Charging equipment regulation and control method and device, electronic equipment and storage medium

Abstract

One or more embodiments of the invention provide a regulation and control method and device of charging equipment, electronic equipment and a storage medium. The method comprises the following steps: receiving power data of a power grid side and a charging equipment side and behavior data of a user equipment side; predicting a first power grid load and a first power grid electricity price corresponding to a future time period based on the power data and the behavior data; in response to the fact that the first power grid load and / or the first power grid electricity price are / is greater than a preset threshold value, generating an ordered charging strategy based on the power data and the behavior data; simulating a second power grid load and a second power grid electricity price corresponding to the future time period based on the ordered charging strategy; and in response to the condition that the second power grid load and / or the second power grid electricity price meet a preset condition, regulating and controlling the charging equipment according to the ordered charging strategy.

Description

Control methods, devices, electronic equipment and storage media for charging equipment Technical Field

[0001] This disclosure relates to one or more embodiments in the technical field, and more particularly to a method, apparatus, electronic device, and storage medium for regulating a charging device. Background Technology

[0002] It should be noted that the above description of the technical background is only for the purpose of providing a clear and complete explanation of the technical solutions of the present invention and facilitating understanding by those skilled in the art. It should not be assumed that the above technical solutions are known to those skilled in the art simply because they have been described in the background section of this invention.

[0003] With the rapid development of the new energy vehicle industry, the number of new energy vehicles on the road continues to rise, and their charging and discharging behavior poses new challenges to the load impact and resource allocation efficiency of the power grid. Vehicle-to-Grid (V2G) technology, relying on intelligent charging and discharging hardware and information communication technology, enables bidirectional energy flow and real-time data interaction between new energy vehicles and the power grid. V2G technology breaks the traditional single-direction power supply model from the power grid to the vehicle, using the vehicle's onboard battery as a mobile energy storage unit. This allows the vehicle to obtain power from the grid to meet its driving needs, and in peak grid load, high electricity prices, or emergency scenarios, it can transmit the stored energy from the battery back to the grid, participating in the load regulation and supply-demand balance of the power system.

[0004] In related technologies, a dynamic adjustment of the grid load-based orderly charging strategy is used to achieve stable grid operation and efficient resource allocation by dynamically adjusting the orderly charging and bidirectional charging and discharging of vehicles.

[0005] However, the relevant technologies still have problems such as the inability to accurately capture the behavioral characteristics of the power grid, charging equipment and user equipment during vehicle-to-grid interaction, and the lack of effective verification of orderly charging strategies. Summary of the Invention

[0006] In view of the above, the purpose of one or more embodiments of this disclosure is to provide a method, apparatus, electronic device and storage medium for regulating a charging device to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, in a first aspect, this disclosure provides a method for regulating a charging device, comprising: receiving power data from the power grid side and the charging device side, and behavioral data from the user equipment side; predicting a first power grid load and a first power grid price for a future time period based on the power data and the behavioral data; generating an ordered charging strategy based on the power data and the behavioral data in response to the first power grid load and / or the first power grid price being greater than a preset threshold; simulating a second power grid load and a second power grid price for the future time period based on the ordered charging strategy; and regulating the charging device according to the ordered charging strategy in response to the second power grid load and / or the second power grid price meeting a preset condition.

[0008] Optionally, predicting the first grid load and the first grid electricity price for a future time period based on the power data and the behavioral data includes: obtaining available power resources for multiple historical time periods and the current time period based on the power data and the behavioral data; using the available power resources for the multiple historical time periods and the current time period as input data, and obtaining the available power resources for the future time period through a first prediction model trained based on a long short-term memory network; using the available power resources for the multiple historical time periods, the current time period, and the future time period as input data, and obtaining the first grid load and the first grid electricity price for the future time period through a second prediction model trained based on a long short-term memory network.

[0009] Optionally, generating an orderly charging strategy based on the power data and the behavioral data includes: constructing a strategy generation model based on the power data and the behavioral data; constructing the objective function and constraints of the strategy generation model based on the environmental protection objectives of the power grid side, the safety objectives of the charging equipment side, and the cost objectives of the user equipment side; solving for the optimal solution of the strategy generation model to generate the orderly charging strategy.

[0010] Optionally, based on the ordered charging strategy, simulating the second grid load and second grid electricity price corresponding to the future time period includes: simulating behavioral data and power data corresponding to the future time period according to the ordered charging strategy; taking behavioral data and power data corresponding to multiple historical time periods and the current time period, and the simulated behavioral data and power data as input, and simulating the available power resources corresponding to the future time period through a third prediction model trained based on a long short-term memory network; taking the available power resources corresponding to the multiple historical time periods and the current time period, and the available resources corresponding to the future time period as input, and simulating the second grid load and second grid electricity price corresponding to the future time period through a fourth prediction model trained based on a long short-term memory network.

[0011] Optionally, the preset conditions include: the second grid load and the second grid electricity price are less than or equal to a preset threshold; or, the peak-valley electricity price difference in the future period is less than or equal to a preset threshold.

[0012] Optionally, regulating the charging equipment according to the ordered charging strategy includes: analyzing the ordered charging strategy to determine the power outage period, power supply period, and discharge period in the future time period; the grid electricity price during the power outage period is higher than a preset threshold, the grid electricity price during the power supply period is lower than a preset threshold, and both the grid electricity price and grid load during the discharge period are higher than preset thresholds; during the power outage period, the charging equipment performs a power outage operation to stop charging the user equipment; during the power supply period, the charging equipment performs a power supply operation to supply power to the user equipment; during the discharge period, the charging equipment performs a discharge operation to extract electrical energy from user equipment whose power exceeds a preset threshold.

[0013] Optionally, it further includes: in response to the second grid load and / or the second grid electricity price not meeting the preset conditions, iteratively optimizing the orderly charging strategy until the second grid load and / or the second grid electricity price meets the preset conditions.

[0014] A second aspect of this disclosure provides a control device for a charging device, comprising: an acquisition module configured to receive power data from the power grid side and the charging device side, and behavioral data from the user equipment side; a first prediction module configured to predict a first power grid load and a first power grid price for a future time period based on the power data and the behavioral data; a generation module configured to generate an ordered charging strategy based on the power data and the behavioral data in response to the first power grid load and / or the first power grid price being greater than a preset threshold; a second prediction module configured to simulate a second power grid load and a second power grid price for the future time period based on the ordered charging strategy; and a control module configured to control the charging device according to the ordered charging strategy in response to the second power grid load and / or the second power grid price meeting preset conditions.

[0015] A third aspect of this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in the first aspect.

[0016] In a fourth aspect, this disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method as described in the first aspect.

[0017] As can be seen from the above, the charging equipment control method, device, electronic device and storage medium provided in this disclosure predict the grid load and grid electricity price in the future based on data from the grid side, the charging equipment side and the user equipment side, generate an orderly charging strategy, and further predict based on the orderly charging strategy to verify the effectiveness of the orderly charging strategy, and ensure that the generated orderly charging strategy can effectively regulate the grid load, thereby further improving grid stability and energy utilization efficiency. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in one or more embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only one or more embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 is a flowchart illustrating a method for controlling a charging device according to one or more embodiments of the present disclosure; Figure 2 is a structural diagram illustrating a control device for a charging device according to one or more embodiments of the present disclosure; Figure 3 is a hardware structure diagram illustrating an electronic device according to one or more embodiments of the present disclosure. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0021] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of this disclosure should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar words used in one or more embodiments of this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0022] As described in the background section, related technologies adjust the orderly charging strategy of charging equipment solely based on historical grid load data, thereby achieving stable grid operation and efficient resource utilization to a certain extent. However, the orderly charging strategy in these technologies adjusts only based on grid load data, without comprehensively considering the behavioral characteristics of both the charging equipment side and the user equipment side in the vehicle-grid interaction system, resulting in poor control effectiveness. Furthermore, the effectiveness of the orderly charging strategy is not verified in these technologies, further leading to a lack of quantitative evaluation criteria for control effects and hindering further optimization of the orderly charging strategy.

[0023] Based on some implementations of this disclosure, a scheme for regulating charging equipment is provided. In this scheme, based on data from the grid side, the charging equipment side, and the user equipment side, the grid load and grid electricity price for future periods are predicted to generate an orderly charging strategy. Further predictions are then made based on this orderly charging strategy to verify its effectiveness. Through this scheme, it is ensured that the generated orderly charging strategy can effectively regulate the grid load, thereby further improving grid stability and energy utilization efficiency.

[0024] Referring to Figure 1, the method for regulating a charging device according to one or more embodiments of this disclosure includes the following steps: Step S101: Receiving power data from the grid side, the charging device side, and behavioral data from the user equipment side; Step S102: Predicting a first grid load and a first grid electricity price for a future time period based on the power data and the behavioral data; Step S103: Generating an orderly charging strategy based on the power data and the behavioral data in response to the first grid load and / or the first grid electricity price being greater than a preset threshold; Step S104: Simulating a second grid load and a second grid electricity price for the future time period based on the orderly charging strategy; Step S105: Regulating the charging device according to the orderly charging strategy in response to the second grid load and / or the second grid electricity price meeting preset conditions.

[0025] In the embodiments of this disclosure, the power data on the grid side may include the power load and electricity price of the grid; the power data on the charging equipment side may include the charging records of the charging equipment; and the data on the user equipment side may include the battery level, battery capacity, charging records, discharging records, and state of charge (SOC) assessment results of the user equipment. In some embodiments, the charging equipment may include charging devices, and the user equipment may include new energy vehicles, etc.

[0026] In some embodiments, data can be received from all parties through data access modules deployed on the grid side, charging equipment side, and user equipment side. These data access modules can be used to establish communication channels between the data processing end (such as the cloud, central server, etc.) and the grid side, charging equipment side, and between the grid side and charging equipment side. Data transmission is achieved using dynamic spectrum allocation technology and channel aggregation technology, thereby reading data from the grid side and charging equipment side, and establishing communication through the Battery Management System (BMS) established on the charging equipment side and user equipment side to obtain data from the user equipment side.

[0027] Electricity data reflects the supply and demand balance of the power grid. Higher charging frequency and charging volume indicate a higher grid load, fewer dispatchable power resources, higher electricity prices, and thus a tighter supply-demand relationship. User data reflects changes in electricity demand; higher charging frequency indicates greater user demand, while higher discharging frequency indicates that basic user needs are being met. Therefore, electricity and user data can be used to measure and predict available resources for future periods. It can be understood that the proportion of grid-side available resources is highly correlated with grid load and grid electricity prices; based on available resources, future grid load and grid electricity prices can be predicted.

[0028] Therefore, in some embodiments, the available power resources corresponding to the historical time period and the current time period can be determined first by analyzing and statistically analyzing the power data and behavioral data corresponding to the historical time period and the current time period; then, the available power resources for future time periods can be predicted based on the available resources for the historical time period and the current time period; and then, the first grid load and the first grid electricity price corresponding to the future time period can be predicted based on the available power resources for the historical time period, the current time period and the predicted future time period.

[0029] In some embodiments, the prediction process can be implemented using deep learning or artificial intelligence technologies based on the aforementioned power data and behavioral data. That is, deep learning or artificial intelligence technologies can be used to predict the available resources, the first grid load, and the first grid electricity price for future time periods.

[0030] Considering that available resources, grid load, and grid electricity prices all change dynamically over time and exhibit periodic characteristics, the current state is strongly dependent on historical data. LSTM, through its gating mechanism, can effectively capture long-term and short-term time series patterns. Combined with the technical solution disclosed herein, LSTM can extract features such as the fluctuation cycle of load and electricity prices during past peak and valley periods, and uncover the temporal relationships between data. In some embodiments, LSTM can be used for prediction.

[0031] In some embodiments, the above-mentioned forecast results can also be displayed to users through visualization technology. For example, the forecast results can be displayed to power grid managers through visualization to facilitate further analysis and scheduling by the managers.

[0032] Orderly charging strategy is the core control method of vehicle-grid interaction technology. It is used to achieve the goals of grid load balance, user electricity cost reduction and power resource efficiency by means of off-peak charging and reverse discharge, while ensuring user vehicle needs and battery safety.

[0033] When the first grid load and / or the first grid electricity price exceed a preset threshold, the grid can be considered to be in a state of load imbalance, which increases user electricity costs and wastes power resources. Therefore, in some embodiments, when the first grid load and / or the first grid electricity price exceed the preset threshold, a new orderly charging strategy can be generated.

[0034] It is understandable that orderly charging strategies are related to power data and behavioral data, so new orderly charging strategies can be generated based on power data and behavioral data.

[0035] In some embodiments, a strategy generation model can be constructed based on power data and behavioral data. The objective function and constraints of the strategy generation model are constructed based on the environmental protection objectives of the power grid, the safety objectives of the charging equipment, and the cost objectives of the user equipment. The optimal solution of the strategy generation model is solved to generate an ordered charging strategy. This ordered charging strategy can adjust the power load on the power grid side, release available power resources, and thus increase the available power resources.

[0036] For example, the environmental goals on the grid side may include increasing the utilization rate of renewable energy, reducing the use of non-renewable energy, and reducing carbon emission intensity; the safety goals on the charging equipment side may include controlling the operating power of the charging equipment to not exceed the rated threshold, avoiding equipment overheating and failure caused by prolonged overload, accurately matching the charging and discharging parameters of the charging equipment and user equipment, preventing overcharging and over-discharging from damaging the battery, and ensuring the operational safety of the charging equipment and user equipment; the cost goals on the user equipment side may include minimizing electricity costs.

[0037] To further ensure the effectiveness of the orderly charging strategy, in the embodiments of this disclosure, the second grid load and the second grid electricity price in future periods can be predicted after the newly generated orderly charging strategy is executed. The effectiveness of the orderly charging strategy can be verified by judging whether the second grid load and the second grid electricity price meet the preset conditions.

[0038] In some embodiments, the process of obtaining the second grid load and the second grid electricity price may include: simulating behavioral data and power data corresponding to the future time period according to the ordered charging strategy; taking behavioral data and power data corresponding to multiple historical time periods and the current time period, and the simulated behavioral data and power data as input, simulating the available power resources corresponding to the future time period through a third prediction model trained based on a long short-term memory network; taking the available power resources corresponding to the multiple historical time periods and the current time period, and the available resources corresponding to the future time period as input, simulating the second grid load and the second grid electricity price corresponding to the future time period through a fourth prediction model trained based on a long short-term memory network.

[0039] In some embodiments, the preset conditions may include: the second grid load and the second grid electricity price are less than or equal to a preset threshold; or, the peak-valley electricity price difference in the future period is less than or equal to a preset threshold.

[0040] In some embodiments, the effectiveness of the orderly charging strategy can also be verified by comparing the second grid load with the first grid load, and the second grid price with the first grid price, based on changes in grid load and grid price.

[0041] When the second grid load and the second grid electricity price meet the preset conditions, the ordered charging strategy can be executed; when the second grid load and the second grid electricity price do not meet the preset conditions, the ordered charging strategy can be iteratively optimized until the second grid load and / or the second grid electricity price meet the preset conditions. The iterative optimization process can be a repetition of the above steps S103~S104.

[0042] In some embodiments, the orderly charging strategy may specifically include adjusting the charging and discharging power according to time-of-use differentiated electricity pricing rules. This disclosure does not limit this aspect.

[0043] In some embodiments, an ordered charging strategy can be executed on the charging device side. This ordered charging strategy can be broken down, and power outage periods, power supply periods, and discharge periods can be determined based on the ordered charging strategy. During the power outage period, the charging device side performs a power outage operation to stop charging the user equipment; during the power supply period, the charging device side performs a power supply operation to supply power to the user equipment; and during the discharge period, the charging device side performs a discharge operation to extract energy from user equipment whose power consumption exceeds a preset threshold, thereby ensuring the balance of the power grid load. Specifically, the power grid price during the power outage period is higher than the preset threshold, the power grid price during the power supply period is lower than the preset threshold, and both the power grid price and the power grid load during the discharge period are higher than the preset threshold.

[0044] It is understandable that this method can be executed by any device, equipment, platform, or cluster of devices with computing and processing capabilities.

[0045] It should be noted that the methods of one or more embodiments of this disclosure can be executed by a single device, such as a computer or server. The methods of this embodiment can also be applied in a distributed scenario, where multiple devices cooperate to complete the process. In such a distributed scenario, one of these devices may execute only one or more steps of the methods of one or more embodiments of this disclosure, and the multiple devices will interact with each other to complete the method described.

[0046] It should be noted that the above description pertains to specific embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0047] Based on the same inventive concept, corresponding to any of the above embodiments, this disclosure also provides a control device for a charging device. As shown in FIG2, the device includes: an acquisition module 11 configured to receive power data from the grid side and the charging device side, and behavioral data from the user equipment side; a first prediction module 12 configured to predict a first grid load and a first grid electricity price corresponding to a future time period based on the power data and the behavioral data; a generation module 13 configured to generate an orderly charging strategy based on the power data and the behavioral data in response to the first grid load and / or the first grid electricity price being greater than a preset threshold; a second prediction module 14 configured to simulate a second grid load and a second grid electricity price corresponding to the future time period based on the orderly charging strategy; and a control module 15 configured to control the charging device according to the orderly charging strategy in response to the second grid load and / or the second grid electricity price meeting preset conditions.

[0048] Optionally, the first prediction module 12 is specifically configured to: obtain available power resources corresponding to multiple historical time periods and the current time period based on the power data and the behavioral data; use the available power resources corresponding to the multiple historical time periods and the current time period as input data, and obtain available power resources corresponding to the future time period through a first prediction model trained based on a long short-term memory network; use the available power resources corresponding to the multiple historical time periods, the current time period, and the future time period as input data, and obtain a first grid load and a first grid electricity price corresponding to the future time period through a second prediction model trained based on a long short-term memory network.

[0049] Optionally, the generation module 13 is specifically configured to: construct a strategy generation model based on the power data and the behavioral data; construct the objective function and constraints of the strategy generation model based on the environmental protection objectives of the power grid side, the safety objectives of the charging equipment side, and the cost objectives of the user equipment side; solve the optimal solution of the strategy generation model to generate the ordered charging strategy.

[0050] Optionally, the second prediction module 14 is specifically configured to: simulate behavioral data and power data corresponding to the future time period according to the ordered charging strategy; take behavioral data and power data corresponding to multiple historical time periods and the current time period, and the simulated behavioral data and power data as input, and simulate the available power resources corresponding to the future time period through a third prediction model trained based on a long short-term memory network; take the available power resources corresponding to the multiple historical time periods and the current time period, and the available power resources corresponding to the future time period as input, and simulate the second grid load and the second grid electricity price corresponding to the future time period through a fourth prediction model trained based on a long short-term memory network.

[0051] Optionally, the preset conditions include: the second grid load and the second grid electricity price are less than or equal to a preset threshold; or, the peak-valley electricity price difference in the future period is less than or equal to a preset threshold.

[0052] Optionally, the control module 15 is specifically configured to: analyze the ordered charging strategy to determine the power outage period, power supply period, and discharge period in the future time period; the grid electricity price during the power outage period is higher than a preset threshold, the grid electricity price during the power supply period is lower than a preset threshold, and both the grid electricity price and grid load during the discharge period are higher than preset thresholds; during the power outage period, the charging equipment performs a power outage operation to stop charging the user equipment; during the power supply period, the charging equipment performs a power supply operation to supply power to the user equipment; during the discharge period, the charging equipment performs a discharge operation to extract electrical energy from user equipment whose power exceeds a preset threshold.

[0053] For ease of description, the above apparatus is described in terms of its functions, divided into various modules. Of course, when implementing one or more embodiments of this disclosure, the functions of each module can be implemented in one or more software and / or hardware.

[0054] The apparatus described above is used to implement the corresponding methods in the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0055] Figure 3 shows a more specific hardware structure diagram of an electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0056] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this disclosure.

[0057] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this disclosure are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0058] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0059] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0060] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0061] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this disclosure, and not necessarily all the components shown in the figures.

[0062] The electronic devices described above are used to implement the corresponding methods in the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0063] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0064] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this disclosure (including the claims) is limited to these examples; within the framework of this disclosure, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this disclosure as described above, which are not provided in detail for the sake of brevity.

[0065] Additionally, to simplify the description and discussion, and to avoid obscuring one or more embodiments of this disclosure, the provided drawings may or may not show well-known power / ground connections to integrated circuit (IC) chips and other components. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring one or more embodiments of this disclosure, and this also takes into account the fact that the details of implementation of these block diagram apparatuses are highly dependent on the platform on which one or more embodiments of this disclosure will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuitry) are set forth to describe exemplary embodiments of this disclosure, it will be apparent to those skilled in the art that one or more embodiments of this disclosure may be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0066] Although this disclosure has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0067] This disclosure includes one or more embodiments intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for controlling a charging device, characterized in that, include: It receives power data from the grid side, charging equipment side, and user equipment side; Based on the power data and the behavioral data, predict the first grid load and the first grid electricity price for the future time period; In response to the first grid load and / or the first grid electricity price being greater than a preset threshold, an orderly charging strategy is generated based on the power data and the behavioral data; based on the orderly charging strategy, the second grid load and the second grid electricity price corresponding to the future time period are simulated; in response to the second grid load and / or the second grid electricity price meeting preset conditions, the charging equipment is regulated according to the orderly charging strategy.

2. The method according to claim 1, characterized in that, Based on the power data and the behavioral data, predicting the first grid load and the first grid electricity price for a future time period includes: obtaining available power resources for multiple historical time periods and the current time period based on the power data and the behavioral data; using the available power resources for the multiple historical time periods and the current time period as input data, and obtaining the available power resources for the future time period through a first prediction model trained based on a long short-term memory network; using the available power resources for the multiple historical time periods, the current time period, and the future time period as input data, and obtaining the first grid load and the first grid electricity price for the future time period through a second prediction model trained based on a long short-term memory network.

3. The method according to claim 1, characterized in that, Generating an orderly charging strategy based on the power data and the behavioral data includes: constructing a strategy generation model based on the power data and the behavioral data; constructing the objective function and constraints of the strategy generation model based on the environmental protection objectives of the power grid side, the safety objectives of the charging equipment side, and the cost objectives of the user equipment side; solving for the optimal solution of the strategy generation model to generate the orderly charging strategy.

4. The method according to claim 1, characterized in that, Based on the ordered charging strategy, simulating the second grid load and second grid electricity price corresponding to the future time period includes: simulating behavioral data and power data corresponding to the future time period according to the ordered charging strategy; taking behavioral data and power data corresponding to multiple historical time periods and the current time period, and the simulated behavioral data and power data as input, and simulating the available power resources corresponding to the future time period through a third prediction model trained based on a long short-term memory network; taking the available power resources corresponding to the multiple historical time periods and the current time period, and the available resources corresponding to the future time period as input, and simulating the second grid load and second grid electricity price corresponding to the future time period through a fourth prediction model trained based on a long short-term memory network.

5. The method according to claim 1, characterized in that, The preset conditions include: the second grid load and the second grid electricity price are less than or equal to a preset threshold; or, the peak-valley electricity price difference in the future period is less than or equal to a preset threshold.

6. The method according to claim 1, characterized in that, The charging equipment is regulated according to the ordered charging strategy, including: analyzing the ordered charging strategy to determine the power outage period, power supply period, and discharge period in the future time period; the grid electricity price during the power outage period is higher than a preset threshold, the grid electricity price during the power supply period is lower than a preset threshold, and both the grid electricity price and grid load during the discharge period are higher than preset thresholds; during the power outage period, the charging equipment performs a power outage operation to stop charging the user equipment; during the power supply period, the charging equipment performs a power supply operation to supply power to the user equipment; during the discharge period, the charging equipment performs a discharge operation to extract electrical energy from user equipment whose power exceeds a preset threshold.

7. The method according to claim 1, characterized in that, Also includes: In response to the second grid load and / or the second grid electricity price not meeting the preset conditions, the orderly charging strategy is iteratively optimized until the second grid load and / or the second grid electricity price meet the preset conditions.

8. A control device for a charging equipment, characterized in that, include: The acquisition module is configured to receive power data from the grid side, the charging equipment side, and behavioral data from the user equipment side. The first prediction module is configured to predict the first grid load and the first grid electricity price for a future time period based on the power data and the behavioral data. The generation module is configured to generate an orderly charging strategy based on the power data and the behavioral data in response to the first grid load and / or the first grid electricity price being greater than a preset threshold. The second prediction module is configured to simulate the second grid load and the second grid electricity price corresponding to the future time period based on the ordered charging strategy. The control module is configured to control the charging equipment according to the orderly charging strategy in response to the second grid load and / or the second grid electricity price meeting preset conditions.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executed by the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 7.

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