Distributed new energy plug and play and hierarchical autonomous control system and method

Abstract

This invention discloses a plug-and-play and hierarchical autonomous control system and method for distributed renewable energy. It relates to the field of renewable energy power system control technology. It includes a device layer, an edge layer, and a coordination layer. The device layer connects to photovoltaic, energy storage, and controllable loads through intelligent terminals to achieve data acquisition and command execution. The edge layer uses a spanning tree algorithm to construct local autonomous clusters, with edge nodes performing regional power optimization and status judgment. The coordination layer relies on a global coordination node and achieves cross-cluster coordination and global power balance through a sparse communication network. This invention solves the problem of unified access and coordinated control of distributed renewable energy by triggering hierarchical control through cluster net power and operating mode exponential triggers, balancing local autonomy and global optimization.

Description

Technical Field

[0001] This invention relates to the field of new energy power system control technology, and more specifically to a distributed new energy plug-and-play and hierarchical autonomous control system and method. Background Technology

[0002] With the acceleration of the global energy transition, distributed renewable energy (such as distributed photovoltaic and wind power) has become an important direction for energy system development. Currently, distributed renewable energy control architectures are mainly divided into two categories: centralized architecture and decentralized architecture. Centralized architecture uses a central controller to manage all terminal devices. While the control logic is simple and easy to implement, it suffers from single-point-of-failure risks, poor scalability, and high communication bandwidth pressure, making it difficult to meet the access requirements of large-scale distributed renewable energy devices. Decentralized architecture, where each terminal device operates independently, avoids single-point-of-failure but lacks global coordination, easily leading to local power imbalances and low energy utilization efficiency, failing to achieve optimized operation of the entire system. Existing technologies have attempted to introduce edge computing nodes to alleviate the pressure on centralized architectures, but most suffer from poor terminal device compatibility, unstable communication connections, and a lack of adaptive adjustment capabilities in control strategies. The shortcomings of existing technologies are as follows: 1. Poor equipment compatibility: Existing new energy terminals lack a unified plug-and-play communication interface. Different types and manufacturers of distributed power generation equipment, energy storage equipment, and controllable loads are difficult to quickly connect to the system. The connection process is cumbersome and the debugging cost is high. It cannot meet the diverse and large-scale access needs of distributed new energy equipment and is out of sync with the plug-and-play technology application of existing energy interconnection access terminals.

[0003] 2. Insufficient communication flexibility: Edge nodes and new energy terminals mostly use fixed communication connections without the introduction of dynamic networking algorithms. When terminal devices fail, are added, or are relocated, the communication network cannot be quickly reconstructed, which can easily lead to local cluster communication interruptions, affecting the transmission efficiency and reliability of control commands, and making it difficult to adapt to the dynamic operating scenarios of distributed new energy devices.

[0004] 3. Poor coordination of hierarchical control: In the existing architecture, there is a lack of effective linkage between the local control of the edge layer and the global control of the coordination layer. Edge nodes cannot dynamically trigger and adapt control strategies according to the cluster's operating status, and the coordination layer cannot obtain the operating data of each local cluster in a timely manner and perform global optimization. This makes it difficult for the system to achieve coordination between local autonomy and global balance, resulting in low energy utilization efficiency.

[0005] 4. Insufficient adaptability of control strategies: The operating mode of new energy terminals is fixed, and no corresponding alternative operating modes and power load values ​​are set according to different working states. It is impossible to adaptively adjust according to changes in system power and equipment operating status, making it difficult to cope with the randomness and volatility of distributed new energy and failing to give full play to the regulating role of energy storage devices and controllable loads.

[0006] Therefore, there is an urgent need for an innovative approach that integrates plug-and-play self-organizing networks, hierarchical autonomous control, and multi-mode adaptive operation to solve the key technical challenges of efficient, reliable, and flexible grid connection of large-scale distributed new energy sources. Summary of the Invention

[0007] In view of this, the present invention provides a distributed new energy plug-and-play and hierarchical autonomous control system and method, which can realize unified access and adaptive control of various distributed new energy devices; with edge autonomous nodes as the core of the edge layer, a local autonomous cluster is constructed through a dynamic spanning tree algorithm to achieve local power optimization and operation status judgment; with global coordination nodes as the core of the collaboration layer, cross-cluster collaboration and global power balance are achieved through a sparse communication network; at the same time, multiple alternative operating modes are set for new energy intelligent terminals, and the edge autonomous nodes trigger hierarchical control strategies according to the cluster net power and operating mode index, realizing the organic combination of local autonomy and global collaboration, and solving the pain points of the prior art.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: A distributed new energy plug-and-play and hierarchical autonomous control system includes a device layer, an edge layer and a collaboration layer connected in sequence. The device layer is used to access distributed new energy devices, collect device data, and execute control commands, including multiple new energy intelligent terminals; The edge layer is used to realize autonomous control and computational optimization of local clusters. It includes multiple edge autonomous nodes, each of which governs a local area and establishes communication connections with nearby new energy intelligent terminals through a tree-generating networking algorithm to form a local autonomous cluster. The coordination layer is used to achieve global power balancing and cross-cluster collaborative control. It includes a global coordination node, which establishes connections with each edge autonomous node through a sparse communication network to balance global power constraints and achieve cross-cluster coordination.

[0009] Preferably, the new energy intelligent terminal is connected to distributed new energy power generation equipment, energy storage equipment and controllable load to form an independent new energy control unit; Distributed new energy power generation equipment is used to convert renewable energy into electrical energy, energy storage equipment is used to store excess electrical energy and smooth power fluctuations, and controllable loads are used to adjust the operating status of the system and participate in peak shaving and valley filling.

[0010] Preferably, the new energy intelligent terminal includes a unified plug-and-play communication interface and an adaptive control module. The adaptive control module is used to collect the operating parameters of the distributed new energy power generation equipment in real time, and receive control commands issued by the edge autonomous node to adaptively adjust the operating status of the connected distributed new energy power generation equipment.

[0011] Preferably, the adaptive control module incorporates a microprocessor and a control algorithm, wherein the control algorithm employs the MPC algorithm, and the specific process includes: An MPC prediction model is constructed based on the operating characteristics of distributed power generation equipment, energy storage equipment, and controllable loads connected to the new energy intelligent terminal. The model input parameters include real-time collected power generation, SOC, load power, voltage, current, frequency, and historical operating data. The model output is the optimal operating state of each device within a future preset time domain. Set constraints for the MPC algorithm, including: SOC constraints for energy storage devices, output constraints for power generation devices, controllable load regulation constraints, and voltage / frequency constraints; The optimization objective of the MPC algorithm is set while taking into account equipment operating losses. The multi-objective optimization is transformed into a single objective optimization by weighted summation, and the objective function is quantified. Based on constraints and optimization objectives, rolling optimization calculations are performed on the equipment operating status to obtain the optimal control command.

[0012] Preferably, the edge autonomous nodes establish communication connections with nearby new energy intelligent terminals by adopting a spanning tree networking algorithm to form a local autonomous cluster.

[0013] Preferably, the edge autonomous node includes a local optimization calculation module and a mode judgment module, which are used to determine the current cluster operation status based on the net power and operation mode index of the local autonomous cluster and trigger the corresponding hierarchical control strategy. The local optimization calculation module is used to collect the operating data of all new energy intelligent terminals in the local autonomous cluster in real time, calculate the net power of the local autonomous cluster, and optimize the operating status of each new energy intelligent terminal in the cluster based on the optimization target, and output local optimization control commands. The mode judgment module is used to determine the current operating status of the local autonomous cluster. It calculates a comprehensive index based on the alternative operating modes, equipment health status, and power regulation capability parameters of all new energy intelligent terminals in the cluster. The calculated comprehensive index characterizes the operating flexibility and regulation capability of the cluster, classifies the cluster operating status into different types, and triggers the corresponding hierarchical control strategy.

[0014] Preferably, the hierarchical control strategy includes: a power surplus state strategy, a power shortage state strategy, a power balance state strategy, and an emergency state strategy.

[0015] Preferably, the global coordination node establishes a connection with each edge autonomous node through a sparse communication network; the global coordination node can receive local cluster net power, operating status and operating mode data uploaded by each edge autonomous node in real time, summarize and calculate the global net power of the entire distributed new energy system, formulate a global power balance strategy in combination with grid dispatch requirements and system operation constraints, issue global control commands to each edge autonomous node, coordinate adjacent local autonomous clusters to perform power complementarity, and realize cross-cluster energy dispatch.

[0016] Preferably, a method for plug-and-play distributed renewable energy and hierarchical autonomous control includes:

[0017] Step 1: The new energy intelligent terminal connects to the distributed new energy power generation equipment, energy storage equipment and controllable load through the plug-and-play communication interface, and collects the equipment operating parameters; Step 2: Edge autonomous nodes establish communication connections with nearby new energy intelligent terminals through a generative tree networking algorithm to form a local autonomous cluster, collect terminal operation data within the cluster, and calculate the cluster's net power and operation mode index; Step 3: Determine the current cluster operating status based on the cluster net power and operating mode index, and trigger the corresponding local control strategy; Step 4: Calculate the global net power based on the operating information of each edge autonomous node, and formulate a global power balancing strategy in combination with system operating constraints to obtain global control commands; Step 5: Adjust the local control strategy according to the global control instructions and local optimization objectives to achieve a coordinated balance between the local cluster and the global system.

[0018] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a distributed new energy plug-and-play and hierarchical autonomous control system and method, which has the following significant beneficial effects: 1. Improve device compatibility and access efficiency: The new energy smart terminal adopts a unified plug-and-play communication interface, which is compatible with multiple communication protocols and different types of devices, enabling rapid access, hot-swapping and plug-and-play of distributed new energy devices, significantly reducing device access and debugging costs, solving the problem of poor device compatibility in existing technologies, adapting to the access needs of large-scale distributed new energy devices, and further improving access stability and compatibility by referring to the technical advantages of existing energy interconnection access terminals.

[0019] 2. Enhance the flexibility and reliability of communication connections: Edge autonomous nodes establish communication connections with new energy intelligent terminals through dynamic spanning tree algorithms. The communication network can be dynamically reconstructed according to the device status, avoiding cluster communication interruption caused by single device failure, ensuring the stability and efficiency of the communication link, reducing communication bandwidth pressure, adapting to the characteristics of distributed new energy devices, and solving the problems of fixed and inflexible communication connections in existing technologies.

[0020] 3. Achieving an organic combination of local autonomy and global collaboration: Through a three-level layered architecture, the edge layer realizes the optimized control and autonomous management of local clusters, while the collaboration layer realizes global power balance and cross-cluster coordination. The two work together to ensure the optimized operation of local clusters and the power balance of the entire system, thereby improving the system's operational stability and energy utilization efficiency, which is in line with the development trend of "cloud-edge-device" collaboration in the energy internet.

[0021] 4. Enhance the adaptability and flexibility of control strategies: Set multiple alternative operating modes and corresponding power load values ​​for new energy intelligent terminals. Edge autonomous nodes can dynamically trigger and adapt control strategies according to the cluster operating status, which can effectively cope with the randomness, intermittency and volatility of distributed new energy, give full play to the regulating role of energy storage equipment and controllable load, improve the level of new energy consumption and reduce system operating costs.

[0022] 5. Enhanced system security and scalability: The sparse communication network employs encrypted transmission technology to ensure data transmission security and prevent unauthorized access; the three-tier architecture design allows the system to be flexibly expanded according to the addition and expansion of new energy equipment without the need to reconstruct the entire system, reducing system upgrade costs and making it suitable for application scenarios of different scales, such as industrial parks, commercial complexes, and new towns. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0024] Figure 1 The system structure diagram provided for this invention.

[0025] Figure 2 The method flowchart provided by the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0027] like Figure 1 As shown, this embodiment of the invention discloses a distributed new energy plug-and-play and hierarchical autonomous control system, including a device layer, an edge layer and a collaboration layer connected in sequence; The device layer is used to access distributed new energy devices, collect device data, and execute control commands, including multiple new energy intelligent terminals; The edge layer is used to realize autonomous control and computational optimization of local clusters. It includes multiple edge autonomous nodes, each of which governs a local area and establishes communication connections with nearby new energy intelligent terminals through a tree-generating networking algorithm to form a local autonomous cluster. The coordination layer is used to achieve global power balancing and cross-cluster collaborative control. It includes a global coordination node, which establishes connections with each edge autonomous node through a sparse communication network to balance global power constraints and achieve cross-cluster coordination.

[0028] Specifically, the new energy intelligent terminal is connected to distributed new energy power generation equipment, energy storage equipment and controllable loads to form an independent new energy control unit; Distributed new energy power generation equipment is used to convert renewable energy into electrical energy, energy storage equipment is used to store excess electrical energy and smooth power fluctuations, and controllable loads are used to adjust the operating status of the system and participate in peak shaving and valley filling.

[0029] Specifically, the new energy intelligent terminal includes a unified plug-and-play communication interface and an adaptive control module. The adaptive control module is used to collect the operating parameters of the distributed new energy power generation equipment in real time, and receive control commands issued by the edge autonomous node to adaptively adjust the operating status of the connected distributed new energy power generation equipment.

[0030] In a specific embodiment of the present invention, each new energy intelligent terminal has a unified plug-and-play communication interface and an adaptive control module, wherein: Unified plug-and-play communication interface: Adopting a standardized communication protocol, it supports multiple communication methods such as HPLC / RF dual-mode and RS485, and is compatible with more than 300 industrial protocols such as DL / T645, Modbus, and IEC 61850. It can adapt to distributed generation equipment, energy storage equipment and controllable loads from different manufacturers and of different types, without the need for complex interface debugging, enabling rapid equipment access and plug-and-play functionality, significantly improving equipment access efficiency and reducing debugging costs. It also supports hot-swapping of equipment, facilitating system expansion and maintenance. Referring to the communication adaptation technology of existing energy interconnection access terminals, it further optimizes interface compatibility and access stability.

[0031] Furthermore, the adaptive control module incorporates a microprocessor and control algorithm, with the core employing a model predictive control (MPC) algorithm to achieve adaptive control. The specific process is as follows: This ensures the algorithm adapts to the randomness and volatility characteristics of distributed new energy equipment, achieving precise adjustment. Specifically, it includes: Establish a predictive model: Based on the operating characteristics of distributed power generation equipment connected to the new energy intelligent terminal, such as photovoltaic inverters, energy storage devices, and controllable loads, construct an MPC predictive model. The model input parameters include real-time collected power generation, energy storage capacity (SOC), load power, voltage, current, frequency, and historical operating data, such as power fluctuation data and equipment response delay data in the past hour. The model output is a future preset time domain, usually set to 5-10 minutes, which can dynamically adjust the optimal operating state of each device according to the device response speed, including photovoltaic output adjustment, energy storage charging and discharging power, and controllable load power adjustment.

[0032] Define constraints: Based on the equipment's own operational limitations and system control requirements, set constraints for the MPC algorithm, including: SOC constraint for energy storage equipment, which is set to 20%-80% in this embodiment to avoid overcharging and over-discharging damage to the equipment; output constraint for power generation equipment (maximum output of photovoltaic inverter does not exceed rated capacity of 50kW); controllable load adjustment constraint (air conditioning power adjustment range is 1-5kW); voltage / frequency constraint (complies with grid standards, voltage fluctuation ≤ ±5%, frequency fluctuation ≤ ±0.2Hz) to ensure the feasibility of control commands.

[0033] Setting optimization objectives: Based on the system operation requirements, the optimization objective of the MPC algorithm is set as maximizing the absorption of new energy and minimizing power fluctuations, while also taking into account equipment operating losses. The multi-objective optimization is transformed into a single objective through weighted summation. The weight allocation is as follows: 60% for new energy absorption, 30% for power fluctuations, and 10% for equipment losses. The objective function is then quantified.

[0034] Rolling optimization calculation: The adaptive control module collects real-time operating parameters every minute and inputs them into the MPC prediction model. Under preset constraints, it performs rolling optimization calculations on the equipment operating status for the next 5-10 minutes to solve for the optimal control command within the current control cycle (1 minute). This eliminates the need to wait for the end of the entire future time domain, ensuring the real-time performance of the control.

[0035] Command execution and feedback correction: The optimal control command for the current control cycle is sent to the corresponding power generation equipment, energy storage equipment, and controllable load. After execution, the actual operating parameters of the equipment are collected, such as the actual energy storage charging and discharging power and the actual photovoltaic output. These parameters are compared with the predicted values ​​of the MPC model, the deviation is calculated, and the deviation is fed back to the prediction model to dynamically correct the model parameters, reduce subsequent prediction errors, and achieve closed-loop adaptive control.

[0036] Furthermore, when the energy storage device's power is too low (SOC=22%, close to the lower limit of 20%), the MPC algorithm predicts the photovoltaic output trend over the next 5 minutes using a predictive model. If it predicts that the photovoltaic output will increase, it will optimize and calculate the control command of "prioritizing photovoltaic power supply to charge the energy storage device, and maintaining the controllable load at minimum operating power." At the same time, it will provide real-time feedback on the charging progress and dynamically adjust the charging power to avoid overcharging or undercharging of the energy storage. When the system has a power surplus, the MPC algorithm predicts future load changes and optimizes the command to charge the energy storage device or increase the operating power of the controllable load, achieving adaptive adjustment of local devices. Simultaneously, this module supports Python secondary development, allowing flexible adjustment of the MPC algorithm's prediction time domain, control cycle, constraints, and optimization target weights according to actual application scenarios, adapting to distributed new energy systems of different scales.

[0037] The distributed new energy plug-and-play and hierarchical autonomous control system of this embodiment is applied to an industrial park, including 10 new energy intelligent terminals, 3 edge autonomous nodes, and 1 global coordination node. Each new energy smart terminal connects to one 50kW photovoltaic inverter (power generation equipment), one 100kWh energy storage battery (energy storage equipment), and several air conditioners / water pumps (controllable loads, power adjustment range 1-5kW). The devices can be quickly connected through a plug-and-play communication interface without the need for interface debugging. The three edge autonomous nodes each govern three areas of the industrial park. Each node forms a local autonomous cluster with new energy intelligent terminals in its area through the improved rapid spanning tree algorithm (RSTP), and collects operating data in real time and calculates net power and operating index K. The global coordination node is deployed in the energy management center of the industrial park. It connects to three edge autonomous nodes through a sparse communication network based on 5G network slicing. It uses AES 128-bit encryption + SM2 encryption to achieve data transmission, and receives the operation information of each cluster in real time to formulate global policies.

[0038] In actual operation, when the net power of a certain area cluster is 20kW (power surplus) and K=7.5, the edge autonomous node triggers the power surplus strategy, instructing the energy storage devices in the area to charge at 15kW and the controllable load to increase power consumption by 5kW; when the net power of another area cluster is -18kW (power shortage) and K=7, the global coordination node coordinates the surplus area to send 18kW of power to the shortage area, realizing cross-cluster power complementarity.

[0039] Specifically, the adaptive control module incorporates a microprocessor and a control algorithm, wherein the control algorithm employs the MPC algorithm, and the specific process includes: An MPC prediction model is constructed based on the operating characteristics of distributed power generation equipment, energy storage equipment, and controllable loads connected to the new energy intelligent terminal. The model input parameters include real-time collected power generation, SOC, load power, voltage, current, frequency, and historical operating data. The model output is the optimal operating state of each device within a future preset time domain. Set constraints for the MPC algorithm, including: SOC constraints for energy storage devices, output constraints for power generation devices, controllable load regulation constraints, and voltage / frequency constraints; The optimization objective of the MPC algorithm is set while taking into account equipment operating losses. The multi-objective optimization is transformed into a single objective optimization by weighted summation, and the objective function is quantified. Based on constraints and optimization objectives, rolling optimization calculations are performed on the equipment operating status to obtain the optimal control command.

[0040] Specifically, the edge autonomous nodes establish communication connections with nearby new energy intelligent terminals by adopting a spanning tree networking algorithm, forming a local autonomous cluster.

[0041] Specifically, the edge autonomous node includes a local optimization computing module and a mode judgment module, which are used to determine the current cluster operation status based on the net power and operation mode index of the local autonomous cluster and trigger the corresponding hierarchical control strategy. The local optimization calculation module is used to collect the operating data of all new energy intelligent terminals in the local autonomous cluster in real time, calculate the net power of the local autonomous cluster, and optimize the operating status of each new energy intelligent terminal in the cluster based on the optimization target, and output local optimization control commands. The mode judgment module is used to determine the current operating status of the local autonomous cluster. It calculates a comprehensive index based on the alternative operating modes, equipment health status, and power regulation capability parameters of all new energy intelligent terminals in the cluster. The calculated comprehensive index characterizes the operating flexibility and regulation capability of the cluster, classifies the cluster operating status into different types, and triggers the corresponding hierarchical control strategy.

[0042] In a specific embodiment of the present invention, the local optimization computing module has a built-in high-performance computing chip that can collect the operating data of all new energy intelligent terminals in the local autonomous cluster in real time, including the power generation, energy storage status, load power, and operating mode of each terminal, and calculate the net power of the local autonomous cluster. Net power = Total output of all power generation equipment in the cluster - Charging power of all energy storage devices - Power consumed by all controllable loads; Based on preset optimization objectives, such as maximizing the absorption of new energy, minimizing operating costs, and minimizing power fluctuations, the system optimizes the operating status of each new energy intelligent terminal within the cluster, outputs local optimization control commands, and realizes local cluster energy optimization allocation.

[0043] In this embodiment, when the net power of a local cluster is positive, i.e. when there is a power surplus, the optimization module can instruct some energy storage devices to charge or some controllable loads to increase power consumption; when the net power is negative, i.e. when there is a power shortage, the module can instruct energy storage devices to discharge or power generation devices to increase output to ensure local cluster power balance.

[0044] In this embodiment, the mode determination module is used to determine the current operating status of the local autonomous cluster. The determination is based on the net power of the local autonomous cluster and the operating mode index. The operating mode index is a comprehensive index calculated based on parameters such as the alternative operating modes, equipment health status, and power regulation capabilities of all new energy intelligent terminals within the cluster. It characterizes the cluster's operational flexibility and regulation capabilities. The mode determination module classifies the cluster's operating status into different types, such as power surplus, power shortage, power balance, and emergency status, based on the sign and absolute value of the net power and the range of the operating mode index, and triggers corresponding hierarchical control strategies.

[0045] Specifically, the hierarchical control strategy includes: power surplus state strategy, power shortage state strategy, power balance state strategy, and emergency state strategy.

[0046] In a specific embodiment of the present invention, the power surplus state strategy includes: the local optimization calculation module of the edge autonomous node generates a local optimization instruction, which instructs some new energy intelligent terminals to switch to energy storage priority mode or load boost mode to increase power consumption; at the same time, it reports power surplus information to the coordination layer. If the surplus power exceeds the local adjustment capacity, the coordination layer coordinates the adjacent clusters to receive power and realize the consumption of surplus power.

[0047] The power shortage strategy includes: the local optimization calculation module generates local optimization instructions, which instruct some new energy intelligent terminals to switch to emergency power supply mode or power generation boost mode to increase power output; at the same time, the power shortage information is reported to the coordination layer, and the coordination layer coordinates adjacent clusters to provide power support to ensure local cluster power balance.

[0048] The power balance strategy includes: maintaining the current operating mode of each new energy intelligent terminal, and the local optimization calculation module monitoring power changes in real time and adjusting operating parameters in a timely manner to ensure power balance.

[0049] Emergency response strategies include: triggering emergency control strategies, instructing all new energy intelligent terminals to switch to emergency operation mode, prioritizing the operation of core equipment; and simultaneously reporting emergency information to the coordination layer, which coordinates global resources, provides emergency support, and reduces the impact of the emergency on the system.

[0050] Specifically, the global coordination node establishes a connection with each edge autonomous node through a sparse communication network. The global coordination node can receive local cluster net power, operating status, and operating mode data uploaded by each edge autonomous node in real time, summarize and calculate the global net power of the entire distributed new energy system, formulate a global power balance strategy in combination with grid dispatch requirements and system operation constraints, issue global control commands to each edge autonomous node, coordinate adjacent local autonomous clusters to perform power complementarity, and realize cross-cluster energy dispatch.

[0051] In a specific embodiment of the present invention, the sparse communication network uses 5G network slicing technology to achieve differentiated QoS guarantee. Combined with a lightweight communication protocol, it realizes efficient data interaction between the global coordination node and each edge autonomous node, ensuring the rapid transmission of control commands and data security. At the same time, it uses AES 128-bit encryption, MD5 authentication and SM2 encryption algorithm to block unauthorized access and improve communication security.

[0052] Specifically, such as Figure 2 As shown, a method for plug-and-play distributed renewable energy and hierarchical autonomous control includes...

[0053] Step 1: The new energy intelligent terminal connects to the distributed new energy power generation equipment, energy storage equipment and controllable load through the plug-and-play communication interface, and collects the equipment operating parameters; Step 2: Edge autonomous nodes establish communication connections with nearby new energy intelligent terminals through a generative tree networking algorithm to form a local autonomous cluster, collect terminal operation data within the cluster, and calculate the cluster's net power and operation mode index; Step 3: Determine the current cluster operating status based on the cluster net power and operating mode index, and trigger the corresponding local control strategy; Step 4: Calculate the global net power based on the operating information of each edge autonomous node, and formulate a global power balancing strategy in combination with system operating constraints to obtain global control commands; Step 5: Adjust the local control strategy according to the global control instructions and local optimization objectives to achieve a coordinated balance between the local cluster and the global system.

[0054] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0055] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A distributed new energy plug-and-play and hierarchical autonomous control system, characterized in that, This includes the device layer, edge layer, and collaboration layer, which are connected in sequence. The device layer is used to access distributed new energy devices, collect device data, and execute control commands, including multiple new energy intelligent terminals; The edge layer is used to realize autonomous control and computational optimization of local clusters. It includes multiple edge autonomous nodes, each of which governs a local area and establishes communication connections with nearby new energy intelligent terminals through a tree-generating networking algorithm to form a local autonomous cluster. The coordination layer is used to achieve global power balancing and cross-cluster collaborative control. It includes a global coordination node, which establishes connections with each edge autonomous node through a sparse communication network to balance global power constraints and achieve cross-cluster coordination.

2. The distributed new energy plug-and-play and hierarchical autonomous control system according to claim 1, characterized in that, The new energy intelligent terminal is connected to distributed new energy power generation equipment, energy storage equipment and controllable load to form an independent new energy control unit. Distributed new energy power generation equipment is used to convert renewable energy into electrical energy, energy storage equipment is used to store excess electrical energy and smooth power fluctuations, and controllable loads are used to adjust the operating status of the system and participate in peak shaving and valley filling.

3. The distributed new energy plug-and-play and hierarchical autonomous control system according to claim 2, characterized in that, The new energy intelligent terminal includes a unified plug-and-play communication interface and an adaptive control module. The adaptive control module is used to collect the operating parameters of the distributed new energy power generation equipment in real time, and receive control commands issued by the edge autonomous node to adaptively adjust the operating status of the connected distributed new energy power generation equipment.

4. A distributed new energy plug-and-play and hierarchical autonomous control system according to claim 3, characterized in that, The adaptive control module incorporates a microprocessor and a control algorithm, wherein the control algorithm employs the MPC algorithm, and the specific process includes: An MPC prediction model is constructed based on the operating characteristics of distributed power generation equipment, energy storage equipment, and controllable loads connected to the new energy intelligent terminal. The model input parameters include real-time collected power generation, SOC, load power, voltage, current, frequency, and historical operating data. The model output is the optimal operating state of each device within a future preset time domain. Set constraints for the MPC algorithm, including: SOC constraints for energy storage devices, output constraints for power generation devices, controllable load regulation constraints, and voltage / frequency constraints; The optimization objective of the MPC algorithm is set while taking into account equipment operating losses. The multi-objective optimization is transformed into a single objective optimization by weighted summation, and the objective function is quantified. Based on constraints and optimization objectives, rolling optimization calculations are performed on the equipment operating status to obtain the optimal control command.

5. A distributed new energy plug-and-play and hierarchical autonomous control system according to claim 1, characterized in that, The edge autonomous nodes establish communication connections with nearby new energy intelligent terminals by adopting a spanning tree networking algorithm, forming a local autonomous cluster.

6. A distributed new energy plug-and-play and hierarchical autonomous control system according to claim 1, characterized in that, The edge autonomous node includes a local optimization computing module and a mode judgment module, which are used to determine the current cluster operation status based on the net power and operation mode index of the local autonomous cluster and trigger the corresponding hierarchical control strategy. The local optimization calculation module is used to collect the operating data of all new energy intelligent terminals in the local autonomous cluster in real time, calculate the net power of the local autonomous cluster, and optimize the operating status of each new energy intelligent terminal in the cluster based on the optimization target, and output local optimization control commands. The mode judgment module is used to determine the current operating status of the local autonomous cluster. It calculates a comprehensive index based on the alternative operating modes, equipment health status, and power regulation capability parameters of all new energy intelligent terminals in the cluster. The calculated comprehensive index characterizes the operating flexibility and regulation capability of the cluster, classifies the cluster operating status into different types, and triggers the corresponding hierarchical control strategy.

7. A distributed new energy plug-and-play and hierarchical autonomous control system according to claim 6, characterized in that, The hierarchical control strategy includes: power surplus state strategy, power shortage state strategy, power balance state strategy, and emergency state strategy.

8. A distributed new energy plug-and-play and hierarchical autonomous control system according to claim 7, characterized in that, The global coordination node establishes a connection with each edge autonomous node through a sparse communication network. The global coordination node can receive local cluster net power, operating status, and operating mode data uploaded by each edge autonomous node in real time, summarize and calculate the global net power of the entire distributed new energy system, formulate a global power balance strategy in combination with grid dispatch requirements and system operation constraints, issue global control commands to each edge autonomous node, coordinate adjacent local autonomous clusters to perform power complementarity, and realize cross-cluster energy dispatch.

9. A method for plug-and-play and hierarchical autonomous control of distributed new energy sources, characterized in that, include Step 1: The new energy intelligent terminal connects to the distributed new energy power generation equipment, energy storage equipment and controllable load through the plug-and-play communication interface, and collects the equipment operating parameters; Step 2: Edge autonomous nodes establish communication connections with nearby new energy intelligent terminals through a generative tree networking algorithm to form a local autonomous cluster, collect terminal operation data within the cluster, and calculate the cluster's net power and operation mode index; Step 3: Determine the current cluster operating status based on the cluster net power and operating mode index, and trigger the corresponding local control strategy; Step 4: Calculate the global net power based on the operating information of each edge autonomous node, and formulate a global power balancing strategy in combination with system operating constraints to obtain global control commands; Step 5: Adjust the local control strategy according to the global control instructions and local optimization objectives to achieve a coordinated balance between the local cluster and the global system.

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