Virtual power plant control system

Abstract

The present invention provides a virtual power plant control system. The system includes: a collection module for collecting environmental data and operating data of distributed energy devices within a target area, performing preliminary verification on the environmental and operating data, and encrypting and sending the data to a processing module. Distributed energy devices include photovoltaic devices, wind power devices, energy storage devices, and metering devices. The processing module performs logical verification on the operating data based on the environmental data, the coordinates of each distributed energy device, and the sampling time of the operating data, and stores and manages the operating data. The present invention enables data collection and processing of distributed energy resources, facilitating resource monitoring, management, and intelligent scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plants, and in particular to a virtual power plant control system. Background Art

[0002] With the continuous growth of energy demand and the transformation of the energy structure, traditional power systems face many challenges, such as imbalances in power supply and demand and difficulties in integrating renewable energy. Virtual Power Plants (VPPs), as a new power system management model, achieve optimal resource scheduling and improve energy efficiency by integrating distributed energy resources (such as solar, wind, and battery storage).

[0003] At present, during the operation of the virtual power plant system, it is necessary to collect, integrate, process and store distributed energy resource data. Distributed energy resource data has the characteristics of dispersion, diversity, intermittency and uncertainty. With the expansion of the scale of the virtual power plant system, the difficulty of processing distributed energy resource data has also increased significantly. Therefore, a virtual power plant control system is needed to realize real-time monitoring and scheduling management of distributed energy resources through intelligent data collection, processing and optimization algorithms, improve energy utilization efficiency, reduce operating costs and promote the development of green energy. Summary of the Invention

[0004] The embodiment of the present invention provides a virtual power plant control system to solve the problem of real-time monitoring and scheduling management of distributed energy resources.

[0005] An embodiment of the present invention provides a virtual power plant control system, including:

[0006] The acquisition module is used to collect environmental data and operating data of distributed energy devices in the target area, perform preliminary verification on the environmental data and operating data, and encrypt and send them to the processing module; the distributed energy devices include photovoltaic equipment, wind power equipment, energy storage equipment, and metering equipment;

[0007] The processing module is used to perform logical verification on the operation data based on the environmental data, the coordinates of each distributed energy device and the sampling time of the operation data, and to store and manage the operation data.

[0008] An embodiment of the present invention provides a virtual power plant control system, which collects environmental data and operating data of distributed energy equipment through an acquisition module, performs preliminary verification during the acquisition process, and sends the data to a processing module, which then performs logical verification to ensure the accuracy of the collected data, realize data collection and processing of distributed energy resources, facilitate resource monitoring, management and intelligent scheduling, and can also monitor and respond to abnormal situations of distributed energy resources in a timely manner based on the data verification results, thereby promoting the development of green energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0010] Figure 1 It is a structural diagram of a virtual power plant control system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0011] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0012] The specific objectives of the virtual power plant control system provided by the present invention are as follows:

[0013] 1. Distributed energy data collection and processing

[0014] 1.1. Realize data collection of various distributed energy resources (such as solar energy, wind energy, battery energy storage, etc.).

[0015] 1.2. Ensure the real-time and accuracy of data collection and support multiple data formats and communication protocols.

[0016] 1.3. Develop data cleaning and verification mechanisms to ensure data quality.

[0017] 2. Intelligent optimization scheduling

[0018] 2.1. Develop advanced optimization algorithms to achieve intelligent scheduling of distributed energy resources.

[0019] 2.2. Provide load forecasting function to accurately predict power demand based on historical data and external factors (such as weather forecast).

[0020] 2.3. Achieve economic scheduling, optimize energy usage strategies, and reduce overall operating costs.

[0021] 3. User-friendly interface

[0022] 3.1. Design and develop an intuitive and easy-to-use user interface that supports access from multiple devices (e.g., PC, tablet, and mobile phone).

[0023] 3.2. Provide real-time monitoring function, users can check the system operation status and key indicators at any time.

[0024] 3.3. Provide data visualization tools, such as charts and dashboards, to help users better understand and analyze data.

[0025] 4. Alarm and notification

[0026] 4.1. Implement system anomaly detection and alarm functions to promptly detect and respond to system failures and abnormal situations.

[0027] 4.2. Provide multiple alarm notification methods (such as SMS, email, push notification) to ensure that users receive important information in a timely manner.

[0028] 4.3. Support user-defined alarm rules and notification strategies.

[0029] 5. Performance and Security

[0030] 5.1. Ensure high system availability and reliability to meet the stringent requirements of the power industry.

[0031] 5.2. Implement data encryption and secure transmission to protect user data privacy and system security.

[0032] 5.3. Provide authentication and authorization mechanisms to ensure that only authorized users can access system functions.

[0033] 6. Scalability and compatibility

[0034] 6.1. The system design has good scalability, which is convenient for future function expansion and upgrade.

[0035] 6.2. Support interfaces with other power systems and third-party platforms to ensure system interoperability and compatibility.

[0036] By achieving the above goals, the virtual power plant system will help power companies and large energy users more efficiently manage and utilize distributed energy resources, promoting the digital and intelligent transformation of the energy industry. To further clarify the objectives, technical solutions, and advantages of this invention, the following will be described through specific embodiments with reference to the accompanying drawings.

[0037] See also Figure 1 , which shows a schematic structural diagram of a virtual power plant control system 1 provided by an embodiment of the present invention, the virtual power plant control system 1 includes:

[0038] The acquisition module 11 is used to collect environmental data and operating data of distributed energy devices in the target area, perform preliminary verification on the environmental data and operating data, and encrypt and send them to the processing module 12. Distributed energy devices include photovoltaic devices, wind power equipment, energy storage equipment, and metering equipment.

[0039] The processing module 12 is used to perform logic verification on the operation data based on the environmental data, the coordinates of each distributed energy device and the sampling time of the operation data, and to store and manage the operation data.

[0040] In this embodiment, the acquisition module is the core component of the virtual power plant system, responsible for collecting real-time data from various distributed energy resources to ensure the comprehensiveness, accuracy, and real-time nature of the data. The data sources, data types, and data usage of the acquisition module are as follows:

[0041] (1) Sensors and smart meters

[0042] •Solar panels: Collect data such as the power generation power, voltage, current, temperature, etc. of the solar panels to monitor their power generation efficiency and health status.

[0043] • Wind turbines: Obtain data such as the speed, power generation, wind speed, and wind direction of wind turbines to analyze their performance and operating conditions.

[0044] •Battery energy storage system: collects data such as battery charge and discharge status, voltage, current, temperature, capacity, etc., and monitors the battery health and remaining power.

[0045] •Smart meter: records electricity consumption data such as power usage, voltage, current, power factor, etc. to help users analyze electricity usage.

[0046] (2) Power system interface

[0047] • Grid load data: Obtain real-time grid load data from the power dispatching center to understand the supply and demand balance of the grid.

[0048] •Electricity price information: Obtain real-time and historical electricity price information to help users conduct economic scheduling and cost optimization.

[0049] • Electricity consumption data: Obtain detailed electricity consumption data from power companies or users to support electricity consumption pattern analysis and optimization.

[0050] (3) Environmental monitoring equipment

[0051] • Weather station data: Collects data such as temperature, humidity, wind speed, wind direction, and rainfall from weather stations to help predict and assess the power generation potential of renewable energy.

[0052] •Solar radiation sensor: records sunlight intensity and radiation, and evaluates the power generation efficiency and potential production capacity of solar panels.

[0053] •Air quality sensor: monitors the concentration of pollutants in the air, such as PM2.5, PM10, CO2, etc., and analyzes the impact of the environment on the equipment.

[0054] The data types and formats are as follows:

[0055] (1) Structured data

[0056] • Equipment operating parameters:

[0057] Voltage: Expressed in volts (V), usually stored in floating point format.

[0058] Current: expressed in amperes (A), usually stored in floating-point format.

[0059] Power: expressed in watts (W) or kilowatts (kW), usually stored in floating point format.

[0060] Temperature: expressed in degrees Celsius (°C), usually stored in floating-point format.

[0061] Electricity: expressed in kilowatt-hours (kWh), usually stored in floating-point format.

[0062] •Environmental monitoring data:

[0063] Temperature: expressed in degrees Celsius (℃), usually stored in floating-point format.

[0064] Humidity: expressed as a percentage (%), usually stored in floating point format.

[0065] Wind speed: expressed in meters per second (m / s), usually stored in floating point format.

[0066] Wind direction: expressed in degrees (°), usually stored in integer format.

[0067] Solar radiation intensity: expressed in watts per square meter (W / m²), usually stored in floating point format.

[0068] • Grid data:

[0069] Grid load: Expressed in megawatts (MW), usually stored in floating point format.

[0070] Electricity price: expressed in monetary units, usually stored in floating point format.

[0071] Electricity usage: expressed in kilowatt-hours (kWh), usually stored in floating-point format.

[0072] (2) Unstructured data

[0073] • Image data:

[0074] Monitoring images: Usually stored in formats such as JPEG and PNG, used for visual monitoring of device operating status.

[0075] • Video data:

[0076] Surveillance video: Usually stored in formats such as MP4 and AVI, used for real-time monitoring and playback analysis of equipment and sites.

[0077] • Text data:

[0078] Log files: Stored in TXT, LOG and other formats, they record information such as equipment operation and system operations.

[0079] (3) Semi-structured data

[0080] •JSON (JavaScript Object Notation): Used to transmit and store complex data structures, such as device configuration, status reports, etc., and is easy to parse and extend.

[0081] •XML (eXtensible Markup Language): used to transmit and store data, especially suitable for data exchange that requires a strict defined format.

[0082] •CSV (Comma-Separated Values): Used to store tabular data, such as historical data, statistical data, etc., which is easy to import and export.

[0083] After the acquisition module completes data acquisition, it needs to communicate with the processing module and send the collected data to the processing module for storage and management. The data transmission and communication protocol between the acquisition module and the processing module are described as follows:

[0084] (1) Communication protocol

[0085] Modbus is a serial communication protocol used for communication between industrial devices. It uses a master-slave architecture and supports physical layers such as RS-232 and RS-485. It is suitable for data collection from power monitoring devices (such as smart meters and transmitters). Due to its simplicity and reliability, it is widely used in industrial automation.

[0086] •OPC (OLE for Process Control) is an industrial communication protocol that supports data exchange between various industrial devices and software applications. It is suitable for complex industrial control systems and integration with SCADA systems, PLC devices, etc., providing cross-platform data access.

[0087] •MQTT (Message Queuing Telemetry Transport) is a lightweight publish / subscribe messaging protocol designed for high-latency, unstable network environments. It is suitable for scenarios requiring real-time data transmission, such as remote device monitoring and sensor data collection, and is particularly widely used in Internet of Things (IoT) applications.

[0088] •HTTP / HTTPS, HTTP is the Hypertext Transfer Protocol, and HTTPS is its encrypted version, ensuring the security of data transmission. It is suitable for web-based application programming interfaces (APIs) for data collection and device management, especially when remote data transmission is carried out over the Internet.

[0089] •WebSocket is a full-duplex communication protocol that allows real-time data exchange between clients and servers. It is suitable for real-time applications that require continuous data flow, such as real-time monitoring systems and dynamic data update scenarios.

[0090] (2) Data transmission mechanism

[0091] •Synchronous transmission: data is transmitted within a predetermined time interval. It is usually used for real-time data with high time requirements. It is suitable for scenarios such as real-time monitoring and fault alarm that require immediate response.

[0092] •Asynchronous transmission, data is transmitted within an uncertain time interval, usually used for non-real-time data with low time requirements. It is suitable for scenarios with low real-time requirements such as environmental monitoring data and historical data uploading.

[0093] (3) Reliability and integrity of data transmission

[0094] •Data verification: During data transmission, the integrity of the data is verified through methods such as checksum and cyclic redundancy check (CRC). It is applicable to all data transmission scenarios to ensure that the data is not tampered with or lost during transmission.

[0095] •Retransmission mechanism, which automatically resends data packets when data transmission errors or losses are detected to ensure reliable data transmission. It is suitable for scenarios where the network environment is unstable or data transmission reliability requirements are high.

[0096] •Data encryption, using encryption algorithms (such as AES, RSA, etc.) to encrypt data during transmission to ensure the security of data during transmission. It is suitable for scenarios involving sensitive data or requiring high security, such as user information, control commands, etc.

[0097] (4) Network Protocol

[0098] •TCP / IP, Transmission Control Protocol / Internet Protocol, is the basic protocol of the Internet and local area networks, providing reliable data transmission services and applicable to all network-based data transmission, ensuring data reliability and integrity.

[0099] •UDP, User Datagram Protocol, provides fast data transmission services without guaranteeing reliability. It is suitable for scenarios with high real-time requirements but with a certain amount of data loss allowed, such as video streaming and audio streaming transmission.

[0100] (5) Data transmission architecture

[0101] •Distributed architecture, data transmission is distributed, processed and stored among multiple nodes, improving the system's fault tolerance and scalability. It is suitable for large-scale, distributed energy systems and improves the system's flexibility and reliability.

[0102] After receiving the data sent by the acquisition module, the processing module can perform logical verification on the data based on the relationship between various types of data, perform data cleaning and verification data analysis and statistical data visualization. Logical verification mainly includes:

[0103] Correlation check: Check the logical relationship between related fields, such as the product of voltage and current should be equal to power (P=U*I).

[0104] Time sequence verification: Ensure that the time sequence of time series data is correct and there is no reverse time sequence.

[0105] In addition to the acquisition module and the processing module, the virtual power plant control system provided in this embodiment may also include:

[0106] Optimization algorithm module for load forecasting and economic dispatch energy optimization strategy;

[0107] User interface module, used to implement real-time monitoring of user login and registration dashboards and chart display reports;

[0108] The alarm and notification module can set the notification method of abnormal detection alarm trigger conditions (such as SMS, email, etc.);

[0109] Backend management module:

[0110] The backend management module is an important part of the virtual power plant system and is used for system configuration, management and maintenance. The following is a detailed functional module division and description:

[0111] 1. User Management

[0112] 1.1. User Registration and Authentication:

[0113] Registration: Administrators can add new user accounts, including username, password, role and other information.

[0114] Login authentication: Users log in to the system using their username and password, and multi-factor authentication (MFA) is supported to improve security.

[0115] Permission management: Role-based access control (RBAC), different roles have different permissions.

[0116] User information management:

[0117] Information View: View user details, including user name, role, last login time, etc.

[0118] Information modification: Administrators can modify user information, such as resetting passwords, changing roles, etc.

[0119] User Deletion: Administrators can delete user accounts that are no longer needed.

[0120] Audit log:

[0121] Operation log: Record all user operations to facilitate auditing and problem tracking.

[0122] Anomaly detection: Detects and alerts abnormal login and operation behaviors.

[0123] 2. Device Management

[0124] 2.1. Device registration and configuration:

[0125] Device registration: Add a new device, including device ID, name, type, location and other information.

[0126] Device configuration: Configure device parameters, such as acquisition frequency, communication protocol, etc.

[0127] 2.2. Equipment monitoring:

[0128] Status monitoring: Real-time monitoring of the operating status of the device, such as online / offline status, power, voltage and other key parameters.

[0129] Fault detection: Detects equipment failures and generates alarm messages.

[0130] Equipment maintenance:

[0131] Device update: Update device firmware to ensure normal operation and security of the device.

[0132] Device deletion: Delete device records that are no longer in use.

[0133] 3. Data Management

[0134] 3.1. Data Storage and Backup:

[0135] Data storage configuration: Configure data storage strategies, including storage location, storage format, etc.

[0136] Data backup: Back up data regularly to ensure data security and recoverability.

[0137] 3.2. Data query and export:

[0138] Data query: Provides flexible data query functions and supports queries based on time, device, data type, etc.

[0139] Data export: Supports exporting query results to Excel, CSV and other format files.

[0140] 3.3. Data cleaning:

[0141] Data archiving: Archive historical data to reduce system burden.

[0142] Data deletion: Regularly delete expired or useless data to free up storage space.

[0143] 4. Alarm management

[0144] 4.1. Alarm configuration:

[0145] Alarm rules: define alarm rules, including alarm conditions, alarm levels, alarm notification methods, etc.

[0146] Alarm threshold: Set the alarm threshold for key parameters such as voltage, current, temperature, etc.

[0147] 4.2. Alarm monitoring:

[0148] Real-time alarm: monitor alarm information in real time to detect and handle abnormal situations in a timely manner.

[0149] Alarm history: View historical alarm records, analyze alarm causes and processing results.

[0150] 4.3. Alarm notification:

[0151] Notification method: Supports multiple alarm notification methods, such as email, SMS, system message, etc.

[0152] Notification Settings: Configure the recipients and notification policies for alarm notifications.

[0153] 5. Report management

[0154] 5.1. Report Generation:

[0155] Periodic reports: Automatically generate reports based on preset templates and time periods, such as daily, weekly, and monthly reports.

[0156] Customized reports: Support users to customize report content and format to meet specific analysis needs.

[0157] 5.2. Report viewing and exporting:

[0158] Report viewing: View report content online, supporting various display formats such as charts and tables.

[0159] Report export: Export reports to PDF, Excel and other formats for easy sharing and archiving.

[0160] 6. System Settings

[0161] 6.1. Basic Configuration:

[0162] System parameters: Configure basic system parameters, such as time format, language, time zone, etc.

[0163] Log management: Set up log recording and storage strategies to ensure the integrity of system operation records.

[0164] 6.2. Security Settings:

[0165] Password policy: Set password complexity requirements and validity period to improve system security.

[0166] Access control: Configure access control policies such as IP whitelists and blacklists to prevent unauthorized access.

[0167] 6.3. Backup and Recovery:

[0168] System backup: Back up system configuration and key data to ensure rapid recovery in case of system failure.

[0169] System recovery: supports restoring system configuration and data from backup to ensure continuous operation of the system.

[0170] 7. Log Management

[0171] 7.1. Operation Log:

[0172] Log records: Record all user operations, including login, configuration modification, data query, etc.

[0173] Log query: Provides flexible log query functions, supporting queries by time, user, operation type, etc.

[0174] 7.2. System log:

[0175] System events: record key events of the system, such as startup, shutdown, exceptions, etc.

[0176] Performance monitoring: Monitor system performance indicators such as CPU, memory, and disk usage.

[0177] 8. Multi-dimensional statistical analysis

[0178] 8.1. Data Analysis Tools:

[0179] Data filtering: Supports filtering data by multiple dimensions such as time, device type, and geographic location, making it easier for users to conduct fine-grained analysis.

[0180] Data aggregation: supports data aggregation by hourly, daily, monthly, and other time granularities, providing a macro perspective.

[0181] 8.2. Statistical reports:

[0182] Preset reports: The system has built-in multiple preset reports, such as equipment operation reports, energy consumption analysis reports, alarm statistics reports, etc., to meet common analysis needs.

[0183] Customized reports: Users can customize report content and format, and flexibly adjust analysis dimensions and indicators.

[0184] 8.3. Visualization chart:

[0185] Interactive charts: Provide interactive chart functions, allowing users to explore data in depth through operations such as clicking, zooming, and dragging.

[0186] Multiple chart types: Supports multiple chart types such as line charts, bar charts, pie charts, heat maps, etc. to meet different data display needs.

[0187] 8.4. Data export and sharing:

[0188] Export function: supports exporting statistical analysis results to PDF, Excel and other formats for easy sharing and archiving.

[0189] Sharing function: supports sharing data analysis results through email, links, etc. to facilitate team collaboration.

[0190] The embodiment of the present invention collects environmental data and operating data of distributed energy equipment through the collection module 11, and performs preliminary verification during the collection process. After sending it to the processing module 12, the processing module 12 performs logical verification to ensure the accuracy of the collected data, realize data collection and processing of distributed energy resources, facilitate resource monitoring, management and intelligent scheduling, and can also monitor and respond to abnormal situations of distributed energy resources in a timely manner based on the data verification results, thereby promoting the development of green energy.

[0191] In one possible implementation, the operating data of photovoltaic equipment includes generated power, voltage, current, and temperature; the operating data of wind power equipment includes speed, generated power, wind speed, and wind direction; the operating data of energy storage equipment includes charge and discharge status, voltage, current, temperature, and capacity; and the operating data of metering equipment includes power consumption, voltage, current, and power factor.

[0192] The acquisition module 11 is specifically used for:

[0193] For each photovoltaic device, determining upper and lower bounds of the photovoltaic device's operating data based on the operating data of photovoltaic devices within a first preset range of distance from the photovoltaic device; if the operating data of the photovoltaic device is within the upper and lower bounds, determining that the operating data of the photovoltaic device has passed the preliminary verification; otherwise, marking the operating data of the photovoltaic device as abnormal based on the degree to which the operating data exceeds the upper and lower bounds, and sending the data to the processing module 12;

[0194] For each wind turbine, determining upper and lower bounds of the wind turbine operating data based on the operating data of wind turbines within a second preset range from the wind turbine; if the operating data of the wind turbine is within the upper and lower bounds, determining that the operating data of the wind turbine has passed the preliminary verification; otherwise, marking the operating data of the wind turbine as abnormal based on the degree to which the operating data of the wind turbine exceeds the upper and lower bounds and sending the data to the processing module 12;

[0195] For each energy storage device, determining upper and lower bounds of the operating data of the energy storage device based on the operating data of energy storage devices within a third preset range from the energy storage device; if the operating data of the energy storage device is within the upper and lower bounds, determining that the operating data of the energy storage device has passed the preliminary verification; otherwise, marking the operating data of the energy storage device as abnormal based on the degree to which the operating data exceeds the upper and lower bounds and sending the data to the processing module 12;

[0196] For each metering device, the upper and lower boundaries of the operating data of the metering device are determined based on the operating data of the metering devices within the fourth preset range of distance from the metering device. If the operating data of the metering device is within the upper and lower boundaries, it is determined that the operating data of the metering device has passed the preliminary verification. Otherwise, an abnormality mark is made based on the degree to which the operating data of the metering device exceeds the upper and lower boundaries, and the data is sent to the processing module 12.

[0197] In this embodiment, the acquisition module can clean and verify the data after collecting it and before sending it to the processing module. The details are as follows:

[0198] (1) Data cleaning

[0199] •The main purpose of data cleaning is to identify and correct errors in raw data, handle missing values, unify data formats, and ensure data quality. The specific steps are as follows:

[0200] 1) Missing value processing:

[0201] Deletion method: For data records with a very small proportion of missing values, you can choose to delete them directly.

[0202] Filling method: Use reasonable values ​​to fill missing data, such as mean, median, nearest value, etc.

[0203] Interpolation method: For time series data, linear interpolation, spline interpolation and other methods can be used to fill missing values.

[0204] 2) Outlier processing:

[0205] Range check: Set a reasonable data range. Data outside the range is considered an abnormal value. For example, the temperature value should be within a reasonable range (such as -40℃ to +85℃) and the voltage value should be within a reasonable range (such as 0V to 1000V).

[0206] Statistical methods: Use box plots, standard deviation and other methods to detect and process outliers. For example, data exceeding 3 times the standard deviation is considered abnormal.

[0207] Model prediction: Use machine learning models to predict reasonable values ​​and compare them with actual values ​​to identify outliers.

[0208] 3) Duplicate data processing:

[0209] Deduplication: Delete duplicate records.

[0210] Data aggregation: Aggregate repeated data that is temporally or logically close, such as taking the average, maximum, or minimum value.

[0211] 4) Unified data format:

[0212] Date format: Unified date and time format, such as the ISO 8601 standard (YYYY-MM-DDTHH:MM).

[0213] Numeric format: Unify the numeric format and keep the number of decimal places consistent.

[0214] Unit conversion: Ensure that all data is in consistent units, such as all temperature values ​​are in degrees Celsius (°C) and all power values ​​are in kilowatt-hours (kWh).

[0215] (2) Data verification

[0216] The main purpose of data verification is to ensure that data remains complete and accurate during transmission, storage, and processing. The specific steps are as follows:

[0217] 1) Format verification:

[0218] Data type verification: Ensure that the data type of each field is correct, such as numeric, string, date, etc.

[0219] Regular expression validation: Use regular expressions to validate field formats, such as email addresses, IP addresses, etc.

[0220] 2) Range check:

[0221] Static range check: pre-define the legal range of each field and perform verification during data collection.

[0222] Dynamic range calibration: Dynamically adjust the calibration range based on historical data to improve calibration flexibility and accuracy.

[0223] 3) Integrity check:

[0224] Foreign key validation: Ensure that the value of the foreign key field exists in the related table, such as the device ID exists in the device table.

[0225] Uniqueness check: Ensures that there are no duplicate values ​​for a primary key or unique field, such as a unique identifier for a data record.

[0226] 4) Checksum:

[0227] Introduction: During data transmission, the checksum of the data is calculated and verified at the receiving end to ensure that the data has not been tampered with or lost.

[0228] Application scenario: Applicable to integrity verification of all transmitted data.

[0229] (3) Data cleaning and verification tools

[0230] 1) Programming tools:

[0231] Python: Use libraries such as Pandas and NumPy for data cleaning and verification.

[0232] R language: Use dplyr, tidyr and other packages for data processing.

[0233] SQL: Use SQL queries and functions to clean and validate data.

[0234] 2) Special software:

[0235] Talend: Provides rich data integration and cleansing capabilities.

[0236] Informatica: Provides data quality management and cleansing tools.

[0237] Apache Nifi: used to automate data stream processing and cleaning.

[0238] The operational data collected from distributed energy devices in this embodiment is primarily influenced by environmental data, which is influenced by geography. Within a relatively small area, the environmental data at each location is essentially the same. Therefore, under normal circumstances, the operational data of two distributed energy devices of the same category and with similar coordinates should be similar at the same time. If the operational data of a device deviates significantly, this may indicate an error in the sampling value or an abnormal operating state of the device. For the four types of distributed energy devices—photovoltaic, wind power, energy storage, and metering—the distance ranges between devices can be set based on the characteristics of each type of device affected by environmental data, and the device operational data used for comparison can be selected.

[0239] For example, for a certain photovoltaic device, the minimum value of the operating data of each photovoltaic device within 500m of the photovoltaic device can be used as the lower boundary of the photovoltaic device's operating data, and the maximum value can be used as the upper boundary. Specifically, the minimum current value of each photovoltaic device within 500m of the photovoltaic device during period A can be used as the lower boundary of the photovoltaic device's current value during period B, and the maximum current value of each photovoltaic device during period A can be used as the upper boundary of the photovoltaic device's current value during period B. Period B starts later than period A and ends earlier than period A. The same principle applies to other types of distributed energy devices and other types of operating data.

[0240] In a possible implementation, the processing module 12 is specifically configured to:

[0241] Clustering the operating data of each photovoltaic device based on the coordinates and sampling time of the photovoltaic device to obtain multiple photovoltaic clusters;

[0242] For each photovoltaic cluster, the photovoltaic area corresponding to the photovoltaic cluster is determined based on the coordinates of the photovoltaic devices corresponding to the operating data within the photovoltaic cluster, multiple predicted operating data of the photovoltaic area are determined based on the environmental data of the photovoltaic area, the upper and lower boundaries of the operating data of the photovoltaic area are determined based on the multiple predicted operating data, and the operating data within the photovoltaic cluster are logically checked based on the upper and lower boundaries of the operating data;

[0243] Clustering the operating data of each wind turbine device based on the coordinates and sampling time of the wind turbine device to obtain multiple wind turbine clusters;

[0244] For each wind power cluster, determine the wind power area corresponding to the wind power cluster based on the coordinates of the wind power equipment corresponding to each operating data in the wind power cluster, determine multiple predicted operating data of the wind power area based on the environmental data of the wind power area, determine the upper and lower boundaries of the operating data of the wind power area based on the multiple predicted operating data, and perform a logical check on each operating data in the wind power cluster based on the upper and lower boundaries of the operating data;

[0245] Clustering the operating data of each energy storage device based on the coordinates and sampling time of the energy storage device to obtain multiple energy storage clusters;

[0246] For each energy storage cluster, determine the energy storage area corresponding to the energy storage cluster based on the coordinates of the energy storage devices corresponding to each operating data within the energy storage cluster, determine multiple predicted operating data of the energy storage area based on the environmental data of the energy storage area, determine the upper and lower boundaries of the operating data of the energy storage area based on the multiple predicted operating data, and perform a logical check on each operating data within the energy storage cluster based on the upper and lower boundaries of the operating data;

[0247] Clustering the operating data of each measuring device based on the coordinates and sampling time of the measuring device to obtain multiple measuring clusters;

[0248] For each metering cluster, the metering area corresponding to the metering cluster is determined based on the coordinates of the metering equipment corresponding to each operating data in the metering cluster, multiple predicted operating data of the metering area are determined based on the environmental data of the metering area, the upper and lower boundaries of the operating data of the metering area are determined based on the multiple predicted operating data, and logical verification is performed on each operating data in the metering cluster based on the upper and lower boundaries of the operating data.

[0249] In this embodiment, the processing module has more comprehensive data resources and data processing capabilities than the acquisition module, and can perform logical verification based on the influence and coordination relationship between devices and the specific operating status of the devices affected by environmental data. Logical verification is to use the logical relationship between various types of operating data for verification, such as the product of voltage and current should be equal to power. In this embodiment, the operating data of various types of distributed energy equipment are verified, such as whether the power generation is consistent with the environmental parameters, whether the current and voltage are consistent with the power generation, and whether the actual energy storage is consistent with the theoretical energy storage. Compared with preliminary verification, logical verification has a more accurate verification effect.

[0250] Specifically, each type of distributed energy device is clustered separately, and distributed energy devices with similar coordinates and operating status can be batch processed. Then, based on environmental data and various different methods, various predicted operating data for the region are estimated, with the minimum value serving as the lower bound and the maximum value serving as the upper bound. For example, for energy storage devices, the environmental data of the storage area can be used to estimate the distributed energy generation and load within the region. The difference between the two can then be calculated to determine the storage capacity or energy supply of the energy storage devices within the region. If the distributed energy generation exceeds the load, the excess energy can be stored, and the difference between the two is the theoretical storage capacity stored in the energy storage device. If the distributed energy generation is less than the load, the energy stored in the energy storage device must be used to meet the load demand, and the difference between the two is the theoretical energy supply capacity of the energy storage device. Using the same method, multiple theoretical storage capacities and theoretical energy supply capacities are obtained, and the maximum and minimum values ​​are selected as the upper and lower bounds, respectively, for logical verification. For example, if the actual storage capacity falls within the upper and lower bounds of the theoretical storage capacity, the logical verification passes; otherwise, if the actual storage capacity exceeds the upper and lower bounds, the logical verification fails. For metering equipment, the distributed energy generation and load can also be estimated based on the environmental data of the metering area, the theoretical metering value of the metering equipment can be determined, and used for logic verification.

[0251] In a possible implementation, the processing module 12 is further configured to:

[0252] For each distributed energy device, if the preliminary verification and logical verification of the operating data of the distributed energy device fail, the distributed energy devices associated with the distributed energy device are searched based on the topological relationships in the target area. If the preliminary verification and logical verification of the operating data of all associated distributed energy devices fail, a fault warning is issued for the distributed energy device. Distributed energy devices with a connection hop count less than the preset number of hops and different categories are considered to be associated distributed energy devices. It should be noted that distributed energy devices of the same category are in a parallel relationship, and their operating states do not affect each other, and are irrelevant quantities. Therefore, this embodiment only considers distributed energy devices of different categories.

[0253] In this embodiment, if the preliminary verification and logical verification of the operating data of a distributed energy device fail, and the preliminary verification and logical verification of the operating data of all associated distributed energy devices fail, it means that the operating data of these distributed energy devices are collected correctly, and the reason for the failure of the verification is that the operating status of the equipment is abnormal, and there may be a fault.

[0254] In a possible implementation, the acquisition module 11 is further configured to:

[0255] Based on preset rules, the data storage status and coordinates of each distributed energy device sent by the processing module 12 at the previous moment are processed to obtain the encryption key at the current moment, and the environmental data and operating data at the current moment are encrypted based on the encryption key at the current moment to obtain the encrypted data at the current moment, and send it to the processing module 12;

[0256] The processing module 12 is further configured to:

[0257] The encrypted data at the current moment is decrypted based on the data storage status at the previous moment and the coordinates of each distributed energy device to obtain the environmental data and operation data at the current moment, and the data storage status at the current moment is determined and sent to the acquisition module 11, so that the acquisition module 11 generates the encryption key at the next moment based on the data storage status at the current moment.

[0258] In this embodiment, to ensure the security of collected data, the following settings can be made for communication and storage:

[0259] (1) Data transmission security

[0260] • Encrypted transmission:

[0261] SSL / TLS: Use the Secure Sockets Layer (SSL) and Transport Layer Security (TLS) protocols to encrypt data transmission, ensuring that data cannot be eavesdropped or tampered with during network transmission.

[0262] VPN: Use virtual private network (VPN) technology to establish a secure communication tunnel to protect the privacy and integrity of data transmission.

[0263] •Data Signature:

[0264] Digital signature: Digitally sign the transmitted data, and the recipient confirms the integrity of the data and the authenticity of the source by verifying the signature.

[0265] • Network Isolation:

[0266] Firewall: Set firewall rules to limit the source and destination IP addresses of data transmission and improve network security.

[0267] VLAN: Use virtual local area network (VLAN) technology to logically isolate the network and prevent unauthorized access between different network areas.

[0268] (2) Data storage security

[0269] • Encrypted storage:

[0270] Disk encryption: Use full disk encryption technology (such as BitLocker and LUKS) to encrypt storage media and prevent data leakage.

[0271] Database encryption: Encrypt and store sensitive data in the database, using encryption algorithms such as AES and RSA to protect data security.

[0272] • Access Control:

[0273] User authentication: Use strong authentication mechanisms (such as two-factor authentication, single sign-on, etc.) to ensure that only authorized users can access systems and data.

[0274] Permission management: Set fine-grained permission management policies to control user access rights to data based on roles (RBAC) or attributes (ABAC).

[0275] •Data backup and recovery:

[0276] Encrypted backup: Encrypted storage of backup data to ensure the security of backup data.

[0277] Offline backup: Perform regular offline backups to prevent data loss due to network attacks.

[0278] In this embodiment, the data storage status may reflect information such as the number of read and write times of each memory and the amount of stored data. For example, the data storage status may be obtained by encoding the number of read and write times and the amount of stored data of each memory.

[0279] In this embodiment, the encryption key for the next moment is determined based on the data generated after the communication at the current moment. Both the acquisition module and the processing module can obtain it directly without the need to additionally calculate meaningless key data, which can reduce the amount of calculation required to generate the key. In addition, the information used to generate the key has high verifiability and non-tamperability, which can achieve better encryption effect.

[0280] In a possible implementation, the acquisition module 11 is specifically configured to:

[0281] Combining the coordinates of each distributed energy device into a coordinate sequence according to a preset order, and calculating a hash value of the coordinate sequence as a first key;

[0282] Calculate the hash value of the data storage state sent by the processing module 12 at the previous moment as the second key at the current moment;

[0283] The environmental data and the operating data at the current moment are triple-DES encrypted based on the first key and the second key at the current moment to obtain the encrypted data at the current moment.

[0284] In this embodiment, the hash function can convert information into a fixed-length hash value. The coordinates of each distributed energy device are preset values. The acquisition module and the processing module use the same hash function to process the coordinate sequence to obtain the same hash value. Similarly, the acquisition module and the processing module can both obtain the data storage status and process it using the same hash function to obtain the same hash value.

[0285] Triple DES encryption requires both communicating parties to understand the encryption rules. The processing module only needs to feedback the processing results after receiving the data sent by the acquisition module. Both parties can then determine the next encryption key to achieve secure and efficient communication.

[0286] In a possible implementation, the processing module 12 is specifically configured to:

[0287] Archive and clean up the data stored in each storage device;

[0288] Calculate the hotness and coldness of each type of data based on the data categories and verification results of each type of data in the current environmental data and operation data;

[0289] Determine the storage device allocation plan for each type of data in the current environmental data and operation data based on the device status of each storage device and the popularity of each type of data;

[0290] Compress, deduplicate, slice, and back up all types of data in the current environmental data and operational data, and store all types of data in the current environmental data and operational data to corresponding storage devices based on the storage device allocation plan;

[0291] The amount of data removed from each storage device and the amount of newly added data are combined in a preset order to obtain the data storage status at the current moment.

[0292] In this embodiment, the processing module is configured to store data as follows:

[0293] (1) Data storage type

[0294] 1) Structured storage:

[0295] Relational Database Management System (RDBMS): Use relational databases such as MySQL, PostgreSQL, and SQL Server to store structured data and support complex queries and transaction processing.

[0296] Table structure design: Design a reasonable table structure based on data type and business requirements, including primary keys, foreign keys, indexes, etc.

[0297] SQL query optimization: Optimize SQL query performance through indexes, views, stored procedures, etc.

[0298] Time Series Database (TSDB): Use a time series database such as InfluxDB and TimescaleDB to store time series data, supporting efficient time series data writing and querying.

[0299] Time series data model: Design a reasonable time series data model, including timestamps, labels, fields, etc.

[0300] Data compression and archiving: Use the compression and archiving functions of the time series database to improve storage efficiency and query performance.

[0301] 2) Unstructured storage:

[0302] Document database: Use document databases such as MongoDB and CouchDB to store unstructured or semi-structured data, supporting flexible data models and high scalability.

[0303] Document structure design: Design a reasonable document structure based on data characteristics, and use nested documents, arrays, etc. to improve data access efficiency.

[0304] Object storage: Use object storage services such as Amazon S3 and Azure Blob Storage to store large amounts of unstructured data, such as images, videos, and log files.

[0305] Object storage management: Improve the management and access efficiency of object storage through directory structure and metadata management.

[0306] 3) Hybrid storage:

[0307] NoSQL database: Use NoSQL databases such as Cassandra and HBase, which combine the advantages of structured and unstructured storage to provide high scalability and high availability.

[0308] Data sharding and replication: Improve the scalability and fault tolerance of storage systems through data sharding and replication.

[0309] Distributed file system: Use distributed file systems such as Hadoop HDFS and Ceph to store large-scale distributed data, supporting high-concurrency access and big data processing.

[0310] Data distribution and redundancy: Improve system reliability and availability through data distribution and redundancy.

[0311] (2) Data storage strategy

[0312] 1) Data tiered storage:

[0313] Hot data: Stores data that is frequently accessed recently and uses high-performance storage media such as SSDs to improve read and write performance.

[0314] Warm data: Stores data that is not frequently accessed, using cost-effective storage media such as SATA hard drives.

[0315] Cold data: Stores rarely accessed historical data using low-cost storage media such as tapes and CDs.

[0316] 2) Data compression and deduplication:

[0317] Data compression: Use compression algorithms such as Gzip and LZ4 to compress stored data and improve storage efficiency.

[0318] Data deduplication: Identify and delete duplicate data to reduce storage space usage.

[0319] 3) Data backup and recovery:

[0320] Regular backup: Set up regular backup strategies to back up important data to off-site storage to improve data security.

[0321] Disaster recovery: Develop a disaster recovery plan and regularly practice data recovery procedures to ensure that data can be quickly restored in the event of an emergency.

[0322] (3) Data storage management

[0323] 1) Storage Monitoring:

[0324] Storage capacity monitoring: Real-time monitoring of storage system capacity usage, and early warning of insufficient storage space.

[0325] Storage performance monitoring: Monitor the read and write performance of storage systems to identify and resolve performance bottlenecks.

[0326] 2) Data lifecycle management:

[0327] Data archiving: Archive old data to low-cost storage media based on the data lifecycle.

[0328] Data cleaning: Regularly clean up useless or expired data to free up storage space.

[0329] 3) Data security management:

[0330] Access control: Set strict access control policies to ensure that only authorized users can access and operate stored data.

[0331] Data encryption: Encrypt stored data to prevent data leakage and unauthorized access.

[0332] In this embodiment, data categories may include various environmental data, photovoltaic equipment operating data, wind power equipment operating data, energy storage equipment operating data, and metering equipment operating data. Verification results are the results of preliminary verification and logical verification. Due to differences in data categories and verification results, the stored data may have different levels of popularity.

[0333] The device status of the storage device includes the number of read and write times and the available capacity. Based on the life balancing strategy, data with high hot and cold data can be preferentially stored in the storage device with low number of read and write times and large available capacity.

[0334] The amount of data removed from and newly added to each storage device is the data generated by the processing module during the data processing process. The processing module only needs to combine these data (for example, the amount of data removed from memory 1 is 10, and the amount of data written is 20; the amount of data removed from memory 2 is 30, and the amount of data written is 40, which can be combined into the data sequence 10203040) and send it to the acquisition module as key information for generating the key, thereby achieving efficient and secure communication.

[0335] In a possible implementation, the processing module 12 is specifically configured to:

[0336] For each type of data in the current environmental data and operating data, the heat and coldness of the data is calculated based on the heat and coldness formula; the heat and coldness formula is:

[0337]

[0338] in, For the The hotness or coldness of class data, For the The proportion of the data volume of this type of data in the total data volume, For the The collection cycle of class data, No. The number of times the class data verification results failed.

[0339] In this embodiment, the The proportion of the data volume of this type of data in the total data volume can be used as the first The weight of the data type is: the larger the data volume, the larger the storage space required for the data type, and the more important the data type is. The higher the collection frequency, the larger the storage space required for the data type and the more times it needs to be stored. Therefore, The collection frequency corresponding to the data of this type is used as a cold and hot influence value. The greater the number of times the verification results of this type of data fail, the higher the fault risk corresponding to this type of data, and the more frequent the collection and review, the more frequent the collection and review. The number of times the verification results of the class data fail is used as another hot / cold impact value.

[0340] The hotness or coldness of data can accurately describe the importance and frequency of use of data, making it easier to sort various types of data according to the hotness or coldness values ​​when allocating storage devices, and allocate storage devices in order to achieve better data storage and usage effects.

[0341] In a possible implementation, the processing module 12 is further configured to:

[0342] When obtaining a data query instruction input by a user, searching for data corresponding to the data query instruction;

[0343] If the data corresponding to the data query instruction is a numerical value, the numerical value is adjusted based on the random number to obtain the data after desensitization;

[0344] If the data corresponding to the data query instruction is an image, the identification elements in the image are blurred to obtain the data after desensitization;

[0345] Send anonymized data to users.

[0346] In this embodiment, to ensure data processing security, the following measures are set:

[0347] •Data desensitization:

[0348] Data masking: When displaying sensitive data, use data masking techniques (such as masking, substitution, etc.) to protect data privacy.

[0349] Anonymization: Anonymize sensitive data to ensure that personal information cannot be identified during data analysis and sharing.

[0350] •Data access log:

[0351] Audit log: records all data access and operation behaviors to facilitate security audits and problem tracking.

[0352] Abnormal monitoring: Real-time monitoring of data access behavior, detection and alarm of abnormal access and operation.

[0353] (1) Data security management

[0354] • Security Policy:

[0355] Security policy formulation: Develop comprehensive data security policies, including data classification, sensitive data protection, and emergency response.

[0356] Security training: Provide regular data security training to staff to improve security awareness and operating standards.

[0357] • Safety Assessment and Testing:

[0358] Penetration testing: Penetration testing is performed regularly to discover and fix security vulnerabilities in the system.

[0359] Security Assessment: Conduct a comprehensive security assessment of the system to ensure compliance with relevant laws, regulations and industry standards.

[0360] • Emergency Response:

[0361] Emergency plan: Develop emergency plans for security incidents such as data leaks and attacks, and clarify the emergency response process and responsible persons.

[0362] Emergency drills: Conduct emergency drills regularly to ensure the effectiveness of emergency plans and personnel's emergency response capabilities.

[0363] When the data queried by the user is numerical, that is, the user needs to analyze the relationship or change trend between the data, the numerical value can be adjusted based on the random number. For example, all the queried values ​​can be multiplied by a random number to modify the numerical value to protect sensitive information in the data. At the same time, the relationship or change trend between the data can be retained to meet the user's analysis needs.

[0364] When the data the user queries is an image, most of the information in the image can be retained, and only the identification elements in the image can be blurred to protect sensitive information in the data.

[0365] In a possible implementation, the acquisition module 11 is specifically configured to:

[0366] For each type of operating data, the collection frequency of the type of operating data is adjusted based on the change rate of the type of operating data.

[0367] In this embodiment, the collection frequency of various types of data can be specifically set as follows:

[0368] (1) Real-time data collection

[0369] •Key equipment parameters: For the core operating parameters (such as voltage, current, power, temperature, etc.) of key equipment such as solar panels, wind turbines, and battery energy storage systems, high-frequency real-time data collection is required.

[0370] Acquisition frequency: once per second (1 Hz) or higher to ensure real-time monitoring and rapid response to equipment operating status.

[0371] • Grid load data: Real-time load data of the grid needs to be collected at a high frequency to ensure that the system can quickly adjust the output of distributed energy and achieve supply and demand balance.

[0372] Acquisition frequency: once per second (1 Hz) or higher.

[0373] (2) Periodic data collection

[0374] •Environmental monitoring data: Environmental monitoring data (such as temperature, humidity, wind speed, wind direction, sunshine intensity, etc.) generally changes slowly, and a longer collection cycle can be set.

[0375] Sampling frequency: once per minute (1 / 60 Hz) or once every 5 minutes (1 / 300 Hz), adjusted according to specific needs.

[0376] •Grid electricity price information: Electricity price information is usually updated every hour or every day, so a longer collection cycle can be set.

[0377] Sampling frequency: once per hour (1 / 3600 Hz) or once per day (1 / 86400 Hz).

[0378] (3) Event-driven data collection

[0379] •Alarm and fault data: When an alarm or fault occurs on the equipment, relevant data is collected immediately and detailed fault information and operating status are recorded.

[0380] Collection frequency: Immediate collection when an event is triggered, irregular collection.

[0381] • Switching operation data: When the device performs a switching operation (such as starting, stopping, switching modes, etc.), detailed data at the time of operation is collected.

[0382] Collection frequency: Immediate collection when an event is triggered, irregular collection.

[0383] (4) Historical data collection

[0384] •Long-term performance data: For the long-term performance data of equipment (such as monthly or annual power generation, power consumption, equipment health status, etc.), a longer collection period can be set for periodic summary and analysis.

[0385] Sampling frequency: once a day (1 / 86400 Hz), once a week (1 / 604800 Hz), or once a month (1 / 2,592,000 Hz), adjusted according to specific needs.

[0386] (5) Adaptive data acquisition

[0387] • Load change monitoring: Adaptively adjust the data collection frequency based on changes in system load and device operating status. For example, when the device load changes dramatically, the collection frequency is increased; when the device is operating stably, the collection frequency is reduced.

[0388] Sampling frequency: Dynamically adjusted according to load changes, ranging from once per second (1 Hz) to once per minute (1 / 60 Hz).

[0389] When adjusting the collection frequency of various types of operating data based on their rate of change, the data rate of change can be segmented and corresponding collection frequencies set. For example, when the difference between two collected energy storage device currents is greater than a first threshold, a first collection frequency with a higher frequency is used; when the difference between two collected energy storage device currents is less than the first threshold but greater than a second threshold, a second collection frequency with a medium frequency is used; and when the difference between two collected energy storage device currents is less than the second threshold, a third collection frequency with a lower frequency is used.

[0390] By determining the acquisition frequency in segments, the appropriate acquisition frequency can be selected based on the actual state of the data, and there is no need to perform complex calculations on the acquisition frequency, thus achieving efficient and accurate acquisition.

[0391] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0392] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0393] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0394] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0395] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, and can be electrical, mechanical, or other forms.

[0396] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0397] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0398] If the integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the module functions of each of the above-mentioned virtual power plant control system embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice within a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0399] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A virtual power plant control system, characterized in that: include: A collection module is used to collect environmental data and operating data of distributed energy devices in the target area, perform preliminary verification on the environmental data and operating data, and encrypt and send them to the processing module; wherein the distributed energy devices include photovoltaic devices, wind power equipment, energy storage equipment, and metering equipment; a processing module, configured to perform a logic check on the operation data based on the environmental data, the coordinates of each distributed energy device, and the sampling time of the operation data, and to store and manage the operation data; The acquisition module is specifically used for: Combining the coordinates of each distributed energy device into a coordinate sequence in a preset order, and calculating a hash value of the coordinate sequence as a first key; calculating a hash value of the data storage state sent by the processing module at the previous moment as a second key at the current moment; performing triple DES encryption on the environmental data and operating data at the current moment based on the first key and the second key at the current moment to obtain encrypted data at the current moment; and sending the encrypted data to the processing module; The processing module is specifically used for: The data stored in each storage device is archived and cleaned; based on the data storage status of the previous moment and the coordinates of each distributed energy device, the encrypted data at the current moment is decrypted to obtain the environmental data and operating data at the current moment; the hotness and coldness of each type of data in the environmental data and operating data at the current moment is calculated based on the data category and verification results of each type of data. The verification results are the results of preliminary verification and logical verification. The hotness and coldness formula is: in, For the The hotness or coldness of class data, For the The proportion of the data volume of this type of data in the total data volume, For the The collection cycle of class data, No. The method comprises the following steps: determining the number of times the verification result of the type of data fails; determining the storage device allocation scheme for each type of data in the environmental data and operation data at the current moment based on the device status of each storage device and the hotness or coldness of each type of data; compressing, deduplicating, slicing and backing up each type of data in the environmental data and operation data at the current moment, and storing each type of data in the environmental data and operation data at the current moment to the corresponding storage device based on the storage device allocation scheme; combining the amount of data removed from each storage device and the amount of newly added data in a preset order to obtain the data storage status at the current moment; sending the data storage status at the current moment to the acquisition module, so that the acquisition module generates the encryption key at the next moment based on the data storage status at the current moment.

2. The virtual power plant control system according to claim 1, characterized in that: The operating data of photovoltaic equipment includes power generation, voltage, current and temperature; the operating data of wind power equipment includes speed, power generation, wind speed and direction; the operating data of energy storage equipment includes charge and discharge status, voltage, current, temperature and capacity; the operating data of metering equipment includes power consumption, voltage, current and power factor; The acquisition module is specifically used for: For each photovoltaic device, determining upper and lower bounds of the photovoltaic device's operating data based on the operating data of photovoltaic devices within a first preset range of distance from the photovoltaic device; if the operating data of the photovoltaic device is within the upper and lower bounds, determining that the operating data of the photovoltaic device has passed the preliminary verification; otherwise, marking the operating data of the photovoltaic device as abnormal based on the degree to which the operating data exceeds the upper and lower bounds and sending the data to the processing module; For each wind turbine, determining upper and lower bounds of the wind turbine operating data based on the operating data of wind turbines within a second preset range from the wind turbine; if the operating data of the wind turbine is within the upper and lower bounds, determining that the operating data of the wind turbine has passed the preliminary verification; otherwise, marking the operating data of the wind turbine as abnormal based on the degree to which the operating data of the wind turbine exceeds the upper and lower bounds and sending the data to the processing module; For each energy storage device, determining upper and lower bounds of the operating data of the energy storage device based on the operating data of energy storage devices within a third preset range from the energy storage device; if the operating data of the energy storage device is within the upper and lower bounds, determining that the operating data of the energy storage device has passed the preliminary verification; otherwise, marking the operating data of the energy storage device as abnormal based on the degree to which the operating data exceeds the upper and lower bounds and sending the data to the processing module; For each measuring device, the upper and lower boundaries of the operating data of the measuring device are determined based on the operating data of the measuring devices within the fourth preset range of distance from the measuring device. If the operating data of the measuring device is within the upper and lower boundaries, it is determined that the operating data of the measuring device has passed the preliminary verification; otherwise, an abnormality mark is made based on the degree to which the operating data of the measuring device exceeds the upper and lower boundaries, and the data is sent to the processing module.

3. The virtual power plant control system according to claim 2, characterized in that: The processing module is specifically used for: Clustering the operating data of each photovoltaic device based on the coordinates and sampling time of the photovoltaic device to obtain multiple photovoltaic clusters; For each photovoltaic cluster, determining the photovoltaic area corresponding to the photovoltaic cluster based on the coordinates of the photovoltaic devices corresponding to each operating data within the photovoltaic cluster, determining multiple predicted operating data of the photovoltaic area based on the environmental data of the photovoltaic area, determining the upper and lower boundaries of the operating data of the photovoltaic area based on the multiple predicted operating data, and performing a logical check on each operating data within the photovoltaic cluster based on the upper and lower boundaries of the operating data; Clustering the operating data of each wind power device based on the coordinates and sampling time of the wind power device to obtain multiple wind power clusters; For each wind power cluster, determining the wind power area corresponding to the wind power cluster based on the coordinates of the wind power equipment corresponding to each operating data within the wind power cluster, determining multiple predicted operating data of the wind power area based on the environmental data of the wind power area, determining upper and lower boundaries of the operating data of the wind power area based on the multiple predicted operating data, and performing a logical check on each operating data within the wind power cluster based on the upper and lower boundaries of the operating data; Clustering the operating data of each energy storage device based on the coordinates and sampling time of the energy storage device to obtain multiple energy storage clusters; For each energy storage cluster, determine the energy storage area corresponding to the energy storage cluster based on the coordinates of the energy storage devices corresponding to each operating data within the energy storage cluster, determine multiple predicted operating data of the energy storage area based on the environmental data of the energy storage area, determine the upper and lower boundaries of the operating data of the energy storage area based on the multiple predicted operating data, and perform a logical check on each operating data within the energy storage cluster based on the upper and lower boundaries of the operating data; Clustering the operating data of each measuring device based on the coordinates and sampling time of the measuring device to obtain multiple measuring clusters; For each metering cluster, the metering area corresponding to the metering cluster is determined based on the coordinates of the metering equipment corresponding to each operating data in the metering cluster, multiple predicted operating data of the metering area are determined based on the environmental data of the metering area, the upper and lower boundaries of the operating data of the metering area are determined based on the multiple predicted operating data, and logical verification is performed on each operating data in the metering cluster based on the upper and lower boundaries of the operating data.

4. The virtual power plant control system according to claim 3, characterized in that: The processing module is further configured to: For each distributed energy device, if the preliminary verification and logical verification of the operating data of the distributed energy device fail, the distributed energy devices associated with the distributed energy device are searched based on the topological relationships in the target area. If the preliminary verification and logical verification of the operating data of all associated distributed energy devices fail, a fault warning is issued for the distributed energy device; among them, distributed energy devices with a connection hop number less than the preset hop number and a different category are associated distributed energy devices.

5. The virtual power plant control system according to claim 1, characterized in that: The processing module is further configured to: When obtaining a data query instruction input by a user, searching for data corresponding to the data query instruction; If the data corresponding to the data query instruction is a numerical value, the numerical value is adjusted based on the random number to obtain data after desensitization; If the data corresponding to the data query instruction is an image, blurring the identification elements in the image to obtain data after desensitization; Send anonymized data to users.

6. The virtual power plant control system according to claim 1, characterized in that: The acquisition module is specifically used for: For each type of operating data, the collection frequency of the type of operating data is adjusted based on the change rate of the type of operating data.

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