Method and system for optimizing operation of alternating-current and direct-current power distribution area of automobile charging station

Abstract

The invention discloses an automobile charging station AC / DC power distribution area operation optimization method and system, and relates to the technical field of new energy traffic infrastructure, and the method comprises the steps: collecting the voltage, current and temperature data of power distribution equipment through a sensor network, and if the voltage exceeds a preset threshold value, or the current exceeds the path load capacity, or the temperature is higher than a safety range, executing the operation; if yes, judging that the equipment is in fault, obtaining a fault equipment identifier and a fault type, generating global operation parameters containing node storage capacity and a control instruction set, and synchronizing the global operation parameters to all distributed controllers through a broadcast mechanism to obtain a consistent system operation state; according to the automobile charging station AC / DC power distribution area operation optimization method and system, the technical bottlenecks of a traditional power distribution system in the aspects of slow fault response, single control structure, static resource allocation and the like are broken through, and an intelligent, high-reliability and high-adaptability operation optimization scheme is provided for new energy automobile charging infrastructures; and the stability and continuity of the charging service are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy transportation infrastructure, and particularly to an operation optimization method and system for the AC / DC distribution area of an electric vehicle charging station. Background Art

[0002] With the popularization of new energy vehicles, the power distribution system of electric vehicle charging stations has become an important infrastructure for energy transformation and urban sustainable development, and its operation optimization is directly related to the reliability and efficiency of charging services. However, the current design and operation of the power distribution system still face many deficiencies, especially in terms of fault response and system stability.

[0003] Most of the existing power distribution optimization methods rely on static configuration or single control strategies. When a key device fails, the system often cannot quickly switch to the backup path, resulting in the interruption of charging services. In addition, the risk of single-point failure of the central control system is widespread. Once the core node fails, the entire charging station may be paralyzed. These limitations make it difficult for the power distribution system to maintain the continuity and stability of services in the face of sudden failures or external disturbances. In this field, the core challenges focus on how to ensure the fault adaptability of the power distribution system and the robustness of the control architecture. Specifically, when a power distribution device fails, the system needs to sense in real time and switch to the backup path to maintain the continuity of charging services; at the same time, the single dependence of the central control system increases the risk of the entire station being paralyzed, and there is an urgent need for distributed or redundant mechanisms to improve the anti-interference ability. In addition, there are also technical bottlenecks in improving the fault recovery speed, and it is necessary to achieve rapid response and resource reallocation in a complex dynamic environment. These technical factors have not been effectively solved, resulting in the charging station being difficult to operate stably under high load or abnormal scenarios. Summary of the Invention

[0004] The purpose of the present invention is to provide an operation optimization method and system for the AC / DC distribution area of an electric vehicle charging station, which can automatically enable the backup path when a power distribution device fails, and at the same time avoid the single-point failure of the central control system by optimizing the control architecture, thereby improving the anti-interference ability and fault recovery speed of the system.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: An operation optimization method for the AC / DC distribution area of an electric vehicle charging station, comprising: Collecting voltage, current, and temperature data of power distribution devices through a sensor network. If the voltage exceeds the preset threshold, the current exceeds the path load capacity, or the temperature is higher than the safe range, it is determined that the device has failed, and the failed device identifier and failure type are obtained; Generating global operation parameters including node storage capacity and control instruction sets, and synchronizing them to all distributed controllers through a broadcast mechanism to obtain a consistent system operation state; By monitoring the running status of the system for consistency in real time, obtaining data on path redundancy and handover response time, analyzing the recovery speed and node connectivity, if the handover response time exceeds the preset threshold, then optimize the resource allocation by adjusting the path length to obtain an optimized power distribution network configuration; Extract the path load capacity and transmission efficiency under high-load scenarios from the optimized power distribution network configuration, generate an anti-interference ability evaluation report including node availability and communication delay, and determine the stable operation ability under abnormal scenarios; According to the anti-interference ability evaluation report, based on the change of node weights in the reconstructed path graph, dynamically update the control instruction set in the path priority and control strategy database, generate a new set of standby paths and a redundant mechanism configuration for node computing power, and obtain the iterated system state.

[0006] Preferably, the specific steps of generating the global operation parameters including node storage capacity and control instruction set include constructing a weighted graph model including node connection relationships and path status parameters based on a graph search algorithm, dynamically searching for path nodes connected to the faulty device according to the faulty device identifier, obtaining several feasible paths using the shortest path search algorithm, calculating the path load capacity and transmission efficiency of each path, and combining the path length and the number of switching devices to determine the standby path with the highest path priority.

[0007] Preferably, the specific steps of generating the global operation parameters including node storage capacity and control instruction set further include generating a path switching instruction including the number of switching devices for the standby path with the highest path priority, sending the instruction to the relevant switching devices through a distributed controller to complete the path switching, and obtaining a new power distribution network operation state.

[0008] Preferably, the specific steps of generating the global operation parameters including node storage capacity and control instruction set further include constructing a capacity constraint graph model composed of nodes and paths based on the current power distribution network operation state, obtaining the load distribution data of each node and analyzing its power demand, if there is a situation where the path load exceeds the capacity limit, then construct a linear optimization model of load scheduling with the minimum objective function, or use the minimum cost maximum flow algorithm for path reallocation to determine the optimal power transfer relationship between nodes.

[0009] Preferably, the specific steps of generating the global operation parameters including node storage capacity and control instruction set further include obtaining the running status of each control node through a distributed control architecture, analyzing the node computing power and communication delay, if a certain node does not respond within the fault detection period, then it is judged as a node failure, and the identifier of the failed node is obtained.

[0010] Preferably, the specific steps of generating the global operating parameters including the node storage capacity and the control instruction set further include: according to the failed node identifier, selecting the standby control node with the highest node availability from the redundancy mechanism, combining the data synchronization frequency and the task migration time, completing the control task migration, obtaining the updated control network state, and generating the global operating parameters including the node storage capacity and the control instruction set according to the updated control network state.

[0011] Preferably, the control instruction set includes node start / stop commands, path switching instructions, energy transfer scheduling parameters, and communication coordination signals.

[0012] Preferably, the broadcast mechanism adopts a multicast protocol based on a ring link to reduce transmission latency and improve the stability of state synchronization.

[0013] Preferably, the determination of equipment failure further includes dynamically analyzing the critical state by referring to the fluctuation frequency and duration in the historical operation data.

[0014] An AC / DC power distribution area operation optimization system for a vehicle charging station, which is used to implement the steps of the vehicle charging station AC / DC power distribution area operation optimization method. The system includes: A sensor network module, which is used to collect the voltage, current, and temperature data of power distribution equipment, and when the voltage exceeds the preset threshold, the current exceeds the path load capacity, or the temperature is higher than the safe range, it is determined as equipment failure, and a failure equipment identifier and a failure type are generated; A parameter generation module, which is used to generate global operating parameters including node storage capacity and control instruction set, and synchronize them to all distributed controllers through a broadcast mechanism to obtain a consistent system operation state; A state monitoring module, which is used to monitor the consistent system operation state in real time, obtain path redundancy and switching response time data, and based on the recovery speed and node connectivity analysis results, determine whether the switching response time exceeds the preset threshold; if it exceeds, optimize the resource allocation by adjusting the path length to obtain an optimized power distribution network configuration; A performance analysis module, which is used to extract the path load capacity and transmission efficiency under high-load scenarios from the optimized power distribution network configuration, and generate an anti-interference ability evaluation report including node availability and communication delay to determine the stable operation ability under abnormal scenarios; A topology and policy update module, which is used to update the path priority in the power distribution network topology diagram and the control instruction set in the control policy database according to the anti-interference ability evaluation report, generate a new set of standby paths and a redundancy mechanism configuration of node computing capabilities, and output the iterated system state.

[0015] From the above technical solutions, it can be seen that the present invention has the following beneficial effects: By introducing a multi-dimensional sensor network to collect key operating parameters such as voltage, current, and temperature in real time, it can promptly sense and determine the fault type when abnormalities occur in power distribution equipment, achieve precise positioning and rapid response, improve the adaptability and recovery efficiency of the system under sudden faults, synchronize global operating parameters to all distributed controllers through a broadcast mechanism, ensure consistent awareness of the system operating state at each node, effectively avoid the single-point failure problem of traditional central control structures, and enhance the robustness and redundancy of the power distribution system. The present invention constructs an operation evaluation model based on switching response time and path redundancy, combines path length and node connectivity analysis, realizes dynamic optimal allocation of resources in the power distribution network, improves power supply sustainability and stability under high-load and abnormal scenarios, generates an anti-interference ability evaluation report, extracts key indicators such as node availability and communication delay, quantitatively analyzes the operating ability under abnormal scenarios, thereby guiding the update of control strategies and the reconstruction of backup paths, improving the anti-interference ability of the overall system. By feeding back the evaluation results for path priority adjustment and control strategy instruction set update, a node computing power redundancy mechanism is constructed to achieve continuous iteration and self-optimization of the system state, and improve the operating intelligence level in a complex power network environment. The present invention breaks through the technical bottlenecks of traditional power distribution systems in aspects such as slow fault response, single control structure, and static resource allocation, provides an intelligent, highly reliable, and highly adaptable operation optimization solution for new energy vehicle charging infrastructure, and significantly improves the stability and continuity of charging services. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flowchart of the method of the present invention; Figure 2 is a connection diagram of system modules of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] As Figure 1 shown, the present invention provides a technical solution: an operation optimization method for an AC / DC power distribution station area of an electric vehicle charging station, including: Collect voltage, current, and temperature data of power distribution equipment through a sensor network. If the voltage exceeds a preset threshold, the current exceeds the path load capacity, or the temperature is higher than the safe range, it is determined that there is a device failure, and the failed device identifier and failure type are obtained; Generate global operating parameters including node storage capacity and control instruction set, and synchronize them to all distributed controllers through broadcast mechanism to obtain consistent system operating status; By real-time monitoring of the consistent system operation status, obtaining path redundancy and switching response time data, analyzing the recovery speed and node connectivity, and optimizing resource allocation by adjusting the path length if the switching response time exceeds the preset threshold, an optimized distribution network configuration is obtained; Extract the path load capacity and transmission efficiency under high-load scenarios from the optimized distribution network configuration, generate an anti-interference capability evaluation report including node availability and communication delay, and determine the stable operation capability under abnormal scenarios; According to the anti-interference capability evaluation report, based on the changes in node weights in the reconstructed path graph, the path priority and control instruction set in the control strategy database are dynamically updated to generate a new set of backup paths and redundant mechanism configurations for node computing power to obtain the iterative system state.

[0019] The method collects key power parameters, including voltage, current and temperature, in real time by deploying a sensor network in the AC and DC distribution area of ​​the car charging station. When any of the monitored parameters exceeds the safe operation threshold, the system can immediately identify and classify the faulty equipment to form an accurate fault diagnosis mechanism. Subsequently, the system generates global operating parameters including the storage capacity and control instruction set of each node based on the current collected data, and synchronizes this information to the distributed controllers in the entire system through a broadcast mechanism, thereby achieving consistency in the system operation state. After obtaining the consistency state, the system further analyzes the path redundancy and switching response time, and identifies the bottleneck in resource configuration by comparing the actual response time with the preset threshold. When the response time is high, the system can automatically optimize the path configuration, adjust the path length or data flow direction, and improve the overall transmission efficiency and switching capability. After optimization, the system can identify the key path and node performance in high-load scenarios, and evaluate the anti-interference ability in combination with indicators such as communication delay to form an evaluation report. Finally, according to the report, the path priority and control strategy in the topology structure are updated, and the redundancy mechanism is configured to enhance the system stability and form a new iteration state.

[0020] The present invention operates based on the following technical mechanisms: 1. Fault judgment mechanism Determine the real-time parameters of the power distribution equipment, assuming that the collected voltage is , the current is , the temperature is , and the corresponding safety thresholds are , , The equipment failure judgment conditions are as follows: ; Among them, , , can be determined according to national electrical equipment standards, equipment specifications, and environmental limit tests.

[0021] 2. Path Switching Analysis and Response Time Evaluation The path redundancy in the system is defined as the number of alternative communication paths from each terminal to the master control center.

[0022] The handover response time is calculated as: ; where: is the data transmission delay; is the controller response delay; is the redundant path activation time. If , the system performs path adjustment and reconstructs the optimal path network using the graph theory shortest path algorithm (such as Dijkstra). is the handover response time threshold set by the system, used to determine whether the current network state requires path reconstruction optimization.

[0023] 3. Resource Optimization and Transmission Efficiency Evaluation The path load capacity and the transmission efficiency are evaluated as follows: ; is the current of the th path node, is the voltage of the th path node, denotes summation, represents the sum of the powers in the current path, that is, the path load capacity , and the parameters are calibrated by the historical operation data of the SCADA system and the simulation model. C 实际传输 is the total amount of data actually transmitted by each path node during a specific period in the actual operation process, which is statistically obtained from the real-time historical data collected by the online monitoring system (such as the SCADA system), reflecting the true workload of the current path; C 理论上限 is the total amount of data that the system can theoretically transmit at most under ideal conditions (i.e., the link is running at full load without interference and loss).

[0024] 4. Calculation of Anti-Interference Ability Index The anti-interference ability depends on the node availability and the data transmission delay : ; where is the environmental noise disturbance factor, which is obtained based on the statistics of communication bit error rate.

[0025] 5. Topology and control instruction set iteration mechanism Path priority and instruction set update frequency satisfy: ; Parameter weight , can be optimized by the AHP (Analytic Hierarchy Process). T 稳定 refers to the maximum time limit allowed for the system to recover to a stable communication state after network switching or topology adjustment. T 稳定 , as a system design parameter, is used to set the goal that the network must complete self - recovery and stabilization within this time after experiencing changes (such as path switching, node failure, load migration), and is an important indicator for regulating the dynamic response speed of the system.

[0026] This method can not only realize the dynamic monitoring of the distribution system of electric vehicle charging stations and multi - factor fault judgment, but also construct a distributed cooperation mechanism by integrating sensor networks and intelligent control technologies, significantly improving the system's resource allocation and path switching capabilities in the case of fault recovery, high load, and sudden anomalies. The formulated technical model supports the quantitative evaluation of the scheme, making the system optimization have data basis and executability, thus realizing the transition from static control to dynamic adaptability.

[0027] Taking an electric vehicle charging station in a certain city as an example, its access volume reaches more than a hundred vehicles per day, and there are serious grid load fluctuations during the morning and evening rush hours. Through the method of the present invention, when a group of charging piles in this charging station trip due to overload, the system can automatically identify the faulty equipment within 5 seconds, construct redundant paths, and allocate adjacent cable channels to complete current transfer. The system completes the update of the control instruction set and the reconstruction of path priority within the subsequent 1 minute, effectively avoiding a power outage of the whole station, and automatically adjusting the control strategy through the anti - interference evaluation report to make the overall operation of the station area return to stability.

[0028] The specific steps for generating the global operation parameters including node storage capacity and control instruction set include constructing a weighted graph model containing node connection relationships and path state parameters based on the graph search algorithm, dynamically searching for path nodes connected to the faulty equipment according to the faulty equipment identifier, obtaining several feasible paths using the shortest path search algorithm, calculating the path load capacity and transmission efficiency of each path, and determining the backup path with the highest path priority in combination with the path length and the number of switching devices.

[0029] After receiving the identification of the faulty device fed back by the sensor network, the system locates the device in the preset topological structure diagram and activates the backup path evaluation mechanism. The set of backup paths represents all alternative paths that are connected to the faulty device node and have the condition of current conduction , .

[0030] 1. Calculation of path load capacity The load capacity of each path is calculated as follows: ; ; where is the maximum load current that the node on the path can bear, is its operating voltage, represents the minimum value of the load capacity of each selected path . The minimum value represents the bottleneck for the safe operation of the system.

[0031] 2. Calculation of transmission efficiency The theoretical transmission efficiency of each path is defined as: ; represents the output power of the th node, represents the input power of the th node. The power of each node is obtained from real-time collected data, represents the sum of the output powers of all output nodes (terminal nodes), represents the sum of the input powers of all input nodes.

[0032] 3. Path priority determination model Considering the path length , the number of switching devices , the load capacity and the efficiency , the path priority is calculated by the following model: ; where is the weight parameter, which can be solved by machine learning regression using the historical operation optimization data of the power station; is the maximum load capacity of the paths in the system, and is normalized.

[0033] Finally, the path with the largest is selected as the preferred backup path after the fault.

[0034] The multi-factor optimization mechanism that combines fault diagnosis and backup paths significantly improves the system's response speed and intelligent scheduling ability. This method not only considers the path load capacity and power transmission efficiency but also takes into account the physical length of the path and the switch complexity, thus achieving a global optimal path decision under multi-dimensional indicators, avoiding energy consumption losses and operation risks caused by path switching, and improving the reliability of the distribution system and energy utilization efficiency.

[0035] In a rapid charging station for elevated electric buses, a power distribution path leading to Charging Pile No. 8 was interrupted due to excessive temperature. After the system identified the fault, it found three backup paths in the topology diagram. After evaluation by the above method, although one of the paths was longer in length, it had a high line load capacity, excellent efficiency, and fewer switches to pass through, so its priority score was the highest. The system automatically switched to this path and completed the path conversion in only 1.2 seconds, avoiding vehicle queuing and interruption of charging tasks. The subsequent operation of this path was stable, effectively verifying the engineering practicability and scheduling intelligence of the method.

[0036] The specific steps for generating the global operating parameters including node storage capacity and control instruction set also include generating a path switching instruction containing the number of switching devices for the backup path with the highest priority, sending the instruction to the relevant switching devices through the distributed controller to complete the path switching, and obtaining the new operating state of the power distribution network.

[0037] After determining the path with the highest priority from the set of backup paths the system needs to generate the corresponding path switching instruction, which should cover all the key switching devices on this path.

[0038] 1. Construction of path switching instruction Suppose the backup optimal path contains a node sequence and the switching devices in between are then the path switching instruction set is defined as: ; where represents the closing operation instruction of switch and its format includes: device address, execution command code, execution delay, priority label, etc., represents the union of multiple switch operation instruction sets in the range from to This range.

[0039] 2. Controller command sending strategy Each distributed controller maintains a switch mapping table within its responsible area and listens for path instructions issued by the dispatching center. The path instructions are sent via a synchronization protocol (such as MQTT or CoAP), and the controller executes the received instructions according to the following timing model: ; where, is the summation function, represents the total time required for a complete routing switch or switch state update in the system, is the communication delay between the controller and device ; is the action response delay after the device receives the instruction; is the number of switches involved.

[0040] The system can implement non - serial closing instructions by setting a parallel operation threshold to reduce the total switching time.

[0041] 3. Distribution network status update model After the path switch is completed, the system compares the distribution diagram state matrices , : ; represents the change amount of the distribution diagram state matrix; represents the rank of the matrix. If , that is, if the rank of the change of the distribution diagram state matrix is greater than 0, it indicates that an effective change has occurred in the network topology, and the current global operating parameters are updated.

[0042] This method realizes intelligent, efficient, and low - latency path switching operations through a structured path switching instruction system, controller partition synchronous operation, and dynamic update mechanism of topological states, and solves the problems of slow manual control response and high risk of misoperation in existing distribution systems. The automation of the switching operation ensures that the system can quickly respond when abnormalities occur in the distribution network or load transfer is required, enhancing the self - healing ability and operation continuity of the power grid.

[0043] At a coastal heavy - load charging port, the main power supply path needs to be temporarily shut down due to external cable maintenance. After receiving the dispatching order, the system immediately identifies the preferred backup path , which involves 3 high - voltage vacuum circuit breakers. The system automatically constructs and issues switching instructions, and the 3 area controllers execute the closing operation in parallel. The total switching time is 2.8 seconds. After the execution is completed, the system updates the topology in real - time and broadcasts the new operating parameters, ensuring the continuous power supply operation of large charging equipment at the port and avoiding charging task interruption and equipment cold start.

[0044] The specific steps for generating global operating parameters including node storage capacity and control instruction set also include constructing a capacity constraint graph model composed of nodes and paths based on the current operating state of the distribution network, obtaining the load distribution data of each node and analyzing its power demand. If there is a situation where the path load exceeds the capacity limit, a linear optimization model of load scheduling with the minimum objective function is constructed, or the minimum cost maximum flow algorithm is used for path reallocation to determine the optimal power transfer relationship between nodes.

[0045] After the distribution path is switched and a new network structure is formed, the system needs to re-evaluate the load conditions of each node to ensure the dynamic matching of power supply and demand and the stability of the system.

[0046] 1. Load Distribution Data Collection and Power Demand Analysis Assume that the current distribution network contains nodes, and the instantaneous power demand of each node is , which is calculated by the following formula: ; where is the node voltage, is the collected load current value, and the collection frequency is 1 - 5 times per second.

[0047] 2. Path Load Capacity Constraint Judgment If the set of all nodes on path is , its total required power is: , ; is the summation function, represents the total load of path , represents the power demand (i.e., load demand) of the th service request for the path, represents the maximum bearable capacity of the th path. If there exists , it is determined that the path is overloaded. represents the number of nodes participating in the load evaluation on the path, that is, on the path, the total number of all nodes (equipment, switches, subsystems, etc.) whose power demands need to be considered. is the maximum allowable current value that the node can withstand, which is given by the node hardware characteristics (such as wire carrying capacity, electrical equipment specifications, power supply module ratings), and is used as a safety limit condition in design and operation. represents the voltage on the th node, represents the weakest link in the overall capacity limitation of the path.

[0048] 3. Dynamic Node Connectivity Adjustment Mechanism The system uses a graph reconstruction method to adjust the connection relationships of some nodes in the network and introduces a connection matrix , and the element represents that node and can be electrically connected: ; Among them, represents the updated connection status or path load after the structure adjustment, represents the original connection status or path load of the node, represents the newly added or alternative path construction relationship, and follows the principle of the shortest power distribution during the adjustment process: ; Among them is the candidate reconstruction edge set, represents the power demand (i.e., load demand) of the th node, is the current available power of node , represents the absolute difference between the demand power and the supply power, that is, the gap between demand and supply, represents the summation of the errors between all paired supply and demand. After adjustment, the updated load distribution vector is denoted as , represents the optimal load distribution vector, that is, after the system is adjusted, the optimal load distribution result of each node, represents the optimal load that the th node or device should bear after optimization, , represents the number of nodes in the system.

[0049] 4. Control Instruction Synchronous Update Mechanism Any connectivity adjustment is accompanied by an update of the switching command set issued by the controller to ensure topological synchronization and execution logic consistency.

[0050] This optimization mechanism realizes dynamic load adjustment based on power demand, and is particularly suitable for power supply balance control in the case of high-fluctuation loads (such as electric bus groups, fast charging pile clusters). Through dynamic connection relationship reconstruction, the system avoids path overload while maintaining the continuity of the power supply and distribution network state update model, improves the load adaptability and operation stability of the power distribution system, significantly reduces the risk of overload tripping, and enhances the flexibility of power allocation between local regions. At a charging hub in a smart highway service area in a mountainous area, a large number of vehicles were concentrated in the west path due to the sudden arrival of tourist groups. The system collected the real-time power demand of each charging node, determined that the original path was overloaded, and automatically started the reconstruction algorithm to divert some high-load nodes to redundant paths, and combined with the prediction model to complete the scheduling for the next 5 minutes. As a result, tripping was effectively prevented, and the average power supply efficiency was improved by 20%. The system ran smoothly without manual intervention. The specific steps of generating global operating parameters including node storage capacity and control instruction set also include obtaining the operating status of each control node through a distributed control architecture, analyzing the node computing power and communication delay, and if a node does not respond within a fault detection cycle, it is judged as a node failure and a failed node identifier is obtained.

[0051] This implementation further enhances the system's fault-tolerant detection and node health status management capabilities under a distributed control architecture.

[0052] 1. Control node operation status monitoring Each control node periodically sends heartbeat packets to the dispatch center (or neighboring nodes). Assuming that the current control node is , and its status monitoring uses the following indicators: Computing capability index : Maximum number of instructions processed per unit time; communication delay : Heartbeat packet round trip time ; Response ID : Set to 1 for normal response, 0 for no response.

[0053] 2. Fault detection cycle mechanism Set the fault detection period to If no node is received during the period If the response is , the failure judgment is triggered: ; Node failure determination: is the current time point, Indicates at time At this moment, whether the response is received successfully, Then the node Invalid.

[0054] 3. Failed node identification and broadcasting Once a node is determined Invalid, the system immediately generates an invalid mark: ; Indicates failure indicator. Indicates the type of failure event, i.e., different types of failures are output, such as communication failures, hardware failures, etc. , Indicates the node identifier where the failure occurs, i.e., the output node , Indicates the timestamp when the failure occurs, i.e., the output time ; And synchronize this identifier to all other controllers and the master control unit in the network through the broadcast mechanism for subsequent control instruction adjustment and network reconstruction.

[0055] 4. Dynamic control strategy adjustment The system can reallocate tasks according to the computing power of the remaining normal nodes: Task reallocation weight ; Indicates the weight of the th active node, Indicates the th active node's ability index,

[0056] Indicates the sum of the ability indexes of all active nodes. The original node tasks are allocated according to the weight to ensure the continuous operation of the system without interruption.

[0057] Through the embedded control node status monitoring mechanism, the system can achieve accurate fault tolerance identification and rapid task migration, enhancing the recovery ability of the distribution system in the event of sudden control node failures. The failure node determination logic is simple and efficient, and is easy to deploy on the industrial real-time bus, ensuring system stability and operation and maintenance response speed, especially suitable for distribution scheduling management in unattended and highly reliable scenarios.

[0058] The specific steps for generating the global operation parameters including the node storage capacity and the control instruction set also include selecting the standby control node with the highest node availability from the redundancy mechanism according to the failure node identifier, combining the data synchronization frequency and the task migration time, completing the control task migration, obtaining the updated control network status, and generating the global operation parameters including the node storage capacity and the control instruction set according to the updated control network status.

[0059] After confirming the failure of the control node, to ensure the continuous operation of the system, while switching the implementation path of the present invention, a hot backup automatic takeover mechanism for control tasks is designed, involving the following key technical steps: 1. Spare control node screening mechanism The system maintains a redundant control node list , represented as a set of backup capacity indicators, consisting of the backup capacities of different backup nodes . The availability of each spare node is calculated by the following comprehensive score : ; wherein is the node computing power, is the maximum computing power in the current system; is the communication delay with the main control center; is a configurable weight parameter used to balance computing and response capabilities.

[0060] Select the node with the largest as the takeover node .

[0061] 2. Task migration scheduling and time calculation The total migration delay of the control task consists of data synchronization time and migration processing time: ; wherein is the data volume to be synchronized (such as control cache, parameter table, etc.), is the current system data synchronization rate, is the time for the spare node to process and load the task.

[0062] If , it is considered that the migration is successful, indicating the upper limit or critical value of the migration time.

[0063] 3. Control network status update and parameter reconstruction After the node migration, a new control network status diagram is formed, and the system generates updated global operation parameters based on this: ; wherein is the control instruction set re-planned according to the capabilities of the takeover node, is the updated global operation parameter, is the new control network status diagram formed after the node migration, is the update algorithm.​

[0064] After the control node fails, the present invention can significantly improve the system's anti-failure ability and control continuity by automatically selecting the optimal takeover node and efficiently completing task migration. The scoring mechanism ensures that the selected standby node has good response speed and computing power. The migration time model guarantees that the scheduling process is predictable and controllable, and the system topology and operating parameters can be updated in real time, endowing the power distribution system with high availability characteristics similar to a "hot standby server" and enhancing the overall robustness.

[0065] During the peak period, a sudden communication interruption occurred in a new energy charging base at a large intercity high-speed rail transfer station, resulting in the failure of the main control node. After detecting the failed node, the system quickly calculated the availability scores of 3 standby nodes and selected the edge controller with the highest score to take over the task. The amount of migrated data was 12 MB, the current synchronization rate was 10 Mbps, and the overall migration process took about 3.3 seconds. The task was seamlessly migrated and maintained the same operating state. The updated control network automatically completed parameter recalculation, ensuring that all fast charging pile scheduling commands in the station area were issued normally without user-perceived interruption.

[0066] The control instruction set includes node start / stop commands, path switching instructions, energy transfer scheduling parameters, and communication coordination signals. In this embodiment, by defining the composition of the control instruction set, comprehensive control over each key node and the operating state of the system in the AC / DC power distribution area of the vehicle charging station is achieved. The control instruction set mainly includes four types of instructions: node start / stop commands, path switching instructions, energy transfer scheduling parameters, and communication coordination signals. These instructions are used in combination to achieve the dynamic optimization and stable operation of the system. Node start / stop commands are used to start or stop functional nodes within the system, such as charging piles, battery energy storage systems, or converter modules. This command includes the node number, start / stop operation instruction (i.e., start or stop), command priority, and the valid time window of the command, which are used to control the operation timing and urgency. Path switching instructions are used to adjust the power path in case of faults or changes in energy demand. This instruction controls a group of switch devices associated with the path, and the command structure includes device numbers, execution instructions (such as closing), execution delay parameters, and path identifiers, ensuring the sequentiality and safety of the system when switching paths. Energy transfer scheduling parameters are used to achieve the power distribution and scheduling between nodes. The system generates scheduling parameters based on the current available power source nodes, energy demand nodes, and path capacities, including the energy start node, energy target node, planned transmission power, and planned scheduling duration. The system will automatically compare the available power of the start node, the maximum carrying capacity of the transmission path, and the actual demand power of the target node, and take the minimum value among the three as the final transmission power to ensure that the energy scheduling is not overloaded or unbalanced. Communication coordination signals are used to transmit task status and synchronization signals between multiple controllers to prevent control errors caused by instruction conflicts or inconsistent states. The signal structure is simple, including the communication session number, signal type (such as synchronization request, status publication, or conflict resolution), and transmission timestamp, which are used for command coordination, priority management, and time synchronization. All control instructions are scheduled by the scheduling center or edge control nodes and executed through the instruction queue sorting mechanism. Commands with higher priorities will be processed first to ensure timely response to critical tasks. The system supports dynamic insertion and withdrawal of instructions, has good scalability and fault tolerance, and can adapt to the rapidly changing operating environment of the power distribution system.

[0067] By clarifying the four major components of the control instruction set, the present invention establishes a complete multi-dimensional control system, which supports the dynamic energy management, topological adaptive adjustment of the power distribution system, and highly reliable execution of control tasks. Node start / stop and path switching enhance the flexibility of network reconfiguration, energy scheduling ensures the optimal allocation of resources, while the communication coordination mechanism guarantees the consistency and timing control accuracy among distributed nodes.

[0068] The broadcast mechanism adopts a multicast protocol based on a ring link to reduce transmission delay and improve the stability of state synchronization. In this embodiment, the system completes the broadcast synchronization operation of global operating parameters and control instructions through a multicast protocol based on a ring link structure. In the traditional broadcast mechanism, information spreads from a central node to all child nodes at the same time, which is easy to cause uneven transmission delay or synchronization failure due to bandwidth bottlenecks or node congestion. This solution adopts a ring link, that is, all control nodes are interconnected in the form of a closed link to form a logical end-to-end data channel. During the state broadcast process, the main control node first sends an operating parameter update data packet to its next node. After receiving the data packet, each node will immediately forward the data to its next adjacent node and perform data analysis and execution locally. Since each node only needs to process a copy of the data and forward it once along the loop, the number of simultaneous broadcast packets is greatly reduced, thereby reducing the probability of link competition and concurrent conflicts. The core mechanism of the multicast protocol includes message sequence number identification, duplicate packet detection, packet loss retransmission and confirmation feedback mechanism. Each data packet carries a unique serial number. After receiving the data packet, the node will verify the validity of the data. If data duplication is detected, it will be discarded; if missing or delayed beyond the limit is detected, the upstream node will be requested to resend. This ensures that even if some nodes are delayed or temporarily interrupted, data completion and synchronization can be completed through the redundant path of the ring link. In addition, the physical implementation of the ring structure can be combined with a bidirectional link to provide dual-channel transmission capabilities. Once a link direction fails, the system can quickly switch to the reverse path to continue the broadcast process, further enhancing the robustness and fault tolerance of the operation parameter synchronization process. Through the multicast mechanism of the ring link, the system realizes the command synchronization and operation status distribution from the master node to all edge controllers. The distribution time is linearly related to the link length, which is significantly better than the stability and delay control capabilities of the star or tree broadcast topology in large-scale node systems.

[0069] The adoption of a multicast protocol based on a ring link effectively solves the problems of packet congestion, large synchronization delay, and poor data consistency that occur in traditional broadcast methods when there are a large number of nodes or a large communication load. This mechanism has the advantages of stable transmission paths, high synchronization efficiency, and strong fault resistance. It is particularly suitable for distributed power supply systems with frequent control command distribution and state sharing. Through the packet sequence identification and redundant channel mechanism, even if some nodes are temporarily disconnected, the continuity and consistency of the overall system control link can be guaranteed.

[0070] In an electric vehicle charging plaza supporting an urban transportation hub, the system deploys more than 30 edge controller nodes for various fast and slow charging piles, energy storage management, and microgrid control. During the daily peak period, the system needs to frequently broadcast load status, path scheduling commands, and parameter adjustment instructions. When using the traditional broadcast method, the system often experienced delays in receiving by some controllers, resulting in inconsistent scheduling. After introducing the multicast protocol based on a ring link, the average time taken for status update broadcasts decreased from 1.8 seconds to 0.4 seconds, and the node - to - node synchronization failure rate dropped below 0.1%. This achieved near - real - time instruction distribution and unified system operation status, significantly improving the stability and security of system operation.

[0071] The judgment of equipment failure also includes referring to the fluctuation frequency and duration in historical operation data for dynamic trend analysis of critical states. In this embodiment, the equipment failure judgment mechanism no longer relies solely on static threshold judgment (such as whether voltage, current, and temperature exceed the standard), but introduces the statistical characteristics of fluctuation behavior in historical data to make a trend judgment on the operation of equipment in the marginal state. Specifically, the system will establish a historical operation database for each device, recording the key parameter values (such as voltage, current, temperature, etc.) and their change behaviors of the device at different time periods. For these data, the system will extract the following two key indicators: one is the fluctuation frequency, that is, the number of times the parameter value fluctuates around the preset threshold per unit time; the other is the fluctuation duration, that is, the length of time the parameter value continuously remains near the threshold (but does not cross the boundary). When the current state of the device is near the safety boundary of a certain indicator, the system will call the historical database to extract the fluctuation data under the same or similar working conditions in the past several cycles and conduct trend analysis. If it is found that the current state conforms to the fluctuation pattern before a failure in history, such as the frequency showing an upward trend and the duration extending, the system will mark the current state as "critical operation", give an early warning of the failure, and even trigger scheduling actions such as preventive path switching or load shunting according to the degree of the trend. The trend analysis process is executed based on a statistical sliding window. The system can adopt a weighted historical data model, giving priority to recent data to improve the sensitivity of the response. At the same time, the system also supports setting dynamic thresholds, for example, appropriately adjusting the judgment sensitivity under extreme environmental conditions such as at night or in low temperature to adapt to different operation scenarios. Through this mechanism, the system has the ability to identify in advance the device states that have not clearly crossed the boundary but have shown abnormal trends, thereby improving the predictive maintenance level and operation robustness of the overall power supply system.

[0072] This embodiment breaks through the limitation of traditional fault judgment relying on a single static threshold. By introducing a trend judgment mechanism based on historical fluctuation behavior, it realizes the early identification and response to potential faults in critical states. Its advantages lie in improving the system's adaptability to complex power disturbances or equipment degradation, significantly reducing the incidence of sudden faults, and enhancing the predictive maintenance ability and stability of the entire distribution substation system. In addition, this mechanism can effectively avoid the judgment blind spots of "short-term out-of-bounds false alarms" and "invisible faults without out-of-bounds", and has higher judgment accuracy and service continuity guarantee ability.

[0073] In a new energy vehicle centralized charging area of an airport, during high-load operation in summer, the temperature of a fast-charging transformer approached the warning upper limit many times but never exceeded it. Through trend analysis, the system found that the frequency of temperature fluctuations of this device increased, and the time continuously maintaining at a high value section lengthened, which was highly similar to the curve trend of another device that had failed in history. The system marked it as a critical state before the temperature exceeded the limit, automatically executed path switching, and smoothly migrated its load to other paths, effectively avoiding the occurrence of equipment overheating and burning accidents, and notified the maintenance personnel for inspection. This not only ensured the continuous power supply of the system but also significantly improved the operation and maintenance efficiency and safety level.

[0074] As Figure 2 shown, there is also provided an AC / DC distribution substation operation optimization system for a vehicle charging station, which is used to implement the steps of the AC / DC distribution substation operation optimization method for the vehicle charging station. The system includes: A sensor network module, which is used to collect voltage, current, and temperature data of distribution equipment, and when the voltage exceeds a preset threshold, the current exceeds the path load capacity, or the temperature is higher than the safe range, it judges that the equipment has failed and generates a failed equipment identifier and a failure type; A parameter generation module, which is used to generate global operation parameters including node storage capacity and control instruction sets, and synchronize them to all distributed controllers through a broadcast mechanism to obtain a consistent system operation state; A status monitoring module, which is used to monitor the consistent system operation state in real time, obtain path redundancy and switching response time data, and based on the analysis results of recovery speed and node connectivity, judge whether the switching response time exceeds a preset threshold; if it exceeds, it optimizes resource allocation by adjusting the path length to obtain an optimized distribution network configuration; A performance analysis module, which is used to extract the path load capacity and transmission efficiency under high-load scenarios from the optimized distribution network configuration, and generate an anti-interference ability evaluation report including node availability and communication delay to determine the stable operation ability under abnormal scenarios; The topology and policy update module is used to update the path priorities in the distribution network topology diagram and the control instruction set in the control policy database according to the anti-interference ability evaluation report, generate a new set of backup paths and a redundant mechanism configuration for the node computing capabilities, and output the iterated system state.

[0075] The system first completes the real-time data collection of the distribution equipment of the vehicle charging station through the sensor network module, including key operating parameters such as voltage, current, and temperature. The module has built-in fault judgment logic and can identify whether there is a situation where the voltage exceeds the equipment's upper limit, the current exceeds the path load capacity, or the temperature exceeds the preset safety threshold. Once the fault condition is met, the corresponding fault device identifier and fault type information can be immediately output. The parameter generation module generates a global operating parameter set containing the computing capabilities, available storage capacities, and scheduling instructions of each control node according to the status information of the current node under the condition of obtaining fault information or the initial state of the system. This parameter set is sent to all distributed controllers through the broadcast mechanism, enabling all nodes in the system to maintain a synchronous operating state, thereby constructing a consistent system view. The status monitoring module tracks the system operating state in real time, focusing on obtaining data on the redundancy and switching response time of each power supply path. By evaluating the recovery speed and node connectivity of these data, the system can judge whether there are performance bottlenecks such as response delays in the current path configuration. Once it is found that the path switching response time exceeds the preset tolerance threshold, the module will trigger path structure optimization operations through the control logic, automatically adjust the path length or reconstruct the distribution link to ensure efficient resource allocation and timeliness optimization of the system structure. The performance analysis module evaluates the bearing capacity and power transmission efficiency of the new path under high-load conditions after the new path is constructed. Combining the current communication response speed and stability of the node, the system automatically generates an anti-interference ability evaluation report to judge its stable operation potential and node emergency response ability in an abnormal working environment. Finally, the topology and policy update module automatically updates the path priority configuration in the distribution network topology diagram and makes corresponding optimization adjustments to the control instruction set in the control policy database according to the stability parameters and fault tolerance ability results in the evaluation report. At the same time, a new set of backup paths and a control node redundant computing resource allocation scheme are generated to ensure that the system can quickly complete iterative upgrades and maintain continuous power supply control in the event of future emergencies. Each module is connected through a high-speed data bus and an edge computing architecture, and the system has high scalability and deployment flexibility under the distributed architecture.

[0076] Through the collaborative work of five major modules, this system realizes a complete operation closed-loop from underlying data collection, intelligent analysis, status judgment, fault prediction to topology reconstruction and strategy optimization. Compared with traditional power distribution management methods, this system has extremely strong real-time response, optimization self-adaptability and fault tolerance, and can significantly improve the operation efficiency and stability of the power system under high-load and complex path environments. At the same time, through modular design, each functional unit is convenient for maintenance and independent upgrade to meet the future expansion and function superposition requirements.

[0077] In a large intelligent charging station in an underground transportation hub, after the system was deployed, during a high-load fluctuation of the summer power grid, it successfully identified the critical state where the temperatures of some charging path nodes continued to fluctuate but did not exceed the limit, and quickly located the source of the problem through the sensor network. The system triggered the path reconstruction process and completed the current transfer and synchronized the operation status of each control node in only 6 seconds. During this process, the parameter generation module achieved the full-station broadcast of control instructions, and the topology module quickly output a new configuration, without interruption of the power supply in the entire station area, ensuring the normal charging order of more than 400 new energy vehicles, fully verifying the practicability and intelligent level of this system.

[0078] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the operation of an AC / DC distribution area of ​​a vehicle charging station, characterized in that: include: The voltage, current, and temperature data of the power distribution equipment are collected through the sensor network. If the voltage exceeds the preset threshold, the current exceeds the path load capacity, or the temperature is higher than the safe range, it is judged as a device failure, and the fault device identification and fault type are obtained; Generate global operating parameters including node storage capacity and control instruction set, and synchronize them to all distributed controllers through broadcast mechanism to obtain consistent system operating status; By real-time monitoring of the consistent system operation status, obtaining path redundancy and switching response time data, analyzing the recovery speed and node connectivity, and optimizing resource allocation by adjusting the path length if the switching response time exceeds the preset threshold, an optimized distribution network configuration is obtained; Extract the path load capacity and transmission efficiency under high-load scenarios from the optimized distribution network configuration, generate an anti-interference capability evaluation report including node availability and communication delay, and determine the stable operation capability under abnormal scenarios; According to the anti-interference capability evaluation report, based on the changes in node weights in the reconstructed path graph, the path priority and control instruction set in the control strategy database are dynamically updated to generate a new set of backup paths and redundant mechanism configurations for node computing power to obtain the iterative system state.

2. The method for optimizing the operation of an AC / DC distribution area of ​​a vehicle charging station according to claim 1, characterized in that: The specific steps of generating global operating parameters including node storage capacity and control instruction set include: constructing a weighted graph model including node connection relationship and path state parameters based on a graph search algorithm, dynamically searching for path nodes connected to the faulty device according to the faulty device identification, obtaining several feasible paths using the shortest path search algorithm, calculating the path load capacity and transmission efficiency of each path, and determining the backup path with the highest path priority in combination with the path length and the number of switch devices.

3. The method for optimizing the operation of an AC / DC distribution area of ​​a vehicle charging station according to claim 2, characterized in that: The specific steps of generating global operating parameters including node storage capacity and control instruction set also include generating a path switching instruction including the number of switch devices for the backup path with the highest path priority, sending the instruction to the relevant switch devices through the distributed controller, completing the path switching, and obtaining a new distribution network operating state.

4. The method for optimizing the operation of an AC / DC distribution area of ​​a vehicle charging station according to claim 3, characterized in that: The specific steps of generating global operating parameters including node storage capacity and control instruction set also include constructing a capacity constraint graph model consisting of nodes and paths based on the current operating status of the distribution network, obtaining load distribution data of each node and analyzing its power demand, and if there is a situation where the path load exceeds the capacity limit, constructing a load scheduling linear optimization model with the minimum objective function, or using the minimum cost maximum flow algorithm to redistribute paths to determine the optimal power transfer relationship between nodes.

5. The method for optimizing the operation of the AC and DC distribution area of ​​a vehicle charging station according to claim 4, characterized in that: The specific steps of generating global operating parameters including node storage capacity and control instruction set also include obtaining the operating status of each control node through a distributed control architecture, analyzing the node computing power and communication delay, and if a node does not respond within a fault detection cycle, it is judged that the node has failed and a failed node identifier is obtained.

6. The method for optimizing the operation of an AC / DC distribution area of ​​a vehicle charging station according to claim 5, characterized in that: The specific step of generating global operating parameters including node storage capacity and control instruction set also includes: According to the failed node identification, the backup control node with the highest node availability is selected from the redundancy mechanism. Combined with the data synchronization frequency and task migration time, the control task migration is completed to obtain the updated control network status. Based on the updated control network status, the global operating parameters including the node storage capacity and the control instruction set are generated.

7. The method for optimizing the operation of an AC / DC distribution area of ​​a vehicle charging station according to claim 1, characterized in that: The control instruction set includes node start and stop commands, path switching instructions, energy transfer scheduling parameters and communication coordination signals.

8. The method for optimizing the operation of an AC / DC distribution area of ​​a vehicle charging station according to claim 1, characterized in that: The broadcast mechanism adopts a multicast protocol based on a ring link to reduce transmission delay and improve state synchronization stability.

9. The method for optimizing the operation of an AC / DC distribution area of ​​a vehicle charging station according to claim 1, characterized in that: The judgment of the equipment failure also includes referring to the fluctuation frequency and duration in the historical operation data and performing dynamic trend analysis on the critical state.

10. An AC / DC distribution area operation optimization system for a vehicle charging station, used to implement the steps of the AC / DC distribution area operation optimization method for a vehicle charging station according to any one of claims 1 to 9, characterized in that: The system comprises: The sensor network module is used to collect voltage, current and temperature data of the power distribution equipment, and when the voltage exceeds the preset threshold, the current exceeds the path load capacity or the temperature is higher than the safe range, it is judged as a device failure and generates a fault device identification and fault type; The parameter generation module is used to generate global operating parameters including node storage capacity and control instruction set, and synchronize them to all distributed controllers through the broadcast mechanism to obtain a consistent system operating status; The status monitoring module is used to monitor the consistent system operation status in real time, obtain the path redundancy and switching response time data, and determine whether the switching response time exceeds the preset threshold based on the recovery speed and node connectivity analysis results; if exceeded, the resource allocation is optimized by adjusting the path length to obtain the optimized distribution network configuration; Performance analysis module, which is used to extract the path load capacity and transmission efficiency under high load scenarios from the optimized distribution network configuration, and generate an anti-interference capability evaluation report including node availability and communication delay to determine the stable operation capability under abnormal scenarios; The update module is used to dynamically update the path priority and control instruction set in the control strategy database based on the changes in node weights in the reconstructed path graph according to the anti-interference capability evaluation report, generate a new set of backup paths and redundant mechanism configuration of node computing power, and obtain the iterated system state.

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