Electric vehicle charging station scheduling method, and device and medium

Abstract

The present invention relates to the technical field of electric vehicle charging stations. Disclosed are an electric vehicle charging station scheduling method for regional energy supply and demand balance, and a device and a medium. In the method, an energy balance equation in a region is first established, and output features of various power sources and charging and discharging constraints of charging stations are taken into consideration; then, correlation analysis is used to evaluate the similarity between the charging stations in the region, and cluster grouping is performed on the charging stations, so as to form charging station groups; and a regional proxy calculates a control signal on the basis of an upper-level load instruction and pushes the control signal to a matched charging station group. With regard to the control signal for the charging station group, a charging and discharging power is iteratively updated by means of minimizing the difference between a load requirement distributed by the regional proxy and the load of the charging station group after clustering is performed, and the charging / discharging amount within a time period is continuously adjusted during an iteration process, so as to accurately track and respond to the regional load requirement.

Description

Electric vehicle charging station scheduling method, device and medium

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on December 18, 2023, with application number 202311732813.5 and invention name “A method for dispatching electric vehicle charging stations for regional energy supply and demand balance”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present invention relates to the technical field of electric vehicle charging stations, and in particular to a method, equipment and medium for scheduling electric vehicle charging stations with regional energy supply and demand balance. Background Art

[0003] The large-scale integration of wind and solar power and electric vehicles into the grid poses significant challenges to power system operation. Coordinating a large number of distributed electric vehicle charging stations to participate in virtual power plants and demand response, achieving supply and demand balance and optimizing control in regional power grids, is a pressing issue. Because the charging and discharging load resources at electric vehicle charging stations are highly dependent on the available battery capacity of electric vehicles, and given the high uncertainty of charging load, failure to effectively regulate the charging load at these stations will negatively impact the safe and stable operation of the regional power grid. Therefore, developing optimized scheduling methods tailored to the characteristics of large-scale distributed electric vehicle charging stations to effectively support regional power grids is a current research hotspot. Summary of the Invention

[0004] The present invention provides a method for dispatching electric vehicle charging stations for regional energy supply and demand balance, comprising the following steps: a power dispatching center generates a load dispatching instruction corresponding to each regional agent; each regional agent evaluates the similarity of the charging stations of each regional agent according to the load dispatching instruction, and clusters the charging stations with the smallest intra-class distance to form a charging station group; with the goal of minimizing the load difference between the charging station group and the load dispatching instruction, the charging and discharging power of the charging station group is continuously and iteratively updated to match the load dispatching instruction.

[0005] The present invention provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the above-mentioned method for scheduling electric vehicle charging stations with balanced regional energy supply and demand.

[0006] The present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for scheduling electric vehicle charging stations for balancing regional energy supply and demand is implemented.

[0007] The advantages of the present invention over the prior art are that: the method provided by the present invention starts from the perspective of regional energy optimization configuration, regards the large number of electric vehicle charging stations distributed in the region as a flexibly adjustable load and energy storage device, and achieves a dynamic balance of source, network, storage and load in the region by coordinating and optimizing the charging and discharging plans of the charging stations in the region.

[0008] This paper establishes a regional economic operation optimization model that considers multiple constraints. It employs a hierarchical control framework that clusters charging stations into regional agents, achieving coordinated regulation of charging station clusters within a region. At the charging station level, a distributed iterative optimization algorithm is designed to calculate the optimal charging and discharging schedule for each charging station in response to the control instructions of the regional agent. This achieves optimal energy allocation within the region while ensuring grid security, improving the grid's economic efficiency.

[0009] The present invention also considers the differences in charging station locations and load characteristics within a region, proposing a scheduling strategy based on clustering charging stations to form regional proxies. This strategy groups charging stations according to their correlation characteristics, enabling coordinated regulation of charging stations within the same regional proxies to improve coordinated control of regional loads. This presents a new approach and technical approach for large-scale coordinated charging station scheduling alongside local wind and solar resources.

[0010] In summary, the present invention conducts comprehensive scheduling from a regional perspective to achieve optimal allocation of regional energy and improve the economic efficiency of the power grid, providing a new solution for the coordinated development of large-scale electric vehicles and power grids.

[0011] Figures in the specification

[0012] The present invention will be further described below in conjunction with the accompanying drawings:

[0013] FIG1 is a flow chart of a method according to an embodiment of the present invention.

[0014] FIG2 is a schematic diagram of the system structure of an embodiment of the present invention.

[0015] FIG3 is a flow chart of another method according to an embodiment of the present invention.

[0016] FIG4 is a schematic structural diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In response to the shortcomings of the existing technology that cannot achieve supply and demand balance and optimized control of regional power grids, the present invention provides an electric vehicle charging station scheduling method, equipment and medium for regional energy supply and demand balance. From the perspective of regional energy optimization configuration, this method regards a large number of electric vehicle charging stations distributed in the region as a kind of flexibly adjustable load and energy storage equipment. By coordinating and optimizing the charging and discharging plans of charging stations in the region, a dynamic balance of source, network, storage and load in the region is achieved.

[0018] Example:

[0019] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] The present invention discloses a method for scheduling electric vehicle charging station resources for regional energy supply and demand balance. The method fully leverages regional characteristics and the collaborative regulation potential of charging station clusters. By balancing the power supply and demand of electric vehicle charging station clusters within a region containing photovoltaic and wind power generation, the present invention optimizes the use of energy in the region and realizes effective power auxiliary services for the power grid.

[0021] This method first establishes a regional energy balance equation, taking into account the output characteristics of various power sources and the charging and discharging constraints of charging stations. Correlation analysis is then used to assess the similarity of charging stations within the region and cluster them into groups to form charging station clusters. Regional agents calculate control signals based on load instructions from higher-level agents and push them to matching charging station clusters. The control signals for the charging station clusters are iteratively updated to minimize the difference between the load demand assigned by the regional agent and the load of the clustered charging station clusters. The iterative process continuously adjusts the charge / discharge amount within each time period to accurately track and respond to regional load demands.

[0022] 1 to 3 , an embodiment of the present invention provides a method for scheduling electric vehicle charging stations for regional energy supply and demand balance, which may specifically include the following steps:

[0023] Step 1: The power dispatch center generates load dispatch instructions corresponding to each regional agent.

[0024] In this step, the power dispatching center establishes and solves the electric vehicle charging station resource scheduling model based on the charging station information, constraints, scheduling capacity and total scheduling tasks of each regional agent, with the goal of minimizing the operating cost of each regional agent, and generates load scheduling instructions corresponding to each regional agent.

[0025] The charging station information obtained from each regional agent is the specific parameter for establishing the resource scheduling model of electric vehicle charging stations.

[0026] The total dispatch task of the regional agent is the sum of all the power loads to be allocated to each regional agent.

[0027] The dispatch capacity is the charging capacity of the charging stations of each regional agent.

[0028] Specifically, the constraints include energy balance constraints. The energy balance constraints require that within any scheduling period t, the wind power generation, photovoltaic power generation, charging capacity of charging stations, discharge capacity of charging stations and local power load in the power system meet the balance relationship. Specifically,

[0029] Where: P wind (t)=∑ w∈W p w (t), P PV (t)=∑ p∈P p p (t),

[0030] Among them, P EP (t) is the amount of electricity purchased by the dispatching management area during period t, P wind (t) is the wind power generation, p w (t) is the power generation of wind turbine w in the power system during period t, W is the set of wind turbines, p p (t) is the power generation of photovoltaic unit p in the power system during the period t, P is the set of photovoltaic units, P PV (t) is the photovoltaic power generation, and are the charging capacity of all charging stations i participating in power auxiliary services in the dispatching management area The sum of the discharge The sum of P load (t) is the load task amount allocated in the scheduling management area within the t period.

[0031] Specifically, the constraints also include wind power and solar power generation constraints, where:

[0032] Wind power generation P wind (t):

[0033] Among them, p w (t) is the power generation of wind turbine w in the power system during the period t, is the available power generation of wind turbine w in period t, is the maximum power generation of wind turbine w in period t, is the amount of wind abandoned by wind turbine w in period t, is the wind power plant’s wind curtailment cost during period t, is the wind curtailment coefficient.

[0034] Photovoltaic power generation P PV (t):

[0035] Among them, p p (t) is the power generation of photovoltaic unit p in the power system during the period t, is the available power generation of photovoltaic unit p in the power system during period t, is the maximum power generation of photovoltaic unit p in the power system during period t, is the amount of abandoned light from photovoltaic unit p in the power system during period t, is the cost of curtailing the photovoltaic power generation unit p in the power system during period t, is the light rejection coefficient.

[0036] Specifically, the constraints also include charging and discharging constraints of the charging station, where:

[0037] Constraints on charging and discharging capacity of electric vehicle charging station i:

[0038] in, is the charging capacity of the i-th charging station in period t, is the maximum charge capacity allowed at the i-th charging station in period t, is the minimum charge allowed at the i-th charging station in period t, is the discharge amount of the i-th charging station in period t, is the maximum discharge capacity allowed at the i-th charging station in period t, is the minimum discharge amount allowed for the i-th charging station in period t.

[0039] Considering the total battery charge and discharge loss in the charging station, the equivalent power of the charging station is defined as:

[0040] in, is the equivalent amount of electricity at the i-th charging station in period t.

[0041] Battery degradation cost of charging station i during period t

[0042] Among them, ρ de is the battery degradation coefficient.

[0043] The total charge SB of all batteries in charging station i i The state change of (t) is:

[0044] Among them, η C is the charging efficiency of the charging station, η D is the discharge efficiency of the charging station, Δt is the scheduling interval, is the charging capacity of the charging station in period t, is the discharge amount of the charging station during period t.

[0045] The total battery capacity SB of the i-th charging station i (t) Must meet the following requirements:

[0046] Among them, S min and S max The minimum and maximum values ​​of the total power of all batteries.

[0047] Specifically, the goal is to minimize the operating costs of each regional agent, including:

[0048] Where Z is the regional power system operation and dispatching cost; T is the dispatching time period set; is the cost of wind power curtailment in the region during period t; is the cost of photovoltaic power generation abandoned in the region during period t, is the battery degradation cost of the i-th charging station in period t, α is a load regulation weight coefficient, which reflects the actual load ∑ k∈K G' k (t) and the predetermined load P load (t) the importance of the difference between EP (t) is the cost of purchased electricity, ρ EP is the electricity price, C EP (t) = ρ EP P EP (t).

[0049] The above steps propose a regional economic operation optimization model that comprehensively considers multiple constraints, including energy balance constraints, wind power and solar power generation constraints, and charging station charging and discharging constraints. This comprehensive consideration of constraints makes the scheduling of electric vehicle charging stations more intelligent and sustainable, solving the shortcomings of the single constraint perspective in existing technologies.

[0050] Specifically, the sum of the load dispatching instructions corresponding to each regional agent is the total dispatching task assigned to the regional agent in the response time period.

[0051] Step 2: Each regional agent evaluates the similarity of its charging stations according to the load dispatch instruction, and clusters the charging stations with the smallest intra-class distance to form a charging station group.

[0052] In this step, each charging station is configured with an electric vehicle charging and discharging station information matrix, wherein the electric vehicle charging and discharging station information matrix includes a classification number, a dispatch participation flag, and an average load. The required auxiliary service power is calculated according to the load dispatch instructions assigned by each regional agent.

[0053] ① Initialize the charging station information matrix X of electric vehicle charging and discharging stations in time period t i (t):

[0054] Among them, A i (t) indicates the classification number, B i (t) indicates whether to participate in scheduling, C i (t) represents the average load of charging station i during period t, x i1 ,x i2 represents the geographic information of the i-th charging station, I is the set of all charging stations, and T is the set of scheduling time periods. Among them, the charging station refers to the electric vehicle charging station.

[0055] The electric vehicle charging and discharging station information matrix is ​​used to calculate the electric vehicle charging station correlation matrix between each charging station in each time period. The charging stations are clustered according to the electric vehicle charging station correlation matrix. The charging station category variable is introduced to represent the category to which each charging station belongs. Clustering is performed with the goal of minimizing the distance between charging stations in the same category, and the clustering results of regional agents are obtained.

[0056] ② Obtain the load dispatch instruction G' of the regional dispatch plan from the power dispatch center k (t), and according to the load dispatch instruction G' k (t) Perform base correlation analysis:

[0057] 1) Calculate the relationship between charging stations within time period t based on correlation analysis:

[0058] Among them, r i,j (t) is the correlation between charging station i and charging station j, v represents the nth element in the electric vehicle charging and discharging station information matrix, v∈V, and V is the set of all elements.

[0059] 2) Correlation matrix R of electric vehicle charging stations in period t ij (t):

[0060] Among them, R ij (t) The main diagonal elements are all 1, indicating that each charging station is 100% related to itself, |N| represents the total number of charging stations in the area, R ij (t) reflects the degree of correlation between each charging station and other charging stations during the period t.

[0061] ③ Based on the correlation matrix R of electric vehicle charging stations ij (t) Perform clustering and assign the results to different regional agents:

[0062] 1) Introducing the charging station category variable: C k represents category k, z i represents the category to which charging station i belongs, where k is the number of categories after clustering, which is also the number of regional agents, K is the cluster set, and z i ∈{C1,C2,…,C K}.

[0063] 2) Define the distance within the same charging station category:

[0064] 3) Define the distance between different charging station categories:

[0065] Indicates that the distance between the same category is 1, which means there is no correlation.

[0066] 4) Minimize the intra-cluster distance after clustering charging stations:

[0067] 5) Meet the number of clusters limit:

[0068] Among them, N max Indicates the maximum number of clusters.

[0069] 6) Satisfy the site category uniqueness constraint:

[0070] 7) Satisfy the constraint that the correlation between charging stations within the same category is large: i,j (t)≥τ

[0071] Among them, τ is the threshold of correlation, and when the correlation coefficient is greater than τ, the correlation is considered strong.

[0072] 8) Get the regional agent clustering results C1, C2, ..., C k , where k is the number of categories after clustering, and also the number of regional agents.

[0073] 9) Fill the result into the electric vehicle charging and discharging station information matrix X during the t period i The first element A in (t) i (t) in.

[0074] The present invention innovatively introduces hierarchical coordinated regulation of regional energy. The above steps form a hierarchical control framework of regional agents by clustering charging stations. This framework not only takes into account the differences in the location and load characteristics of electric vehicle charging stations, but also enables the charging stations under the same regional agent to achieve coordinated regulation, thereby improving the overall control capability of regional load.

[0075] Furthermore, the embodiment of this step adopts a hierarchical control framework that clusters charging stations to form regional agents. The hierarchical control framework can group charging stations according to their correlation characteristics, so that charging stations under the same regional agent can be coordinated and adjusted to improve the coordinated control capability of regional loads.

[0076] Step 3: With the goal of minimizing the load difference between the charging station group and the load dispatch instruction, continuously iteratively update the charging and discharging power of the charging station group to match the load dispatch instruction.

[0077] In this step, the load dispatching instructions are matched with the clustered charging station groups according to the clustering results. Each regional agent calculates the corresponding control signal based on the received load dispatching instructions, and uses a distributed iterative optimization algorithm to update the charging and discharging power of each charging station group to track the control signal.

[0078] Among them, the load dispatching instruction is used to instruct the charging station groups under the jurisdiction of each regional agent to match the load dispatching instruction with the clustered charging station group according to the service invitation results, and generate an optimized charging and discharging plan for the charging station group in this area. The optimized charging and discharging plan is used to instruct each charging station in the charging station group in this area to adjust charging and discharging according to the optimized charging and discharging plan.

[0079] ④ According to the clustering result matching response, the regional agent will match the received load dispatch instruction with the clustered charging station group in each region, and calculate the signal r of any regional agent k to control the charging station group. k (t):

[0080] Among them, N k is the number of charging station clusters clustered by regional agent k participating in the response during period t, G' k (t) is the load dispatching instruction received by regional agent k from the power dispatching center during period t.

[0081] For each clustered charging station group that responds to grid demand, the charging and discharging power is iteratively updated as follows: is the minimum value of charge and discharge power, is the maximum value of the charge and discharge power, and the result is fed back to the corresponding regional agent.

[0082] Assume that at the kth iteration, the charging station group C k The charge and discharge power is:

[0083] Among them, α and β are penalty factors. The goal of the above formula is to make the control signal r n The power of the previous iteration The difference between the control signal With the previous signal The iterative power difference is minimized to meet the charging power constraint. Through the iterative optimization process, the charging power of the charging station can be changed with the control signal to respond to the grid demand.

[0084] ⑤Update the number of iterations n so that n=n+1.

[0085] ⑥When the number of iterations reaches a predetermined value or the error between two adjacent iterations is less than a certain value δ, the iterative operation is terminated, otherwise steps ②-④ are repeated.

[0086] ⑦ According to the final results, calculate the average load C of each charging station within each cluster during the t period i (t), assign tasks to each charging station.

[0087] The above steps achieve the optimal charging and discharging schedule for electric vehicle charging stations by designing a distributed iterative optimization algorithm. This algorithm not only improves scheduling efficiency but also ensures the safe operation of the power grid. This distributed computing approach effectively addresses the computational complexity and real-time performance issues of traditional centralized optimization methods.

[0088] Furthermore, the above-mentioned embodiments take into account the differences in charging station locations and load characteristics within a region, proposing a scheduling strategy based on clustering charging stations to form regional proxies. This strategy can group charging stations based on their correlation characteristics, enabling coordinated regulation of charging stations within the same regional proxies to enhance the coordinated control of regional loads. This present invention provides a new approach and technical means for large-scale coordination of charging stations and local wind and solar resources in grid scheduling.

[0089] In summary, this invention provides a novel scheduling scheme for large-scale coordination of electric vehicle charging station resources from the perspective of regional energy supply and demand balance. This method fully leverages regional characteristics and the collaborative regulation potential of charging station clusters. By balancing power supply and demand for electric vehicle charging station clusters within a region containing photovoltaic and wind power generation, this invention optimizes regional energy utilization and provides effective power auxiliary services to the power grid. This provides a more feasible and sustainable path for the coordinated development of electric vehicles and the power grid, and offers an innovative solution for the large-scale grid integration of renewable energy and electric vehicle charging stations.

[0090] This method first establishes a regional energy balance equation, taking into account the output characteristics of various power sources and the charging and discharging constraints of charging stations. Correlation analysis is then used to assess the similarity of charging stations within the region and cluster them into groups to form charging station clusters. Regional agents calculate control signals based on load instructions from higher-level agents and push them to matching charging station clusters. The control signals for the charging station clusters are iteratively updated to minimize the difference between the load demand assigned by the regional agent and the load of the clustered charging station clusters. The iterative process continuously adjusts the charge / discharge amount within each time period to accurately track and respond to regional load demands.

[0091] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal, and its internal structure diagram may be shown in FIG4 . The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, the memory, and the I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data. The I / O interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for scheduling electric vehicle charging stations for balancing regional energy supply and demand is implemented.

[0092] Those skilled in the art will understand that the structure shown in FIG4 is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0093] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements a method for scheduling electric vehicle charging stations for regional energy supply and demand balance when executing the computer program.

[0094] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which, when executed by a processor, implements a method for scheduling electric vehicle charging stations for regional energy supply and demand balance.

[0095] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0096] The above embodiments are provided for the purpose of describing the present invention only and are not intended to limit the scope of the present invention. The scope of the present invention is defined by the appended claims. Various equivalent substitutions and modifications made without departing from the spirit and principles of the present invention are intended to be within the scope of the present invention.

Claims

1. A method for dispatching electric vehicle charging stations for regional energy supply and demand balance, characterized in that: The following steps are involved: The power dispatch center generates load dispatch instructions corresponding to each regional agent; Each of the regional agents evaluates the similarity of the charging stations of each of the regional agents according to the load dispatch instruction, and clusters the charging stations with the smallest intra-class distance to form a charging station group; With the goal of minimizing the load difference between the charging station group and the load scheduling instruction, the charging and discharging power of the charging station group is continuously updated iteratively to match the load scheduling instruction.

2. The method for dispatching electric vehicle charging stations for regional energy supply and demand balance according to claim 1, characterized in that: The power dispatching center establishes and solves the resource dispatching model of electric vehicle charging stations based on the charging station information, constraints, dispatching capacity and total dispatching tasks of each of the regional agents, with the goal of minimizing the operating cost of each of the regional agents, and generates load dispatching instructions corresponding to each of the regional agents.

3. The method for dispatching electric vehicle charging stations for regional energy supply and demand balance according to claim 1, characterized in that: Clustering the charging stations with the smallest intra-class distance to form a charging station group includes the following steps: ① Initialize the information matrix X of the charging stations of each regional agent in the t period of time for electric vehicle charging and discharging stations i (t): Among them, A i (t) indicates the classification number, B i (t) indicates whether to participate in scheduling, C i (t) represents the average load of charging station i during period t, x i1 ,x i2 represents the geographical information of the i-th charging station, I is the set of all charging stations, and T is the set of scheduling time periods; ② Obtain the load dispatching instruction G' of the regional dispatching plan from the power dispatching center k (t), and according to the load dispatch instruction G' k (t) Perform base correlation analysis: 1) Calculate the relationship between charging stations within time period t based on correlation analysis: in, v represents the nth element in the electric vehicle charging and discharging station information matrix, v∈V; 2) The correlation matrix R of electric vehicle charging stations in period t ij (t): Among them, R ij (t) The main diagonal elements are all 1, indicating that each charging station is 100% related to itself, |N| represents the total number of charging stations in the area, R ij (t) reflects the correlation degree of each charging station with other charging stations in time period t; ③ Based on the correlation matrix R of electric vehicle charging stations ij (t) Perform clustering and assign the results to different regional agents: 1) Introduce charging station category variable: C k represents category k, Among them, k is the number of categories after clustering, which is also the number of regional agents, K is the cluster set, and z i represents the category to which charging station i belongs, z i ∈{C1,C2,…,C K }; 2) Define the distance within the same charging station category: 3) Define the distances between different charging station categories: Indicates that the distance between the same categories is 1, which is irrelevant; 4) Minimize the intra-class distance after charging station clustering: 5) Satisfy the number of clusters limit: Among them, N max Indicates the maximum number of clusters; 6) Satisfy the site category uniqueness constraint: 7) Constraints with high correlation between charging stations within a category: r i,j (t)≥τ Among them, τ is the threshold of correlation, and when the correlation coefficient is greater than τ, the correlation is considered strong; 8) Get the regional agent clustering results C1, C2, ..., C k , where k is the number of categories after clustering, which is also the number of regional agents; 9) Fill in the electric vehicle charging and discharging station information matrix X in the t period according to the results i The first element A in (t) i (t) in.

4. The method for dispatching electric vehicle charging stations for regional energy supply and demand balance according to claim 3, characterized in that: Taking the load difference between the charging station group and the load dispatching instruction as the minimum as the goal, the charging and discharging power of the charging station group is continuously updated iteratively, including the steps of: ④ According to the clustering result matching response, the regional agent will match the received load dispatch instruction with the clustered charging station group in each region, and calculate the signal of any regional agent k controlling the charging station group: Among them, N k is the number of charging station clusters clustered by regional agent k participating in the response during period t, G' k (t) The regional agent k receives the load dispatching instruction from the power dispatching center during period t; For each clustered charging station group that responds to grid demand, the charging and discharging power is iteratively updated according to the following formula: And feedback the results to the corresponding regional agent; Assume that at the kth iteration, the charging station group C k The charge and discharge power is: Among them, α and β are penalty factors. The goal of the above formula is to make the control signal r n The power of the previous iteration The difference between the control signal With the previous signal The iterative power difference is minimized to meet the charging power limit constraint; through the iterative optimization process, the charging power of the charging station changes with the control signal to achieve the response to the grid demand; ⑤ Update the number of iterations n so that n=n+1; ⑥When the number of iterations reaches a predetermined value or the error between two adjacent iterations is less than a certain value δ, the iterative operation is terminated, otherwise steps ②-④ are repeated; ⑦ According to the final result, calculate the average load C of each charging station i within each cluster within the t period i (t), assign tasks to each charging station.

5. The method for dispatching electric vehicle charging stations for regional energy supply and demand balance according to claim 2, characterized in that: The constraints include energy balance constraints, where The energy balance constraint is that in any dispatching period t, the wind power generation, photovoltaic power generation, charging station charging, charging station discharge and local power load in the power system meet the balance relationship, specifically: Where: P wind (t)=∑ w∈W p w (t), P PV (t)=∑ p∈P p p (t), Among them, P EP (t) is the purchased electricity in the dispatching management area during the period t, P wind (t) is wind power generation; p w (t) is the power generation of wind turbine w in the power system during the period t; p p (t) is the power generation of photovoltaic unit p in the power system during the period t, P PV (t) is the photovoltaic power generation; and are the charging capacity of all charging stations i participating in power auxiliary services in the dispatching management area The sum and discharge amount The sum of P load (t) is the load task amount allocated in the scheduling management area within period t.

6. The method for dispatching electric vehicle charging stations for regional energy supply and demand balance according to claim 2, characterized in that: The constraints include wind power and solar power generation constraints, where: Wind power generation P wind (t): Among them, p w (t) is the power generation of wind turbine w in the power system during the period t, is the available power generation of wind turbine w in period t, is the maximum power generation of wind turbine w in period t, is the wind abandonment volume of wind turbine w in period t, is the wind power plant’s wind curtailment cost during period t, is the wind abandonment factor; Photovoltaic power generation P PV (t): Among them, p p (t) is the power generation of the photovoltaic unit p in the power system during the period t, is the available power generation of photovoltaic unit p in the power system during period t, is the maximum power generation of the photovoltaic unit p in the power system during the period t, is the amount of abandoned light from photovoltaic unit p in the power system during period t, is the cost of abandoning light of photovoltaic unit p in the power system during period t, is the light rejection coefficient.

7. The method for dispatching electric vehicle charging stations for regional energy supply and demand balance according to claim 2, characterized in that: The constraints include charging and discharging constraints of the charging station, where: Constraints on charging and discharging capacity of electric vehicle charging station i: in, is the charging capacity of the i-th charging station in period t, is the maximum allowed charging capacity of the i-th charging station in time period t, is the minimum allowed charging capacity of the i-th charging station in period t, is the discharge amount of the i-th charging station in period t, is the maximum discharge capacity allowed at the i-th charging station in period t, is the minimum discharge amount allowed for the i-th charging station in period t; Considering the total battery charge and discharge loss in the charging station, the equivalent power of the charging station is defined as: in, is the equivalent power of the i-th charging station in time period t; Battery degradation cost of charging station i during period t Among them, ρ de is the battery degradation coefficient; Total charge SB of all batteries in charging station i i The state change of (t) is: Among them, η C is the charging efficiency of the charging station, η D is the discharge efficiency of the charging station, Δt is the scheduling interval, is the charging amount of the charging station in period t, is the discharge amount of the charging station in period t; The total battery charge SB of the i-th charging station i (t) Must meet the following requirements: Among them, S min and S max The minimum and maximum values ​​of the total power of all batteries.

8. The method for dispatching electric vehicle charging stations for regional energy supply and demand balance according to claim 2, characterized in that: The goal is to minimize the operating cost of each regional agent, specifically including: Among them, Z is the operation and dispatching cost of the regional power system; T is the dispatching time period set; is the wind power abandonment cost of the wind power plants in the region during period t; is the cost of photovoltaic power generation abandonment in the region during period t, is the battery degradation cost of the i-th charging station in period t, α is a load regulation weight coefficient, which reflects the actual load ∑ k∈K G' k (t) and the predetermined load P load (t) the importance of the difference between EP (t) is the cost of purchased electricity, ρ EP is the electricity price, C EP (t) = ρ EP P EP (t).

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the electric vehicle charging station scheduling method for regional energy supply and demand balance as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the electric vehicle charging station scheduling method for regional energy supply and demand balance described in any one of claims 1-8 is implemented.

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