Intelligent charging control method, device, equipment, system and storage medium

Abstract

The invention is suitable for the technical field of charging control, and provides an intelligent charging control method, device, equipment and system and a storage medium, and the method comprises the steps: obtaining multi-dimensional operation data which comprises power grid state information and charging station and vehicle information; according to the multi-dimensional operation data, a scheduling distribution scheme is obtained, the scheduling distribution scheme comprises a vehicle charging strategy, and the vehicle charging strategy is used for stipulating the charging time and the charging power condition of each vehicle charged at the charging station; and the vehicle charging strategy is issued to the charging station, so that the charging station charges each vehicle charged at the charging station according to the vehicle charging strategy. Therefore, the vehicle of the charging station can be charged as much as possible according to the power grid, the vehicle and user requirements.

Description

Technical Field

[0001] This application belongs to the field of charging control technology, and in particular relates to intelligent charging control methods, devices, equipment, systems and storage media. Background Technology

[0002] With the rapid increase in the number of electric vehicles, their centralized charging load connected to the power distribution network has become a key factor affecting the safe and stable operation and economic benefits of the power system. Due to the sudden and random nature of their charging behavior, improper scheduling can easily lead to surges in power grid peak loads and overloads of local equipment.

[0003] Currently, as the mainstream communication protocol, the Open Charge Point Protocol (OCPP) schedules charging stations through the charging profile function. The scheduling usually includes time-of-use pricing or simple power limiting, such as low-price slow charging at night and high-power fast charging during the day.

[0004] However, in the face of large-scale electric vehicle access, increasingly complex grid dispatching, and diversified user needs, the above methods suffer from insufficient dispatching granularity and difficulty in achieving charging according to vehicle demand. Summary of the Invention

[0005] This application provides intelligent charging control methods, devices, equipment, systems, and storage media, which can charge vehicles at charging stations as much as possible according to the needs of the power grid, vehicles, and users.

[0006] In a first aspect, embodiments of this application provide an intelligent charging control method, including: Acquire multi-dimensional operational data, which includes grid status information and charging station and vehicle information. Grid status information includes at least one of the following: current electricity price, load forecast information, and load constraints. Charging station and vehicle information includes at least one of the following: current state of charge (SOC), target SOC, expected charging time, and vehicle priority information of vehicles charging at the charging station. Based on multi-dimensional operational data, a scheduling and allocation scheme is obtained. The scheduling and allocation scheme includes a vehicle charging strategy, which specifies the charging time and charging power of each vehicle charging at the charging station. The system issues vehicle charging strategies to charging stations so that they can charge each vehicle at the station in accordance with the strategies.

[0007] In one possible implementation of the first aspect, the multi-dimensional operational data also includes distributed power output information and energy storage power station operational status information. The distributed power output information includes the power of renewable energy equipment, which includes photovoltaic power generation equipment and / or wind power generation equipment. The energy storage power station operational status information includes the current SOC and power capacity of the energy storage power station. The scheduling and allocation scheme also includes an energy storage charging and discharging strategy, which specifies the charging time and charging power of the energy storage power station when charging from the power generation equipment and / or the grid, as well as the discharging time and discharging power to the charging station. Sending vehicle charging strategies to charging stations to enable charging stations to charge vehicles charging at charging piles in accordance with the vehicle charging strategies includes: sending vehicle charging strategies to charging stations and sending energy storage charging and discharging strategies to energy storage power stations to enable charging stations to charge each vehicle charging at the charging station in accordance with the vehicle charging strategies, and enabling energy storage power stations to charge from renewable energy devices and / or the power grid in accordance with the energy storage charging and discharging strategies, and to discharge to the charging stations.

[0008] In one possible implementation of the first aspect, the method further includes: If the first event is detected, the changed electricity price is used as the current electricity price, and the current SOC, target SOC, and estimated charging time of the new vehicle are updated in the charging station and vehicle information. The first event includes at least one of the following: the electricity price changes or the new vehicle accesses the charging station. Then, the step of obtaining the scheduling and allocation scheme based on multi-dimensional operating data is executed again.

[0009] In one possible implementation of the first aspect, the first event also includes a change in the power of renewable energy devices.

[0010] In one possible implementation of the first aspect, a scheduling and allocation scheme is obtained based on multi-dimensional operational data, including: Multi-dimensional operational data is input into the target optimization algorithm to obtain a scheduling and allocation scheme. The target optimization algorithm is used to generate a charging strategy related to each vehicle charging at the charging station based on the power grid status information and the charging station and vehicle information.

[0011] In one possible implementation of the first aspect, the process of generating the objective optimization algorithm includes: Obtain a sample operation dataset, which includes multiple sets of sample operation data. Each set of sample operation data is associated with an actual scheduling and allocation scheme. Each set of sample operation data includes sample power grid status information and sample charging station and vehicle information. Input the sample operation dataset into the original optimization algorithm to obtain a set of predicted scheduling and allocation schemes. Based on the difference between the predicted scheduling and allocation scheme set and the actual scheduling and allocation scheme set, train the original scheduling algorithm to obtain the target optimization algorithm.

[0012] Secondly, embodiments of this application provide an intelligent charging control device, comprising: The data acquisition and distribution module is used to acquire multi-dimensional operational data, which includes power grid status information and charging station and vehicle information. Power grid status information includes at least one of the following: current electricity price, load forecast information, and load constraints. Charging station and vehicle information includes at least one of the following: current state of charge (SOC), target SOC, expected charging time, and vehicle priority information of vehicles charging at the charging station. The intelligent scheduling module is used to obtain a scheduling and allocation scheme based on multi-dimensional operational data. The scheduling and allocation scheme includes a vehicle charging strategy, which specifies the charging time and charging power of each vehicle charging at the charging station. The data acquisition and distribution module is also used to distribute vehicle charging strategies to charging stations, so that charging stations can charge each vehicle charging at the charging station in accordance with the vehicle charging strategies.

[0013] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any one of the first aspects above.

[0014] Fourthly, embodiments of this application provide an intelligent charging system, which includes an electronic device, a charging station, and an energy storage station. The charging station includes multiple charging piles and is used to charge vehicles through the multiple charging piles. The energy storage station includes multiple energy storage cabinets and is used to store energy through the multiple energy storage cabinets and discharge to the charging station. The electronic device is used to perform the method as described in any one of the first aspects above.

[0015] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in any one of the first aspects above.

[0016] Sixthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute the method described in any one of the first aspects.

[0017] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0018] The beneficial effects of the embodiments in this application compared with the prior art are: Electronic devices can obtain grid status information from the power grid, including current electricity price, load forecast information, and load constraint information, as well as charging station and vehicle information, including current SOC, target SOC, estimated charging time, and priority information, thereby preparing data for electronic devices to generate more reasonable vehicle charging strategies and laying the foundation for charging optimization of smart charging stations.

[0019] Electronic devices can generate reasonable vehicle charging strategies that meet multiple dimensions, including safety, user satisfaction, and efficiency, based on grid status information and charging station and vehicle information. Based on this, the electronic devices can distribute the vehicle charging strategies to charging stations, enabling each charging pile at the station to charge according to the strategy for each vehicle. This allows for dynamic scheduling and optimal power allocation for multiple vehicles and charging scenarios, ensuring personalized charging needs at charging stations. Furthermore, charging can be performed according to various vehicle conditions, preventing power waste and providing a high level of user satisfaction. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of a system provided in an embodiment of this application; Figure 2 This is a schematic flowchart of an embodiment of the intelligent charging control method provided in this application; Figure 3 This is a flowchart illustrating another embodiment of the intelligent charging control method provided in this application; Figure 4 This is a flowchart illustrating another embodiment of the intelligent charging control method provided in this application; Figure 5 This is a schematic diagram of the structure of the intelligent charging control device provided in the embodiments of this application; Figure 6 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. Detailed Implementation

[0022] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0023] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0024] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0025] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0026] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0028] For ease of understanding, the examples provided are for reference only and are related to the concepts in the embodiments of this application.

[0029] 1. Current State of Charge (SOC) and Target SOC: Current SOC refers to the percentage of battery capacity remaining in the vehicle at a specific moment. Target SOC refers to the percentage of battery capacity the vehicle expects to reach at the end of charging.

[0030] This application provides an intelligent charging control method, device, equipment, system, storage medium, and program product. The method is applicable to various scenarios where charging piles at charging stations are used to charge vehicles.

[0031] This method is suitable for large commercial parks that may include charging stations with several charging piles, energy storage power stations, and renewable energy equipment (such as photovoltaic power generation equipment and wind power generation equipment). For example, the commercial park may be a highway service area.

[0032] This intelligent charging control method can be executed by an electronic device, which can be a cloud server for the power grid, or other types of equipment within the power grid, such as laptops, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), etc. This application does not impose any restrictions on the specific type of electronic device.

[0033] This intelligent charging method is applied to intelligent charging systems, which may include electronic devices, charging stations, and energy storage power stations.

[0034] like Figure 1 As shown, the electronic equipment includes a power grid / dispatch center, a back-end management system, an artificial intelligence (AI) dispatch module (which can be simply referred to as the intelligent dispatch module), and a data acquisition and distribution module. The power grid / dispatch center provides power grid status information, which may include current electricity prices, load forecasting information, and load constraints. The back-end management system manages user information, configures policies, stores and displays data, and provides operational support for system operation. The intelligent dispatch module generates dispatch allocation schemes. The data acquisition and distribution module, as the system's execution interface, is responsible for collecting relevant data from the power grid / dispatch center, energy storage power stations, and charging stations, and distributing the dispatch allocation schemes to these stations. Typically, the data acquisition and distribution module can obtain charging station and vehicle information from charging stations, distributed power output information and operating status information from energy storage power stations, and power grid status information from the intelligent dispatch module, the back-end management system, and the power grid / dispatch center.

[0035] Renewable energy equipment includes photovoltaic power generation equipment and / or wind power generation equipment, which is used to provide distributed power output information to electronic devices.

[0036] A charging station includes multiple charging piles. The charging station is used to provide charging station and vehicle information to electronic devices, receive vehicle charging strategies from electronic devices, and charge the vehicle through multiple charging piles in accordance with the vehicle charging strategies, thereby realizing distributed and flexible charging control.

[0037] An energy storage power station includes multiple energy storage cabinets (each containing an energy storage battery). The energy storage power station is used to provide electronic devices with information on the operating status of the energy storage power station, receive energy storage charging and discharging strategies from electronic devices, and charge and discharge to the charging station through multiple energy storage cabinets in accordance with the energy storage charging and discharging strategies, thereby realizing the functions of peak shaving and valley filling of electricity and backup power.

[0038] The above-mentioned components are interconnected through network communication, forming a coordinated operation system of power grid, energy storage, and charging.

[0039] Based on the above description, the intelligent charging control method provided in the embodiments of this application will be described in detail below, taking an electronic device as the execution subject.

[0040] Please see Figure 2 , Figure 2 A flowchart illustrating an embodiment of the intelligent charging control method provided in this application is shown. Figure 2 As shown, the intelligent charging control method provided in this application may include: S101. Obtain multi-dimensional operational data, which includes power grid status information and charging station and vehicle information. Power grid status information includes at least one of the following: current electricity price, load forecast information, and load constraints. Charging station and vehicle information includes at least one of the following: current SOC, target SOC, expected charging time, and vehicle priority information of vehicles charging at the charging station.

[0041] Among them, the power grid status information usually comes from the upper-level power grid, such as power plants and substations that need to supply power to charging stations.

[0042] Electricity prices may vary depending on the time of day. For example, at 2 PM, during the flat electricity price period, the price is 0.8 yuan per kilowatt-hour. However, during the evening peak (7-9 PM), the price may surge to 1.5 yuan per kilowatt-hour. At the current time of 2 PM, the current electricity price is 0.8 yuan per kilowatt-hour.

[0043] The load forecast information is obtained by the upstream power grid based on the historical power load information of the charging station. The load forecast information can be for the next day, the next hour, or even the next half hour; this application does not limit this. For example, if the load forecast information is for the next hour, and the current time is 2 PM, the load forecast information is for the period from 2 PM to 3 PM, and the total power load of the charging station will reach a peak of 800 kW between 2 PM and 3 PM.

[0044] Load constraints typically refer to the maximum load of a substation, such as a maximum load of 1000 kW, otherwise there is a risk of tripping.

[0045] Among them, the charging station and vehicle information are obtained by the charging station from the vehicle and / or based on the vehicle's information.

[0046] It should be understood that each charging vehicle has a current State of Charge (SOC) and a target SOC. After any vehicle connects to a charging pile at a charging station, the charging station can request the current SOC and target SOC from the vehicle through the charging pile. The vehicle can then send its current SOC and target SOC back to the charging station through the charging pile. Alternatively, after any vehicle connects to a charging pile at a charging station, it can directly send its current SOC and target SOC to the charging station through the charging pile. This application does not limit the method by which the charging station obtains the vehicle's current SOC and target SOC.

[0047] The estimated charging time can be calculated by the vehicle based on the current charging power and remaining battery level, and sent to the charging station actively or passively. Alternatively, the estimated charging time can be calculated based on the user's desired charging end time set on the vehicle, the current charging power, and the remaining battery level, and sent to the charging station actively or passively. For example, the vehicle's display screen can show a reminder to set a "planned charging end time," and the display screen can also show a time setting area, making it convenient for the user to set the desired charging end time (e.g., if the current time is 2 PM and the user finishes get off work at 6 PM, the user can set the desired charging end time to 6 PM).

[0048] Vehicle priority information can exist in various forms, including first-to-first-charge priority and / or priority determined by vehicle brand. This application does not limit the type of priority information. First-to-first-charge priority is determined by the time charging begins; the earlier the charging begins, the higher the priority. When priority is determined by vehicle brand, if the charging station connected to the vehicle corresponds to its brand, the vehicle has a higher priority. For example, a charging station includes charging station 1, charging station 2, and charging station 3, all of which are brand A charging stations. If vehicle a is brand A and its charging time is the earliest, vehicle a has the highest priority. If vehicle b is not brand A and its charging time is the same as vehicle a, vehicle b has a lower priority than vehicle a.

[0049] S102. Based on multi-dimensional operational data, a scheduling and allocation scheme is obtained. The scheduling and allocation scheme includes a vehicle charging strategy, which specifies the charging time and charging power of each vehicle charging at the charging station.

[0050] The vehicle charging strategy includes a charging plan for each charging station's charging piles, taking into account safety, user satisfaction, and efficiency. Safety: Ensure the total electrical load does not exceed the grid's limit (1000kW). User satisfaction: Meet the charging needs of all vehicle owners as much as possible, especially high-priority vehicles. Efficiency: Ensure each vehicle is fully charged as quickly as possible.

[0051] For example, a current charging station includes three vehicles: vehicle A, vehicle B, and vehicle C. Vehicle C has a higher priority than vehicle B, which in turn has a higher priority than vehicle A. The current vehicle charging strategy could be: immediately fast-charge vehicle C at 60kW for 50 minutes; medium-speed charge vehicle B at 30kW; and slow-speed charge vehicle A at 20kW, with off-peak charging.

[0052] S103. Issue vehicle charging strategies to the charging stations so that the charging stations can charge each vehicle charging at the charging stations in accordance with the vehicle charging strategies.

[0053] For example, if the current vehicle charging strategy is the charging strategy in example S102, then the charging station can control the charging pile that charges vehicle C to immediately allocate charging power such as 60kW to vehicle C, ensuring that it is charged within 50 minutes. The charging station can control the charging pile that charges vehicle B to charge vehicle B at a medium speed of 30kW. The charging station can control the charging pile that charges vehicle A to charge vehicle A at a slow speed of 20kW.

[0054] It should be noted that when electronic devices send vehicle charging strategies to charging stations, they can usually convert the vehicle charging strategy into a charging profile before sending it. The charging profile is used to indicate the charging power of each vehicle in the future.

[0055] In this embodiment, the electronic device can obtain grid status information, including current electricity price, load forecast information, and load constraint information, from the grid, and charging station and vehicle information, including current SOC, target SOC, expected charging time, and priority information, from the charging station. This prepares the data for the electronic device to generate a more reasonable vehicle charging strategy and lays the foundation for the charging optimization of smart charging stations.

[0056] Electronic devices can generate reasonable vehicle charging strategies that meet multiple dimensions, including safety, user satisfaction, and efficiency, based on grid status information and charging station and vehicle information. Based on this, the electronic devices can distribute the vehicle charging strategies to charging stations, enabling each charging pile at the station to charge according to the strategy for each vehicle. This allows for dynamic scheduling and optimal power allocation for multiple vehicles and charging scenarios, ensuring personalized charging needs at charging stations. Furthermore, charging can be performed according to various vehicle conditions, preventing power waste and providing a high level of user satisfaction.

[0057] As the penetration rate of renewable energy in the power system gradually increases, the volatility of new energy output is enhanced. Based on renewable energy, energy synergy and energy optimization can be achieved.

[0058] based on Figure 2 As described in the illustrated embodiments, the charging station may also include renewable energy equipment, which may include photovoltaic power generation equipment and / or wind power generation equipment. The photovoltaic power generation equipment is used to generate solar power, and the wind power generation equipment is used to generate wind power. When charging each vehicle, the charging station may also consider the electricity generated by more economical renewable energy equipment.

[0059] Below, in conjunction with Figure 3 This paper details the specific implementation process of the intelligent charging control method of this application.

[0060] Please see Figure 3 , Figure 3 A flowchart illustrating an embodiment of the intelligent charging control method provided in this application is shown. Figure 3 As shown, the intelligent charging control method provided in this application includes: S201. Obtain multi-dimensional operational data, including grid status information, charging station and vehicle information, distributed power output information, and energy storage station operational status information.

[0061] The grid status information includes at least one of the following: current electricity price, load forecast information, and load constraints; the charging station and vehicle information includes at least one of the following: current SOC, target SOC, expected charging time, and vehicle priority information of the vehicles charging at the charging station; the distributed power output information includes the power of renewable energy equipment, which includes photovoltaic power generation equipment and / or wind power generation equipment; and the energy storage station's operating status information includes the current SOC and power capacity of the energy storage station.

[0062] Among them, the output information of distributed power sources comes from renewable energy equipment. For example, the power of photovoltaic power generation equipment comes from photovoltaic power generation equipment, and the power of wind power generation equipment comes from wind power generation equipment.

[0063] The power output of both photovoltaic (PV) and wind power generation equipment is weather-dependent. For PV equipment, the power output is higher on sunny days, for example, generating 200 kW. For wind power equipment, the power output is higher on windy days, for example, generating 220 kW.

[0064] The operational status information of the energy storage power station comes from the energy storage power station itself.

[0065] The current SOC of an energy storage power station can be understood as the current SOC of the energy storage battery in the energy storage power station, for example, the current SOC of the energy storage power station is 40%.

[0066] The power capacity of an energy storage power station can be understood as the total power capacity of the energy storage batteries in the energy storage power station, that is, the maximum power of charging or discharging. For example, if the energy storage power station charges or discharges at a power of 300kW, then the power capacity of the energy storage power station is 300kW.

[0067] For further details regarding S202, please refer to [link / reference]. Figure 2 S101 in the illustrated embodiment will not be described in detail here.

[0068] S202. Based on multi-dimensional operational data, a scheduling and allocation scheme is obtained, which includes vehicle charging strategy and energy storage charging and discharging strategy.

[0069] Among them, the vehicle charging strategy is used to specify the charging time and charging power of each vehicle charging at the charging station, and the energy storage charging and discharging strategy is used to specify the charging time and charging power of the energy storage power station charging from the power generation equipment and / or the grid, as well as the discharging time and discharging power to the charging station.

[0070] For vehicle charging strategies, economic efficiency can also be considered. Economic efficiency means using electricity from cheaper renewable energy devices as much as possible, and using all renewable energy devices at night, such as using more solar power, to avoid buying electricity from the grid during peak electricity prices, so that the electricity from renewable energy devices is consumed by the charging stations as much as possible and waste is reduced.

[0071] In a specific embodiment, assuming the current charging station includes three vehicles, namely vehicle A, vehicle B, and vehicle C, with vehicle C having a higher priority than vehicle B, which in turn has a higher priority than vehicle A, then: The current vehicle charging strategy can be as follows: Vehicle C is immediately fast-charged at 60kW for 50 minutes; Vehicle B is charged at a medium speed of 30kW; and Vehicle A is slowly charged at 20kW, with off-peak charging. The electricity for Vehicles A, B, and C is supplied jointly by the photovoltaic power generation equipment and the power grid.

[0072] The current energy storage charging and discharging strategy can be as follows: if the power generation of the current photovoltaic power generation equipment exceeds the immediate consumption, it can supply power to the energy storage power station in the next 2 hours with a power supply of 30kW, which is also the charging power of 30kW, to charge the energy storage battery of the energy storage power station, and the energy storage power station provides power to the charging station.

[0073] S203, issue a vehicle charging strategy to the charging station and send an energy storage charging and discharging strategy to the energy storage power station, so that the charging station charges each vehicle charged by the charging station in accordance with the vehicle charging strategy, and the energy storage power station charges from renewable energy equipment and / or the grid in accordance with the energy storage charging and discharging strategy, and discharges to the charging station.

[0074] In a specific embodiment, assuming the current vehicle charging strategy is the charging strategy in the example of S202, and the current energy storage charging and discharging strategy is the energy storage charging and discharging strategy in the example of S202, then: The charging station can control the charging pile for vehicle C, immediately allocating a charging power of 60kW to vehicle C to ensure it is fully charged within 50 minutes. The charging station can also control the charging pile for vehicle B, charging it at a medium speed of 30kW. Furthermore, the charging station can control the charging pile for vehicle A, charging it slowly at 20kW. In the next two hours, the energy storage station can obtain power from the photovoltaic power generation equipment and supply power to the charging station, transmitting a power of 30kW.

[0075] In this embodiment, the electronic device can acquire distributed power output information including the power of renewable energy devices, and operating status information of the energy storage station including the current SOC and power capacity of the energy storage station. This prepares the data for the electronic device to generate more reasonable vehicle charging strategies and more reasonable energy storage charging and discharging strategies, laying the foundation for the charging optimization of smart charging stations.

[0076] Electronic devices can generate reasonable vehicle charging strategies and energy storage charging / discharging strategies that meet multiple dimensions of ensuring economy, safety, user satisfaction, and efficiency, based on grid status information, charging station and vehicle information, distributed power output information, and energy storage station operating status information. Based on this, the electronic devices can distribute the vehicle charging strategies to charging stations, enabling each charging pile at the station to charge according to the specific vehicle charging strategy. They can also distribute the energy storage charging / discharging strategies to energy storage stations, allowing the stations to charge from renewable energy equipment and / or the grid, and discharge to the charging stations according to their own strategies. This not only enables dynamic scheduling and optimal power allocation for multiple vehicles and charging scenarios, ensuring personalized charging needs at charging stations, but also allows for flexible linkage with photovoltaic, wind power, and energy storage stations. It considers grid peak and valley loads, renewable energy equipment output, and energy storage status to optimize charging and discharging, which is beneficial for peak shaving and valley filling and the efficient utilization of renewable energy.

[0077] based on Figure 3 As described in the illustrated embodiment, the intelligent charging system can also iterate and optimize the scheduling and allocation scheme when a new vehicle is plugged into a charging pile, in order to continuously achieve closed-loop control.

[0078] Below, in conjunction with Figure 4 This paper details the specific implementation process of the intelligent charging control method of this application.

[0079] Please see Figure 4 , Figure 4 A flowchart illustrating an embodiment of the intelligent charging control method provided in this application is shown. Figure 4 As shown, the intelligent charging control method provided in this application includes: S301. Obtain multi-dimensional operational data, including grid status information, charging station and vehicle information, distributed power output information, and energy storage station operational status information.

[0080] S302. Based on multi-dimensional operational data, a scheduling and allocation scheme is obtained, which includes vehicle charging strategy and energy storage charging and discharging strategy.

[0081] S303, issue a vehicle charging strategy to the charging station and send an energy storage charging and discharging strategy to the energy storage station, so that the charging station charges each vehicle charged by the charging station in accordance with the vehicle charging strategy, and the energy storage station charges from renewable energy equipment and / or the grid in accordance with the energy storage charging and discharging strategy, and discharges to the charging station.

[0082] S304. If the first event is detected, the changed electricity price is used as the current electricity price, and the current SOC of the new vehicle, the target SOC of the new vehicle, and the estimated charging time of the new vehicle are updated in the charging station and vehicle information, and S302 is executed again. The first event includes at least one of the following: the grid electricity price changes or the new vehicle connects to the charging station.

[0083] It should be understood that during the charging process of any vehicle, events may occur such as changes in the power of renewable energy equipment due to weather changes, changes in electricity prices, or new vehicles connecting to charging stations. The power grid and charging piles need to treat these events as the first events and report them to the electronic equipment.

[0084] In addition, the first event may also include situations such as a vehicle ending its charging prematurely or a decrease in the energy storage SOC. This application does not limit the specific type of the first event.

[0085] For example, if the weather suddenly changes, causing the photovoltaic output of the photovoltaic power generation equipment to drop from 120kW to 40kW, the total demand is still 110kW (60kW from vehicle C + 30kW from vehicle B + 20kW from vehicle A), but the photovoltaic system can only provide 40kW, resulting in a shortfall of 70kW. In this case, the photovoltaic power generation equipment needs to report the change in its power output to the electronic equipment.

[0086] Renewable energy equipment includes photovoltaic (PV) power generation equipment and wind power generation equipment. After receiving an event indicating a change in the power of the PV power generation equipment, the electronic equipment can update the power of the PV power generation equipment in multi-dimensional operational data and execute S302 again to allow the electronic equipment to regenerate the scheduling and allocation scheme. For example, in the regenerated scheduling and allocation scheme, the energy storage charging and discharging strategy can be set to obtain electricity from the wind power generation equipment (charging), combine it with the electricity from the grid, and continue discharging to the charging station to maintain the combined power of vehicle C (60kW), vehicle B (30kW), and vehicle A (20kW).

[0087] In this embodiment, after the electronic device sends the vehicle charging strategy to the charging station and the energy storage charging and discharging strategy to the energy storage power station, if the upper-level power grid, energy storage power station, charging station, and renewable energy equipment detect the first event, they can report to the electronic device. In this way, the electronic device can iterate and optimize the scheduling and allocation scheme to regenerate a scheduling and allocation scheme that is more suitable for the current situation. This enables multi-objective coordinated operation of the power grid side, energy storage side, and user side, and can continuously achieve closed-loop control, realizing dynamic and real-time intelligent scheduling.

[0088] Furthermore, in some embodiments, a scheduling and allocation scheme is obtained based on multi-dimensional operational data, including: Multi-dimensional operational data is input into the target optimization algorithm to obtain a scheduling and allocation scheme. The target optimization algorithm is used to generate a charging strategy related to each vehicle charging at the charging station based on the power grid status information and the charging station and vehicle information.

[0089] The objective optimization algorithm can be a genetic algorithm, a particle swarm optimization algorithm, or a simulated annealing algorithm. This application does not specifically limit the type of objective optimization algorithm.

[0090] Since the target optimization algorithm is used to generate a charging strategy related to each vehicle charging at the charging station based on the grid state information and the charging station and vehicle information, the charging strategy needs to meet the grid load and vehicle charging demand. After the power station equipment receives multi-dimensional operating data, it can input the multi-dimensional operating data into the target optimization algorithm. The target optimization algorithm can calculate the optimal charging situation, i.e., the vehicle charging strategy, based on the multi-dimensional operating data and under various constraints (such as safety, user satisfaction and efficiency).

[0091] It should be noted that if the multi-dimensional operational data also includes distributed power generation output information and energy storage station operation status information, the multi-dimensional operational data input to the target optimization algorithm also includes distributed power generation output information and energy storage station operation status information. The target optimization algorithm is used to generate a charging strategy related to each vehicle charging at the charging station, as well as a charging and discharging strategy for the energy storage station, based on the grid status information, charging station and vehicle information, distributed power generation output information, and energy storage station operation status information.

[0092] Based on the above description, in some embodiments, the generation process of the target optimization algorithm includes: Obtain a sample operation dataset, which includes multiple sets of sample operation data. Each set of sample operation data is associated with an actual scheduling and allocation scheme. Each set of sample operation data includes sample power grid status information and sample charging station and vehicle information. Input the sample operation dataset into the original optimization algorithm to obtain a set of predicted scheduling and allocation schemes. Based on the difference between the predicted scheduling and allocation scheme set and the actual scheduling and allocation scheme set, train the original scheduling algorithm to obtain the target optimization algorithm.

[0093] It should be understood that the sample power grid status information may include sample electricity prices, sample load forecast information and sample load constraints for the past N days, and the sample charging station and vehicle information may include the sample current SOC, sample target SOC, sample estimated charging duration and sample vehicle priority information for each vehicle charging at the charging station in the past M days.

[0094] The actual scheduling and allocation scheme can be specified by experts in the relevant field, or it can be generated by other simple algorithms and adjusted by experts in the relevant field. This application does not limit the way the actual scheduling and allocation scheme is obtained.

[0095] The original optimization algorithm adjusts its model parameters by comparing the predicted scheduling allocation scheme with the actual scheduling allocation scheme until the stopping condition is met (such as the loss value being lower than a certain threshold, the number of training rounds reaching the upper limit, or the loss value no longer decreasing significantly), at which point training can stop.

[0096] It should be noted that the operational data for each sample may also include output information of the sample distributed power source and operational status information of the sample energy storage power station.

[0097] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0098] Corresponding to the intelligent charging control method described in the above embodiments, Figure 5 A structural block diagram of the intelligent charging control device 400 provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0099] Reference Figure 5 The device includes: The data acquisition and distribution module 401 is used to acquire multi-dimensional operational data, which includes power grid status information and charging station and vehicle information. The power grid status information includes at least one of the following: current electricity price, load forecast information, and load constraints. The charging station and vehicle information includes at least one of the following: current state of charge (SOC), target SOC, expected charging time, and vehicle priority information of the vehicles charging at the charging station. The intelligent scheduling module 402 is used to obtain a scheduling and allocation scheme based on multi-dimensional operational data. The scheduling and allocation scheme includes a vehicle charging strategy, which specifies the charging time and charging power of each vehicle charging at the charging station. The data acquisition and distribution module 401 is also used to distribute vehicle charging strategies to the charging station so that the charging station can charge each vehicle charging at the charging station in accordance with the vehicle charging strategy.

[0100] In some embodiments, the multi-dimensional operational data also includes distributed power generation output information and energy storage power station operational status information. Distributed power generation output information includes the power of renewable energy equipment, which includes photovoltaic power generation equipment and / or wind power generation equipment. Energy storage power station operational status information includes the current SOC and power capacity of the energy storage power station. The scheduling and allocation scheme also includes an energy storage charging and discharging strategy, which specifies the charging time and charging power of the energy storage power station when charging from the power generation equipment and / or the grid, as well as the discharging time and discharging power to the charging station.

[0101] In some embodiments, the data acquisition and distribution module 401 is specifically used for: The system issues vehicle charging strategies to charging stations and sends energy storage charging and discharging strategies to energy storage power stations, so that charging stations charge each vehicle at the charging station in accordance with the vehicle charging strategies, and that energy storage power stations charge from renewable energy devices and / or the grid in accordance with the energy storage charging and discharging strategies, and discharge to the charging stations.

[0102] In some embodiments, the data acquisition and distribution module 401 is specifically used for: If the first event is detected, the changed electricity price is used as the current electricity price, and the current SOC of the new vehicle, the target SOC of the new vehicle, and the estimated charging time of the new vehicle are updated in the charging station and vehicle information. The first event includes at least one of the following: the electricity price changes or the new vehicle accesses the charging station; the intelligent scheduling module 402 is specifically used to: re-execute the step of obtaining the scheduling and allocation scheme based on multi-dimensional operating data.

[0103] In some embodiments, the first event also includes a change in the power of the renewable energy device.

[0104] In some embodiments, the intelligent scheduling module 402 is specifically used for: Multi-dimensional operational data is input into the target optimization algorithm to obtain a scheduling and allocation scheme. The target optimization algorithm is used to generate a charging strategy related to each vehicle charging at the charging station based on the power grid status information and the charging station and vehicle information.

[0105] In some embodiments, the intelligent charging control device 400 further includes a generation module, which is specifically used for: Obtain a sample operation dataset, which includes multiple sets of sample operation data. Each set of sample operation data is associated with an actual scheduling and allocation scheme. Each set of sample operation data includes sample power grid status information and sample charging station and vehicle information. Input the sample operation dataset into the original optimization algorithm to obtain a set of predicted scheduling and allocation schemes. Based on the difference between the predicted scheduling and allocation scheme set and the actual scheduling and allocation scheme set, train the original scheduling algorithm to obtain the target optimization algorithm.

[0106] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0107] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0108] This application also provides an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0109] For example, this application also provides a schematic diagram of the structure of an electronic device. Figure 6 As shown, the electronic device 400 includes a processor 401, a memory 402, a communication interface 403, and a bus 404. The processor 401, memory 402, and communication interface 403 communicate via the bus 404, or via other means such as wireless transmission. The memory 402 stores instructions, and the processor 401 executes the instructions stored in the memory 402. The memory 402 stores program code 4021, and the processor 401 can call the program code 4021 stored in the memory 402 to execute the methods described in the above embodiments.

[0110] It should be understood that in this application, processor 401 can be a CPU, or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor, etc.

[0111] The memory 402 may include read-only memory and random access memory, and provides instructions and data to the processor 401. The memory 402 may also include non-volatile random access memory. The memory 402 may be volatile memory or non-volatile memory, or may include both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0112] In addition to the data bus, the 404 bus may also include a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 6 The general will label all buses as Bus 404.

[0113] This application also provides an intelligent charging system, which includes an electronic device, a charging station, and an energy storage power station. The charging station is used to: provide charging station and vehicle information to the electronic device, receive vehicle charging strategies from the electronic device, and charge the vehicle through multiple charging piles according to the vehicle charging strategies. The energy storage power station includes multiple energy storage cabinets and is used to: provide operating status information of the energy storage power station to the electronic device, receive energy storage charging and discharging strategies from the electronic device, and charge and discharge to the charging station through multiple energy storage cabinets according to the energy storage charging and discharging strategies. The electronic device is used to execute the steps in the above-described method embodiments.

[0114] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0115] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0116] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0117] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0118] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0119] In the embodiments provided in this application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0120] The units described as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0121] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A smart charging control method, characterized in that, The method comprises: obtaining multi-dimensional operation data, wherein the multi-dimensional operation data comprises power grid state information and charging station and vehicle information, the power grid state information comprises at least one of current electricity price, load prediction information and load constraint condition, and the charging station and vehicle information comprises at least one of current state of charge (SOC) of a vehicle being charged in the charging station, target SOC, expected charging duration of the vehicle and priority information of the vehicle; obtaining a scheduling allocation scheme according to the multi-dimensional operation data, wherein the scheduling allocation scheme comprises a vehicle charging strategy, and the vehicle charging strategy is used to specify charging time and charging power of each vehicle being charged in the charging station; issuing the vehicle charging strategy to the charging station, so that the charging station charges each vehicle being charged in the charging station according to the vehicle charging strategy.

2. The method of claim 1, wherein, The multi-dimensional operation data further comprises distributed power output information and operation state information of an energy storage station, the distributed power output information comprises power of a renewable energy device, and the renewable energy device comprises a photovoltaic power generation device and / or a wind power generation device, and the operation state information of the energy storage station comprises current SOC and power capacity of the energy storage station. The scheduling allocation scheme further comprises an energy storage charging and discharging strategy, and the energy storage charging and discharging strategy is used to specify charging time and charging power of the energy storage station from the power generation device and / or the power grid, and discharging time and discharging power of the energy storage station to the charging station. The step of issuing the vehicle charging strategy to the charging station, so that the charging station charges each vehicle being charged in the charging station according to the vehicle charging strategy, comprises: issuing the vehicle charging strategy to the charging station and sending the energy storage charging and discharging strategy to the energy storage station, so that the charging station charges each vehicle being charged in the charging station according to the vehicle charging strategy, and the energy storage station charges from the renewable energy device and / or the power grid according to the energy storage charging and discharging strategy, and discharges to the charging station.

3. The method of claim 2, wherein, The method further comprises: if a first event is detected, updating the current electricity price as the changed electricity price, and updating the current SOC of a new vehicle, the target SOC of the new vehicle and the expected charging duration of the new vehicle into the charging station and vehicle information, wherein the first event comprises at least one of a change in electricity price and a new vehicle accessing the charging station; performing again the step of obtaining the scheduling allocation scheme according to the multi-dimensional operation data.

4. The method of claim 3, wherein, The first event further comprises a change in power of the renewable energy device.

5. The method of any one of claims 1 to 4, wherein, The step of obtaining the scheduling allocation scheme according to the multi-dimensional operation data comprises: inputting the multi-dimensional operation data into a target optimization algorithm to obtain the scheduling allocation scheme, wherein the target optimization algorithm is used to generate a charging strategy related to each vehicle being charged in the charging station according to the power grid state information and the charging station and vehicle information.

6. The method of claim 5, wherein, The generation process of the target optimization algorithm comprises: obtain a sample operation data set, the sample operation data set comprising a plurality of groups of sample operation data, each group of sample operation data being associated with an actual scheduling allocation scheme, each group of sample operation data comprising sample power grid state information and sample charging station and vehicle information; input the sample operation data set into an original optimization algorithm to obtain a predicted scheduling allocation scheme set; train the original scheduling algorithm according to differences between the predicted scheduling allocation scheme set and the actual scheduling allocation scheme set to obtain the target optimization algorithm.

7. An intelligent charging control device, characterized by, comprise: a data acquisition and delivery module configured to obtain multi-dimensional operation data, the multi-dimensional operation data comprising power grid state information and charging station and vehicle information, the power grid state information comprising at least one of a current electricity price, load prediction information, and load constraint conditions, and the charging station and vehicle information comprising at least one of a current state of charge (SOC) of a vehicle being charged in the charging station, a target SOC, a predicted charging duration, and priority information of the vehicle; an intelligent scheduling module configured to obtain a scheduling allocation scheme according to the multi-dimensional operation data, the scheduling allocation scheme comprising a vehicle charging strategy for specifying charging time and charging power of each vehicle being charged in the charging station; the data acquisition and delivery module is further configured to deliver the vehicle charging strategy to the charging station, so that the charging station charges each vehicle being charged in the charging station according to the vehicle charging strategy.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the method of any one of claims 1-6.

9. An intelligent charging system characterized by, The intelligent charging system comprises an electronic device, a charging station, and a storage power station; The charging station comprises a plurality of charging piles, and the charging station is configured to provide charging station and vehicle information to the electronic device, receive a vehicle charging strategy from the electronic device, and charge vehicles through the plurality of charging piles according to the vehicle charging strategy; The storage power station comprises a plurality of storage cabinets, and the storage power station is configured to provide operation state information of the storage power station to the electronic device, receive a storage charging and discharging strategy from the electronic device, and charge and discharge to the charging station through the plurality of storage cabinets according to the storage charging and discharging strategy; The electronic device is configured to execute the method of any one of claims 1-6, and the charging station comprises a plurality of charging piles.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the method of any one of claims 1-6. The computer program is executed by the processor to implement the method of any one of claims 1-6.

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