges power with the power grid at time t; p m,t p represents the power output of the gas turbine at time t. b,t p represents the battery charging and discharging power at time t. e，t p is the power of the electric chiller at time t; d,t The electrical power of the data center at time t during operation; Cooling power balance constraints: ； In the formula: η HE For cooling efficiency, Q r Q is the refrigeration power of the expansion mechanism. a For the refrigeration power of an absorption refrigeration system, Q e Q represents the refrigeration power of an electric refrigeration unit. d This refers to the cooling power required by the data center to maintain normal operating temperature. Equipment power constraints: ； ； ； in, , Let represent the upper and lower limits of the natural gas pressure and power generation at time t in the expander, respectively. , These represent the upper and lower limits of the power exchanged between the data center's power supply system and the power grid at time t, respectively. , These represent the upper and lower limits of the battery charging and discharging power at time t, respectively. Data center temperature constraints: ， In the formula, This represents the ambient temperature value of the servers in the data center at time t. , These represent the upper and lower limits of the ambient temperature for the servers in the data center at time t, respectively. Energy storage constraints: ， In the formula, W b This refers to the energy stored by the energy storage device during charging and discharging. , These represent the upper and lower limits of the energy storage device's charging and discharging energy storage, respectively.

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