Operation management system of optical storage supercharging and management method thereof

Abstract

The application discloses an operation management system of light storage and charging and a management method thereof, and the system comprises a sensing layer, a network layer, a platform layer and an application layer. The application realizes deep cooperation of photovoltaic, energy storage and charging equipment through the four-layer architecture of sensing-network-platform-application, optimizes energy configuration through AI intelligent scheduling, improves photovoltaic on-site consumption rate and peak-valley arbitrage income, covers all scenes of sites, orders, vehicle fleets and marketing through a refined operation management system, improves operation efficiency and user experience in combination with multi-terminal application, shortens fault response time and guarantees the availability of the system in combination with real-time equipment monitoring, intelligent fault diagnosis and mobile operation and maintenance, and builds a security line through hierarchical permissions, data encryption and log auditing, is compatible with V2G and orderly charging innovative functions, simultaneously reduces operation cost and carbon emission, and realizes triple win of economic benefit, ecological benefit and social benefit.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic, energy storage, and supercharging technology, and in particular to an operation and management system and method for photovoltaic, energy storage, and supercharging. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the demand for charging infrastructure continues to grow. Integrated photovoltaic-storage-supercharging stations, which effectively integrate photovoltaic power generation, energy storage peak shaving, and fast charging functions, have become an industry trend. However, existing photovoltaic-storage-supercharging systems have many problems: 1. Poor equipment coordination: photovoltaic, energy storage, and charging equipment are mostly controlled independently, lacking unified scheduling, resulting in low energy utilization efficiency; 2. Inefficient operation and management: insufficient accuracy in predicting charging load and photovoltaic output, making dynamic optimization difficult; 3. Insufficient control precision: lack of refined regulation of orderly charging and V2G interaction; 4. Inadequate safety protection and operation and maintenance system: delayed response to equipment failures affects system stability. In light of the above, this application proposes an operation and management system and method for photovoltaic-storage-supercharging. Summary of the Invention

[0003] Based on the technical problems existing in the background technology, this invention proposes an operation and management system and management method for photovoltaic storage and supercharging.

[0004] The present invention proposes an operation and management system for photovoltaic, energy storage and supercharging, which includes a perception layer, a network layer, a platform layer and an application layer. The perception layer includes a photovoltaic inverter, an energy storage cabinet, a liquid-cooled supercharging pile, a V2G device, an environmental sensor, an AI camera and a video monitoring device, used to collect photovoltaic power generation data, energy storage charging and discharging data, charging equipment operation data, environmental parameters, site safety status data and vehicle occupancy data. The network layer includes a smart gateway, a 4G / 5G communication module, an optical fiber transmission device, and a protocol conversion unit, which are used to realize data transmission between the perception layer device and the platform layer, and support multiple protocol adaptation such as MQTT, HTTP, and Modbus. The platform layer includes a data center, an intelligent decision engine, an equipment management module, an operations management module, a financial settlement module, and a security protection module. The data center stores, cleans, and integrates various types of data collected by the perception layer, including basic equipment data, operational status data, order data, user data, and environmental data. The intelligent decision engine, based on AI algorithms and combined with peak-valley electricity prices, equipment load, photovoltaic output, and energy storage status, generates orderly charging strategies, energy storage charging and discharging scheduling schemes, and power control commands. The equipment management module enables remote control, fault diagnosis, and parameter configuration of photovoltaic, energy storage, and charging equipment. The operations management module handles site management, order management, fleet management, and marketing activity configuration. The financial settlement module handles order clearing, revenue sharing, invoice management, and account management. The security protection module provides access control, encrypted data transmission, and operation log auditing. The application layer includes a web management backend for B-end managers, a charging mini-program for C-end car owners, an operation mini-program for merchants, a mobile operation and maintenance mini-program for operation and maintenance personnel, and a group fleet terminal for group fleets.

[0005] Preferably, the intelligent decision engine includes a load forecasting unit, an orderly charging control unit, an energy storage scheduling unit, and a V2G coordination unit. The load forecasting unit predicts the charging load demand and photovoltaic output within a preset time period based on historical data and real-time parameters. The orderly charging control unit dynamically adjusts the output power of the charging piles according to the grid load, the remaining energy storage capacity, and the user's charging priority. The energy storage scheduling unit generates optimized strategies for energy storage charging, discharging, and backup based on the peak-valley electricity price difference and energy supply and demand balance. The V2G coordination unit is used to manage the interaction between vehicles and the grid, and to optimize the discharge period and calculate revenue.

[0006] Preferably, the application layer further includes a 3D digital twin module for photovoltaic, energy storage and supercharging, used to restore the site layout and equipment status in a 1:1 ratio, and to display energy flow, equipment operating parameters and alarm information in real time; The Web management backend is the core operation entry point for photovoltaic, energy storage and supercharging operation management. It integrates full-scenario functions such as power station management, charging pile control, order statistics, fleet management, marketing configuration, financial settlement and data center. It supports basic information configuration of sites / equipment, orderly charging strategy formulation, multi-dimensional data report generation, revenue sharing and invoice management, real-time monitoring of equipment operation status, handling of fault alarms, configuration of marketing activities, and mastering core operation indicators through data visualization dashboards, realizing refined control and decision support across the entire chain. The group fleet terminal mainly provides fleet users with vehicle charging management and driving behavior analysis assistance functions, realizing centralized control of fleet vehicle charging data, charging cost accounting, driver driving behavior standard analysis and optimization suggestions output, adapting to the large-scale and standardized operation and management needs of group fleets; The charging mini-program provides car owners with convenient full-process charging services, supporting site search and map navigation, QR code / VIN scanning for plug-and-charge, dual-gun charging, real-time charging status viewing, historical order inquiry and invoice application. It also integrates functions such as receiving promotional activities, saving frequently used sites, and appealing for parking fees. Car owners can customize the charging amount, keep track of the charging progress in real time, and enjoy an efficient and convenient green charging experience. The operation mini-program focuses on the daily operation and management needs of the site, supports real-time viewing of core data such as charging volume, order number, and revenue, remote monitoring of equipment operation status, management of occupancy fee schemes and operation announcements, and viewing of marketing campaign effects. It can also handle user inquiries and review occupancy appeals, enabling lightweight operation and control anytime and anywhere, and improving site management efficiency and revenue conversion. The mobile maintenance mini-program supports real-time reception of equipment alarms, creation and tracking of maintenance work orders, guidance on equipment fault diagnosis and troubleshooting, and reporting of inspection records. Users can view detailed equipment operation logs, bind equipment to parking spaces, and process equipment start-up and shutdown operations in batches. It also generates maintenance statistical reports to achieve rapid fault response and closed-loop maintenance processes.

[0007] This invention also proposes an operation and management method for photovoltaic-storage-supercharging systems, comprising the following steps: S1: Data Acquisition and Preprocessing: The sensing layer devices collect real-time data from the entire photovoltaic, energy storage, and charging chain, as well as environmental and safety data. The data is then transmitted to the platform layer via the network layer. The data center cleans, deduplicates, converts the format, and removes outliers from the raw data. S2: Intelligent Scheduling Decision: The intelligent decision engine generates multi-dimensional optimization instructions based on preprocessed data, combined with preset rules and AI algorithms; S3: Equipment Collaborative Management and Control: The equipment management and control module will send optimization instructions to the corresponding equipment to realize the collaborative operation of photovoltaic, energy storage and charging equipment, and at the same time provide real-time feedback on the equipment execution status to form a closed-loop control; S4: Operational Analysis and Optimization: The operations management module generates power station operation reports, user behavior analysis reports, and fleet charging statistics based on operational and order data, supporting the configuration of marketing activities and the adjustment of operational strategies. Meanwhile, the financial settlement module automatically completes order clearing, revenue sharing, and invoice issuance. S5: Security Protection and Operation and Maintenance Assurance: The security protection module verifies system access permissions, encrypts data transmission, and logs operation behavior. At the same time, the operation and maintenance module monitors the device status in real time, receives fault alarms, generates operation and maintenance work orders, and guides operation and maintenance personnel to troubleshoot faults.

[0008] Preferably, in step S2, the generated multi-dimensional optimization instructions include: (1) When the power grid is in a valley and the photovoltaic output is insufficient, control the energy storage cabinet to charge and store energy; (2) When the power grid is in peak period and the charging load is high, control the energy storage cabinet to discharge to assist power supply, and at the same time start the orderly charging strategy to regulate the power of the charging pile. (3) When a V2G device is detected to be connected and the grid load is low, a vehicle discharge dispatch command is generated; User charging priority is determined based on a combination of user type, vehicle remaining battery power, reservation information, and membership level. Whitelisted users and fleet users can be assigned priority charging permissions.

[0009] Preferably, the specific logical steps of S2 are as follows: S201: Based on the raw data of photovoltaic output, energy storage SOC, grid load, peak-valley electricity price, and user charging demand collected by the sensing layer, calculate the average photovoltaic output and remaining available energy storage capacity for the time period, and statistically prioritize user charging demand. The specific formula used is as follows: The formula used to calculate the average photovoltaic output over the period is: Where T is the number of data collection points within the time period; the formula used to calculate the remaining available energy storage capacity is: ,in This refers to the rated capacity of the energy storage. The formula used to prioritize user charging needs is: ,in , , As weight, + =1, The remaining battery power of the vehicle; S202: Based on the LSTM algorithm, the charging load for future periods is predicted. Combined with the predicted photovoltaic output and energy storage status, the supply and demand balance is determined. The formula used is as follows: Total charging load for the forecast period: Where k = 1, 2, ..., n, This indicates historical load sequences, time-period characteristics, and weather factors; Supply and demand gap calculation: ,in For energy storage discharge power, Power for energy storage charging; S203: Combining peak-valley electricity price differences, a genetic algorithm is used to optimize the energy storage charging and discharging strategy. The specific formula used is as follows: Energy storage charging decision: ,in For charging time, The remaining power supply capacity of the power grid; Energy storage discharge decision: ; Energy storage revenue objective function: ,in Costs associated with energy storage charging and discharging losses; S204: Dynamically allocate charging pile power based on grid load threshold and user priority; The maximum charging power allowed by the power grid is ,in The power grid safety load factor is taken as 0.8-0.9; Total distributable power is ,in The rated charging power for user i is N, and the number of charging users is N. Single-user power allocation: ; Power limit correction: ,in The amount of charge required for user i; S205: When the grid load is below the threshold, start vehicle discharge. Its vehicle's discharge power is: ,in Reserve the minimum amount of battery power for users. Rated power for vehicle V2G; The total power control for the entire V2G network is as follows: Where M is the number of vehicles participating in V2G; S206: Power balance check: If the balance is not met, reduce the power of non-critical users in reverse order of priority until the constraint is met; repeat steps S201-S206 every 5 minutes based on real-time data and dynamically update the instructions.

[0010] Preferably, the specific logical steps of S3 are as follows: S301: The device management module receives optimization instructions from the intelligent decision engine, and splits and matches the instructions according to device type and device ID. The formula used is as follows: Its photovoltaic equipment directive: ,in Let i be the output regulation of the i-th inverter. Contribute to the goal To contribute to the present; Energy storage device instructions: ,in The command power for the j-th energy storage cabinet is positive for charging and negative for discharging. Charging station instructions: ,in The final power allocation for the kth charging pile is taken from the intelligent scheduling decision result; S302: The split instructions are sent to the corresponding devices in real time via the MQTT / Modbus protocol. An execution timeout threshold is set. The device returns the execution status and real-time operating parameters within one second of execution, and calculates the instruction execution deviation rate to determine if it meets the requirements. The formula used is: Deviation rate: ,like If the execution is effective, it will enter the stable monitoring phase. If the timeout is not corrected, a retry mechanism is triggered; if it still fails, a device fault alarm is reported. S303: Based on device feedback data, the system power balance is checked in real time. If an imbalance exists, it is dynamically corrected. The formula used is as follows: Total actual power of the system: ; Total target power of the system: ; Power gap: ; Based on the calculation results, the energy storage equipment is adjusted first, and the correction amount is: If the energy storage regulation is insufficient, then fine-tune the charging pile power. in Indicates actual effort, Indicates the actual charge and discharge power. This represents the real-time value of SOC. Indicates the actual output power; S304: When the equipment reports a fault code or parameters exceeding the safety threshold, the following is immediately triggered: the photovoltaic system stops output adjustment, maintains current stable operation, exits charging and discharging, switches to standby mode, stops power supply to the charging pile, locks the equipment, and pushes a maintenance work order. If the faulty equipment affects power balance, redundant equipment is activated to compensate. ,in This is the redundancy factor, ranging from 0.05 to 0.1. S305: Automatically records key data for each collaborative management and control operation, including command issuance time, device execution results, power balance data, and anomaly handling records. The log format meets the requirements for operation and maintenance traceability and supports exporting and querying by device ID and time range.

[0011] Preferably, the specific logical steps of S5 are as follows: S501: Configure permission matrices according to management roles, clearly define module operation permissions, and when users log in, the system determines operation permissions through a permission verification formula: ,in For user role permissions set, To request operation permissions, To indicate the validity of the login token, 1 = valid, 0 = invalid; like =0, directly intercept the operation and return an insufficient permission prompt, while recording the interception log; S502: Generate a temporary key based on timestamp and device identifier: ,in This is the current timestamp. To access the device's unique identifier, This is the system root key; It uses SSL / TLS protocol for transmission, and sensitive data is additionally encrypted with AES-256; S503: Records operator ID, operation time, operation module, operation content, IP address, and device information, and stores logs in time-segmented chunks. It also supports quick filtering of key operations using formulas. ; in For the target operator ID, For time range, For the target module; When the operation type is a sensitive operation, an alarm will be automatically triggered and the log priority will be marked as "high risk"; S504: Collects equipment operating parameters every 15 seconds and uses a formula to determine whether the parameters exceed the safe range. ; in As an anomaly marker, For voltage safety threshold, For current safety threshold, The maximum allowable temperature for the equipment. Communication status: 1 = normal, 0 = offline; And classify according to the severity of the abnormality: ,in For parameter weights, , For single parameter anomaly identification; Its grading criteria are as follows: For emergency fault, This is a common fault. This is a minor warning; S505: When When the system detects an error, it immediately sends an alarm and generates a maintenance work order to guide maintenance personnel in troubleshooting.

[0012] Compared with existing technologies, the beneficial effects of this invention are: By prioritizing direct supply of photovoltaic power to meet charging demand, surplus electricity is stored in the energy storage system in real time. When the grid is at its peak or photovoltaic power output is insufficient, the energy storage system can accurately discharge to replenish the energy, avoiding the waste of resources such as "photovoltaic curtailment, idle energy storage, and charging dependence on the grid". At the same time, by dynamically adjusting the power of charging piles with the help of orderly charging strategy, and optimizing the charging and discharging time of energy storage by combining peak and valley electricity price differences, "peak shaving and valley filling" and peak and valley arbitrage are achieved. The average annual local photovoltaic consumption rate of a single station is effectively improved, the ineffective operation of energy storage charging and discharging is effectively reduced, the comprehensive energy utilization efficiency is effectively improved, and the peak and valley arbitrage income is effectively increased. AI algorithms enable the prediction of charging load and photovoltaic output, providing accurate data for dynamic scheduling; support multiple charging strategies such as unified pricing, time-of-use pricing, and revenue-sharing pricing; temporary solutions can be configured for holidays and site maintenance; the operation dashboard displays key indicators such as site power consumption, revenue, and equipment utilization in real time; combined with user behavior analysis, marketing strategies are optimized, effectively improving the operating revenue of a single site; at the same time, automated financial settlement and batch operation and maintenance reduce manual intervention and effectively reduce operating labor costs. B-end administrators can remotely monitor and diagnose equipment through the web backend, effectively shortening the operation and maintenance response time; C-end vehicle owners can use the charging mini-program to complete site queries, scan codes for charging, and order inquiries, supporting VIN plug-and-charge, dual-gun simultaneous charging, and appeals for parking fees; fleet users can use the operation mini-program to manage fleet wallets, analyze charging data, and issue invoices in batches, meeting the cost accounting needs of enterprises. In addition, the stations are equipped with facilities such as convenience stores, restrooms, and free WIFI, and combined with marketing activities, user repurchase rates are effectively improved; By implementing tiered access control, encrypted data transmission, and audited operation logs, the system ensures access and data privacy. At the device level, voltage, current, and temperature parameters are monitored in real time. In case of abnormalities, charging is automatically cut off and alarms are triggered. Combined with fault database matching and troubleshooting solutions, the device fault resolution rate is effectively improved, and availability is effectively maintained. In terms of compliance, it supports connection with the national clearing platform and local regulatory platforms to meet data reporting requirements. At the same time, through the review of revenue-sharing merchants and the supervision of reserve fund accounts, the compliance of fund transfers is ensured, and financial risks are avoided. This invention achieves deep collaboration between photovoltaic, energy storage, and charging equipment through a four-layer architecture of "sensing-network-platform-application." It optimizes energy allocation with AI-powered intelligent scheduling, improving local photovoltaic absorption rates and peak-valley arbitrage profits. A refined operation management system covers all scenarios, including sites, orders, fleets, and marketing, while multi-terminal applications enhance operational efficiency and user experience. Real-time equipment monitoring, intelligent fault diagnosis, and mobile maintenance shorten fault response time and ensure system availability. Furthermore, it strengthens security through tiered permissions, data encryption, and log auditing, while also supporting innovative V2G and orderly charging functions. Simultaneously, it reduces operating costs and carbon emissions, achieving a triple win of economic, ecological, and social benefits. Attached Figure Description

[0013] Figure 1 This is a block diagram of an operation and management system for photovoltaic, energy storage and supercharging proposed in this invention; Figure 2 This is a flowchart of an operation and management method for photovoltaic, energy storage and supercharging proposed in this invention. Detailed Implementation

[0014] The present invention will be further explained below with reference to specific embodiments. Example

[0015] Reference Figure 1 This embodiment proposes an operation and management system for photovoltaic, energy storage and supercharging, including a perception layer, a network layer, a platform layer and an application layer. The perception layer includes photovoltaic inverters, energy storage cabinets, liquid-cooled supercharging piles, V2G equipment, environmental sensors, AI cameras and video monitoring equipment, which are used to collect photovoltaic power generation data, energy storage charging and discharging data, charging equipment operation data, environmental parameters, site safety status data and vehicle occupancy data. The network layer includes a smart gateway, a 4G / 5G communication module, fiber optic transmission equipment, and a protocol conversion unit, which are used to realize data transmission between the perception layer devices and the platform layer, and support multiple protocol adaptations such as MQTT, HTTP, and Modbus. The platform layer includes a data center, an intelligent decision engine, an equipment management module, an operations management module, a financial settlement module, and a security protection module. The data center stores, cleans, and integrates various data collected from the perception layer, including basic equipment data, operational status data, order data, user data, and environmental data. The intelligent decision engine, based on AI algorithms, combines peak-valley electricity prices, equipment load, photovoltaic output, and energy storage status to generate orderly charging strategies, energy storage charging and discharging scheduling schemes, and power control commands. The equipment management module enables remote control, fault diagnosis, and parameter configuration of photovoltaic, energy storage, and charging equipment. The operations management module handles site management, order management, fleet management, and marketing activity configuration. The financial settlement module handles order clearing, revenue sharing, invoice management, and account management. The security protection module provides access control, encrypted data transmission, and operation log auditing. The intelligent decision engine includes a load forecasting unit, an orderly charging control unit, an energy storage scheduling unit, and a V2G coordination unit. The load forecasting unit predicts the charging load demand and photovoltaic output within a preset time period based on historical data and real-time parameters. The orderly charging control unit dynamically adjusts the output power of the charging piles according to the grid load, the remaining energy storage capacity, and the user's charging priority. The energy storage scheduling unit generates optimized strategies for energy storage charging, discharging, and backup based on the peak-valley electricity price difference and energy supply and demand balance. The V2G coordination unit is used to manage the interaction between vehicles and the grid, and to optimize the discharge time and calculate revenue. The application layer includes a web management backend for B-end managers, a charging mini-program for C-end car owners, an operation mini-program for merchants, a mobile operation and maintenance mini-program for operation and maintenance personnel, and a group fleet terminal for group fleets. The application layer also includes a 3D digital twin module for photovoltaic, energy storage and supercharging, which is used to restore the site layout and equipment status in a 1:1 ratio and display energy flow, equipment operating parameters and alarm information in real time; The Web management backend is the core operation entry point for photovoltaic, energy storage and supercharging operation management. It integrates full-scenario functions such as power station management, charging pile control, order statistics, fleet management, marketing configuration, financial settlement and data center. It supports basic information configuration of sites / equipment, orderly charging strategy formulation, multi-dimensional data report generation, revenue sharing and invoice management, real-time monitoring of equipment operation status, handling of fault alarms, configuration of marketing activities, and mastering core operation indicators through data visualization dashboards, realizing refined control and decision support across the entire chain. The group fleet terminal mainly provides fleet users with vehicle charging management and driving behavior analysis assistance functions, realizing centralized control of fleet vehicle charging data, charging cost accounting, driver driving behavior standard analysis and optimization suggestions output, adapting to the large-scale and standardized operation and management needs of group fleets; The charging mini-program provides car owners with convenient full-process charging services, supporting site search and map navigation, QR code / VIN scanning for plug-and-charge, dual-gun charging, real-time charging status viewing, historical order inquiry and invoice application. It also integrates functions such as receiving promotional activities, saving frequently used sites, and appealing for parking fees. Car owners can customize the charging amount, keep track of the charging progress in real time, and enjoy an efficient and convenient green charging experience. The operation mini-program focuses on the daily operation and management needs of the site, supports real-time viewing of core data such as charging volume, order number, and revenue, remote monitoring of equipment operation status, management of occupancy fee schemes and operation announcements, and viewing of marketing campaign effects. It can also handle user inquiries and review occupancy appeals, enabling lightweight operation and control anytime and anywhere, and improving site management efficiency and revenue conversion. The mobile maintenance mini-program supports real-time reception of equipment alarms, creation and tracking of maintenance work orders, guidance on equipment fault diagnosis and troubleshooting, and reporting of inspection records. Users can view detailed equipment operation logs, bind equipment to parking spaces, and process equipment start-up and shutdown operations in batches. It also generates maintenance statistical reports to achieve rapid fault response and closed-loop maintenance processes.

[0016] Reference Figure 2 This embodiment also proposes an operation and management method for photovoltaic-storage-supercharging, including the following steps: S1: Data Acquisition and Preprocessing: The sensing layer devices collect real-time data from the entire photovoltaic, energy storage, and charging chain, as well as environmental and safety data. The data is then transmitted to the platform layer via the network layer. The data center cleans, deduplicates, converts the format, and removes outliers from the raw data. S2: Intelligent Scheduling Decision: The intelligent decision engine generates multi-dimensional optimization instructions based on preprocessed data, combined with preset rules and AI algorithms; The generated multi-dimensional optimization instructions include: (1) When the power grid is in a valley and the photovoltaic output is insufficient, control the energy storage cabinet to charge and store energy; (2) When the power grid is in peak period and the charging load is high, control the energy storage cabinet to discharge to assist power supply, and at the same time start the orderly charging strategy to regulate the power of the charging pile. (3) When a V2G device is detected to be connected and the grid load is low, a vehicle discharge dispatch command is generated; User charging priority is determined based on a combination of user type, vehicle remaining battery power, reservation information and membership level. Whitelisted users and fleet users can be set with priority charging permissions. The specific logical steps of S2 are as follows: S201: Based on the raw data of photovoltaic output, energy storage SOC, grid load, peak-valley electricity price, and user charging demand collected by the sensing layer, calculate the average photovoltaic output and remaining available energy storage capacity for the time period, and statistically prioritize user charging demand. The specific formula used is as follows: The formula used to calculate the average photovoltaic output over the period is: Where T is the number of data collection points within the time period; the formula used to calculate the remaining available energy storage capacity is: ,in This refers to the rated capacity of the energy storage. The formula used to prioritize user charging needs is: ,in , , As weight, + =1, The remaining battery power of the vehicle; S202: Based on the LSTM algorithm, the charging load for future periods is predicted. Combined with the predicted photovoltaic output and energy storage status, the supply and demand balance is determined. The formula used is as follows: Total charging load for the forecast period: Where k = 1, 2, ..., n, This indicates historical load sequences, time-period characteristics, and weather factors; Supply and demand gap calculation: ,in For energy storage discharge power, Power for energy storage charging; S203: Combining peak-valley electricity price differences, a genetic algorithm is used to optimize the energy storage charging and discharging strategy. The specific formula used is as follows: Energy storage charging decision: ,in For charging time, The remaining power supply capacity of the power grid; Energy storage discharge decision: ; Energy storage revenue objective function: ,in Costs associated with energy storage charging and discharging losses; S204: Dynamically allocate charging pile power based on grid load threshold and user priority; The maximum charging power allowed by the power grid is ,in The power grid safety load factor is taken as 0.8-0.9; Total distributable power is ,in The rated charging power for user i is N, and the number of charging users is N. Single-user power allocation: ; Power limit correction: ,in The amount of charge required for user i; S205: When the grid load is below the threshold, start vehicle discharge. Its vehicle's discharge power is: ,in Reserve the minimum amount of battery power for users. Rated power for vehicle V2G; The total power control for the entire V2G network is as follows: Where M is the number of vehicles participating in V2G; S206: Power balance check: If the balance is not met, reduce the power of non-critical users in reverse order of priority until the constraint is met; repeat steps S201-S206 every 5 minutes based on real-time data and dynamically update the instructions.

[0017] S3: Equipment Collaborative Management and Control: The equipment management and control module will send optimization instructions to the corresponding equipment to realize the collaborative operation of photovoltaic, energy storage and charging equipment, and at the same time provide real-time feedback on the equipment execution status to form a closed-loop control; The specific logical steps are as follows: S301: The device management module receives optimization instructions from the intelligent decision engine, and splits and matches the instructions according to device type and device ID. The formula used is as follows: Its photovoltaic equipment directive: ,in Let i be the output regulation of the i-th inverter. Contribute to the goal To contribute to the present; Energy storage device instructions: ,in The command power for the j-th energy storage cabinet is positive for charging and negative for discharging. Charging station instructions: ,in The final power allocation for the kth charging pile is taken from the intelligent scheduling decision result; S302: The split instructions are sent to the corresponding devices in real time via the MQTT / Modbus protocol. An execution timeout threshold is set. The device returns the execution status and real-time operating parameters within one second of execution, and calculates the instruction execution deviation rate to determine if it meets the requirements. The formula used is: Deviation rate: ,like If the execution is effective, it will enter the stable monitoring phase. If the timeout is not corrected, a retry mechanism is triggered; if it still fails, a device fault alarm is reported. S303: Based on device feedback data, the system power balance is checked in real time. If an imbalance exists, it is dynamically corrected. The formula used is as follows: Total actual power of the system: ; Total target power of the system: ; Power gap: ; Based on the calculation results, the energy storage equipment is adjusted first, and the correction amount is: If the energy storage regulation is insufficient, then fine-tune the charging pile power. in Indicates actual effort, Indicates the actual charge and discharge power. This represents the real-time value of SOC. Indicates the actual output power; S304: When the equipment reports a fault code or parameters exceeding the safety threshold, the following is immediately triggered: the photovoltaic system stops output adjustment, maintains current stable operation, exits charging and discharging, switches to standby mode, stops power supply to the charging pile, locks the equipment, and pushes a maintenance work order. If the faulty equipment affects power balance, redundant equipment is activated to compensate. ,in This is the redundancy factor, ranging from 0.05 to 0.1. S305: Automatically records key data for each collaborative management and control operation, including command issuance time, device execution results, power balance data, and anomaly handling records. The log format meets the requirements for operation and maintenance traceability and supports exporting and querying by device ID and time range.

[0018] S4: Operational Analysis and Optimization: The operations management module generates power station operation reports, user behavior analysis reports, and fleet charging statistics based on operational and order data, supporting the configuration of marketing activities and the adjustment of operational strategies. Meanwhile, the financial settlement module automatically completes order clearing, revenue sharing, and invoice issuance. S5: Security Protection and Operation and Maintenance Assurance: The security protection module verifies system access permissions, encrypts data transmission, and logs operation behavior. Meanwhile, the operation and maintenance module monitors the device status in real time, receives fault alarms, generates operation and maintenance work orders, and guides operation and maintenance personnel to troubleshoot faults. The specific logical steps are as follows: S501: Configure permission matrices according to management roles, clearly define module operation permissions, and when users log in, the system determines operation permissions through a permission verification formula: ,in For user role permissions set, To request operation permissions, To indicate the validity of the login token, 1 = valid, 0 = invalid; like =0, directly intercept the operation and return an insufficient permission prompt, while recording the interception log; S502: Generate a temporary key based on timestamp and device identifier: ,in This is the current timestamp. To access the device's unique identifier, This is the system root key; It uses SSL / TLS protocol for transmission, and sensitive data is additionally encrypted with AES-256; S503: Records operator ID, operation time, operation module, operation content, IP address, and device information, and stores logs in time-segmented chunks. It also supports quick filtering of key operations using formulas. ; in For the target operator ID, For time range, For the target module; When the operation type is a sensitive operation, an alarm will be automatically triggered and the log priority will be marked as "high risk"; S504: Collects equipment operating parameters every 15 seconds and uses a formula to determine whether the parameters exceed the safe range. ; in As an anomaly marker, For voltage safety threshold, For current safety threshold, The maximum allowable temperature for the equipment. Communication status: 1 = normal, 0 = offline; And classify according to the severity of the abnormality: ,in For parameter weights, , For single parameter anomaly identification; Its grading criteria are as follows: For emergency fault, This is a common fault. This is a minor warning; S505: When When the system detects an error, it immediately sends an alarm and generates a maintenance work order to guide maintenance personnel in troubleshooting.

[0019] This embodiment achieves deep collaboration between photovoltaic, energy storage, and charging equipment through a four-layer architecture of "sensing-network-platform-application". It optimizes energy allocation with AI intelligent scheduling, improving the local photovoltaic consumption rate and peak-valley arbitrage benefits. With a refined operation management system covering all scenarios of sites, orders, fleets, and marketing, it improves operational efficiency and user experience by combining multi-terminal applications. Relying on real-time equipment monitoring, intelligent fault diagnosis, and mobile operation and maintenance, it shortens fault response time and ensures system availability. Furthermore, it strengthens security defenses through hierarchical permissions, data encryption, and log auditing, and is compatible with innovative V2G and orderly charging functions. At the same time, it reduces operating costs and carbon emissions, achieving a triple win of economic, ecological, and social benefits.

[0020] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An operation and management system for photovoltaic, energy storage, and supercharging systems, characterized in that, It includes a perception layer, a network layer, a platform layer, and an application layer. The perception layer includes photovoltaic inverters, energy storage cabinets, liquid-cooled supercharging piles, V2G equipment, environmental sensors, AI cameras, and video surveillance equipment, which are used to collect photovoltaic power generation data, energy storage charging and discharging data, charging equipment operation data, environmental parameters, site safety status data, and vehicle occupancy data. The network layer includes a smart gateway, a 4G / 5G communication module, an optical fiber transmission device, and a protocol conversion unit, which are used to realize data transmission between the perception layer device and the platform layer, and support multiple protocol adaptation such as MQTT, HTTP, and Modbus. The platform layer includes a data center, an intelligent decision engine, an equipment management module, an operations management module, a financial settlement module, and a security protection module. The data center stores, cleans, and integrates various types of data collected by the perception layer, including basic equipment data, operational status data, order data, user data, and environmental data. The intelligent decision engine, based on AI algorithms and combined with peak-valley electricity prices, equipment load, photovoltaic output, and energy storage status, generates orderly charging strategies, energy storage charging and discharging scheduling schemes, and power control commands. The equipment management module enables remote control, fault diagnosis, and parameter configuration of photovoltaic, energy storage, and charging equipment. The operations management module handles site management, order management, fleet management, and marketing activity configuration. The financial settlement module handles order clearing, revenue sharing, invoice management, and account management. The security protection module provides access control, encrypted data transmission, and operation log auditing. The application layer includes a web management backend for B-end managers, a charging mini-program for C-end car owners, an operation mini-program for merchants, a mobile operation and maintenance mini-program for operation and maintenance personnel, and a group fleet terminal for group fleets.

2. The operation and management system for photovoltaic, energy storage, and supercharging according to claim 1, characterized in that, The intelligent decision engine includes a load forecasting unit, an orderly charging control unit, an energy storage scheduling unit, and a V2G coordination unit. The load forecasting unit predicts the charging load demand and photovoltaic output within a preset time period based on historical data and real-time parameters. The orderly charging control unit dynamically adjusts the output power of the charging piles according to the grid load, the remaining energy storage capacity, and the user's charging priority. The energy storage scheduling unit generates optimized strategies for energy storage charging, discharging, and backup based on the peak-valley electricity price difference and energy supply and demand balance. The V2G coordination unit is used to manage the interaction between vehicles and the grid, and to optimize the discharge period and calculate revenue.

3. The operation and management system for photovoltaic, energy storage, and supercharging according to claim 1, characterized in that, The application layer also includes a 3D digital twin module for photovoltaic, energy storage and supercharging, which is used to restore the site layout and equipment status in 1:1 and display energy flow, equipment operating parameters and alarm information in real time. The Web management backend is the core operation entry point for photovoltaic, energy storage and supercharging operation management. It integrates full-scenario functions such as power station management, charging pile control, order statistics, fleet management, marketing configuration, financial settlement and data center. It supports basic information configuration of sites / equipment, orderly charging strategy formulation, multi-dimensional data report generation, revenue sharing and invoice management, real-time monitoring of equipment operation status, handling of fault alarms, configuration of marketing activities, and mastering core operation indicators through data visualization dashboards, realizing refined control and decision support across the entire chain. The group fleet terminal mainly provides fleet users with vehicle charging management and driving behavior analysis assistance functions, realizing centralized control of fleet vehicle charging data, charging cost accounting, driver driving behavior standard analysis and optimization suggestions output, adapting to the large-scale and standardized operation and management needs of group fleets; The charging mini-program provides car owners with convenient full-process charging services, supporting site search and map navigation, QR code / VIN scanning for plug-and-charge, dual-gun charging, real-time charging status viewing, historical order inquiry and invoice application. It also integrates functions such as receiving promotional activities, saving frequently used sites, and appealing for parking fees. Car owners can customize the charging amount, keep track of the charging progress in real time, and enjoy an efficient and convenient green charging experience. The operation mini-program focuses on the daily operation and management needs of the site, supports real-time viewing of core data such as charging volume, order number, and revenue, remote monitoring of equipment operation status, management of occupancy fee schemes and operation announcements, and viewing of marketing campaign effects. It can also handle user inquiries and review occupancy appeals, enabling lightweight operation and control anytime and anywhere, and improving site management efficiency and revenue conversion. The mobile maintenance mini-program supports real-time reception of equipment alarms, creation and tracking of maintenance work orders, guidance on equipment fault diagnosis and troubleshooting, and reporting of inspection records. Users can view detailed equipment operation logs, bind equipment to parking spaces, and process equipment start-up and shutdown operations in batches. It also generates maintenance statistical reports to achieve rapid fault response and closed-loop maintenance processes.

4. An operation and management method for photovoltaic, energy storage, and supercharging systems, based on the system described in any one of claims 1-3, characterized in that, Includes the following steps: S1: Data Acquisition and Preprocessing: The sensing layer devices collect real-time data from the entire photovoltaic, energy storage, and charging chain, as well as environmental and safety data. The data is then transmitted to the platform layer via the network layer. The data center cleans, deduplicates, converts the format, and removes outliers from the raw data. S2: Intelligent Scheduling Decision: The intelligent decision engine generates multi-dimensional optimization instructions based on preprocessed data, combined with preset rules and AI algorithms; S3: Equipment Collaborative Management and Control: The equipment management and control module will send optimization instructions to the corresponding equipment to realize the collaborative operation of photovoltaic, energy storage and charging equipment, and at the same time provide real-time feedback on the equipment execution status to form a closed-loop control; S4: Operational Analysis and Optimization: The operations management module generates power station operation reports, user behavior analysis reports, and fleet charging statistics based on operational and order data, supporting the configuration of marketing activities and the adjustment of operational strategies. Meanwhile, the financial settlement module automatically completes order clearing, revenue sharing, and invoice issuance. S5: Security Protection and Operation and Maintenance Assurance: The security protection module verifies system access permissions, encrypts data transmission, and logs operation behavior. At the same time, the operation and maintenance module monitors the device status in real time, receives fault alarms, generates operation and maintenance work orders, and guides operation and maintenance personnel to troubleshoot faults.

5. The operation and management method for photovoltaic-storage-supercharging according to claim 4, characterized in that, In S2, the generated multi-dimensional optimization instructions include: (1) When the power grid is in a valley and the photovoltaic output is insufficient, control the energy storage cabinet to charge and store energy; (2) When the power grid is in peak period and the charging load is high, control the energy storage cabinet to discharge to assist power supply, and at the same time start the orderly charging strategy to regulate the power of the charging pile. (3) When a V2G device is detected to be connected and the grid load is low, a vehicle discharge dispatch command is generated; User charging priority is determined based on a combination of user type, vehicle remaining battery power, reservation information, and membership level. Whitelisted users and fleet users can be assigned priority charging permissions.

6. The operation and management method for photovoltaic-storage-supercharging according to claim 4, characterized in that, The specific logical steps of S2 are as follows: S201: Based on the raw data of photovoltaic output, energy storage SOC, grid load, peak-valley electricity price, and user charging demand collected by the sensing layer, calculate the average photovoltaic output and remaining available energy storage capacity for the time period, and statistically prioritize user charging demand. The specific formula used is as follows: The formula used to calculate the average photovoltaic output over the period is: , where T is the number of data collection points within the time period; The formula used to calculate the remaining available capacity of energy storage is: ,in This refers to the rated capacity of the energy storage. The formula used to prioritize user charging needs is: ,in , , As weight, + =1, The remaining battery power of the vehicle; S202: Based on the LSTM algorithm, the charging load for future periods is predicted. Combined with the predicted photovoltaic output and energy storage status, the supply and demand balance is determined. The formula used is as follows: Total charging load for the forecast period: Where k = 1, 2, ..., n, This indicates historical load sequences, time-period characteristics, and weather factors; Supply and demand gap calculation: ,in For energy storage discharge power, Power for energy storage charging; S203: Combining peak-valley electricity price differences, a genetic algorithm is used to optimize the energy storage charging and discharging strategy. The specific formula used is as follows: Energy storage charging decision: ,in For charging time, The remaining power supply capacity of the power grid; Energy storage discharge decision: ; Energy storage revenue objective function: ,in Costs associated with energy storage charging and discharging losses; S204: Dynamically allocate charging pile power based on grid load threshold and user priority; The maximum charging power allowed by the power grid is ,in The power grid safety load factor is taken as 0.8-0.9; Total distributable power is ,in The rated charging power for user i is N, and the number of charging users is N. Single-user power allocation: ; Power limit correction: ,in The amount of charge required for user i; S205: When the grid load is below the threshold, start vehicle discharge. Its vehicle's discharge power is: ,in Reserve the minimum amount of battery power for users. Rated power for vehicle V2G; The total power control for the entire V2G network is as follows: Where M is the number of vehicles participating in V2G; S206: Power balance check: If the balance is not met, reduce the power of non-critical users in reverse order of priority until the constraint is met; repeat steps S201-S206 every 5 minutes based on real-time data and dynamically update the instructions.

7. The operation and management method for photovoltaic-storage-supercharging according to claim 6, characterized in that, The specific logical steps of S3 are as follows: S301: The device management module receives optimization instructions from the intelligent decision engine, and splits and matches the instructions according to device type and device ID. The formula used is as follows: Its photovoltaic equipment directive: ,in Let i be the output regulation of the i-th inverter. Contribute to the goal To contribute to the present; Energy storage device instructions: ,in The command power for the j-th energy storage cabinet is positive for charging and negative for discharging. Charging station instructions: ,in The final power allocation for the kth charging pile is taken from the intelligent scheduling decision result; S302: The split instructions are sent to the corresponding devices in real time via the MQTT / Modbus protocol. An execution timeout threshold is set. The device returns the execution status and real-time operating parameters within one second of execution, and calculates the instruction execution deviation rate to determine if it meets the requirements. The formula used is: Deviation rate: ,like If the execution is effective, it will enter the stable monitoring phase. If the timeout is not corrected, a retry mechanism is triggered; if it still fails, a device fault alarm is reported. S303: Based on device feedback data, the system power balance is checked in real time. If an imbalance exists, it is dynamically corrected. The formula used is as follows: Total actual power of the system: ; Total target power of the system: ; Power gap: ; Based on the calculation results, the energy storage equipment is adjusted first, and the correction amount is: If the energy storage regulation is insufficient, then fine-tune the charging pile power. in Indicates actual effort, Indicates the actual charge and discharge power. This represents the real-time value of SOC. Indicates the actual output power; S304: When the equipment reports a fault code or parameters exceeding the safety threshold, the following is immediately triggered: the photovoltaic system stops output adjustment, maintains current stable operation, exits charging and discharging, switches to standby mode, stops power supply to the charging pile, locks the equipment, and pushes a maintenance work order. If the faulty equipment affects power balance, redundant equipment is activated to compensate. ,in This is the redundancy factor, ranging from 0.05 to 0.

1. S305: Automatically records key data for each collaborative management and control operation, including command issuance time, device execution results, power balance data, and anomaly handling records. The log format meets the requirements for operation and maintenance traceability and supports exporting and querying by device ID and time range.

8. The operation and management method for photovoltaic-storage-supercharging according to claim 7, characterized in that, The specific logical steps of S5 are as follows: S501: Configure permission matrices according to management roles, clearly define module operation permissions, and when users log in, the system determines operation permissions through a permission verification formula: ,in For user role permissions set, To request operation permissions, To indicate the validity of the login token, 1 = valid, 0 = invalid; like =0, directly intercept the operation and return an insufficient permission prompt, while recording the interception log; S502: Generate a temporary key based on timestamp and device identifier: ,in This is the current timestamp. To access the device's unique identifier, This is the system root key; It uses SSL / TLS protocol for transmission, and sensitive data is additionally encrypted with AES-256; S503: Records operator ID, operation time, operation module, operation content, IP address, and device information, and stores logs in time-segmented chunks. It also supports quick filtering of key operations using formulas. ； in For the target operator ID, For time range, For the target module; When the operation type is a sensitive operation, an alarm will be automatically triggered and the log priority will be marked as "high risk"; S504: Collects equipment operating parameters every 15 seconds and uses a formula to determine whether the parameters exceed the safe range. ; in As an anomaly marker, For voltage safety threshold, For current safety threshold, The maximum allowable temperature for the equipment. Communication status: 1 = normal, 0 = offline; And classify according to the severity of the abnormality: ,in For parameter weights, , For single parameter anomaly identification; Its grading criteria are as follows: For emergency fault, This is a common fault. This is a minor warning; S505: When When the system detects an error, it immediately sends an alarm and generates a maintenance work order to guide maintenance personnel in troubleshooting.

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