An operating optimization method and system for AC / DC power distribution areas of an electric vehicle charging station

Abstract

The present invention discloses a method and system for optimizing the operation of the AC / DC power distribution area of an electric vehicle charging station, which relates to the technical field of new energy transportation infrastructure. It includes collecting 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 failure device identifier and failure type are obtained. Global operation parameters including node storage capacity and control instruction sets are generated and synchronized to all distributed controllers through a broadcast mechanism to obtain a consistent system operation state. The method and system for optimizing the operation of the AC / DC power distribution area of the electric vehicle charging station break through the technical bottlenecks of traditional power distribution systems in aspects such as slow fault response, single control structure, and static resource allocation, providing an intelligent, highly reliable, and highly adaptable operation optimization solution for new energy vehicle charging infrastructure, and significantly improving the stability and continuity of charging services.

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 power 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 faults 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 difficulty of the charging station 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 power 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 power distribution area of an electric vehicle charging station, comprising:

[0006] Collect 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 is faulty, and the faulty device identifier and fault type are obtained;

[0007] 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;

[0008] By monitoring the operating 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, optimize resource allocation by adjusting the path length to obtain an optimized distribution network configuration;

[0009] Extract the path load capacity and transmission efficiency under high-load scenarios from the optimized distribution network configuration, generate an anti-interference ability assessment report including node availability and communication delay, and determine the stable operating ability under abnormal scenarios;

[0010] According to the anti-interference ability assessment report, based on the change of node weights in the reconstructed path graph, dynamically update the path priority and the control instruction set in the 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.

[0011] Preferably, the specific steps of generating the global operating 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 the 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.

[0012] Preferably, the specific steps of generating the global operating 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 the distributed controller to complete the path switching, and obtaining the new operating state of the distribution network.

[0013] Preferably, the specific steps of generating the global operating 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 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, then constructing a linear optimization model of load scheduling with the minimum objective function, or using the minimum cost maximum flow algorithm for path reallocation to determine the optimal power transfer relationship between nodes.

[0014] Preferably, the specific steps of generating the global operating parameters including node storage capacity and control instruction set further include obtaining the operating status of each control node through the 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 to obtain the identifier of the failed node.

[0015] Preferably, the specific steps of generating the global operation 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 operation parameters including the node storage capacity and the control instruction set according to the updated control network state.

[0016] Preferably, the control instruction set includes node start / stop commands, path switching instructions, energy transfer scheduling parameters, and communication coordination signals.

[0017] Preferably, the broadcast mechanism adopts a multicast protocol based on a ring - shaped link to reduce the transmission delay and improve the stability of state synchronization.

[0018] Preferably, the judgment of equipment failure also includes dynamically analyzing the critical state by referring to the fluctuation frequency and duration in the historical operation data.

[0019] An AC / DC distribution area operation optimization system for an electric vehicle charging station, which is used to implement the steps of the above - mentioned AC / DC distribution area operation optimization method for an electric vehicle charging station. The system includes:

[0020] A sensor network module, which is used to collect the 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 equipment failure, and a failure equipment identifier and a failure type are generated;

[0021] A parameter generation module, which is used to generate global operation parameters including the node storage capacity and the control instruction set, and synchronize them to all distributed controllers through a broadcast mechanism to obtain a consistent system operation state;

[0022] A state monitoring module, which is used to monitor the consistent system operation state in real - time, obtain the path redundancy and the switching response time data, and based on the analysis results of the recovery speed and the node connectivity, judge 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;

[0023] A performance analysis module, which is used to extract the path load capacity and the transmission efficiency under high - load scenarios from the optimized power distribution network configuration, and generate an anti - interference ability evaluation report including the node availability and the communication delay to determine the stable operation ability under abnormal scenarios;

[0024] The topology and strategy update module is used to update the path priorities in the distribution network topology diagram and the control instruction set in the control strategy database according to the anti-interference ability evaluation report, generate a new set of backup paths and a redundant mechanism configuration for node computing capabilities, and output the iterated system state.

[0025] As can be seen from the above technical solutions, the present invention has the following beneficial effects:

[0026] By introducing a multi-dimensional sensor network to collect key operating parameters such as voltage, current, and temperature in real time, it is possible to timely sense and judge the fault type when abnormal conditions occur in distribution equipment, achieve precise positioning and rapid response, improve the adaptability and recovery efficiency of the system under sudden faults. By synchronizing global operating parameters to all distributed controllers through a broadcast mechanism, it ensures a consistent understanding of the system operating state by each node, effectively avoids the single-point failure problem of traditional central control structures, and enhances the robustness and redundancy of the 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 the dynamic optimal allocation of resources in the distribution network, and improves the power supply sustainability and stability under high-load and abnormal scenarios. By generating an anti-interference ability evaluation report, extracting key indicators such as node availability and communication delay, and quantitatively analyzing the operating ability under abnormal scenarios, it guides the update of control strategies and the reconstruction of backup paths, improves the anti-interference ability of the overall system. By using the evaluation results to feedback for path priority adjustment and control strategy instruction set update, constructing a redundant mechanism for node computing capabilities, realizing the continuous iteration and self-optimization of the system state, and enhancing the operating intelligence level in complex power network environments. The present invention breaks through the technical bottlenecks of traditional 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 scheme for new energy vehicle charging infrastructure, and significantly improves the stability and continuity of charging services. Brief Description of the Drawings

[0027] Figure 1 It is the flowchart of the method of the present invention;

[0028] Figure 2 It is the connection diagram of the system modules of the present invention. Detailed Embodiments

[0029] 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 of 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.

[0030] Such asFigure 1 As shown, the present invention provides a technical solution: a method for optimizing the operation of an AC / DC distribution station area of a vehicle charging station, comprising:

[0031] 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;

[0032] 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;

[0033] 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;

[0034] 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;

[0035] 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.

[0036] 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.

[0037] The present invention operates based on the following technical mechanisms:

[0038] 1. Fault judgment mechanism

[0039] Judge the real-time parameters of the power distribution equipment. Let the collected voltage be , the current be , and the temperature be . The corresponding safety thresholds are , , . The equipment fault judgment conditions are as follows:

[0040] ;

[0041] Among them, , , can be determined according to national electrical equipment standards, equipment specifications, and environmental limit tests.

[0042] 2. Path switching analysis and response time evaluation

[0043] The path redundancy in the system is defined as the number of optional communication paths from each terminal to the main control center.

[0044] The switching response time is calculated as: ;

[0045] Among them: is the data transmission delay; is the controller response delay; is the redundant path activation time. If , the system performs path adjustment and uses the graph theory shortest path algorithm (such as Dijkstra) to reconstruct the optimal path network. is the switching response time threshold set by the system, which is used to judge whether the current network state needs to perform path reconstruction optimization.

[0046] 3. Resource optimization and transmission efficiency evaluation

[0047] Evaluate the path load capacity and the transmission efficiency as follows:

[0048] ;

[0049] is the current of the th path node, is the voltage of the th path node, represents summation, Represents the sum of the powers in the current path, i.e., 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 time period in the actual operation process, which is obtained by statistically analyzing the real-time historical data collected by the on-line monitoring system (such as the SCADA system), and reflects 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).

[0050] 4. Calculation of anti-interference ability index

[0051] Anti-interference ability depends on node availability and data transmission delay :

[0052] ;

[0053] where is the environmental noise perturbation factor, which is obtained based on the statistics of the communication error rate.

[0054] 5. Topology and control instruction set iteration mechanism

[0055] Path priority and instruction set update frequency satisfy:

[0056] ;

[0057] 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 return to a stable communication state after network switching or topology adjustment, T 稳定 As a system design parameter, it 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 index for regulating the dynamic response speed of the system.

[0058] This method can not only realize the dynamic monitoring of the distribution system of electric vehicle charging stations and the multi-factor fault judgment, but also construct a distributed cooperation mechanism by integrating sensor networks and intelligent control technologies, significantly improving the resource allocation and path switching capabilities of the system under fault recovery, high load and sudden anomalies. The formulaic 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.

[0059] Taking a new energy vehicle charging station in a certain city as an example, the number of connected vehicles reaches more than 100 per day, and there are serious grid load fluctuations during the morning and evening peak hours. Through the method of the present invention, when a group of charging piles are overloaded and tripped, the charging station can automatically identify the faulty equipment within 5 seconds, build redundant paths, and deploy adjacent cable channels to complete the current transfer. The system completes the control instruction set update and path priority reconstruction within the next 1 minute, effectively avoiding power outages in the entire station, and automatically adjusts the control strategy through the anti-interference evaluation report, so that the overall operation of the station area returns to stability.

[0060] 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.

[0061] After receiving the faulty device identification from the sensor network, the system locates the device in the preset topology diagram and starts the backup path evaluation mechanism. Represents all alternative paths connected to the faulty device node and capable of conducting current , .

[0062] 1. Path load capacity calculation

[0063] Each path Load capacity Calculated as: ;

[0064] in Nodes on the path The maximum current that can be carried is For its working voltage, Indicates that each path is selected Load capacity The minimum value is used to represent the bottleneck of safe operation of the system.

[0065] 2. Transmission efficiency calculation

[0066] Theoretical transmission efficiency of each path Defined as: ;

[0067] Expressed as The output power of each node, Expressed as The input power of each node, and 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). is expressed as the sum of the input powers of all input nodes.

[0068] 3. Path priority determination model

[0069] Comprehensive path length , the number of switching devices , load capacity and efficiency , path priority The calculation model is as follows:

[0070] ;

[0071] Among them, is the weight parameter, which can be solved by machine learning regression through the historical operation optimization data of the power station; is the maximum carrying capacity of the path in the system, and is normalized.

[0072] Finally, select the path with the largest

[0073] as the preferred standby path after the fault.

[0074] Through the multi-factor optimization mechanism that combines fault diagnosis and standby paths, the response speed and intelligent scheduling ability of the system are significantly improved. 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 realizing the global optimal path decision under multi-dimensional indicators, avoiding the energy consumption loss and operation risk brought by path switching, and improving the reliability and energy utilization efficiency of the distribution system.

[0075] The specific steps for generating the global operation parameters including the node storage capacity and the control instruction set also 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 the distributed controller to complete the path switching, and obtaining the new operation state of the distribution network.

[0076] After determining the path with the highest priority from the set of alternative paths After that, the system needs to generate a corresponding path switching instruction, which should cover all the key switching devices on this path.

[0077] 1. Construction of path switching instruction

[0078] Suppose the preferred alternative path contains a node sequence and the switching devices in between are then the path switching instruction set is defined as: ;

[0079] where represents the closing operation instruction of switch and its format includes: device address, execution command code, execution delay, priority label, etc., represents to the union of multiple switch operation instruction sets within this range.

[0080] 2. Controller command - issuing strategy

[0081] 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 down through a synchronization protocol (such as MQTT or CoAP), and the controller executes the received instructions according to the following timing model:

[0082] ;

[0083] 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.

[0084] The system can reduce the total switching time by setting a parallel operation threshold to implement non - serial closing instructions.

[0085] 3. Distribution network state update model

[0086] After the path switching is completed, the system compares the distribution map state matrices , before and after the switching:

[0087] ; represents the change amount of the distribution map state matrix;

[0088] represents the rank of the matrix. If , that is, it means that the rank of the change in the distribution diagram state matrix is greater than 0, indicating that an effective change has occurred in the network topology, and the current global operating parameters are updated.

[0089] This method realizes intelligent, efficient, and low-latency path switching operations through a structured path switching instruction system, controller partition synchronization operations, and a dynamic update mechanism for 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.

[0090] In a coastal heavy-duty 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 standby optimal path , which involves 3 high-voltage vacuum circuit breakers. The system automatically constructs and issues switching instructions, and the closing operations are executed in parallel by 3 area controllers. The total switching time is 2.8 seconds. After the execution is completed, the system updates the topological structure in real time and broadcasts the new operating parameters, ensuring the continuous power supply operation of large charging equipment in the port and avoiding the interruption of charging tasks and cold start of equipment.

[0091] The specific steps for generating the global operating parameters including node storage capacity and control instruction sets 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.

[0092] 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.

[0093] 1. Load distribution data collection and power demand analysis

[0094] Assume that the current distribution network contains nodes, and the instantaneous power demand of each node is , which is calculated by the following formula: ;

[0095] Among them, is the node voltage, is the collected load current value, and the collection frequency is 1 - 5 times per second.

[0096] , 2. Path load capacity constraint determination

[0097] If the path The set of all nodes on it is , and its total required power is:

[0098] , ;

[0099] is the summation function, represents the total load of the path , represents the th service request's power demand (i.e., load demand) for the path, represents the th path's maximum bearable capacity. 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, the total number of nodes (devices, switches, subsystems, etc.) on the path that need to have their power demands considered, is the maximum allowable current value that the node can bear, given by the node's 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 th node's voltage, represents the weakest link in the overall capacity limitation of the path.

[0100] 3. Dynamic adjustment of node connectivity mechanism

[0101] The system uses the graph reconstruction method to adjust the connection relationships of some nodes in the network, introducing the connection matrix , and the element represents that node and can be electrically connected:

[0102] ;

[0103] Among them, represents the updated connection state or path load after the structural adjustment, represents the original connection state or path load of the node, represents the newly added or alternative path construction relationship, and follows the principle of the shortest power delivery during the adjustment process: ;

[0104] Among them is the candidate reconstruction edge set, represents the th node's power demand (i.e., load demand), For the node The current available power 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, the optimal load distribution result of each node in the system after adjustment represents the th node or device should bear the optimal load after optimization , represents the number of nodes in the system.

[0105] 4. Control instruction synchronous update mechanism

[0106] 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.

[0107] This optimization mechanism realizes dynamic load adjustment based on power demand, and is especially 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, while maintaining the continuity of the power supply and distribution network state update model, the system avoids path overload, improves the load adaptability and operation stability of the distribution system, significantly reduces the risk of overload tripping, and enhances the flexibility of power allocation between local regions.

[0108] In a charging hub in a mountainous area intelligent highway service area, when a large number of tourist groups arrive suddenly, a large number of vehicles are concentratedly connected, resulting in overload of the west path. By collecting the real-time power demand of each charging node, the system judges that the original path is overloaded, automatically starts the reconstruction algorithm, shunts some high-load nodes to redundant paths, and completes the scheduling for the next 5 minutes in combination with the prediction model. The result effectively prevents tripping, while improving the average power supply efficiency by 20%, and the system runs smoothly without manual intervention.

[0109] The specific steps for generating the global operation parameters including the node storage capacity and the control instruction set further include obtaining the operation 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, it is judged as a node failure, and the failure node identifier is obtained.

[0110] This embodiment further strengthens the fault tolerance detection and node health status management capabilities of the system under the distributed control architecture.

[0111] 1. Monitoring the operation status of control nodes

[0112] Each control node periodically sends a heartbeat packet to the scheduling center (or neighboring nodes). Assume the current control node is , and the following metrics are used for its status monitoring:

[0113] Computing power metric : The maximum number of instructions processed per unit time; Communication delay : The round-trip time of the heartbeat packet ; Response identifier : Set to 1 for a normal response and 0 for no response.

[0114] 2. Fault detection cycle mechanism

[0115] Set the fault detection cycle to . If no response from the node is received within the cycle, a failure judgment is triggered:

[0116] ;

[0117] Node failure determination: is the current time point, indicates whether a response was successfully received at time . If , then the node fails.

[0118] 3. Failure node identification and broadcast

[0119] Once a node is determined to have failed, the system immediately generates a failure identifier:

[0120] ;

[0121] represents the failure identifier, represents the type of the failure event, i.e., different failure types are output, such as communication failure, hardware failure, etc., , represents the identifier of the node where the failure occurred, i.e., the output node , represents the timestamp when the failure occurred, i.e., the output time ;

[0122] 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.

[0123] 4. Dynamic control strategy adjustment

[0124] The system can reallocate tasks according to the computing power of the remaining normal nodes:

[0125] Task redistribution weight ;

[0126] represents the weight of the th active node, represents the ability index of the th active node, represents the sum of the ability indices of all active nodes. The original node tasks are allocated according to the weight to ensure the continuous operation of the system without interruption.

[0127] 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 power 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, and is especially suitable for the power distribution scheduling management of unattended and high-reliability scenarios.

[0128] In a highway multi-functional service area with high operating pressure in winter, due to extreme low temperature, some controllers failed due to frozen internal components. The system identified that two control nodes were unresponsive within 5 seconds and confirmed them as permanent faults through the failure determination mechanism. After the failure identification was broadcast in the system, its control tasks were immediately taken over by adjacent controllers. This move avoided the failure of power path switching exceeding 200kW, the system maintained continuous power supply to the whole station, and recorded the event in the background for later operation and maintenance troubleshooting.

[0129] The specific steps for generating the global operation parameters including the node storage capacity and 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 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 control instruction set according to the updated control network status.

[0130] After confirming the failure of the control node, in order to ensure the continuous operation of the system, while implementing path switching, the present invention designs a hot backup automatic takeover mechanism for control tasks, which involves the following key technical steps:

[0131] 1. Standby control node screening mechanism

[0132] The system maintains a list of redundant control nodes , represented as a set of backup ability indices, consisting of the backup capacities of different backup nodes , and the availability of each standby node is calculated by the following comprehensive score, :

[0133] ;

[0134] Among them, is the node computing power, is the maximum computing power in the current system; is the communication delay with the master control center; is a configurable weight parameter used to balance computing and response capabilities.

[0135] Select the largest node as the takeover node .

[0136] 2. Task Migration Scheduling and Time Calculation

[0137] Control the total migration delay of the task Consists of data synchronization time and migration processing time:

[0138] ;

[0139] Among them, 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 standby node to process and load the task.

[0140] If , it is considered that the migration is successful, represents the upper limit or critical value of the migration time.

[0141] 3. Control Network State Update and Parameter Reconfiguration

[0142] After the node migration, a new control network state diagram is formed , and the system generates an updated version of the global operating parameters accordingly: ;

[0143] Among them is the control instruction set re-planned according to the capabilities of the takeover nodes, is the updated global operating parameter, is the new control network state diagram formed after the node migration, is the update algorithm.

[0144] After the control node fails, the present invention significantly improves the anti-failure ability and control continuity of the system by automatically selecting the optimal takeover node and efficiently completing task migration. The scoring mechanism ensures that the selected standby nodes have good response speeds and computing capabilities. 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, enabling the power distribution system to possess high availability characteristics similar to "hot standby servers" and enhancing the overall robustness.

[0145] During the peak period, a sudden communication interruption occurred at a new energy charging base in 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 its availability score from 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 running state. The updated control network automatically recalculated the parameters, ensuring that all fast charging pile scheduling commands in the station area were sent normally without user-perceived interruption.

[0146] The control instruction set includes node start / stop commands, path switching instructions, energy transfer scheduling parameters, and communication coordination signals. In this implementation, by defining the composition of the control instruction set, comprehensive control over each key node and the operating state of 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. Each instruction is 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 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 to ensure the sequentiality and safety of the system when switching paths. Energy transfer scheduling parameters are used to achieve power distribution and scheduling between nodes. The system generates scheduling parameters based on the current available power 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 of the three as the final transmission power to ensure that 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 send timestamp 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 priority 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.

[0147] By clearly controlling the four major components of the instruction set, the present invention establishes a complete multi-dimensional control system, which supports the dynamic energy management of the power distribution system, the topological adaptive adjustment, and the highly reliable execution of control tasks. The start / stop of nodes and path switching enhance the flexibility of network reconfiguration, energy scheduling ensures the optimal allocation of resources, and the communication coordination mechanism guarantees the consistency and timing control accuracy among distributed nodes.

[0148] 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 the ring link structure. In the traditional broadcast mechanism, information diffuses from a central node to all child nodes simultaneously, which is prone to uneven transmission delays or synchronization failures due to bandwidth bottlenecks or node congestion. This solution uses a ring link, that is, all control nodes are interconnected in a closed link form to form a logically end-to-end data channel. During the state broadcast process, the master control node first sends a running parameter update data packet to its next node. After receiving the data packet, each node immediately forwards the data to its next adjacent node and performs data parsing and execution locally. Since each node only needs to process one copy of the data and forward it once along the loop, the number of data packets sent simultaneously during the broadcast is significantly reduced, thereby reducing the probability of link contention and concurrent conflicts. The core mechanisms of the multicast protocol include message sequence number identification, duplicate packet detection, lost packet retransmission, and acknowledgment feedback mechanism. Each data packet carries a unique sequence number. After receiving the data, the node will verify the data validity. If duplicate data is detected, it will be discarded; if missing or delay exceeds the limit is detected, the upstream node will be requested to retransmit. This ensures that even if some nodes experience delays or temporary interruptions, the data can be completed and synchronized through the redundant paths 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 failure occurs in one link direction, the system can quickly switch to the reverse path to continue the broadcast process, further enhancing the robustness and fault tolerance during the synchronization of operating parameters. Through the multicast mechanism of this ring link, the system realizes the instruction synchronization and operation state distribution from the master control node to all edge controllers. Its distribution time is linearly related to the link length, which is significantly better than the stability and delay control capabilities of star or tree broadcast topologies in large-scale node systems.

[0149] Adopting a multicast protocol based on a ring link effectively solves the problems of data packet congestion, large synchronization delay, and poor data consistency that occur in the traditional broadcast method when the number of nodes is large or the communication load is high. This mechanism has the advantages of stable transmission path, high synchronization efficiency, and strong fault tolerance, and is especially suitable for distributed power supply systems with frequent distribution of control instructions and state sharing. Through the data packet sequence identification and redundant channel mechanism, even if some nodes are briefly disconnected, the continuity and consistency of the overall system control link can be guaranteed.

[0150] 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 state update broadcasts was reduced 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 enhancing the stability and security of system operation.

[0151] The judgment of equipment failure also includes dynamically analyzing the trend of critical states by referring to the fluctuation frequency and duration in historical operation data. 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, extract the fluctuation data under the same or similar working conditions in the past several cycles, and conduct a 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 increasing, the system will mark the current state as "critical operation", and give an early warning of the failure. It can 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, thus improving the predictive maintenance level and operation robustness of the overall power supply system.

[0152] 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 the critical state. 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 area system. In addition, this mechanism can effectively avoid the judgment blind spots of "short-term out-of-bounds false alarms" and "non-out-of-bounds hidden faults", and has higher judgment accuracy and service continuity guarantee ability.

[0153] In a new energy vehicle centralized charging area of an airport, when a fast charging transformer is operating at high load in summer, its temperature approaches the warning upper limit many times but never exceeds it. Through trend analysis, the system finds that the temperature fluctuation frequency of this equipment increases, and the time continuously maintaining at the high value section lengthens, which is highly similar to the curve trend of another faulty equipment in history. The system marks it as a critical state before the temperature exceeds the limit, automatically executes path switching, smoothly migrates its load to other paths, effectively avoids the occurrence of equipment overheating and burning accidents, and notifies the maintenance personnel for inspection. This not only ensures the continuous power supply of the system but also significantly improves the operation and maintenance efficiency and safety level.

[0154] As Figure 2 shown, there is also provided an AC-DC distribution substation area operation optimization system for a vehicle charging station, which is used to implement the steps of the AC-DC distribution substation area operation optimization method for the vehicle charging station. The system includes:

[0155] A sensor network module, which is used to collect 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 determines that the equipment is faulty and generates a faulty equipment identifier and a fault type;

[0156] 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;

[0157] 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 analysis results of recovery speed and node connectivity, determine whether the switching response time exceeds the preset threshold; if it exceeds, it optimizes resource allocation by adjusting the path length to obtain an optimized power distribution network configuration;

[0158] A performance analysis module, which is used to extract the path load capacity and transmission efficiency in 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 in abnormal scenarios;

[0159] 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.

[0160] 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. This module has built-in fault judgment logic and can identify whether there are situations 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 conditions are 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 of each control node, available storage capacity, and scheduling instructions 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 status 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 determine 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 the path structure optimization operation 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.

[0161] Through the collaborative cooperation of five major modules, this system realizes a complete operation closed-loop from underlying data acquisition, intelligent analysis, status judgment, fault prediction to topology reconstruction and strategy optimization. Compared with traditional power distribution management methods, this system has extremely strong response real-time performance, optimization self-adaptability and fault tolerance ability, 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, meeting the needs of future expansion and function superposition.

[0162] 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. The power supply of the entire station area was not interrupted, ensuring the normal charging order of more than 400 new energy vehicles, fully verifying the practicality and intelligent level of this system.

[0163] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood 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. An operation optimization method for the AC / DC power distribution area of an electric vehicle charging station, characterized in that Including: Collect voltage, current, and temperature data of power distribution equipment 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 equipment has failed, and the failed equipment identifier and failure type are obtained; Generate global operating 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 operating state; By continuously monitoring the consistent system operating state, obtain path redundancy and handover response time data, analyze the recovery speed and node connectivity. If the handover response time exceeds the preset threshold, optimize 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 operating 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 path priority and the control instruction set in the control strategy database, generate a new set of backup paths and a redundant mechanism configuration for node computing capabilities, and obtain the iterated system state.

2. The operation optimization method of the AC / DC power distribution area of an electric vehicle charging station according to claim 1, wherein: The specific steps for generating global operating parameters including node storage capacity and control instruction sets are as follows: Based on a graph search algorithm, construct a weighted graph model including node connection relationships and path status parameters. Dynamically search for path nodes connected to the failed equipment according to the failed equipment identifier, use the shortest path search algorithm to obtain several feasible paths, calculate the path load capacity and transmission efficiency of each path, and combine the path length and the number of switching devices to determine the backup path with the highest path priority.

3. A method for optimizing the operation of the AC / DC distribution area of an electric vehicle charging station according to claim 2, characterized in that: The specific steps for generating global operating parameters including node storage capacity and control instruction sets also include: For the backup path with the highest path priority, generate a path switching instruction including the number of switching devices, and send the instruction to the relevant switching devices through the distributed controller to complete the path switching and obtain the new power distribution network operating state.

4. A method for optimizing the operation of the AC / DC distribution area of an electric vehicle charging station according to claim 3, characterized in that: The specific steps for generating global operating parameters including node storage capacity and control instruction sets also include: Based on the current power distribution network operating state, construct a capacity constraint graph model composed of nodes and paths, obtain the load distribution data of each node and analyze its power demand. If there is a situation where the path load exceeds the capacity limit, construct a linear optimization model for 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.

5. A method for optimizing the operation of the AC / DC power distribution area of an electric vehicle charging station according to claim 4, characterized in that: The specific steps for generating global operating parameters including node storage capacity and control instruction sets also include: Through the distributed control architecture, obtain the operating states of each control node, analyze the node computing capabilities and communication delays. If a certain node does not respond within the fault detection period, it is determined that the node has failed, and the failed node identifier is obtained.

6. The operation optimization method for the AC / DC power distribution area of an electric vehicle charging station according to claim 5, wherein: The specific steps for generating global operating parameters including node storage capacity and control instruction sets also include, Select the standby control node with the highest node availability from the redundancy mechanism according to the failed node identifier, combine the data synchronization frequency and the task migration time, complete the control task migration, obtain the updated control network state, and generate global operation parameters including node storage capacity and control instruction set according to the updated control network state.

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

8. A method for optimizing the operation of the AC / DC power distribution area of an electric 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 the stability of state synchronization.

9. A method for optimizing the operation of AC-DC distribution areas in an electric vehicle charging station according to claim 1, characterized in that: The determination of equipment failure also includes dynamically analyzing the trend of the critical state by referring to the fluctuation frequency and duration in the historical operation data.

10. An AC / DC power distribution station operation optimization system for an electric vehicle charging station, which is used to implement the steps of the AC / DC power distribution station operation optimization method for an electric vehicle charging station according to any one of claims 1-9, characterized in that, The system includes: A sensor network module for collecting voltage, current, and temperature data of power distribution equipment, and determining equipment failure, generating a failed equipment identifier and a failure type when the voltage exceeds a preset threshold, the current exceeds the path load capacity, or the temperature is higher than the safe range. A parameter generation module for generating global operation parameters including node storage capacity and control instruction set, and synchronizing them to all distributed controllers through the broadcast mechanism to obtain a consistent system operation state. A state monitoring module for real-time monitoring of the consistent system operation state, obtaining path redundancy and handover response time data, and judging whether the handover response time exceeds a preset threshold based on the recovery speed and the node connectivity analysis result; if it exceeds, optimize the resource allocation by adjusting the path length to obtain an optimized power distribution network configuration. A performance analysis module for extracting the path load capacity and transmission efficiency in high-load scenarios from the optimized power distribution network configuration, and generating an anti-interference ability evaluation report including node availability and communication delay to determine the stable operation ability in abnormal scenarios. An update module for dynamically updating the path priority and the control instruction set in the control strategy database based on the change of node weights in the reconstructed path graph according to the anti-interference ability evaluation report, generating a new set of standby paths and a redundancy mechanism configuration of node computing capabilities, and obtaining an iterative system state.

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