Grid-connected loop collaborative management system suitable for multi-power access

Abstract

The invention relates to the technical field of grid-connected loop collaborative management, and particularly discloses a grid-connected loop collaborative management system suitable for multi-power-supply access, which comprises a multi-power-supply access module, a multi-power-supply management module, a multi-power-supply management module and a multi-power-supply management module, comprising but not limited to a solar photovoltaic power generation device, a wind power generation device, a traditional thermal power generation access end, an energy storage battery pack access end and the like, so that collection of output power of each power supply is realized, and a function of adapting to different power supply interfaces is provided to ensure that each power supply can be stably accessed to the system; and the grid-connected loop monitoring unit is arranged at a key node of the grid-connected loop and is used for acquiring electrical parameters such as current, voltage, power factor and frequency in the grid-connected loop in real time. According to the grid-connected loop collaborative management system suitable for multi-power-supply access, the operation convenience and the fault handling capacity are improved, the requirements of multi-power-supply flexible access and dynamic adjustment under a complex grid-connected loop topological structure are integrally met, and the energy utilization efficiency is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of grid-connected circuit collaborative management technology, specifically a grid-connected circuit collaborative management system suitable for multiple power source access. Background Technology

[0002] As the global energy structure shifts towards cleaner and more diversified sources, the coordinated grid connection of multiple power sources, including renewable energy such as solar and wind power, as well as traditional thermal power generation and energy storage systems, has become a core trend in the development of the energy internet. However, grid connection management in multi-power source access scenarios faces multiple technical challenges:

[0003] On the one hand, different types of power sources exhibit significant differences in their power generation characteristics. Photovoltaic power generation is affected by sunlight intensity and temperature, resulting in intermittent fluctuations. Wind power generation relies on wind speed variations, leading to unstable output power. Traditional thermal power, while providing stable output, has a slow peak-shaving response. Energy storage systems are limited by charging and discharging efficiency and cycle life constraints. These differences make it easy for multiple power sources to experience power quality problems such as voltage fluctuations, frequency deviations, and power factor imbalances when connected to the grid. In severe cases, this may cause grid-connected circuit protection devices to malfunction, affecting the safe operation of the power grid.

[0004] On the other hand, existing grid-connected management systems mostly adopt a single power source independent control mode, lacking a global coordination mechanism: decentralized local controllers can only achieve local adjustment of a single power source, and cannot dynamically allocate power according to overall load demand and power status, resulting in problems such as low clean energy consumption rate, insufficient utilization of energy storage resources, and poor system operation economy. At the same time, the massive amount of operational data (such as electrical parameters, environmental data, and equipment status information) generated by multiple power sources faces security risks such as data leakage and tampering during transmission. Traditional encryption methods are difficult to meet the real-time and security requirements of industrial-grade communication due to cumbersome key management and insufficient algorithm adaptability.

[0005] Furthermore, with the increasing penetration of distributed power sources, the topology of grid-connected circuits is becoming increasingly complex. Traditional centralized control architectures suffer from drawbacks such as large response delays and poor scalability, making it difficult to adapt to the needs of flexible access and dynamic adjustment of multiple power sources. Therefore, developing a grid-connected circuit collaborative management system with accurate perception of multiple power source status, global collaborative control, secure data transmission, and intelligent optimization decision-making capabilities is crucial for solving these problems and is of great significance for improving energy utilization efficiency and ensuring stable grid operation. Summary of the Invention

[0006] To achieve the above objectives, the present invention provides the following technical solution: a grid-connected loop collaborative management system suitable for multi-power source access, comprising:

[0007] Multi-power access module: Used to connect various types of power sources, including but not limited to solar photovoltaic power generation devices, wind power generation devices, traditional thermal power generation access terminals, energy storage battery access terminals, etc., to realize the aggregation of power output from various power sources, and has the function of adapting to different power interfaces to ensure that each power source can be stably connected to the system.

[0008] Grid-connected circuit monitoring unit: Located at key nodes of the grid-connected circuit, it collects electrical parameters such as current, voltage, power factor, and frequency in the grid-connected circuit in real time, as well as power generation status information of each power source, such as the light intensity of solar panels and the wind speed and rotation speed of wind turbines. The collected data is preliminarily processed and packaged, and then transmitted to the data processing and analysis module.

[0009] Data processing and analysis module: Receives data from the grid-connected loop monitoring unit, uses preset data algorithms and models to perform in-depth analysis of the data, identifies the generation characteristics of each power source and the power quality status of the grid-connected loop, predicts the trend of power load change, and at the same time makes early warning judgments on abnormal data and generates corresponding control commands to send to the collaborative control module.

[0010] Collaborative control module: Based on the instructions of the data processing and analysis module, it coordinates the output power of each power source, controls the on / off state of each power source connected to the grid and adjusts the connection ratio, realizes collaborative power generation among multiple power sources, ensures stable and efficient power supply of the grid-connected circuit, and also has the function of interactive coordination with the external power grid, and can adjust the output of this system according to the needs of the external power grid.

[0011] Energy storage management subsystem: Connected to the access point of the energy storage battery pack, it is responsible for managing the charging and discharging process of the energy storage battery. According to the power supply and demand of the system, it controls the charging of the energy storage battery when there is excess power and controls the discharging of the energy storage battery to supplement power when there is insufficient power, so as to maintain the power balance of the system and extend the service life of the energy storage battery.

[0012] Communication network module: Constructs communication links between various modules within the system and between the system and the external monitoring center. It adopts a combination of wired and wireless communication to ensure the real-time, accuracy, and stability of data transmission, and to ensure smooth information exchange between modules and remote monitoring and management of the system from the outside.

[0013] Preferably, the grid-connected circuit monitoring unit uses high-precision sensors, including a Rogowski coil current sensor for measuring current and a capacitive voltage sensor for measuring voltage. The sensors have self-calibration and fault self-checking functions, and can promptly send fault signals to the data processing and analysis module when a fault is detected.

[0014] Preferably, the data algorithm in the data processing and analysis module includes an artificial intelligence-based neural network algorithm for predicting power load. By learning from historical power data, environmental data, and time series data, a load prediction model is established, and the prediction accuracy error is controlled within ±5%.

[0015] Preferably, the collaborative control module adopts a distributed control architecture, with a local controller at each power access point. The local controller can autonomously adjust the power output according to the real-time status of the local power supply and the global instructions of the collaborative control module, so as to achieve fast response and flexible control. The local controller and the collaborative control module maintain real-time communication through a communication network module.

[0016] Preferably, the energy storage management subsystem has an intelligent charging and discharging strategy. During the charging process, based on the remaining capacity of the energy storage battery and the current grid electricity price, a time-sharing charging strategy is adopted to increase the charging power during off-peak hours. During the discharging process, priority is given to ensuring the power supply of critical loads. The system also counts and manages the number of charge and discharge cycles of the energy storage battery. When the number of charge and discharge cycles reaches a certain threshold, the system reminds the user to perform battery maintenance or replacement.

[0017] Preferably, the communication network module has a data encryption function, which uses the SM4 national cryptographic algorithm to encrypt the transmitted data to prevent the data from being stolen or tampered with during transmission. It also has a network congestion control mechanism, which automatically adjusts the data transmission rate and priority when network congestion occurs to ensure the priority transmission of important data.

[0018] Preferably, the multi-power supply access module further includes lightning strike, overvoltage, and overcurrent protection circuits to protect against possible lightning surges, abnormal overvoltages, and overcurrents at the power supply access end. When an abnormal electrical signal is detected, the power supply access is quickly cut off to protect the safety of the internal equipment of the system, and the access is automatically restored after the abnormal situation is eliminated.

[0019] Preferably, the system is also equipped with a human-machine interface, which is set in a local monitoring room or accessed remotely via a web page. It displays the operating status of each power source, the electrical parameters of the grid-connected circuit, and the system's early warning information in a visual manner. Operators can perform operations such as setting system parameters and issuing control commands through the human-machine interface. The human-machine interface also has an operation log recording function to facilitate the tracing of operation history.

[0020] Preferably, the collaborative management method executed by the system includes the following steps:

[0021] Multi-power source status assessment: The grid-connected loop monitoring unit collects the core operating parameters of each power source in real time. For solar photovoltaic power generation devices, it collects the photovoltaic panel temperature, module string open-circuit voltage, and actual output power; for wind power generation devices, it collects the wind turbine speed, blade angle, and real-time power generation; for traditional thermal power generation devices, it collects the turbine inlet steam pressure, boiler outlet temperature, and active power output; and for energy storage battery packs, it collects the individual battery voltage, total charging and discharging current, and remaining power.

[0022] Load demand forecasting: Based on historical electricity consumption data, weather forecast data, and holiday electricity consumption patterns, the data processing and analysis module uses the random forest algorithm to predict the hourly load demand for the next 24 hours, with the prediction error controlled within ±8%.

[0023] Power allocation strategy: The collaborative control module allocates the output power of each power source according to the load forecast results and power status score, following the principle of "clean energy priority, energy storage peak shaving assistance, and traditional power source as a backup". When the output of clean energy is insufficient, the energy storage battery is used first. If it is still insufficient, traditional thermal power generation is started to supplement it.

[0024] Real-time dynamic adjustment: The system status is refreshed every 5 minutes, and the deviation between the actual load and the predicted load is compared. When the deviation exceeds ±10%, the collaborative control module automatically adjusts the output ratio of each power source and sends charging and discharging adjustment commands to the energy storage management subsystem.

[0025] Collaborative Fault Handling: When a power supply failure is detected, the collaborative control module cuts off the grid-connected circuit of the power supply within 100ms, while temporarily replenishing power through the energy storage battery to maintain stable total output power, and sends fault alarm information to the human-machine interface.

[0026] It has the following beneficial effects:

[0027] This grid-connected circuit collaborative management system, applicable to multiple power source access, effectively addresses the differences in power generation characteristics of various power sources through interface adaptation and protection circuit design of the multi-power source access module, combined with the high-precision sensing capabilities of the grid-connected circuit monitoring unit. It reduces power quality issues such as voltage fluctuations and frequency deviations, lowers the risk of malfunction of grid-connected circuit protection devices, and ensures safe grid operation. Utilizing the distributed architecture and global collaborative mechanism of the collaborative control module, along with the accurate load forecasting of the data processing and analysis module, it achieves dynamic power allocation among multiple power sources, improving the clean energy absorption rate and energy storage resource utilization rate. Simultaneously, it reduces the system's unit cost per kilowatt-hour through economic optimization algorithms, improving operational economy. The communication network module employs SM4 national cryptographic algorithm encryption and network congestion control mechanisms, solving the security and real-time issues of data transmission under multiple power source access and meeting industrial-grade communication requirements. Furthermore, the system's human-machine interface and fault collaborative handling mechanism enhance operational convenience and fault response capabilities. Overall, it adapts to the needs of flexible access and dynamic adjustment of multiple power sources under complex grid-connected circuit topologies, significantly improving energy utilization efficiency. Detailed Implementation

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] This invention provides a technical solution: a grid-connected loop collaborative management system suitable for multiple power source access, comprising:

[0030] Multi-power access module: Used to connect various types of power sources, including but not limited to solar photovoltaic power generation devices, wind power generation devices, traditional thermal power generation access terminals, energy storage battery access terminals, etc., to realize the aggregation of power output from various power sources, and has the function of adapting to different power interfaces to ensure that each power source can be stably connected to the system.

[0031] Grid-connected circuit monitoring unit: Located at key nodes of the grid-connected circuit, it collects electrical parameters such as current, voltage, power factor, and frequency in the grid-connected circuit in real time, as well as power generation status information of each power source, such as the light intensity of solar panels and the wind speed and rotation speed of wind turbines. The collected data is preliminarily processed and packaged, and then transmitted to the data processing and analysis module.

[0032] Data processing and analysis module: Receives data from the grid-connected loop monitoring unit, uses preset data algorithms and models to perform in-depth analysis of the data, identifies the generation characteristics of each power source and the power quality status of the grid-connected loop, predicts the trend of power load change, and at the same time makes early warning judgments on abnormal data and generates corresponding control commands to send to the collaborative control module.

[0033] Collaborative control module: Based on the instructions of the data processing and analysis module, it coordinates the output power of each power source, controls the on / off state of each power source connected to the grid and adjusts the connection ratio, realizes collaborative power generation among multiple power sources, ensures stable and efficient power supply of the grid-connected circuit, and also has the function of interactive coordination with the external power grid, and can adjust the output of this system according to the needs of the external power grid.

[0034] Energy storage management subsystem: Connected to the access point of the energy storage battery pack, it is responsible for managing the charging and discharging process of the energy storage battery. According to the power supply and demand of the system, it controls the charging of the energy storage battery when there is excess power and controls the discharging of the energy storage battery to supplement power when there is insufficient power, so as to maintain the power balance of the system and extend the service life of the energy storage battery.

[0035] Communication network module: Constructs communication links between various modules within the system and between the system and the external monitoring center. It adopts a combination of wired and wireless communication to ensure the real-time, accuracy, and stability of data transmission, and to ensure smooth information exchange between modules and remote monitoring and management of the system from the outside.

[0036] The communication network module has data encryption capabilities, employing the SM4 national cryptographic algorithm to encrypt transmitted data, preventing data theft or tampering during transmission. It also features a network congestion control mechanism, automatically adjusting data transmission rate and priority when network congestion occurs, ensuring priority transmission of important data. The specific steps are as follows:

[0037] Step 1, Key Generation: A 128-bit initial key is generated through the system's built-in key management center. This key is generated using a true random number generator and is automatically updated every 72 hours. After the key is generated, it is stored through a hardware encryption module and can only be accessed by the communication module during encryption and decryption operations. It cannot be read by external programs.

[0038] Step 2, Data Grouping: Divide the raw data to be transmitted into 128-bit groups. If the last group of data is less than 128 bits, padding is performed using PKCS#7 padding. The padding byte value is the number of bytes to be padded.

[0039] Step 3, Round Key Expansion: Based on the 128-bit initial key, 32 subkeys are generated using the round key expansion function of the SM4 algorithm. The expansion process uses a combination of nonlinear and linear transformations to ensure the security of the subkeys.

[0040] Step 4, Encryption Operation: Perform 32 rounds of iterative encryption on each 128-bit data block. Each round of iteration includes nonlinear transformation, linear transformation and subkey XOR operation. After 32 rounds of iteration, reverse the order of the result to obtain a 128-bit ciphertext block.

[0041] Step 5: Ciphertext Concatenation: Concatenate all encrypted ciphertext groups in their original order to form complete ciphertext data. At the same time, add a 16-bit checksum to the ciphertext header for the receiving end to verify data integrity.

[0042] Step 6, Decryption and Verification: After receiving the ciphertext, the receiving end first verifies the CRC16 checksum. If the verification fails, it requires retransmission. If the verification passes, it performs 32 rounds of decryption using the same round key as the encryption to obtain the original data packets. After removing padding bits, the packets are concatenated to form the complete original data.

[0043] The collaborative management method executed by the system includes the following steps:

[0044] Multi-power source status assessment: The grid-connected loop monitoring unit collects the core operating parameters of each power source in real time. Among them, the solar photovoltaic power generation device collects the photovoltaic panel temperature, module string open-circuit voltage and actual output power; the wind power generation device collects the wind turbine speed, blade angle and real-time power generation; the traditional thermal power generation connection end collects the turbine inlet steam pressure, boiler outlet temperature and active power output; and the energy storage battery pack collects the individual battery voltage, total charging and discharging current and remaining power.

[0045] Load demand forecasting: Based on historical electricity consumption data, weather forecast data, and holiday electricity consumption patterns, the data processing and analysis module uses the random forest algorithm to predict the hourly load demand for the next 24 hours, with the prediction error controlled within ±8%. The specific steps are as follows:

[0046] Data preprocessing: Collect hourly load data for the past 90 days, environmental data for the corresponding time period, and holiday identification data. Use linear interpolation to fill in missing data, remove outliers and re-interpolate, normalize the environmental data, and convert the holiday identification data into binary variables.

[0047] Feature engineering: Construct time features, lag features, environmental features, and holiday features, and use Pearson correlation coefficient to screen features with a correlation higher than 0.3 with the load to form a feature set;

[0048] Model training: The dataset is divided into training and validation sets in a 7:3 ratio. The random forest model is initialized with 200 decision trees, a maximum depth of 15 for each tree, and a minimum number of samples per leaf node of 5. The model is trained using the training set and the hyperparameters are adjusted using 5-fold cross-validation on the validation set. The optimal parameters are determined with the goal of minimizing the root mean square error.

[0049] Load forecasting: Input the feature data of the next 24 hours into the trained random forest model, and output the hourly load forecast values ​​for the next 24 hours, with the forecast accuracy error controlled within ±7%;

[0050] Power allocation strategy: The collaborative control module allocates the output power of each power source according to the load forecast results and power status score, following the principle of "clean energy priority, energy storage peak shaving assistance, and traditional power source as a backup". When the output of clean energy is insufficient, the energy storage battery is used first. If it is still insufficient, traditional thermal power generation is started to supplement it.

[0051] Real-time dynamic adjustment: The system status is refreshed every 5 minutes, and the deviation between the actual load and the predicted load is compared. When the deviation exceeds ±10%, the collaborative control module automatically adjusts the output ratio of each power source and sends charging and discharging adjustment commands to the energy storage management subsystem.

[0052] Collaborative Fault Handling: When a power supply failure is detected, the collaborative control module cuts off the grid-connected circuit of the power supply within 100ms, while temporarily replenishing power through the energy storage battery to maintain stable total output power, and sends fault alarm information to the human-machine interface.

[0053] Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art and related fields based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described and explained in the present invention, unless otherwise specified or limited, shall be implemented according to conventional means in the art.

Claims

1. A grid-connected loop collaborative management system suitable for multiple power source access, characterized in that, include: Multi-power access module: Used to connect various types of power sources, including but not limited to solar photovoltaic power generation devices, wind power generation devices, traditional thermal power generation access terminals, and energy storage battery access terminals, to realize the aggregation of power output from various power sources, and has the function of adapting to different power interfaces to ensure that each power source can be stably connected to the system. Grid-connected circuit monitoring unit: Located at key nodes of the grid-connected circuit, it collects electrical parameters such as current, voltage, power factor, and frequency in the grid-connected circuit in real time, as well as the power generation status information of each power source. The collected data is preliminarily processed and packaged, and then transmitted to the data processing and analysis module. Data processing and analysis module: Receives data from the grid-connected loop monitoring unit, uses preset data algorithms and models to perform in-depth analysis of the data, identifies the generation characteristics of each power source and the power quality status of the grid-connected loop, predicts the trend of power load change, and at the same time makes early warning judgments on abnormal data and generates corresponding control commands to send to the collaborative control module. Collaborative control module: Based on the instructions of the data processing and analysis module, it coordinates the output power of each power source, controls the on / off state of each power source connected to the grid and adjusts the connection ratio, realizes collaborative power generation among multiple power sources, ensures stable and efficient power supply of the grid-connected circuit, and also has the function of interactive coordination with the external power grid, and can adjust the output of this system according to the needs of the external power grid. Energy storage management subsystem: Connected to the access point of the energy storage battery pack, it is responsible for managing the charging and discharging process of the energy storage battery. According to the power supply and demand of the system, it controls the charging of the energy storage battery when there is excess power and controls the discharging of the energy storage battery to supplement power when there is insufficient power, so as to maintain the power balance of the system and extend the service life of the energy storage battery. Communication network module: Constructs communication links between various modules within the system and between the system and the external monitoring center. It adopts a combination of wired and wireless communication to ensure the real-time, accuracy, and stability of data transmission, and to ensure smooth information exchange between modules and remote monitoring and management of the system from the outside.

2. The grid-connected circuit collaborative management system suitable for multi-power source access according to claim 1, characterized in that: The grid-connected circuit monitoring unit uses high-precision sensors, including a Rogowski coil current sensor for measuring current and a capacitive voltage sensor for measuring voltage. The sensors have self-calibration and fault self-checking functions, and can send fault signals to the data processing and analysis module in a timely manner when they detect their own faults.

3. The grid-connected circuit collaborative management system suitable for multi-power source access according to claim 1, characterized in that: The data processing and analysis module includes an artificial intelligence-based neural network algorithm for predicting power load. By learning from historical power data, environmental data, and time series data, a load prediction model is established, and the prediction accuracy error is controlled within ±5%.

4. A grid-connected loop collaborative management system suitable for multi-power source access according to claim 1, characterized in that: The collaborative control module adopts a distributed control architecture, with a local controller at each power access point. The local controller can autonomously adjust the power output according to the real-time status of the local power supply and the global commands of the collaborative control module, so as to achieve fast response and flexible control. The local controller and the collaborative control module maintain real-time communication through a communication network module.

5. A grid-connected loop collaborative management system suitable for multi-power source access according to claim 1, characterized in that: The energy storage management subsystem has an intelligent charging and discharging strategy. During the charging process, it adopts a time-sharing charging strategy based on the remaining power of the energy storage battery and the current grid electricity price. During off-peak hours, it increases the charging power. During the discharging process, it prioritizes the power supply to critical loads. It also counts and manages the number of charge and discharge cycles of the energy storage battery. When the number of charge and discharge cycles reaches a certain threshold, it reminds the user to perform battery maintenance or replacement.

6. A grid-connected loop collaborative management system suitable for multi-power source access according to claim 1, characterized in that: The communication network module has a data encryption function, which uses the SM4 national cryptographic algorithm to encrypt the transmitted data to prevent the data from being stolen or tampered with during transmission. It also has a network congestion control mechanism, which automatically adjusts the data transmission rate and priority when network congestion occurs to ensure the priority transmission of important data.

7. A grid-connected circuit collaborative management system suitable for multi-power source access according to claim 1, characterized in that: The multi-power access module also includes lightning strike, overvoltage, and overcurrent protection circuits, which protect against possible lightning surges, abnormal overvoltages, and overcurrents at the power access end. When an abnormal electrical signal is detected, the power access is quickly cut off to protect the safety of the internal equipment of the system, and the access is automatically restored after the abnormal situation is eliminated.

8. A grid-connected loop collaborative management system suitable for multi-power source access according to claim 1, characterized in that: The system is also equipped with a human-machine interface, which is set in a local monitoring room or accessed remotely via a web page. It displays the operating status of each power source, the electrical parameters of the grid-connected circuit, and the system's early warning information in a visual manner. Operators can use the human-machine interface to set system parameters and issue control commands. The human-machine interface also has an operation log recording function, which makes it easy to trace the operation history.

9. A grid-connected loop collaborative management system suitable for multi-power source access according to claim 1, characterized in that, The collaborative management method executed by the system includes the following steps: Multi-power source status assessment: The grid-connected loop monitoring unit collects the core operating parameters of each power source in real time. Among them, the solar photovoltaic power generation device collects the photovoltaic panel temperature, module string open circuit voltage and actual output power; the wind power generation device collects the wind turbine speed, blade angle and real-time power generation; the traditional thermal power generation access terminal collects the steam turbine inlet pressure, boiler outlet temperature and active power output; and the energy storage battery pack collects the individual battery voltage, total charging and discharging current and remaining power. Load demand forecasting: Based on historical electricity consumption data, weather forecast data, and holiday electricity consumption patterns, the data processing and analysis module uses the random forest algorithm to predict the hourly load demand for the next 24 hours, with the prediction error controlled within ±8%. Power allocation strategy: The collaborative control module allocates the output power of each power source according to the load forecast results and power status score, following the principle of prioritizing clean energy, supplementing with energy storage for peak shaving, and ensuring the backup of traditional power sources. When the output of clean energy is insufficient, the energy storage battery is used first, and if it is still insufficient, traditional thermal power generation is started to supplement it. Real-time dynamic adjustment: The system status is refreshed every 5 minutes, and the deviation between the actual load and the predicted load is compared. When the deviation exceeds ±10%, the collaborative control module automatically adjusts the output ratio of each power source and sends charging and discharging adjustment commands to the energy storage management subsystem. Collaborative Fault Handling: When a power supply failure is detected, the collaborative control module cuts off the grid-connected circuit of the power supply within 100ms, while temporarily replenishing power through the energy storage battery to maintain stable total output power, and sends fault alarm information to the human-machine interface.