Method, device and equipment for stabilizing impact load of electric vehicle and medium

Abstract

The invention discloses an electric vehicle impact load stabilizing method, device and equipment and a medium, and relates to the technical field of computers, the charge state of an electric vehicle, the selection strategy of a target aggregator and a hypergraph structure comprising the target aggregator of each region are set; calculating the average charging payment price of the target aggregator; constructing a game matrix of the target aggregator under different selection strategies; calculating an expected payment price; determining the number of current iterations, randomly selecting other aggregators, judging whether the selection strategies of the other aggregators are consistent with the selection strategy of the target aggregator, and if not, updating the selection strategy of the target aggregator based on the expected payment price; and judging whether the current number of iterations is smaller than the maximum number of iterations, if the current number of iterations is not smaller than the maximum number of iterations, calculating the impact load of the electric vehicle based on the updated selection strategy, and stabilizing the impact load of the electric vehicle by using a game strategy, thereby effectively relieving and coping with the impact load caused by the electric vehicle.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, device, equipment and medium for smoothing impact load of an electric vehicle. Background Art

[0002] With the large-scale popularization of electric vehicles, the management of electric vehicles by electric vehicle aggregators has gradually become more intelligent. After parking, electric vehicle users only need to connect to the charging socket, and the aggregator's backend system will choose the night off-peak period for charging. However, this speculative charging arbitrage behavior of aggregators may cause a large number of electric vehicles to charge at the same time, thus generating impact load at a certain moment. This not only increases the burden on the power grid, but may also have a negative impact on the price mechanism of the power market and the stability of the power grid.

[0003] As can be seen from the above, how to achieve the smoothing of the impact load of electric vehicles and effectively alleviate and deal with the impact load caused by electric vehicles is a problem to be solved in the field. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for smoothing the impact load of electric vehicles, which can achieve the smoothing of the impact load of electric vehicles and effectively alleviate and deal with the impact load caused by electric vehicles. The specific scheme is as follows:

[0005] In a first aspect, the present application discloses a method for smoothing an impact load of an electric vehicle, comprising:

[0006] Setting the state of charge of the electric vehicle, the selection strategy of the target aggregator, and the hypergraph structure including the target aggregators in each region; the types of the selection strategy include speculative strategy and non-speculative strategy; the hypergraph structure includes a first hypergraph structure and a second hypergraph structure; the nodes in the first hypergraph structure are the set of all target aggregators; the nodes in the second hypergraph structure are the set of target aggregators under the speculative strategy;

[0007] Calculate the average charging payment price of the corresponding target aggregator based on the type of the selection strategy, the state of charge, and the hypergraph structure;

[0008] Using the average charging payment price, construct a game matrix of the target aggregator under different selection strategies;

[0009] Calculating the expected payment price of the target aggregator in the game matrix under different selection strategies;

[0010] Determine the current iteration number, randomly select other aggregators within the same area as the target aggregator in the hypergraph structure, and determine whether the selection strategy of the other aggregator is consistent with that of the target aggregator. If the selection strategy of the other aggregator is inconsistent with that of the target aggregator, update the selection strategy of the target aggregator based on the expected payment price to obtain the updated selection strategy;

[0011] Determine whether the current iteration number is less than the maximum iteration number. If the current iteration number is not less than the maximum iteration number, calculate the impact load of the electric vehicle based on the updated selection strategy, and suppress the impact load using the game strategy.

[0012] Optionally, calculating the average charging payment price of the corresponding target aggregator based on the type of the selection strategy, the state of charge, and the hypergraph structure includes:

[0013] If the type of the selection strategy is a speculative strategy, calculate the average charging payment price of the corresponding target aggregator using the first average charging payment price calculation formula;

[0014] If the type of the selection strategy is a non-speculative strategy, calculate the average charging payment price of the corresponding target aggregator under the preset high state of charge, the preset medium state of charge, and the preset low state of charge using the second average charging payment price calculation formula or the third average charging payment price calculation formula or the fourth average charging payment price calculation formula.

[0015] Optionally, the first average charging payment price calculation formula is:

[0016] ;

[0017] Wherein, is the first average charging payment price, is the charging electricity price, is the rated power of the electric vehicle battery, and SoC is the state of charge of the electric vehicle;

[0018] The second average charging payment price calculation formula is:

[0019] ;

[0020] Wherein, is the second average charging payment price, is the charging electricity price spread after exiting speculation;

[0021] The third average charging payment price calculation formula is:

[0022] ;

[0023] Among them, is the third average charging payment price, is the charging completion ratio, is the anxiety factor, and ;

[0024] The calculation formula for the fourth average charging payment price is:

[0025] ;

[0026] Among them, is the fourth average charging payment price, is the probability of the charging payment of the target aggregator under the non-speculative strategy.

[0027] Optionally, calculating the expected payment price of the target aggregator in different selection strategies in the game matrix includes:

[0028] If the type of the selection strategy of the target aggregator is a non-speculative strategy, the calculation formula for the expected payment price is:

[0029] ;

[0030] Among them, is the non-speculative expected payment price corresponding to the type of the selection strategy of the target aggregator being a non-speculative strategy, is the proportion of other aggregators in the same area as the target aggregator that choose the speculative strategy, , , are the average charging payment prices corresponding to the electric vehicle being in the preset high state of charge, the preset medium state of charge, and the preset low state of charge respectively when the types of the selection strategies of the target aggregator and other aggregators are both non-speculative strategies, , , are the average charging payment prices corresponding to the electric vehicle being in the preset high state of charge, the preset medium state of charge, and the preset low state of charge respectively when the type of the selection strategy of the target aggregator is a non-speculative strategy and the type of the selection strategy of other aggregators is a speculative strategy, and are the proportions of the preset high state of charge and the preset low state of charge in the electric vehicles aggregated by other aggregators respectively;

[0031] If the type of the selection strategy of the target aggregator is a speculative strategy, the calculation formula for the expected payment price is:

[0032] ;

[0033] Among them, is the speculative expected payment corresponding to the type of the selection strategy of the target aggregator being the speculative strategy, , , are respectively the average charging payment prices corresponding to the electric vehicle being in the preset high state of charge, the preset medium state of charge, and the preset low state of charge when the type of the selection strategy of the target aggregator is the speculative strategy and the types of the selection strategies of other aggregators are non-speculative strategies, , , are respectively the average charging payment prices corresponding to the electric vehicle being in the preset high state of charge, the preset medium state of charge, and the preset low state of charge when the types of the selection strategies of both the target aggregator and other aggregators are speculative strategies.

[0034] Optionally, if the selection strategy of the other aggregator is inconsistent with the selection strategy of the target aggregator, then updating the selection strategy of the target aggregator based on the expected payment price includes:

[0035] If the selection strategy of the other aggregator is inconsistent with the selection strategy of the target aggregator, then use the Fermi golden rule and update the selection strategy of the target aggregator based on the expected payment price.

[0036] Optionally, if the selection strategy of the other aggregator is inconsistent with the selection strategy of the target aggregator, then using the Fermi golden rule and updating the selection strategy of the target aggregator based on the expected payment price includes:

[0037] If the type of the selection strategy of the target aggregator is the speculative strategy and the type of the selection strategy of the other aggregator is the non-speculative strategy, then determine whether the expected payment price of the other aggregator under the non-speculative strategy is greater than the expected payment price of the target aggregator under the speculative strategy;

[0038] If the expected payment price of the other aggregator under the non-speculative strategy is greater than the expected payment price of the target aggregator under the speculative strategy, then use the Fermi golden rule to update the expected payment price of the target aggregator under the speculative strategy;

[0039] If the type of the selection strategy of the target aggregator is the non-speculative strategy and the type of the selection strategy of the other aggregator is the speculative strategy, then determine whether the expected payment price of the other aggregator under the speculative strategy is greater than the expected payment price of the target aggregator under the non-speculative strategy;

[0040] If the expected payment price of other aggregators under the speculative strategy is greater than the expected payment price of the target aggregator under the non-speculative strategy, the Fermi golden rule is used to update the expected payment price of the target aggregator under the non-speculative strategy.

[0041] Optionally, the formula for calculating the impact load of an electric vehicle is:

[0042] ;

[0043] Where, is the impact load, m is the number of aggregators that choose the speculative strategy, is the number of hyperedges associated with node i in the speculative hypergraph.

[0044] In a second aspect, the present application discloses a device for suppressing the impact load of an electric vehicle, including:

[0045] A setting module for setting the state of charge of the electric vehicle, the selection strategy of the target aggregator, and the hypergraph structure including the target aggregators in each region; the types of the selection strategy include a speculative strategy and a non-speculative strategy; the hypergraph structure includes a first hypergraph structure and a second hypergraph structure; the nodes in the first hypergraph structure are the set of all target aggregators; the nodes in the second hypergraph structure are the set of target aggregators under the speculative strategy;

[0046] A first calculation module for calculating the average charging payment price of the corresponding target aggregator based on the type of the selection strategy, the state of charge, and the hypergraph structure;

[0047] A game matrix construction module for constructing a game matrix of the target aggregator under different selection strategies using the average charging payment price;

[0048] A second calculation module for calculating the expected payment price of the target aggregator in the game matrix under different selection strategies;

[0049] A selection strategy update module for determining the current iteration number, randomly selecting other aggregators in the same region as the target aggregator from the hypergraph structure, and determining whether the selection strategy of the other aggregator is the same as the selection strategy of the target aggregator. If the selection strategy of the other aggregator is different from the selection strategy of the target aggregator, the selection strategy of the target aggregator is updated based on the expected payment price to obtain an updated selection strategy;

[0050] The impact load suppression module is used to determine whether the current iteration number is less than the maximum iteration number. If the current iteration number is not less than the maximum iteration number, the impact load of the electric vehicle is calculated based on the updated selection strategy, and the game strategy is used to suppress the impact load.

[0051] In a third aspect, the present application discloses an electronic device, including:

[0052] A memory for storing a computer program;

[0053] A processor for executing the computer program to implement the aforementioned method for suppressing the impact load of an electric vehicle.

[0054] In a fourth aspect, the present application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the aforementioned method for suppressing the impact load of an electric vehicle are implemented.

[0055] It can be seen that the present application provides a method for suppressing the impact load of electric vehicles, including setting the state of charge of electric vehicles, the selection strategy of target aggregators, and a hypergraph structure including target aggregators in each region; the types of the selection strategy include a speculative strategy and a non-speculative strategy; the hypergraph structure includes a first hypergraph structure and a second hypergraph structure; the nodes in the first hypergraph structure are the set of all target aggregators; the nodes in the second hypergraph structure are the set of target aggregators under the speculative strategy; calculating the average charging payment price of the corresponding target aggregators based on the type of the selection strategy, the state of charge, and the hypergraph structure; constructing a game matrix of the target aggregators under different selection strategies by using the average charging payment price; calculating the expected payment price of the target aggregators in the game matrix under different selection strategies; determining the current iteration number, and randomly selecting other aggregators from the same region as the target aggregator in the hypergraph structure, and judging whether the selection strategy of the other aggregators is consistent with the selection strategy of the target aggregator. If the selection strategy of the other aggregators is not consistent with the selection strategy of the target aggregator, then updating the selection strategy of the target aggregator based on the expected payment price to obtain an updated selection strategy; judging whether the current iteration number is less than the maximum iteration number. If the current iteration number is not less than the maximum iteration number, then calculating the impact load of the electric vehicle based on the updated selection strategy, and suppressing the impact load by using a game strategy. The present application proposes a method for suppressing the impact load of electric vehicles. Aiming at the problem of impact load caused by the arbitrage behavior of electric vehicle aggregators that concentrate a large number of electric vehicles for speculative high-power charging during low electricity price periods, first, the state of charge of electric vehicles, the selection strategy of target aggregators, and a hypergraph structure including target aggregators in each region are set, and then the average charging payment price of the target aggregators is calculated, and a game matrix is constructed, which can depict the evolution process of the strategy selection of the target aggregators, calculate the expected payment price of the target aggregators under different selection strategies, determine the current iteration number, and randomly select other aggregators from the same region as the target aggregator in the hypergraph structure, and judge whether the selection strategy of the other aggregators is consistent with the selection strategy of the target aggregator. If not, then update the selection strategy of the target aggregator; judge whether the current iteration number is less than the maximum iteration number. If not less, then calculate the impact load of the electric vehicle, and suppress the impact load by using a game strategy, which can effectively alleviate and cope with the impact load caused by electric vehicles. Description of the Drawings

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.

[0057] Figure 1 Flowchart of a method for suppressing the impact load of an electric vehicle disclosed in this application;

[0058] Figure 2 Specific flowchart for realizing the suppression of the impact load of an electric vehicle disclosed in this application;

[0059] Figure 3 Relationship diagram between the number of speculative aggregators and the impact load disclosed in this application;

[0060] Figure 4 Relationship diagram between the expected payment and the steady-state solution disclosed in this application;

[0061] Figure 5 Structure schematic diagram of a device for suppressing the impact load of an electric vehicle disclosed in this application;

[0062] Figure 6 Structure diagram of an electronic device provided by this application. Detailed implementation manners

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0064] With the large-scale popularization of electric vehicles, the management of electric vehicles by aggregators is gradually becoming more intelligent. After an electric vehicle user parks and simply plugs into the charging socket, the aggregator's back-end system will select the low-demand period at night for charging. However, this arbitrage behavior of speculative charging by aggregators may cause a large number of electric vehicles to charge simultaneously at the same time, resulting in an impact load at a certain moment. This not only increases the burden on the power grid but also may have a negative impact on the price mechanism and grid stability of the power market. As can be seen from the above, how to suppress the impact load of electric vehicles and effectively alleviate and cope with the impact load caused by electric vehicles is a problem to be solved in this field.

[0065] See Figure 1As shown in the figure, an embodiment of the present invention discloses a method for suppressing the impact load of an electric vehicle, which may specifically include:

[0066] Step S11: Set the state of charge of the electric vehicle, the selection strategy of the target aggregator, and the hypergraph structure including the target aggregators in each region; the types of the selection strategy include a speculative strategy and a non-speculative strategy; the hypergraph structure includes a first hypergraph structure and a second hypergraph structure; the nodes in the first hypergraph structure are the set of all target aggregators; the nodes in the second hypergraph structure are the set of target aggregators under the speculative strategy.

[0067] In this embodiment, setting the initial state of charge of the electric vehicle includes preset high state of charge, preset medium state of charge, and preset low state of charge; the types of the selection strategy of the aggregator include a speculative strategy and a non-speculative strategy; the hypergraph structure includes a first hypergraph structure and a second hypergraph structure, where the structure of the first hypergraph structure always remains unchanged, while the structure of the second hypergraph structure is dynamically adjusted with the change of the aggregator strategy selection.

[0068] The first hypergraph structure G T Consists of M nodes and N hyperedges, where the nodes represent the set of all aggregator entities. This hypergraph can be represented by a fixed M×N incidence matrix A T Indicates. If the aggregator Is in region g, then the matrix element ; Otherwise, .

[0069] The second hypergraph structure G S Consists of m nodes and N hyperedges, where the nodes represent the set of speculative aggregator entities. This hypergraph can be represented by an m×N incidence matrix A S Indicates. If the speculative aggregator Is in region g, then the matrix element ; Otherwise, .

[0070] Step S12: Calculate the average charging payment price of the corresponding target aggregator based on the type of the selection strategy, the state of charge, and the hypergraph structure.

[0071] In this embodiment, if the type of the selection strategy is a speculative strategy, the average charging payment price of the corresponding target aggregator is calculated using the first average charging payment price calculation formula; if the type of the selection strategy is a non-speculative strategy, the average charging payment price of the corresponding target aggregator under the preset high state of charge, preset medium state of charge, and preset low state of charge is calculated using the second average charging payment price calculation formula or the third average charging payment price calculation formula or the fourth average charging payment price calculation formula.

[0072] Furthermore, the calculation formula for the first average charging payment price is:

[0073] ;

[0074] wherein, is the first average charging payment price, is the charging electricity price, is the rated power of the electric vehicle battery, and SoC is the state of charge of the electric vehicle;

[0075] The calculation formula for the second average charging payment price is:

[0076] ;

[0077] wherein, is the second average charging payment price, is the charging electricity price spread after exiting speculation;

[0078] The calculation formula for the third average charging payment price is:

[0079] ;

[0080] wherein, is the third average charging payment price, is the charging completion ratio, is the anxiety factor, and ;

[0081] The calculation formula for the fourth average charging payment price is:

[0082] ;

[0083] wherein, is the fourth average charging payment price, is the probability of the target aggregator's charging payment under the non-speculative strategy;

[0084] Considering that a large number of aggregators adopt the non-speculative strategy, which may trigger new impact loads during other low electricity price periods, assume is positively correlated with its co-membership degree in the speculative hypergraph, and is calculated as follows:

[0085] ;

[0086] wherein, is the proportion of members who choose the speculative strategy among all co-members of aggregator , which is the steady-state solution when the game system evolves to stability, ; and are respectively aggregators The number of co - members in the topological hyper - graph and the speculative hyper - graph.

[0087] In this embodiment, when the aggregator adopts a speculative strategy, regardless of the state of charge of the electric vehicles it aggregates, the average charging payment price of the corresponding target aggregator is calculated using the first average charging payment price calculation formula; when the aggregator adopts a non - speculative strategy, according to three different situations of the preset high state of charge, the preset medium state of charge, and the preset low state of charge, the average charging payment of the aggregator is calculated by the following formula in a gradient manner:

[0088] ;

[0089] Wherein, and are both threshold values of the preset high state of charge and the preset low state of charge;

[0090] If the electric vehicle is in a high state of charge, the electric vehicle can be regarded as a transferable load, and the power grid dispatching department can transfer it to a low - price period for charging, thereby reducing the charging payment of the aggregator. Compared with the speculative strategy, the payment at this time is less. At this time, the second average charging payment price calculation formula is used for calculation.

[0091] If the electric vehicle is in the preset low state of charge, even if it is transferred to a low - price period for charging, the electric vehicle faces the risk of incomplete charging. Since incomplete charging may cause travel anxiety the next day, the charging payment in this case includes the charging cost and the anxiety cost. The charging payment in this situation is higher than that in the speculative strategy. At this time, the third average charging payment price calculation formula is used for calculation.

[0092] Step S13: Construct a game matrix of the target aggregator under different selection strategies by using the average charging payment price.

[0093] In this embodiment, the game matrix is shown in Table 1:

[0094] Table 1

[0095]

[0096] Among them, the first row of the payment matrix represents the payment of the aggregator , and the second row represents the payment of its co - members; when the aggregator and its co - members both choose non - speculative strategies, , , are the average charging payment prices corresponding to the electric vehicle in the preset high state of charge, the preset medium state of charge, and the preset low state of charge respectively when the types of selection strategies of the target aggregator and other aggregators are both non - speculative strategies, , , When the type of the selection strategy of the target aggregator is the non-speculative strategy and the types of the selection strategies of other aggregators are the speculative strategies, the average charging payment prices corresponding to the electric vehicle in the preset high state of charge, the preset medium state of charge, and the preset low state of charge, , , When the type of the selection strategy of the target aggregator is the speculative strategy and the types of the selection strategies of other aggregators are the non-speculative strategies, the average charging payment prices corresponding to the electric vehicle in the preset high state of charge, the preset medium state of charge, and the preset low state of charge, , , When the types of the selection strategies of both the target aggregator and other aggregators are the speculative strategies, the average charging payment prices corresponding to the electric vehicle in the preset high state of charge, the preset medium state of charge, and the preset low state of charge.

[0097] Step S14: Calculate the expected payment prices of the target aggregator in the game matrix under different selection strategies.

[0098] In this embodiment, if the type of the selection strategy of the target aggregator is the non-speculative strategy, the calculation formula for calculating the expected payment price is:

[0099] ;

[0100] Wherein, is the non-speculative expected payment price corresponding to the type of the selection strategy of the target aggregator being the non-speculative strategy, is the proportion of other aggregators in the same area as the target aggregator that choose the speculative strategy, , , are the average charging payment prices corresponding to the electric vehicle in the preset high state of charge, the preset medium state of charge, and the preset low state of charge when the types of the selection strategies of both the target aggregator and other aggregators are the non-speculative strategies, , , are the average charging payment prices corresponding to the electric vehicle in the preset high state of charge, the preset medium state of charge, and the preset low state of charge when the type of the selection strategy of the target aggregator is the non-speculative strategy and the types of the selection strategies of other aggregators are the speculative strategies, and are the proportions of the preset high state of charge and the preset low state of charge in the electric vehicles aggregated by other aggregators respectively;

[0101] If the type of the selection strategy of the target aggregator is a speculative strategy, the calculation formula for the expected payment price is as follows:

[0102] ;

[0103] Wherein, is the speculative expected payment corresponding to the type of the selection strategy of the target aggregator being a speculative strategy, , , are the average charging payment prices corresponding to the electric vehicle being in the preset high state of charge, the preset medium state of charge, and the preset low state of charge, respectively, when the type of the selection strategy of the target aggregator is a speculative strategy and the types of the selection strategies of other aggregators are non-speculative strategies. , , are the average charging payment prices corresponding to the electric vehicle being in the preset high state of charge, the preset medium state of charge, and the preset low state of charge, respectively, when the types of the selection strategies of both the target aggregator and other aggregators are speculative strategies.

[0104] Step S15: Determine the current iteration number, randomly select other aggregators within the same area as the target aggregator from the hypergraph structure, and determine whether the selection strategy of the other aggregator is the same as that of the target aggregator. If the selection strategy of the other aggregator is not the same as that of the target aggregator, update the selection strategy of the target aggregator based on the expected payment price to obtain the updated selection strategy.

[0105] In this embodiment, it is determined whether the selection strategy of the other aggregator is the same as that of the target aggregator. If the selection strategy of the other aggregator is not the same as that of the target aggregator, the selection strategy of the target aggregator is updated using the Fermi golden rule and based on the expected payment price.

[0106] Specifically, if the type of the selection strategy of the target aggregator is a speculative strategy and the type of the selection strategies of other aggregators is a non-speculative strategy, then it is determined whether the expected payment price of other aggregators under the non-speculative strategy is greater than the expected payment price of the target aggregator under the speculative strategy; if the expected payment price of other aggregators under the non-speculative strategy is greater than the expected payment price of the target aggregator under the speculative strategy, then the Fermi golden rule is used to update the expected payment price of the target aggregator under the speculative strategy; if the type of the selection strategy of the target aggregator is a non-speculative strategy and the type of the selection strategies of other aggregators is a speculative strategy, then it is determined whether the expected payment price of other aggregators under the speculative strategy is greater than the expected payment price of the target aggregator under the non-speculative strategy; if the expected payment price of other aggregators under the speculative strategy is greater than the expected payment price of the target aggregator under the non-speculative strategy, then the Fermi golden rule is used to update the expected payment price of the target aggregator under the non-speculative strategy.

[0107] In this embodiment, the strategy is updated according to the Fermi rule based on the payment difference and calculated as follows:

[0108] ;

[0109] where P is the probability that the subject learns the strategies of co-members, and U i and U j are the expected payments of the current subject and its co-members respectively. The parameter k is used to describe the degree of individual rationality. When k → 0, it indicates that the subject's decision-making is completely rational; if k → ∞, it indicates that the subject lacks rationality and can only randomly select and update the strategy.

[0110] Step S16: Determine whether the current iteration number is less than the maximum iteration number. If the current iteration number is not less than the maximum iteration number, then calculate the impact load of the electric vehicle based on the updated selection strategy, and suppress the impact load using the game strategy.

[0111] In this embodiment, the formula for calculating the impact load of the electric vehicle is:

[0112] ;

[0113] ;

[0114] where is the impact load, m is the number of aggregators that select the speculative strategy, and is the number of hyperedges associated with node i in the speculative hypergraph.

[0115] In this embodiment, the specific process of suppressing the impact load of the electric vehicle is as shown in Figure 2As shown below, the steps are as follows: Step 1: Set the state of charge of the electric vehicle, the initial strategy of the target aggregator, and the hypergraph structure; Step 2: Analyze the relationship between the selection strategy of the target aggregator and the state of charge of the electric vehicles it aggregates, and calculate the average charging payment price of the target aggregator; Step 3: Construct the game matrix of the target aggregator under different selection strategies; Step 4: Calculate the expected payment price of the target aggregator under different selection strategies; Step 5: For each target aggregator, randomly select other aggregators in the same area as the target aggregator, and determine whether the selection strategies of other aggregators are consistent with those of the target aggregator. If they are not consistent, update the selection strategy of the target aggregator based on the payment difference according to the Fermi rule. If they are consistent, skip this step; Step 6: Determine whether the current iteration number is less than the maximum iteration number. If it is not less, calculate the impact load. Otherwise, return to Step 3.

[0116] This application aims at the problem of impact load caused by the arbitrage behavior of electric vehicle aggregators that concentrate a large number of electric vehicles for speculative high-power charging during low electricity price periods. First, a hypergraph model of electric vehicle aggregators is constructed to analyze the interaction degree and correlation of aggregators in the hypergraph. Then, within the main body, a dilemma game model is established to calculate the charging payment of aggregators. Among the main bodies, an evolutionary game model is established to depict the evolutionary process of aggregator strategy selection. Finally, simulation analysis is carried out on the impact of key factors on the suppression effect and degree of impact load, providing a solution to effectively alleviate and cope with the problem of impact load caused by electric vehicles.

[0117] In a specific implementation, a certain area is divided into 50 regions (hyperedges), and the number of aggregators is 100 (nodes). It is assumed that in the topological hypergraph, the node degree follows a power-law tail distribution, and the exponential parameter is set to 2. After calculation, the average size of the hyperedges of the first hypergraph structure is 48.1, and the sum of the degrees of each node is 2405, that is, the total impact load is 2405. In addition, it is assumed that the rated power of each electric vehicle is 150 kW, and the high, medium, and low states of charge are 0.7, 0.5, and 0.3 respectively; the charging electricity price is set at 0.3 yuan / kWh, and the charging price difference is 0.05 yuan / kWh; the charging shortage ratio is 0.5, and the anxiety factor is 1.5; the degree of rationality of the main body k = 0.1; at the initial moment, 90% of the aggregators are randomly selected to choose the speculative strategy.

[0118] The high, medium, and low states of charge of electric vehicles are set at 100% respectively, denoted as Scenario 1, Scenario 2, and Scenario 3. In Scenario 1, the impact load is completely suppressed. In Scenario 2, the impact load is effectively alleviated. In Scenario 3, the impact load persists. In addition, there is the same changing trend between the number of speculative aggregators and the impact load. The more the number of speculative aggregators, the greater the impact load. The relationship between the number of speculative aggregators and the impact load is as Figure 3 shown.

[0119] The relationship between the expected payment and the steady-state solution is as follows Figure 4 shown in the figure. The abscissa z* represents the steady-state solution of the system, that is, the proportion of members who choose the speculative strategy among the co-members of the aggregator. The ordinate represents the expected payment of the aggregator. It can be seen that the suppression effect and degree of the electric vehicle impact load are related to the expected payment of the aggregator's choice strategy. In Scenario 1, regardless of the proportion of members who choose the speculative strategy among the co-members of the aggregator, the expected payment of choosing the non-speculative strategy is always lower than that of choosing the speculative strategy. Therefore, the aggregator finally chooses the non-speculative strategy, thus achieving complete suppression of the impact load. On the contrary, in Scenario 3, since the expected payment of choosing the non-speculative strategy is always higher than that of choosing the speculative strategy, the aggregator finally chooses the speculative strategy.

[0120] In this embodiment, the state of charge of the electric vehicle, the selection strategy of the target aggregator, and the hypergraph structure including the target aggregators in each region are set; the types of the selection strategy include a speculative strategy and a non-speculative strategy; the hypergraph structure includes a first hypergraph structure and a second hypergraph structure; the nodes in the first hypergraph structure are the set of all target aggregators; the nodes in the second hypergraph structure are the set of target aggregators under the speculative strategy; based on the type of the selection strategy, the state of charge, and the hypergraph structure, the average charging payment price of the corresponding target aggregator is calculated; the game matrix of the target aggregator under different selection strategies is constructed by using the average charging payment price; the expected payment price of the target aggregator in different selection strategies in the game matrix is calculated; the current iteration number is determined, and other aggregators are randomly selected from the same region as the target aggregator in the hypergraph structure, and it is judged whether the selection strategy of the other aggregator is the same as the selection strategy of the target aggregator. If the selection strategy of the other aggregator is not the same as the selection strategy of the target aggregator, the selection strategy of the target aggregator is updated based on the expected payment price to obtain the updated selection strategy; it is judged whether the current iteration number is less than the maximum iteration number. If the current iteration number is not less than the maximum iteration number, the impact load of the electric vehicle is calculated based on the updated selection strategy, and the impact load is smoothed by using the game strategy. The present application proposes a method for smoothing the impact load of electric vehicles. Aiming at the problem of impact load caused by the arbitrage behavior of electric vehicle aggregators to conduct high-power charging speculatively with a large number of electric vehicles during low electricity price periods, first, the state of charge of the electric vehicle, the selection strategy of the target aggregator, and the hypergraph structure including the target aggregators in each region are set, and then the average charging payment price of the target aggregator is calculated, and the game matrix is constructed, which can depict the evolution process of the target aggregator's strategy selection, calculate the expected payment price of the target aggregator under different selection strategies, determine the current iteration number, and randomly select other aggregators from the same region as the target aggregator in the hypergraph structure, and judge whether the selection strategy of the other aggregator is the same as the selection strategy of the target aggregator. If not, the selection strategy of the target aggregator is updated; it is judged whether the current iteration number is less than the maximum iteration number. If not less, the impact load of the electric vehicle is calculated, and the impact load is smoothed by using the game strategy, which can effectively alleviate and cope with the impact load caused by electric vehicles.

[0121] See Figure 5 As shown, an embodiment of the present invention discloses a device for smoothing the impact load of an electric vehicle, which may specifically include:

[0122] A setting module 11 is configured to set the state of charge of the electric vehicle, the selection strategy of the target aggregator, and a hypergraph structure including target aggregators in each region; the types of the selection strategy include a speculative strategy and a non-speculative strategy; the hypergraph structure includes a first hypergraph structure and a second hypergraph structure; the nodes in the first hypergraph structure are the set of all target aggregators; the nodes in the second hypergraph structure are the set of target aggregators under the speculative strategy;

[0123] A first calculation module 12 is configured to calculate the average charging payment price of the corresponding target aggregator based on the type of the selection strategy, the state of charge, and the hypergraph structure;

[0124] A game matrix construction module 13 is configured to construct a game matrix of the target aggregator under different selection strategies by using the average charging payment price;

[0125] A second calculation module 14 is configured to calculate the expected payment price of the target aggregator in the game matrix under different selection strategies;

[0126] A selection strategy update module 15 is configured to determine the current iteration number, randomly select other aggregators in the same region as the target aggregator from the hypergraph structure, and judge whether the selection strategy of the other aggregator is consistent with the selection strategy of the target aggregator. If the selection strategy of the other aggregator is inconsistent with the selection strategy of the target aggregator, the selection strategy of the target aggregator is updated based on the expected payment price to obtain an updated selection strategy;

[0127] A shock load suppression module 16 is configured to judge whether the current iteration number is less than the maximum iteration number. If the current iteration number is not less than the maximum iteration number, the shock load of the electric vehicle is calculated based on the updated selection strategy, and the shock load is suppressed by using a game strategy.

[0128] In this embodiment, the state of charge of the electric vehicle, the selection strategy of the target aggregator, and the hypergraph structure including the target aggregators in each region are set; the types of the selection strategy include a speculative strategy and a non-speculative strategy; the hypergraph structure includes a first hypergraph structure and a second hypergraph structure; the nodes in the first hypergraph structure are the set of all target aggregators; the nodes in the second hypergraph structure are the set of target aggregators under the speculative strategy; based on the type of the selection strategy, the state of charge, and the hypergraph structure, the average charging payment price of the corresponding target aggregator is calculated; the game matrix of the target aggregator under different selection strategies is constructed by using the average charging payment price; the expected payment price of the target aggregator in different selection strategies in the game matrix is calculated; the current iteration number is determined, and other aggregators are randomly selected from the same region as the target aggregator in the hypergraph structure, and it is judged whether the selection strategy of the other aggregator is the same as that of the target aggregator. If the selection strategy of the other aggregator is not the same as that of the target aggregator, the selection strategy of the target aggregator is updated based on the expected payment price to obtain the updated selection strategy; it is judged whether the current iteration number is less than the maximum iteration number. If the current iteration number is not less than the maximum iteration number, the impact load of the electric vehicle is calculated based on the updated selection strategy, and the game strategy is used to suppress the impact load. The present application proposes a method for suppressing the impact load of electric vehicles. Aiming at the problem of the impact load caused by the arbitrage behavior of electric vehicle aggregators speculatively charging a large number of electric vehicles at a high power during low electricity price periods, first, the state of charge of the electric vehicle, the selection strategy of the target aggregator, and the hypergraph structure including the target aggregators in each region are set, and then the average charging payment price of the target aggregator is calculated, and the game matrix is constructed, which can depict the evolution process of the target aggregator's strategy selection, calculate the expected payment price of the target aggregator under different selection strategies, determine the current iteration number, and randomly select other aggregators from the same region as the target aggregator in the hypergraph structure, and judge whether the selection strategy of the other aggregator is the same as that of the target aggregator. If not, the selection strategy of the target aggregator is updated; it is judged whether the current iteration number is less than the maximum iteration number. If not less, the impact load of the electric vehicle is calculated, and the game strategy is used to suppress the impact load, which can effectively alleviate and cope with the impact load caused by electric vehicles.

[0129] In some specific embodiments, the first calculation module 12 may specifically include:

[0130] The first average charging payment price calculation module is configured to calculate the average charging payment price of the corresponding target aggregator by using the first average charging payment price calculation formula if the type of the selection strategy is a speculative strategy.

[0131] The second average charging payment price calculation module is configured to, if the type of the selected strategy is a non-speculative strategy, calculate the average charging payment price of the target aggregator corresponding to the preset high state of charge, the preset medium state of charge, and the preset low state of charge by using the second average charging payment price calculation formula, the third average charging payment price calculation formula, or the fourth average charging payment price calculation formula.

[0132] In some specific embodiments, the first average charging payment price calculation formula is:

[0133] ;

[0134] Wherein, is the first average charging payment price, is the charging electricity price, is the rated power of the electric vehicle battery, and SoC is the state of charge of the electric vehicle;

[0135] The second average charging payment price calculation formula is:

[0136] ;

[0137] Wherein, is the second average charging payment price, is the charging electricity price spread after exiting speculation;

[0138] The third average charging payment price calculation formula is:

[0139] ;

[0140] Wherein, is the third average charging payment price, is the charging completion ratio, is the anxiety factor, and ;

[0141] The fourth average charging payment price calculation formula is:

[0142] ;

[0143] Wherein, is the fourth average charging payment price, is the probability of the charging payment of the target aggregator under the non-speculative strategy.

[0144] In some specific embodiments, the second calculation module 14 may specifically include:

[0145] The first expected payment price calculation module is configured to, if the type of the selected strategy of the target aggregator is a non-speculative strategy, calculate the formula for the expected payment price as:

[0146] ;

[0147] wherein, is the non-speculative expected payment price corresponding to the type of the selection strategy of the target aggregator being the non-speculative strategy, is the proportion of the speculative strategy selected among other aggregators in the same region as the target aggregator, , , are respectively the average charging payment prices corresponding to the electric vehicle being in the preset high state of charge, preset medium state of charge, and preset low state of charge when the types of the selection strategies of the target aggregator and other aggregators are both non-speculative strategies, , , are respectively the average charging payment prices corresponding to the electric vehicle being in the preset high state of charge, preset medium state of charge, and preset low state of charge when the type of the selection strategy of the target aggregator is the non-speculative strategy and the type of the selection strategy of other aggregators is the speculative strategy, and are respectively the proportions of the preset high state of charge and preset low state of charge among the electric vehicles aggregated by other aggregators;

[0148] If the type of the selection strategy of the target aggregator is the speculative strategy, the calculation formula for the expected payment price is:

[0149] ;

[0150] wherein, is the speculative expected payment corresponding to the type of the selection strategy of the target aggregator being the speculative strategy, , , are respectively the average charging payment prices corresponding to the electric vehicle being in the preset high state of charge, preset medium state of charge, and preset low state of charge when the type of the selection strategy of the target aggregator is the speculative strategy and the type of the selection strategy of other aggregators is the non-speculative strategy, , , are respectively the average charging payment prices corresponding to the electric vehicle being in the preset high state of charge, preset medium state of charge, and preset low state of charge when the types of the selection strategies of the target aggregator and other aggregators are both the speculative strategies.

[0151] In some specific embodiments, the selection strategy update module 15 may specifically include:

[0152] A selection strategy update module for the target aggregator, which is used to update the selection strategy of the target aggregator by using Fermi's golden rule and based on the expected payment price if the selection strategy of the other aggregator is inconsistent with that of the target aggregator.

[0153] In some specific embodiments, the selection strategy update module 15 may specifically include:

[0154] A first judgment module, which is used to judge whether the expected payment price of the other aggregator under the non-speculative strategy is greater than the expected payment price of the target aggregator under the speculative strategy if the type of the selection strategy of the target aggregator is the speculative strategy and the type of the selection strategy of the other aggregator is the non-speculative strategy;

[0155] A first expected payment price update module, which is used to update the expected payment price of the target aggregator under the speculative strategy by using Fermi's golden rule if the expected payment price of the other aggregator under the non-speculative strategy is greater than the expected payment price of the target aggregator under the speculative strategy;

[0156] A second judgment module, which is used to judge whether the expected payment price of the other aggregator under the speculative strategy is greater than the expected payment price of the target aggregator under the non-speculative strategy if the type of the selection strategy of the target aggregator is the non-speculative strategy and the type of the selection strategy of the other aggregator is the speculative strategy;

[0157] A second expected payment price update module, which is used to update the expected payment price of the target aggregator under the non-speculative strategy by using Fermi's golden rule if the expected payment price of the other aggregator under the speculative strategy is greater than the expected payment price of the target aggregator under the non-speculative strategy.

[0158] In some specific embodiments, the formula for calculating the impact load of an electric vehicle is:

[0159] ;

[0160] where is the impact load, m is the number of aggregators that choose the speculative strategy, is the number of hyperedges associated with node i in the speculative hypergraph.

[0161] Figure 6Schematic diagram of the structure of an electronic device provided by an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the method for suppressing the impact load of an electric vehicle executed by the electronic device disclosed in any of the foregoing embodiments.

[0162] In this embodiment, the power supply 23 is used to provide operating voltages for the various hardware devices on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows is any communication protocol applicable to the technical solution of the present application, and no specific limitation is imposed thereon herein; the input / output interface 25 is used to obtain external input data or output data to the outside, and its specific interface type can be selected according to specific application requirements, and no specific limitation is made herein.

[0163] In addition, as a carrier for resource storage, the memory 22 may be a read-only memory, a random access memory, a magnetic disk, or an optical disc, etc., and the resources stored thereon include an operating system 221, a computer program 222, and data 223, etc., and the storage method may be temporary storage or permanent storage.

[0164] Among them, the operating system 221 is used to manage and control the various hardware devices and the computer program 222 on the electronic device 20 to implement the operation and processing of the data 223 in the memory 22 by the processor 21, and it may be Windows, Unix, Linux, etc. In addition to the computer program that can be used to complete the method for suppressing the impact load of an electric vehicle executed by the electronic device 20 disclosed in any of the foregoing embodiments, the computer program 222 may further include a computer program that can be used to complete other specific tasks. In addition to the data that can be transmitted by external devices received by the electric vehicle impact load suppression device, the data 223 may also include data collected by its own input / output interface 25, etc.

[0165] The steps of the method or algorithm described in combination with the embodiments disclosed in this article can be directly implemented by hardware, a software module executed by a processor, or a combination of the two. The software module can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0166] Further, the embodiment of the present application also discloses a computer-readable storage medium. A computer program is stored in the storage medium. When the computer program is loaded and executed by a processor, the method steps for suppressing the impact load of the electric vehicle disclosed in any of the foregoing embodiments are implemented.

[0167] Finally, it should also be noted that in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0168] The method, device, equipment and storage medium for suppressing the impact load of an electric vehicle provided by the present invention have been introduced in detail above. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for smoothing the impact load of an electric vehicle, characterized in that: include: Setting the state of charge of the electric vehicle, the selection strategy of the target aggregator, and the hypergraph structure including the target aggregators in each region; the types of the selection strategy include speculative strategy and non-speculative strategy; the hypergraph structure includes a first hypergraph structure and a second hypergraph structure; the nodes in the first hypergraph structure are the set of all target aggregators; the nodes in the second hypergraph structure are the set of target aggregators under the speculative strategy; Calculate the average charging payment price of the corresponding target aggregator based on the type of the selection strategy, the state of charge, and the hypergraph structure; Using the average charging payment price, construct a game matrix of the target aggregator under different selection strategies; Calculating the expected payment price of the target aggregator in the game matrix under different selection strategies; Determine the current number of iterations, and randomly select other aggregators from the same region as the target aggregator in the hypergraph structure, and determine whether the selection strategy of the other aggregators is consistent with the selection strategy of the target aggregator. If the selection strategy of the other aggregators is inconsistent with the selection strategy of the target aggregator, update the selection strategy of the target aggregator based on the expected payment price to obtain an updated selection strategy; It is determined whether the current number of iterations is less than the maximum number of iterations. If the current number of iterations is not less than the maximum number of iterations, the impact load of the electric vehicle is calculated based on the updated selection strategy, and the impact load is smoothed using a game strategy.

2. The method for smoothing the impact load of an electric vehicle according to claim 1, characterized in that: The calculating the corresponding average charging payment price of the target aggregator based on the type of the selection strategy, the state of charge, and the hypergraph structure includes: If the type of the selected strategy is a speculative strategy, the average charging payment price of the corresponding target aggregator is calculated using the first average charging payment price calculation formula; If the type of strategy selected is a non-speculative strategy, the second average charging payment price calculation formula, the third average charging payment price calculation formula, or the fourth average charging payment price calculation formula is used to calculate the average charging payment price of the target aggregator corresponding to the preset high state of charge, the preset medium state of charge, and the preset low state of charge.

3. The method for smoothing the impact load of an electric vehicle according to claim 2, characterized in that: The first average charging payment price calculation formula is: ； in, The price paid for the first average charge, is the charging electricity price, is the rated power of the electric vehicle battery, and SoC is the state of charge of the electric vehicle; The second average charging payment price calculation formula is: ； in, The price paid for the second average charge, To exit speculation after charging electricity price difference; The third average charging payment price calculation formula is: ； in, The third average charging price paid, is the charging completion ratio, is the anxiety factor, and ; The fourth average charging payment price calculation formula is: ； in, The fourth average charging price is The probability of charging payment for the target aggregator under the non-speculative strategy.

4. The method for smoothing the impact load of an electric vehicle according to claim 1, characterized in that: The calculating the expected payment price of the target aggregator in the game matrix under different selection strategies includes: If the target aggregator's selection strategy type is a non-speculative strategy, the formula for calculating the expected payment price is: ； in, The type of selection strategy for the target aggregator is the non-speculative expected payment price corresponding to the non-speculative strategy, is the proportion of other aggregators in the same region as the target aggregator that choose speculative strategies, , , When the selection strategies of the target aggregator and other aggregators are all non-speculative strategies, the average charging payment price corresponding to the electric vehicle in the preset high state of charge, the preset medium state of charge, and the preset low state of charge, , , The target aggregator’s selection strategy type is non-speculative strategy, and the other aggregators’ selection strategies type is speculative strategy. The corresponding average charging payment price of electric vehicles in the preset high state of charge, the preset medium state of charge, and the preset low state of charge, respectively. and are the proportion of electric vehicles aggregated with other aggregators in the preset high state of charge and the preset low state of charge, respectively; If the target aggregator's selection strategy type is a speculative strategy, the formula for calculating the expected payment price is: ； in, The type of selection strategy for the target aggregator is the speculative expected payoff corresponding to the speculative strategy, , , The target aggregator’s selection strategy type is a speculative strategy, and the other aggregators’ selection strategies type is a non-speculative strategy. The corresponding average charging payment price of the electric vehicle in the preset high state of charge, the preset medium state of charge, and the preset low state of charge, , , The average charging payment prices corresponding to the electric vehicles being in a preset high state of charge, a preset medium state of charge, and a preset low state of charge when the selection strategies of the target aggregator and other aggregators are all speculative strategies.

5. The method for smoothing the impact load of an electric vehicle according to claim 1, characterized in that: If the selection strategy of the other aggregators is inconsistent with the selection strategy of the target aggregator, updating the selection strategy of the target aggregator based on the expected payment price includes: If the selection strategies of the other aggregators are inconsistent with the selection strategy of the target aggregator, the selection strategy of the target aggregator is updated based on the expected payment price using the Fermi Golden Rule.

6. The method for smoothing the impact load of an electric vehicle according to claim 5, characterized in that: If the selection strategy of the other aggregators is inconsistent with the selection strategy of the target aggregator, the selection strategy of the target aggregator is updated based on the expected payment price using the Fermi Golden Rule, including: If the target aggregator's selection strategy is a speculative strategy and the other aggregators' selection strategies are non-speculative strategies, then determine whether the expected payment price of the other aggregators under the non-speculative strategy is greater than the expected payment price of the target aggregator under the speculative strategy; If the expected payment price of other aggregators under the non-speculative strategy is greater than the expected payment price of the target aggregator under the speculative strategy, the expected payment price of the target aggregator under the speculative strategy is updated using the Fermi Golden Rule; If the target aggregator's selection strategy is a non-speculative strategy and the other aggregators' selection strategies are speculative strategies, then determine whether the expected payment price of the other aggregators under the speculative strategy is greater than the expected payment price of the target aggregator under the non-speculative strategy; If the expected payment price of other aggregators under the speculative strategy is greater than the expected payment price of the target aggregator under the non-speculative strategy, the expected payment price of the target aggregator under the non-speculative strategy is updated using the Fermi Golden Rule.

7. The method for smoothing the impact load of an electric vehicle according to any one of claims 1 to 6, characterized in that: The formula for calculating the impact load of electric vehicles is: ； in, is the shock load, m is the number of aggregators that choose speculative strategies, is the number of hyperedges associated with node i in the speculative hypergraph.

8. A device for smoothing impact load of electric vehicles, characterized in that: include: A setting module is used to set the state of charge of the electric vehicle, the selection strategy of the target aggregator, and the hypergraph structure including the target aggregators in each region; the types of the selection strategy include speculative strategy and non-speculative strategy; the hypergraph structure includes a first hypergraph structure and a second hypergraph structure; the nodes in the first hypergraph structure are the set of all target aggregators; the nodes in the second hypergraph structure are the set of target aggregators under the speculative strategy; A first calculation module, configured to calculate an average charging payment price of the corresponding target aggregator based on the type of the selection strategy, the state of charge, and the hypergraph structure; A game matrix construction module, used to construct a game matrix of the target aggregator under different selection strategies using the average charging payment price; A second calculation module is used to calculate the expected payment price of the target aggregator in the game matrix under different selection strategies; A selection strategy update module is used to determine the current number of iterations, and randomly select other aggregators from the same area as the target aggregator in the hypergraph structure, and determine whether the selection strategy of the other aggregators is consistent with the selection strategy of the target aggregator. If the selection strategy of the other aggregators is inconsistent with the selection strategy of the target aggregator, the selection strategy of the target aggregator is updated based on the expected payment price to obtain an updated selection strategy; The impact load smoothing module is used to determine whether the current number of iterations is less than the maximum number of iterations. If the current number of iterations is not less than the maximum number of iterations, the impact load of the electric vehicle is calculated based on the updated selection strategy, and the impact load is smoothed using a game strategy.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor is used to execute the computer program to implement the method for smoothing the impact load of an electric vehicle as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program; wherein, when the computer program is executed by a processor, the method for smoothing the impact load of an electric vehicle as described in any one of claims 1 to 7 is implemented.

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