Scheduling method, device, equipment and product of electric vehicle cluster

Abstract

The invention discloses a scheduling method, device, equipment and product of an electric vehicle cluster, and relates to the technical field of an electric power auxiliary service market.The method comprises the steps that the electric vehicle clusters are graded according to historical credibility indexes, and the initial reserve capacity of each grade of cluster is determined; the historical credibility index is used for measuring the credit performance of the electric vehicle cluster when the reserve capacity is provided in the past, inputting the initial reserve capacity of each level of cluster into the bidding strategy optimization model, optimizing the initial reserve capacity of each level of cluster through an optimization algorithm, obtaining the target reserve capacity of each level of cluster, and obtaining the target reserve capacity of each level of cluster. The bidding strategy optimization model is used for maximizing the risk net income, and scheduling the electric vehicle cluster according to the target reserve capacity of each level cluster. The electric vehicle cluster is preliminarily graded through the historical credibility index, and the grading is further optimized by adopting the optimization algorithm, so that the electricity collector can obtain reliable standby resources in emergency, and the economic benefit of the electricity collector is maximized at the same time.

Description

Technical Field

[0001] The present application relates to the technical field of power auxiliary service market, and in particular to dispatching methods, devices, equipment and products for electric vehicle clusters. Background Art

[0002] With the transformation of the global energy structure and the increasingly severe climate change problem, the development of clean energy and the improvement of energy efficiency have become global challenges and development directions. Electric vehicles (EVs), as an important part of new energy vehicles, have attracted widespread attention because they can effectively reduce greenhouse gas emissions and improve energy efficiency. At the same time, with the rapid growth of the number of electric vehicles, the application of vehicle-to-grid (V2G) technology has become possible. Electric vehicles can not only meet personal travel needs, but also serve as a new type of regulating resource in the power system, participate in the power market, especially the reserve market, to provide reserve capacity for the power grid and enhance the flexibility and reliability of the power grid. The reserve market of the power system is an important part of the power market. Its main function is to ensure that the power system can quickly call on reserve resources to maintain the stable operation of the power grid through market mechanisms when unexpected events occur, such as generator failures or demand forecast errors. The reliability and response speed of reserve resources are crucial to the stability of the power system.

[0003] In the existing technology, decentralized electric vehicle resources are centrally managed to form a large-scale electric vehicle cluster that can be uniformly dispatched to participate in the power market to provide frequency regulation, backup and other services. However, this implementation does not fully solve the problem of electric vehicle cluster dishonesty, that is, the random travel behavior of electric vehicle users may cause the actual available backup capacity to be lower than the promised capacity, thus affecting the stable operation of the power system and the trust of the backup market.

[0004] Therefore, how to improve the reliability of electric vehicle clusters as backup resources is an urgent problem to be solved. Summary of the invention

[0005] The main purpose of the present application is to provide a scheduling method, device, equipment and product for an electric vehicle cluster, aiming to solve the technical problem of low reliability of electric vehicle clusters as backup resources.

[0006] To achieve the above purpose, the present application proposes a dispatching method for an electric vehicle cluster, the method comprising:

[0007] Classifying electric vehicle clusters according to historical credibility indicators and determining initial backup capacity for clusters at each level, wherein the historical credibility indicators are used to measure the credit performance of electric vehicle clusters in providing backup capacity in the past;

[0008] Inputting the initial spare capacity of the clusters at each level into the bidding strategy optimization model, optimizing the initial spare capacity of the clusters at each level by an optimization algorithm, and obtaining the target spare capacity of the clusters at each level, wherein the bidding strategy optimization model is used to maximize the net risk return;

[0009] The electric vehicle cluster is dispatched according to the target spare capacity of the clusters at each level.

[0010] In one embodiment, the step of grading the electric vehicle clusters according to the historical credibility index and determining the initial spare capacity of each level of clusters includes:

[0011] Determining the historical credibility indicator by calculating the proportion of the electric vehicle cluster successfully responding to the backup call in the past;

[0012] The historical credibility indicators are sorted from large to small, and the electric vehicle clusters are correspondingly divided into one or more levels to obtain a hierarchical backup framework for electric vehicle clusters;

[0013] Based on the electric vehicle cluster hierarchical reserve framework, the initial reserve capacity of each level of cluster is determined according to a preset capacity allocation coefficient.

[0014] In one embodiment, the bidding strategy optimization model includes an objective function mathematical model, and before the step of inputting the initial spare capacity of the clusters at each level into the bidding strategy optimization model, the step includes:

[0015] Constructing the initial function mathematical model;

[0016] Based on the initial spare capacity of the clusters at each level, construct constraint conditions;

[0017] The initial function mathematical model is updated according to the constraint conditions to obtain the objective function mathematical model.

[0018] In one embodiment, the step of constructing the initial function mathematical model includes:

[0019] Calculate deterministic revenue based on declared capacity, declared price and capacity cost;

[0020] Calculate the uncertainty benefit based on the call benefit, breach of trust cost and breach of trust penalty;

[0021] Based on the deterministic benefit and the uncertain benefit, the initial function mathematical model is constructed.

[0022] In one embodiment, the step of inputting the initial spare capacity of the clusters at each level into the bidding strategy optimization model, optimizing the initial spare capacity of the clusters at each level by an optimization algorithm, and obtaining the target spare capacity of the clusters at each level includes:

[0023] Inputting the initial spare capacity of the clusters at each level into the objective function mathematical model, and using multiple optimization algorithms for parallel processing to obtain a risk net return result interval;

[0024] Selecting an optimal solution in the result interval, wherein the optimal solution corresponds to the maximum value of the net risk return;

[0025] Optimizing the preset capacity allocation coefficient according to the maximum value of the risk net benefit to determine the target capacity allocation coefficient;

[0026] The target spare capacity of the clusters at each level is determined according to the target capacity allocation coefficient.

[0027] In one embodiment, the historical credibility index is expressed as:

[0028]

[0029] In the formula, H i is the historical credibility index; R it is a binary variable, indicating whether the i-th cluster successfully responded to the backup call at time t, with successful response recorded as 1 and no response recorded as 0; T is the total number of calls in the evaluation period.

[0030] In addition, to achieve the above-mentioned purpose, the present application also proposes a dispatching device for an electric vehicle cluster, the dispatching device for an electric vehicle cluster comprising:

[0031] A preliminary classification module, used to classify electric vehicle clusters according to historical credibility indicators and determine the initial spare capacity of clusters at each level, wherein the historical credibility indicators are used to measure the credit performance of electric vehicle clusters in providing spare capacity in the past;

[0032] A hierarchical optimization module, used for inputting the initial spare capacity of the clusters at each level into a bidding strategy optimization model, optimizing the initial spare capacity of the clusters at each level by an optimization algorithm, and obtaining the target spare capacity of the clusters at each level, wherein the bidding strategy optimization model is used for maximizing the net risk return;

[0033] The cluster scheduling module is used to schedule the electric vehicle cluster according to the target spare capacity of the clusters at each level.

[0034] In addition, to achieve the above-mentioned purpose, the present application also proposes a scheduling device for an electric vehicle cluster, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the scheduling method for an electric vehicle cluster as described above.

[0035] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the scheduling method of the electric vehicle cluster as described above are implemented.

[0036] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the scheduling method for an electric vehicle cluster as described above are implemented.

[0037] One or more technical solutions proposed in this application have at least the following technical effects:

[0038] Electric vehicle clusters are classified according to historical credibility indicators, and the initial spare capacity of each level of clusters is determined. The historical credibility indicators are used to measure the credit performance of electric vehicle clusters in providing spare capacity in the past. The initial spare capacity of each level of clusters is input into the bidding strategy optimization model. The initial spare capacity of each level of clusters is optimized by the optimization algorithm to obtain the target spare capacity of each level of clusters. The bidding strategy optimization model is used to maximize the net risk return and dispatch electric vehicle clusters according to the target spare capacity of each level of clusters. The electric vehicle clusters are preliminarily classified according to the historical credibility indicators, and their reliability is evaluated based on the past response of the clusters. The electric vehicle clusters are divided into different levels of spare resources. The optimization algorithm is used to adjust and optimize the classification strategy to ensure that the collector can obtain reliable spare resources in an emergency and maximize its economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] Figure 1 This is a flow chart of a first embodiment of a method for dispatching an electric vehicle cluster according to the present application;

[0042] Figure 2 This is a flow chart of a second embodiment of the scheduling method for electric vehicle clusters of the present application;

[0043] Figure 3 This is a flow chart of a third embodiment of the method for dispatching an electric vehicle cluster of the present application;

[0044] Figure 4 A schematic diagram of the hierarchical backup framework for this application;

[0045] Figure 5 This is a schematic diagram of the hierarchical spare wheel control strategy for this application;

[0046] Figure 6 This is a schematic diagram of the effect of the hierarchical backup strategy for this application;

[0047] Figure 7 Analysis chart of default capacity of the next level of backup for different secondary backup configurations for this application;

[0048] Figure 8 This is the analysis chart for filling the first-level standby default capacity for this application;

[0049] Fig. 9 This is the effect analysis chart of the grading strategy for this application;

[0050] Fig.10 This is a schematic diagram of the module structure of the dispatching device of the electric vehicle cluster according to the embodiment of the present application;

[0051] Fig.11 Schematic diagram of the device structure of the hardware operating environment involved in the scheduling method of the electric vehicle cluster in the embodiment of the present application.

[0052] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0054] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0055] Since the reserve market of the power system is an important part of the power market, its main function is to ensure that the power system can quickly call on reserve resources to maintain the stable operation of the power grid through market mechanisms when unexpected events occur, such as generator failures or demand forecast errors. The reliability and response speed of reserve resources are crucial to the stability of the power system. In existing technologies, participants in the reserve market mainly include traditional large-scale generators and some emerging distributed energy resources, such as energy storage equipment and renewable energy power generation facilities. As a new type of reserve resource, the participation of electric vehicle clusters in the reserve market has potential cost advantages and environmental benefits compared to traditional reserve resources. However, the participation of electric vehicle clusters also faces a series of challenges, mainly because the use of electric vehicles has a high degree of uncertainty, which affects their reliability as a reserve resource.

[0056] One of the most similar implementation schemes is the concept of electric vehicle aggregators (EVAs), which centrally manages decentralized electric vehicle resources to form a large-scale electric vehicle cluster that can be uniformly dispatched to participate in the power market to provide frequency regulation, backup and other services. This scheme improves the operability and economy of electric vehicle clusters as participants in the power market through the intermediary role of aggregators. However, this implementation scheme does not fully solve the problem of electric vehicle cluster default, that is, the random travel behavior of electric vehicle users may result in the actual available backup capacity being lower than the promised capacity, thus affecting the stable operation of the power system and the trust in the backup market.

[0057] The present application provides a solution to classify electric vehicle clusters according to historical credibility indicators and determine the initial spare capacity of each level of clusters. The historical credibility indicators are used to measure the credit performance of electric vehicle clusters in providing spare capacity in the past. The initial spare capacity of each level of clusters is input into the bidding strategy optimization model. The initial spare capacity of each level of clusters is optimized by the optimization algorithm to obtain the target spare capacity of each level of clusters. The bidding strategy optimization model is used to maximize the net risk return and schedule electric vehicle clusters according to the target spare capacity of each level of clusters. The electric vehicle clusters are preliminarily classified by historical credibility indicators, and their reliability is evaluated based on the past response of the clusters. The electric vehicle clusters are divided into different levels of spare resources. The optimization algorithm is used to adjust and optimize the classification strategy to ensure that the aggregator can obtain reliable spare resources in an emergency while maximizing its economic benefits.

[0058] Based on this, the present application embodiment provides a scheduling method for an electric vehicle cluster, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the scheduling method for electric vehicle clusters of the present application.

[0059] In this embodiment, the dispatching method of the electric vehicle cluster includes steps S10 to S30:

[0060] Step S10, classifying electric vehicle clusters according to historical credibility indicators, and determining the initial spare capacity of clusters at each level.

[0061] It should be noted that the electric vehicle cluster can be understood as the centralized management of scattered electric vehicle resources to form a large-scale electric vehicle cluster that can be uniformly dispatched and participate in the electricity market as a backup resource. The historical credibility index can be a designed indicator for evaluating the historical performance of the electric vehicle cluster. This indicator is based on the performance of the electric vehicle cluster in the past period of time and is used to measure the credit performance of the electric vehicle cluster when providing backup capacity in the past. The initial backup capacity can be a preliminary and basic backup capacity value set for each level of electric vehicle cluster before the optimization process begins.

[0062] It should be noted that step S10 includes: determining a historical credibility index by calculating the proportion of electric vehicle clusters that successfully responded to backup calls in the past; sorting the historical credibility index from large to small, and correspondingly dividing the electric vehicle clusters into one to multiple levels to obtain a hierarchical backup framework for electric vehicle clusters; based on the hierarchical backup framework for electric vehicle clusters, determining the initial backup capacity of clusters at each level according to a preset capacity allocation coefficient.

[0063] The historical credibility index is expressed as formula (1):

[0064]

[0065] In formula (1), H i is the historical credibility index; R it is a binary variable, indicating whether the i-th cluster successfully responded to the backup call at time t, with a successful response recorded as 1 and a failure to respond recorded as 0; T is the total number of calls in the evaluation period. The hierarchical backup framework can be a structure or framework obtained by grading electric vehicle clusters according to historical credibility indicators. In the hierarchical backup framework for electric vehicle clusters, electric vehicle clusters are divided into different levels (such as level one, level two, level three, etc.), and each level corresponds to different credit and reliability. The preset capacity allocation coefficient can be a pre-set parameter or coefficient used to determine the initial backup capacity of the electric vehicle cluster according to its level. Different levels correspond to different preset capacity allocation coefficients to reflect the differences in the provision of backup capacity among clusters of different levels.

[0066] Exemplarily, the hierarchical status of each electric vehicle cluster in the backup market is determined based on its past response performance. High-credibility clusters are classified as high-level backups, and low-credibility clusters are classified as low-level backups. Electric vehicle clusters can be divided into primary backup resources and secondary backup resources. It should be noted that the historical credibility is often classified as a higher-level backup (i.e., primary backup resources). Secondary backup resources are composed of electric vehicle clusters with slightly lower reliability. Because the capacity they provide is unreliable, they do not directly participate in the market, but provide capacity for primary backup in the form of secondary backup. And it has nothing to do with the capacity size of the electric vehicle cluster. When configuring the secondary backup, the sum of its capacity will be controlled, which is generally not greater than the capacity of the primary backup. The response success rate (historical credibility, HCI) of the electric vehicle cluster can be calculated by collecting the backup call data of the electric vehicle cluster during the evaluation period. According to the HCI sorting, the clusters are divided into different levels of backup resources to ensure that clusters with high reliability are used first in emergency situations. After classification, a fixed capacity allocation coefficient is set for the backup resources of adjacent levels, the capacity allocation ratio is determined, and the model is simplified. If the high-level resources are sufficient, they are allocated first. If they are insufficient, they are allocated to the next level of resources according to a fixed coefficient.

[0067] Step S20, inputting the initial spare capacity of clusters at each level into the bidding strategy optimization model, optimizing the initial spare capacity of clusters at each level through the optimization algorithm, and obtaining the target spare capacity of clusters at each level.

[0068] It should be noted that the bidding strategy optimization model can be a mathematical model used to optimize the bidding strategy of the electric vehicle cluster under given conditions (such as market demand, price signals, cluster costs, etc.) to maximize the risk net benefit. The optimization algorithm can be used to adjust the spare capacity of clusters at all levels to find the optimal solution that maximizes the risk net benefit. The target spare capacity can be the spare capacity that should be provided by clusters at all levels after being processed by the bidding strategy optimization model and the optimization algorithm.

[0069] Step S30, scheduling the electric vehicle cluster according to the target spare capacity of each level of cluster.

[0070] For example, the electric vehicle clusters can be orderly controlled and managed according to the target spare capacity of clusters at each level to ensure that spare capacity can be provided on time and in quantity when needed.

[0071] In this embodiment, electric vehicle clusters are classified according to historical credibility indicators, and the initial spare capacity of each level of clusters is determined. The historical credibility indicators are used to measure the credit performance of electric vehicle clusters in providing spare capacity in the past. The initial spare capacity of each level of clusters is input into the bidding strategy optimization model. The initial spare capacity of each level of clusters is optimized by the optimization algorithm to obtain the target spare capacity of each level of clusters. The bidding strategy optimization model is used to maximize the net risk return and dispatch the electric vehicle clusters according to the target spare capacity of each level of clusters. The classification method based on historical credibility can better coordinate the abundance and economy of spare resources, divide electric vehicle clusters into different levels of spare resources, and use optimization algorithms to adjust and optimize the classification strategy to ensure that the collector can obtain reliable spare resources in an emergency and maximize its economic benefits, providing a new perspective and technical support for electric vehicle clusters to participate in the power market spare resources more effectively and reliably.

[0072] Reference Figure 2 , Figure 2 This is a flow chart of the second embodiment of the scheduling method for electric vehicle clusters of the present application, based on the above Figure 1 The first embodiment shown is a second embodiment of the scheduling method for an electric vehicle cluster of the present application.

[0073] In the second embodiment, before step S20, the following steps are included:

[0074] Step S11, constructing an initial function mathematical model.

[0075] It should be noted that the step S11 includes: calculating deterministic benefits based on declared capacity, declared price and capacity cost; calculating uncertain benefits based on call benefits, breach of trust costs and breach of trust fines; and constructing an initial function mathematical model based on deterministic benefits and uncertain benefits.

[0076] For example, the initial function model is formed by two parts: deterministic benefits and uncertain benefits. The time axis is discretized, the entire quotation cycle T is divided into n time periods of length Δt, and the time variability of the backup data provided by the EV cluster within Δt is frozen. It is expressed as formula (2):

[0077]

[0078] In formula (2), G is the total risk net return of the electricity supplier; m represents the number of EV clusters that the electricity supplier can manage; M i is the net risk benefit brought by the i-th EV cluster. Among them, the deterministic benefit f i,det (t) reflects the deterministic profit obtained by each EV cluster i through its declared capacity in time period t. The uncertain profit f i,s,uncert(t) This part captures the additional profit or economic loss that each EV cluster may obtain or face under a specific scenario s due to uncertain factors such as market fluctuations and changes in scheduling requirements. The overall goal is to maximize the sum of these two parts while considering the probability of occurrence of different scenarios γ s The deterministic benefit is fixed and does not depend on any scenario, so it is only calculated once. The uncertain benefit depends on the scenario, so it is summed for each scenario and the probability of the scenario is taken into account. The deterministic benefit is expressed as formula (3):

[0079] f i,det (t) = a i (t)(p′ i (t)-p i (t))Δt (3)

[0080] In formula (3), f i,det (t) represents the deterministic return, a i (t) is the declared capacity of the i-th EV cluster in time period t, p′ i (t) is the unit capacity declared price of the i-th EV cluster in time period t, p i (t) is the corresponding unit capacity cost. The uncertainty benefit is expressed as formula (4):

[0081] f i,s,uncert (t) = f i,s,call (t)-f i,s,backup (t)-f i,s,agr (t) (4)

[0082] In formula (4), f i,s,uncert (t) represents the uncertainty return, f i,s,call (t) represents the benefit obtained by the i-th EV cluster being called in time period t in scenario s; f i,s,backup (t) refers to the cost required for the i-th EV cluster to call its secondary reserve to compensate for the default capacity in this scenario at time period t; f i,s,agr (t) represents the penalty that the u-th EV cluster must pay for breach of trust in time period t in scenario s. The specific expressions of the call benefit and breach of trust penalty are as follows:

[0083] f i,s,cakk (t) = p i,s (t)·a i,s (t)·π i,s (t)·Δt (5)

[0084]

[0085] In formula (5), p i,s(t) is a 0-1 integer variable, p i,s (t) = 1 means that the i-th cluster is called in time period t, p i,s (t) = 0 means it has not been called, a i,s (t) represents the cluster capacity, π i,s (t) is the unit price being called; in formula (6), It is also a 0-1 integer variable. Indicates that cluster i needs to pay a fine in time period t. No fine is required; i,incred,s (t) is the actual default capacity of cluster i; and π′ i,s (t) is the unit capacity penalty corresponding to the default capacity. Among them, the initial default capacity of the cluster is the natural default capacity. The calculation formula of the natural default capacity is formula (7):

[0086] a i,incred,s (t) = η i,s (t)·a i,s (t)·Δt (7)

[0087] In formula (7), the natural default rate η i,s (t) and cluster capacity a i,s (t) to evaluate the natural default capacity of cluster i in time period t, and propose the natural default rate η i,s (t) = 10%, which means that a cluster initially has 10% of its capacity at risk of default. The default capacity share of the cluster can be filled through the secondary backup. In the optimization process, once the default capacity ratio is determined, the corresponding secondary backup cost, that is, the additional cost required to activate the secondary backup resources, becomes a key consideration. The hierarchical backup cost can be calculated using formula (8) and formula (9):

[0088]

[0089] a j,backup,s (t) = σ·a j,s (t) (9)

[0090] In formula (8) and formula (9), n i+1 represents the number of EV clusters serving as the i+1th level backup; a j,backup,s (t) represents the spare capacity provided by cluster j to cluster i, which is composed of the spare capacity allocation coefficient σ and the capacity a of cluster j in this period. j,s (t) Joint decision-making; j,vackup,s (t) is the unit price of spare capacity when cluster j is used as the next-level spare.

[0091] Step S12: construct constraint conditions based on the initial spare capacity of clusters at each level.

[0092] Exemplarily, the constraints involved in step S11 are as follows:

[0093] The reserve capacity allocation ratio constraint is that the reserve provides a certain proportion of its own capacity to supplement the default capacity of its upper level reserve. The constraint condition of the reserve capacity allocation ratio is formula (10):

[0094] 0≤σ≤1 (10)

[0095] In formula (10), when σ = 0, it means that the backup does not provide any capacity, and σ = 1 means that the backup provides its full capacity as a supplement. Specifically, the sum of the 90% of the capacity that can be stably supplied in the capacity provided by the next-level backup should be greater than or equal to the default capacity of the previous-level backup to ensure that the previous-level backup can supply the promised capacity. The constraint on the capacity provided by the backup is formula (11):

[0096]

[0097] According to the model setting, for backup, its default capacity is always a minority of the total, so in the configuration process, as the backup level increases, the number of backups shows a non-increasing rule. Assuming there are n clusters in total, these n clusters are divided into m levels of backup, then there are the following constraints (Formula (12) and Formula (13)):

[0098]

[0099] n m +…+n2+n1=n (13)

[0100] In formula (12) and formula (13), n m represents the number of level m reserves. This constraint ensures that the number of lower-level reserves does not exceed the number of upper-level reserves. When m=1, it means that all EV clusters directly participate in the bidding of the reserve market as first-level reserves.

[0101] Step S13, updating the initial function mathematical model according to the constraint conditions to obtain the target function mathematical model.

[0102] It should be noted that the objective function mathematical model may be a mathematical model obtained by adjusting and optimizing the initial function mathematical model after considering all constraints.

[0103] In this embodiment, by constructing an initial function mathematical model, and based on the initial spare capacity of clusters at each level, constructing constraints, and updating the initial function mathematical model according to the constraints, the objective function mathematical model is obtained, which can decompose the complex economic decision-making process into quantifiable benefit parts, making the model more refined. By integrating the constraints and the initial function model, the objective function mathematical model obtained can find the optimal decision-making plan under the premise of meeting the system stability and resource allocation requirements. The objective function mathematical model covers multiple aspects such as the formulation of a hierarchical spare mechanism, spare capacity assessment, risk analysis, and resource allocation, allowing market participants to consider the issue of spare capacity supply from a more comprehensive perspective and improve the credibility and economic benefits of spare capacity. By providing more credible spare capacity and more optimized economic benefits, more electric vehicle clusters are encouraged to participate in the spare market, promoting the application and popularization of clean energy, and meeting the goal of sustainable development.

[0104] Reference Figure 3 , Figure 3 This is a flow chart of the third embodiment of the scheduling method for electric vehicle clusters of the present application, based on the above Figure 2 The second embodiment shown is a third embodiment of the scheduling method for an electric vehicle cluster of the present application.

[0105] In the third embodiment, the step S20 includes:

[0106] Step S201, input the initial spare capacity of each level of cluster into the objective function mathematical model, and use multiple optimization algorithms for parallel processing to obtain the risk net return result interval.

[0107] It should be noted that the risk net benefit result interval can be a set of risk net benefit values ​​obtained by applying multiple optimization algorithms in parallel during the optimization process, after the initial spare capacity of clusters at each level is input into the objective function mathematical model, which reflects the risk-adjusted net benefit level that the system may achieve under different conditions. The use of multiple optimization algorithms in parallel can be understood as actually running different algorithms, evaluating their performance on specific problems, and finally selecting the algorithm with the best effect.

[0108] For example, in the optimization of the tiered backup strategy, multiple tests can be conducted on historical data and market conditions, and the best algorithm can be selected by comparing the performance of different algorithms (genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm and Harris Eagle optimization algorithm) in terms of optimization efficiency, calculation time and final risk net return.

[0109] Step S202, selecting the optimal solution in the result interval, the optimal solution corresponding to the maximum value of the net risk return.

[0110] It should be noted that the optimal solution corresponds to the optimal capacity allocation scheme, which maximizes the risk net benefit. Exemplarily, the genetic algorithm (GA), particle swarm optimization algorithm (PSO), simulated annealing algorithm (SA) and Harris Hawk optimization algorithm (HHO) can be run to evaluate their respective results. After multiple rounds of testing, if HHO can better optimize the risk net benefit in this scenario, HHO is selected as the main algorithm.

[0111] Step S203, optimizing the preset capacity allocation coefficient according to the maximum value of the risk net benefit, and determining the target capacity allocation coefficient.

[0112] It should be noted that the target capacity allocation coefficient may be an optimal capacity allocation ratio or coefficient obtained through an optimization process, and is used to determine a more reasonable capacity allocation plan for clusters at each level.

[0113] For example, by adjusting the preset capacity allocation coefficient, the risk net benefit of the entire system is maximized. The adjustment process can be repeated and compared multiple times until a set of coefficients is found so that the risk net benefit of the system is optimal under given conditions.

[0114] Step S204: determining the target spare capacity of clusters at each level according to the target capacity allocation coefficient.

[0115] It should be noted that the target spare capacity of each level of cluster is determined according to the target capacity allocation coefficient, thereby obtaining the final hierarchical spare capacity framework. Figure 4 The final hierarchical reserve framework diagram is shown, which divides the EV cluster into multiple levels of reserve capacity, where each level of reserve capacity provides capacity support for the level above it, up to the top-level first-level reserve. In this hierarchical reserve mechanism, in order to reduce the coupling effect between different levels of reserve, only the top-level first-level reserve is eligible to participate in the reserve market bidding to obtain the benefits of successful bidding, and if it fails to provide the promised capacity, it must pay a penalty; other levels of reserve only act on the reserve above it. In the actual operation process, when there is a capacity gap in the first-level reserve, the second-level reserve will be called to provide it with capacity; if the second-level reserve still cannot fill the capacity gap of the first-level reserve, the third-level reserve will continue to be called to fill the gap of the second-level reserve. And so on, until the capacity promised by the first-level reserve when participating in the reserve market is reliable. Finally, if the second-level reserve still has remaining capacity, it will be used as a new first-level reserve to obtain another part of the benefits. Figure 5The wheel control strategy of hierarchical reserve is shown. The horizontal and vertical axes in the figure represent time and the capacity of hierarchical reserve respectively. In this strategy, a logical network structure is constructed between adjacent hierarchical reserves. When the upper level reserve needs capacity replenishment, all EV clusters of the lower level reserve will be mobilized to participate, and the lower level will be guided to fill the capacity for the upper level reserve according to the optimized strategy. Each wheel control process will be completed within a scheduling period, including the capacity judgment of the upper level reserve, the feedback to the lower level reserve, and the time required for the lower level reserve to respond. From the perspective of capacity, in the hierarchical reserve mechanism, the capacity of the upper level reserve is often greater than that of the lower level reserve.

[0116] In this embodiment, in the EV cluster hierarchical backup strategy, when a cluster loses its credibility, other clusters can be used to make up for its lost capacity. This strategy effectively improves the actual supply capacity of the backup capacity, thereby enhancing the credibility and stability of the backup capacity. In this way, market participants can more reliably obtain the promised backup capacity, making the market operation more stable and reliable. The objective function of maximizing the net risk benefit of the aggregator is introduced to provide a more optimized solution for the economic benefits of electric vehicle aggregators in the backup market.

[0117] In order to provide strong support for the embodiments of the present application, the method proposed in the present application was experimentally verified.

[0118] In one implementation, based on the above example, the electric vehicle cluster can be divided into two levels of backup. Exemplary, the impact of the hierarchical backup strategy based on historical credibility on the net risk return of the aggregator. The electric vehicle (EV) cluster is divided into primary and secondary backup resources according to historical credibility, and five historical credibility standards are set: 0.5, 0.6, 0.7, 0.8 and 0.9, such as Figure 6 As shown in the figure, the secondary reserve capacity increases with the increase of historical credibility standards, which are 46.7596MW, 64.5801MW, 80.3491MW, 94.2123MW, and 110.2891MW respectively. The net risk income of the collector increases when the historical credibility standard is 0.5 to 0.8, with the values ​​of 14.8351 million yuan, 15.8739 million yuan, 16.79315 million yuan, and 17.60128 million yuan respectively, and decreases to 16.99591 million yuan when the historical credibility standard is 0.9. Figure 6 This shows that improving the historical credibility standard can initially increase the net risk return of the collector, but after exceeding a certain threshold, the net risk return decreases. Selecting an appropriate historical credibility classification standard is crucial to maximizing the economic benefits of the collector and ensuring the effective allocation of backup resources. Since the first-level backup default capacity affects the reliability as the second-level backup capacity changes, the first-level backup default capacity is analyzed, such as Figure 7As shown in the figure, as the secondary reserve capacity increases, the primary reserve default capacity decreases significantly. When the secondary reserve capacity increases from 0MW to 94.2123MW, the primary reserve default capacity decreases from 89.3165MW to -11.7383MW. This shows that increasing the secondary reserve capacity can effectively reduce the potential risk of the primary reserve, and redundancy may occur under high-capacity reserve configuration. Figure 8 The results show the supplementation of the default capacity of the primary reserve and the redundant capacity generated under the optimal configuration of the secondary reserve. The green part represents the redundant capacity, and the light yellow part represents the total amount of default capacity supplemented by the primary reserve cluster. The results highlight the importance of properly configuring the secondary reserve resources, which can not only reduce the burden of the primary reserve, but also provide additional safety reserves for the power system when the system capacity allows. Fig. 9 The effect of the grading strategy was analyzed. Fig. 9 In the results, the final average risk-net return of the hierarchical strategy based on historical credibility reached 17.60128 million yuan, and the average returns of the two random hierarchical strategies were 16.97794 million yuan and 17.40261 million yuan, respectively. The non-classified strategy, the hierarchical strategy based on historical credibility, and the two random hierarchical strategies were compared. The results show that when the hierarchical strategy is not adopted, the risk-net return is a horizontal line, because all clusters participate in the market bidding under the same conditions and there is no optimization of capacity allocation coefficients. In contrast, the implementation of the hierarchical strategy significantly improves the risk-net return, verifying its effectiveness in optimizing resource allocation and enhancing economic benefits. Significantly, the strategy based on historical credibility performs well in improving risk-net return, outperforming the non-classified and other random strategies. This strategy uses historical data to accurately formulate market strategies and optimizes the allocation of spare resources through data analysis, highlighting its unique advantages in enhancing the economic benefits of cluster operators. These results demonstrate the obvious advantages of the hierarchical strategy based on historical credibility over other strategies and emphasize the key role of accurate data analysis in formulating effective market strategies.

[0119] In one implementation, based on the above embodiments, a cooperation mechanism among aggregators is proposed. For example, aggregators can establish a partnership to share spare capacity to ensure the stable operation of the spare market. By sharing spare capacity, when one aggregator fails to provide the promised spare capacity, other aggregators can provide additional spare capacity to avoid the occurrence of spare default.

[0120] This implementation method not only helps to improve the credibility of the backup capacity by introducing a cooperation mechanism between electricity collectors, but also promotes cooperation and mutual benefit between electricity collectors, thereby enhancing the stability of the entire market.

[0121] In one embodiment, based on the above-mentioned embodiments, cross-regional spare capacity trading, cross-energy interconnection spare, cross-market multi-objective optimization and energy market participation are proposed. Among them, cross-regional spare capacity trading can be understood as different regions may face different spare capacity demands and supply situations, and electric vehicle aggregators can use EV cluster spare strategies to trade spare capacity between different regions. Cross-energy interconnection spare can be understood as electric vehicle aggregators can cooperate with other energy participants to incorporate renewable energy and energy storage facilities into the reserve market. Cross-market multi-objective optimization can be understood as in addition to maximizing economic benefits, this application can also be expanded to a multi-objective optimization problem that considers other objectives. For example, while considering economic benefits, it can optimize the credibility of spare capacity, reduce carbon emissions, increase energy sustainability, etc. Energy market participation can be understood as applying the method of this application to the energy market to improve the participation and credibility of electric vehicle aggregators in the energy market.

[0122] In this implementation, by proposing cross-regional reserve capacity transactions, effective allocation of reserve resources between regions can be achieved, the risk of insufficient reserve capacity can be reduced, and the supply reliability of the overall market can be improved. At the same time, cross-regional reserve capacity transactions can also help optimize the operation of power systems in different regions and improve the utilization rate of clean energy. Through cross-energy reserves, the complementarity between different energy sources can be better utilized to achieve a diversified supply of reserve capacity. This not only helps to improve the credibility of reserve capacity, but also promotes the overall sustainable development of clean energy. Through cross-market multi-objective optimization and energy market participation, electric vehicle aggregators can participate in the energy market more flexibly, provide power output or energy storage services according to market demand, and achieve effective management and optimization of energy.

[0123] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the scheduling method of the electric vehicle cluster of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0124] This application also provides a dispatching device for electric vehicle clusters, please refer to Fig.10 , the dispatching device of the electric vehicle cluster includes:

[0125] A preliminary classification module 10, for classifying electric vehicle clusters according to historical credibility indicators and determining initial spare capacity of clusters at each level, wherein the historical credibility indicators are used to measure the credit performance of electric vehicle clusters in providing spare capacity in the past;

[0126] The hierarchical optimization module 20 is used to input the initial spare capacity of the clusters at each level into the bidding strategy optimization model, optimize the initial spare capacity of the clusters at each level by an optimization algorithm, and obtain the target spare capacity of the clusters at each level. The bidding strategy optimization model is used to maximize the net risk benefit.

[0127] The cluster scheduling module 30 is used to schedule the electric vehicle cluster according to the target spare capacity of the clusters at each level.

[0128] The dispatching device of the electric vehicle cluster provided by the present application adopts the dispatching method of the electric vehicle cluster in the above embodiment, which can solve the technical problem of low reliability of the electric vehicle cluster as a backup resource. Compared with the prior art, the beneficial effects of the dispatching device of the electric vehicle cluster provided by the present application are the same as the beneficial effects of the dispatching method of the electric vehicle cluster provided by the above embodiment, and other technical features in the dispatching device of the electric vehicle cluster are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0129] The present application provides a scheduling device for an electric vehicle cluster, the scheduling device for an electric vehicle cluster comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the scheduling method for the electric vehicle cluster in the above-mentioned embodiment one.

[0130] Reference below Fig.11 , which shows a schematic diagram of the structure of the dispatching device of the electric vehicle cluster suitable for implementing the embodiment of the present application. The dispatching device of the electric vehicle cluster in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig.11 The dispatching device of the electric vehicle cluster shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0131] like Fig.11As shown, the dispatching device of the electric vehicle cluster may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the dispatching device of the electric vehicle cluster are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the dispatching device of the electric vehicle cluster to communicate with other devices wirelessly or by wire to exchange data. Fig.11 , a dispatching device of an electric vehicle cluster having various systems is shown, but it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented or possessed alternatively.

[0132] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0133] The dispatching device of the electric vehicle cluster provided by the present application adopts the dispatching method of the electric vehicle cluster in the above embodiment, which can solve the technical problem of low reliability of the electric vehicle cluster as a backup resource. Compared with the prior art, the beneficial effects of the dispatching device of the electric vehicle cluster provided by the present application are the same as the beneficial effects of the dispatching method of the electric vehicle cluster provided by the above embodiment, and the other technical features in the dispatching device of the electric vehicle cluster are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0134] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0135] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0136] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the scheduling method for electric vehicle clusters in the above-mentioned embodiment.

[0137] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0138] The computer-readable storage medium may be included in the dispatching device of the electric vehicle cluster; or may exist independently without being assembled into the dispatching device of the electric vehicle cluster.

[0139] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the scheduling device of the electric vehicle cluster, the scheduling device of the electric vehicle cluster: classifies the electric vehicle clusters according to the historical credibility index, and determines the initial spare capacity of each level of clusters, wherein the historical credibility index is used to measure the credit performance of the electric vehicle cluster when providing spare capacity in the past; inputs the initial spare capacity of the clusters at each level into the bidding strategy optimization model, optimizes the initial spare capacity of the clusters at each level through the optimization algorithm, and obtains the target spare capacity of the clusters at each level, wherein the bidding strategy optimization model is used to maximize the net risk return; and schedules the electric vehicle cluster according to the target spare capacity of the clusters at each level.

[0140] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0141] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0142] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0143] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned dispatching method for electric vehicle clusters, and can solve the technical problem of low reliability of electric vehicle clusters as backup resources. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the dispatching method for electric vehicle clusters provided in the above-mentioned embodiments, and will not be elaborated here.

[0144] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned scheduling method for electric vehicle clusters when executed by a processor.

[0145] The computer program product provided by the present application can solve the technical problem of low reliability of electric vehicle clusters as backup resources. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the scheduling method of the electric vehicle cluster provided by the above embodiment, which will not be repeated here.

[0146] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for dispatching electric vehicle clusters, characterized in that: The method includes: Classifying electric vehicle clusters according to historical credibility indicators and determining initial backup capacity for clusters at each level, wherein the historical credibility indicators are used to measure the credit performance of electric vehicle clusters in providing backup capacity in the past; Inputting the initial spare capacity of the clusters at each level into the bidding strategy optimization model, optimizing the initial spare capacity of the clusters at each level by an optimization algorithm, and obtaining the target spare capacity of the clusters at each level, wherein the bidding strategy optimization model is used to maximize the net risk return; The electric vehicle cluster is dispatched according to the target spare capacity of the clusters at each level.

2. The method according to claim 1, characterized in that The step of grading the electric vehicle clusters according to the historical credibility index and determining the initial spare capacity of each level of clusters includes: Determining the historical credibility indicator by calculating the proportion of the electric vehicle cluster successfully responding to the backup call in the past; The historical credibility indicators are sorted from large to small, and the electric vehicle clusters are correspondingly divided into one or more levels to obtain a hierarchical backup framework for electric vehicle clusters; Based on the electric vehicle cluster hierarchical reserve framework, the initial reserve capacity of each level of cluster is determined according to a preset capacity allocation coefficient.

3. The method according to claim 2, characterized in that The bidding strategy optimization model includes an objective function mathematical model. Before the step of inputting the initial spare capacity of the clusters at each level into the bidding strategy optimization model, the step includes: Constructing the initial function mathematical model; Based on the initial spare capacity of the clusters at each level, construct constraint conditions; The initial function mathematical model is updated according to the constraint conditions to obtain the objective function mathematical model.

4. The method according to claim 3, characterized in that The step of constructing the initial function mathematical model comprises: Calculate deterministic revenue based on declared capacity, declared price and capacity cost; Calculate the uncertainty benefit based on the call benefit, breach of trust cost and breach of trust penalty; Based on the deterministic benefit and the uncertain benefit, the initial function mathematical model is constructed.

5. The method according to claim 4, characterized in that The step of inputting the initial spare capacity of the clusters at each level into the bidding strategy optimization model, optimizing the initial spare capacity of the clusters at each level by an optimization algorithm, and obtaining the target spare capacity of the clusters at each level includes: Inputting the initial spare capacity of the clusters at each level into the objective function mathematical model, and using multiple optimization algorithms for parallel processing to obtain a risk net return result interval; Selecting an optimal solution in the result interval, wherein the optimal solution corresponds to the maximum value of the net risk return; Optimizing the preset capacity allocation coefficient according to the maximum value of the risk net benefit to determine the target capacity allocation coefficient; The target spare capacity of the clusters at each level is determined according to the target capacity allocation coefficient.

6. The method according to any one of claims 1 to 5, characterized in that The historical credibility index is expressed as: In the formula, H i is the historical credibility index; R it is a binary variable, indicating whether the i-th cluster successfully responded to the backup call at time t, with successful response recorded as 1 and no response recorded as 0; T is the total number of calls in the evaluation period.

7. A management device for electric vehicle clusters, characterized in that: The device comprises: A preliminary classification module, used to classify electric vehicle clusters according to historical credibility indicators and determine the initial spare capacity of clusters at each level, wherein the historical credibility indicators are used to measure the credit performance of electric vehicle clusters in providing spare capacity in the past; A hierarchical optimization module, used for inputting the initial spare capacity of the clusters at each level into a bidding strategy optimization model, optimizing the initial spare capacity of the clusters at each level by an optimization algorithm, and obtaining the target spare capacity of the clusters at each level, wherein the bidding strategy optimization model is used for maximizing the net risk return; The cluster scheduling module is used to schedule the electric vehicle cluster according to the target spare capacity of the clusters at each level.

8. A management device for electric vehicle clusters, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for managing an electric vehicle cluster according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for managing an electric vehicle cluster as claimed in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method for managing an electric vehicle cluster according to any one of claims 1 to 6 are implemented.

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