A method and terminal for automatic execution of optical storage and charging inspection station

Abstract

The present invention discloses a method and terminal for automated execution of a photovoltaic storage charging and inspection station. A virtual power plant operation platform generates a corresponding execution strategy according to preset invitation content, based on a pre-trained AI model and a time period electricity price. The AI ​​model includes a user charging prediction model and a photovoltaic prediction model. An equipment-side energy management system EMS generates a backup strategy according to the invitation content issued by the virtual power plant operation platform. The backup strategy controls the power value of the power grid to be the difference between an operating baseline and a response capacity. If the execution strategy takes effect normally, the equipment is automatically controlled according to the execution strategy during the invitation period. Otherwise, the equipment-side energy management system EMS executes the backup strategy to control the equipment. Two sets of automated control strategies are generated according to data analysis and AI technology. At the same time, the equipment control strategy is intelligently adjusted according to the equipment data collected during the execution process, thereby improving the safety and stability of the automated invitation execution process.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage charging and discharging regulation, and in particular to a method and terminal for automated execution of an optical storage charging inspection station. Background Art

[0002] Virtual power plant is a concept that centrally manages and dispatches decentralized energy resources (such as solar photovoltaic, wind power generation, energy storage system, etc.) through software and intelligent technology. It can optimize energy production, storage and consumption, improve energy efficiency and grid stability; demand-side response service is that the grid guides users to reduce electricity consumption during peak grid demand or increase electricity consumption during troughs through additional subsidies to balance the grid load. It is one of the main sources of operating income for virtual power plants; photovoltaic storage charging and testing stations integrate multiple functions such as photovoltaic power generation, energy storage, charging and battery testing. This site can be used as part of a virtual power plant to provide flexible energy supply and demand response for the grid.

[0003] Currently, most demand-side responses are invitation-based and executed offline without system involvement, resulting in slow response, low efficiency, and difficulty in quickly adapting to changes in grid load demand. At the same time, the manual adjustment of equipment strategies has poor accuracy, cannot fully utilize the advantages of equipment to maximize profits, and fails to fully utilize historical data and real-time data for prediction and optimization decisions.

[0004] Therefore, how to optimize the demand-side response strategy, implement a reliable and stable automation solution, and ensure that the demand-side response service can be executed quickly and accurately; at the same time, how to ensure the security and stability of the system during the automation execution process is the current problem to be solved. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method and terminal for the automated execution of an optical storage charging and inspection station, realize multi-strategy automated response to invitations and intelligently adjust equipment control strategies, improve site revenue, and ensure the security and stability of the system during automated execution.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for automated execution of an optical storage and charging inspection station, comprising the steps of:

[0008] S1. The virtual power plant operation platform generates a corresponding execution strategy according to the preset invitation content, based on the pre-trained AI model and the time-slot electricity price, wherein the AI ​​model includes a user charging prediction model and a photovoltaic prediction model;

[0009] S2. The device-side energy management system EMS generates a backup strategy according to the invitation content issued by the virtual power plant operation platform, and the backup strategy controls the power value of the power grid to be the difference between the operating baseline and the response capacity;

[0010] S3. If the execution strategy is in effect normally, the device is automatically controlled according to the execution strategy during the invitation period;

[0011] Otherwise, the device-side energy management system EMS executes the backup strategy to control the device.

[0012] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0013] A terminal for automated execution of an optical storage and charging inspection station comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0014] S1. The virtual power plant operation platform generates a corresponding execution strategy according to the preset invitation content, based on the pre-trained AI model and the time-slot electricity price, wherein the AI ​​model includes a user charging prediction model and a photovoltaic prediction model;

[0015] S2. The device-side energy management system EMS generates a backup strategy according to the invitation content issued by the virtual power plant operation platform, and the backup strategy controls the power value of the power grid to be the difference between the operating baseline and the response capacity;

[0016] S3. If the execution strategy is in effect normally, the device is automatically controlled according to the execution strategy during the invitation period;

[0017] Otherwise, the device-side energy management system EMS executes the backup strategy to control the device.

[0018] The beneficial effects of the present invention are: providing a method and terminal for the automated execution of an optical storage and charging inspection station, using data analysis and AI technology to generate execution strategies and backup strategies based on the invitation content, giving priority to using execution strategies to control the equipment during the invitation period and automatically adjusting the execution strategies, and using backup strategies to control the equipment when the execution strategies fail. On the one hand, it realizes automated execution of invitations and intelligent adjustment of equipment control strategies, thereby increasing site revenue; on the other hand, it generates two sets of equipment control strategies, thereby ensuring the safety and stability of the automated execution process. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flowchart of a method for automated execution of an optical storage and charging inspection station according to an embodiment of the present invention;

[0020] Figure 2A schematic flow chart of a method for automatically executing an optical storage and charging inspection station according to an embodiment of the present invention;

[0021] Figure 3 An architecture diagram of a method for automated execution of an optical storage and charging inspection station according to an embodiment of the present invention;

[0022] Figure 4 A structural diagram of a terminal for automated execution of an optical storage and charging inspection station according to an embodiment of the present invention;

[0023] Description of labels:

[0024] 1. A terminal for automated execution of an optical storage and charging inspection station; 2. A processor; 3. A memory. DETAILED DESCRIPTION

[0025] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0026] Please refer to Figure 1 , a method for automated execution of an optical storage and charging inspection station, comprising the steps of:

[0027] S1. The virtual power plant operation platform generates a corresponding execution strategy according to the preset invitation content, based on the pre-trained AI model and the time-slot electricity price, wherein the AI ​​model includes a user charging prediction model and a photovoltaic prediction model;

[0028] S2. The device-side energy management system EMS generates a backup strategy according to the invitation content issued by the virtual power plant operation platform, and the backup strategy controls the power value of the power grid to be the difference between the operating baseline and the response capacity;

[0029] S3. If the execution strategy is in effect normally, the device is automatically controlled according to the execution strategy during the invitation period;

[0030] Otherwise, the device-side energy management system EMS executes the backup strategy to control the device.

[0031] From the above description, it can be seen that the beneficial effects of the present invention are: providing a method for automated execution of an optical storage and charging inspection station, using data analysis and AI technology to generate execution strategies and backup strategies based on the invitation content, giving priority to using execution strategies to control the equipment during the invitation period and automatically adjusting the execution strategies, and using backup strategies to control the equipment when the execution strategies fail. On the one hand, it realizes automated execution of invitations and intelligent adjustment of equipment control strategies, thereby increasing site revenue; on the other hand, it generates two sets of equipment control strategies, ensuring the safety and stability of the automated execution process.

[0032] Furthermore, before step S1, the following steps are also included:

[0033] S01. The power grid platform initiates a demand-side response invitation to the virtual power plant operation platform and triggers a notification reminder to notify the invited user;

[0034] S02, the inviting user independently chooses whether to accept the invitation. If the invitation is accepted, the inviting user determines the invitation content through the virtual power plant operation platform and provides invitation feedback. The power grid platform receives and provides feedback of the invitation content provided by the inviting user to the virtual power plant operation platform to confirm the invitation execution.

[0035] Otherwise, end this demand-side response invitation.

[0036] From the above description, it can be seen that the power grid platform initiates a demand-side response invitation to the virtual power plant operation platform and notifies the user. The invited user can choose whether to accept the invitation, making the demand-side response more in line with the user's needs and capabilities; at the same time, the power grid platform initiates an invitation to the virtual power plant operation platform, and the platform notifies the user. This method broadens the participation channels for demand-side response. More users can receive invitation information and have the opportunity to participate in demand-side response, which promotes the optimal allocation of power resources and improves the flexibility and adaptability of the entire power system.

[0037] Furthermore, after step S02, the following steps are also included:

[0038] S03, monitoring the execution process of the invitation in real time through the virtual power plant operation platform, and sending a notification to remind the inviting user when an abnormality of a key node is triggered;

[0039] The key node anomalies include failure to generate the execution strategy or the backup strategy, failure to issue the execution strategy or the backup strategy, device adjustment accuracy not meeting the invitation requirements, and device offline or abnormality leading to data collection failure.

[0040] From the above description, it can be seen that by real-time monitoring of key nodes and timely notification and reminder of invited customers, users can understand the system status in a timely manner, reduce execution deviations caused by abnormal situations, and improve the stability and reliability of the system.

[0041] Furthermore, the invitation content includes the invitation period, response capacity and subsidized electricity price;

[0042] In step S1, based on the pre-trained AI model and the electricity price during the time period, a corresponding execution strategy is generated, including:

[0043] Adjusting the device charging and discharging power and the execution period in the execution strategy according to the user charging prediction model, the photovoltaic prediction model, the invitation period and the response capacity;

[0044] The photovoltaic prediction model is established based on historical weather data and historical operation data of the photovoltaic system of the photovoltaic storage and charging inspection station, and the user charging prediction model is established based on historical charging data of the charging piles of the photovoltaic storage and charging inspection station;

[0045] According to the subsidized electricity price, the charging capacity of the equipment in the execution strategy is adjusted by comparing the grid electricity price and the user charging fee.

[0046] From the above description, it can be seen that targeted adjustments can be made according to different invitation contents and related models, and execution strategies can be generated by comprehensively considering multiple factors to improve the rationality and adaptability of the strategies. At the same time, better use can be made of electricity price differences and subsidy policies to improve the economic benefits of sites in demand-side response services.

[0047] Furthermore, step S3 is specifically as follows:

[0048] If the execution strategy is effective normally, the energy storage equipment is automatically controlled according to the execution strategy during the invitation period, the equipment status is monitored in real time through the virtual power plant operation platform, and the execution strategy is adjusted according to the pre-trained AI model and the collected equipment data, and the execution strategy is re-issued at a preset interval;

[0049] Otherwise, the device-side energy management system EMS executes the backup strategy to control the device.

[0050] From the above description, it can be seen that the pre-trained AI model adjusts the execution strategy in combination with the collected device data, and can continuously optimize the control strategy according to the actual situation, making the system more adaptable to different operating environments and enhancing the stability of the system; re-issuing the execution strategy at preset intervals ensures that the system is always in the optimal control state, thereby improving the reliability of the system.

[0051] Please refer to Figure 4 , a terminal for automated execution of an optical storage and charging inspection station, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0052] S1. The virtual power plant operation platform generates a corresponding execution strategy according to the preset invitation content, based on the pre-trained AI model and the time-slot electricity price, wherein the AI ​​model includes a user charging prediction model and a photovoltaic prediction model;

[0053] S2. The device-side energy management system EMS generates a backup strategy according to the invitation content issued by the virtual power plant operation platform, and the backup strategy controls the power value of the power grid to be the difference between the operating baseline and the response capacity;

[0054] S3. If the execution strategy is in effect normally, the device is automatically controlled according to the execution strategy during the invitation period;

[0055] Otherwise, the device-side energy management system EMS executes the backup strategy to control the device.

[0056] From the above description, it can be seen that the beneficial effects of the present invention are: providing a terminal for automated execution of an optical storage and charging inspection station, using data analysis and AI technology to generate execution strategies and backup strategies based on the invitation content, giving priority to using execution strategies to control the equipment during the invitation period and automatically adjusting the execution strategies, and using backup strategies to control the equipment when the execution strategies fail. On the one hand, it realizes automated execution of invitations and intelligent adjustment of equipment control strategies, thereby increasing site revenue; on the other hand, it generates two sets of equipment control strategies, ensuring the safety and stability of the automated execution process.

[0057] Furthermore, before step S1, the following steps are also included:

[0058] S01. The power grid platform initiates a demand-side response invitation to the virtual power plant operation platform and triggers a notification reminder to notify the invited user;

[0059] S02, the inviting user independently chooses whether to accept the invitation. If the invitation is accepted, the inviting user determines the invitation content through the virtual power plant operation platform and provides invitation feedback. The power grid platform receives and provides feedback of the invitation content provided by the inviting user to the virtual power plant operation platform to confirm the invitation execution.

[0060] Otherwise, end this demand-side response invitation.

[0061] From the above description, it can be seen that the power grid platform initiates a demand-side response invitation to the virtual power plant operation platform and notifies the user. The invited user can choose whether to accept the invitation, making the demand-side response more in line with the user's needs and capabilities; at the same time, the power grid platform initiates an invitation to the virtual power plant operation platform, and the platform notifies the user. This method broadens the participation channels for demand-side response. More users can receive invitation information and have the opportunity to participate in demand-side response, which promotes the optimal allocation of power resources and improves the flexibility and adaptability of the entire power system.

[0062] Furthermore, after step S02, the following steps are further included:

[0063] S03, monitoring the execution process of the invitation in real time through the virtual power plant operation platform, and sending a notification to remind the inviting user when an abnormality of a key node is triggered;

[0064] The key node anomalies include failure to generate the execution strategy or the backup strategy, failure to issue the execution strategy or the backup strategy, device adjustment accuracy not meeting the invitation requirements, and device offline or abnormality leading to data collection failure.

[0065] From the above description, it can be seen that by real-time monitoring of key nodes and timely notification and reminder of invited customers, users can understand the system status in a timely manner, reduce execution deviations caused by abnormal situations, and improve the stability and reliability of the system.

[0066] Furthermore, the invitation content includes the invitation period, response capacity and subsidized electricity price;

[0067] In step S1, based on the pre-trained AI model and the electricity price during the time period, a corresponding execution strategy is generated, including:

[0068] Adjusting the device charging and discharging power and the execution period in the execution strategy according to the user charging prediction model, the photovoltaic prediction model, the invitation period and the response capacity;

[0069] The photovoltaic prediction model is established based on historical weather data and historical operation data of the photovoltaic system of the photovoltaic storage and charging inspection station, and the user charging prediction model is established based on historical charging data of the charging piles of the photovoltaic storage and charging inspection station;

[0070] According to the subsidized electricity price, the charging capacity of the equipment in the execution strategy is adjusted by comparing the grid electricity price and the user charging fee.

[0071] From the above description, it can be seen that targeted adjustments can be made according to different invitation contents and related models, and execution strategies can be generated by comprehensively considering multiple factors to improve the rationality and adaptability of the strategies. At the same time, better use can be made of electricity price differences and subsidy policies to improve the economic benefits of sites in demand-side response services.

[0072] Furthermore, step S3 is specifically as follows:

[0073] If the execution strategy is effective normally, the energy storage equipment is automatically controlled according to the execution strategy during the invitation period, the equipment status is monitored in real time through the virtual power plant operation platform, and the execution strategy is adjusted according to the pre-trained AI model and the collected equipment data, and the execution strategy is re-issued at a preset interval;

[0074] Otherwise, the device-side energy management system EMS executes the backup strategy to control the device.

[0075] From the above description, it can be seen that the pre-trained AI model adjusts the execution strategy in combination with the collected device data, and can continuously optimize the control strategy according to the actual situation, making the system more adaptable to different operating environments and enhancing the stability of the system; re-issuing the execution strategy at preset intervals ensures that the system is always in the optimal control state, thereby improving the reliability of the system.

[0076] The present invention provides a method and terminal for automated execution of a photovoltaic storage charging and inspection station, which is suitable for the photovoltaic storage charging and inspection station to automatically execute demand-side response based on a virtual power plant operation platform.

[0077] Please refer to Figures 1 to 3 , Embodiment 1 of the present invention is:

[0078] A method for automated execution of an optical storage and charging inspection station, comprising the steps of:

[0079] S01. The power grid platform initiates a demand-side response invitation to the virtual power plant operation platform and triggers a notification reminder to notify the invited user.

[0080] In this embodiment, data is transmitted between the power grid platform and the virtual power plant operation platform via MQTT (in other equivalent embodiments, it is not limited to the MQTT communication protocol) to synchronize invitation requests and execution process data.

[0081] like Figure 3 The virtual power plant platform performs equipment scheduling and control through a cloud-edge-end architecture, where the cloud is the virtual power plant operation platform, which is responsible for inviting demand filling, executing data monitoring, and AI strategy calculations. The edge is the EMS energy management system deployed on the on-site industrial computer, which is responsible for equipment control, equipment data processing, and data reporting. The device end refers to photovoltaic, energy storage, charging piles and other equipment in the photovoltaic storage and charging inspection station, which accepts scheduling control, completes equipment actions and reports data.

[0082] In this embodiment, the power grid platform initiates an invitation to the virtual power plant operation platform through the MQTT protocol, the content of which includes the invited user, demand type, execution period, execution capacity, etc. The platform triggers a notification reminder and notifies the user through SMS, in-site messages, emails, etc.

[0083] Among them, the invited users are electricity users registered in the power grid. The virtual power plant operation platform establishes user files and equipment files for each invited user. The file information is synchronized from the virtual power plant to the power grid system for scheduling of the power grid.

[0084] S02, the inviting user independently chooses whether to accept the invitation. If the invitation is accepted, the inviting user determines the invitation content through the virtual power plant operation platform and provides invitation feedback. The power grid platform receives and provides feedback of the invitation content provided by the inviting user to the virtual power plant operation platform to confirm the invitation execution.

[0085] Otherwise, end this demand-side response invitation.

[0086] In this embodiment, the inviting user provides invitation feedback on the virtual power plant operation platform. The feedback content includes whether to participate in this demand invitation and fill in the response capacity. The operation platform transmits the information to the power grid platform through the MQTT protocol. After the power grid platform receives the invitation information, it feeds back to the virtual power plant operation platform, and both parties confirm the execution of the invitation.

[0087] S03, monitoring the execution process of the invitation in real time through the virtual power plant operation platform, and sending a notification to remind the inviting user when an abnormality of a key node is triggered;

[0088] The key node anomalies include failure to generate the execution strategy or the backup strategy, failure to issue the execution strategy or the backup strategy, device adjustment accuracy not meeting the invitation requirements, and device offline or abnormality leading to data collection failure.

[0089] In this embodiment, the virtual power plant operation platform will monitor the entire invitation process in real time, perform detection and alarm at each key node of the invitation response. When a key node is abnormal or requires manual processing, the system will send reminders to the corresponding users via in-site messages, text messages, emails, etc.

[0090] S1. The virtual power plant operation platform generates a corresponding execution strategy based on the preset invitation content, the pre-trained AI model and the time-period electricity price, wherein the AI ​​model includes a user charging prediction model and a photovoltaic prediction model; the invitation content includes the invitation time period, the response capacity and the subsidized electricity price;

[0091] Among them, based on the pre-trained AI model and the electricity price in the time period, the corresponding execution strategy is generated, including:

[0092] Adjusting the device charging and discharging power and the execution period in the execution strategy according to the user charging prediction model, the photovoltaic prediction model, the invitation period and the response capacity;

[0093] The photovoltaic prediction model is established based on historical weather data and historical operation data of the photovoltaic system of the photovoltaic storage and charging inspection station, and the user charging prediction model is established based on historical charging data of the charging piles of the photovoltaic storage and charging inspection station;

[0094] According to the subsidized electricity price, the charging capacity of the equipment in the execution strategy is adjusted by comparing the grid electricity price and the user charging fee.

[0095] In this embodiment, a photovoltaic prediction model is established based on historical weather data and historical photovoltaic power generation data of the site, and the weather prediction data is combined to obtain the predicted photovoltaic power generation; a prediction model is established based on the charging conditions of the historical charging piles at the site combined with periodic changes (working days, holidays, seasonality), and user charging data is predicted for planning energy storage backup and discharge strategies. The combination of the two allows a certain margin to be reserved when charging energy storage in order to absorb photovoltaic power generation and reduce the abandonment rate; among them, the invitation period and response capacity affect the charging and discharging power size and execution period of the AI ​​strategy control equipment; the subsidized electricity price affects the AI ​​weighing the energy storage charging, and it is necessary to compare the grid electricity price and the user charging fee to ensure that the benefits of executing this invitation can be maximized.

[0096] Among them, at least the following requirements must be met when the strategy is generated: ensure that photovoltaic power generation is supplied to charging piles first, and then charged into energy storage to avoid photovoltaic abandonment; electricity prices are divided into peak, valley and normal periods, and energy storage batteries are charged as much as possible during valley hours and discharged during peak hours. During normal hours, charging, discharging or standby will be selected according to the consumption situation predicted by the model to ensure the maximum peak shaving and valley filling benefits; when the virtual power plant invites demand intervention, the subsidized electricity price will generally be greater than the peak-valley price difference, so the strategy required by the virtual power plant is prioritized for execution.

[0097] In this embodiment, by combining charging power prediction, photovoltaic power generation prediction, invitation content, etc., the amount of energy storage is controlled to a certain range at the appropriate time.

[0098] Specifically, if the demand-side response is a peak-shaving demand, at an appropriate time when the electricity price is low, the energy storage will be charged with a certain amount of electricity in advance before the response period, while taking into account the photovoltaic absorption margin. During the peak-shaving response period, stop drawing electricity from the grid and switch to photovoltaic and energy storage power supply, and appropriately reduce the charging power of the charging pile to ensure the normal power consumption of users during the response period, achieve the maximum power conservation effect during the peak-shaving period, and maximize user benefits;

[0099] If the demand-side response is for valley filling, the energy storage power will be discharged in advance. During the execution of the invitation, the energy storage will be charged at the required maximum power. At the same time, users can be guided to charge in an orderly manner to ensure the maximum valley filling effect.

[0100] In this implementation, the generated execution strategy will be sent from the virtual power plant operation platform to the device side for priority execution during the invitation period to control the device to automatically perform demand-side response.

[0101] S2. The device-side energy management system EMS generates a backup strategy according to the invitation content issued by the virtual power plant operation platform. The backup strategy controls the power value of the power grid to be the difference between the operating baseline and the response capacity.

[0102] S3. If the execution strategy is effective normally, the energy storage device is automatically controlled according to the execution strategy during the invitation period, the device status is monitored in real time through the virtual power plant operation platform, and the execution strategy is adjusted according to the pre-trained AI model and the collected device data, and the execution strategy is re-issued at a preset interval;

[0103] Otherwise, the device-side energy management system EMS executes the backup strategy to control the device.

[0104] In this embodiment, the content of the strategy includes the execution period, target SOC, execution power, battery protection parameters, etc. The photovoltaic, energy storage, charging piles and other equipment in the photovoltaic storage and charging inspection station will perform charging and discharging, limit power usage, adjust the power grid power supply and other equipment operations to ensure that the invitation response requirements are met. During the execution of the strategy, the platform will collect equipment operating parameters in real time, import the data results into the AI ​​model calculation, and generate equipment scheduling strategies based on factors such as grid electricity prices, charging costs, weather and photovoltaic forecasts, user charging forecasts, and invitation execution requirements when power deviations or inconsistencies occur. The strategy is adjusted within a certain period of time and re-issued to the device to ensure normal equipment control. The minimum granularity of the strategy control is half an hour, and the day is divided into 48 time periods by half an hour to issue the following strategies.

[0105] In this implementation, whether the execution strategy is effective is mainly determined based on the performance of the device during operation. For details, please refer to the following scenarios:

[0106] The device adjustment accuracy does not meet the invitation requirements: During the invitation execution process, the device adjustment accuracy does not meet the requirements specified in the invitation. For example, if the invitation requires that the power be controlled within a certain range during a specific period of time, but the actual device operating power deviates from the range, this indicates that the device is not accurately controlled according to the predetermined strategy, and there may be problems with the execution of the strategy, and it has not taken effect normally;

[0107] Device offline or abnormal: If the device is offline and cannot normally receive or execute the execution policy instructions, the execution policy cannot be normally implemented on the device, and it can be determined that the execution policy is not normally effective;

[0108] Data collection failure: During the operation of the equipment, problems occurred in the data collection process, and it was impossible to accurately obtain the equipment's operating data, such as power, electricity and other parameters. Due to the lack of accurate data feedback, the virtual power plant operation platform cannot effectively monitor the operation of the equipment, nor can it adjust the strategy according to the actual situation, which means that the execution strategy cannot function normally and can be judged as abnormally effective.

[0109] In this embodiment, the backup strategy will only be adopted when the execution strategy fails abnormally, and the device-side energy management system EMS will control the device accordingly according to the backup strategy; EMS will strictly follow the requirements of the invitation response and control the power value of the power grid to be the difference between the operating baseline and the response capacity. Although this strategy can ensure the completion of the invitation, it cannot intelligently adjust the energy storage power to maximize the benefits.

[0110] Please refer to Figure 4 , Embodiment 2 of the present invention is:

[0111] A terminal 1 for automated execution of an optical storage and charging inspection station comprises a processor 2, a memory 3 and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, the steps in the method for automated execution of an optical storage and charging inspection station described in the first embodiment above are implemented.

[0112] In summary, the method and terminal for automated execution of a photovoltaic storage and charging inspection station provided by the present invention solves the integration problem among the three systems of the power grid, the virtual power plant operation platform, and the equipment, ensuring the efficient collaboration of the system; at the same time, using data analysis and AI technology, an execution strategy and a backup strategy are generated according to the invitation content. During the invitation period, the execution strategy is preferentially used to control the equipment and the execution strategy can be automatically adjusted. When the execution strategy fails, the backup strategy is used to control the equipment. On the one hand, the automated execution of invitations and the intelligent adjustment of equipment control strategies are realized, thereby increasing site revenue; on the other hand, two sets of equipment control strategies are generated, ensuring the safety and stability of the automated execution process.

[0113] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for automated execution of an optical storage and charging inspection station, characterized in that: Includes steps: S1. The virtual power plant operation platform generates a corresponding execution strategy according to the preset invitation content, based on the pre-trained AI model and the time-slot electricity price, wherein the AI ​​model includes a user charging prediction model and a photovoltaic prediction model; The invitation content includes the invitation period, response capacity and subsidized electricity price; In step S1, based on the pre-trained AI model and the electricity price during the time period, a corresponding execution strategy is generated, including: Adjusting the device charging and discharging power and the execution period in the execution strategy according to the user charging prediction model, the photovoltaic prediction model, the invitation period and the response capacity; The photovoltaic prediction model is established based on historical weather data and historical operation data of the photovoltaic system of the photovoltaic storage and charging inspection station, and the user charging prediction model is established based on historical charging data of the charging piles of the photovoltaic storage and charging inspection station; According to the subsidized electricity price, the charging capacity of the equipment in the execution strategy is adjusted by comparing the grid electricity price and the user charging fee; S2. The device-side energy management system EMS generates a backup strategy according to the invitation content issued by the virtual power plant operation platform, and the backup strategy controls the power value of the power grid to be the difference between the operating baseline and the response capacity; S3. If the execution strategy is in effect normally, the device is automatically controlled according to the execution strategy during the invitation period; Otherwise, the device-side energy management system EMS executes the backup strategy to control the device.

2. The method for automated execution of an optical storage and charging inspection station according to claim 1, characterized in that: Before step S1, the method further includes the following steps: S01. The power grid platform initiates a demand-side response invitation to the virtual power plant operation platform and triggers a notification reminder to notify the invited user; S02, the inviting user independently chooses whether to accept the invitation. If the invitation is accepted, the inviting user determines the invitation content through the virtual power plant operation platform and provides invitation feedback. The power grid platform receives and provides feedback of the invitation content provided by the inviting user to the virtual power plant operation platform to confirm the invitation execution. Otherwise, end this demand-side response invitation.

3. The method for automated execution of an optical storage and charging inspection station according to claim 2, characterized in that: The step S02 further includes the following steps: S03, monitoring the execution process of the invitation in real time through the virtual power plant operation platform, and sending a notification to remind the inviting user when an abnormality of a key node is triggered; The key node anomalies include failure to generate the execution strategy or the backup strategy, failure to issue the execution strategy or the backup strategy, device adjustment accuracy not meeting the invitation requirements, and device offline or abnormality leading to data collection failure.

4. The method for automated execution of an optical storage and charging inspection station according to claim 1, characterized in that: Step S3 is specifically as follows: If the execution strategy is effective normally, the energy storage equipment is automatically controlled according to the execution strategy during the invitation period, the equipment status is monitored in real time through the virtual power plant operation platform, and the execution strategy is adjusted according to the pre-trained AI model and the collected equipment data, and the execution strategy is re-issued at a preset interval; Otherwise, the device-side energy management system EMS executes the backup strategy to control the device.

5. A terminal for automated execution of an optical storage and charging inspection station, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: S1. The virtual power plant operation platform generates a corresponding execution strategy according to the preset invitation content, based on the pre-trained AI model and the time-slot electricity price, wherein the AI ​​model includes a user charging prediction model and a photovoltaic prediction model; The invitation content includes the invitation period, response capacity and subsidized electricity price; In step S1, based on the pre-trained AI model and the electricity price during the time period, a corresponding execution strategy is generated, including: Adjusting the device charging and discharging power and the execution period in the execution strategy according to the user charging prediction model, the photovoltaic prediction model, the invitation period and the response capacity; The photovoltaic prediction model is established based on historical weather data and historical operation data of the photovoltaic system of the photovoltaic storage and charging inspection station, and the user charging prediction model is established based on historical charging data of the charging piles of the photovoltaic storage and charging inspection station; According to the subsidized electricity price, the charging capacity of the equipment in the execution strategy is adjusted by comparing the grid electricity price and the user charging fee; S2. The device-side energy management system EMS generates a backup strategy according to the invitation content issued by the virtual power plant operation platform, and the backup strategy controls the power value of the power grid to be the difference between the operating baseline and the response capacity; S3. If the execution strategy is in effect normally, the device is automatically controlled according to the execution strategy during the invitation period; Otherwise, the device-side energy management system EMS executes the backup strategy to control the device.

6. The terminal for automated execution of an optical storage and charging inspection station according to claim 5, characterized in that: Before step S1, the method further includes the following steps: S01. The power grid platform initiates a demand-side response invitation to the virtual power plant operation platform and triggers a notification reminder to notify the invited user; S02, the inviting user independently chooses whether to accept the invitation. If the invitation is accepted, the inviting user determines the invitation content through the virtual power plant operation platform and provides invitation feedback. The power grid platform receives and provides feedback of the invitation content provided by the inviting user to the virtual power plant operation platform to confirm the invitation execution. Otherwise, end this demand-side response invitation.

7. The terminal for automated execution of an optical storage and charging inspection station according to claim 6, characterized in that: The step S02 further includes the following steps: S03, monitoring the execution process of the invitation in real time through the virtual power plant operation platform, and sending a notification to remind the inviting user when an abnormality of a key node is triggered; The key node anomalies include failure to generate the execution strategy or the backup strategy, failure to issue the execution strategy or the backup strategy, device adjustment accuracy not meeting the invitation requirements, and device offline or abnormality leading to data collection failure.

8. The terminal for automated execution of an optical storage and charging inspection station according to claim 5, characterized in that: Step S3 is specifically as follows: If the execution strategy is effective normally, the energy storage equipment is automatically controlled according to the execution strategy during the invitation period, the equipment status is monitored in real time through the virtual power plant operation platform, and the execution strategy is adjusted according to the pre-trained AI model and the collected equipment data, and the execution strategy is re-issued at a preset interval; Otherwise, the device-side energy management system EMS executes the backup strategy to control the device.

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