Charging station load characteristic prediction method and device, equipment and storage medium

Abstract

The invention relates to a charging station load characteristic prediction method and device, equipment and a storage medium. According to the invention, the charging load curve of the charging station is taken as the final charging load curve of the charging station when the total satisfaction degree of the users at the preset charging price of the charging station is maximum by combining the total satisfaction degree of the users of various vehicle types to the charging station after charging at the charging station and the attributes of the charging station; charging behavior characteristics of users of various vehicle types can be fully considered, the accuracy of load characteristic prediction of the charging station is improved, and scientific guidance is provided for operation of the charging station.

Description

Technical Field

[0001] The present disclosure relates to the field of electric power technology, and in particular to a method, device, equipment and storage medium for predicting load characteristics of a charging station. Background Art

[0002] As a green and clean means of transportation and a high-quality alternative to fuel vehicles, new energy vehicles are gradually becoming a new development direction for the future automotive industry. The charging load of new energy vehicles is flexible and the charging behavior of new energy vehicles is relatively random. If the regulation capacity of large-scale new energy vehicle charging load can be fully utilized, it will provide charging station operators with more diversified profit and regulation methods.

[0003] At present, the charging load characteristics of charging stations are mainly estimated through the properties of the charging stations themselves (such as the capacity of charging equipment, the number of charging equipment, etc.) to guide the charging station operators to regulate the operation of the charging stations. However, since only the properties of the charging stations themselves are considered, the charging load characteristics of the charging stations obtained are relatively limited, and the charging load curve reflecting the charging load characteristics of the charging stations is far from the actual ones and is not accurate enough. Summary of the invention

[0004] In order to solve the above technical problems, the present disclosure provides a method, device, equipment and storage medium for predicting load characteristics of a charging station.

[0005] A first aspect of the present disclosure provides a method for predicting load characteristics of a charging station, comprising:

[0006] Based on the distribution law of historical vehicle charging data of the charging station, determine the target charging data of a preset number of target vehicles at the charging station in a preset future period, and the target charging data of the target vehicles meets the distribution law;

[0007] Based on the target charging data, construct an initial charging load curve of the charging station corresponding to a preset number of target vehicles in a preset future period;

[0008] Based on the arrival time of each target vehicle at the charging station, the charging time and the charging fee before and after each target vehicle responds to the preset charging price of the charging station, describe the overall satisfaction of users of a preset number of target vehicles with the charging station at the preset charging price;

[0009] With the goal of maximizing the total satisfaction at a preset charging price, the load parameters in the initial charging load curve are optimized and solved to obtain a modified charging load curve of the charging station. The modified charging load curve has the maximum total satisfaction at the preset charging price.

[0010] A second aspect of the present disclosure provides a charging station load characteristic prediction device, comprising:

[0011] A first determination module is used to determine target charging data of a preset number of target vehicles at a charging station in a preset future period based on a distribution law of historical vehicle charging data of the charging station, and the target charging data of the target vehicles meets the distribution law;

[0012] A construction module, for constructing an initial charging load curve of a charging station corresponding to a preset number of target vehicles in a preset future period based on the target charging data;

[0013] A description module is used to describe the total satisfaction of users of a preset number of target vehicles with the charging station at the preset charging price of the charging station based on the arrival time of each target vehicle at the charging station, the charging time and the charging fee before and after each target vehicle responds to the preset charging price of the charging station;

[0014] The solution module is used to optimize and solve the load parameters in the initial charging load curve with the goal of maximizing the total satisfaction under a preset charging price, and obtain a modified charging load curve of the charging station, wherein the modified charging load curve has the maximum total satisfaction under the preset charging price.

[0015] A third aspect of the present disclosure provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the charging station load characteristic prediction method of the first aspect can be implemented.

[0016] A fourth aspect of the present disclosure provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the charging station load characteristic prediction method of the first aspect can be implemented.

[0017] Compared with the prior art, the technical solution provided by the present invention has the following advantages:

[0018] The present disclosure determines the target charging data of a preset number of target vehicles in a preset future period at a charging station based on the distribution law of the historical vehicle charging data of the charging station, and the target charging data of the target vehicles meets the distribution law; based on the target charging data, constructs the initial charging load curve of the charging station corresponding to the preset number of target vehicles in the preset future period; based on the arrival time period of each target vehicle at the charging station before and after each target vehicle responds to the preset charging price of the charging station, the charging time and charging fee at the charging station, describes the total satisfaction of the users of the preset number of target vehicles with the charging station under the preset charging price; with the maximum total satisfaction under the preset charging price as the goal, optimizes and solves the load parameters in the initial charging load curve to obtain the modified charging load curve of the charging station, and the modified charging load curve has the maximum total satisfaction under the preset charging price. The present disclosure can combine the total satisfaction of users of each vehicle type with the charging station after charging at the charging station and the attributes of the charging station itself, and take the charging load curve of the charging station when the total satisfaction of users under the preset charging price of the charging station is the maximum as the final charging load curve of the charging station, which can fully consider the charging behavior characteristics of users of each vehicle type, improve the accuracy of the prediction of the load characteristics of the charging station, and thus provide scientific guidance for the operation of the charging station. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0020] In order to more clearly illustrate the embodiments of the present invention 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.

[0021] Figure 1 is a flow chart of a method for predicting load characteristics of a charging station provided by an embodiment of the present disclosure;

[0022] Figure 2 is a flow chart of a method for determining target charging data provided by an embodiment of the present disclosure;

[0023] Figure 3 is a flow chart of a comprehensive satisfaction determination method provided by an embodiment of the present disclosure;

[0024] Figure 4 is a flow chart of a method for determining a modified charging load curve provided by an embodiment of the present disclosure;

[0025] Figure 5 It is a structural schematic diagram of a charging station load characteristic prediction device provided by an embodiment of the present disclosure;

[0026] Figure 6 It is a structural diagram of a computer device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0028] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0029] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0030] It should be noted that, in this article, 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 such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0031] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0032] The charging station load characteristic prediction method and other methods provided in the embodiments of the present disclosure can be executed by a computer device, which can be understood as any device with processing and computing capabilities. The device can include but is not limited to mobile terminals such as smart phones, laptops, tablet computers (PADs), and fixed electronic devices such as digital TVs and desktop computers.

[0033] In order to better understand the inventive concept of the embodiments of the present disclosure, the technical solutions of the embodiments of the present disclosure are described below in conjunction with exemplary embodiments.

[0034] Figure 1 is a flow chart of a method for predicting charging station load characteristics provided by an embodiment of the present disclosure. The method can be executed by a computer device, such as Figure 1 As shown, the charging station load characteristic prediction method provided in this embodiment includes the following steps:

[0035] Step 110 : Based on the distribution pattern of historical vehicle charging data of the charging station, determine target charging data of a preset number of target vehicles at the charging station in a preset future time period, and the target charging data satisfies the distribution pattern.

[0036] In the disclosed embodiment, the computer device may determine target charging data for a preset number of target vehicles at the charging station in a preset future time period based on the distribution pattern of historical vehicle charging data at the charging station, wherein the target charging data satisfies the distribution pattern.

[0037] The historical vehicle charging data can be understood as the vehicle charging data of a charging station in a certain historical period (eg, the past year).

[0038] The preset future time period can be understood as a preset future time period, such as the next day, the next week, etc., which can be set as needed and is not limited here.

[0039] The preset number can be set as needed, for example, 100, which is not limited here.

[0040] Step 120: Based on the target charging data, construct an initial charging load curve of the charging station corresponding to a preset number of target vehicles in a preset future time period.

[0041] In the embodiment of the present disclosure, the initial charging load curve of the charging station can be understood as the total average charging power of the charging station in each time period.

[0042] Step 130: Describe the overall satisfaction of users of the preset number of target vehicles with the charging station at the preset charging price based on the arrival time of each target vehicle at the charging station, the charging time and the charging fee before and after each target vehicle responds to the preset charging price of the charging station.

[0043] In the disclosed embodiment, the computer device can describe the overall satisfaction of users of a preset number of target vehicles with the charging station at a preset charging price based on the arrival time of each target vehicle at the charging station, the charging time at the charging station and the charging cost before and after each target vehicle responds to the preset charging price of the charging station.

[0044] The preset charging price is the current charging price set by the charging station operator. The preset charging price can be set by the charging station operator as needed and is not limited here.

[0045] Step 140: With the goal of maximizing the total satisfaction at a preset charging price, the load parameters in the initial charging load curve are optimized and solved to obtain a modified charging load curve of the charging station, and the modified charging load curve maximizes the total satisfaction at the preset charging price.

[0046] In the embodiment of the present disclosure, the load parameter in the initial charging load curve can be understood as the total average charging power of vehicles charged in the charging station corresponding to each time period.

[0047] Therefore, we can combine the total satisfaction of users of each vehicle type with the charging station after charging at the charging station and the attributes of the charging station itself, and use the charging load curve of the charging station when the total satisfaction of users is maximum under the preset charging price of the charging station as the final charging load curve of the charging station. This can fully consider the charging behavior characteristics of users of each vehicle type, improve the accuracy of the prediction of the load characteristics of the charging station, and thus provide scientific guidance for the operation of the charging station.

[0048] In some embodiments of the present disclosure, the computer device may execute the above-mentioned determination of the target charging data of a preset number of target vehicles at the charging station in a preset future period based on the distribution law of the historical vehicle charging data of the charging station. Figure 2 A flowchart of a method for determining target charging data is provided, such as Figure 2 As shown, the target charging data determination method provided in this embodiment includes the following steps:

[0049] Step 210: classify the historical vehicle charging data of the charging station by vehicle type to obtain vehicle charging data corresponding to each vehicle type.

[0050] Vehicle types may include private cars, commercial vehicles, etc.

[0051] Step 220: From the vehicle charging data corresponding to each vehicle type, obtain the arrival time of each vehicle of the vehicle type at the charging station, the battery charge state of each vehicle when arriving at the charging station, the charge amount of each vehicle at the charging station, and the average charging power.

[0052] For example, historical vehicle charging data can be represented as:

[0053] D={(t a (i) SOC a (i), E(i), P(i), v(i)); i∈N}

[0054] Among them, t a (i) is the arrival time of the i-th vehicle at the charging station;

[0055] SOC a (i) is the battery charge state of the i-th vehicle when it arrives at the charging station;

[0056] E(i) is the charging amount of the i-th vehicle coming to the charging station;

[0057] P(i) is the average charging power of the i-th vehicle coming to the charging station;

[0058] v(i) is the vehicle type of the i-th vehicle, which can be divided into two categories: commercial vehicles and private cars;

[0059] N is the number of vehicles.

[0060] Step 230: Based on the arrival times of vehicles of each vehicle type, construct the arrival time distribution characteristics of vehicles of each vehicle type.

[0061] The arrival time distribution characteristics can be understood as the distribution law of arrival time.

[0062] For example, when the vehicle type is a commercial vehicle CV, the corresponding vehicle arrival time dataset can be:

[0063]

[0064] When the vehicle type is a private car PV, the corresponding vehicle arrival time dataset can be:

[0065]

[0066] According to the above data after vehicle type grouping, it is found that the number of vehicles arriving at the station at time t0 is the least. The arrival time of the vehicles is transformed to

[0067]

[0068] Get the arrival time after the time change;

[0069] Assume that the vehicle arrival change time in each vehicle type data Obey a Gaussian distribution with specific parameters, that is, The probability density function of the arrival time distribution characteristics is as follows:

[0070]

[0071] According to the historical vehicle charging data of the charging station, the maximum likelihood estimation method is used to obtain the parameters of the above Gaussian distribution:

[0072] For vehicles of the operating type, the characteristic parameters of the vehicle's arrival time distribution characteristics It can be obtained by the following formula:

[0073]

[0074] Among them, n CV Indicates the number of operating vehicles;

[0075] For private cars, the characteristic parameters of the vehicle's arrival time distribution characteristics are It can be obtained by the following formula:

[0076]

[0077] Among them, n PV Represents the number of private cars.

[0078] Step 240: Based on the battery state of charge of each vehicle type at each arrival time, construct a battery state of charge distribution feature of each vehicle type at each preset arrival time period.

[0079] The preset arrival time period can be set as needed, for example, it can include 0:00-6:00, 6:00-18:00 and 18:00-24:00, which is not limited here.

[0080] The battery state of charge (SOC) is the ratio between the current remaining power of the battery and its capacity when fully charged. This ratio is usually expressed as a percentage, with a value range of 0 to 100%, which intuitively reflects the state of the battery from fully discharged (SOC is 0) to fully charged (SOC is 100%).

[0081] For example, the data of operating vehicles and private cars in the historical vehicle charging data of the charging station are further divided into multiple groups. Here, the arrival time periods are divided into three groups as an example, that is, 0<t a ≤6, 6<t a ≤18, 18<t a ≤24, the historical vehicle charging data is further divided into 6 groups: CV0_6, CV6_18, CV18_24, PV0_6, PV6_18, PV18_24:

[0082] <![CDATA[0<t a ≤6]]> <![CDATA[6<t a ≤18]]> <![CDATA[18<t a ≤24]]> CV CV0_6 CV6_18 CV18_24 PV PV0_6 PV6_18 PV18_24

[0083] The data subset about the vehicle arrival SOC in the above 6 sets of data is defined as follows:

[0084]

[0085] Assume that the vehicle arrival SOC in each set of data above obeys a Gaussian distribution with specific parameters, that is

[0086]

[0087] The SOC probability density functions of the operating vehicles arriving at the charging station from 0:00 to 6:00, 6:00 to 18:00, and 18:00 to 24:00 are:

[0088]

[0089] The SOC probability density functions of private cars arriving at the charging station from 0:00 to 6:00, 6:00 to 18:00, and 18:00 to 24:00 are:

[0090]

[0091] According to the historical vehicle charging data of the charging station, the maximum likelihood estimation method is used to obtain the parameters of the above Gaussian distribution:

[0092] For the operating vehicles that arrive at the station between 0:00 and 6:00, 6:00 and 18:00, and 18:00 and 24:00, the characteristic parameters of their SOC distribution characteristics are It can be obtained through the following formulas:

[0093]

[0094] For private cars arriving at the station between 0:00 and 6:00, 6:00 and 18:00, and 18:00 and 24:00, the characteristic parameters of the SOC distribution characteristics are and It can be obtained through the following formulas:

[0095]

[0096] Step 250: construct a distribution feature of the charge amount of each vehicle type at each battery state of charge based on the charge amount of each vehicle type at each battery state of charge.

[0097] For example, first, according to the SOC distribution characteristics of the vehicles arriving at the station at each preset arrival time period, the data of the operating vehicles and private cars are further divided into multiple groups. Here, the data is divided into three groups of SOC as an example, that is, 0<SOC a ≤30%, 30%<SOC a ≤70%, 70%<SOC a ≤100%, the historical vehicle charging data can be further divided into 18 groups as shown in the following table:

[0098] CV <![CDATA[0<SOC a ≤30%]]> <![CDATA[30%<SOC a ≤70%]]> <![CDATA[70%<SOC a ≤100%]]> <![CDATA[0<t a ≤6]]> CV1_0_30 CV1_30_70 CV1_70_100 <![CDATA[6<t a ≤18]]> CV2_0_30 CV2_30_70 CV2_70_100 <![CDATA[18<t a ≤24]]> CV3_0_30 CV3_30_70 CV3_70_100

[0099] PV <![CDATA[0<SOC a ≤30%]]> <![CDATA[30%<SOC a ≤70%]]> <![CDATA[70%<SOC a ≤100%]]> <![CDATA[0<t a ≤6]]> PV1_0_30 PV1_30_70 PV1_70_100 <![CDATA[6<t a ≤18]]> PV2_0_30 PV2_30_70 PV2_70_100 <![CDATA[18<t a ≤24]]> PV3_0_30 PV3_30_70 PV3_70_100 ;

[0100] With "CV, 0<t a ≤6, 0<SOC a ≤30%”, “PV, 18<t a ≤24, 70%<SOCa Taking the two groups of "≤100%" as an example, the definition of the vehicle charging amount data at the station in the above groups is as follows, and the relevant definitions of other groups can be deduced by analogy.

[0101]

[0102] Assume that the charging amount of the vehicle coming to this station in each set of data above follows a Gaussian distribution with specific parameters, that is

[0103]

[0104] Based on the historical vehicle charging data of the charging station, the maximum likelihood estimation method is used to estimate the characteristic parameters of the charging capacity distribution characteristics of the operating vehicle at each battery state of charge. and Characteristic parameter E of the charge capacity distribution characteristics of private cars at various battery charge states PV and Right now:

[0105]

[0106] Step 260: construct an average charging power distribution feature of vehicles of each vehicle type at each battery state of charge based on the average charging power of vehicles of each vehicle type at each battery state of charge.

[0107] For example, historical vehicle charging data is divided into 18 groups according to vehicle type, arrival time, and arrival SOC as shown in the following table:

[0108] CV <![CDATA[0<SOC a ≤30%]]> <![CDATA[30%<SOC a ≤70%]]> <![CDATA[70%<SOC a ≤100%]]> <![CDATA[0<t a ≤6]]> CV1_0_30 CV1_30_70 CV1_70_100 <![CDATA[6<t a ≤18]]> CV2_0_30 CV2_30_70 CV2_70_100 <![CDATA[18<t a ≤24]]> CV3_0_30 CV3_30_70 CV3_70_100

[0109] PV <![CDATA[0<SOC a ≤30%]]> <![CDATA[30%<SOC a ≤70%]]> <![CDATA[70%<SOC a ≤100%]]> <![CDATA[0<t a ≤6]]> PV1_0_30 PV1_30_70 PV1_70_100 <![CDATA[6<t a ≤18]]> PV2_0_30 PV2_30_70 PV2_70_100 <![CDATA[18<t a ≤24]]> PV3_0_30 PV3_30_70 PV3_70_100 ;

[0110] With "CV, 0<t a ≤6, 0<SOC a ≤30%”, “PV, 18<t a ≤24, 70%<SOC a ≤100%” for example, the average charging power P of the operating vehicles at the station in the above groupings CV , the average charging power P of private cars at the station PV The definition of is as follows, and the definitions of other groups can be deduced by analogy:

[0111]

[0112] Assume that the average charging power of vehicles arriving at the station in each set of data above obeys a Gaussian distribution with specific parameters, that is:

[0113]

[0114] The probability density function of the average charging power of vehicles arriving at the station is as follows:

[0115]

[0116] According to the historical vehicle charging data of the charging station, the maximum likelihood estimation method is used to estimate the parameters of the Gaussian distribution, that is, the characteristic parameters of the average charging power distribution characteristics of vehicles of the operating vehicle type at each battery charge state and Characteristic parameters of the average charging power distribution characteristics of private cars at various battery charge states and They are:

[0117]

[0118] Step 270: Determine the target arrival time at the charging station, the target charge amount at the charging station, and the target average charging power for each target vehicle among a preset number of target vehicles in a preset future time period based on the arrival time distribution characteristics, the battery state of charge distribution characteristics, the charge amount distribution characteristics, and the average charging power distribution characteristics.

[0119] In some embodiments, the above step 270 may include steps 2701-2705:

[0120] Step 2701: Sample arrival time data based on the probability density function of the arrival time distribution characteristics of each vehicle type to obtain the target arrival time of each target vehicle in a preset future time period.

[0121] Step 2702: Sampling battery state of charge data based on the probability density function of the battery state of charge distribution characteristics of each vehicle type in each preset arrival time period to obtain a target battery state of charge for each target vehicle in a preset future time period.

[0122] Step 2703: Sample the charge capacity data based on the probability density function of the charge capacity distribution characteristics of each vehicle type at each battery state of charge to obtain a target charge capacity for each target vehicle in a preset future time period.

[0123] Step 2704: Perform data sampling based on the probability density function of the average charging power distribution characteristics of each vehicle type at each battery state of charge to obtain a target average charging power for each target vehicle in a preset future time period.

[0124] Step 280: According to the target arrival time of each target vehicle, the target average charging power in each time period to which the target arrival time belongs is superimposed to obtain the total average charging power corresponding to each time period.

[0125] For example, the target arrival times of the three target vehicles a, b, and c are 12:00, 13:00, and 13:30, respectively. The time period of 12:00, 13:00, and 13:30 is 12:00-14:00. Then, the target average charging powers of the three target vehicles a, b, and c can be added together to obtain the total average charging power of the charging station corresponding to the time period of 12:00-14:00.

[0126] Step 290: construct an initial charging load curve of the charging station based on the total average charging power corresponding to each time period.

[0127] Therefore, the target charging data of a preset number of target vehicles in a preset future time period at the charging station can be obtained more accurately through the distribution pattern of the historical vehicle charging data of the charging station, and the initial charging load curve of the charging station corresponding to the preset number of target vehicles in the preset future time period can be constructed more accurately.

[0128] In some embodiments of the present disclosure, the above-mentioned description of the total satisfaction of users of a preset number of target vehicles with the charging station at a preset charging price based on the arrival time of each target vehicle at the charging station, the charging time and the charging fee before and after each target vehicle responds to the preset charging price of the charging station, the computer device can execute Figure 3 A flowchart of a method for determining overall satisfaction is provided, such as Figure 3 As shown, the overall satisfaction determination method provided in this embodiment includes the following steps:

[0129] Step 310: Calculate the sensitivity coefficient of each vehicle type user to the charging cost of the charging station, the sensitivity coefficient of each vehicle type user to the time period when the vehicle arrives at the charging station, and the sensitivity coefficient of each vehicle type user to the charging time of the charging station.

[0130] The charging cost sensitivity coefficient can be understood as an evaluation of the sensitivity of users of each vehicle type to charging costs. The greater the sensitivity of users of a certain vehicle type to charging costs, the more concerned users of this vehicle type are about charging costs, and the greater the charging cost sensitivity coefficient.

[0131] In some embodiments, the above calculation of the charging cost sensitivity coefficient of each vehicle type user to the charging station may include S11-S15:

[0132] S11. For each vehicle type user, obtain satisfaction of a target number of users with the historical charging fees of the charging station, and obtain a target number of first satisfactions of users of the vehicle type.

[0133] The number of targets can be set as needed, for example, 100, and there is no limit here.

[0134] S12. Standardize each first satisfaction degree to obtain a first standard satisfaction degree corresponding to each first satisfaction degree.

[0135] S13. Calculate a first ratio of each first standard satisfaction degree to the sum of a target number of first standard satisfaction degrees.

[0136] Specifically, the sum of the target number of first standard satisfactions may be calculated, and then a first ratio of each first standard satisfaction to the sum of the target number of first standard satisfactions may be calculated.

[0137] S14. Based on the first ratio, calculate the entropy value of the target number of first standard satisfactions to obtain a first entropy value.

[0138] S15. Based on the first entropy value, calculate a charging cost sensitivity coefficient of the user of the vehicle type to the charging station.

[0139] The arrival time sensitivity coefficient can be understood as the sensitivity of each vehicle type user to the time when the vehicle arrives at the charging station. The greater the sensitivity of a certain vehicle type user to the arrival time, the more the user of this vehicle type cares about the arrival time, and the greater the arrival time sensitivity coefficient.

[0140] In some embodiments, the above calculation of the user sensitivity coefficient of each vehicle type to the time period when the vehicle arrives at the charging station may include S21-S25:

[0141] S21. For each vehicle type user, obtain satisfaction of a target number of users with respect to historical arrival times at a charging station, and obtain a target number of second satisfactions of users of the vehicle type.

[0142] S22: Standardize each second satisfaction degree to obtain a second standard satisfaction degree corresponding to each second satisfaction degree.

[0143] S23. Calculate a second ratio of each second standard satisfaction degree to the sum of the second standard satisfaction degrees.

[0144] S24. Based on the second ratio, calculate the entropy value of the target number of second standard satisfactions to obtain a second entropy value.

[0145] S25. Calculate, based on the second entropy value, a sensitivity coefficient of the user of the vehicle type to the arrival time period of the charging station.

[0146] The charging time sensitivity coefficient can be understood as the sensitivity of each vehicle type user to the charging time of the charging station. The greater the sensitivity of a certain vehicle type user to the charging time, the more concerned the user of the vehicle type is about the charging time, and the greater the charging time sensitivity coefficient.

[0147] In some embodiments, the above calculation of the sensitivity coefficient of the charging time of the user of each vehicle type to the charging station may include S31-S35:

[0148] S31. For each vehicle type user, obtain a target number of satisfactions of the users with respect to the historical charging duration of the charging station, to obtain a target number of third satisfactions of the users of the vehicle type.

[0149] S32: Standardize each third satisfaction degree to obtain a third standard satisfaction degree corresponding to each third satisfaction degree.

[0150] S33: Calculate a third ratio of each third standard satisfaction degree to the sum of the third standard satisfaction degrees.

[0151] S34. Based on the third ratio, calculate the entropy value of the target number of third standard satisfactions to obtain a third entropy value.

[0152] S35. Calculate, based on the third entropy value, a sensitivity coefficient of the user of the vehicle type to the charging time of the charging station.

[0153] Specifically, a sampling survey (such as a questionnaire survey) can be conducted on users of each vehicle type to collect the satisfaction of a target number of users with the historical charging costs of the charging station, and obtain a target number of first satisfactions of users of that vehicle type; the satisfaction of a target number of users with the historical arrival times at the charging station can be collected to obtain a target number of second satisfactions of users of that vehicle type; the satisfaction of a target number of users with the historical charging times of the charging station can be collected to obtain a target number of third satisfactions of users of that vehicle type.

[0154] Specifically, in order to eliminate the dimension effect, each of the first satisfaction, the second satisfaction, and the third satisfaction can be standardized respectively. For example, the Min-Max standardization method can be used for standardization, and the standardized expression can be:

[0155]

[0156] Where i is the user's ID; j is the satisfaction ID, when j is 1, it indicates the first satisfaction, when j is 2, it indicates the second satisfaction, when j is 3, it indicates the third satisfaction; x min (j) is the minimum value of the jth satisfaction among the target number of users; x max(j) is the maximum value of the jth satisfaction among the target number of users; Z ij is the standard satisfaction corresponding to the j-th satisfaction of the i-th user;

[0157] For example, when i is 3 and j is 1, x ij is the first satisfaction of the third user among the target number of users; x min (j) is the minimum value of the first satisfaction among the target number of users, x max (j) is the maximum value of the first satisfaction among the target number of users, Z ij It is the first standard satisfaction degree corresponding to the first satisfaction degree of the third user.

[0158] Specifically, the first ratio of each first standard satisfaction degree to the sum of the target number of first standard satisfaction degrees, the second ratio of each second standard satisfaction degree to the sum of the second standard satisfaction degrees, and the third ratio of each third standard satisfaction degree to the sum of the third standard satisfaction degrees can be calculated by the following formula:

[0159]

[0160] Where n represents the number of targets; P ij represents the ratio corresponding to the jth satisfaction of the i-th user; when j is 1, P ij is the first ratio corresponding to the first satisfaction of the i-th user; when j is 2, P ij is the second ratio corresponding to the second satisfaction of the i-th user; when j is 3, P ij is the third ratio corresponding to the third satisfaction of the i-th user.

[0161] Specifically, based on the first ratio, the entropy value of the first standard satisfaction of the target number is calculated to obtain the first entropy value; based on the second ratio, the entropy value of the second standard satisfaction of the target number is calculated to obtain the second entropy value; based on the third ratio, the entropy value of the third standard satisfaction of the target number is calculated to obtain the third entropy value, which can be calculated by the following formula:

[0162]

[0163] Among them, e j represents the entropy value corresponding to the jth satisfaction. When j is 1, e j represents the first entropy value corresponding to the first satisfaction; when j is 2, e j represents the second entropy value corresponding to the second satisfaction; when j is 3, e j Indicates the third entropy value corresponding to the third satisfaction.

[0164] Specifically, based on the first entropy value, the sensitivity coefficient of the user of the vehicle type to the charging cost of the charging station is calculated; based on the second entropy value, the sensitivity coefficient of the user of the vehicle type to the arrival time of the charging station is calculated; based on the third entropy value, the sensitivity coefficient of the user of the vehicle type to the charging time of the charging station is calculated, which can be calculated by the following formula:

[0165]

[0166] Among them, γ j represents the sensitivity coefficient corresponding to the jth satisfaction. When j is 1, γ j represents the charging cost sensitivity coefficient; when j is 2, γ j represents the sensitivity coefficient of the arrival period; when j is 3, γ j Indicates the charging time sensitivity coefficient.

[0167] Step 320 , describing the satisfaction of each target vehicle user with the charging cost of the charging station, the satisfaction with the planned arrival time of the target vehicle at the charging station, and the satisfaction with the charging time of the charging station under the preset charging price of the charging station.

[0168] In the disclosed embodiment, the computer device can describe the user's satisfaction with the charging cost of the charging station, the satisfaction with the planned arrival time of the target vehicle at the charging station, and the satisfaction with the charging time of the charging station under the influence of the preset charging price of the charging station.

[0169] The planned arrival time can be understood as the arrival time recommended by the operator of the charging station to the user under the preset charging price of the charging station.

[0170] In some embodiments, under the preset charging price of the charging station described above, the user satisfaction of each target vehicle with the charging cost of the charging station, the satisfaction with the planned arrival time of the target vehicle at the charging station, and the satisfaction with the charging time of the charging station may include steps 3201-3207:

[0171] Step 3201: For each target vehicle, obtain the historical charging cost used to fully charge the preset charging amount after the target vehicle arrives at the charging station at the target arrival time before the target vehicle responds to the preset charging price.

[0172] The preset charging amount can be set as needed and is not limited here.

[0173] Step 3202: After obtaining the target vehicle's response to the preset charging price, the actual charging cost used to fully charge the preset charging amount after arriving at the charging station at the scheduled arrival time, and the additional revenue brought to the charging station by fully charging the preset charging amount.

[0174] Step 3203: Based on the historical charging costs, actual charging costs and additional benefits, predict the user satisfaction of the target vehicle with the charging cost of the charging station.

[0175] For example, the user satisfaction A of the target vehicle with the charging cost of the charging station can be calculated by the following formula:

[0176]

[0177] cost0=E0*EP(t aI );

[0178] cost1=E0*EP(t aR );

[0179] Among them, CIC is the profit attractiveness constant, and its value reflects the amount of profit that is attractive to users;

[0180] cost0 represents the historical charging cost used by the target vehicle to fully charge the preset amount after the target vehicle arrives at the charging station at the target arrival time before the target vehicle responds to the preset charging price;

[0181] cost1 represents the actual charging cost for the target vehicle to reach the preset charging capacity after responding to the preset charging price at the scheduled arrival time;

[0182] E0 represents the preset charge capacity;

[0183] EP(t aI ) represents the daily time-of-use electricity price curve of the power market before the target vehicle responds to the preset charging price;

[0184] EP(t aR ) represents the daily time-of-use electricity price curve in the power market after the target vehicle responds to the preset charging price;

[0185] Profit represents the additional revenue brought to the charging station by the target vehicle responding to the preset charging price and fully charging to the preset amount after arriving at the charging station at the scheduled arrival time.

[0186] Step 3204: For each target vehicle, calculate the time difference between the planned arrival time of the target vehicle after responding to the preset charging price and the target arrival time of the target vehicle before responding to the preset charging price.

[0187] Step 3205: Based on the time difference, predict the user satisfaction of the target vehicle with the planned arrival time of the target vehicle at the charging station.

[0188] For example, the user satisfaction B of the target vehicle with the planned arrival time of the target vehicle at the charging station can be calculated by the following formula:

[0189] B = -lg(|Δt|++1);

[0190] Δt=t aR -t aI ;

[0191] Among them, t aI Indicates the target arrival time of the target vehicle before responding to the preset charging price;

[0192] t aR Indicates the planned arrival time of the target vehicle after responding to the preset charging price;

[0193] Δt represents the time difference.

[0194] Step 3206: For each target vehicle, obtain the first average charging power after the target vehicle responds to the preset charging price and the first total number of vehicles visiting the charging station during the time period of the planned arrival time, as well as the target average charging power before the target vehicle responds to the preset charging price and the second total number of vehicles visiting the charging station during the time period of the target arrival time.

[0195] Step 3207: Calculate the user satisfaction of the target vehicle with the charging time of the charging station based on the first average charging power, the first total number of vehicles, the target average charging power, and the second total number of vehicles.

[0196] For example, the user satisfaction C of the target vehicle with the charging time of the charging station can be calculated by the following formula:

[0197]

[0198] Among them, P R represents the first average charging power of the target vehicle after responding to the preset charging price;

[0199] P I represents the target average charging power of the target vehicle before responding to the preset charging price;

[0200] n(t aI ) represents the total number of second vehicles that visit the charging station during the time period before the target vehicle responds to the preset charging price and the target arrival time;

[0201] n(t aR ) represents the total number of first vehicles that visit the charging station during the time period during which the target vehicle is scheduled to arrive at the station after responding to the preset charging price.

[0202] Step 330: For each target vehicle, sum the product of the target vehicle's corresponding charging cost satisfaction and the target vehicle's corresponding charging cost sensitivity coefficient, the product of the target vehicle's corresponding planned arrival time satisfaction and the target vehicle's corresponding arrival time period sensitivity coefficient, and the product of the target vehicle's corresponding charging time satisfaction and the target vehicle's corresponding charging time sensitivity coefficient to obtain the target vehicle's user's comprehensive satisfaction with the charging station at the preset charging price.

[0203] For example, the comprehensive satisfaction θ of the target vehicle user with the charging station under the preset charging price can be calculated by the following formula:

[0204] θ=γ1A+γ2B+γ3C;

[0205] Among them, γ1 represents the charging cost sensitivity coefficient corresponding to the target vehicle;

[0206] γ2 represents the sensitivity coefficient of the arrival time period corresponding to the target vehicle;

[0207] γ3 represents the charging time sensitivity coefficient corresponding to the target vehicle;

[0208] A represents the charging cost satisfaction of the target vehicle;

[0209] B represents the satisfaction level of the planned arrival time corresponding to the target vehicle;

[0210] C represents the charging time satisfaction of the target vehicle.

[0211] Step 340: sum up the comprehensive satisfaction of the users of a preset number of target vehicles with the charging station to obtain the total satisfaction of the users of a preset number of target vehicles with the charging station at a preset charging price.

[0212] Therefore, based on the arrival time of each target vehicle at the charging station, the charging time and the charging cost before and after each target vehicle responds to the preset charging price of the charging station, the overall satisfaction of users of a preset number of target vehicles with the charging station at the preset charging price can be accurately predicted.

[0213] In some embodiments of the present disclosure, the load parameters in the initial charging load curve are adjusted with the goal of maximizing the total satisfaction at the preset charging price to obtain a modified charging load curve for the charging station. The computer device may execute Figure 4 A flowchart of a method for determining a modified charging load curve is provided, such as Figure 4 As shown, the method for determining the modified charging load curve provided in this embodiment includes the following steps:

[0214] Step 410, taking the maximum total satisfaction under the preset charging price as the goal, calculate the planned arrival time and the first average charging power of each target vehicle after responding to the preset charging price when the total satisfaction is maximized.

[0215] Step 420: Determine the planned arrival time of each target vehicle after responding to the preset charging price when the total satisfaction is maximized as the ideal arrival time of each target vehicle, and determine the first average charging power of each target vehicle after responding to the preset charging price when the total satisfaction is maximized as the ideal average charging power of each target vehicle.

[0216] Step 430: According to the ideal arrival time of each target vehicle, the ideal average charging power in each time period to which the ideal arrival time belongs is superimposed to obtain the total ideal average charging power corresponding to each time period.

[0217] For example, the ideal arrival times of the three target vehicles a, b and c are 12:05, 13:05 and 13:40 respectively, and the time periods of 12:05, 13:05 and 13:40 are 12:02-14:00. Then, the ideal average charging powers of the three target vehicles a, b and c can be added together to obtain the total ideal average charging power of the charging station corresponding to the time period of 12:02-14:00.

[0218] Step 440: Adjust the total average charging power corresponding to each time period in the initial charging load curve to the total ideal average charging power to obtain a modified charging load curve of the charging station. The modified charging load curve has the maximum total satisfaction at the preset charging price.

[0219] Therefore, we can combine the total satisfaction of users of each vehicle type with the charging station after charging at the charging station and the attributes of the charging station itself, and use the charging load curve of the charging station when the total satisfaction of users is maximum under the preset charging price of the charging station as the final charging load curve of the charging station. This can fully consider the charging behavior characteristics of users of each vehicle type, improve the accuracy of the prediction of the load characteristics of the charging station, and thus provide scientific guidance for the operation of the charging station.

[0220] Figure 5 is a schematic diagram of the structure of a charging station load characteristic prediction device provided by an embodiment of the present disclosure, and the device can be understood as the above-mentioned computer device or some functional modules in the above-mentioned computer device. Figure 5 As shown, the charging station load characteristic prediction device 500 includes:

[0221] A first determination module 510 is used to determine target charging data of a preset number of target vehicles at a charging station in a preset future period based on a distribution law of historical vehicle charging data of the charging station, and the target charging data of the target vehicles meets the distribution law;

[0222] A construction module 520 is used to construct an initial charging load curve of a charging station corresponding to a preset number of target vehicles in a preset future period based on the target charging data;

[0223] A description module 530 is used to describe the total satisfaction of users of a preset number of target vehicles with the charging station at the preset charging price of the charging station based on the arrival time period of each target vehicle at the charging station, the charging time and the charging fee at the charging station before and after each target vehicle responds to the preset charging price of the charging station;

[0224] The solution module 540 is used to optimize and solve the load parameters in the initial charging load curve with the goal of maximizing the total satisfaction at a preset charging price, and obtain a modified charging load curve of the charging station, where the modified charging load curve maximizes the total satisfaction at the preset charging price.

[0225] Optionally, the first determining module includes:

[0226] The classification submodule is used to classify the historical vehicle charging data of the charging station into vehicle types and obtain the vehicle charging data corresponding to each vehicle type;

[0227] An acquisition submodule is used to acquire, from the vehicle charging data corresponding to each vehicle type, the arrival time of each vehicle in the vehicle type at the charging station, the battery charge state of each vehicle when arriving at the charging station, the charge amount of each vehicle at the charging station, and the average charging power;

[0228] A first construction submodule is used to construct the arrival time distribution characteristics of vehicles of each vehicle type based on the arrival time of vehicles of each vehicle type;

[0229] The second construction submodule is used to construct the battery state of charge distribution characteristics of vehicles of each vehicle type in each preset arrival time period based on the battery state of charge of vehicles of each vehicle type at each arrival time;

[0230] A third construction submodule is used to construct a charge amount distribution feature of vehicles of each vehicle type at each battery state of charge based on the charge amount of vehicles of each vehicle type at each battery state of charge;

[0231] A fourth construction submodule, for constructing an average charging power distribution feature of vehicles of each vehicle type at each battery state of charge based on the average charging power of vehicles of each vehicle type at each battery state of charge;

[0232] The first determination submodule is used to determine the target arrival time of each target vehicle at the charging station, the target charge amount and the target average charging power at the charging station among a preset number of target vehicles in a preset future time period based on the arrival time distribution characteristics, the battery state of charge distribution characteristics, the charge amount distribution characteristics and the average charging power distribution characteristics;

[0233] The first superposition submodule is used to superimpose the target average charging power in each time period to which the target arrival time belongs according to the target arrival time of each target vehicle, so as to obtain the total average charging power corresponding to each time period;

[0234] The fifth construction submodule is used to construct an initial charging load curve of the charging station based on the total average charging power corresponding to each time period.

[0235] Optionally, the first determining submodule includes:

[0236] A first sampling unit is used to sample arrival time data based on a probability density function of the arrival time distribution characteristics of vehicles of each vehicle type, and obtain a target arrival time of each target vehicle in a preset future period;

[0237] A second sampling unit is used to sample battery state of charge data based on a probability density function of a battery state of charge distribution characteristic of vehicles of each vehicle type at each preset arrival time period, and obtain a target battery state of charge for each target vehicle in a preset future time period;

[0238] A third sampling unit is used to sample charging capacity data based on a probability density function of charging capacity distribution characteristics of each vehicle type at each battery state of charge, to obtain a target charging capacity of each target vehicle in a preset future time period;

[0239] The fourth sampling unit is used to perform data sampling based on the probability density function of the average charging power distribution characteristics of vehicles of each vehicle type at each battery state of charge, so as to obtain a target average charging power for each target vehicle in a preset future time period.

[0240] Optionally, the above description module includes:

[0241] A sensitivity coefficient calculation submodule, used to calculate the sensitivity coefficient of each vehicle type user to the charging cost of the charging station, the sensitivity coefficient of each vehicle type user to the time period when the vehicle arrives at the charging station, and the sensitivity coefficient of each vehicle type user to the charging time of the charging station;

[0242] A description submodule is used to describe the user satisfaction of each target vehicle with respect to the charging cost of the charging station, the target vehicle's planned arrival time at the charging station, and the charging time of the charging station under the preset charging price of the charging station;

[0243] The first summing submodule is used to sum, for each target vehicle, the product of the charging cost satisfaction corresponding to the target vehicle and the charging cost sensitivity coefficient corresponding to the target vehicle, the product of the planned arrival time satisfaction corresponding to the target vehicle and the arrival time sensitivity coefficient corresponding to the target vehicle, and the product of the charging time satisfaction corresponding to the target vehicle and the charging time sensitivity coefficient corresponding to the target vehicle, to obtain the comprehensive satisfaction of the user of the target vehicle with the charging station under the preset charging price;

[0244] The second summing submodule is used to sum the comprehensive satisfaction of users of a preset number of target vehicles with the charging station to obtain the total satisfaction of users of a preset number of target vehicles with the charging station at a preset charging price.

[0245] Optionally, the sensitivity coefficient calculation submodule includes:

[0246] The first acquisition unit is used to acquire, for each vehicle type user, satisfaction of a target number of users with respect to historical charging costs of a charging station, to obtain a target number of first satisfactions of users of the vehicle type; acquire satisfaction of a target number of users with respect to historical arrival times at a charging station, to obtain a target number of second satisfactions of users of the vehicle type; acquire satisfaction of a target number of users with respect to historical charging durations at a charging station, to obtain a target number of third satisfactions of users of the vehicle type;

[0247] A standardization unit, used for standardizing each first satisfaction degree, the second satisfaction degree and the third satisfaction degree to obtain a first standard satisfaction degree corresponding to each first satisfaction degree, a second standard satisfaction degree corresponding to each second satisfaction degree and a third standard satisfaction degree corresponding to each third satisfaction degree;

[0248] a first calculation unit, configured to calculate a first ratio of each first standard satisfaction degree to the sum of a target number of first standard satisfaction degrees, a second ratio of each second standard satisfaction degree to the sum of the second standard satisfaction degrees, and a third ratio of each third standard satisfaction degree to the sum of the third standard satisfaction degrees;

[0249] A second calculation unit is used to calculate the entropy value of the first standard satisfaction of the target number based on the first ratio to obtain a first entropy value; based on the second ratio, calculate the entropy value of the second standard satisfaction of the target number to obtain a second entropy value; based on the third ratio, calculate the entropy value of the third standard satisfaction of the target number to obtain a third entropy value;

[0250] The third calculation unit is used to calculate the charging cost sensitivity coefficient of the user of the vehicle type to the charging station based on the first entropy value; calculate the arrival time period sensitivity coefficient of the user of the vehicle type to the charging station based on the second entropy value; and calculate the charging time sensitivity coefficient of the user of the vehicle type to the charging station based on the third entropy value.

[0251] Optionally, the above description submodule includes:

[0252] The second acquisition unit is used to acquire, for each target vehicle, a historical charging fee used to fully charge a preset amount of the target vehicle after the target vehicle arrives at the charging station at the target arrival time before the target vehicle responds to the preset charging price;

[0253] The third acquisition unit is used to obtain the actual charging cost used by the target vehicle to fully charge the preset charging amount after arriving at the charging station at the scheduled arrival time after responding to the preset charging price, and the additional income brought to the charging station by fully charging the preset charging amount;

[0254] A first prediction unit, configured to predict the user satisfaction of the target vehicle with respect to the charging cost of the charging station based on the historical charging cost, the actual charging cost and the additional benefit;

[0255] A fourth calculation unit is used to calculate, for each target vehicle, a time difference between a planned arrival time of the target vehicle after responding to a preset charging price and a target arrival time of the target vehicle before responding to the preset charging price;

[0256] A second prediction unit is used to predict, based on the time difference, the satisfaction of the user of the target vehicle with the planned arrival time of the target vehicle at the charging station;

[0257] A fourth acquisition unit is used to acquire, for each target vehicle, a first average charging power after the target vehicle responds to the preset charging price and a first total number of vehicles visiting the charging station during the time period of the planned arrival time, as well as a target average charging power before the target vehicle responds to the preset charging price and a second total number of vehicles visiting the charging station during the time period of the target arrival time;

[0258] The fifth calculation unit is used to calculate the user's satisfaction with the charging time of the charging station based on the first average charging power, the first total number of vehicles, the target average charging power and the second total number of vehicles.

[0259] Optionally, the above-mentioned solution module includes:

[0260] A calculation submodule, for calculating the planned arrival time and the first average charging power of each target vehicle after responding to the preset charging price when the total satisfaction is maximized, with the goal of maximizing the total satisfaction under the preset charging price;

[0261] The second determination submodule is used to determine the planned arrival time of each target vehicle after responding to the preset charging price when the total satisfaction is maximized to determine the ideal arrival time of each target vehicle, and to determine the first average charging power of each target vehicle after responding to the preset charging price when the total satisfaction is maximized to determine the ideal average charging power of each target vehicle;

[0262] The second superposition submodule is used to superimpose the ideal average charging power in each time period to which the ideal arrival time belongs according to the ideal arrival time of each target vehicle, so as to obtain the total ideal average charging power corresponding to each time period;

[0263] The adjustment submodule is used to adjust the total average charging power corresponding to each time period in the initial charging load curve to the total ideal average charging power, so as to obtain a modified charging load curve of the charging station.

[0264] The charging station load characteristic prediction device provided in the embodiments of the present disclosure can implement the method of any of the above embodiments, and its execution method and beneficial effects are similar, which will not be repeated here.

[0265] The embodiments of the present disclosure also provide a computer device, which includes a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method of any of the above embodiments can be implemented, and its execution method and beneficial effects are similar and will not be repeated here.

[0266] Figure 6 is a schematic diagram of the structure of a computer device provided by an embodiment of the present disclosure, such as Figure 6 As shown, the computer device 600 may include a processor 610 and a memory 620, wherein the memory 620 stores a computer program 621, and when the computer program 621 is executed by the processor 610, the method provided by any of the above embodiments can be implemented, and its execution method and beneficial effects are similar and will not be repeated here.

[0267] Of course, to simplify, Figure 6 Only some of the components related to the present invention in the computer device 600 are shown, and components such as a bus, an input / output interface, an input device, and an output device are omitted. In addition, according to specific application conditions, the computer device 600 may also include any other appropriate components.

[0268] An embodiment of the present disclosure provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method of any of the above embodiments can be implemented. The execution method and beneficial effects are similar and will not be repeated here.

[0269] The above-mentioned computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable 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.

[0270] The computer program may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the disclosed embodiments, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer device, partially on the user's device, as a separate software package, partially on the user's computer device and partially on a remote computer device, or entirely on a remote computer device or server.

[0271] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0272] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0273] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting charging station load characteristics, characterized in that: include: Determining target charging data of a preset number of target vehicles at the charging station in a preset future period based on a distribution law of historical vehicle charging data of the charging station, wherein the target charging data of the target vehicles satisfies the distribution law; Based on the target charging data, constructing an initial charging load curve of the charging station corresponding to the preset number of target vehicles in the preset future time period; Based on the arrival time of each target vehicle at the charging station, the charging time and the charging fee at the charging station before and after each target vehicle responds to the preset charging price of the charging station, describe the overall satisfaction of users of the preset number of target vehicles with the charging station at the preset charging price; With the goal of maximizing the total satisfaction at the preset charging price, the load parameters in the initial charging load curve are optimized and solved to obtain a modified charging load curve of the charging station, wherein the total satisfaction at the preset charging price is maximized.

2. The method according to claim 1, characterized in that The step of determining target charging data of a preset number of target vehicles at the charging station in a preset future time period based on the distribution law of historical vehicle charging data at the charging station includes: Classify the historical vehicle charging data of the charging station by vehicle type to obtain the vehicle charging data corresponding to each vehicle type; From the vehicle charging data corresponding to each vehicle type, obtain the arrival time of each vehicle of the vehicle type at the charging station, the battery charge state of each vehicle when arriving at the charging station, the charge amount of each vehicle at the charging station, and the average charging power; Based on the arrival time of vehicles of each vehicle type, construct the arrival time distribution characteristics of vehicles of each vehicle type; Based on the battery state of charge of vehicles of each vehicle type at each arrival time, construct the battery state of charge distribution characteristics of vehicles of each vehicle type at each preset arrival time period; Based on the charge amount of vehicles of each vehicle type at each battery state of charge, constructing a charge amount distribution feature of vehicles of each vehicle type at each battery state of charge; Based on the average charging power of vehicles of each vehicle type at each battery state of charge, construct an average charging power distribution feature of vehicles of each vehicle type at each battery state of charge; Based on the arrival time distribution characteristics, battery state of charge distribution characteristics, charge amount distribution characteristics and average charging power distribution characteristics, determine the target arrival time of each target vehicle at the charging station, the target charge amount and the target average charging power at the charging station among a preset number of target vehicles in a preset future time period; According to the target arrival time of each target vehicle, the target average charging power in each time period to which the target arrival time belongs is superimposed to obtain the total average charging power corresponding to each time period; Based on the total average charging power corresponding to each time period, an initial charging load curve of the charging station is constructed.

3. The method according to claim 2, characterized in that The step of determining the target arrival time of each target vehicle at the charging station, the target charge amount and the target average charging power at the charging station among a preset number of target vehicles in a preset future time period based on the arrival time distribution characteristics, the battery state of charge distribution characteristics, the charge amount distribution characteristics and the average charging power distribution characteristics comprises: Sampling arrival time data based on the probability density function of the arrival time distribution characteristics of vehicles of each vehicle type to obtain a target arrival time for each target vehicle in a preset future period; Sampling battery state of charge data based on a probability density function of battery state of charge distribution characteristics of vehicles of each vehicle type at each preset arrival time period to obtain a target battery state of charge for each target vehicle in a preset future time period; Based on the probability density function of the charge capacity distribution characteristics of each vehicle type at each battery state of charge, the charge capacity data is sampled to obtain the target charge capacity of each target vehicle in a preset future period; Data sampling is performed based on the probability density function of the average charging power distribution characteristics of vehicles of each vehicle type at each battery state of charge to obtain the target average charging power of each target vehicle in a preset future time period.

4. The method according to claim 1, characterized in that: The overall satisfaction of users of the preset number of target vehicles with the charging station at the preset charging price is described based on the arrival time of each target vehicle at the charging station, the charging time and the charging fee at the charging station before and after each target vehicle responds to the preset charging price of the charging station, including: Calculate the sensitivity coefficient of users of each vehicle type to the charging cost of the charging station, the sensitivity coefficient of users of each vehicle type to the time period when the vehicle arrives at the charging station, and the sensitivity coefficient of users of each vehicle type to the charging time of the charging station; Describe the satisfaction of each target vehicle user with the charging cost of the charging station, the satisfaction with the planned arrival time of the target vehicle at the charging station, and the satisfaction with the charging time of the charging station under the preset charging price of the charging station; For each target vehicle, the product of the charging cost satisfaction corresponding to the target vehicle and the charging cost sensitivity coefficient corresponding to the target vehicle, the product of the planned arrival time satisfaction corresponding to the target vehicle and the arrival time period sensitivity coefficient corresponding to the target vehicle, and the product of the charging time satisfaction corresponding to the target vehicle and the charging time sensitivity coefficient corresponding to the target vehicle are summed to obtain the comprehensive satisfaction of the user of the target vehicle with the charging station under the preset charging price; The comprehensive satisfaction of the users of the preset number of target vehicles with the charging station is summed to obtain the total satisfaction of the users of the preset number of target vehicles with the charging station at the preset charging price.

5. The method according to claim 4, characterized in that The calculation of the sensitivity coefficient of the user of each vehicle type to the charging fee of the charging station, the sensitivity coefficient of the user of each vehicle type to the time period when the vehicle arrives at the charging station, and the sensitivity coefficient of the user of each vehicle type to the charging time of the charging station includes: For each vehicle type user, obtain a target number of satisfactions of the users with the historical charging fees of the charging station, and obtain a target number of first satisfactions of the users of the vehicle type; obtain a target number of satisfactions of the users with the historical arrival times at the charging station, and obtain a target number of second satisfactions of the users of the vehicle type; obtain a target number of satisfactions of the users with the historical charging durations at the charging station, and obtain a target number of third satisfactions of the users of the vehicle type; Standardizing each of the first satisfaction, the second satisfaction, and the third satisfaction to obtain a first standard satisfaction corresponding to each of the first satisfaction, a second standard satisfaction corresponding to each of the second satisfaction, and a third standard satisfaction corresponding to each of the third satisfaction; Calculating a first ratio of each first standard satisfaction degree to the sum of a target number of first standard satisfaction degrees, a second ratio of each second standard satisfaction degree to the sum of the second standard satisfaction degrees, and a third ratio of each third standard satisfaction degree to the sum of the third standard satisfaction degrees; Based on the first ratio, the entropy value of the first standard satisfaction of the target number is calculated to obtain a first entropy value; based on the second ratio, the entropy value of the second standard satisfaction of the target number is calculated to obtain a second entropy value; based on the third ratio, the entropy value of the third standard satisfaction of the target number is calculated to obtain a third entropy value; Based on the first entropy value, the sensitivity coefficient of users of the vehicle type to the charging cost of the charging station is calculated; based on the second entropy value, the sensitivity coefficient of users of the vehicle type to the arrival time period of the charging station is calculated; based on the third entropy value, the sensitivity coefficient of users of the vehicle type to the charging time of the charging station is calculated.

6. The method according to claim 4, characterized in that The description of the preset charging price of the charging station, the satisfaction of each target vehicle user with the charging cost of the charging station, the satisfaction with the planned arrival time of the target vehicle at the charging station, and the satisfaction with the charging time of the charging station, respectively, includes: For each target vehicle, obtaining the historical charging cost used by the target vehicle to fully charge the preset amount after the target vehicle arrives at the charging station at the target arrival time before the target vehicle responds to the preset charging price; Obtaining the actual charging cost for the target vehicle to be fully charged to the preset charge amount after arriving at the charging station at the planned arrival time after responding to the preset charging price, and the additional revenue brought to the charging station by being fully charged to the preset charge amount; Predicting the satisfaction of the user of the target vehicle with the charging cost of the charging station based on the historical charging cost, the actual charging cost and the additional benefit; For each target vehicle, calculating the time difference between the planned arrival time of the target vehicle after responding to the preset charging price and the target arrival time of the target vehicle before responding to the preset charging price; Based on the time difference, predicting the satisfaction of the user of the target vehicle with the planned arrival time of the target vehicle at the charging station; For each target vehicle, obtain the first total number of vehicles that visit the charging station during the time period of the first average charging power and the planned arrival time of the target vehicle after the target vehicle responds to the preset charging price, and the second total number of vehicles that visit the charging station during the time period of the target average charging power and the target arrival time of the target vehicle before the target vehicle responds to the preset charging price; Based on the first average charging power, the first total number of vehicles, the target average charging power, and the second total number of vehicles, the satisfaction of the user of the target vehicle with the charging time of the charging station is calculated.

7. The method according to claim 1, characterized in that The step of optimizing and solving the load parameters in the initial charging load curve with the goal of maximizing the total satisfaction under the preset charging price to obtain the modified charging load curve of the charging station includes: Taking the maximum total satisfaction under the preset charging price as the goal, calculating the planned arrival time and the first average charging power of each target vehicle after responding to the preset charging price when the total satisfaction is maximized; The planned arrival time of each target vehicle after responding to the preset charging price when the total satisfaction is the maximum is determined as the ideal arrival time of each target vehicle, and the first average charging power of each target vehicle after responding to the preset charging price when the total satisfaction is the maximum is determined as the ideal average charging power of each target vehicle; According to the ideal arrival time of each target vehicle, the ideal average charging power in each time period to which the ideal arrival time belongs is superimposed to obtain the total ideal average charging power corresponding to each time period; The total average charging power corresponding to each time period in the initial charging load curve is adjusted to the total ideal average charging power to obtain a modified charging load curve of the charging station.

8. A charging station load characteristic prediction device, characterized in that: include: A first determination module is used to determine target charging data of a preset number of target vehicles at the charging station in a preset future period based on a distribution law of historical vehicle charging data of the charging station, and the target charging data of the target vehicles meets the distribution law; A construction module, configured to construct, based on the target charging data, an initial charging load curve of the charging station corresponding to the preset number of target vehicles in the preset future period; a description module, for describing the total satisfaction of users of the preset number of target vehicles with the charging station at the preset charging price of the charging station based on the arrival time period of each target vehicle at the charging station, the charging time and the charging fee at the charging station before and after each target vehicle responds to the preset charging price of the charging station; A solution module is used to optimize and solve the load parameters in the initial charging load curve with the goal of maximizing the total satisfaction at the preset charging price, so as to obtain a modified charging load curve of the charging station, wherein the modified charging load curve maximizes the total satisfaction at the preset charging price.

9. A computer device, characterized in that: include: A memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the charging station load characteristic prediction method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting the load characteristics of a charging station according to any one of claims 1 to 7 is implemented.

Images