Virtual power plant low-carbon economic dispatching method and system

Abstract

The invention relates to a virtual power plant low-carbon economic dispatching method and system. The method comprises the following steps: determining an internal structure composition of a virtual power plant system; constructing an output model of each internal structure of the virtual power plant system; carrying out aggregation modeling on the output model of each internal structure of the virtual power plant system, taking minimization of the total operation cost as a target, incorporating tenant interaction data and data center interaction data, and constructing a target function; determining constraint conditions of the target function; the constraint conditions at least comprise a gas turbine constraint, an electric vehicle constraint, a power transaction constraint and a power balance constraint; and obtaining a virtual power plant low-carbon economic dispatching strategy according to the objective function. According to the technical scheme, tenant interaction data and data center interaction data are included, in the scheduling process, the virtual power plant not only pays attention to power cost and carbon emission, personalized scheduling can be carried out according to specific requirements of the tenants and environment requirements, and therefore more refined and efficient power services are provided.

Description

Technical Field

[0001] The present application relates to the technical field of power plant dispatching, and in particular to a low-carbon economic dispatching method and system for a virtual power plant. Background Art

[0002] Under the dual carbon goals, how to achieve efficient utilization and absorption of renewable energy and ensure the economic and stable operation of the power system has become a key issue. Virtual power plants can manage the aggregation of large amounts of energy and realize flexible regulation of distributed resources. Virtual power plants aggregate distributed power sources and flexibility resources so that the power units participating in the aggregation can also participate in the flexibility regulation function. In order to improve the virtual power plant's ability to absorb renewable energy while ensuring the reliability and safety of the operation process, it is essential to adopt appropriate optimization scheduling strategies and control methods.

[0003] Existing optimization scheduling methods mostly focus only on the economic efficiency of virtual grid business systems, but ignore tenants' wishes and environmental benefits, resulting in the inability of existing optimization scheduling methods to meet the needs of multiple parties. Summary of the invention

[0004] In order to overcome, at least to a certain extent, the problem that the optimization scheduling method in the related art cannot meet the needs of multiple parties, the present application provides a virtual power plant low-carbon economic scheduling method and system.

[0005] The scheme of this application is as follows:

[0006] According to a first aspect of an embodiment of the present application, a low-carbon economic dispatching method for a virtual power plant is provided, comprising:

[0007] Determine the internal structure of the virtual power plant system;

[0008] Construct output models of each internal structure of the virtual power plant system;

[0009] Aggregate the output models of each internal structure of the virtual power plant system, take the minimization of total operating cost as the goal, incorporate tenant interaction data and data center interaction data, and construct the objective function;

[0010] Determining the constraint conditions of the objective function; the constraint conditions at least include: gas turbine constraint, electric vehicle constraint, power trading constraint and power balance constraint;

[0011] A low-carbon economic dispatch strategy for the virtual power plant is obtained according to the objective function.

[0012] Preferably, the internal structure of the virtual power plant system includes: a gas turbine, a wind turbine, a photovoltaic unit, an electric vehicle and a carbon capture system.

[0013] Preferably, the method further comprises:

[0014] Build the output model of the gas turbine:

[0015] P i GT =Q i η GT L;

[0016] Among them, P i GT represents the power of the gas generator set; Q i represents the natural gas consumption; η GT represents the power generation efficiency of the gas generator set; L represents the lower calorific value of natural gas.

[0017] Preferably, the method further includes:

[0018] Build the output model of the wind turbine:

[0019]

[0020] Among them, P WP represents the wind power output, v t represents the actual wind speed at time t, v ci represents the cut-in wind speed, v co represents the cut-out wind speed, v R represents the rated wind speed, P R represents the rated power;

[0021] Build the probability density function of wind power output:

[0022]

[0023] Among them, f o (P WP ) represents the probability density of wind power output; v represents the wind speed; k represents the shape parameter; c represents the speed parameter; h = v R -v ci -1; ρ = (P R +hP WP )v ci .

[0024] Preferably, the method further includes:

[0025] Build the output model of the photovoltaic unit:

[0026]

[0027] Among them, f P (P PV ) follows the β distribution and represents the probability density function of the photovoltaic output power; P PV represents the photovoltaic output power, P PV= ξAη, where ξ represents the solar irradiation intensity; A represents the area of the photovoltaic panel; η represents the energy conversion coefficient; a and b respectively represent the location parameter and shape parameter of the β distribution.

[0028] Preferably, the method further includes:

[0029] Constructing an output model for an electric vehicle:

[0030]

[0031] where Z t = 0 represents the charging state of the electric vehicle, and Z t = 1 represents the discharging state of the electric vehicle; C dc,t represents the capacity state of the electric vehicle at time t; P c and P f respectively represent the charging and discharging powers; ε represents the charging and discharging losses; T c and T f respectively represent the charging and discharging times.

[0032] Preferably, the method further includes:

[0033] Constructing an output model for a carbon capture system:

[0034]

[0035] where E BP (t), E CG (t) respectively represent the total amount of CO 2 generated by the power plant in the t period, the mass of CO 2 captured by the power plant in the t period, and the mass of CO 2 to be captured provided by the solution storage tank in the t period; β, ζ, and η respectively represent the carbon capture efficiency, the energy consumption per unit of CO 2 captured, and the maximum operating coefficients of the regeneration tower and the compressor; e BP and ω respectively represent the carbon emission intensity of the power plant and the flue gas diversion ratio; P BPmax represents the maximum output power of the power plant.

[0036] Preferably, constructing an objective function, including:

[0037] minC vpp = C G + C ESS + C Grid + C CO2 + C inc

[0038]

[0039] where Cvpp Denote the total operating cost; C G Denote the operating cost of the gas turbine; C ESS Denote the charging and discharging cost of the electric vehicle; C Grid Denote the power trading cost of the distribution network; C CO2 Denote the carbon trading cost; C lnc Denote the incentive cost; δ G Denote the unit power generation cost of the gas turbine; δ ESS Denote the charging and discharging cost of the electric vehicle; Denote the power purchase price; Denote the power selling price; δ Inc And δ′ Inc Respectively denote the subsidy prices for the changes in the tenant's calculated demand in the time domain and space; η denotes the charging and discharging efficiency of the energy storage; Denote the output power of the gas turbine in data center i at time t; Denote the power purchased by data center i from the distribution network at time t; Denote the power sold by data center i to the distribution network at time t; And Respectively denote the charging and discharging powers of the electric vehicle in data center i at time t; Denote the load transfer volume of tenant j in data center i from time t to time i'; Denote the load transfer volume of tenant j from data center i to data center i' at time t.

[0040] Preferably, the gas turbine constraints include:

[0041]

[0042] Wherein, P i GT,max , P i GT,min Are respectively the upper and lower limits of the gas turbine output; Are respectively the maximum downward and maximum upward ramp powers of the gas turbine;

[0043] The electric vehicle constraints include:

[0044]

[0045] In the formula: P i ESS,max Is the maximum allowable charging and discharging power of the electric vehicle; Is the capacity of the energy storage at the initial moment of scheduling, And Are respectively the minimum and maximum allowable remaining capacities of the energy storage;

[0046] The power trading constraints include:

[0047]

[0048] In the formula, P i Grid,max is the maximum value of the trading power;

[0049] The power balance constraints include:

[0050]

[0051] According to the second aspect of the embodiments of the present application, a virtual power plant low-carbon economic dispatching system is provided, including:

[0052] A determination module, configured to determine the internal structure composition of the virtual power plant system;

[0053] A model construction module, configured to construct an output model for each internal structure of the virtual power plant system;

[0054] A function construction module, configured to perform aggregated modeling on the output models of each internal structure of the virtual power plant system, with the goal of minimizing the total operating cost, incorporating tenant interaction data and data center interaction data, and constructing an objective function;

[0055] A constraint module, configured to determine the constraint conditions of the objective function; the constraint conditions at least include: gas turbine constraints, electric vehicle constraints, power trading constraints, and power balance constraints;

[0056] A dispatching module, configured to obtain a virtual power plant low-carbon economic dispatching strategy according to the objective function.

[0057] The technical solution provided by the present application may include the following beneficial effects:

[0058] The virtual power plant low-carbon economic dispatching method in the present application includes: determining the internal structure composition of the virtual power plant system; constructing an output model for each internal structure of the virtual power plant system; performing aggregated modeling on the output models of each internal structure of the virtual power plant system, with the goal of minimizing the total operating cost, incorporating tenant interaction data and data center interaction data, and constructing an objective function; determining the constraint conditions of the objective function; the constraint conditions at least include: gas turbine constraints, electric vehicle constraints, power trading constraints, and power balance constraints; obtaining a virtual power plant low-carbon economic dispatching strategy according to the objective function.

[0059] In this technical solution, when constructing the objective function, tenant interaction data and data center interaction data are incorporated to obtain the objective function under the economic and low-carbon dual-objective optimization of the virtual power plant considering tenants and the environment. During the scheduling process, the virtual power plant not only focuses on power costs and carbon emissions but can also perform personalized scheduling according to the specific needs of tenants (such as the service quality requirements of the data center, the flexibility of the electric vehicle charging period, etc.) and environmental requirements, thereby providing more refined and efficient power services. Incorporating tenant interaction data and data center interaction data into the objective function of the virtual power plant's low-carbon economic scheduling helps achieve more accurate power scheduling and cost control, while promoting the development of the low-carbon economy. By optimizing power scheduling, not only can the energy utilization efficiency be improved and costs be reduced, but also the power service quality of users can be enhanced while achieving environmental protection goals.

[0060] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit this application. Brief Description of the Drawings

[0061] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0062] Figure 1 is a schematic flowchart of a method for low-carbon economic scheduling of a virtual power plant provided by an embodiment of this application;

[0063] Figure 2 is a schematic structural diagram of a low-carbon economic scheduling system of a virtual power plant provided by an embodiment of this application.

[0064] Reference Signs: Determination Module - 21; Model Construction Module - 22; Function Construction Module - 23; Constraint Module - 24; Scheduling Module - 25. Detailed Description of the Embodiments

[0065] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. On the contrary, they are merely examples of devices and methods consistent with some aspects of this application as detailed in the appended claims.

[0066] Embodiment 1

[0067] Figure 1 is a schematic flowchart of a method for low-carbon economic scheduling of a virtual power plant provided by an embodiment of this application. Referring to Figure 1 , a method for low-carbon economic scheduling of a virtual power plant includes:

[0068] S11: Determine the internal structure composition of the virtual power plant system;

[0069] S12: Build the output models of the internal structures of the virtual power plant system;

[0070] S13: Aggregate and model the output models of the internal structures of the virtual power plant system. With the goal of minimizing the total operating cost, incorporate tenant interaction data and data center interaction data to build the objective function;

[0071] S14: Determine the constraint conditions of the objective function; The constraint conditions include at least: gas turbine constraints, electric vehicle constraints, power trading constraints, and power balance constraints;

[0072] S15: Obtain the low-carbon economic dispatch strategy of the virtual power plant according to the objective function.

[0073] It should be noted that the internal structure of the virtual power plant system includes: gas turbines, wind turbines, photovoltaic units, electric vehicles, and carbon capture systems.

[0074] Build the output model of the gas turbine:

[0075] P i GT = Q i η GT L;

[0076] Among them, P i GT represents the power of the gas generator set; Q i represents the natural gas consumption; η GT represents the power generation efficiency of the gas generator set; L represents the lower calorific value of natural gas.

[0077] Build the output model of the wind turbine:

[0078]

[0079] Among them, P WP represents the wind power output, v t represents the actual wind speed at time t, v c i represents the cut-in wind speed, v co represents the cut-out wind speed, v R represents the rated wind speed, P R represents the rated power;

[0080] Build the probability density function of wind power output:

[0081]

[0082] Among them, f o(P WP ) represents the probability density of wind power output; v represents the wind speed; k represents the shape parameter; c represents the speed parameter; h = v R -v ci -1; ρ = (P R +hP WP )v ci .

[0083] Build the output model of the photovoltaic unit:

[0084]

[0085] Among them, f P (P PV ) follows the β distribution and represents the probability density function of the photovoltaic output power; P PV represents the photovoltaic output power, P PV = ξAη, where ξ represents the solar irradiance; A represents the area of the photovoltaic panel; η represents the energy conversion coefficient; a and b respectively represent the location parameter and shape parameter of the β distribution.

[0086] Build the output model of the electric vehicle:

[0087]

[0088] Among them, Z t = 0 represents the charging state of the electric vehicle, Z t = 1 represents the discharging state of the electric vehicle; C dc,t represents the capacity state of the electric vehicle at time t; P c , P f respectively represent the charging and discharging powers; ε represents the charging and discharging loss; T c , T f respectively represent the charging and discharging times.

[0089] Build the output model of the carbon capture system:

[0090]

[0091] Among them, E BP (t), E CG (t) respectively represent the total amount of CO 2 generated by the power plant in the t period, the mass of CO 2 captured by the power plant in the t period, and the mass of CO 2 to be captured provided by the solution storage in the t period; β, ζ, η respectively represent the carbon capture efficiency, the energy consumption per unit of CO 2 captured, and the maximum working coefficients of the regeneration tower and the compressor; e BP and ω respectively represent the carbon emission intensity of the power plant and the flue gas diversion ratio; PBPmax Indicates the maximum output power of the power plant.

[0092] Then, according to the system structure of the virtual power plant, aggregation modeling is carried out to obtain the economic and low-carbon dual-objective optimal scheduling model of the virtual power plant.

[0093] Construct the objective function, including:

[0094] minC vpp = C G + C ESS + C Grid + C CO2 + C inc

[0095]

[0096] Among them, C vpp represents the total operating cost; C G represents the operating cost of the gas turbine; C ESS represents the charging and discharging cost of the electric vehicle; C Grid represents the power trading cost of the distribution network; C CO2 represents the carbon trading cost; C lnc represents the incentive cost; δ G represents the unit power generation cost of the gas turbine; δ ESS represents the charging and discharging cost of the electric vehicle; represents the power purchase price; represents the power selling price; δ Inc and δ' Inc respectively represent the subsidy prices for the change of the tenant's computing demand in the time domain and space; η represents the charging and discharging efficiency of the energy storage; represents the output power of the gas turbine in data center i at time t; represents the power purchased by data center i from the distribution network at time t; represents the power sold by data center i to the distribution network at time t; and respectively represent the charging and discharging powers of the electric vehicle in data center i at time t; represents the load transfer volume of tenant j in data center i from time t to time i'; represents the load transfer volume of tenant j from data center i to data center i' at time t.

[0097] As can be seen from the above, in this technical solution, when constructing the objective function of the optimized scheduling model, the subsidy price for the change of the tenant's computing demand in the time domain and space, the output power of the gas turbine in data center i at time t, the power purchased by data center i from the distribution network at time t, the power sold by data center i to the distribution network at time t, the charging and discharging power of electric vehicles in data center i at time t are included. The load transfer volume of tenant j in data center i from time t to time i'. The load transfer volume of tenant j from data center i to data center i' at time t. By incorporating tenant interaction data and data center interaction data, an objective function under the economic and low-carbon dual objectives of the virtual power plant considering tenants and the environment is obtained. During the scheduling process, the virtual power plant not only focuses on power costs and carbon emissions, but also can perform personalized scheduling according to the specific needs of tenants (such as the service quality requirements of data centers, the flexibility of electric vehicle charging periods, etc.) and environmental requirements, so as to provide more refined and efficient power services. Incorporating tenant interaction data and data center interaction data into the objective function of the low-carbon economic scheduling of the virtual power plant helps to achieve more accurate power scheduling and cost control, while promoting the development of the low-carbon economy. By optimizing power scheduling, not only can the energy utilization efficiency be improved and costs be reduced, but also the power service quality of users can be enhanced while achieving environmental protection goals.

[0098] It should be noted that in some embodiments, the constraint conditions include gas turbine constraints, electric vehicle constraints, power-based constraints, and power balance constraints.

[0099] Among them, the gas turbine constraints include:

[0100]

[0101] Among them, P i GT,max , P i GT,min are the upper and lower limits of the gas turbine output respectively; are the maximum downward and maximum upward ramp powers of the gas turbine respectively;

[0102] The gas turbine constraints ensure that the gas turbine operates within a safe range, avoiding losses caused by overload or frequent start-stop. It helps the system to respond quickly to sudden increases or fluctuations in demand and improves system stability.

[0103] The electric vehicle constraints include:

[0104]

[0105] In the formula: P i ESS,max is the maximum allowable charging and discharging power of the electric vehicle; is the capacity of the energy storage at the initial moment of scheduling, and are the minimum and maximum remaining capacities allowed for energy storage, respectively;

[0106] The electric vehicle constraint enhances the dispatchability of the electric vehicle load, supports charging during low demand periods and discharging during peak demand periods, and improves the load balancing ability. It supports demand response and provides flexibility for low-carbon economic dispatch.

[0107] The power trading constraints include:

[0108]

[0109] where P i Grid,max is the maximum value of the trading power;

[0110] The power trading constraints are used to maximize the power trading revenue, reduce the operating cost, support making up the local power gap through market trading, and enhance the flexibility and adaptability of the virtual power plant.

[0111] The power balance constraints include:

[0112]

[0113] It should be noted that the output of the gas turbine, the output of the photovoltaic power generation, the output of the wind power generation, the charging and discharging power of the electric vehicle, the carbon capture power in the data center, the two-way trading power with the distribution network, and the power consumption of the data center should always be balanced. The power balance constraints ensure the real-time balance of the power system, maintain the stability of the grid frequency, improve the comprehensive coordination ability of the virtual power plant, and ensure reliable power supply.

[0114] In the low-carbon economic dispatch method of the virtual power plant, the setting of the constraint conditions directly affects the rationality and practical operability of the dispatch strategy.

[0115] Embodiment 2

[0116] A low-carbon economic dispatch system for a virtual power plant, comprising:

[0117] A determination module 21, configured to determine the internal structure composition of the virtual power plant system;

[0118] A model construction module 22, configured to construct an output model for each internal structure of the virtual power plant system;

[0119] A function construction module 23, configured to perform aggregated modeling on the output models of each internal structure of the virtual power plant system, with the goal of minimizing the total operating cost, incorporating tenant interaction data and data center interaction data, and constructing an objective function;

[0120] A constraint module 24 for determining the constraint conditions of the objective function; the constraint conditions at least include: gas turbine constraints, electric vehicle constraints, power trading constraints, and power balance constraints;

[0121] A scheduling module 25 for obtaining a low-carbon economic scheduling strategy for the virtual power plant according to the objective function.

[0122] It can be understood that the same or similar parts in the above embodiments can be referred to each other, and the content not detailed in some embodiments can be seen in the same or similar content of other embodiments.

[0123] It should be noted that in the description of the present application, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present application, unless otherwise specified, the meaning of "a plurality of" refers to at least two.

[0124] Any process or method description in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process. And the scope of the preferred embodiments of the present application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the technical field of the embodiments of the present application.

[0125] It should be understood that each part of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following well-known technologies in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0126] Those of ordinary skill in the technical field of the present application can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0127] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, may exist physically alone for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0128] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, or the like.

[0129] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples.

[0130] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A low-carbon economic dispatching method for a virtual power plant, characterized in that: include: Determine the internal structure of the virtual power plant system; Construct output models of each internal structure of the virtual power plant system; Aggregate the output models of each internal structure of the virtual power plant system, take the minimization of total operating cost as the goal, incorporate tenant interaction data and data center interaction data, and construct the objective function; Determining constraints of the objective function; The constraints include at least: gas turbine constraints, electric vehicle constraints, power trading constraints and power balance constraints; A low-carbon economic dispatch strategy for the virtual power plant is obtained according to the objective function.

2. The method according to claim 1, characterized in that The internal structure of the virtual power plant system includes: gas turbines, wind turbines, photovoltaic units, electric vehicles and carbon capture systems.

3. The method according to claim 2, characterized in that The method further comprises: Construct a gas turbine output model: P i GT =Q i the GT L; Among them, P i GT Indicates the power of the gas generator set; Q i Indicates natural gas consumption; η GT It indicates the power generation efficiency of the gas-fired generator set; L indicates the lower calorific value of natural gas.

4. The method according to claim 2, characterized in that: The method further comprises: Constructing the output model of wind turbines: Among them, P WP Represents wind power output power, v t represents the actual wind speed at time t, v ci represents the cut-in wind speed, v co Indicates the cut-out wind speed, v R Indicates rated wind speed, P R Indicates rated power; Construct wind power output probability density function: Among them, f o (P WP ) represents the probability density of wind power output; v represents the wind speed; k represents the shape parameter; c represents the speed parameter; h = v R -v ci -1;ρ=(P R +hP WP )v ci .

5. The method according to claim 2, characterized in that: The method further comprises: Constructing the output model of photovoltaic units: Among them, f P (P PV ) obeys β distribution, which represents the probability density function of photovoltaic output power; P PV Represents photovoltaic output power, P PV =ξAη, ξ represents the solar radiation intensity; A represents the area of ​​the photovoltaic panel; η represents the energy conversion coefficient; a and b represent the location parameter and shape parameter of the β distribution respectively.

6. The method according to claim 2, characterized in that The method further comprises: Constructing an electric vehicle output model: Among them, Z t =0 indicates the charging state of the electric vehicle, Z t =1 indicates the discharge state of the electric vehicle; C dc,t represents the capacity state of the electric vehicle at time t; P c , P f Respectively represent the charge and discharge power; ε represents the charge and discharge loss; T c , T f Respectively represent the charge and discharge time.

7. The method according to claim 2, characterized in that: The method further comprises: Modeling the output of a carbon capture system: Among them, E BP (t) E CG (t) represents the total amount of CO2 generated by the power plant in period t, the mass of CO2 captured by the power plant in period t, and the mass of CO2 to be captured provided by the solution storage in period t; β, ζ, η represent the carbon capture efficiency, the energy consumption per unit CO2 captured, and the maximum working coefficient of the regeneration tower and compressor, respectively; e BP and ω represent the carbon emission intensity and flue gas diversion ratio of the power plant respectively; P BPmax Indicates the maximum output power of the power plant.

8. The method according to claim 1, characterized in that: Construct the objective function, including: minC vpp =C G +C ESS +C Grid +C CO2 +C inc Among them, C vpp represents the total operating cost; C G represents the gas turbine operating cost; C ESS represents the charging and discharging cost of electric vehicles; C Grid represents the electricity transaction cost of the distribution network; C CO2 represents the carbon trading cost; C lnc represents the incentive cost; δ G represents the unit power generation cost of gas turbine; δ ESS represents the cost of charging and discharging electric vehicles; Indicates the electricity purchase price; represents the electricity selling price; δ Inc and δ I ' nc They represent the subsidy prices of tenant computing demands that change in time and space respectively; η represents the energy storage charging and discharging efficiency; represents the output power of the gas turbine in data center i at time period t; represents the power purchased by data center i from the distribution network during period t; represents the power sold by data center i to the distribution network during period t; and They represent the charging and discharging power of electric vehicles in data center i during time period t; represents the load transfer amount of tenant j in data center i from period t to period i'; represents the load transfer amount of tenant j from data center i to data center i' in time period t.

9. The method according to claim 1, characterized in that: The gas turbine constraints include: Among them, P i GT,max , P i GT,min are the upper and lower limits of gas turbine output respectively; are the maximum downward and maximum upward climbing power of the gas turbine, respectively; The electric vehicle constraints include: Where: P i ESS,max The maximum charging and discharging power allowed for electric vehicles; is the capacity of energy storage at the initial scheduling time, and are the minimum and maximum remaining capacities allowed for energy storage, respectively; The power trading constraints include: Where P i Grid,max is the maximum value of transaction power; The power balance constraints include:

10. A virtual power plant low-carbon economic dispatching system, characterized in that: include: A determination module, used to determine the internal structure of the virtual power plant system; Model building module, used to build output models of various internal structures of the virtual power plant system; The function building module is used to aggregate the output models of the internal structures of the virtual power plant system, take the minimization of the total operating cost as the goal, incorporate tenant interaction data and data center interaction data, and build the objective function; A constraint module, used to determine the constraint conditions of the objective function; The constraints include at least: gas turbine constraints, electric vehicle constraints, power trading constraints and power balance constraints; The scheduling module is used to obtain a low-carbon economic scheduling strategy for the virtual power plant according to the objective function.

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