Grid-connected micro-grid scheduling method based on V2G energy storage accurate schedulable potential

Abstract

The invention discloses a grid-connected micro-grid scheduling method based on V2G energy storage accurate schedulable potential, which comprises the following steps of: constructing an electric vehicle monomer model under the condition of considering the charging behavior randomness and the charging demand uncertainty of an electric vehicle, and further utilizing a Minkowski sum aggregation method based on the monomer model so as to obtain the accurate schedulable potential of the V2G energy storage. A physical constraint model of large-scale V2G energy storage is established; based on a physical process and a scheduling target of operation of the V2G-containing energy storage grid-connected micro-grid, the micro-grid can realize consumption of renewable energy sources through reasonable scheduling for any uncertainty realization; according to the method, constraint structure characteristics of a grid-connected micro-grid optimization scheduling model containing large-scale V2G energy storage are utilized, sufficient and necessary conditions for meeting feasibility and safety in each scheduling period of a micro-grid system are established, and random implementation of uncertainties can be guaranteed through reasonable scheduling of the micro-grid; the method meets the economical and safe conditions of a micro-grid system, and is suitable for industrial large-scale application and popularization.

Description

Technical Field

[0001] This invention belongs to the field of microgrid application technology of V2G energy storage and renewable energy, specifically involving a grid-connected microgrid scheduling method based on the precise dispatchable potential of V2G energy storage. Background Technology

[0002] Grid-connected microgrids integrate distributed renewable energy generation units to provide clean, efficient, and economically controllable power supply, while enhancing the grid's flexibility. The introduction of renewable energy leads to significant power volatility and source-load uncertainty, posing a serious challenge to system operational stability. While traditional stationary battery energy storage can partially alleviate these problems, its application is limited by bottlenecks such as high initial investment costs, poor scalability, and lack of operational flexibility.

[0003] In recent years, the large-scale development of electric vehicles has provided new solutions. Based on vehicle-to-grid (V2G) technology, electric vehicles can serve as flexible distributed energy storage units, supporting the safe operation of grid-connected microgrids while meeting user charging needs. This dual-functional role gives electric vehicle energy storage significant advantages in scalability, flexibility, enhanced renewable energy absorption capacity, and system operational safety.

[0004] However, there are still several technical challenges in connecting large numbers of electric vehicles to the grid-connected microgrid, including the uncertainty of user travel behavior and the complexity of large-scale electric vehicle coordinated scheduling. Currently, there is a lack of a multi-stage optimization scheduling method for microgrids that meets the economic and safety requirements of microgrid systems under the influence of uncertain factors such as renewable energy and electric vehicle charging demand, and that considers large-scale V2G energy storage. Summary of the Invention

[0005] The purpose of this invention is to provide a grid-connected microgrid scheduling method based on the precise dispatchable potential of V2G energy storage, in order to solve the problem that the existing technology lacks a multi-stage optimization scheduling method for microgrids that meets the economic and safety conditions of microgrid systems under the influence of uncertain factors and considers large-scale V2G energy storage.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A grid-connected microgrid dispatching method based on the precise dispatchable potential of V2G energy storage includes the following steps: Step 1: Establish an uncertainty set based on historical information of random factors in the grid-connected microgrid, and obtain the key parameters of the grid-connected microgrid; Step 2: Establish an energy storage model for a single electric vehicle based on its individual energy storage parameters; aggregate multiple electric vehicles into a V2G generalized energy storage device, determine the key parameters of the V2G generalized energy storage device, and then establish a V2G generalized energy storage model. Step 3: Based on the key parameters of the grid-connected microgrid obtained in Step 1 and the key parameters of the V2G generalized energy storage device obtained in Step 2, establish a multi-stage scheduling model of the grid-connected microgrid based on the dispatchable potential of V2G energy storage, with the goal of minimizing the operating cost of the grid-connected microgrid. Step 4: Establish the necessary and sufficient conditions for the feasibility and security of the scheduling solutions for each scheduling period of the grid-connected microgrid; Step 5: Based on the robust optimization scheduling model of the grid-connected microgrid with V2G energy storage obtained in Step 3 and the necessary and sufficient conditions obtained in Step 4, establish a mixed integer programming model of the grid-connected microgrid based on the precise dispatchable potential of V2G energy storage.

[0007] The present invention also has the following features: Furthermore, in step 1, the historical information on the random factors of the grid-connected microgrid includes historical information on load demand, historical information on wind power output, historical information on photovoltaic power output, and historical information on the initial power of electric vehicles; The aforementioned uncertainties specifically include the load demand uncertainty set, wind power output uncertainty set, solar power output uncertainty set, and initial power uncertainty set of electric vehicles, which correspond one-to-one with the aforementioned historical information, as shown in the following formulas:

[0008]

[0009]

[0010]

[0011] in, t This represents any scheduling period of a grid-connected microgrid; T The first microgrid connected to the grid. T One scheduling period; and They represent t Electric vehicles during dispatching periods i The initial lower and upper limits of battery power; express t Electric vehicles during dispatching periods i The initial charge; express t Load demand of grid-connected microgrids during dispatch periods; express t Wind power output of grid-connected microgrids during dispatch periods; express t Photovoltaic output of grid-connected microgrids during dispatch periods; This represents the total number of electric vehicles connected to the grid-connected microgrid. i Represents any electric vehicle; The key parameters specifically include the energy storage parameters of a single electric vehicle and the operating parameters of the grid-connected microgrid; Among them, the energy storage parameters of a single electric vehicle include: the upper and lower limits of the energy storage capacity of a single electric vehicle, the energy storage charge and discharge efficiency coefficient of a single electric vehicle, and the upper and lower limits of the maximum charge and discharge power of a single electric vehicle. The operating parameters of a grid-connected microgrid include: the total scheduling cycle, the duration of each scheduling period, and the upper and lower limits of the transmission power capacity.

[0012] Furthermore, step 2 specifically includes the following steps: Step 21: Establish an energy storage model for a single electric vehicle based on its individual energy storage parameters, as shown in the following formula:

[0013]

[0014]

[0015] in, and They represent t A single electric vehicle during the dispatch period i The upper and lower limits of charging and discharging power; Indicates electric vehicles i exist t Charging and discharging power during the scheduling period; For electric vehicles i exist t The amount of energy stored during the dispatch period; and They represent t A single electric vehicle during the dispatch period i The upper and lower limits of energy storage capacity; τ The length of a scheduling period; For electric vehicles i The scheduling period during which the microgrid is connected to the grid; Step 22, Introduce a description of electric vehicles i exist tVariables of grid connection status during scheduling period This integrates multiple electric vehicles into a generalized V2G energy storage device. Among them, electric vehicles i exist t Variables of grid connection status during scheduling period As shown in the following formula:

[0016] Where 1 represents electric vehicle i It is in grid-connected state; 0 indicates electric vehicle i Currently offline; Indicates electric vehicles i The moment of connection to the grid-connected microgrid; Indicates electric vehicles i The moment when the microgrid is disconnected from the grid; Multiple electric vehicles can be aggregated into a generalized V2G energy storage device, as shown in the following formula:

[0017]

[0018]

[0019] Step 23: Use the Minkowski summation method to obtain the key parameters of the V2G generalized energy storage device. The key parameters specifically include:

[0020]

[0021]

[0022]

[0023]

[0024]

[0025] in, and They represent t Upper and lower limits of charging and discharging power of V2G generalized energy storage devices during dispatch periods; and They represent t Upper and lower limits of the change in energy storage capacity of V2G generalized energy storage devices caused by changes in the grid connection status of electric vehicles during the dispatch period; express t The charging and discharging power of V2G generalized energy storage devices during the dispatch period; express t The energy stored in V2G generalized energy storage devices during the dispatch period; Step 24: Establish a V2G generalized energy storage model for a single electric vehicle throughout its entire scheduling cycle based on the key parameters of the V2G generalized energy storage device, as shown in the following equation:

[0026]

[0027]

[0028]

[0029] in, express t The charging and discharging power of V2G generalized energy storage devices during the dispatch period; express t The change in energy storage capacity of V2G generalized energy storage devices caused by changes in the grid connection status of electric vehicles during the dispatch period; express t The energy storage level of V2G generalized energy storage devices during the dispatch period.

[0030] Furthermore, the multi-stage dispatch model for grid-connected microgrids based on the dispatchable potential of V2G energy storage in step 3 is as follows: The objective function that minimizes the total operating cost of a grid-connected microgrid while meeting operational requirements is:

[0031] in, This represents the total operating cost of a grid-connected microgrid over the entire dispatch cycle; express t Transmission power between the grid-connected microgrid and the main grid during the dispatching period; This indicates the load demand of the grid-connected microgrid until... t The row vector of the realized values ​​for the scheduling period; This indicates the cutoff time for wind power output in the grid-connected microgrid. t The row vector of the realized values ​​for the scheduling period; This indicates that the photovoltaic output of the grid-connected microgrid has been cut off. t The row vector of the realized values ​​for the scheduling period; Δ The row vector represents the realized value of the energy storage change of the generalized energy storage device in the grid-connected microgrid up to the scheduling period t, caused by the change in the grid connection status of electric vehicles. and They represent t The unit cost of purchasing electricity from the main grid and the unit revenue of selling electricity to the grid-connected microgrid during the dispatch period; Energy storage energy level upper and lower limits constraints:

[0032] Limitations on power exchange between grid-connected microgrids and the main grid:

[0033] Power balance constraints between grid-connected microgrids and the main grid:

[0034] Charging power limitations of V2G generalized energy storage devices:

[0035] Dynamic equation of SOC for V2G generalized energy storage devices:

[0036] in, , , and These represent the uncertainties of load demand, wind power output, solar power output, and V2G energy storage capacity, respectively. express t The energy storage level of V2G generalized energy storage devices at the end of the scheduling period; and They represent t The lower and upper limits of the energy storage level of V2G generalized energy storage devices at the end of the scheduling period; and They represent t Upper and lower limits of power exchanged between the microgrid and the main grid during the dispatch period; express t Power exchanged between the microgrid and the main grid during the dispatch period; express t Energy storage charging and discharging power of V2G generalized energy storage devices during dispatch periods; express t Wind power output during dispatch periods; express t Photovoltaic output during the scheduling period; express t Load demand during the scheduling period; express t The change in energy storage capacity of V2G generalized energy storage devices caused by changes in the grid connection status of electric vehicles during the dispatch period; and They represent t Charging and discharging power limitations of V2G generalized energy storage devices during dispatch periods; τ Indicates the length of a scheduling period; and These represent the charge / discharge efficiency coefficients of grid-connected microgrids and V2G generalized energy storage devices, respectively, and are generally 0.9; The independent variable represents the auxiliary variable, corresponding to the discharge and charging power of the V2G energy storage device. ; as well as The auxiliary equation is as follows: .

[0037] Furthermore, the necessary and sufficient conditions for the feasibility and security of the scheduling solution for each scheduling period of the grid-connected microgrid in step 4 are specifically as follows:

[0038] .

[0039] express t -1 The lower limit of the energy storage level of V2G generalized energy storage devices at the end of the scheduling period; and They represent t -1 The lower and upper limits of the energy storage level of V2G generalized energy storage devices at the end of the scheduling period; and They represent t Upper and lower limits of photovoltaic output of grid-connected microgrids during dispatch periods; and They represent t-1 The feasible safe lower and upper limits of energy storage level at the end of the scheduling period.

[0040] Furthermore, the hybrid integer programming model for grid-connected microgrids based on the precise dispatchable potential of V2G energy storage in step 5 is as follows:

[0041] Constraints:

[0042] Prerequisites:

[0043] Feasibility: necessary and sufficient conditions

[0044] in, and They represent t Upper and lower limits of power transmission between grid-connected microgrids and the main grid during the dispatching period; and They represent t The lower and upper limits of load demand for grid-connected microgrids during the dispatch period; and They represent t Lower and upper limits of wind power output of grid-connected microgrids during dispatch periods; and They represent t Lower and upper limits of photovoltaic output of grid-connected microgrids during dispatch periods; and They represent t Charging and discharging power limitations of V2G generalized energy storage devices during dispatch periods; and They represent t -1 The feasible safe lower and upper limits of the energy storage level of V2G generalized energy storage devices at the end of the scheduling period; and express t The lower and upper limits of the change in energy storage capacity of V2G generalized energy storage devices caused by changes in the grid connection status of electric vehicles during the dispatch period; τ Indicates the length of a scheduling period; and They represent t-1 The lower and upper limits of energy storage levels at the end of the scheduling period.

[0045] A processing unit for grid-connected microgrid dispatching, the electronic equipment comprising: Memory, used to store executable instructions; The processor, when executing executable instructions or computer programs stored in the memory, implements the above-described method.

[0046] A computer-readable storage medium storing executable instructions or a computer program, wherein the executable instructions, when executed by a processor, implement the method described above.

[0047] Compared with the prior art, the present invention has the following technical effects: The grid-connected microgrid scheduling method based on the precise dispatchable potential of V2G energy storage in this invention constructs a single electric vehicle model under the condition of considering the randomness of electric vehicle charging behavior and the uncertainty of charging demand. Furthermore, based on the single vehicle model, a physical constraint model of large-scale V2G energy storage is established using the Minkowski sum aggregation method. Based on the physical processes and scheduling objectives of grid-connected microgrids with V2G energy storage, and with the goal of minimizing operating costs, an optimal scheduling model for grid-connected microgrids is established that satisfies unpredictability and multi-stage robustness, and considers the uncertain information of electric vehicles. This ensures that for any uncertainties, the microgrid can achieve the absorption of renewable energy through reasonable scheduling. Utilizing the constraint structure characteristics of the optimal scheduling model for grid-connected microgrids with large-scale V2G energy storage, sufficient and necessary conditions for the feasibility and safety of each scheduling period of the microgrid system are established, ensuring that any realization of uncertainties can be guaranteed through reasonable scheduling of the microgrid. The feasible dispatch range of energy storage for each dispatch period can be obtained by solving the problem in one go. Then, a rolling optimization dispatch model of grid-connected microgrid based on the precise dispatchable potential of V2G energy storage can be established. This model aims to minimize the operating cost. It obtains the optimal dispatch solution for each dispatch period by rolling the solution of the single-layer mixed integer programming model for each dispatch period. It meets the economic and safety conditions of microgrid system and is suitable for large-scale use and promotion in industry. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of a grid-connected microgrid; Figure 2 This is a flowchart of the grid-connected microgrid scheduling method based on the precise dispatchable potential of V2G energy storage according to the present invention; Figure 3 This is a schematic diagram of the load demand on a typical day in one embodiment of the present invention; Figure 4 This is a schematic diagram of wind power output on a typical day in one embodiment of the present invention; Figure 5 This is a schematic diagram of photoelectric output in one embodiment of the present invention; Figure 6 This is a schematic diagram of V2G energy storage energy change in one embodiment of the present invention; Figure 7 This is a schematic diagram illustrating the feasible range of energy levels for V2G energy storage at different time periods in one embodiment of the present invention. Detailed Implementation

[0049] It should be noted that, unless otherwise specified, all components in this invention are components known in the prior art. Similarly, all methods in this invention, unless otherwise specified, are methods known in the prior art.

[0050] The following are specific embodiments of the present invention. It should be noted that the present invention is not limited to the following specific embodiments. All equivalent modifications made based on the technical solutions of this application fall within the protection scope of the present invention.

[0051] Please see Figure 1 A grid-connected microgrid with large-scale V2G energy storage mainly comprises V2G energy storage, electrical loads (including interruptible loads, portable loads, and flexible loads), wind power plants, and photovoltaic power plants. The microgrid system is connected to the main grid system, enabling energy exchange—either purchasing electricity from or selling electricity to the main grid. Furthermore, V2G energy storage, electrical loads, wind power, and photovoltaic power are connected via the same bus, allowing for direct energy exchange within the microgrid system. This means loads can obtain electricity from electric vehicle energy storage, wind power, and photovoltaic renewable energy power plants; V2G energy storage can supply electricity to electrical loads and also obtain electricity from wind and photovoltaic renewable energy power plants; and wind and photovoltaic renewable energy generation can supply electricity to electrical loads and V2G energy storage.

[0052] The specific structural characteristics of a microgrid with V2G energy storage are as follows: V2G energy storage systems consist of electric vehicle battery packs, battery aggregation management systems, power conversion systems, collection lines, and other auxiliary equipment, and are used for the storage, conversion, and release of electrical energy. Wind power generation consists of a wind turbine, an asynchronous or synchronous wind turbine generator, a tower supporting the generator set, a battery charging controller, an inverter, a load unloader, a grid connection controller, and a battery bank, which converts the kinetic energy of the wind into electrical energy.

[0053] A photovoltaic power generation system consists of solar cell modules (arrays), controllers, energy storage battery banks, AC / DC inverters, etc., which directly convert solar radiation energy into electrical energy.

[0054] Electrical loads can be categorized into various industrial loads, agricultural loads, transportation loads, and residential loads, consuming electrical energy to enable the operation of industry, agriculture, transportation, and daily life. The total load of a microgrid is the sum of the total power consumed by all electrical equipment in the system.

[0055] The robust optimal scheduling model for grid-connected microgrids based on the precise dispatchable potential of V2G energy storage determines the economic performance and security of microgrid operation. Therefore, the establishment of a robust optimal scheduling model and algorithm for grid-connected microgrids based on the precise dispatchable potential of V2G energy storage is of great significance.

[0056] For the dispatching problem of grid-connected microgrids with V2G energy storage, the realization of uncertainties and the implementation of dispatching schemes occur alternately and iteratively due to the influence of uncertainties such as renewable energy sources like wind and solar power, load demand, and the initial charging capacity of electric vehicles. Therefore, the robust optimization dispatching problem of grid-connected microgrids with V2G energy storage is essentially a multi-stage dispatching problem, which needs to consider unpredictability and multi-stage robustness requirements to cope with all possible random factors.

[0057] However, solving the optimal scheduling model considering unpredictability and multi-stage robustness requirements is quite challenging. Currently, there is a lack of a feasible method for accurately and quickly solving the multi-stage robust optimal scheduling model of grid-connected microgrids with V2G energy storage, especially considering the uncertain initial charging capacity of electric vehicles. Therefore, the multi-stage robust optimal scheduling method for grid-connected microgrids based on the precise dispatchability potential of V2G energy storage proposed in this invention is of great significance.

[0058] The technical problem this embodiment aims to solve is to provide a method for directly solving the multi-stage robust optimization scheduling problem of grid-connected microgrids based on the precise dispatchability potential of V2G energy storage. The result obtained by this method represents the necessary and sufficient conditions for ensuring the safe and economical operation of grid-connected microgrids containing V2G energy storage. That is, within the obtained feasible decision region, the grid-connected microgrid system containing V2G energy storage can achieve safe operation under uncertainties through reasonable scheduling; outside the obtained feasible region, the system cannot cope with arbitrary realization values ​​of wind power output, initial electric vehicle power, and uncertain load demand.

[0059] The grid-connected microgrid scheduling method based on the precise dispatchable potential of V2G energy storage in this embodiment constructs a single electric vehicle model under the condition of considering the randomness of electric vehicle charging behavior and the uncertainty of charging demand. Furthermore, based on the single vehicle model, a physical constraint model of large-scale V2G energy storage is established using the Minkowski sum aggregation method. Based on the physical processes and scheduling objectives of grid-connected microgrids with V2G energy storage, and with the goal of minimizing operating costs, an optimal scheduling model for grid-connected microgrids is established that satisfies unpredictability and multi-stage robustness, and considers the uncertain information of electric vehicles. This ensures that for any uncertainties, the microgrid can achieve the absorption of renewable energy through reasonable scheduling. Utilizing the constraint structure characteristics of the optimal scheduling model for grid-connected microgrids with large-scale V2G energy storage, sufficient and necessary conditions for the feasibility and safety of each scheduling period of the microgrid system are established, ensuring that any realization of uncertainties can be guaranteed through reasonable scheduling of the microgrid. The feasible dispatch range of energy storage for each dispatch period can be obtained by solving the problem in one go. Then, a rolling optimization dispatch model of grid-connected microgrid based on the precise dispatchable potential of V2G energy storage can be established. This model aims to minimize the operating cost and obtains the optimal dispatch solution for each dispatch period by rolling the solution of the single-layer mixed integer programming model for each dispatch period.

[0060] Furthermore, obtaining relevant information such as equipment parameters and uncertain parameters (electric load, wind power output, photovoltaic power output, and initial electric vehicle power) can provide data support for realizing multi-stage robust optimization scheduling of grid-connected microgrids based on the precise and dispatchable potential of V2G energy storage.

[0061] Furthermore, based on historical data on the random factors of load demand, wind power output, solar power output, and initial electric vehicle power, uncertainty sets for load demand, wind power output, solar power output, and initial electric vehicle power are established respectively to reflect the range of variation of uncertainties.

[0062] Furthermore, taking minimum operating cost as the objective function and comprehensively considering the operational requirements of each device in the grid-connected microgrid, an optimal scheduling model for the grid-connected microgrid, incorporating V2G energy storage, is established. This model is a conceptual model, and theoretically, the scheduling scheme obtained from it can guarantee the safety and feasibility of the microgrid system operation.

[0063] Furthermore, based on the structural analysis of a robust optimization scheduling model for a grid-connected microgrid considering V2G energy storage, the feasible range for ensuring the safe operation of the grid-connected microgrid system and the precise dispatchable potential of V2G energy storage are presented. Under different operating and equipment parameters, as long as a solution exists within the given feasible range, and the scheduling schemes of the grid-connected system are all within this feasible range, then for any uncertain value realized in the grid-connected microgrid, there is a feasible scheduling scheme. In fact, this feasible range is a necessary and sufficient condition for ensuring the feasibility of the grid-connected microgrid.

[0064] Furthermore, based on the aforementioned necessary and sufficient conditions, and with the goal of minimizing operating costs, a mixed-integer programming model for robust optimal scheduling of grid-connected microgrids based on the precise dispatchability potential of V2G energy storage is established. Unlike the conceptual model for robust optimal scheduling of grid-connected microgrids considering V2G energy storage, this mixed-integer programming model can be solved quickly using a commercial solver. Therefore, once the realized values ​​and system parameters are known, the safe and feasible scheduling range, optimal scheduling solution, and minimum operating cost for each scheduling period of the grid-connected microgrid can be obtained.

[0065] In summary, the multi-stage robust optimization scheduling method for grid-connected microgrids based on the precise dispatchability potential of V2G energy storage in this embodiment provides technical support for the formulation of economically feasible scheduling schemes for grid-connected microgrids containing V2G energy storage.

[0066] Specifically, such as Figure 2 As shown, a grid-connected microgrid dispatching method based on the precise dispatchable potential of V2G energy storage includes the following steps: Step 1: Establish an uncertainty set based on historical information of random factors in the grid-connected microgrid, and obtain the key parameters of the grid-connected microgrid; Step 2: Establish an energy storage model for a single electric vehicle based on its individual energy storage parameters; aggregate multiple electric vehicles into a V2G generalized energy storage device, determine the key parameters of the V2G generalized energy storage device, and then establish a V2G generalized energy storage model. Step 3: Based on the key parameters of the grid-connected microgrid obtained in Step 1 and the key parameters of the V2G generalized energy storage device obtained in Step 2, establish a multi-stage scheduling model of the grid-connected microgrid based on the dispatchable potential of V2G energy storage, with the goal of minimizing the operating cost of the grid-connected microgrid. Step 4: Establish the necessary and sufficient conditions for the feasibility and security of the scheduling solutions for each scheduling period of the grid-connected microgrid; Step 5: Based on the robust optimization scheduling model of the grid-connected microgrid with V2G energy storage obtained in Step 3 and the necessary and sufficient conditions obtained in Step 4, establish a mixed integer programming model of the grid-connected microgrid based on the precise dispatchable potential of V2G energy storage.

[0067] Specifically, in step 1, the historical information on the random factors of the grid-connected microgrid includes historical information on load demand, historical information on wind power output, historical information on photovoltaic power output, and historical information on the initial power of electric vehicles. The aforementioned uncertainties specifically include the load demand uncertainty set, wind power output uncertainty set, solar power output uncertainty set, and initial power uncertainty set of electric vehicles, which correspond one-to-one with the aforementioned historical information, as shown in the following formulas:

[0068]

[0069]

[0070]

[0071] in, t This represents any scheduling period of a grid-connected microgrid; T The first microgrid connected to the grid. T One scheduling period; and They represent t Electric vehicles during dispatching periods i The initial lower and upper limits of battery power; express t Electric vehicles during dispatching periods i The initial charge; express t Load demand of grid-connected microgrids during dispatch periods; express t Wind power output of grid-connected microgrids during dispatch periods; express t Photovoltaic output of grid-connected microgrids during dispatch periods; This represents the total number of electric vehicles connected to the grid-connected microgrid. i Represents any electric vehicle; The key parameters specifically include the energy storage parameters of a single electric vehicle and the operating parameters of the grid-connected microgrid; Among them, the energy storage parameters of a single electric vehicle include: the upper and lower limits of the energy storage capacity of a single electric vehicle, the energy storage charge and discharge efficiency coefficient of a single electric vehicle, and the upper and lower limits of the maximum charge and discharge power of a single electric vehicle. The operating parameters of a grid-connected microgrid include: the total scheduling cycle, the duration of each scheduling period, and the upper and lower limits of the transmission power capacity.

[0072] Specifically, step 2 is to establish a single electric vehicle energy storage model based on the relevant parameters obtained in step 1, including charging and discharging power boundary constraints and battery capacity safety boundaries. Based on the Minkowski summation principle, the individual energy storage models of electric vehicles are spatially superimposed and aggregated to obtain the parameters of the generalized energy storage device. Specifically, the following steps are included: Step 21, based on the acquired individual electric vehicle energy storage parameters, including tSingle electric vehicle during dispatch period i Upper and lower limits of charging and discharging power and ; t Single electric vehicle during dispatch period i Upper and lower limits of energy storage capacity and Therefore, an energy storage model for a single electric vehicle is established, as shown in the following equation:

[0073]

[0074]

[0075] in, and They represent t A single electric vehicle during the dispatch period i The upper and lower limits of charging and discharging power; Indicates electric vehicles i exist t Charging and discharging power during the scheduling period; For electric vehicles i exist t The amount of energy stored during the dispatch period; and They represent t A single electric vehicle during the dispatch period i The upper and lower limits of energy storage capacity; τ The length of a scheduling period; For electric vehicles i The scheduling period during which the microgrid is connected to the grid; Step 22, Introduce a description of electric vehicles i exist t Variables of grid connection status during scheduling period This integrates multiple electric vehicles into a generalized V2G energy storage device. Among them, electric vehicles i exist t Variables of grid connection status during scheduling period As shown in the following formula:

[0076] Where 1 represents electric vehicle i It is in grid-connected state; 0 indicates electric vehicle i Currently offline; Indicates electric vehicles i The moment of connection to the grid-connected microgrid; Indicates electric vehicles i The moment when the microgrid is disconnected from the grid; Multiple electric vehicles can be aggregated into a generalized V2G energy storage device, as shown in the following formula:

[0077]

[0078]

[0079] Step 23: Use the Minkowski summation method to obtain the key parameters of the V2G generalized energy storage device. The key parameters specifically include:

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] in, and They represent t Upper and lower limits of charging and discharging power of V2G generalized energy storage devices during dispatch periods; and They represent t Upper and lower limits of the change in energy storage capacity of V2G generalized energy storage devices caused by changes in the grid connection status of electric vehicles during the dispatch period; express t The charging and discharging power of V2G generalized energy storage devices during the dispatch period; express t The energy stored in V2G generalized energy storage devices during the dispatch period; Step 24: Establish a V2G generalized energy storage model for a single electric vehicle throughout its entire scheduling cycle based on the key parameters of the V2G generalized energy storage device, as shown in the following equation:

[0086]

[0087]

[0088]

[0089] in, express t The charging and discharging power of V2G generalized energy storage devices during the dispatch period; express t The change in energy storage capacity of V2G generalized energy storage devices caused by changes in the grid connection status of electric vehicles during the dispatch period; express t The energy storage level of V2G generalized energy storage devices during the dispatch period.

[0090] Specifically, step 3 is based on the key parameters obtained in steps 1 and 2, and with the goal of minimizing the operating cost of the microgrid system, establishes a multi-stage scheduling model for grid-connected microgrids based on the dispatchable potential of V2G energy storage that satisfies unpredictability and multi-stage robustness. Considering the physical laws governing the actual operation and scheduling of grid-connected microgrids, decision-making must meet the requirements of unpredictability.

[0091] make , , and .

[0092] Based on the requirement of unexpectedness, a decision is defined as: (1) Furthermore, the multi-stage dispatch model for grid-connected microgrids based on the dispatchable potential of V2G energy storage in step 3 is as follows: The objective function that minimizes the total operating cost of a grid-connected microgrid while meeting operational requirements is:

[0093] in, This represents the total operating cost of a grid-connected microgrid over the entire dispatch cycle; express t Transmission power between the grid-connected microgrid and the main grid during the dispatching period; This indicates the load demand of the grid-connected microgrid until... t The row vector of the realized values ​​for the scheduling period; This indicates the cutoff time for wind power output in the grid-connected microgrid. t The row vector of the realized values ​​for the scheduling period; This indicates that the photovoltaic output of the grid-connected microgrid has been cut off. t The row vector of the realized values ​​for the scheduling period; Δ The row vector represents the realized value of the energy storage change of the generalized energy storage device in the grid-connected microgrid up to the scheduling period t, caused by the change in the grid connection status of electric vehicles. and They represent t The unit cost of purchasing electricity from the main grid and the unit revenue of selling electricity to the grid-connected microgrid during the dispatch period; Operational constraints: The establishment of a multi-stage robust optimization scheduling model for grid-connected microgrids with V2G energy storage needs to consider the relevant constraints of grid-connected microgrids in actual operation and scheduling.

[0094] The specific content of the constraints includes: Equation (3) represents the upper and lower limits of V2G energy storage energy level constraints, which are the physical operation requirements of V2G energy storage devices in actual operation.

[0095] Energy storage energy level upper and lower limits constraints: (3) Equation (4) defines the power exchange limitations between the grid-connected microgrid and the main grid during operation: (4) Equation (5) is a power balance constraint, which requires that the algebraic sum of the exchange power between the microgrid and the main grid, the charging and discharging power of V2G energy storage, the wind power output, and the photovoltaic power output equal the load demand.

[0096] Power balance constraints between grid-connected microgrids and the main grid: (5) Equation (6) characterizes the charging and discharging power limitations of V2G energy storage. Charging power limitations of V2G generalized energy storage devices: (6) The dynamic equation of the state of charge (SOC) of V2G generalized energy storage devices reflects the evolution of the energy storage level of V2G energy storage, specifically: (7) in, , , and These represent the uncertainties in load demand, wind power output, solar power output, and V2G energy storage capacity, respectively. express tV2G energy storage energy level at the end of the scheduling period; and They represent t The lower and upper limits of V2G energy storage energy levels at the end of the scheduling period; and They represent t Upper and lower limits of power exchanged between the microgrid and the main grid during the dispatch period; express t During the dispatching period, the microgrid exchanges power with the main grid; express t V2G energy storage charging and discharging power during the scheduling period; express t Wind power output during the dispatch period; express t Photovoltaic output during scheduling periods; express t Load demand during scheduling periods; express t The change in energy storage capacity of V2G generalized energy storage devices caused by changes in the grid connection status of electric vehicles during the dispatch period; and They represent t Charging and discharging power limitations of V2G energy storage systems during dispatch periods; τ Indicates the length of a scheduling period; and These represent the charge / discharge efficiency coefficients, respectively. The independent variable represents the auxiliary variable, corresponding to the discharge and charging power of the V2G energy storage device. ; as well as The auxiliary equation is as follows: (7) Specifically, in step 4, based on the multi-stage robust optimization scheduling model of the grid-connected microgrid with V2G energy storage obtained in step S3, sufficient and necessary conditions are established for the scheduling solutions of the microgrid system to meet feasibility and security in each scheduling period. First, to facilitate subsequent derivation, the expression of the decision variables is simplified: (9) Based on equations (5), (6), and (7), the following formula can be obtained: (10) (11) (12) According to equation (10), equation (4) can be transformed into (13) According to the above formula, the multi-stage robust optimization scheduling model of the grid-connected microgrid containing V2G energy storage in step S3 can be transformed into... Equations (3), (11), and (13) are the core variables.

[0097] Further simplification and rearrangement of equations (3), (11), and (13) yields: (14) (15) (16) During the scheduling period t The known information includes t -1 scheduling period decision (such as ),as well as t The realized values ​​of load demand, wind power output, solar power output, and V2G energy storage capacity changes during the dispatch period. Based on this known information, formulate... t Scheduling time slot decision .

[0098] Due to the existence of V2G energy storage system, there will be a coupling relationship between adjacent scheduling periods during the grid-connected microgrid system scheduling process (Equation (14)-Equation (16)).

[0099] Therefore, if the decision The formulation was inappropriate, during the scheduling period. t For grid-connected microgrid systems, there may be no feasible scheduling solution.

[0100] Based on the above analysis, it can be seen that, despite the Decision-making is based on After the decision was made, but The permitted range must be based on The allowable range is used to determine this.

[0101] Based on equations (7) and (14), the following formula can be obtained: (17) Combining the relationships between equations (6) and (17) with those between equations (15) and (16), we can conclude that only when the intersection of equations (6) and (17) is non-empty, does the following hold true regarding... Feasible decisions, namely (18) Equations (15) and (16) are both about For interval constraints, the condition for the existence of a feasible solution is that the intersection of the two intervals is non-empty, i.e. (19) Furthermore, considering box-type uncertain sets, equation (19) transforms into a tighter constraint form: (20) According to equation (20) and The physical upper and lower limits are guaranteed to be within There are feasible decisions. Permitted range: (twenty one) Equation (22) is related to the scheduling period. t -1 and t The relevant equations. For scheduling periods t and t +1, the conclusion should still hold. Therefore, in equation (22) and Should be replaced with and Thus, a recursive equation was established.

[0102] (twenty two) Based on the above analysis, the lower limit is... It must be the maximum value among all lower bounds, and It must be the minimum of all upper bounds.

[0103] Equation (22) is The precise lower and upper bounds. If and only if Not empty, There is a feasible solution.

[0104] Furthermore, it has been pointed out that equation (18) is a necessary condition for performing the above analysis. According to equations (4) and (6), inequality (18) holds if and only if equation (18) applies to all... d t , w t and p v,t Both are valid.

[0105] (twenty three) Equations (22) and (23) provide the scheduling time period. t The grid-connected microgrid system has feasible conditions for a scheduling solution. Therefore, the feasible range for each scheduling period under the rolling optimization framework is: .

[0106] In summary, the necessary and sufficient conditions for the feasibility and security of the scheduling solution for each scheduling period of the grid-connected microgrid in step 4 are as follows:

[0107] .

[0108] Specifically, the hybrid integer programming model for grid-connected microgrids based on the precise dispatchable potential of V2G energy storage in step 5 is as follows:

[0109] Constraints:

[0110] Prerequisites:

[0111] Feasibility: necessary and sufficient conditions

[0112] in, and They represent t Upper and lower limits of power transmission between grid-connected microgrids and the main grid during the dispatching period; and They represent t The lower and upper limits of load demand for grid-connected microgrids during the dispatch period; and They represent t Lower and upper limits of wind power output of grid-connected microgrids during dispatch periods; and They represent t Lower and upper limits of photovoltaic output of grid-connected microgrids during dispatch periods; and They represent t Charging and discharging power limitations of V2G generalized energy storage devices during dispatch periods; and They represent t-1 The feasible safe lower and upper limits of the energy storage level of V2G generalized energy storage devices at the end of the scheduling period; and express t The lower and upper limits of the change in energy storage capacity of V2G generalized energy storage devices caused by changes in the grid connection status of electric vehicles during the dispatch period; τ Indicates the length of a scheduling period; and They represent t -1 The lower and upper limits of energy storage levels at the end of the scheduling period.

[0113] As a specific implementation method, this embodiment provides a grid-connected microgrid dispatching system with V2G energy storage. This system can be used to implement the above-mentioned multi-stage robust optimization dispatching method for grid-connected microgrids with V2G energy storage. Specifically, the grid-connected microgrid dispatching system with V2G energy storage includes a data module, a construction module, a range module, and a dispatching module.

[0114] The data module, based on historical information about acquired random factors, establishes uncertain sets for wind power output, solar power output, load demand, and initial charging capacity of electric vehicles; and acquires key parameters of equipment in the system. The module constructs a large-scale V2G energy storage model based on the energy storage model of a single electric vehicle and the Minkowski summation method; based on the uncertainty set of stochastic factors, a multi-stage robust optimization scheduling model for grid-connected microgrids with V2G energy storage is established with the goal of minimizing the total operating cost. The scope module establishes the range of feasibility and safety requirements for each scheduling period of the grid-connected microgrid system based on the multi-stage robust optimization scheduling model of grid-connected microgrids with V2G energy storage obtained from the construction module. The scheduling module establishes a multi-stage robust optimization scheduling mixed integer programming model for grid-connected microgrids with V2G energy storage based on the multi-stage robust optimization scheduling model of grid-connected microgrids with V2G energy storage obtained from the construction module and the feasibility and safety range obtained from the range module. By directly solving this model, the minimum total operating cost and feasible scheduling decisions for each scheduling period are obtained.

[0115] As a specific implementation, this embodiment provides a terminal device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor in this embodiment of the invention can be used for operating a grid-connected microgrid optimal scheduling model based on the precise dispatchable potential of V2G energy storage to address the uncertainty of electric vehicle charging demand, including: Historical information on wind power output, solar power output, load demand, and the random factor of initial electric vehicle charging power, along with key parameters in a grid-connected microgrid system containing V2G energy storage, is obtained to establish an uncertainty set. With the goal of minimizing total operating cost, a multi-stage robust optimization scheduling model for the grid-connected microgrid containing V2G energy storage is established, satisfying unpredictability and multi-stage robustness while considering the uncertainty of initial electric vehicle charging power. The constraint structure features in the model are fully utilized to establish sufficient and necessary conditions for the feasibility and safety of each scheduling period in the microgrid system. Based on these sufficient and necessary conditions, a mixed-integer programming model for minimizing the total operating cost of the grid-connected microgrid system based on the precise dispatchable potential of V2G energy storage is established. The minimum total operating cost and feasible scheduling solutions and ranges for each scheduling period are obtained by directly solving this model.

[0116] The terminal device is a chip. In this embodiment, the chip includes a processor, which may be one or more, and a memory for storing a computer program executable by the processor. The computer program stored in the memory may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor may be configured to execute the computer program to perform the aforementioned multi-stage robust optimization scheduling method for grid-connected microgrids based on the precise dispatchable potential of V2G energy storage.

[0117] Additionally, the chip may include a power supply component and a communication component. The power supply component can be configured to perform power management for the chip, and the communication component can be configured to enable communication within the chip, such as wired or wireless communication. Furthermore, the chip may include input / output (I / O) interfaces. The chip can operate on an operating system stored in memory.

[0118] This embodiment also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0119] One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the multi-stage robust optimization scheduling method for grid-connected microgrids with V2G energy storage in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps: Historical information on wind power output, solar power output, load demand, and the random factors of initial electric vehicle charging power, along with key parameters in a grid-connected microgrid system containing V2G energy storage, is obtained to establish an uncertainty set. With the goal of minimizing total operating cost, a multi-stage robust optimization scheduling model for the grid-connected microgrid containing V2G energy storage is established, satisfying unpredictability and multi-stage robustness while considering the uncertainty of initial electric vehicle charging power. The constraint structure features in the model are fully utilized to establish sufficient and necessary conditions for the minimum total operating cost of the microgrid system to meet feasibility and safety requirements during each scheduling period. Based on these sufficient and necessary conditions, a mixed-integer programming model for the grid-connected microgrid based on the precise dispatchable potential of V2G energy storage is established. The minimum total operating cost, feasible scheduling solutions for each scheduling period, and feasible scheduling ranges are obtained by directly solving this model.

[0120] As a specific implementation method, this embodiment aims to verify a multi-stage robust optimization scheduling method for a grid-connected microgrid system with V2G energy storage based on the precise dispatchable potential of V2G energy storage, and obtain relevant information in the microgrid system. Table 1 shows the main parameters of the equipment in the grid-connected microgrid system. Figure 3, Figure 4 , Figure 5 and Figure 6 The data displays information related to load demand, wind power output, solar power output, and V2G energy storage changes on a typical day, covering 24 scheduling periods, each lasting one hour.

[0121] Table 1. Main Parameters of Microgrid System

[0122] Based on the above parameters and uncertainties, the minimum total cost of a grid-connected microgrid, considering the uncertainty of electric vehicle charging demand, is $50,925. The feasible range of energy levels for V2G energy storage during various dispatch periods is as follows: Figure 7 As shown.

[0123] In summary, the multi-stage robust optimization scheduling method for grid-connected microgrids based on the precise dispatchability potential of V2G energy storage in this embodiment can provide the minimum operating cost scheduling solution to ensure the safe operation of the system under given grid-connected microgrid parameters. Simultaneously, it provides a feasible range for scheduling decisions during the specified scheduling period; operators only need to operate within this given feasible range to ensure the reliable operation of the system.

[0124] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

Claims

1. A grid-connected microgrid dispatching method based on the precise dispatchable potential of V2G energy storage, characterized in that, Includes the following steps: Step 1: Establish an uncertainty set based on historical information of random factors in the grid-connected microgrid, and obtain the key parameters of the grid-connected microgrid; Step 2: Establish an energy storage model for a single electric vehicle based on its individual energy storage parameters; aggregate multiple electric vehicles into a V2G generalized energy storage device, determine the key parameters of the V2G generalized energy storage device, and then establish a V2G generalized energy storage model. Step 3: Based on the key parameters of the grid-connected microgrid obtained in Step 1 and the key parameters of the V2G generalized energy storage device obtained in Step 2, establish a multi-stage scheduling model of the grid-connected microgrid based on the dispatchable potential of V2G energy storage, with the goal of minimizing the operating cost of the grid-connected microgrid. Step 4: Establish the necessary and sufficient conditions for the feasibility and security of the scheduling solutions for each scheduling period of the grid-connected microgrid; Step 5: Based on the robust optimization scheduling model of the grid-connected microgrid with V2G energy storage obtained in Step 3 and the necessary and sufficient conditions obtained in Step 4, establish a mixed integer programming model of the grid-connected microgrid based on the precise dispatchable potential of V2G energy storage.

2. The grid-connected microgrid dispatching method based on the precise dispatchable potential of V2G energy storage as described in claim 1, characterized in that, In step 1, the historical information of the random factors of the grid-connected microgrid includes historical information of load demand, historical information of wind power output, historical information of photovoltaic power output, and historical information of the initial power of electric vehicles; The aforementioned uncertainties specifically include the load demand uncertainty set, wind power output uncertainty set, solar power output uncertainty set, and initial power uncertainty set of electric vehicles, which correspond one-to-one with the aforementioned historical information, as shown in the following formulas: in, t This represents any scheduling period of a grid-connected microgrid; T The first microgrid connected to the grid. T One scheduling period; and They represent t Electric vehicles during dispatching periods i The initial lower and upper limits of battery power; express t Electric vehicles during dispatching periods i The initial charge; express t Load demand of grid-connected microgrids during dispatch periods; express t Wind power output of grid-connected microgrids during dispatch periods; express t Photovoltaic output of grid-connected microgrids during dispatch periods; This represents the total number of electric vehicles connected to the grid-connected microgrid. i Represents any electric vehicle; The key parameters specifically include the energy storage parameters of a single electric vehicle and the operating parameters of the grid-connected microgrid; Among them, the energy storage parameters of a single electric vehicle include: the upper and lower limits of the energy storage capacity of a single electric vehicle, the energy storage charge and discharge efficiency coefficient of a single electric vehicle, and the upper and lower limits of the maximum charge and discharge power of a single electric vehicle. The operating parameters of a grid-connected microgrid include: the total scheduling cycle, the duration of each scheduling period, and the upper and lower limits of the transmission power capacity.

3. The grid-connected microgrid dispatching method based on the precise dispatchable potential of V2G energy storage as described in claim 2, characterized in that, Step 2 specifically includes the following steps: Step 21: Establish an energy storage model for a single electric vehicle based on its individual energy storage parameters, as shown in the following formula: in, and They represent t A single electric vehicle during the dispatch period i The upper and lower limits of charging and discharging power; Indicates electric vehicles i exist t Charging and discharging power during the scheduling period; For electric vehicles i exist t The amount of energy stored during the dispatch period; and They represent t A single electric vehicle during the dispatch period i The upper and lower limits of energy storage capacity; τ The length of a scheduling period; For electric vehicles i The scheduling period during which the microgrid is connected to the grid; Step 22, Introduce a description of electric vehicles i exist t Variables of grid connection status during scheduling period This integrates multiple electric vehicles into a generalized V2G energy storage device. Among them, electric vehicles i exist t Variables of grid connection status during scheduling period As shown in the following formula: Where 1 represents electric vehicle i It is in grid-connected state; 0 indicates electric vehicle i Currently offline; Indicates electric vehicles i The moment of connection to the grid-connected microgrid; Indicates electric vehicles i The moment when the microgrid is disconnected from the grid; Multiple electric vehicles can be aggregated into a generalized V2G energy storage device, as shown in the following formula: Step 23: Use the Minkowski summation method to obtain the key parameters of the V2G generalized energy storage device. The key parameters specifically include: in, and They represent t Upper and lower limits of charging and discharging power of V2G generalized energy storage devices during dispatch periods; and They represent t Upper and lower limits of the change in energy storage capacity of V2G generalized energy storage devices caused by changes in the grid connection status of electric vehicles during the dispatch period; express t The charging and discharging power of V2G generalized energy storage devices during the dispatch period; express t The energy stored in V2G generalized energy storage devices during the dispatch period; Step 24: Establish a V2G generalized energy storage model for a single electric vehicle throughout its entire scheduling cycle based on the key parameters of the V2G generalized energy storage device, as shown in the following equation: in, express t The charging and discharging power of V2G generalized energy storage devices during the dispatch period; express t The change in energy storage capacity of V2G generalized energy storage devices caused by changes in the grid connection status of electric vehicles during the dispatch period; express t The energy storage level of V2G generalized energy storage devices during the dispatch period.

4. The grid-connected microgrid dispatching method based on the precise dispatchable potential of V2G energy storage as described in claim 3, characterized in that, Step 3, the multi-stage dispatch model for grid-connected microgrids based on the dispatchable potential of V2G energy storage, is as follows: The objective function that minimizes the total operating cost of a grid-connected microgrid while meeting operational requirements is: in, This represents the total operating cost of a grid-connected microgrid over the entire dispatch cycle; express t Transmission power between the grid-connected microgrid and the main grid during the dispatching period; This indicates the load demand of the grid-connected microgrid until... t The row vector of the realized values ​​for the scheduling period; This indicates that the wind power output of the grid-connected microgrid has been cut off until... t The row vector of the realized values ​​for the scheduling period; This indicates that the photovoltaic output of the grid-connected microgrid has been cut off. t The row vector of the realized values ​​for the scheduling period; Δ The row vector represents the realized value of the energy storage change of the generalized energy storage device in the grid-connected microgrid up to the scheduling period t, caused by the change in the grid connection status of electric vehicles. and They represent t The unit cost of purchasing electricity from the main grid and the unit revenue of selling electricity to the grid-connected microgrid during the dispatch period; Energy storage energy level upper and lower limits constraints: Limitations on power exchange between grid-connected microgrids and the main grid: Power balance constraints between grid-connected microgrids and the main grid: Charging power limitations of V2G generalized energy storage devices: Dynamic equation of SOC for V2G generalized energy storage devices: in, , , and These represent the uncertainties of load demand, wind power output, solar power output, and V2G energy storage capacity, respectively. express t The energy storage level of V2G generalized energy storage devices at the end of the scheduling period; and They represent t The lower and upper limits of the energy storage level of V2G generalized energy storage devices at the end of the scheduling period; and They represent t Upper and lower limits of power exchanged between the microgrid and the main grid during the dispatch period; express t Power exchanged between the microgrid and the main grid during the dispatch period; express t Energy storage charging and discharging power of V2G generalized energy storage devices during dispatch periods; express t Wind power output during dispatch periods; express t Photovoltaic output during the scheduling period; express t Load demand during the scheduling period; express t The change in energy storage capacity of V2G generalized energy storage devices caused by changes in the grid connection status of electric vehicles during the dispatch period; and They represent t Charging and discharging power limitations of V2G generalized energy storage devices during dispatch periods; τ Indicates the length of a scheduling period; and These represent the charge / discharge efficiency coefficients of grid-connected microgrids and V2G generalized energy storage devices, respectively, and are generally 0.9; The independent variable represents the auxiliary variable, corresponding to the discharge and charging power of the V2G energy storage device. ; as well as The auxiliary equation is as follows: 。 5. The grid-connected microgrid dispatching method based on the precise dispatchable potential of V2G energy storage as described in claim 4, characterized in that, The necessary and sufficient conditions for the feasibility and security of the scheduling solution for each scheduling period of the grid-connected microgrid in step 4 are as follows: 。 express t -1 The lower limit of the energy storage level of V2G generalized energy storage devices at the end of the scheduling period; and They represent t -1 The lower and upper limits of the energy storage level of V2G generalized energy storage devices at the end of the scheduling period; and They represent t Upper and lower limits of photovoltaic output of grid-connected microgrids during dispatch periods; and They represent t -1 The feasible safe lower and upper limits of energy storage level at the end of the scheduling period.

6. The grid-connected microgrid dispatching method based on the precise dispatchable potential of V2G energy storage as described in claim 5, characterized in that, The hybrid integer programming model for grid-connected microgrids based on the precise dispatchable potential of V2G energy storage in step 5 is as follows: Constraints: Prerequisites: Feasibility: necessary and sufficient conditions in, and They represent t Upper and lower limits of power transmission between grid-connected microgrids and the main grid during the dispatching period; and They represent t The lower and upper limits of load demand for grid-connected microgrids during the dispatch period; and They represent t Lower and upper limits of wind power output of grid-connected microgrids during dispatch periods; and They represent t Lower and upper limits of photovoltaic output of grid-connected microgrids during dispatch periods; and They represent t Charging and discharging power limitations of V2G generalized energy storage devices during dispatch periods; and They represent t -1 The feasible safe lower and upper limits of the energy storage level of V2G generalized energy storage devices at the end of the scheduling period; and express t The lower and upper limits of the change in energy storage capacity of V2G generalized energy storage devices caused by changes in the grid connection status of electric vehicles during the dispatch period; τ Indicates the length of a scheduling period; and They represent t -1 The lower and upper limits of energy storage levels at the end of the scheduling period.

7. A processing unit for implementing grid-connected microgrid dispatching, characterized in that, The electronic device includes: Memory, used to store executable instructions; A processor, when executing executable instructions or computer programs stored in the memory, implements the method as described in any one of claims 1 to 6.

8. A computer-readable storage medium storing executable instructions or a computer program, characterized in that, When the executable instructions are executed by the processor, they implement the method as described in any one of claims 1 to 6.

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