A charging pile utilization rate considering electric vehicle real-time electricity price calculation method and system

Abstract

The application relates to the technical field of power demand response, and discloses a charging pile utilization rate considering electric vehicle real-time electricity price calculation method and system, which comprises the following steps: considering the charging price and date attribute of a charging station on the charging willingness of an electric vehicle user, and constructing a user charging intention model; determining a charging pile utilization rate correction coefficient based on the utilization rate and congestion degree of the charging station; constructing a target function in the form of maximizing the comprehensive benefit of a charging station operator; determining the expected charging user quantity at any moment according to the user charging intention model, and adjusting the real-time electricity price at the corresponding moment according to the charging pile utilization rate correction coefficient; and obtaining the electric vehicle real-time charging electricity price planning result by solving the target function. Compared with a traditional method, the application optimizes the charging station electricity price composition structure, improves the charging station operation benefit, effectively alleviates the problem of insufficient charging pile utilization rate, optimizes power resource allocation, and has a wide application prospect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power demand response, and more particularly to a method and system for calculating real-time electricity price of electric vehicles considering charging pile utilization rate. BACKGROUND

[0002] In recent years, with the increasing global concern for environmental protection and sustainable development, new energy vehicles, especially electric vehicles, have grown rapidly. Large-scale disordered charging will increase the burden of the distribution network, leading to increased power transmission network loss and heavy overload, which is not conducive to the safe and economic operation of the power grid. Charging infrastructure provides charging and battery swapping services for electric vehicles. Time-of-use electricity price is usually used to guide electric vehicle users to charge during off-peak hours when the electricity price is low, so as to ensure user charging demand while reducing the pressure on the power grid during peak hours, achieving balance between power supply and demand and smooth operation of the power grid.

[0003] However, at present, charging infrastructure still has problems such as unreasonable structure, uneven service, and non-standard operation, resulting in poor dispatching optimization effect. At the same time, there is a contradiction between low utilization rate of electric vehicle charging piles and overcrowding. Some electric vehicle charging operators are in a loss situation, which has discouraged the enthusiasm of charging infrastructure builders. Therefore, when formulating charging electricity price to dispatch users, not only the fluctuation of new energy on the power generation side should be considered, but also the economic efficiency of power transmission on the power consumption side should be considered to ensure the economic benefits of electric vehicle charging operators. SUMMARY

[0004] The present application provides a method and system for calculating real-time electricity price of electric vehicles considering charging pile utilization rate to overcome the defects of unreasonable structure and uneven service of existing charging infrastructure.

[0005] To solve the above technical problems, the technical solutions of the present application are as follows:

[0006] A method for calculating real-time electricity price of electric vehicles considering charging pile utilization rate, comprising the following steps:

[0007] Considering the charging price and date attribute of the charging station on the charging willingness of electric vehicle users, a user charging intention model is constructed;

[0008] Based on the utilization rate and congestion degree of the charging station, a charging pile utilization rate correction coefficient is determined;

[0009] A target function is constructed to maximize the comprehensive benefits of the charging station operator;

[0010] The expected number of charging users at any time is determined according to the user charging intention model, and the real-time electricity price at the corresponding time is adjusted according to the charging pile utilization rate correction coefficient, and the real-time electricity price planning result of electric vehicles is obtained by solving the target function.

[0011] Further, the application also proposes an electric vehicle real-time electricity price calculation system considering charging pile utilization, which applies the electric vehicle real-time electricity price calculation method considering charging pile utilization.

[0012] The charging intention updating module is used for updating the user charging intention model based on the charging station charging price and the date attribute of the electric vehicle user charging intention.

[0013] The charging pile utilization correction module is used for determining the charging pile utilization correction coefficient based on the utilization and congestion degree of the charging station.

[0014] The real-time electricity price calculation module is used for determining the expected charging user quantity at any time according to the user charging intention model, adjusting the real-time electricity price at the corresponding time according to the charging pile utilization correction coefficient, and obtaining the electric vehicle real-time electricity price planning result by solving the objective function of maximizing the comprehensive benefit of the charging station operator.

[0015] Compared with the prior art, the beneficial effects of the technical scheme of the application are:

[0016] The application takes maximizing the comprehensive benefit of the charging station operator as the target, determines the charging user quantity at different times and under different charging prices according to the user charging intention model, and obtains the optimal electric vehicle real-time charging electricity price planning result by solving the objective function after the data correction of the correction coefficient, so as to maximize the comprehensive benefit of the charging station operator while meeting the user charging demand and improving the charging pile utilization.

[0017] Compared with the traditional method, the application optimizes the charging station electricity price composition structure, improves the charging station operation income, effectively alleviates the problem of insufficient charging pile utilization, optimizes the power resource allocation, and has a wide application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The flowchart of the electric vehicle real-time electricity price calculation method according to an embodiment of the application is shown.

[0019] Figure 2 The architecture diagram of the electric vehicle real-time electricity price calculation system according to an embodiment of the application is shown. DETAILED DESCRIPTION

[0020] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The description of the exemplary embodiments is intended to apply to any exemplary embodiment, unless specified otherwise. It is understood that the following description is not intended to limit the application to the specific embodiments described. It is also understood that the exemplary embodiments are not intended to limit the application to the particular forms disclosed. Rather, the intention is to cover all modifications, equivalents and alternatives falling within the scope of the application as defined by the appended claims.

[0021] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0022] It is to be understood that the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It is to be understood that the term "and / or" as used herein encompasses all possible combinations of particular items listed apart from disjunctively interpreting such phrases. It is further understood that the terms "comprise" (and any form of comprise, e.g., comprised of, comprises, and comprising) "contain" "include" "have" "hold" "possess" "wear" and "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0023] The application will be described in detail below with reference to the attached drawings and specific embodiments.

[0024] Embodiment 1

[0025] The embodiment proposes a real-time electricity price calculation method for electric vehicles considering charging pile utilization rate, as shown in Figure 1 The flowchart of the real-time electricity price calculation method for electric vehicles considering charging pile utilization rate of the embodiment is shown in

[0026] The real-time electricity price calculation method for electric vehicles considering charging pile utilization rate proposed in the embodiment includes the following steps:

[0027] S1, considering the charging price and date attribute of the charging station and the charging willingness of the electric vehicle user, a user charging intention model is constructed;

[0028] S2, based on the utilization rate and congestion degree of the charging station, a charging pile utilization rate correction coefficient is determined;

[0029] S3, a target function is constructed to maximize the comprehensive benefit of the charging station operator;

[0030] S4, determining the expected number of charging users at any time according to the user charging intention model, adjusting the real-time electricity price at the corresponding time according to the charging pile utilization correction coefficient, and obtaining the electric vehicle real-time electricity price planning result by solving the objective function.

[0031] The embodiment collects charging station historical operation data, adopts time-of-use step charging control strategy, formulates charging station electric vehicle real-time electricity price, and provides theoretical basis and practical guidance for time-of-use charging control of electric vehicles.

[0032] Specifically, the embodiment considers different charging prices and date attributes of each user, constructs a user charging intention model by fitting historical data; the embodiment also considers the utilization and congestion of the charging station, and constructs a correction coefficient for the utilization of the charging station; further, the embodiment maximizes the comprehensive benefit of the charging station operator, determines the number of charging users at different times and different charging prices according to the user charging intention model, and obtains the optimal electric vehicle real-time charging price planning result by solving the objective function after data correction of the correction coefficient.

[0033] In the specific implementation process, the historical data set is constructed by collecting charging station historical operation data and charging station unit power purchase cost.

[0034] The charging station historical operation data includes charging station charging price, charging station electric vehicle quantity, charging station electric vehicle battery capacity, charging station electric vehicle charging power, charging station maximum allowable electric vehicle quantity, charging station electric vehicle queuing time, etc.

[0035] In an optional embodiment, the user charging intention model is constructed, including the following steps:

[0036] S1.1, collecting charging station historical operation data, including real-time charging price, charging user quantity and / or charging station electric vehicle queuing time of the charging station at each sampling time within at least one week in history;

[0037] S1.2, fitting the charging station historical operation data based on the sampling time and the corresponding charging price, and constructing a user charging intention model; the expression is:

[0038]

[0039] wherein c i，j represents the user intended charging price at the jth time of the ith day, represents the charging user at the jth time of the ith day; α i，j , β i，j , γ i，j are model parameters.

[0040] For example, the model parameter α of the user charging intention model i，j β i，j γ i，j Adjust the settings according to the actual application scenario.

[0041] Where i∈[1,7] is the Sunday number in the weekly time series. In particular, for cases with national statutory holidays, i∈[1,6] can be optionally set.

[0042] In an optional embodiment, determining the charging pile utilization correction coefficient based on the charging station's utilization rate and congestion includes the following steps:

[0043] S2.1 Collect historical operational data of charging stations, including the number of charging piles in operation, the number of charging users, the maximum number of electric vehicles that the charging station can accommodate, the average queuing time of electric vehicles at the charging station, and / or the average charging time of electric vehicles at the charging station at each sampling time within at least one week in history.

[0044] S2.2 Calculate the charging station utilization rate μus at time j on day i. e , i, j; its expression is:

[0045]

[0046] Where, m i，j n represents the number of charging users at the charging station at time j on day i; i，j This refers to the number of charging piles at the charging station that are in normal operational condition at time j on day i.

[0047] S2.3 Calculate the charging station congestion level μ at time j on day i. queue，i，j Its expression is:

[0048]

[0049] in, This represents the average queuing time for electric vehicles at charging stations. This represents the average charging time for electric vehicles at charging stations; n p This indicates the maximum number of electric vehicles that a charging station is allowed to accommodate.

[0050] S2.4, Based on the charging station utilization rate μ at any given time use，i，j and charging station congestion μ queue，i，j Calculate the normalized coefficient β of charging station utilization rate. use and the normalized coefficient β of charging station congestion queue Further determine the charging pile utilization correction coefficient A i，j Its expression is:

[0051]

[0052] Where, μ queue，max and μ queue，min These represent the maximum and minimum values ​​of charging station congestion, respectively; μ use，max and μ use，min This indicates the maximum and minimum utilization rates of the charging station.

[0053] In an optional embodiment, in step S3, the objective function aims to maximize the overall benefits of the charging station operator and determines the real-time electricity price of the charging station at various times of the day; the expression for the overall benefits R of the charging station operator is:

[0054]

[0055] Among them, C i，j c represents the real-time electricity price at the charging station at time j on day i. i，j，base P represents the unit electricity purchase cost of the charging station at time j on day i. c，i，j ΔT represents the total charging power of the charging station at time j on day i, and ΔT is the preset electricity price update interval.

[0056] Furthermore, in an optional embodiment, obtaining the real-time electricity price planning result for electric vehicles by solving the objective function includes the following steps:

[0057] S4.1 Determine the user's intended charging price c at each moment based on the user charging intention model. i，j Number of charging users m i，j And the corresponding charging power of electric vehicles;

[0058] S4.2, Based on the charging pile utilization rate correction coefficient, determine the user's intended charging price c at the corresponding time. i j is adjusted, and the total charging power of the charging station is determined based on the number of charging users at the corresponding time and the charging power of the corresponding electric vehicles; its expression is:

[0059] C i，j =A i，j ·c i，j

[0060]

[0061]

[0062] in, The charging price c represents the user's charging intention model. i，j The corresponding number of users intending to charge; A i，jThis is the correction factor for the utilization rate of charging piles; m i，j n represents the number of charging users at the charging station at time j on day i; i，j p represents the number of charging piles with normal operational capability at time j on day i; c，i，j，k This represents the electric vehicle charging power of user k at time j on day i;

[0063] S4.3 Input the adjusted user's intended charging price and the total charging power of the charging station into the objective function and solve it to obtain the real-time electricity price of the charging station at each time of day.

[0064] Compared with traditional methods, this embodiment optimizes the electricity price composition structure of charging stations, improves the operating revenue of charging stations, effectively alleviates the problem of insufficient utilization of charging piles, optimizes the allocation of power resources, and has broad application prospects.

[0065] Example 2

[0066] This embodiment applies the real-time electricity price calculation method for electric vehicles that considers the utilization rate of charging piles proposed in Embodiment 1, and proposes a real-time electricity price calculation system for electric vehicles that considers the utilization rate of charging piles. For example... Figure 2 The diagram shown is an architecture diagram of the real-time electricity price calculation system for electric vehicles that takes into account the utilization rate of charging piles in this embodiment.

[0067] The real-time electricity price calculation system for electric vehicles that considers the utilization rate of charging piles proposed in this embodiment includes:

[0068] The charging intention update module is used to update the user charging intention model based on the charging price and date attributes of charging stations for electric vehicles.

[0069] The charging pile utilization correction module is used to determine the charging pile utilization correction coefficient based on the utilization rate and congestion of the charging station.

[0070] The real-time electricity price calculation module is used to determine the expected number of charging users at any given time based on the user charging intention model, and to adjust the real-time electricity price at the corresponding time based on the charging pile utilization correction coefficient. By solving the objective function that maximizes the comprehensive benefits of the charging station operator, the real-time electricity price planning result for electric vehicles is obtained.

[0071] In an optional embodiment, the charging intention update module performs the following steps:

[0072] Collect historical operational data of charging stations, including real-time charging prices, number of charging users, and / or queuing time of electric vehicles at charging stations for each sampling time within at least one week in history;

[0073] Based on the sampling time and the corresponding charging price, the historical operating data of the charging station is fitted to construct a user charging intention model; its expression is:

[0074]

[0075] Among them, c i，j This represents the user's intended charging price at time j on day i. α represents the charging user at time j on day i; i，j β i，j γ i，j These are the model parameters.

[0076] In an optional embodiment, the charging pile utilization correction module performs the following steps:

[0077] Collect historical operational data of charging stations, including the number of charging piles in operation, the number of charging users, the maximum number of electric vehicles that the charging station can accommodate, the average queuing time of electric vehicles at the charging station, and / or the average charging time of electric vehicles at the charging station at each sampling time within at least one week in history.

[0078] Calculate the charging station utilization rate μ at time j on day i. use，i，j Its expression is:

[0079]

[0080] Where, m i，j n represents the number of charging users at the charging station at time j on day i; i，j This refers to the number of charging piles at the charging station that are in normal operational condition at time j on day i.

[0081] Calculate the charging station congestion level μ at time j on day i. queue，i，j Its expression is:

[0082]

[0083] in, This represents the average queuing time for electric vehicles at charging stations. This represents the average charging time for electric vehicles at charging stations; n p This indicates the maximum number of electric vehicles that a charging station is allowed to accommodate.

[0084] Based on the charging station utilization rate μ at any given time use，i，j and charging station congestion μ queue，i，j Calculate the normalized coefficient β of charging station utilization rate. use and the normalized coefficient β of charging station congestion queue Further determine the charging pile utilization correction coefficient A i，j Its expression is:

[0085]

[0086] Where, μ queue，max and μ queue，min These represent the maximum and minimum values ​​of charging station congestion, respectively; μ use，max and μ use，min This indicates the maximum and minimum utilization rates of the charging station.

[0087] In an optional embodiment, the objective function aims to maximize the overall benefits of the charging station operator and determines the real-time electricity price of the charging station at various times of the day; the expression for the overall benefits R of the charging station operator is:

[0088]

[0089] Among them, C i，j c represents the real-time electricity price at the charging station at time j on day i. i，j，base P represents the unit electricity purchase cost of the charging station at time j on day i. c，i，j ΔT represents the total charging power of the charging station at time j on day i, and ΔT is the preset electricity price update interval.

[0090] In an optional embodiment, the real-time electricity price calculation module performs the following steps:

[0091] The user's intended charging price c at each moment is determined based on the user charging intention model. i，j Number of charging users m i，j And the corresponding charging power of electric vehicles;

[0092] Based on the charging pile utilization correction coefficient, the user's intended charging price c at the corresponding time. i，j Adjustments are made, and the total charging power of the charging station is determined based on the number of charging users at the corresponding time and the charging power of the corresponding electric vehicles; its expression is:

[0093] C i，j =A i，j ·c i，j

[0094]

[0095] in, The charging price c represents the user's charging intention model. i，j The corresponding number of users intending to charge; A i，j This is the correction factor for the utilization rate of charging piles; m i，j n represents the number of charging users at the charging station at time j on day i; i，jp represents the number of charging piles with normal operational capability at time j on day i; c，i，j，k This represents the electric vehicle charging power of user k at time j on day i;

[0096] The adjusted user-intended charging price and the total charging power of the charging station are input into the objective function and solved to obtain the real-time electricity price of the charging station at each time of day.

[0097] It is understood that the system in this embodiment corresponds to the method in Embodiment 1 above, and the options in Embodiment 1 above are also applicable to this embodiment, so they will not be described again here.

[0098] Example 3

[0099] This embodiment proposes a computer device, including a memory and a processor. The memory stores computer-readable instructions, which, when executed by the processor, cause the processor to perform all or part of the steps of the real-time electricity price calculation method for electric vehicles as proposed in Embodiment 1.

[0100] Example 4

[0101] This embodiment proposes a storage medium storing computer-readable instructions, wherein when the computer-readable instructions are executed by a processor, they implement all or part of the steps of the real-time electricity price calculation method for electric vehicles proposed in Embodiment 1.

[0102] By way of example, the storage medium includes, but is not limited to, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks or optical disks, and other media capable of storing program code.

[0103] By way of example, the instructions, programs, code sets, or instruction sets may be implemented using conventional programming languages.

[0104] By way of example, the processor includes, but is not limited to, smartphones, personal computers, servers, network devices, etc., for performing all or part of the steps of the real-time electricity price calculation method for electric vehicles described in Example 1.

[0105] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The device embodiments described above are merely exemplary. The modules described as separate components may or may not be physically separate. When implementing the present invention, the functions of each module can be implemented in one or more software and / or hardware. Alternatively, some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0106] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for calculating real-time electricity prices for electric vehicles considering the utilization rate of charging piles, characterized in that, Includes the following steps: Considering the impact of charging station pricing and date attributes on electric vehicle users' willingness to charge, a user charging intention model is constructed. The process of constructing a user charging intention model includes the following steps: Collect historical operational data of charging stations, including real-time charging prices, number of charging users, and / or queuing time of electric vehicles at charging stations for each sampling time within at least one week in history; Based on the sampling time and the corresponding charging price, the historical operating data of the charging station is fitted to construct a user charging intention model; its expression is: Among them, c i,j This represents the user's intended charging price at time j on day i. α represents the charging user at time j on day i; i,j β i,j γ i,j These are model parameters; Based on the utilization rate and congestion of charging stations, determine the charging pile utilization rate correction coefficient; The determination of the charging pile utilization correction coefficient based on the utilization rate and congestion of the charging station includes the following steps: Collect historical operational data of charging stations, including the number of charging piles in operation, the number of charging users, the maximum number of electric vehicles that the charging station can accommodate, the average queuing time of electric vehicles at the charging station, and / or the average charging time of electric vehicles at the charging station at each sampling time within at least one week in history. Calculate the charging station utilization rate μ at time j on day i. use,i,j Its expression is: Where, m i,j n represents the number of charging users at the charging station at time j on day i; i,j This refers to the number of charging piles at the charging station that are in normal operational condition at time j on day i. Calculate the charging station congestion level μ at time j on day i. queue,i,j Its expression is: in, This represents the average queuing time for electric vehicles at charging stations. This represents the average charging time for electric vehicles at charging stations; n p This indicates the maximum number of electric vehicles that a charging station is allowed to accommodate. Based on the charging station utilization rate μ at any given time use,i,j and charging station congestion μ queue,i,j Calculate the normalized coefficient β of charging station utilization rate. use and the normalized coefficient β of charging station congestion queue Further determine the charging pile utilization correction coefficient A i,j Its expression is: Where, μ queue,max and μ queue,min These represent the maximum and minimum values ​​of charging station congestion, respectively; μ use,max and μ use,min This indicates the maximum and minimum utilization rates of the charging station. The objective function is constructed to maximize the overall benefits for charging station operators; The expected number of charging users at any given time is determined based on the user charging intention model, and the real-time electricity price at the corresponding time is adjusted according to the charging pile utilization correction coefficient. The real-time charging electricity price planning result for electric vehicles is obtained by solving the objective function.

2. The method for calculating real-time electricity prices for electric vehicles according to claim 1, characterized in that, The objective function aims to maximize the overall benefits for charging station operators, and determines the real-time electricity price for charging stations at various times throughout the day; the expression for the overall benefits R of the charging station operator is: Among them, C i,j c represents the real-time electricity price at the charging station at time j on day i. i,j,base P represents the unit electricity purchase cost of the charging station at time j on day i. c,i,j ΔT represents the total charging power of the charging station at time j on day i, and ΔT is the preset electricity price update interval.

3. The method for calculating real-time electricity prices for electric vehicles according to claim 2, characterized in that, The process of obtaining the real-time electricity price planning result for electric vehicles by solving the objective function includes the following steps: The user's intended charging price c at each moment is determined based on the user charging intention model. i,j Number of charging users m i,j And the corresponding charging power of electric vehicles; Based on the charging pile utilization correction coefficient, the user's intended charging price c at the corresponding time. i,j Adjustments are made, and the total charging power of the charging station is determined based on the number of charging users at the corresponding time and the charging power of the corresponding electric vehicles; its expression is: C i,j =A i,j ·c i,j in, The charging price c represents the user's charging intention model. i,j The corresponding number of users intending to charge; A i,j This is the correction factor for the utilization rate of charging piles; m i,j n represents the number of charging users at the charging station at time j on day i; i,j p represents the number of charging piles with normal operational capability at time j on day i; c,i,j,k This represents the electric vehicle charging power of user k at time j on day i; The adjusted user-intended charging price and the total charging power of the charging station are input into the objective function and solved to obtain the real-time electricity price of the charging station at each time of day.

4. A real-time electricity price calculation system for electric vehicles that considers the utilization rate of charging piles, using the real-time electricity price calculation method for electric vehicles as described in any one of claims 1 to 3, characterized in that, include: The charging intention update module is used to update the user charging intention model based on the impact of charging station charging prices and date attributes on electric vehicle users' charging intentions. The charging intention update module performs the following steps: Collect historical operational data of charging stations, including real-time charging prices, number of charging users, and / or queuing time of electric vehicles at charging stations for each sampling time within at least one week in history; Based on the sampling time and the corresponding charging price, the historical operating data of the charging station is fitted to construct a user charging intention model; its expression is: Among them, c i,j This represents the user's intended charging price at time j on day i. α represents the charging user at time j on day i; i,j β i,j γ i,j These are model parameters; The charging pile utilization rate correction module is used to determine the charging pile utilization rate correction coefficient based on the utilization rate and congestion level of the charging station; the charging pile utilization rate correction module performs the following steps: Collect historical operational data of charging stations, including the number of charging piles in operation, the number of charging users, the maximum number of electric vehicles that the charging station can accommodate, the average queuing time of electric vehicles at the charging station, and / or the average charging time of electric vehicles at the charging station at each sampling time within at least one week in history. Calculate the charging station utilization rate μ at time j on day i. use,i,j Its expression is: Where, m i,j n represents the number of charging users at the charging station at time j on day i; i,j This refers to the number of charging piles at the charging station that are in normal operational condition at time j on day i. Calculate the charging station congestion level μ at time j on day i. queue,i,j Its expression is: in, This represents the average queuing time for electric vehicles at charging stations. This represents the average charging time for electric vehicles at charging stations; n p This indicates the maximum number of electric vehicles that a charging station is allowed to accommodate. Based on the charging station utilization rate μ at any given time use,i,j and charging station congestion μ queue,i,j Calculate the normalized coefficient β of charging station utilization rate. use and the normalized coefficient β of charging station congestion queue Further determine the charging pile utilization correction coefficient A i,j Its expression is: Where, μ queue,max and μ queue,min These represent the maximum and minimum values ​​of charging station congestion, respectively; μ use,max and μ use,min This indicates the maximum and minimum utilization rates of the charging station. The real-time electricity price calculation module is used to determine the expected number of charging users at any given time based on the user charging intention model, and to adjust the real-time electricity price at the corresponding time based on the charging pile utilization correction coefficient. By solving the objective function that maximizes the comprehensive benefits of the charging station operator, the real-time electricity price planning result for electric vehicles is obtained.

5. The real-time electricity price calculation system for electric vehicles according to claim 4, characterized in that, The objective function aims to maximize the overall benefits for charging station operators, and determines the real-time electricity price for charging stations at various times throughout the day; the expression for the overall benefits R of the charging station operator is: Among them, C i,j c represents the real-time electricity price at the charging station at time j on day i. i,j,base P represents the unit electricity purchase cost of the charging station at time j on day i. c,i,j ΔT represents the total charging power of the charging station at time j on day i, and ΔT is the preset electricity price update interval.

6. The real-time electricity price calculation system for electric vehicles according to claim 5, characterized in that, The real-time electricity price calculation module performs the following steps: The user's intended charging price c at each moment is determined based on the user charging intention model. i,j Number of charging users m i,j And the corresponding charging power of electric vehicles; Based on the charging pile utilization correction coefficient, the user's intended charging price c at the corresponding time. i,j Adjustments are made, and the total charging power of the charging station is determined based on the number of charging users at the corresponding time and the charging power of the corresponding electric vehicles. Its expression is: C i,j =A i,j ·c i,j in, The charging price c represents the user's charging intention model. i,j The corresponding number of users intending to charge; A i,j This is the correction factor for the utilization rate of charging piles; m i,j n represents the number of charging users at the charging station at time j on day i; i,j p represents the number of charging piles with normal operational capability at time j on day i; c,i,j,k This represents the electric vehicle charging power of user k at time j on day i; The adjusted user-intended charging price and the total charging power of the charging station are input into the objective function and solved to obtain the real-time electricity price of the charging station at each time of day.

Images