Scheduling method for electric vehicle aggregation to participate in power market auxiliary peak regulation service

Abstract

The invention discloses a scheduling method for electric vehicle aggregation to participate in power market auxiliary peak regulation service, which relates to the technical field of smart power grids, and comprises the steps of integrating vehicle-mounted and user APP data, dividing regions through a GIS technology, managing and analyzing a charging margin by an aggregator according to power grid requirements, dividing a scheduling cluster, and evaluating the scheduling potential of the aggregator. According to user travel characteristics, automobiles are classified, a peak regulation strategy is optimized, an electric automobile cluster peak regulation optimization model is constructed, charging and discharging are planned for load fluctuation, a particle swarm algorithm is adopted to optimize a charging and discharging strategy, electric automobiles are efficiently dispatched to participate in power grid peak regulation, and the flexibility and efficiency of a power grid are improved. Through integration of vehicle-mounted and APP data and combination of a GIS technology and an optimization model, electric vehicle charging, efficient scheduling and quantification of schedulable capacity are accurately analyzed, regulation and control are classified according to travel characteristics, the peak regulation efficiency of an electric vehicle auxiliary power grid is greatly improved, resource utilization is optimized, the flexibility and stability of the power grid are enhanced, and power supply and demand balance, energy conservation and emission reduction are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid peak regulation, and in particular to a dispatching method for electric vehicles to participate in the auxiliary peak regulation service of the power market. Background Art

[0002] With the advancement of global energy transformation and sustainable development goals, the market size of electric vehicles, as an important part of low-carbon travel, has expanded rapidly. Electric vehicles have not only changed the traditional transportation energy consumption model, but also become an indispensable flexible resource in smart grids due to their energy storage characteristics.

[0003] The present invention proposes a dispatching method for electric vehicles to participate in the auxiliary peak-shaving service of the electricity market through aggregation. The method aims to realize deep mining and preprocessing of data by highly integrating the on-board intelligent system and user APP data, so as to accurately calculate the charging time margin of each electric vehicle, provide a scientific basis for dividing the dispatchable clusters and ordinary clusters, and use the spatial analysis capability of the geographic information system (GIS) to reasonably divide and manage the electric vehicle distribution area, ensure that the electric vehicle resources in each sub-area can be efficiently integrated, and flexibly coordinate according to the power grid instructions through the aggregator to improve the dispatch response speed and accuracy at the regional level. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a scheduling method for electric vehicles to aggregate and participate in auxiliary peak-shaving services in the electricity market to solve the problems of insufficient scheduling flexibility and inefficient resource optimization configuration when electric vehicles aggregate and efficiently participate in peak-shaving services in the electricity market.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides a scheduling method for electric vehicle aggregation to participate in auxiliary peak-shaving services in the electricity market, which includes integrating an on-board intelligent system and user APP data, preprocessing the data, calculating the charging time margin, identifying the charging time margin through data analysis, dividing the dispatchable cluster and the ordinary cluster, and using GIS technology for spatial analysis. The entire area is divided into multiple sub-areas, each sub-area is managed by an aggregator, and the aggregator coordinates according to the power grid instructions, evaluates the dispatchable capacity of the electric vehicle cluster, determines the ability of the electric vehicle cluster to participate in power grid scheduling as a flexible resource, classifies electric vehicles based on user travel characteristics, and connects them to the power grid for regulation according to the classification ratio, optimizes the power grid peak-shaving plan, constructs an electric vehicle cluster peak-shaving optimization model, divides the electric vehicle response time period, and formulates the charging and discharging plan of the electric vehicle cluster according to the fluctuation of the power grid load in each time period, and explores efficient electric vehicle charging and discharging management strategies through the particle swarm optimization algorithm.

[0008] As a preferred solution of the scheduling method for electric vehicles to participate in the auxiliary peak-shaving service of the power market, the method integrates the vehicle-mounted intelligent system and the user APP data, pre-processes the data, calculates the charging time margin, identifies the charging time margin through data analysis, and divides the dispatchable cluster and the ordinary cluster. The specific steps are as follows:

[0009] By integrating the in-vehicle intelligent system with the user's APP to collect data, check whether the received data has missing values ​​and abnormal values, and perform necessary data cleaning to preliminarily analyze the user's charging habits;

[0010] The calculation of charging time margin divides electric vehicles into conventional clusters and dispatchable clusters, and the expression is:

[0011] Ta = Tc - Tb - Td;

[0012] Among them, Ta is the charging time margin, Tc is the off-grid time of the electric vehicle, Tb is the time difference between the expected charging completion time and the next driving time, and Td is the charging demand time;

[0013] By judging the charging time margin, it can be determined whether the electric vehicle has extra time to participate in the regulation. When Ta>0, such vehicles are regarded as having charging time margin and are suitable to be classified as dispatchable clusters to participate in the auxiliary services of the power grid. When Ta≤0, the vehicle does not have the flexibility of scheduling and is classified as an ordinary cluster.

[0014] As a preferred solution of the scheduling method for electric vehicles to participate in the auxiliary peak load regulation service in the power market according to the present invention, the spatial analysis is performed using GIS technology, and the entire area is divided into multiple sub-areas, each sub-area is managed by an aggregator, and the aggregator coordinates according to the grid instructions. The specific steps are as follows:

[0015] Use hot spot analysis tools in GIS software to identify high-frequency areas of electric vehicle use, use network analysis tools to simulate traffic flow, and reasonably divide the entire area into multiple sub-areas. Design the size and shape of each sub-area based on the actual road network, charging station distribution, and user needs;

[0016] In each sub-area Pi, the number of electric vehicles Ni should be determined based on the user demand, the capacity of charging facilities and the road carrying capacity of the area, ensuring that Ni ≤ Nmax,i;

[0017] GIS and GPS technologies are used to monitor and manage the location of electric vehicles, meta-heuristic algorithms are used to calculate the optimal number of electric vehicles and dispatch strategies for each area, and aggregators use cloud platforms to receive and process real-time instructions from the power grid.

[0018] As a preferred solution of the dispatching method for electric vehicles to participate in the auxiliary peak load regulation service of the power market, the dispatchable capacity of the electric vehicle cluster is evaluated to determine the ability of the electric vehicle cluster to participate in the power grid dispatching. The specific steps are as follows:

[0019] After electric vehicles are dispatched, the dispatchable capacity is divided into upward dispatch capacity and downward dispatch capacity;

[0020] The upward scheduling capacity expression is:

[0021]

[0022] The downward scheduling capacity expression is:

[0023]

[0024] in, and are the upward and downward adjustment of the capacity of the i-th electric vehicle, SOC min The minimum value allowed to participate in regulation, n is the number of electric vehicles, m is the number of electric vehicles currently charging, C i is the battery capacity of the ith electric vehicle, SOC act is the percentage of available battery remaining, η represents the percentage of charging efficiency, P is the number of electric vehicles with current energy storage capacity, Ei represents the amount of electricity currently being used by the jth electric vehicle, and E k Indicates the current remaining power of the kth electric car;

[0025] By evaluating the dispatchable potential of the electric vehicle cluster in the region, the expression is:

[0026]

[0027] In the formula, h is the adjustment factor, R act is the discharge capacity;

[0028] The total dispatch capacity of an electric vehicle is expressed as:

[0029]

[0030] Among them, Vc is the battery capacity, SOC i is the current state of the battery, Ce is the total capacity of the battery, i represents the i-th electric vehicle, D T is the dispatch capacity of electric vehicles;

[0031] The total functional capacity of a single electric vehicle is expressed as:

[0032]

[0033] Among them, De is the discharge efficiency, F T is the total functional capacity of the electric vehicle;

[0034] The dispatchable potential expression of a single electric vehicle is:

[0035] P = FT–DT;

[0036] Extending a single electric vehicle to the cluster mode, the potential matrix Hi is expressed as:

[0037] Hi=[P1,P2,P3...Pn];

[0038] Where Pn is the dispatchable potential of a single electric vehicle in a cluster of n electric vehicles;

[0039] The dispatchable potential expression of the electric vehicle cluster in the region is:

[0040]

[0041] Among them, S i The potential value that aggregators can be allowed to participate in regulation.

[0042] As a preferred solution of the scheduling method for electric vehicles to participate in the auxiliary peak-shaving service of the power market, the specific steps are as follows: the electric vehicles are classified based on the travel characteristics of users, and connected to the power grid for regulation according to the classification ratio, so as to optimize the peak-shaving plan of the power grid;

[0043] The electric vehicles in the region are classified and sorted. There are multiple clusters in each region based on random spatial distribution. Each region is managed by an aggregator. Five types of electric vehicles on the market are selected. Electric vehicles with different performances are divided into six categories from A to F, including a diverse combination of large capacity and high power to low capacity and high power. They are divided into three categories based on user travel characteristics;

[0044] Based on the classification results, grid operators adopt the method of peak shaving and valley filling to reduce loads and formulate differentiated charging strategies and dynamic electricity price mechanisms.

[0045] As a preferred solution of the scheduling method for electric vehicles to participate in the auxiliary peak load regulation service in the power market according to the present invention, the charging and discharging plan of the electric vehicle cluster is formulated according to the fluctuation of the power grid load in each time period, and the specific steps are as follows:

[0046] Formulate the electric vehicle cluster power E i The upper and lower limits of are expressed as:

[0047] E min ≤E i ≤E max ;

[0048] Among them, E min is the upper limit of the electric vehicle cluster power, E max It is the offline limit of the electric vehicle cluster power;

[0049] Evaluate and calculate the discharge and charging capabilities of the electric vehicle cluster. The SOC lower limit capacity is set to be no less than 30% based on battery performance, vehicle operation requirements, and safety factors. The expression is as follows:

[0050] SOC i,k ≥SOC min ;

[0051] C i =(SOC i,k -SOC min )C e ;

[0052] C c =(SOC max -SOC i,k )C e ;

[0053]

[0054]

[0055] Among them, SOC i,k is the battery state of charge of electric vehicle i at time k, SOCmax C is the maximum value allowed to participate in regulation. c is the charging capacity of the electric vehicle at a certain moment, C g is the discharge capacity of the electric vehicle cluster at a certain moment;

[0056] Establish constraints for electric vehicle clusters to control charging and discharging capabilities within a safe and efficient range. The specific steps are as follows:

[0057] The charging and discharging capability constraint expressions are as follows:

[0058] P charge ≤P1;

[0059] P discharge ≤P2;

[0060] R min ≤R act ≤R max ;

[0061] Among them, P charge and P discharge , respectively, the actual charging and discharging power, R min is the minimum discharge capacity of electric vehicles involved in regulation, R max To participate in the regulation of the maximum discharge capacity, P1 is the upper limit of charging efficiency, and P2 is the upper limit of discharge efficiency;

[0062] The cluster size constraint expression is as follows:

[0063] N min <N act <N max ;

[0064] Among them, N min The minimum number of electric vehicles in the cluster included in the aggregator management area, N act The actual number of electric vehicles in the regional cluster managed by the aggregator, N max Managing the maximum number of electric vehicles in a regional cluster for aggregators

[0065] The peak shaving constraint expression is as follows:

[0066]

[0067] Among them, X d is the peak load demand issued by the dispatch center to the aggregator, ΔP i,t represents the charging and discharging power of the electric vehicle cluster in a specific period of time t, t0 represents the starting point of the peak demand, and Δt represents the length of the time period;

[0068] The particle swarm optimization algorithm is used to solve the above model. By optimizing the charging and discharging power that electric vehicle aggregators need to obtain from the power grid in different time periods, the optimal electric vehicle charging and discharging management strategy is formulated.

[0069] As a preferred solution of the scheduling method for electric vehicles to participate in the auxiliary peak load regulation service in the power market according to the present invention, the particle swarm optimization algorithm is used to explore efficient electric vehicle charging and discharging management strategies. The specific steps are as follows:

[0070] A set of particles is defined for each sub-region under the management of the EV aggregator, each particle represents a specific charging and discharging strategy;

[0071] According to the particle initial velocity, individual optimal solution and global optimal solution, the particle velocity is updated. The expression is as follows:

[0072] v i,t+1 =w·v i,t +c1·r1·(p best,i -x i,t )+c2·r2·(g best -x i,t );

[0073] Among them, v i,t+1 is the velocity vector of the ith particle at time t+1, the current velocity vector of the ith particle at time t, w is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random weights, p best,i is the optimal solution found by the ith particle, g best is the global optimal solution, x i,t is the position of the ith particle at time t;

[0074] Calculate the new strategy based on the updated speed, the expression is as follows:

[0075] x i,t+1 =x i,t +v i,t+1 ;

[0076] Where is the new position of the i-th particle at time t+1;

[0077] The updated strategy needs to obtain the electric vehicle charging and discharging management strategy based on the particle swarm optimization algorithm under the constraints of meeting the upper and lower limits of charging power and discharging power, and the minimum and maximum values ​​of battery SOC.

[0078] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the dispatching method for electric vehicles to aggregate and participate in auxiliary peak-shaving services in the electricity market as described in the first aspect of the present invention is implemented.

[0079] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the dispatching method for electric vehicles to aggregate and participate in auxiliary peak-shaving services in the electricity market as described in the first aspect of the present invention is implemented.

[0080] The beneficial effects of the present invention are as follows: by integrating the on-board intelligent system and user APP data, using GIS technology, refined management and optimization models, accurate analysis of electric vehicle charging behavior, efficient scheduling in geographic space, quantitative evaluation of the dispatchable capacity of electric vehicle clusters, and classified regulation based on user travel characteristics are achieved, which significantly improves the ability of electric vehicles to participate in the auxiliary peak-shaving services of the power market as flexible resources. This series of steps not only optimizes data processing, resource management and power grid response efficiency, but also maximizes the auxiliary peak-shaving potential of electric vehicles, while ensuring user experience, enhancing the flexibility and stability of the power grid, and effectively contributing to the load balance of the power system and the realization of energy conservation and emission reduction goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0082] Figure 1 This is a flow chart of the dispatching method for electric vehicles to participate in the auxiliary peak-shaving service in the electricity market in Example 1.

[0083] Figure 2 This is a flow chart of user characteristic classification for the dispatching method of electric vehicles aggregating and participating in auxiliary peak load regulation services in the electricity market in Example 1. DETAILED DESCRIPTION

[0084] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0085] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0086] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0087] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a dispatching method for electric vehicles to participate in the auxiliary peak load regulation service of the power market, including the following steps:

[0088] S1. Integrate the on-board intelligent system and user APP data, pre-process the data, calculate the charging time margin, identify the charging time margin through data analysis, and divide the dispatchable cluster into ordinary clusters.

[0089] Furthermore, by integrating the vehicle intelligent system with the user APP to collect data (including but not limited to the vehicle's current power, total battery capacity, expected next driving time, and expected charging completion time), check whether the received data has missing values ​​and abnormal values, and perform necessary data cleaning to preliminarily analyze the user's charging habits and provide reference for subsequent analysis;

[0090] The calculation of charging time margin refers to the difference between the total time that electric vehicles are expected to connect to charging facilities for charging and the shortest charging time actually required to meet the next travel demand. Electric vehicles are divided into conventional clusters and dispatchable clusters. The expression is:

[0091] Ta=Tc-Tb-Td

[0092] Among them, Ta is the charging time margin, Tc is the off-grid time of the electric vehicle, Tb is the time difference between the expected charging completion time and the next driving time, and Td is the charging demand time;

[0093] By judging the charging time margin, it can be determined whether the electric vehicle has extra time to participate in the regulation. When Ta>0, there is extra time to flexibly adjust the charging. Such vehicles are considered to have charging time margin and are suitable to be classified as dispatchable clusters to participate in the auxiliary services of the power grid. If Ta≤0, the vehicle does not have the flexibility of dispatch and is classified as a normal cluster.

[0094] Specifically: The core task of this step is to integrate data from the vehicle's intelligent system and the user's APP, including the vehicle's current power level, total battery capacity, estimated time for the next trip, and the time the user wants charging to be completed. Subsequently, these data are checked to identify and process any missing or abnormal values, perform necessary data cleaning, and preliminarily analyze the user's charging habits to provide a basis for subsequent analysis;

[0095] Next, the focus is on calculating the so-called "charging time margin," which is the difference between the total time the electric vehicle is expected to be connected to a charging facility for charging and the shortest charging time actually required to meet the next travel demand. This difference is expressed through a formula that takes into account factors such as the time the electric vehicle is off the grid, the time difference between the expected charging completion time and the next travel time, and the charging demand time;

[0096] The calculation result of the charging time margin is used to determine whether the electric vehicle has flexible time to participate in grid regulation. Specifically, if the charging time margin of an electric vehicle is greater than zero (Ta>0), it means that the vehicle has extra time to flexibly adjust charging, so it is regarded as having a charging time margin and is suitable for classification as a dispatchable cluster, which means that it can participate in the auxiliary services of the grid. On the contrary, if the charging time margin is less than or equal to zero (Ta≤0), it means that the vehicle does not have the flexibility of scheduling, so it is classified as an ordinary cluster and does not participate in the peak-shaving service of the grid. This step is the basis of the entire scheduling method and provides key data support for subsequent cluster peak-shaving optimization.

[0097] S2. Using GIS technology analysis, the entire area is divided into multiple sub-areas. Each sub-area is managed by an aggregator, who coordinates according to the grid instructions. The specific steps are as follows:

[0098] Furthermore, GIS software is used for spatial analysis. Hot spot analysis tools in GIS software are used to identify high-frequency areas of electric vehicle use. Network analysis tools are used to simulate traffic flow, and the entire area is reasonably divided into multiple sub-areas to ensure that the number of electric vehicles in each sub-area is moderate and easy to manage. At the same time, the distribution of grid access points is considered so that each sub-area can be connected to the grid. The size and shape of each sub-area are designed according to the actual road network, charging station distribution and user needs;

[0099] In each sub-area Pi, the number of electric vehicles Ni should be determined based on the user demand, the capacity of charging facilities and the road carrying capacity of the area, ensuring that Ni≤Nmax,i (Ni≤Nmax,i is the maximum capacity based on safety and efficiency considerations);

[0100] Using GIS and GPS technology to monitor and manage the location of electric vehicles, using meta-heuristic algorithms to calculate the optimal number of electric vehicles and dispatch strategies for each area, aggregators use cloud platforms to receive and process real-time instructions from the power grid;

[0101] Specifically: Following the data integration and pre-processing steps, by combining advanced Geographic Information System (GIS) and Global Positioning System (GPS) technologies, aggregators are able to monitor and manage the location of electric vehicles in real time, which makes it possible to dynamically adjust the dispatch plan and optimize the charge state. This integrated technical approach not only improves the utilization rate of electric vehicles and user satisfaction, but also ensures the stable operation of the power grid;

[0102] Through the precise division and management of sub-regions, aggregators can more effectively calculate the optimal number of EVs and dispatch strategies in each region to adapt to the needs of the grid and the actual use of EVs. This lays the foundation for the next step - building an EV cluster peak-shaving optimization model, which will optimize the load of the grid and improve energy efficiency based on the division of these regions and the dispatch strategies of EVs.

[0103] S3. Evaluate the dispatchable capacity of the electric vehicle cluster and determine the ability of the electric vehicle cluster to participate in grid dispatch.

[0104] Furthermore, after the electric vehicle performs power dispatching for a period of Δt, the electric vehicle's power is not less than SOC min , that is, the SOC value shall not be less than 30% of the rated capacity of the battery.

[0105] The capacity is divided into upward scheduling capacity and downward scheduling capacity. The upward scheduling capacity expression is:

[0106]

[0107] The downward scheduling capacity expression is:

[0108]

[0109] in, and are the upward and downward adjustment of the capacity of the i-th electric vehicle, SOC min The minimum value allowed to participate in regulation, n is the number of electric vehicles, m is the number of current charging electric vehicles, C i is the battery capacity of the ith electric vehicle, SOC act Is the percentage of available battery remaining η represents the charging efficiency percentage, P is the number of electric vehicles with current energy storage capacity, Ei represents the amount of electricity currently being used by the jth electric vehicle, and E k Indicates the current remaining power of the kth electric car;

[0110] By evaluating the dispatchable potential of the electric vehicle cluster in the region, the expression is:

[0111]

[0112] In the formula, h is the adjustment factor, which indicates the influence of the discharge capacity of electric vehicles on the dispatchable capacity, R act is the discharge capability, when R act When it is greater than 0, it indicates that the discharge capacity is strong. act When it is close to or equal to 0, it means that the discharge capacity and the charge capacity are equivalent. act When it is less than 0, it means that the discharge capacity is weak and is not suitable as an adjustable area;

[0113] The total dispatch capacity of an electric vehicle is expressed as:

[0114]

[0115] Among them, Vc is the battery capacity, SOC i is the current state of the battery, Ce is the total capacity of the battery, i represents the i-th electric vehicle, D T is the dispatch capacity of electric vehicles;

[0116] The total functional capacity of a single electric vehicle is expressed as:

[0117]

[0118] Among them, De is the discharge efficiency, F T is the total functional capacity of the electric vehicle;

[0119] The dispatchable potential expression of a single electric vehicle is:

[0120] P=FT-DT

[0121] Extending a single electric vehicle to the cluster mode, the potential matrix Hi is expressed as:

[0122] Hi=[P1,P2,P3...Pn]

[0123] Among them, P1, ..., Pn is the dispatchable potential of a single electric vehicle in the electric vehicle cluster.

[0124] The dispatchable potential expression of the electric vehicle cluster in the region is:

[0125]

[0126] Among them, Si is the potential value that the aggregator is allowed to participate in regulation, which helps to ensure that even in the worst case, the electric vehicle cluster can provide minimum scheduling support.

[0127] Aggregators use GIS and GPS technologies to monitor and manage the location of electric vehicles, and combine meta-heuristic algorithms to calculate the optimal number of electric vehicles and dispatch strategies for each area. Through the cloud platform, aggregators receive and process real-time instructions from the power grid, optimize the charge state, and respond to changes in the power grid load in real time. This method improves the utilization rate of electric vehicles and user satisfaction, while ensuring the stable operation of the power grid;

[0128] Specifically: the distribution data of each sub-region and its electric vehicles obtained through GIS technology are used to further analyze and calculate the dispatchable capacity of each electric vehicle and the entire cluster;

[0129] By calculating the upward and downward dispatch capacity of each electric vehicle, that is, the maximum amount of power they can provide or absorb to the grid when needed, this includes taking into account factors such as battery capacity, current charge, charging efficiency, etc., and then aggregating the data of all vehicles, the total dispatchable capacity of the entire cluster can be obtained. This value represents the ability of the electric vehicle cluster to respond to grid demand;

[0130] Detailed data on the physical limitations and potential capabilities of EV clusters to actually participate in grid regulation, which supports the development of optimization models that enable subsequent peak-shaving strategies to ensure that EV stored energy is effectively used to balance grid loads without compromising EV battery health or user demand;

[0131] The evaluation of the dispatchable capacity of electric vehicle clusters is based on the data of spatial analysis results, which provides the necessary quantitative basis for formulating effective peak-shaving strategies, ensuring that electric vehicles can participate in grid peak-shaving in the best way, thereby improving the operation efficiency of the grid and the utilization rate of electric vehicles;

[0132] S4. Classify electric vehicles based on travel characteristics, optimize the grid peak-shaving plan, and adapt to changes and ambiguity in electricity prices.

[0133] Furthermore, the electric vehicles in the region are classified and sorted. Based on the random distribution of space, there are multiple clusters in each region. Each region is managed by an aggregator. The parameters of five types of electric vehicles on the market are randomly selected as follows:

[0134] Class A: large capacity, high power;

[0135] Class B: large capacity, low power;

[0136] Class C: medium capacity, high power;

[0137] Class D: medium capacity, medium power;

[0138] Class E: low capacity, high power;

[0139] Class F: low capacity, high power;

[0140] Electric vehicles are divided into three types according to user travel characteristics, and the grid entry and exit times are as follows:

[0141] Type 1: Normal hours are 7:00-8:00, 8:00-17:00 and 17:00-18:00;

[0142] Type 2: Users who work far away use the car between 6:00-8:00 and 17:00-19:00;

[0143] Type 3: Night shift users, using the car from 20:00 to 6:00 the next day;

[0144] Different types of electric vehicles correspond to different SOC values ​​for participating in regulation. Different SOC values ​​correspond to specific strategies and capabilities for participating in peak load regulation. The number of three types of electric vehicles and their SOC for participating in regulation are as follows:

[0145] type quantity Participate in regulating SOC Type 1 1000 0.85 Type 2 800 0.65 Type 3 200 0.15

[0146] Assume that the aggregator sets the weight of all clusters to ω = 0.5, otherwise it is considered to have no ability to participate in regulation;

[0147] Specifically, the classification of electric vehicles based on travel characteristics directly utilizes the data obtained from the previous evaluation of the dispatchable capacity of the electric vehicle cluster. By analyzing the types of electric vehicles and the travel patterns of users, this step divides the vehicles into different categories to optimize the grid peak-shaving plan and adapt to changes in electricity prices.

[0148] Electric vehicles are divided into three types according to their capacity and power characteristics, as well as the travel time of users. Each type has different available time windows to participate in grid regulation according to its usage pattern, as well as different SOC (state of charge) values ​​for participating in regulation. These data help aggregators develop specific peak-shaving strategies for each cluster.

[0149] This step is crucial for the subsequent formulation of peak-shaving strategies because it takes into account the actual travel needs of users and the performance characteristics of electric vehicles, thereby enabling a more accurate prediction of the potential time periods when each vehicle can be used for peak-shaving. This ensures that the peak-shaving capacity of the electric vehicle cluster is maximized without interfering with users' daily use.

[0150] S5. Construct an electric vehicle cluster peak-shaving optimization model and divide the electric vehicle response time periods.

[0151] Furthermore, the electric vehicle response period is divided into 24 hours, and the charging and discharging power of the electric vehicle cluster is formulated according to the fluctuation of the grid load in each time period. Then the mean square error between the dispatching power of the electric vehicle cluster and the grid load is, and the mean square error expression between the dispatching power and the grid load is:

[0152]

[0153] Among them, P c The dispatch power required by the dispatch center at time t received by the aggregator, P i,t is the dispatch power of the dispatchable electric vehicle cluster during period t;

[0154] Furthermore, the specific steps for formulating the charging and discharging power of the electric vehicle cluster according to the fluctuation of the grid load in each time period are as follows:

[0155] Electric vehicle cluster power E i The upper and lower limits of are:

[0156] E min ≤E i ≤E max

[0157] Among them, E min is the upper limit of the electric vehicle cluster power, E max It is the offline limit of the electric vehicle cluster power;

[0158] Since electric vehicle batteries must ensure a certain amount of power reserve, evaluating and calculating the discharge and charging capacity of electric vehicle clusters is crucial for the dispatch and demand response management of electric vehicles participating in the power market. The SOC lower limit capacity is set to no less than 30%.

[0159] SOC i,k ≥SOC min

[0160] C i =(SOC i,k -SOC min )C e

[0161] C c =(SOC max -SOC i,k )C e

[0162]

[0163] Among them, SOC i,k is the battery state of charge of electric vehicle i at time k, SOC maxC is the maximum value allowed to participate in regulation. c is the charging capacity of the electric vehicle cluster at a certain moment, C g is the discharge capacity of the electric vehicle cluster at a certain moment, C z The discharge efficiency of the electric vehicle cluster at a certain moment;

[0164] The actual power P of users participating in peak load regulation act The interval is:

[0165] P min <P act <P max

[0166] This constraint ensures that the output power of electric vehicles remains within a safe and economic range when participating in peak load regulation. min and P max Respectively represent the minimum and maximum power thresholds at which the vehicle can operate safely, which helps protect the battery from excessive charge and discharge and ensures that the vehicle can provide enough power when needed;

[0167] The discharge capacity constraint is:

[0168] P charge ≤P1

[0169] P discharge ≤P2

[0170] R min ≤R act ≤R max

[0171] Among them, P charge and P discharge , is the actual charge and discharge power during the discharge process, R min is the minimum discharge capacity of electric vehicles involved in regulation, R max To participate in the regulation of the maximum discharge capacity, P1 is the upper limit of charging efficiency, and P2 is the upper limit of discharge efficiency;

[0172] These constraints ensure that during peak load regulation, the charging and discharging power of electric vehicles will not exceed the set upper limits P1 and P2, thereby preventing damage to the battery or increasing the load on the system due to excessive power. The discharge capacity of electric vehicles R act The minimum value R min and the maximum value R max To ensure that the vehicle operates according to the designed performance parameters and can fully discharge and peak load when needed;

[0173] Aggregator-side constraints include:

[0174] The cluster size constraints are:

[0175] Nmin <N act <N max

[0176] Among them, N min The minimum number of electric vehicles N in the cluster included in the aggregator management area act The actual number of electric vehicles N in the regional cluster managed by the aggregator max Managing the maximum number of electric vehicles in a regional cluster for aggregators;

[0177] This constraint ensures that the number of electric vehicles managed by the aggregator N act Maintain at minimum N min and the maximum value N max A reasonable fleet size is conducive to optimizing resource allocation and improving peak load efficiency for aggregators;

[0178] The peak shaving constraint is:

[0179]

[0180] Among them, X d is the peak-shaving demand issued by the dispatch center to the aggregator. Pi,t represents the charging and discharging power of the electric vehicle cluster in a specific period t, represents the starting point of the peak-shaving demand, and t, represents the length of the time period. This is a time interval, indicating the change of P: between t0 and t0+Δt;

[0181] This constraint is calculated within a specific period of time t0+Δt, according to the scheduling instruction X d The peak power that needs to be reduced. By adjusting the charging and discharging power P of the electric vehicle cluster i,t , aggregators can help the grid reduce electricity demand during high-load periods, contributing to the stable and efficient operation of the grid.

[0182] By optimizing the charging and discharging power that electric vehicle aggregators need to obtain from the power grid in different time periods, they can find the optimal electric vehicle charging and discharging management strategy, so that the electric vehicle cluster can play the greatest auxiliary role in the peak load regulation process of the power grid, better meet the expected load curve, minimize the mean square error, and ensure the safe and stable operation of the power grid;

[0183] Specifically: Based on the previous electric vehicle classification and dispatchable capacity assessment, the electric vehicle response period is divided into 24 hours, and the charging and discharging power of the electric vehicle cluster is formulated according to the fluctuation of the grid load in each time period. The purpose of this is to minimize the mean square error between the dispatch power and the grid load, so as to improve the stability and efficiency of the grid operation;

[0184] When formulating the charging and discharging strategy, the upper and lower limits of the electric vehicle cluster power are taken into account to ensure that the remaining battery power is within a safe range, and the actual power range for users to participate in peak load regulation is set to ensure that the output power of electric vehicles remains within a safe and economic range when participating in peak load regulation. In addition, by setting the discharge capacity constraint, the battery is protected from damage caused by excessive charging and discharging, while ensuring that the vehicle can provide sufficient power when needed;

[0185] The constraints faced by aggregators include cluster size and peak load reduction, which ensure that the number of electric vehicles managed by aggregators is maintained within a reasonable range and can effectively meet the peak load demand. Through these optimization measures, electric vehicle clusters can play the greatest auxiliary role in the peak load reduction process of the power grid, better meet the expected load curve, and thus ensure the safe and stable operation of the power grid.

[0186] S6. Use the particle swarm optimization algorithm to discover efficient electric vehicle charging and discharging management strategies. The specific steps are as follows:

[0187] Furthermore, a set of particles is defined for each sub-region under the management of the EV aggregator, and each particle represents a specific charging and discharging strategy;

[0188] A set of particles is defined for each sub-region under the management of the EV aggregator, each particle represents a specific charging and discharging strategy;

[0189] According to the particle initial velocity, individual optimal solution and global optimal solution, the particle velocity is updated. The expression is as follows:

[0190] v i,t+1 =w·v i,t +c1·r1·(p best,i -x i,t )+c2·r2·(g best -x i,t );

[0191] Among them, v i,t+1 is the velocity vector of the ith particle at time t+1, the current velocity vector of the ith particle at time t, w is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random weights, p best,i is the optimal solution found by the ith particle, g best is the global optimal solution, x i,t is the position of the ith particle at time t;

[0192] Calculate the new strategy based on the updated speed, the expression is as follows:

[0193] x i,t+1 =x i,t +v i,t+1 ;

[0194] Where is the new position of the i-th particle at time t+1;

[0195] The updated strategy needs to be based on the particle swarm optimization algorithm to obtain the electric vehicle charging and discharging management strategy under the constraints of meeting the upper and lower limits of charging power and discharging power, and the minimum and maximum values ​​of battery SOC;

[0196] The electric vehicle charging and discharging management strategy obtained based on the particle swarm optimization algorithm includes: the aggregator responds to the peak load-shaving task issued by the control center and controls the charging and discharging behavior of the electric vehicle group;

[0197] Specifically: Particle swarm optimization algorithm is used to discover efficient charging and discharging management strategies. A set of particles is defined for each sub-region electric vehicle cluster. Each particle represents a specific charging and discharging strategy. The algorithm continuously explores and optimizes these strategies through particle speed updates and position adjustments.

[0198] The speed update takes into account the particle's current speed, individual optimal solution, and global optimal solution, while the new position is calculated based on the updated speed. These update strategies must meet a series of constraints, such as the upper and lower limits of charging and discharging power, and the minimum and maximum values ​​of battery SOC;

[0199] This process enables aggregators to efficiently respond to peak load shaving tasks from control centers and implement them by controlling the charging and discharging behavior of electric vehicle groups. This approach not only improves the peak load shaving capacity of the power grid, but also ensures the health of electric vehicle batteries and user satisfaction.

[0200] This embodiment also provides a computer device, which is suitable for the case where electric vehicles are aggregated to participate in the dispatching method of auxiliary peak-shaving services in the electricity market, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the dispatching method of electric vehicles aggregated to participate in the electricity market auxiliary peak-shaving services as proposed in the above embodiment.

[0201] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. 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 and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0202] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the scheduling method for realizing the aggregation of electric vehicles to participate in the auxiliary peak-shaving service of the electricity market as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0203] In summary, the present invention integrates on-board data, GIS technology and intelligent algorithms to accurately dispatch electric vehicles to participate in grid peak load regulation, optimizes charging and discharging strategies based on user behavior, and improves grid flexibility and efficiency through fine classification and dynamic management, which has a positive and far-reaching impact on promoting the development of green smart grids and realizing the sustainable transformation of energy systems.

[0204] Example 2, referring to Table 1, is the second example of the present invention. To further verify the advancement of the present invention, experimental simulation data of a dispatching method for electric vehicles to participate in auxiliary peak load regulation services in the electricity market are provided.

[0205] Five electric vehicle samples were selected, and different sub-areas, SOC, dispatch capacity, and functional capacity were set for each electric vehicle sample.

[0206] First, the real-time data of the vehicle-mounted intelligent system and the user's APP are integrated, and strict quality control is performed on the data to eliminate outliers and missing values ​​to ensure the accuracy of the data.

[0207] Subsequently, the charging margin (Ta) of each vehicle is calculated, and whether the vehicle is qualified to participate in the peak load regulation service is determined based on the charging margin of each vehicle.

[0208] Then, using GIS technology, the entire study area was subdivided into five sub-areas (P1-P5), and each sub-area was assigned an aggregator for refined management.

[0209] Finally, the aggregator receives and responds to grid instructions through the cloud platform to achieve dynamic scheduling.

[0210] The details are shown in the following table:

[0211] Table 1 Experimental record table

[0212]

[0213]

[0214] Through the analysis of the above data sets, it can be intuitively observed that most vehicles (such as EV01) have considerable dispatchable potential (P) while meeting basic travel needs. They can effectively participate in the auxiliary peak-shaving service of the power market. By adjusting the dispatch capacity (D T ) and functional capacity (F T ) by comparing with the previous data, we found that the average dispatchable potential reached 10kWh per vehicle, indicating that the overall cluster contributes significantly to the peak load regulation of the grid.

[0215] The dispatching method of the electric vehicles aggregated to participate in the auxiliary peak-shaving service of the power market of the present invention not only enhances the stability and flexibility of the power system, but also promotes the efficient integration of renewable energy through precise electric vehicle dispatching strategies, demonstrating its innovation and advantages in the auxiliary peak-shaving service of the power market. The formed optimization model and strategy provide strong technical support for the development of future smart grids.

[0216] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A dispatching method for electric vehicles to participate in power market auxiliary peak load regulation service, characterized by: include, Integrate the vehicle intelligent system and user APP data, pre-process the data, calculate the charging time margin, identify the charging time margin through data analysis, and divide the dispatchable clusters and ordinary clusters; Using GIS technology for spatial analysis, the entire region is divided into multiple sub-regions, each of which is managed by an aggregator, who coordinates according to grid instructions; Evaluate the dispatchable capacity of electric vehicle clusters and determine their ability to participate in grid dispatch as flexible resources; Electric vehicles are classified based on user travel characteristics and connected to the grid for regulation according to the classification ratio to optimize the grid peak load regulation plan; Construct an electric vehicle cluster peak load optimization model, divide the electric vehicle response time period, and formulate a charging and discharging plan for the electric vehicle cluster based on the fluctuation of the grid load in each time period; Discover efficient electric vehicle charging and discharging management strategies through particle swarm optimization algorithm.

2. The dispatching method for electric vehicles to participate in the power market auxiliary peak load regulation service as claimed in claim 1, characterized in that: The integrated vehicle intelligent system and user APP data are pre-processed, the charging time margin is calculated, the charging time margin is identified through data analysis, and the dispatchable cluster and the ordinary cluster are divided. The specific steps are as follows: By integrating the in-vehicle intelligent system with the user's APP to collect data, check whether the received data has missing values ​​and abnormal values, and perform necessary data cleaning to preliminarily analyze the user's charging habits; The calculation of charging time margin divides electric vehicles into conventional clusters and dispatchable clusters, and the expression is: Ta = Tc - Tb - Td; Among them, Ta is the charging time margin, Tc is the off-grid time of the electric vehicle, Tb is the time difference between the expected charging completion time and the next driving time, and Td is the charging demand time; By judging the charging time margin, it can be determined whether the electric vehicle has extra time to participate in the regulation. When Ta>0, such vehicles are regarded as having charging time margin and are suitable to be classified as dispatchable clusters to participate in the auxiliary services of the power grid. When Ta≤0, the vehicle does not have the flexibility of scheduling and is classified as an ordinary cluster.

3. The dispatching method for electric vehicles to participate in the power market auxiliary peak load regulation service as claimed in claim 2, characterized in that: The spatial analysis using GIS technology divides the entire area into multiple sub-areas, each of which is managed by an aggregator, who coordinates according to the grid instructions. The specific steps are as follows: Use hot spot analysis tools in GIS software to identify high-frequency areas of electric vehicle use, use network analysis tools to simulate traffic flow, and reasonably divide the entire area into multiple sub-areas. Design the size and shape of each sub-area based on the actual road network, charging station distribution, and user needs; In each sub-region Pi, the number of electric vehicles Ni should be determined based on the user demand, the capacity of charging facilities and the road carrying capacity of the region, ensuring that Ni ≤ Nmax,i; GIS and GPS technologies are used to monitor and manage the location of electric vehicles, meta-heuristic algorithms are used to calculate the optimal number of electric vehicles and dispatch strategies for each area, and aggregators use cloud platforms to receive and process real-time instructions from the power grid.

4. The dispatching method for electric vehicles to participate in the power market auxiliary peak load regulation service as claimed in claim 3, characterized in that: The specific steps of evaluating the dispatchable capacity of the electric vehicle cluster and determining the ability of the electric vehicle cluster to participate in power grid dispatch are as follows: After electric vehicles are dispatched, the dispatchable capacity is divided into upward dispatch capacity and downward dispatch capacity; The upward scheduling capacity expression is: The downward scheduling capacity expression is: in, and are the upward and downward adjustment of the capacity of the i-th electric vehicle, SOC min The minimum value allowed to participate in regulation, n is the number of electric vehicles, m is the number of electric vehicles currently charging, C i is the battery capacity of the ith electric vehicle, SOC act is the percentage of available battery remaining, η represents the percentage of charging efficiency, P is the number of electric vehicles with current energy storage capacity, Ei represents the amount of electricity currently being used by the jth electric vehicle, and E k Indicates the current remaining power of the kth electric car; By evaluating the dispatchable potential of the electric vehicle cluster in the region, the expression is: In the formula, h is the adjustment factor, R act is the discharge capacity; The total dispatch capacity of an electric vehicle is expressed as: Among them, Vc is the battery capacity, SOC i is the current state of the battery, Ce is the total capacity of the battery, i represents the i-th electric vehicle, D T is the dispatch capacity of electric vehicles; The total functional capacity of a single electric vehicle is expressed as: Among them, De is the discharge efficiency, F T is the total functional capacity of the electric vehicle; The dispatchable potential expression of a single electric vehicle is: P = FT–DT; Extending a single electric vehicle to the cluster mode, the potential matrix Hi is expressed as: Hi=[P1,P2,P3...Pn]; Where Pn is the dispatchable potential of a single electric vehicle in the nth electric vehicle cluster; The dispatchable potential expression of the electric vehicle cluster in the region is: Among them, S i The potential value that aggregators can be allowed to participate in regulation.

5. The dispatching method for electric vehicles to participate in the power market auxiliary peak load regulation service as claimed in claim 4, characterized in that: The electric vehicles are classified based on the user travel characteristics, and connected to the power grid for regulation according to the classification ratio to optimize the power grid peak regulation plan. The specific steps are as follows; The electric vehicles in the region are classified and sorted. There are multiple clusters in each region based on random spatial distribution. Each region is managed by an aggregator. Five types of electric vehicles on the market are selected. Electric vehicles with different performances are divided into six categories from A to F, including a diverse combination of large capacity and high power to low capacity and high power. They are divided into three categories based on user travel characteristics; Based on the classification results, grid operators adopt the method of peak shaving and valley filling to reduce loads and formulate differentiated charging strategies and dynamic electricity price mechanisms.

6. The dispatching method for electric vehicles to participate in the power market auxiliary peak load regulation service as claimed in claim 5, characterized in that: The electric vehicle response time period is divided to construct an electric vehicle cluster peak load optimization model, and the specific steps are as follows: Based on the classification results of user travel characteristics, the response time of electric vehicles in a day is divided by hour. The charging and discharging power of the electric vehicle cluster is formulated according to the fluctuation of the grid load in each time period, and the mean square error between the dispatching power of the electric vehicle cluster and the grid load is calculated. The expression is as follows: Among them, P c The dispatch power required by the dispatch center at time t received by the aggregator, P i,t is the dispatch power of the dispatchable electric vehicle cluster during period t, F min is to minimize the scheduling error.

7. The dispatching method for electric vehicles to participate in the power market auxiliary peak load regulation service as claimed in claim 6, characterized in that: According to the fluctuation of grid load in each time period, a charging and discharging plan for the electric vehicle cluster is formulated. The specific steps are as follows: Formulate the electric vehicle cluster power E i The upper and lower limits of are expressed as: AND min ≤E i ≤E max ; Among them, E min is the upper limit of the electric vehicle cluster power, E max It is the offline limit of the electric vehicle cluster power; Evaluate and calculate the discharge and charging capabilities of the electric vehicle cluster. The SOC lower limit capacity is set to be no less than 30% based on battery performance, vehicle operation requirements, and safety factors. The expression is as follows: SOC i,k ≥SOC min ; C i =(SOC i,k -SOC min )C e ; C c =(SOC max -SOC i,k )C e ; Among them, SOC i,k is the battery state of charge of electric vehicle i at time k, SOC max C is the maximum value allowed to participate in regulation. c is the charging capacity of the electric vehicle at a certain moment, C g is the discharge capacity of the electric vehicle cluster at a certain moment; Establish constraints for electric vehicle clusters to control charging and discharging capabilities within a safe and efficient range. The specific steps are as follows: The charging and discharging capability constraint expressions are as follows: P charge ≤P1; P discharge ≤P2; R min ≤R act ≤R max ; Among them, P charge and P discharge , respectively, the actual charging and discharging power, R min is the minimum discharge capacity of electric vehicles involved in regulation, R max To participate in the regulation of the maximum discharge capacity, P1 is the upper limit of charging efficiency, and P2 is the upper limit of discharge efficiency; The cluster size constraint expression is as follows: N min <N act <N max ; Among them, N min The minimum number of electric vehicles in the cluster included in the aggregator management area, N act The actual number of electric vehicles in the regional cluster managed by the aggregator, N max Managing the maximum number of electric vehicles in a regional cluster for aggregators The peak shaving constraint expression is as follows: Among them, X d is the peak load demand issued by the dispatch center to the aggregator, ΔP i,t represents the charging and discharging power of the electric vehicle cluster in a specific period of time t, t0 represents the starting point of the peak demand, and Δt represents the length of the time period; The particle swarm optimization algorithm is used to solve the above model. By optimizing the charging and discharging power that electric vehicle aggregators need to obtain from the power grid in different time periods, the optimal electric vehicle charging and discharging management strategy is formulated.

8. The dispatching method for electric vehicles to participate in the power market auxiliary peak load regulation service as claimed in claim 7, characterized in that: Through the particle swarm optimization algorithm, an efficient electric vehicle charging and discharging management strategy is discovered. The specific steps are as follows: A set of particles is defined for each sub-region under the management of the EV aggregator, each particle represents a specific charging and discharging strategy; According to the particle initial velocity, individual optimal solution and global optimal solution, the particle velocity is updated. The expression is as follows: v i,t+1 =w·v i,t +c1·r1·(p best,i -x i,t )+c2·r2·(g best -x i,t ); Among them, v i,t+1 is the velocity vector of the ith particle at time t+1, the current velocity vector of the ith particle at time t, w is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random weights, p best,i is the optimal solution found by the ith particle, g best is the global optimal solution, x i,t is the position of the ith particle at time t; Calculate the new strategy based on the updated speed, the expression is as follows: x i,t+1 =x i,t +v i,t+1 ; Where is the new position of the i-th particle at time t+1; The updated strategy needs to obtain the electric vehicle charging and discharging management strategy based on the particle swarm optimization algorithm under the constraints of meeting the upper and lower limits of charging power and discharging power, and the minimum and maximum values ​​of battery SOC.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the dispatching method for electric vehicles to aggregate and participate in auxiliary peak-shaving services in the electricity market as described in any one of claims 1 to 8 are implemented.

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 steps of a dispatching method for electric vehicles to aggregate and participate in auxiliary peak-shaving services in a power market as described in any one of claims 1 to 8 are implemented.

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