A charging pile grid-connected management method

Abstract

The present invention discloses a charging pile grid connection management method, including dividing a user's charging behavior at a charging pile into a reservation mode and a direct charging mode. The reservation mode refers to a user reserving a charging pile for grid connection through a smart terminal, uploading user parameter information to a charging station communication control system, calculating an optimal charge and discharge control strategy based on the user's reserved grid connection period, and pushing the strategy to the user's smart terminal. The direct charging mode refers to a user going directly to the charging pile, uploading charging mode information to the charging station communication control system through a smart terminal, calculating an optimal charge and discharge control strategy, and feeding it back to the user's smart terminal. The beneficial effect of the present invention is to propose a charging pile grid connection management method, calculate an optimal charge and discharge control strategy push mode based on the user's reserved grid connection period, and significantly reduce the operating costs of the charging pile control system.

Description

Technical Field

[0001] The present invention relates to the field of intelligent power distribution network communications, and in particular to a charging pile grid-connected management method. Background Art

[0002] Existing electric vehicle load models are mostly based on the charge and discharge power constraints of power batteries or on statistically analyzing EV grid connection patterns from existing public datasets to establish standardized, unified EV load models. However, in real-world EV grid-connected scenarios, EV users are highly autonomous individuals, and EV grid-connected behavior is highly uncertain, making it difficult to accurately predict EV grid-connected demand. Furthermore, vehicle-grid interaction will be a defining characteristic of EV grid-connected scenarios, and this interaction is inherently instantaneous. From a user's perspective, they desire a variety of charging modes to choose from, interact with charging stations, and autonomously adjust their charging modes, reflecting the diverse needs of these users. From the perspective of charging station power supply, user satisfaction will become an increasingly important metric for power supply quality. While incentivizing EV participation in system operation, it is necessary to account for the diverse needs of users. Therefore, to improve the relevance of research to real-world scenarios, further exploration of EV control models that account for diverse user needs is necessary. Furthermore, these control models must be highly responsive to vehicle-grid interaction.

[0003] Furthermore, existing coordination mechanisms between electric vehicles and charging stations and other distributed resources fail to account for user needs and preferences and are unsuitable for vehicle-grid interaction scenarios. Furthermore, optimizing the active power of charging stations in electric vehicle grid-connected scenarios is an optimization problem involving multiple, multidimensional uncertainties. Multiplicity is reflected in the simultaneous existence of uncertainties in multiple resources, such as the intermittent nature of renewable energy power and the volatility of electric vehicle cluster power. Multidimensionality is reflected in the multidimensional nature of certain uncertainties in real-world scenarios, such as the randomness of electric vehicle access sites and grid access times, which results in fluctuations in electric vehicle cluster power having both temporal and spatial dimensions. Therefore, further collaborative analysis methods for multiple, multidimensional uncertainties are needed. Summary of the Invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0005] In view of the above problems and / or the problems existing in the existing charging pile grid connection management methods, the present invention is proposed.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a charging pile grid connection management method, which includes:

[0007] The user's charging behavior at the charging pile is divided into reservation mode and direct charging mode;

[0008] The reservation mode refers to the user making an appointment for the charging pile to access the network through the smart terminal, uploading the user parameter information to the charging station communication control system, and the charging station communication control system initializing the grid connection cost calculation based on the user's reservation information. In the reservation mode, the optimal charge and discharge control strategy is calculated based on the user's reserved grid connection time period and pushed to the user's smart terminal. In the reservation mode, the user can select the charge and discharge control strategy through the smart terminal;

[0009] The direct charging mode means that the user goes directly to the charging pile, and the user uploads the charging mode information to the charging station communication control system through the smart terminal. The charging station communication control system reads the user's vehicle parameter information, calculates the grid connection cost, and at the same time calculates the optimal charging and discharging control strategy in the direct charging mode and feeds it back to the user's smart terminal. In the direct charging mode, the user selects the charging and discharging control strategy through the smart terminal.

[0010] As a preferred solution of the charging pile grid connection management method of the present invention, wherein: the calculation of the optimal charging and discharging control strategy based on the grid connection time period reserved by the user in the reservation mode and pushing it to the user's smart terminal includes:

[0011] Appointment model strategy equation:

[0012]

[0013] Among them, P （t） is the reservation strategy priority, Γ p Refers to the capacity-time coupling coefficient, t s With t e They refer to the start and end time of the reservation period, erf(·) refers to the capacity time deviation, and v p is the theoretical optimal charging time, δ p is the time tolerance standard deviation, M is the number of concurrent appointments, η i is the priority of the i-th user, C b （i） is the battery capacity, k p is the grid base load rate, ε p is the time period compression factor, Δt i It is the appointment time margin.

[0014] As a preferred solution of the charging pile grid-connected management method of the present invention, the step of calculating the optimal charging and discharging control strategy in the direct charging mode and feeding it back to the user's smart terminal includes:

[0015] Direct-flush strategy equation:

[0016]

[0017] Where I(t) is the responsiveness of the direct-charge strategy, To avoid negative responses, N is the number of direct requests. is the urgency of the jth request, is the three-dimensional state tensor of the charging pile, represents spatiotemporal convolution, is the demand decay rate, is the request initiation time, Λ(t) is the real-time capacity of the grid, and Ξ is the impedance compensation coefficient;

[0018] The three-dimensional state tensor of the charging pile includes power, temperature and voltage.

[0019] As a preferred solution of the charging pile grid connection management method of the present invention, the user parameter information includes:

[0020] Power battery type, capacity, rated charging power, charging efficiency, rated discharge power, and discharge efficiency.

[0021] As a preferred solution of the charging pile grid-connected management method of the present invention, the user can select the charging and discharging control strategy through the smart terminal in the reservation mode, including:

[0022] The user selects the charging and discharging control strategy through the smart terminal. If the user confirms, the charging pile control center will upload the demand information to the EVA control center; if the user does not confirm, the reservation information can be changed through the smart terminal.

[0023] As a preferred solution of the charging pile grid-connected management method of the present invention, wherein: the optimal charging and discharging control strategy is calculated in the direct charging mode and fed back to the user's smart terminal, and the user selects the charging and discharging control strategy through the smart terminal in the direct charging mode, including:

[0024] The user selects the charging and discharging control strategy through the smart terminal. If the user confirms, the charging pile control center will upload the demand information to the smart terminal. If the user does not confirm, the grid connection demand method can be changed through the smart terminal. The user cannot modify the grid connection location and charging time.

[0025] As a preferred solution of the charging pile grid connection management method of the present invention, the uploading of user parameter information to the charging station communication control system includes:

[0026] The charging pile control system consists of five key parts: ADNO control center, EVA control center, charging station control center, smart terminal and trusted authentication control center;

[0027] The communication link of the charging pile control system can be bidirectional. ADNO is responsible for the scheduling of the entire system by aggregating operating status data and data uploaded by EVA: the EVA control center aggregates and analyzes the data uploaded by the charging station control center and sends charging station power control instructions; the charging station is an entity that directly communicates with the smart terminal. The charging station can predict the network access needs of the smart terminal or obtain the reservation information of the smart terminal; when the smart terminal is connected to the grid, the charging station can dynamically read the smart terminal parameter information. The charging station control center aggregates and uploads the smart terminal grid connection related information to the EVA control center, and controls the charging and discharging power of the electric vehicle according to the received instructions;

[0028] The trusted authentication control center is responsible for the encryption and authentication of the data transmission link of the communication control system;

[0029] The ADNO is an open source electronic prototyping platform and the EVA is an auxiliary computer.

[0030] As a preferred solution of the charging pile grid-connected management method of the present invention, the ADNO aggregates the operating status data including:

[0031] From the perspective of the charging station communication control system, the grid-connected status of the power batteries belonging to the smart terminals that have been connected to the network is divided into three categories: charging, idle and discharging.

[0032] In a second aspect, some embodiments of the present invention provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation method of the above-mentioned first aspect.

[0033] In a third aspect, some embodiments of the present invention provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any one of the implementations of the first aspect is implemented.

[0034] The beneficial effect of the present invention is that a charging pile grid connection management method is proposed, which calculates the optimal charging and discharging control strategy push mode according to the grid connection period reserved by the user, so that the operating cost of the charging pile control system can be greatly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0036] Figure 1 This is a flow chart of the charging pile grid-connected management method in Example 1.

[0037] Figure 2 Schematic diagram of the electronic structure of the charging pile grid-connected management method in Example 3. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0039] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0040] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0041] Example 1

[0042] Reference Figure 1 This is the first embodiment of the present invention, which provides a charging pile grid connection management method, comprising:

[0043] like Figure 1 As shown, the user's charging behavior at the charging pile is divided into reservation mode and direct charging mode;

[0044] S1: Reservation mode means that the user reserves the charging pile for network access through the smart terminal and uploads the user parameter information to the charging station communication control system. The charging station communication control system initializes the grid connection cost calculation based on the user's reservation information. In the reservation mode, the optimal charge and discharge control strategy is calculated based on the user's reserved grid connection period and pushed to the user's smart terminal. In the reservation mode, the user can select the charge and discharge control strategy through the smart terminal. If the user confirms, the charging pile control center uploads the demand information to the EVA control center; if the user does not confirm, the reservation information can be changed through the smart terminal; the user parameter information includes the power battery type, capacity, rated charging power, charging efficiency, rated discharge power, and discharge efficiency.

[0045] In the reservation mode, the optimal charge and discharge control strategy is calculated based on the user's scheduled grid connection period and pushed to the user's smart terminal, including:

[0046] Appointment model strategy equation:

[0047]

[0048] Among them, P （t） is the reservation strategy priority, Γ p Refers to the capacity-time coupling coefficient, t s With t e They refer to the start and end time of the reservation period, erf(·) refers to the capacity time deviation, and v p is the theoretical optimal charging time, δ p is the time tolerance standard deviation, M is the number of concurrent appointments, η i is the priority of the i-th user, C b （i） is the battery capacity, k p is the grid base load rate, ε p is the time period compression factor, Δt i It is the appointment time margin.

[0049] S2: Direct charging mode means that the user goes directly to the charging pile and uploads the charging mode information to the charging station communication control system through the smart terminal. The charging station communication control system reads the user's vehicle parameter information, calculates the grid connection cost, and calculates the optimal charging and discharging control strategy in the direct charging mode and feeds it back to the user's smart terminal. In the direct charging mode, the user selects the charging and discharging control strategy through the smart terminal. If the user confirms, the charging pile control center uploads the demand information to the smart terminal; if the user does not confirm, the grid connection demand method can be changed through the smart terminal. The user cannot modify the grid connection location and charging time.

[0050] Direct-flush strategy equation:

[0051]

[0052] Where I(t) is the responsiveness of the direct-charge strategy, To avoid negative responses, N is the number of direct requests. is the urgency of the jth request, is the three-dimensional state tensor of the charging pile, represents spatiotemporal convolution, is the demand decay rate, is the request initiation time, Λ(t) is the real-time capacity of the grid, and Ξ is the impedance compensation coefficient;

[0053] The three-dimensional state tensor of the charging pile includes power, temperature and voltage.

[0054] Time period compression factor Definition: The coupling coefficient between the peak-to-valley ratio of grid load and the time offset of user demand.

[0055] Calculation formula:

[0056]

[0057] Where N is the registered user database of the charging station scheduling system, η i is the user contract priority weight, T peak is the peak period corresponding to the grid benchmark load rate, T valley is the load factor during the valley period.

[0058] Proof of feasibility:

[0059] Based on the grid interaction requirements of Article 7.3 of the State Grid's "Technical Guidelines for Charging Facilities Connected to Distribution Networks", the real-time data of the grid SCADA system is collected and periodic calculation is performed. (Update cycle 15 minutes).

[0060] Appointment time margin Definition: The buffer between the user's expected charging time and the time the system can allocate.

[0061] Calculation method:

[0062]

[0063] Where T min is the minimum interval required for equipment protection (mandatory ≥ 3 minutes). β is based on the user priority η i Dynamic adjustment (range 0.1-0.3), C b (i) is the battery capacity, P rated is the rated power, T min is the minimum time threshold, β is the gain coefficient, and Class(i) is the user category index.

[0064] Existing technical support: Refer to the charging time estimation method in Article 5.2.3 of GB / T 27930-2015 Electric Vehicle BMS Communication Protocol.

[0065] Ξ Definition: Equivalent line impedance correction from the charging pile to the distribution and transformation node.

[0066] Real-time acquisition method:

[0067]

[0068] R line With X lineThey are line resistance and reactance parameters (read from the GIS system), l refers to the physical distance from the charging pile to the transformer, α refers to the line attenuation coefficient (determined by looking up the table according to the cable model), j is the imaginary unit, S base Belongs to the baseline power.

[0069] The charging pile control system consists of five key parts: ADNO control center, EVA control center, charging station control center, smart terminal and trusted authentication control center;

[0070] The communication link of the charging pile control system can transmit in both directions. ADNO is responsible for the scheduling of the entire system by aggregating operating status data and data uploaded by EVA. The EVA control center aggregates and analyzes data uploaded by the charging station control center and sends charging station power control instructions. The charging station is an entity that directly communicates with the smart terminal. The charging station can predict the smart terminal's network access needs or obtain the smart terminal's reservation information. When the smart terminal is connected to the grid, the charging station can dynamically read the smart terminal's parameter information. The charging station control center aggregates and uploads the smart terminal's grid connection related information to the EVA control center, and controls the charging and discharging power of the electric vehicle based on the received instructions.

[0071] The trusted authentication control center is responsible for the encryption and authentication of data transmission in the communication control system;

[0072] ADNO is an open source electronic prototyping platform and EVA is an auxiliary computer.

[0073] ADNO aggregates operational status data including:

[0074] From the perspective of the charging station communication control system, the grid-connected status of the power batteries belonging to the smart terminals that have been connected to the network is divided into three categories: charging, idle and discharging.

[0075] Example 2

[0076] The second embodiment of the present invention is different from the first embodiment in that it further includes:

[0077] 1. Experimental preparation and implementation process

[0078] This experiment used a smart charging station in a central urban area as the test site, equipped with six 120kW DC fast-charging stations (numbered CZ01-CZ06) and four 22kW AC charging stations (numbered JL01-JL04). The experiment ran from May 1 to May 7, 2024, covering both weekdays and weekends. The test vehicles included 12 mainstream electric vehicles (such as the BYD Han EV and Tesla Model 3), with battery capacities ranging from 60 to 100 kWh and an initial SOC value of 20% to 40%.

[0079] Hardware configuration:

[0080] Charging pile equipment:

[0081] Dynamic power regulation module (±15% rated power).

[0082] Dual-mode communication controller (supports 5G / fiber dual channels).

[0083] Temperature / voltage triaxial sensor array (sampling rate 1kHz).

[0084] Grid monitoring:

[0085] Distribution network PMU synchronous measurement device (accuracy level 0.2).

[0086] Load rate recorder (time resolution 0.1s).

[0087] User terminal:

[0088] Customized APP (time accuracy in appointment mode ±30 seconds).

[0089] Dynamic strategy visualization interactive interface.

[0090] Experimental process:

[0091] Phase 1 (appointment mode testing):

[0092] Next-day charging reservations are open at 18:00 every day. Users submit parameters such as the desired charging period (accurate to 15 minutes), target SOC (80% / 90% / 100%), vehicle identification code, etc. through the APP.

[0093] The communication control system uses a rolling time domain optimization algorithm to generate a 24-hour charging strategy plan based on historical load data and weather forecasts (temperature prediction error ±1.5°C).

[0094] At 2:00 a.m., a strategy preview calculation is performed to generate the power curve of each charging pile and the grid interaction cost matrix. Users receive strategy push notifications between 7:00 and 8:00 a.m. and can choose to accept system recommendations or adjust charging parameters.

[0095] Phase 2 (Washback Mode Test):

[0096] Direct charging requests are randomly triggered during the daily charging peak hours (10:00-12:00, 18:00-20:00).

[0097] The charging pile collects vehicle BMS data in real time (including 20 parameters such as battery internal resistance and temperature gradient).

[0098] The communication system combines the current grid capacity margin (accuracy ±3%), the status of adjacent charging piles (temperature, voltage imbalance) and electricity price signals to generate a dynamic strategy within 300ms.

[0099] The strategy execution stage adopts PID-PWM compound control, and the power adjustment step is ≤5kW / s.

[0100] Comparison group settings:

[0101] Traditional single-mode policy group (fixed-time reservation only).

[0102] No coordinated control group (no grid status feedback in direct-charge mode).

[0103] 2. Experimental Data Record Sheet

[0104] Table 1: Reservation mode strategy parameter record

[0105]

[0106] Table 2: Real-time data recording of direct-flow mode

[0107]

[0108] Table 3: Grid status comparison record

[0109]

[0110] Table 4: User behavior characteristics record

[0111]

[0112] Table 5: Comparison of strategy execution effects

[0113]

[0114] Table 6: Comprehensive Benefit Analysis

[0115]

[0116] Experimental data demonstrates that the dual-mode collaborative strategy proposed in this paper demonstrates significant advantages in grid adaptability, user satisfaction, and device reliability. In the reservation mode (Table 1), the system's recommended charging power exhibits a strong correlation with the grid load factor (R² = 0.912), and a user confirmation rate of 93.2% demonstrates excellent strategy acceptability. The actual completion time deviation is kept within 7.9 minutes, a 49.4% improvement compared to the traditional reservation system (average deviation of 15.6 minutes).

[0117] The real-time response performance of the direct charging mode (Table 2) is demonstrated in two key aspects: First, the strategy generation time remains consistently below 300ms, a 48.3% improvement over the uncoordinated control group (average response time of 580ms); second, the temperature control effect is significant, with the charging pile operating temperature consistently below the safety threshold of 45°C, and the number of abnormalities reduced by 82.4% (Table 5). The strategy optimization index remains stable in the range of 1.65-1.85, verifying the effectiveness of the dynamic adjustment algorithm.

[0118] A comparison of grid conditions (Table 3) shows that this invention reduced peak load by 11.4 percentage points and valley fluctuation by 45.1%. Harmonic distortion dropped from 5.8% to 2.1%, meeting the requirements of the GB / T 14549-93 power quality standard. Three-phase imbalance was improved by 56.3%, effectively extending transformer life.

[0119] User behavior analysis (Table 4) reveals that commuters' reliance on the reservation model (reserving 14.2 hours in advance) contrasts sharply with their low frequency of adjustments (0.8 times per week). Meanwhile, emergency users' tolerance for rush mode (5.4 minutes) demonstrates the necessity of a rapid response mechanism. The satisfaction score reached 4.6, a 21.1% increase over the traditional system (3.8).

[0120] In terms of comprehensive benefits (Table 6), average daily operating costs decreased by 38.8%, primarily due to: ① a 29.7% increase in peak-valley electricity price arbitrage efficiency; and ② a 41.2% decrease in equipment maintenance costs. CO2 emissions were reduced by 52.0% compared to the industry benchmark, thanks to the intelligent matching of the charging process with the photovoltaic power generation curve. Regarding social benefits, the complaint rate decreased by 75.5%, confirming a substantial improvement in the user experience.

[0121] Example 3

[0122] Reference Figure 2 , which is the third embodiment of the present invention, differs from the first three embodiments in that the application module for implementing the travel-related enterprise assistant based on the large model includes:

[0123] Reference below Figure 2 , which shows a schematic structural diagram of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 2 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0124] like Figure 2 As shown, electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage device 308 into a random access memory (RAM) 303. RAM 303 also stores various programs and data required for the operation of electronic device 300. Processing device 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0125] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 2 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 2 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0126] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a terminal device such as a computer, server, mobile phone, or tablet.

[0127] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A charging pile grid connection management method, characterized in that: The following steps are involved: The user's charging behavior at the charging pile is divided into reservation mode and direct charging mode; The reservation mode refers to the user making an appointment for the charging pile to access the network through the smart terminal, uploading the user parameter information to the charging station communication control system, and the charging station communication control system initializing the grid connection cost calculation based on the user's reservation information. In the reservation mode, the optimal charge and discharge control strategy is calculated based on the user's reserved grid connection time period and pushed to the user's smart terminal. In the reservation mode, the user can select the charge and discharge control strategy through the smart terminal; The direct charging mode means that the user goes directly to the charging pile and uploads the charging mode information to the charging station communication control system through the smart terminal. The charging station communication control system reads the user's vehicle parameter information, calculates the grid connection cost, and calculates the optimal charging and discharging control strategy in the direct charging mode and feeds it back to the user's smart terminal. In the direct charging mode, the user selects the charging and discharging control strategy through the smart terminal. The method of calculating the optimal charge and discharge control strategy according to the grid connection period reserved by the user in the reservation mode and pushing it to the user's smart terminal includes: Appointment model strategy equation: Among them, P (t) is the reservation strategy priority, Γ p Refers to the capacity-time coupling coefficient, t s With t e They refer to the start and end time of the reservation period, erf(·) refers to the capacity time deviation, and v p is the theoretical optimal charging time, δ p is the time tolerance standard deviation, M is the number of concurrent appointments, η i is the priority of the i-th user, C b (i) is the battery capacity, k p is the grid base load rate, ε p is the time period compression factor, Δt i is the appointment time margin; The step of calculating the optimal charge and discharge control strategy in the direct charge mode and feeding it back to the user's smart terminal includes: Direct-flush strategy equation: Where I(t) is the responsiveness of the rush strategy, ReLU(·) is the avoidance of negative responses, and N is the number of rush requests. is the urgency of the jth request, Φ j (t) is the three-dimensional state tensor of the charging pile, represents spatiotemporal convolution, is the demand decay rate, is the request initiation time, Λ(t) is the real-time capacity of the grid, and Ξ is the impedance compensation coefficient; The three-dimensional state tensor of the charging pile includes power, temperature and voltage; The user parameter information includes: Power battery type, capacity, rated charging power, charging efficiency, rated discharge power, and discharge efficiency; In the reservation mode, the user can select the charge and discharge control strategy through the smart terminal, including: The user selects the charging and discharging control strategy through the smart terminal. If the user confirms, the charging pile control center will upload the demand information to the EVA control center; if the user does not confirm, the reservation information can be changed through the smart terminal.

2. The charging pile grid connection management method according to claim 1, characterized in that: The calculation of the optimal charge and discharge control strategy in the direct charge mode and the feedback to the user's smart terminal, wherein the user selects the charge and discharge control strategy through the smart terminal in the direct charge mode, includes: The user selects the charging and discharging control strategy through the smart terminal. If the user confirms, the charging pile control center will upload the demand information to the smart terminal. If the user does not confirm, the grid connection demand method can be changed through the smart terminal. The user cannot modify the grid connection location and charging time.

3. The charging pile grid connection management method according to claim 1, characterized in that: The uploading of user parameter information to the charging station communication control system includes: The charging pile control system consists of five key parts: ADNO control center, EVA control center, charging station control center, smart terminal and trusted authentication control center; The communication link of the charging pile control system can be bidirectionally transmitted. ADNO is responsible for the scheduling of the entire system by aggregating operating status data and data uploaded by EVA: the EVA control center aggregates and analyzes the data uploaded by the charging station control center and sends charging station power control instructions; the charging station is an entity that directly communicates with the smart terminal. The charging station can predict and obtain the network access needs of the smart terminal or obtain the reservation information of the smart terminal; when the smart terminal is connected to the grid, the charging station can dynamically read the smart terminal parameter information. The charging station control center aggregates and uploads the smart terminal grid connection related information to the EVA control center, and controls the charging and discharging power of the electric vehicle according to the received instructions; The trusted authentication control center is responsible for the encryption and authentication of the data transmission link of the communication control system; The ADNO is an open source electronic prototyping platform and the EVA is an auxiliary computer.

4. The charging pile grid connection management method according to claim 3, characterized in that: The ADNO aggregates operational status data including: From the perspective of the charging station communication control system, the grid-connected status of the power batteries belonging to the smart terminals that have been connected to the network is divided into three categories: charging, idle and discharging.

5. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 4.

6. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to implement the method according to any one of claims 1 to 4.

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