Intelligent charging and discharging control method for coordinated operation of energy storage charging piles and buses

Abstract

This invention provides an intelligent charging and discharging control method for the coordinated operation of energy storage charging piles and buses. This method relates to the field of energy storage control technology and includes: real-time acquisition of bus battery status data, grid load data, and energy storage system status data; calculation of charging demand for a preset future time period using a forecasting model based on historical operating data and real-time traffic information; selection of a mode optimization strategy based on electricity price signals and the energy storage SOC status; selection of a peak-time mode optimization strategy and a valley-time mode optimization strategy; generation of power allocation instructions that meet on-time schedule constraints and grid load constraints; and dynamic power allocation via a SiC bidirectional converter. This method improves charging efficiency and reduces bus delays caused by charging queues.

Description

Technical Field

[0001] The present invention relates to the field of charge and discharge control technology, and in particular to an intelligent charge and discharge control method for the coordinated operation of an energy storage charging pile and a bus. Background Art

[0002] With the increasing popularity of new energy buses, charging infrastructure faces significant challenges. Currently, the number of new energy buses far outnumbers the number of charging stations. Therefore, the coordination between charging stations and buses is extremely important.

[0003] In related technologies, mainstream charging piles use a fixed power output mode and cannot dynamically respond to changes in grid conditions. During the morning rush hour, when many vehicles converge on the station, the demand for charging power can be high. However, the limited functionality of a single pile can lead to long queues and, in turn, bus delays. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide an intelligent charging and discharging control method for the coordinated operation of energy storage charging piles and buses, thereby improving charging efficiency and reducing the delay rate of buses caused by charging queues.

[0005] In a first aspect, an embodiment of the present invention provides an intelligent charging and discharging control method for the coordinated operation of energy storage charging piles and buses, the method comprising: obtaining bus battery status data, grid load data, and energy storage system status data in real time; calculating charging demand for a preset future time period through a prediction model based on historical operating data and real-time traffic information; selecting a mode optimization strategy based on electricity price signals and the energy storage SOC status; the mode optimization strategy comprising: a peak mode optimization strategy and a valley mode optimization strategy; generating power allocation instructions that meet on-time schedule constraints and grid load constraints; and performing dynamic power allocation through a SiC bidirectional converter.

[0006] In a preferred embodiment of the present invention, the above-mentioned real-time acquisition of bus battery status data, grid load data and energy storage system status data includes: obtaining the SOC, SOH and GPS estimated arrival time of each bus through the on-board BMS; collecting real-time electricity price signals and load rate data from the grid dispatching system; and monitoring the SOC status and health status (SOH) of the energy storage system.

[0007] In a preferred embodiment of the present invention, the above-mentioned calculation of charging demand for a preset time period in the future based on historical operating data and real-time traffic information through a prediction model includes: constructing an LSTM neural network model and inputting dimensional data; the dimensional data includes: historical charging power data, real-time traffic index and weather data and holiday marks; and outputting the expected charging load matrix of each charging pile in the future.

[0008] In a preferred embodiment of the present invention, the peak mode strategy includes: setting the optimization objective function: Max(α*punctuality rate+β*energy storage utilization rate); applying hard constraints; the valley mode optimization strategy includes: setting the cost minimization objective function: Min(∑i=1nPiC*electricity price); applying slow charging protection constraints.

[0009] In a preferred embodiment of the present invention, the above-mentioned dynamic power distribution performed by the SiC bidirectional converter includes: selecting the power supply mode according to the power distribution instruction: when the electricity price is greater than the threshold and the energy storage SOC is greater than 30%, enabling pure energy storage power supply; when the electricity price is greater than the threshold and the energy storage SOC is less than or equal to 30%, enabling hybrid power supply; when the electricity price is less than or equal to the threshold, enabling direct power supply from the grid; and completing power switching within a preset time through the SiC converter.

[0010] In a preferred embodiment of the present invention, the above-mentioned method also includes: real-time monitoring of the fault status of the charging pile and the sudden change of the grid load; when an abnormality is detected, calling the reserved 10% energy storage capacity to establish a backup power pool, and reallocating the charging power of healthy vehicles, and migrating the vehicle at the faulty pile to an idle charging pile.

[0011] In a second aspect, an embodiment of the present invention further provides an intelligent charging and discharging control device for the coordinated operation of an energy storage charging pile and a bus. The device includes: a real-time data acquisition module for acquiring bus battery status data, grid load data, and energy storage system status data in real time; a charging demand determination module for calculating the charging demand for a preset future time period through a prediction model based on historical operating data and real-time traffic information; a mode optimization strategy selection module for selecting a mode optimization strategy based on electricity price signals and energy storage SOC status; the mode optimization strategies include: peak mode optimization strategy and valley mode optimization strategy; a power distribution instruction generation module for generating power distribution instructions that meet shift punctuality constraints and grid load constraints; and a dynamic power distribution execution module for executing dynamic power distribution through a SiC bidirectional converter.

[0012] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the intelligent charging and discharging control method for the coordinated operation of the energy storage charging pile and the bus according to the first aspect.

[0013] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the intelligent charging and discharging control method for the coordinated operation of the energy storage charging pile and the bus according to the first aspect.

[0014] The embodiments of the present invention bring the following beneficial effects:

[0015] An embodiment of the present invention provides an intelligent charging and discharging control method for the coordinated operation of energy storage charging piles and buses. This method uses real-time bus battery status data, grid load data, and energy storage system status data. Based on historical operating data and real-time traffic information, a prediction model is used to calculate charging demand for a preset future time period. Based on electricity price signals and the energy storage SOC status, a mode optimization strategy is selected. These strategies include peak-time mode optimization and off-peak mode optimization. Power allocation instructions are generated to meet on-time schedule constraints and grid load constraints. Dynamic power allocation is then implemented using a SiC bidirectional converter. This approach improves charging efficiency and reduces bus delays caused by charging queues.

[0016] Other features and advantages of the present disclosure will be set forth in the following description, or some features and advantages may be inferred or unambiguously determined from the description, or may be learned by practicing the above-mentioned technology of the present disclosure.

[0017] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A flow chart of an intelligent charging and discharging control method for the coordinated operation of an energy storage charging pile and a bus provided by an embodiment of the present invention;

[0020] Figure 2 A flow chart of another intelligent charging and discharging control method for the coordinated operation of an energy storage charging pile and a bus provided by an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of the structure of an intelligent charging and discharging control device for coordinated operation of an energy storage charging pile and a bus provided by an embodiment of the present invention;

[0022] Figure 4 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0024] With the increasing popularity of new energy buses, charging infrastructure faces significant challenges. Currently, the number of new energy buses far outnumbers the number of charging stations. Therefore, the coordination between charging stations and buses is extremely important.

[0025] In related technologies, mainstream charging piles use a fixed power output mode and cannot dynamically respond to changes in grid conditions. During the morning rush hour, when many vehicles converge on the station, the demand for charging power can be high. However, the limited functionality of a single pile can lead to long queues and, in turn, bus delays.

[0026] Moreover, relevant technologies do not consider the coordination between electricity price signals and energy storage, which leads to excessive dependence on the power grid during peak periods with high electricity prices, and a low daily average utilization rate of photovoltaic energy storage systems.

[0027] Based on this, an embodiment of the present invention provides an intelligent charging and discharging control method for the coordinated operation of energy storage charging piles and buses. This method can obtain real-time bus battery status data, grid load data, and energy storage system status data. Based on historical operating data and real-time traffic information, a prediction model is used to calculate charging demand for a preset future time period. Based on electricity price signals and the energy storage SOC status, a mode optimization strategy is selected. The mode optimization strategies include: peak mode optimization strategy and valley mode optimization strategy. Power allocation instructions are generated to meet the on-time rate constraint and grid load constraint. Dynamic power allocation is performed through a SiC bidirectional converter. This method improves charging efficiency and reduces the delay rate of buses caused by charging queues.

[0028] To facilitate understanding of this embodiment, a method for intelligent charging and discharging control of coordinated operation of an energy storage charging pile and a bus disclosed in an embodiment of the present invention is first introduced in detail.

[0029] Example 1

[0030] The embodiment of the present invention provides an intelligent charging and discharging control method for the coordinated operation of an energy storage charging pile and a bus. Figure 1 This is a flow chart of an intelligent charging and discharging control method for the coordinated operation of an energy storage charging pile and a bus provided by an embodiment of the present invention. Figure 1 As shown, the intelligent charging and discharging control method for the coordinated operation of the energy storage charging pile and the bus may include the following steps:

[0031] Step S101 , obtaining bus battery status data, grid load data, and energy storage system status data in real time.

[0032] Among them, corresponding data can be obtained through on-board BMS, power grid dispatching, energy storage monitoring and other aspects.

[0033] Step S102 : Based on historical operation data and real-time traffic information, the charging demand for a preset time period in the future is calculated using a prediction model.

[0034] For charging demand prediction, we can obtain historical charging records and the current real-time traffic index, i.e., the real-time traffic congestion index, to output the load matrix of charging piles in the future.

[0035] Step S103: Select a mode optimization strategy based on the electricity price signal and the energy storage SOC state.

[0036] Among them, the mode optimization strategy includes: peak time mode optimization strategy and valley time mode optimization strategy.

[0037] Step S104: generating a power allocation instruction that satisfies the shift punctuality constraint and the grid load constraint.

[0038] Among them, shift punctuality constraints, grid load constraints, energy storage protection constraints and single pile current limiting constraints can be implemented.

[0039] Step S105 , performing dynamic power distribution through the SiC bidirectional converter.

[0040] Specifically, dynamic power distribution through the SiC bidirectional converter can include: selecting the power supply mode according to the power distribution instruction: when the electricity price is greater than the threshold and the energy storage SOC is greater than 30%, enabling pure energy storage power supply; when the electricity price is greater than the threshold and the energy storage SOC is ≤30%, enabling hybrid power supply; when the electricity price is ≤ the threshold, enabling direct power supply from the grid; and completing power switching within a preset time through the SiC converter.

[0041] The intelligent charging and discharging control method for the coordinated operation of energy storage charging piles and buses, provided in an embodiment of the present invention, can obtain real-time bus battery status data, grid load data, and energy storage system status data. Based on historical operating data and real-time traffic information, a prediction model is used to calculate charging demand for a preset future time period. Based on electricity price signals and the energy storage SOC status, a mode optimization strategy is selected. The mode optimization strategies include peak-time mode optimization and off-peak mode optimization strategies. Power allocation instructions are generated to meet on-time schedule constraints and grid load constraints. Dynamic power allocation is performed using a SiC bidirectional converter. This approach improves charging efficiency and reduces bus delays caused by charging queues.

[0042] Example 2

[0043] An embodiment of the present invention also provides another intelligent charging and discharging control method for the coordinated operation of an energy storage charging pile and a bus; this method is implemented on the basis of the method of the above embodiment.

[0044] Figure 2 A flow chart of another intelligent charging and discharging control method for the coordinated operation of an energy storage charging pile and a bus provided by an embodiment of the present invention, such as Figure 2 As shown, the intelligent charging and discharging control method for the coordinated operation of the energy storage charging pile and the bus may include the following steps:

[0045] Step S201 , obtaining bus battery status data, grid load data, and energy storage system status data in real time.

[0046] Specifically, real-time acquisition of bus battery status data, grid load data, and energy storage system status data can include: obtaining each bus's SOC, SOH, and GPS estimated arrival time through the on-board BMS; collecting real-time electricity price signals and load rate data from the grid dispatching system; and monitoring the SOC status and health SOH of the energy storage system.

[0047] Step S202 : Based on historical operation data and real-time traffic information, the charging demand for a future preset period is calculated using a prediction model.

[0048] Specifically, based on historical operating data and real-time traffic information, the charging demand for a preset time period in the future is calculated through a prediction model, which can include: building an LSTM neural network model and inputting dimensional data; the dimensional data includes: historical charging power data, real-time traffic index, weather data and holiday marks; and outputting the expected charging load matrix of each charging pile in the future.

[0049] Step S203: Select a mode optimization strategy based on the electricity price signal and the energy storage SOC state.

[0050] Among them, the mode optimization strategy includes: peak time mode optimization strategy and valley time mode optimization strategy.

[0051] Among them, the peak mode strategy may include: setting the optimization objective function: Max (α*punctuality rate + β*energy storage utilization rate); imposing hard constraints.

[0052] Among them, the valley mode optimization strategy may include: setting the cost minimization objective function: Min (∑i=1nPiC*electricity price); imposing slow charging protection constraints.

[0053] Step S204: Generate a power allocation instruction that satisfies the shift punctuality constraint and the grid load constraint.

[0054] Among them, the constraints on flight punctuality are: , indicating that the charging completion time is less than or equal to the planned departure time - safety redundancy time, where Including the gun plug-in and unplug time and BMS handshake time; grid load constraints: , indicating that the total power is ≤ the minimum available capacity of the grid / energy storage; energy storage protection constraint: , Indicates the current remaining power percentage of the energy storage system; Single pile current limit constraint: , Indicates the hardware power of the charging pile.

[0055] Specifically, the feasible solution space is constructed: first calculate the minimum charging time for each vehicle using the following formula: ;in, Charge the vehicle to target power. is the current power of the vehicle, Q is the battery capacity, and C is the charging rate; then filter out those that do not meet vehicles, is the vehicle charging time. Multi-objective optimization solution, the objective function is: ,in, is the punctuality weight, is the grid dependence penalty factor, is the grid power, is the energy storage fluctuation penalty coefficient, Dynamic adjustment of weights: when SOC is less than the preset value, the punctuality weight can be reduced; when the grid load is greater than the preset value, the grid dependence penalty factor can be increased; when the battery health SOH is less than the preset value, a life protection item can be added. .

[0056] Step S205 , performing dynamic power distribution through the SiC bidirectional converter.

[0057] Specifically, the above step S105 has been described in detail and will not be repeated here.

[0058] Step S206: monitor the fault status of the charging pile and the sudden change of the grid load in real time.

[0059] Step S207: When an abnormality is detected, the reserved 10% energy storage capacity is called to establish a backup power pool, and the charging power of healthy vehicles is reallocated, and the vehicle at the faulty charging pile is migrated to an idle charging pile.

[0060] Example 3

[0061] Corresponding to the above method embodiment, the embodiment of the present invention provides an intelligent charging and discharging control device for coordinated operation of an energy storage charging pile and a bus. Figure 3A schematic diagram of the structure of an intelligent charging and discharging control device for coordinated operation of an energy storage charging pile and a bus provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the intelligent charging and discharging control device for the coordinated operation of the energy storage charging pile and the bus may include:

[0062] The real-time data acquisition module 301 is used to obtain the bus battery status data, grid load data and energy storage system status data in real time;

[0063] The charging demand determination module 302 is used to calculate the charging demand for a preset time period in the future through a prediction model based on historical operation data and real-time traffic information.

[0064] The mode optimization strategy selection module 303 is used to select a mode optimization strategy according to the electricity price signal and the energy storage SOC state; the mode optimization strategy includes: a peak mode optimization strategy and a valley mode optimization strategy.

[0065] The power allocation instruction generation module 304 is used to generate a power allocation instruction that meets the shift punctuality constraint and the grid load constraint.

[0066] The dynamic power allocation execution module 305 is configured to execute dynamic power allocation through the SiC bidirectional converter.

[0067] The intelligent charging and discharging control device for energy storage charging piles and buses, provided in an embodiment of the present invention, can obtain real-time bus battery status data, grid load data, and energy storage system status data. Based on historical operating data and real-time traffic information, it calculates charging demand for a preset future time period using a prediction model. Based on electricity price signals and the energy storage SOC status, it selects a mode optimization strategy, including peak-time mode optimization and off-peak mode optimization strategies. It generates power allocation instructions that meet on-time schedule constraints and grid load constraints, and implements dynamic power allocation via a SiC bidirectional converter. This approach improves charging efficiency and reduces bus delays caused by charging queues.

[0068] In some embodiments, the real-time data acquisition module is also used to obtain the SOC, SOH and GPS estimated arrival time of each bus through the on-board BMS; collect real-time electricity price signals and load rate data from the power grid dispatching system; and monitor the SOC status and health SOH of the energy storage system.

[0069] In some embodiments, the charging demand determination module is also used to construct an LSTM neural network model, input dimensional data; the dimensional data includes: historical charging power data, real-time traffic index and weather data and holiday marks; and output the expected charging load matrix of each charging pile in the future.

[0070] In some embodiments, the mode optimization strategy selection module is also used to set the optimization objective function: Max(α*punctuality rate+β*energy storage utilization rate); impose hard constraints; the valley mode optimization strategy includes: setting the cost minimization objective function: Min(∑i=1nPiC*electricity price); and imposing slow charging protection constraints.

[0071] In some embodiments, the power allocation instruction generation module is also used to enable pure energy storage power supply when the electricity price is greater than the threshold and the energy storage SOC is greater than 30%; to enable hybrid power supply when the electricity price is greater than the threshold and the energy storage SOC is less than or equal to 30%; to enable direct power supply from the grid when the electricity price is less than or equal to the threshold; and to complete power switching within a preset time through the SiC converter.

[0072] In some embodiments, the dynamic power allocation execution module is also used to monitor the fault status of charging piles and sudden changes in grid load in real time; when an abnormality is detected, the reserved 10% energy storage capacity is called to establish a backup power pool, and the charging power of healthy vehicles is reallocated, and vehicles at faulty piles are migrated to idle charging piles.

[0073] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those in the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding content in the aforementioned method embodiment.

[0074] Example 4

[0075] The embodiment of the present invention also provides an electronic device for operating the intelligent charging and discharging control method for the coordinated operation of the energy storage charging pile and the bus; Figure 4 The structure diagram of an electronic device shown in the figure includes a memory 400 and a processor 401, wherein the memory 400 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor 401 to implement the above-mentioned intelligent charging and discharging control method for the coordinated operation of the energy storage charging pile and the bus.

[0076] Further, Figure 4 The electronic device shown further includes a bus 402 and a communication interface 403 , and the processor 401 , the communication interface 403 and the memory 400 are connected via the bus 402 .

[0077] The memory 400 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is achieved through at least one communication interface 403 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 402 may be an ISA bus, a PCI bus, or an EISA bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0078] The processor 401 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor 401 or by software instructions. The above processor 401 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in memory 400, and processor 401 reads the information in memory 400 and, in conjunction with its hardware, completes the steps of the method of the aforementioned embodiment.

[0079] An embodiment of the present invention further provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the above-mentioned intelligent charging and discharging control method for the coordinated operation of the energy storage charging pile and the bus. For specific implementation, please refer to the method embodiment and will not be repeated here.

[0080] The computer program product of the intelligent charging and discharging control method for the coordinated operation of an energy storage charging pile and a bus provided in an embodiment of the present invention includes a computer-readable storage medium storing non-volatile program code executable by a processor. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be found in the method embodiment and will not be repeated here.

[0081] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0082] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. There may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some communication interface, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0083] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0084] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0085] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0086] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An intelligent charging and discharging control method for the coordinated operation of energy storage charging piles and buses, characterized in that: The method comprises: Real-time acquisition of bus battery status data, grid load data, and energy storage system status data; Based on historical operating data and real-time traffic information, a prediction model is used to calculate charging demand during future preset periods. Selecting a mode optimization strategy based on the electricity price signal and the energy storage SOC state; the mode optimization strategy includes: a peak mode optimization strategy and a valley mode optimization strategy; Generate power allocation instructions that meet shift punctuality constraints and grid load constraints; Dynamic power distribution via SiC bidirectional converters; The real-time acquisition of bus battery status data, grid load data, and energy storage system status data includes: Obtain each bus's SOC, SOH, and GPS estimated arrival time through the onboard BMS; Collect real-time electricity price signals and load rate data from the power grid dispatching system; Monitor the SOC status and health status (SOH) of the energy storage system; The method of calculating charging demand for a preset period of time in the future based on historical operating data and real-time traffic information through a prediction model includes: Build an LSTM neural network model and input dimensional data; the dimensional data includes: historical charging power data, real-time traffic index and weather data and holiday marks; Output the expected charging load matrix of each charging pile in the future; The peak mode strategy includes: Set the optimization objective function: Max(α*punctuality rate + β*energy storage utilization rate); impose hard constraints; The valley time mode optimization strategy includes: Set the cost minimization objective function: Min (∑i=1nPiC*electricity price); impose slow charging protection constraints.

2. The method according to claim 1, characterized in that The method of performing dynamic power distribution by the SiC bidirectional converter includes: selecting a power supply mode according to a power distribution instruction; When the electricity price is greater than the threshold and the energy storage SOC is greater than 30%, pure energy storage power supply is enabled; When the electricity price is greater than the threshold and the energy storage SOC is ≤30%, hybrid power supply is enabled; When the electricity price is less than or equal to the threshold, direct supply from the grid is enabled; Power switching is completed within a preset time through the SiC converter.

3. The method according to claim 2, characterized in that The method further comprises: Real-time monitoring of charging pile fault status and grid load mutation; When an abnormality is detected, the reserved 10% energy storage capacity is called upon to establish a backup power pool, and the charging power of healthy vehicles is reallocated, and vehicles at faulty charging piles are migrated to idle charging piles.

4. An intelligent charging and discharging control device for coordinated operation of an energy storage charging pile and a bus, characterized in that: An intelligent charging and discharging control method for realizing the coordinated operation of an energy storage charging pile and a bus according to any one of claims 1 to 3, the device comprising: Real-time data acquisition module, used to obtain real-time bus battery status data, grid load data and energy storage system status data; The charging demand determination module is used to calculate the charging demand for a preset period in the future through a prediction model based on historical operating data and real-time traffic information; A mode optimization strategy selection module is used to select a mode optimization strategy based on the electricity price signal and the energy storage SOC state; the mode optimization strategy includes: a peak mode optimization strategy and a valley mode optimization strategy; A power allocation instruction generation module is used to generate power allocation instructions that meet the shift punctuality constraint and grid load constraint; The dynamic power distribution execution module is used to execute dynamic power distribution through the SiC bidirectional converter.

5. An electronic device, characterized in that: It includes a processor and a memory, the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the intelligent charging and discharging control method for the coordinated operation of the energy storage charging pile and the bus as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the intelligent charging and discharging control method for the coordinated operation of the energy storage charging pile and the bus as described in any one of claims 1 to 3.

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