Data center micro energy network load control method under orderly power utilization scene

Abstract

The invention discloses a data center micro energy network load control method in an orderly power utilization scene, which comprises the following steps of: establishing a data center energy consumption model by considering the energy consumption of IT equipment and the energy consumption of a cooling system; a micro-energy network thermoelectric coupling energy management framework considering renewable energy source access and data center waste heat recovery is built in combination with various adjustable and controllable devices; carbon transaction and green certificate transaction are introduced, and an interaction mechanism between the two is applied; establishing a micro-energy grid low-carbon economic optimization scheduling model, and obtaining a data center micro-energy grid load control scheme considering economy and low-carbon property in an orderly power utilization scene; the method provides reference for the problem of load control of the micro-energy network of the data center in an orderly power utilization scene, and has a good application prospect.

Description

Technical Field

[0001] The present invention belongs to the field of power demand side management, and more specifically, relates to a method for controlling the load of a data center micro energy network in an orderly power consumption scenario. Background Art

[0002] In recent years, in order to achieve low-carbon power generation, the traditional power system has gradually evolved into a new power system. In this process, the demand-side resources have diversified. In addition to traditional loads, emerging loads are also growing continuously and gradually showing the characteristics of "producers and consumers", with many points and wide areas, and frequent interactions. Its flexible and diverse characteristics enable it to undertake more regulation tasks.

[0003] As a typical high-energy consumption and high-carbon emission load, data centers have become a new type of demand-side resource and have attracted a lot of attention. As a producer and consumer with two-way supply and demand, it consumes a lot of electricity to maintain the normal operation of the system, and is an important electricity consumer and carbon emitter in the regional energy system. On the other hand, due to the high heat dissipation rate of its equipment and the requirement of uninterrupted operation, it is a stable waste heat producer all year round. Improving energy efficiency and reducing greenhouse gas emissions are crucial for data centers. The existing data center micro-energy network optimization operation method provides relevant solutions for data center energy conservation and carbon reduction, but it does not systematically and comprehensively consider the data center micro-energy network load control method under the orderly power consumption scenario, and it is difficult to obtain a more low-carbon economic load control solution. How to make the data center micro-energy network operate more optimally under the orderly power consumption scenario is an urgent problem to be solved. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the purpose of the present invention is to provide a data center micro energy network load control method in an orderly power consumption scenario, aiming at the problem of data center demand management.

[0005] The present invention adopts the following technical solution.

[0006] A method for controlling a data center micro energy grid load in an orderly power consumption scenario comprises the following steps:

[0007] Step S1, considering the energy consumption of IT equipment and cooling system, establishing a data center energy consumption model;

[0008] Step S2, combining a variety of adjustable devices to build a micro energy network thermoelectric coupling energy management framework that considers renewable energy access and data center waste heat recovery;

[0009] Step S3, introduce carbon trading and green certificate trading and apply the interaction mechanism between the two;

[0010] Step S4, establishing a low-carbon economic optimization scheduling model for the micro-energy network, and obtaining an energy management optimization scheduling solution that takes into account both economy and low carbon.

[0011] Furthermore, the energy consumption model of the cooling system is:

[0012]

[0013] in, are the energy consumption of the data center cooling system and IT equipment in period t, LF t is the comprehensive load factor of the data center in period t, and its value is 2;

[0014] The total energy consumption of a data center is the sum of the energy consumption of IT equipment and cooling system:

[0015]

[0016] in, is the total energy consumption of the data center in period t.

[0017] Furthermore,

[0018] The micro energy network considering renewable energy access and waste heat recovery of data centers consists of two parts: an electric power network and a thermal power network.

[0019] The power network uses data centers as the main power load. While interacting with the upper power grid, it is connected to distributed new energy generators and is equipped with self-contained generators and electric energy storage equipment. The distributed new energy generators are wind and solar new energy generators.

[0020] The interaction between the micro energy grid system and the upper power grid must meet the power transmission constraints:

[0021]

[0022] in, are the electricity purchase and sales in time period t, is the power purchase and sales status indicator of the system in period t (interaction is 1, no interaction is 0), is the maximum value of line power transmission;

[0023] The system's cost of purchasing and selling electricity to the upper-level power grid is:

[0024]

[0025] in, are the purchase and sale prices of electricity in time period t respectively;

[0026] The actual output of the connected distributed wind and solar energy generating units is often less than the predicted output, and the wind and solar abandonment constraints are:

[0027]

[0028]

[0029] in, is the actual wind power and photovoltaic power generation in period t, Predict wind and photovoltaic power generation for time period t;

[0030] Self-provided generator sets need to meet the start-stop constraints and ramp constraints:

[0031]

[0032] in, is the start / stop status indicator of the generator set during period t, where the generator set is running and is 1, and the generator set is shut down and is 0. is the output of the generator set in period t, is the upper limit of the unit’s power generation capacity, V OG It is the climbing power limit of the unit;

[0033] The operating cost of a self-provided generator set consists of fixed costs, energy costs, and start-up and shutdown costs:

[0034]

[0035] in, is the total cost of the self-provided diesel generator set in period t, is the output of the diesel generator set in period t, C OG,fix is the fixed cost of diesel generator operation, p OG,fuel is the fuel cost, C OG,SU , C OG,SD They are the startup and shutdown costs of diesel generator sets respectively;

[0036] Electric energy storage devices need to meet charging and discharging constraints:

[0037]

[0038] in, is the power of the energy storage device, They are the minimum and maximum power of the energy storage device, is the charge and discharge efficiency, is the charge and discharge power in time period t, is the maximum charge and discharge power; They are the state indicators of charge and discharge, 1. When not charging or discharging is 0.

[0039] The loss cost of electric energy storage equipment is:

[0040]

[0041] in, is the loss cost of the electric energy storage device during period t, λ ES,loss is the loss coefficient.

[0042] The thermal network considers recovering waste heat from the data center to provide heat for users with heat loads in the region. The network is also equipped with cogeneration units, electric boilers and thermal energy storage equipment.

[0043] The waste heat recovery power of a data center is proportional to the power consumption:

[0044]

[0045] in, is the waste heat recovery power of the data center during period t, η DC is the thermoelectric energy conversion efficiency.

[0046] The power generation and heat generation of the cogeneration unit are in a certain proportion:

[0047]

[0048] in, are the electrical and thermal power of the CHP unit in time period t, η CHP is the unit's electric-to-heat ratio, is the upper limit of the unit’s electrical power, It is the start / stop status indicator of the unit in time period t (1 for operation and 0 for shutdown).

[0049] The cost of a combined heat and power unit consists of three parts: fixed cost, energy cost, and start-up and shutdown cost:

[0050]

[0051] in, is the total cost of the CHP unit in period t, C CHP,fix is the fixed cost of CHP unit operation, p CHP ,fuel 、h CHP,fuel are the fuel costs related to electrical power and thermal power, respectively, and C CHP,SU , C CHP,SD It is the startup and shutdown cost of CHP unit.

[0052] The energy conversion model of the electric boiler is:

[0053]

[0054] in, is the heat and electricity power of the electric boiler in time period t, η boiler is the electrothermal energy conversion efficiency, It is the maximum thermal power of the electric boiler.

[0055] Thermal energy storage equipment needs to meet charging and discharging constraints:

[0056]

[0057] in, For the energy of thermal energy storage equipment, It is the smallest and largest capacity thermal energy storage device. For heat storage and release efficiency, is the heat storage and release power of the equipment in period t, is the maximum heat storage and release power, They are the state indicators of heat storage and release respectively (heat storage and release is 1, and no heat storage and release is 0).

[0058] The loss cost of thermal energy storage equipment is:

[0059]

[0060] in, is the loss cost of the thermal energy storage device during period t, λ TS,loss is the loss coefficient.

[0061] The power and heat networks of the data center micro energy grid need to meet the following balance constraints:

[0062]

[0063] Furthermore,

[0064] The carbon trading market is aimed at various carbon emission entities. In essence, one carbon emission entity pays a certain fee to another carbon emission entity to obtain additional carbon emission permits to meet government assessment requirements. The model is:

[0065]

[0066] CE i,t =CI i ·P i,t

[0067] CC i,t =CF i ·P i,t

[0068] in, is the carbon trading cost of the system in period t, p CET is the unit carbon emission rights trading price, G fossil is the collection of units emitting carbon dioxide, CEi,t is the actual carbon emission of unit i in period t, CI i Its unit output carbon emission intensity, CC i,t is the carbon quota of unit i in period t, CF i Its carbon quota coefficient, P i,t is the output power of unit i in time period t.

[0069] The green certificate trading market is aimed at power generation entities and electricity users. Every unit of electricity produced by a renewable energy unit will receive a green certificate, which can be freely traded in the green certificate market at the current certificate price. The model is:

[0070]

[0071] in, is the green certificate transaction cost of the system in period t, p GCT is the unit green certificate transaction price, is the number of green certificates required by the system to meet the consumption responsibility weight in time period t, is the number of green certificates obtained by the system during period t, is the electricity demand of new energy quota in period t, is the total power consumption of the system during period t, NF is the new energy quota coefficient, G newenergy P is a collection of new energy units. j,t is the output power of unit j in time period t.

[0072] The interaction between the two focuses on calculating the carbon emission reduction of renewable energy power generation reflected in the green certificate. The interaction model is:

[0073]

[0074] in, is the carbon transaction cost when the system considers the interaction between the two in period t, Green certificates offset carbon emissions in period t. is the carbon emission reduction amount reflected by the jth type of green certificate, CE fossil The carbon emissions of traditional coal-fired energy throughout its life cycle, The carbon emissions of the jth type of new energy throughout its entire life cycle.

[0075] The system participation market cost is:

[0076]

[0077] in, is the total cost of the system participating in the market during period t.

[0078] Furthermore,

[0079] The objective function of the low-carbon economic optimization scheduling model of the micro-energy grid is to minimize the total cost of the system, and the constraints are as shown above:

[0080]

[0081] Among them, C MEG is the total cost of the micro energy grid.

[0082] The beneficial effects of the present invention are that, compared with the prior art, the present invention has the following advantages:

[0083] For the data center micro energy grid containing a variety of controllable devices, renewable energy access and data center waste heat recovery are considered. Based on the bidirectional supply and demand of the data center, a thermoelectric coupling energy management framework of the micro energy grid is built. It is incorporated into the carbon trading market and the green certificate trading market and the interaction mechanism between the two is applied. A load control method for the data center micro energy grid under the orderly electricity consumption scenario is proposed, and a more low-carbon economic energy management optimization scheduling plan is obtained, which has good practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 It is a flow chart of a method for controlling a data center micro energy network load in an orderly power consumption scenario of the present invention;

[0085] Figure 2 This is a schematic diagram of the micro energy network architecture of a data center in step S2 of the method of the present invention;

[0086] Figure 3 The predicted power generation of distributed wind power and photovoltaic power in step S2 of the method of this aspect;

[0087] Figure 4 It is the electric balance diagram of the data center micro energy network in step S4 of the method of the present invention;

[0088] Figure 5 It is the heat balance diagram of the data center micro energy network in step S4 of the method of the present invention. DETAILED DESCRIPTION

[0089] The present application is further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present application.

[0090] like Figure 1 As shown, a method for controlling the load of a data center micro energy network in an orderly power consumption scenario includes the following steps:

[0091] Step S1, considering the energy consumption of IT equipment and cooling system, establish a data center energy consumption model.

[0092] The total energy consumption of a data center includes two important parts: IT equipment and cooling system. The energy consumption of the cooling system is directly related to the energy consumption of its IT equipment. The energy consumption model is:

[0093]

[0094] in, are the energy consumption of the data center cooling system and IT equipment in period t, LF t is the comprehensive load factor of the data center in period t, and its value is 2.

[0095] The total energy consumption of a data center is the sum of the energy consumption of IT equipment and cooling system:

[0096]

[0097] in, is the total energy consumption of the data center in period t.

[0098] Step S2, combining a variety of adjustable devices to build a micro energy network thermoelectric coupling energy management framework that considers renewable energy access and data center waste heat recovery.

[0099] The micro energy grid considering renewable energy access and waste heat recovery from data centers consists of two parts: the power network and the thermal network. Figure 2 shown.

[0100] The power network uses data centers as its main power load. While interacting with the upper-level power grid, it is connected to distributed new energy generators and is equipped with self-contained generators and energy storage equipment.

[0101] The interaction between the micro energy grid system and the upper power grid must meet the power transmission constraints:

[0102]

[0103] in, are the electricity purchase and sales in time period t, is the power purchase and sales status indicator of the system in period t (interaction is 1, no interaction is 0), It is the maximum value of line power transmission, which is 10MW.

[0104] The system's cost of purchasing and selling electricity to the superior power grid is:

[0105]

[0106] in, are the purchase and sale prices of electricity in time period t, as shown in Table 1.

[0107] Table 1 Purchase and sale prices of electricity

[0108]

[0109]

[0110] The actual output of the connected distributed wind and solar energy generating units is often less than the predicted output, and the wind and solar abandonment constraints are:

[0111]

[0112] in, is the actual wind power and photovoltaic power generation in period t, Predict wind power and photovoltaic power generation for time period t, such as Figure 3 shown.

[0113] Self-provided generator sets need to meet the start-stop constraints and ramp constraints:

[0114]

[0115] in, is the start / stop status indicator of the generator set in period t (1 for running and 0 for shutting down), is the output of the generator set in period t, is the upper limit of the unit's power generation capacity, which is 100MW. OG It is the climbing power limit of the unit and its value is 25MW.

[0116] The operating costs of self-provided generator sets are mainly composed of fixed costs, energy costs and start-up and shutdown costs:

[0117]

[0118] in, is the total cost of the self-provided diesel generator set in period t, is the output of the diesel generator set in period t, C OG,fix is the fixed cost of diesel generator set operation, which is 2.5$. OG,fuel is the fuel cost, which is 2.5$, C OG,SU , C OG,SD They are the startup and shutdown costs of the diesel generator set, and the value is 5.5$.

[0119] Electric energy storage devices need to meet charging and discharging constraints:

[0120]

[0121]

[0122] in, is the power of the energy storage device, They are the minimum and maximum power of the energy storage device, which are 0 and 40MW respectively. is the charge and discharge efficiency, both are taken as 0.9, is the charge and discharge power in time period t, is the maximum charge and discharge power, both are taken as 10MW, They are respectively the status indicators of charge and discharge (1 for charge and discharge, 0 for no charge and discharge).

[0123] The loss cost of electric energy storage equipment is:

[0124]

[0125] in, is the loss cost of the electric energy storage device during period t, λ ES,loss is the loss coefficient, and its value is 1.4$ / MW.

[0126] The thermal network considers recovering waste heat from the data center to provide heat for users with heat loads in the region. The network is also equipped with cogeneration units, electric boilers and thermal energy storage equipment.

[0127] The waste heat recovery power of a data center is proportional to the power consumption:

[0128]

[0129] in, is the waste heat recovery power of the data center during period t, η DC is the thermoelectric energy conversion efficiency, which is 0.7.

[0130] The power generation and heat generation of the cogeneration unit are in a certain proportion:

[0131]

[0132] in, are the electrical and thermal power of the CHP unit in time period t, η CHP is the unit's electric-to-heat ratio, which is 0.35 / 0.45. is the upper limit of the unit's electrical power, which is 50MW / h. It is the start / stop status indicator of the unit in time period t (1 for operation and 0 for shutdown).

[0133] The cost of a combined heat and power unit consists of three parts: fixed cost, energy cost, and start-up and shutdown cost:

[0134]

[0135] Among them, C t CHP is the total cost of the CHP unit in period t, CCHP,fix is the fixed cost of CHP unit operation, which is 2.1$ / MW, p CHP,fuel 、h CHP,fuel are the fuel costs related to electrical power and thermal power, which are 2.1 and 1.4$ / MW respectively. CHP,SU , C CHP,SD is the startup and shutdown cost of the CHP unit, which is 4.2$ / MW.

[0136] The energy conversion model of the electric boiler is:

[0137]

[0138] in, is the heat and electricity power of the electric boiler in time period t, η boiler is the electrothermal energy conversion efficiency, which is 0.9. It is the maximum thermal power of the electric boiler, which is 30MW / h.

[0139] Thermal energy storage equipment needs to meet charging and discharging constraints:

[0140]

[0141] in, For the energy of thermal energy storage equipment, The minimum and maximum capacities of thermal energy storage equipment are 0 and 20MW respectively. is the heat storage and release efficiency, both are taken as 0.95, is the heat storage and release power of the equipment in period t, is the maximum heat storage and release power, which is 20MW. They are the state indicators of heat storage and release respectively (heat storage and release is 1, and no heat storage and release is 0).

[0142] The loss cost of thermal energy storage equipment is:

[0143]

[0144] in, is the loss cost of the thermal energy storage device during period t, λ TS,loss is the loss coefficient, and its value is 1.4$ / MW.

[0145] The power and heat networks of the data center micro energy grid need to meet the following balance constraints:

[0146]

[0147]

[0148] Step S3, introduce carbon trading and green certificate trading and apply the interaction mechanism between the two.

[0149] The carbon trading market is aimed at various carbon emission entities. In essence, one carbon emission entity pays a certain fee to another carbon emission entity to obtain additional carbon emission permits to meet government assessment requirements. The model is:

[0150]

[0151] CE i,t =CI i ·P i,t

[0152] CC i,t =CF i ·P i,t

[0153] in, is the carbon trading cost of the system in period t, p CET is the unit carbon emission rights trading price, which is 11$ / t, G fossil is the collection of units emitting carbon dioxide, CE i,t is the actual carbon emission of unit i in period t, CI i Its unit output carbon emission intensity, CC i,t is the carbon quota of unit i in period t, CF i Its carbon quota coefficient, P i,t is the output power of unit i in time period t.

[0154] The green certificate trading market is aimed at power generation entities and electricity users. Every unit of electricity produced by a renewable energy unit will receive a green certificate, which can be freely traded in the green certificate market at the current certificate price. The model is:

[0155]

[0156] in, is the green certificate transaction cost of the system in period t, p GCT is the unit green certificate transaction price, which is 11$ / piece. is the number of green certificates required by the system to meet the consumption responsibility weight in time period t, is the number of green certificates obtained by the system during period t, is the electricity demand of new energy quota in period t, is the total power consumption of the system during period t, NF is the new energy quota coefficient, G newenergy P is a collection of new energy units. j,t is the output power of unit j in time period t.

[0157] The interaction between the two focuses on calculating the carbon emission reduction of renewable energy power generation reflected in the green certificate. The interaction model is:

[0158]

[0159]

[0160] in, is the carbon transaction cost when the system considers the interaction between the two in period t, Green certificates offset carbon emissions in period t. is the carbon emission reduction amount reflected by the jth type of green certificate, CE fossil The carbon emissions of traditional coal-fired energy throughout its life cycle, The carbon emissions of the jth type of new energy throughout its entire life cycle.

[0161] The system participation market cost is:

[0162]

[0163] in, is the total cost of the system participating in the market during period t.

[0164] Step S4, establish a low-carbon economic optimization scheduling model for the micro-energy network, and obtain an energy management optimization scheduling scheme that takes into account both economy and low carbon. At this time, the minimum cost is $8087, and the electricity and heat balance diagram is as follows: Figure 4 and Figure 5 shown.

[0165] The objective function of the low-carbon economic optimization scheduling model of the micro-energy grid is to minimize the total system cost, and the constraints are shown in S1, S2, and S3:

[0166]

[0167] Among them, C MEG is the total cost of the micro energy grid.

[0168] The applicant of the present invention has made a detailed explanation and description of the implementation examples of the present invention in conjunction with the drawings in the specification. However, those skilled in the art should understand that the above implementation examples are only preferred implementation schemes of the present invention, and the detailed description is only to help readers better understand the spirit of the present invention, but not to limit the scope of protection of the present invention. On the contrary, any improvements or modifications based on the inventive spirit of the present invention should fall within the scope of protection of the present invention.

Claims

1. A method for controlling the load of a data center micro energy network in an orderly power consumption scenario, characterized in that: The following steps are involved: Step S1, considering the energy consumption of IT equipment and cooling system, establishing a data center energy consumption model; Step S2, combining various adjustable devices to build a data center micro energy network thermoelectric coupling energy management framework that considers renewable energy access and waste heat recovery; Step S3, introduce carbon trading and green certificate trading and apply the interaction mechanism between the two; Step S4, establishing a low-carbon economic optimization scheduling model for the micro-energy network under an orderly power consumption scenario, and obtaining a load control solution for the data center micro-energy network that takes into account both economy and low carbon under an orderly power consumption scenario.

2. According to the method for controlling the load of a data center micro energy network in an orderly power consumption scenario according to claim 1, it is characterized in that: In step S1: The energy consumption model of the data center is: in, are the energy consumption of the data center cooling system and IT equipment in period t, LF t is the comprehensive load factor of the data center in period t, and its value is 2; The total energy consumption of a data center is the sum of the energy consumption of IT equipment and cooling system: in, is the total energy consumption of the data center in period t.

3. According to the method for controlling the load of a data center micro energy network in an orderly power consumption scenario according to claim 1, it is characterized in that: In step S2: The micro energy grid considering renewable energy access and waste heat recovery from data centers consists of two parts: a power network and a thermal network, wherein: The power network uses data centers as the main power load. While interacting with the upper power grid, it is connected to distributed new energy generators and is equipped with self-contained generators and electric energy storage equipment; The interaction between the micro energy grid system and the upper power grid must meet the power transmission constraints: in, are the electricity purchase and sales in time period t, is the power purchase and sales status indicator of the system in period t, with interaction as 1 and no interaction as 0. is the maximum value of line power transmission; The system's cost of purchasing and selling electricity to the upper-level power grid is: in, are the purchase and sale prices of electricity in time period t respectively; The actual output of the connected distributed wind and solar energy generating units is often less than the predicted output, and the wind and solar abandonment constraints are: in, is the actual wind power and photovoltaic power generation in period t, Predict wind and photovoltaic power generation for time period t; Self-provided generator sets need to meet the start-stop constraints and ramp constraints: in, is the start / stop status indicator of the generator set during period t, with running being 1 and shutting down being 0; is the output of the generator set in period t, is the upper limit of the unit’s power generation capacity, V OG It is the climbing power limit of the unit; The operating costs of self-provided generator sets are mainly composed of fixed costs, energy costs and start-up and shutdown costs: in, is the total cost of the self-provided diesel generator set in period t, is the output of the diesel generator set in period t, C OG,fix is the fixed cost of diesel generator operation, p OG,fuel is the fuel cost, C OG,SU , C OG,SD They are the startup and shutdown costs of diesel generator sets respectively; The electric energy storage device needs to meet the charging and discharging constraints: in, is the power of the energy storage device, are the minimum and maximum power of the energy storage device, is the charge and discharge efficiency, is the charge and discharge power in time period t, is the maximum charge and discharge power; They are the status indicators of charge and discharge, with charge and discharge being 1 and no charge and discharge being 0; The loss cost of electric energy storage equipment is: in, is the loss cost of the electric energy storage device during period t, λ ES,loss is the loss coefficient; The thermal network considers waste heat recovery from the data center to provide heat for users with heat loads in the region. The network is also equipped with cogeneration units, electric boilers and thermal energy storage equipment. The waste heat recovery power of a data center is proportional to the power consumption: in, is the waste heat recovery power of the data center during period t, η DC is the thermoelectric energy conversion efficiency; The power generation and heat generation of the cogeneration unit are in a certain proportion: in, are the electrical and thermal power of the CHP unit in time period t, η CHP is the unit's electric-to-heat ratio, is the upper limit of the unit’s electrical power, is the start / stop status indicator of the unit in period t, 1 for operation and 0 for shutdown; The cost of a combined heat and power unit consists of three parts: fixed cost, energy cost, and start-up and shutdown cost: in, is the total cost of the CHP unit in period t, C CHP,fix is the fixed cost of CHP unit operation, p CHP,fuel 、h CHP,fuel are the fuel costs related to electrical power and thermal power, respectively, and C CHP,SU , C CHP,SD Startup and shutdown costs of CHP units; The energy conversion model of the electric boiler is: in, is the heat and electricity power of the electric boiler in time period t, η boiler is the electrothermal energy conversion efficiency, is the maximum thermal power of the electric boiler; The thermal energy storage device needs to meet the charging and discharging constraints: in, For the energy of thermal energy storage equipment, It is the smallest and largest capacity thermal energy storage device. For heat storage and release efficiency, is the heat storage and release power of the equipment in period t, is the maximum heat storage and release power, They are the state indicators of heat storage and release, 1 for heat storage and release, and 0 for no heat storage and release; The loss cost of the thermal energy storage device is: in, is the loss cost of the thermal energy storage device during period t, λ TS,loss is the loss coefficient; The power and thermal networks of the data center micro energy grid need to meet the balance constraints:

4. According to the method for controlling the load of a data center micro energy network in an orderly power consumption scenario according to claim 1, it is characterized in that: In step S3: The carbon trading model is: in, is the carbon trading cost of the system in period t, p CET is the unit carbon emission rights trading price, G fossil is the collection of units emitting carbon dioxide, CE i,t is the actual carbon emission of unit i in period t, CI i Its unit output carbon emission intensity, CC i,t is the carbon quota of unit i in period t, CF i Its carbon quota coefficient, P i,t is the output power of unit i in period t; The green certificate trading model is: in, is the green certificate transaction cost of the system in period t, p GCT is the unit green certificate transaction price, is the number of green certificates required by the system to meet the consumption responsibility weight in time period t, is the number of green certificates obtained by the system during period t, is the electricity demand of new energy quota in period t, is the total power consumption of the system during period t, NF is the new energy quota coefficient, G newenergy P is a collection of new energy units. j,t is the output power of unit j in period t; The interaction between the two focuses on calculating the carbon emission reduction of renewable energy power generation reflected in the green certificate. The interaction model is: in, is the carbon transaction cost when the system considers the interaction between the two in period t, Green certificates offset carbon emissions in period t. is the carbon emission reduction amount reflected by the jth type of green certificate, CE fossil The carbon emissions of traditional coal-fired energy throughout its life cycle, Carbon emissions during the entire life cycle of the jth type of new energy supply; The system participation market cost is: in, is the total cost of the system participating in the market during period t.

5. According to the method for controlling the load of a data center micro energy network in an orderly power consumption scenario as described in claim 1, it is characterized in that: In step S4: The objective function of the low-carbon economic optimization scheduling model of the micro-energy grid is to minimize the total cost of the system, that is, Among them, C MEG is the total cost of the micro energy grid.

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