Electric vehicle aggregation and source load response virtual power plant optimization scheduling method and system

Abstract

The invention is suitable for the technical field of virtual power plant collaborative scheduling, and provides a virtual power plant optimal scheduling method and system for electric vehicle aggregation and source load response, and the method comprises the steps: building a virtual power plant frame considering an electric vehicle aggregator and a source load response mechanism through building an access mode of the electric vehicle aggregator and the source load response mechanism; establishing an economic operation scheduling model of the virtual power plant by taking the minimum operation cost of the virtual power plant as a target; considering an electric power balance constraint, a gas turbine constraint, an electric energy storage constraint and an EV aggregator constraint, and establishing a constraint condition of virtual power plant operation; basic parameters are set, basic parameter values are input into the economic operation scheduling model and the constraint conditions, the virtual power plant economic operation scheduling model is solved, and a virtual power plant optimization scheduling result is output. According to the method, the communication, scheduling and management pressure of the virtual power plant is effectively reduced, the overall operation cost and carbon emission of the virtual power plant are reduced, and the wind and light absorption capability is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of virtual power plant collaborative scheduling, and particularly relates to a virtual power plant optimal scheduling method and system of electric vehicle aggregation and source-load response. BACKGROUND

[0002] As an important means of integrating distributed energy and flexible resources, virtual power plants can realize the coordinated and optimized operation of source-side power generation equipment and load-side power consumption equipment. Moreover, with the access of large-scale electric vehicles to the power grid, they meet the access conditions of virtual power plants as flexible adjustable resources. However, how to effectively access large-scale electric vehicles to virtual power plants and fully exert their flexible adjustment potential has become a key problem to improve the economic operation of virtual power plants and the new energy consumption capacity.

[0003] In the research of electric vehicles accessing virtual power plants, the existing methods mostly directly aggregate electric vehicles to access virtual power plants for participation in scheduling, without effective grouping and aggregation management of electric vehicles. Due to the randomness and dispersion of large-scale electric vehicle charging and discharging behaviors, this method leads to a huge amount of information interaction between virtual power plants and single electric vehicles, which brings serious pressure to the communication, scheduling and management of virtual power plants, and it is difficult to realize efficient scheduling.

[0004] In the research of source-load response mechanism of virtual power plants, the existing methods mainly consider the coordinated response of source-side power generation equipment and energy storage systems and load-side electrical loads, without including electric vehicle aggregators into the source-load response mechanism. This method cannot fully exert the adjustment potential of electric vehicle aggregators as load-side flexible resources, and limits the further optimization space of virtual power plants in reducing the operation economy and improving the new energy consumption capacity. SUMMARY

[0005] The purpose of the embodiment of the application is to provide a virtual power plant optimal scheduling method and system of electric vehicle aggregation and source-load response, aiming to solve the problems proposed in the background.

[0006] The embodiment of the application is implemented in this way. The virtual power plant optimal scheduling method of electric vehicle aggregation and source-load response comprises the following steps:

[0007] Step 1: establishing an access mode of electric vehicle aggregators;

[0008] Step 2: establishing a source-load response mechanism;

[0009] Step 3: constructing a virtual power plant framework considering electric vehicle aggregators and the source-load response mechanism;

[0010] Step 4: establishing a virtual power plant economic operation scheduling model considering electric vehicle aggregators and the source-load response mechanism;

[0011] Step 5: Establish constraints for the operation of the virtual power plant;

[0012] Step 6: Set the basic parameters, solve the economic operation scheduling model, and output the virtual power plant optimization scheduling results.

[0013] A further technical solution, in step 1, describes the specific method by which electric vehicle aggregators access the virtual power plant as follows:

[0014] Virtual power plant access There are several electric vehicle aggregators, and each aggregator uses a clustering method to divide large-scale electric vehicles with ordered charging and discharging characteristics into groups. Each cluster is managed separately, and the charging and discharging power of each electric vehicle cluster is aggregated using an aggregation method, and then connected to the virtual power plant in the form of an aggregate to participate in optimized scheduling.

[0015] In a further technical solution, in step 2, the source-load response mechanism uses waste heat power generation equipment and electric energy storage as adjustable resources on the source side, and time-shiftable loads, interruptible loads and electric vehicle aggregators as flexible resources on the load side, and coordinates them uniformly through a virtual power plant control platform.

[0016] In a further technical solution, in step 3, the constructed virtual power plant framework is configured with wind power generation, photovoltaic power generation, gas turbine units, waste heat power generation equipment, carbon capture equipment and electric energy storage on the source side, and with electric load and electric vehicle aggregator on the load side, and realizes energy management, data acquisition, intelligent metering and system evaluation through the virtual power plant control platform.

[0017] Electrical loads include interruptible loads, time-shiftable loads, and fixed loads, with interruptible loads and time-shiftable loads exhibiting demand response characteristics.

[0018] In a further technical solution, in step 4, the virtual power plant economic operation scheduling model takes the minimum operating cost of the virtual power plant as its objective function, and its expression is:

[0019]

[0020] In the formula, The operating cost of the virtual power plant; Costs of purchasing electricity and gas for virtual power plants; Carbon emission costs for virtual power plants; Costs of curtailing wind and solar power in virtual power plants; Cost of load demand response; For equipment operation and maintenance costs; Costs for virtual power plant dispatching EV aggregators;

[0021] The expression for the electricity and gas purchase costs of the virtual power plant is as follows:

[0022]

[0023] In the formula, and These are time-of-use electricity pricing and gas pricing, respectively. and The respective power and gas purchase capacity of the virtual power plant; This is the total scheduling time;

[0024] The expression for the carbon emission cost of a virtual power plant is:

[0025]

[0026] In the formula, , and These are the carbon dioxide production, carbon quota, and carbon capture volume of the virtual power plant, respectively. The length of the carbon emission tiers; The benchmark price for carbon trading; This is the step growth coefficient;

[0027] The expression for the cost of curtailing wind and solar power in a virtual power plant is:

[0028]

[0029] In the formula, To compensate for the loss of scenery, Curtailed wind and solar power;

[0030] The expression for load demand response cost is:

[0031]

[0032] In the formula, Power interrupted by electrical load; This is the cost coefficient for power load interruption.

[0033] The expression for equipment operation and maintenance costs is:

[0034]

[0035] In the formula, For gas turbine Operation and maintenance cost coefficient; for Time gas turbine Output power; Energy storage devices The operation and maintenance cost coefficient; and These are energy storage devices The charging power and discharging power;

[0036] The expression for the cost of virtual power plant dispatching EV aggregators is:

[0037]

[0038] In the formula, This is the battery degradation cost coefficient; The unit price for discharge subsidies for aggregators; For the first Among the EV aggregators, the first Each cluster Discharge power at any given moment.

[0039] In a further technical solution, in step 5, the constraints include electric power balance constraints, gas turbine constraints, electric energy storage constraints, and EV aggregator constraints.

[0040] The expression for the power balance constraint is:

[0041]

[0042] In the formula, This refers to the output power of wind and solar power. This provides electrical power output for the gas turbine. To output electrical power to thermal power units; This refers to the output power of the waste heat power generation equipment. For the first Among the EV aggregators, the first Each cluster The charging power at any given moment; Power consumption of carbon capture equipment; Basic electrical load; For time-transferable loads; For interruptible electrical loads;

[0043] The expression for the constraints of the gas turbine unit is:

[0044]

[0045] In the formula, and Gas turbine Upper and lower limits of output ramp power; For gas turbine The amount of energy consumed; For gas turbine Operating efficiency; for Time gas turbine ; output power;

[0046] The expression for the energy storage constraint is:

[0047]

[0048] In the formula, and These are energy storage devices The upper and lower limits of capacity; and These are energy storage devices The charging and discharging efficiency; for Real-time energy storage devices The capacity.

[0049] The expression for the EV aggregator constraint is:

[0050]

[0051] In the formula, and They are respectively Time of the first Upper and lower limits of charging power for individual EV clusters; and The first Upper and lower limits of discharge power for each EV cluster; for Time of the first The capacity of an EV cluster; and The first Capacity upper and lower limits for each EV cluster; and The first The charging and discharging efficiency of an individual EV cluster; For the first The charging needs of individual EV clusters.

[0052] In a further technical solution, step 6 includes the following steps:

[0053] First, the predicted wind and solar power values, electricity load data, gas turbine parameters, energy storage system parameters, electric vehicle aggregator parameters, carbon trading mechanism parameters, and time-of-use electricity prices and natural gas prices are input into the virtual power plant economic operation scheduling model and operating constraints.

[0054] Then, considering the operational constraints, the virtual power plant economic operation scheduling model is solved using the CPLEX solver in Matlab.

[0055] Finally, the output includes the optimal operating cost of the virtual power plant, the curtailed wind and solar power of the virtual power plant, the carbon emissions of the virtual power plant, the output of each access unit of the virtual power plant, and the charging and discharging power of each cluster of electric vehicle aggregators.

[0056] Another objective of this invention is to provide a virtual power plant optimization scheduling system for electric vehicle aggregation and source-load response, based on the above-described method, comprising:

[0057] The aggregator management module is used to establish the access methods for electric vehicle aggregators;

[0058] The source-load response module is used to establish the source-load response mechanism;

[0059] The framework building module is used to build a virtual power plant framework that takes into account electric vehicle aggregators and source-load response mechanisms;

[0060] The model building module is used to establish an economic operation and scheduling model for virtual power plants that takes into account the response mechanisms of electric vehicle aggregators and source loads, with the goal of minimizing the operating cost of virtual power plants.

[0061] The constraint establishment module is used to establish the constraints for the operation of the virtual power plant;

[0062] The solver module is used to set basic parameters, solve the economic operation scheduling model, and output the virtual power plant optimization scheduling results.

[0063] The present invention provides a method and system for optimizing the scheduling of virtual power plants based on electric vehicle aggregation and source-load response. This method incorporates electric vehicles into a virtual power plant after the electric vehicle aggregator performs grouping and aggregation management, and constructs a source-load response mechanism together with the load-side demand response load, the source-side waste heat power generation equipment and energy storage. This effectively reduces the communication, scheduling and management pressure of the virtual power plant, reduces the overall operating cost and carbon emissions of the virtual power plant, and improves the wind and solar power absorption capacity. Attached Figure Description

[0064] Figure 1 A flowchart of a virtual power plant optimization scheduling method for electric vehicle aggregation and source-load response provided in an embodiment of the present invention;

[0065] Figure 2 This is a framework diagram of a virtual power plant.

[0066] Figure 3 A graph showing wind, solar, and electrical load data;

[0067] Figure 4 This is the output diagram of each access unit under the optimal operating cost of the virtual power plant;

[0068] Figure 5The diagram shows the charging and discharging power of each cluster of electric vehicle aggregators (where a is aggregator 1 and b is aggregator 2). Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0070] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0071] Example 1:

[0072] like Figure 1 As shown, a virtual power plant optimization scheduling method for electric vehicle aggregation and source-load response provided in an embodiment of the present invention includes the following steps:

[0073] Step 1: Establish access methods for electric vehicle aggregators;

[0074] The specific method for electric vehicle aggregators to connect to virtual power plants is described as follows: Virtual power plant access There are several electric vehicle aggregators, and each aggregator uses a clustering method to divide large-scale electric vehicles with ordered charging and discharging characteristics into groups. Each cluster is managed separately, and the charging and discharging power of each electric vehicle cluster is aggregated using an aggregation method, and then connected to the virtual power plant in the form of an aggregate to participate in optimized scheduling.

[0075] Step 2: Establish a source-load response mechanism;

[0076] Considering the access methods of electric vehicle aggregators, waste heat power generation equipment and electric energy storage are used as adjustable resources on the source side, while time-shiftable loads, interruptible loads, and electric vehicle aggregators are used as flexible resources on the load side. A source-load response mechanism is formed through unified coordination by a virtual power plant control platform.

[0077] The specific working methods of each resource in the source-load response mechanism are as follows:

[0078] On the source side, waste heat power generation equipment recovers waste heat generated during the operation of gas turbines for secondary power generation, improving the overall energy utilization efficiency. Electric energy storage systems utilize their time-shifting charging and discharging characteristics to charge and store energy during periods of abundant wind and solar power output or low electricity prices, and release electricity during peak load or peak electricity price periods, achieving peak shaving and valley filling. On the load side, time-shiftable loads achieve load shifting by transferring electricity consumption periods from peak to off-peak times, interruptible loads temporarily interrupt power consumption when the system needs it, and electric vehicle aggregators aggregate multiple electric vehicle clusters, utilizing the flexible scheduling capabilities of orderly charging and discharging of vehicles to centrally charge during off-peak electricity price periods and feed back power to the grid during peak electricity price periods.

[0079] The specific control strategy for the virtual power plant's source-load response mechanism is as follows:

[0080] The virtual power plant control platform coordinates the resources on the source and load sides in a unified manner, and dynamically adjusts the output or load level of each response resource according to the system's operating status and optimization objectives.

[0081] Step 3: Construct a virtual power plant framework that takes into account electric vehicle aggregators and source-load response mechanisms;

[0082] After establishing the access method and source-load response mechanism for electric vehicle aggregators, the aggregators are connected to the virtual power plant, and the source-load response mechanism is considered within the virtual power plant to construct a system such as... Figure 2 The diagram illustrates a virtual power plant framework that incorporates electric vehicle aggregators and a source-load response mechanism. This virtual power plant is equipped with wind power generation, photovoltaic power generation, gas turbine units, waste heat power generation equipment, carbon capture equipment, and energy storage on the source side, and with electrical loads and electric vehicle aggregators on the load side. It utilizes a virtual power plant control platform to achieve functions such as energy management, data acquisition, intelligent metering, and system evaluation.

[0083] Electrical loads include interruptible loads, time-shiftable loads, and fixed loads, and interruptible loads and time-shiftable loads have demand response characteristics.

[0084] Step 4: Establish a virtual power plant economic operation scheduling model that takes into account the electric vehicle aggregator and source-load response mechanism.

[0085] Based on the established virtual power plant framework, and considering the costs incurred by each access unit of the virtual power plant, an economic operation and scheduling model for the virtual power plant, taking into account the electric vehicle aggregator and source-load response mechanism, is constructed with the objective function of minimizing the virtual power plant operating cost. Its expression is as follows:

[0086]

[0087] In the formula, The operating cost of the virtual power plant; Costs of purchasing electricity and gas for virtual power plants; Carbon emission costs for virtual power plants; Costs of curtailing wind and solar power in virtual power plants; Cost of load demand response; For equipment operation and maintenance costs; Costs for virtual power plant dispatching EV aggregators.

[0088] The expression for the electricity and gas purchase costs of the virtual power plant is as follows:

[0089]

[0090] In the formula, and These are time-of-use electricity pricing and gas pricing, respectively. and The respective power and gas purchase capacity of the virtual power plant; This is the total scheduling time.

[0091] The expression for the carbon emission cost of a virtual power plant is:

[0092]

[0093] In the formula, , and These are the carbon dioxide production, carbon quota, and carbon capture volume of the virtual power plant, respectively. The length of the carbon emission tiers; The benchmark price for carbon trading; This represents the step growth coefficient.

[0094] The expression for the cost of curtailing wind and solar power in a virtual power plant is:

[0095]

[0096] In the formula, To compensate for the loss of scenery, Wasted wind and solar power.

[0097] The expression for load demand response cost is:

[0098]

[0099] In the formula, Power interrupted by electrical load; This is the cost coefficient for power load interruption.

[0100] The expression for equipment operation and maintenance costs is:

[0101]

[0102] In the formula, For gas turbine Operation and maintenance cost coefficient; for Time gas turbine Output power; Energy storage devices The operation and maintenance cost coefficient; and These are energy storage devices The charging power and discharging power.

[0103] The expression for the cost of virtual power plant dispatching EV aggregators is:

[0104]

[0105] In the formula, This is the battery degradation cost coefficient; The unit price for discharge subsidies for aggregators; For the first Among the EV aggregators, the first Each cluster Discharge power at any given moment.

[0106] Step 5: Establish constraints for the operation of the virtual power plant.

[0107] Based on the established economic operation and scheduling model, constraints for the operation of the virtual power plant are established, taking into account power balance constraints, gas turbine constraints, energy storage constraints, and EV aggregator constraints.

[0108] The expression for the power balance constraint is:

[0109]

[0110] In the formula, This refers to the output power of wind and solar power. This provides electrical power output for the gas turbine. To output electrical power to thermal power units; This refers to the output power of the waste heat power generation equipment. For the first Among the EV aggregators, the first Each cluster The charging power at any given moment; Power consumption of carbon capture equipment; Basic electrical load; For time-transferable loads; It is an interruptible electrical load.

[0111] The expression for the constraints of the gas turbine unit is:

[0112]

[0113] In the formula, and Gas turbine Upper and lower limits of output ramp power; For gas turbine The amount of energy consumed; For gas turbine Operating efficiency; for Time gas turbine ; output power;

[0114] The expression for the energy storage constraint is:

[0115]

[0116] In the formula, and These are energy storage devices The upper and lower limits of capacity; and These are energy storage devices The charging and discharging efficiency; for Real-time energy storage devices The capacity.

[0117] The expression for the EV aggregator constraint is:

[0118]

[0119] In the formula, and They are respectively Time of the first Upper and lower limits of charging power for individual EV clusters; and The first Upper and lower limits of discharge power for each EV cluster; for Time of the first The capacity of an EV cluster; and The first Capacity upper and lower limits for each EV cluster; and The first The charging and discharging efficiency of an individual EV cluster; For the first The charging needs of individual EV clusters.

[0120] Step 6: Set the basic parameters, solve the economic operation scheduling model, and output the virtual power plant optimization scheduling results.

[0121] First, the predicted wind and solar power values, electrical load data, gas turbine parameters, energy storage system parameters, electric vehicle aggregator parameters, carbon trading mechanism parameters, and time-of-use electricity prices and natural gas prices are input into the virtual power plant economic operation scheduling model and operating constraints.

[0122] Then, considering the operational constraints, the virtual power plant economic operation scheduling model is solved using the CPLEX solver in Matlab.

[0123] Finally, the output includes the optimal operating cost of the virtual power plant, the curtailed wind and solar power of the virtual power plant, the carbon emissions of the virtual power plant, the output of each access unit of the virtual power plant, and the charging and discharging power of each cluster of electric vehicle aggregators.

[0124] An embodiment of the present invention provides a virtual power plant optimization scheduling system for electric vehicle aggregation and source-load response, based on the above-described method, comprising:

[0125] The aggregator management module is used to establish the access methods for electric vehicle aggregators;

[0126] The source-load response module is used to establish the source-load response mechanism;

[0127] The framework building module is used to build a virtual power plant framework that takes into account electric vehicle aggregators and source-load response mechanisms;

[0128] The model building module is used to establish an economic operation and scheduling model for virtual power plants that takes into account the response mechanisms of electric vehicle aggregators and source loads, with the goal of minimizing the operating cost of virtual power plants.

[0129] The constraint establishment module is used to establish the constraints for the operation of the virtual power plant;

[0130] The solver module is used to set basic parameters, solve the economic operation scheduling model, and output the virtual power plant optimization scheduling results.

[0131] Example 2:

[0132] Reference Figures 3-5 Tables 1-4 provide a specific application example and a comparison example to verify the effectiveness of this method.

[0133] Obtain wind, solar and electrical load data such as Figure 3 As shown in Table 1; the parameters of the gas turbine are shown in Table 2; the parameters of the energy storage system are shown in Table 3; two electric vehicle aggregators are set up to connect to the virtual power plant, each electric vehicle aggregator contains 5 clusters, and each aggregator has aggregated the electric vehicles of each cluster; the time-of-use electricity price parameters are shown in Table 3.

[0134] Table 1 Gas Turbine Parameters

[0135] Parameter Upper limit of output Lower limit of output Ramp rate Cogeneration efficiency Gas-electric conversion efficiency Value 50 MW 5 MW 25 MW / h 70.4% 20%

[0136] Table 2 Energy Storage System Parameters

[0137] Parameter Upper limit of charging power Upper limit of discharging power Upper limit of capacity Charging and discharging efficiency Value 11 MW 11 MW 30 MW 90%

[0138] Table 3 Time-of-use electricity price parameters

[0139] Time period Electricity price Type 1:00-5:00, 22:00-24:00 0.295 yuan / kWh Valley time period 6:00-7:00, 12:00-16:00, 20:00-21:00 0.55 yuan / kWh Flat time period 8:00-11:00, 17:00-19:00 0.804 yuan / kWh Peak time period

[0140] Based on the above simulation parameter settings, the economic operation scheduling model proposed in Example 1 is solved using the CPLEX solver.

[0141] The output of each access unit in the virtual power plant proposed in Example 1 under the optimal operating cost is obtained, such as... Figure 4 As shown. By Figure 4 It can be seen that during off-peak hours at night, the virtual power plant mainly relies on grid power purchase and wind power generation to maintain supply and demand balance. Electric vehicle aggregators and energy storage are charged simultaneously to improve wind power absorption capacity. During peak load periods, the system meets load demand through wind and solar power generation, aggregator discharge, and energy storage discharge.

[0142] The charging and discharging power of each cluster of electric vehicle aggregators in the virtual power plant proposed in Example 1 is obtained, such as... Figure 5 As shown. By Figure 5 a and Figure 5 As can be seen from b, the electric vehicle clusters of each aggregator mainly charge during off-peak hours at night and during the peak photovoltaic period from 12:00 to 15:00, and discharge during peak load periods, exhibiting obvious "peak shaving and valley filling" characteristics.

[0143] To verify the advantages of the virtual power plant optimization scheduling method proposed in Example 1 in reducing operational economics and absorbing renewable energy, the following scenarios were set for comparison. Scenario 1: Based on the virtual power plant proposed in Example 1, without considering electric vehicle aggregators, interruptible loads, and time-shiftable loads; Scenario 2: Based on the virtual power plant proposed in Example 2, without considering electric vehicle aggregators; Scenario 3: The virtual power plant proposed in Example 1. The optimization scheduling comparison results for the three scenarios under the same parameter settings are shown in Table 4.

[0144] Table 4 Comparison of Optimized Scheduling Results in Different Scenarios

[0145] Scenario Operation cost Abandoned wind power Carbon emission Scenario 1 1,040,000 yuan 310 MW 139 tons Scenario 2 68,000 yuan 207 MW 80 tons Scenario 3 52,000 yuan 73 MW 51 tons

[0146] As shown in Table 4, the operating cost of the method proposed in Example 1 (Scenario 3) is 520,000 yuan, which is 50% and 23.5% lower than that in Scenario 1 and Scenario 2, respectively; the curtailed wind and solar power is only 73MW, which is 76.5% and 64.7% lower than that in Scenario 1 and Scenario 2, respectively, demonstrating a significant improvement in renewable energy consumption; and carbon emissions are 51 tons, which is 63.3% and 36.3% lower than those in Scenario 1 and Scenario 2, respectively. The results indicate that the virtual power plant optimization scheduling method proposed in Example 1, which takes into account the electric vehicle aggregator and source-load response mechanism, shows significant advantages in terms of operational economy, renewable energy consumption capacity, and low-carbon operation through the synergistic optimization of multiple flexible resources.

[0147] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A virtual power plant optimization scheduling method for electric vehicle aggregation and source-load response, characterized in that, Includes the following steps: Step 1: Establish access methods for electric vehicle aggregators; Step 2: Establish a source-load response mechanism; Step 3: Construct a virtual power plant framework that takes into account electric vehicle aggregators and source-load response mechanisms; Step 4: Establish a virtual power plant economic operation scheduling model that takes into account the electric vehicle aggregator and source-load response mechanism; Step 5: Establish constraints for the operation of the virtual power plant; Step 6: Set the basic parameters, solve the economic operation scheduling model, and output the virtual power plant optimization scheduling results.

2. The virtual power plant optimization scheduling method for electric vehicle aggregation and source-load response according to claim 1, characterized in that, In step 1, the specific method by which electric vehicle aggregators connect to the virtual power plant is described as follows: Virtual power plant access There are several electric vehicle aggregators, and each aggregator uses a clustering method to divide large-scale electric vehicles with ordered charging and discharging characteristics into groups. Each cluster is managed separately, and the charging and discharging power of each electric vehicle cluster is aggregated using an aggregation method, and then connected to the virtual power plant in the form of an aggregate to participate in optimized scheduling.

3. The virtual power plant optimization scheduling method for electric vehicle aggregation and source-load response according to claim 1, characterized in that, In step 2, the source-load response mechanism uses waste heat power generation equipment and electric energy storage as adjustable resources on the source side, and time-shiftable loads, interruptible loads and electric vehicle aggregators as flexible resources on the load side, and coordinates them uniformly through a virtual power plant control platform.

4. The virtual power plant optimization scheduling method for electric vehicle aggregation and source-load response according to claim 1, characterized in that, In step 3, the constructed virtual power plant framework is configured with wind power generation, photovoltaic power generation, gas turbine units, waste heat power generation equipment, carbon capture equipment and electric energy storage on the source side, and electric load and electric vehicle aggregator on the load side. Energy management, data acquisition, intelligent metering and system evaluation are realized through the virtual power plant control platform. Electrical loads include interruptible loads, time-shiftable loads, and fixed loads, with interruptible loads and time-shiftable loads exhibiting demand response characteristics.

5. The virtual power plant optimization scheduling method for electric vehicle aggregation and source-load response according to claim 1, characterized in that, In step 4, the virtual power plant economic operation scheduling model takes the minimum operating cost of the virtual power plant as its objective function, and its expression is: In the formula, The operating cost of the virtual power plant; Costs of purchasing electricity and gas for virtual power plants; Carbon emission costs for virtual power plants; Costs of curtailing wind and solar power in virtual power plants; Cost of load demand response; For equipment operation and maintenance costs; Costs for virtual power plant dispatching EV aggregators; The expression for the electricity and gas purchase costs of the virtual power plant is as follows: In the formula, and These are time-of-use electricity pricing and gas pricing, respectively. and The respective power and gas purchase capacity of the virtual power plant; This is the total scheduling time; The expression for the carbon emission cost of a virtual power plant is: In the formula, , and These are the carbon dioxide production, carbon quota, and carbon capture volume of the virtual power plant, respectively. The length of the carbon emission tiers; The benchmark price for carbon trading; This is the step growth coefficient; The expression for the cost of curtailing wind and solar power in a virtual power plant is: In the formula, To compensate for the loss of scenery, Curtailed wind and solar power; The expression for load demand response cost is: In the formula, Power interrupted by electrical load; This is the cost coefficient for power load interruption. The expression for equipment operation and maintenance costs is: In the formula, For gas turbine Operation and maintenance cost coefficient; for Time gas turbine Output power; Energy storage devices The operation and maintenance cost coefficient; and These are energy storage devices The charging power and discharging power; The expression for the cost of virtual power plant dispatching EV aggregators is: In the formula, This is the battery degradation cost coefficient; The unit price for discharge subsidies for aggregators; For the first Among the EV aggregators, the first Each cluster Discharge power at any given moment.

6. The virtual power plant optimization scheduling method for electric vehicle aggregation and source-load response according to claim 5, characterized in that, In step 5, the constraints include electric power balance constraints, gas turbine constraints, electric energy storage constraints, and EV aggregator constraints. The expression for the power balance constraint is: In the formula, This refers to the output power of wind and solar power. This provides electrical power output for the gas turbine. To output electrical power to thermal power units; This refers to the output power of the waste heat power generation equipment. For the first Among the EV aggregators, the first Each cluster The charging power at any given moment; Power consumption of carbon capture equipment; Basic electrical load; For time-transferable loads; For interruptible electrical loads; The expression for the constraints of the gas turbine unit is: In the formula, and Gas turbine Upper and lower limits of output ramp power; For gas turbine The amount of energy consumed; For gas turbine Operating efficiency; for Time gas turbine ; output power; The expression for the energy storage constraint is: In the formula, and These are energy storage devices The upper and lower limits of capacity; and These are energy storage devices The charging and discharging efficiency; for Real-time energy storage devices The capacity; The expression for the EV aggregator constraint is: In the formula, and They are respectively Time of the first Upper and lower limits of charging power for individual EV clusters; and The first Upper and lower limits of discharge power for each EV cluster; for Time of the first The capacity of an EV cluster; and The first Capacity upper and lower limits for each EV cluster; and The first The charging and discharging efficiency of an individual EV cluster; For the first The charging needs of individual EV clusters.

7. The virtual power plant optimization scheduling method for electric vehicle aggregation and source-load response according to claim 6, characterized in that, Step 6 includes the following steps: First, the predicted wind and solar power values, electricity load data, gas turbine parameters, energy storage system parameters, electric vehicle aggregator parameters, carbon trading mechanism parameters, and time-of-use electricity prices and natural gas prices are input into the virtual power plant economic operation scheduling model and operating constraints. Then, considering the operational constraints, the virtual power plant economic operation scheduling model is solved using the CPLEX solver in Matlab. Finally, the output includes the optimal operating cost of the virtual power plant, the curtailed wind and solar power of the virtual power plant, the carbon emissions of the virtual power plant, the output of each access unit of the virtual power plant, and the charging and discharging power of each cluster of electric vehicle aggregators.

8. A virtual power plant optimization scheduling system for electric vehicle aggregation and source-load response, based on the virtual power plant optimization scheduling method for electric vehicle aggregation and source-load response as described in any one of claims 1-7, characterized in that, include: The aggregator management module is used to establish the access methods for electric vehicle aggregators; The source-load response module is used to establish the source-load response mechanism; The framework building module is used to build a virtual power plant framework that takes into account electric vehicle aggregators and source-load response mechanisms; The model building module is used to establish an economic operation and scheduling model for virtual power plants that takes into account the response mechanisms of electric vehicle aggregators and source loads, with the goal of minimizing the operating cost of virtual power plants. The constraint establishment module is used to establish the constraints for the operation of the virtual power plant; The solver module is used to set basic parameters, solve the economic operation scheduling model, and output the virtual power plant optimization scheduling results.

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