A method for active load balancing in distribution networks based on multi-charging station collaboration

Abstract

This invention relates to a method for active load balancing in distribution networks based on multi-charging station collaboration, belonging to the field of power and energy systems. This method aggregates multiple charging stations within the same regional distribution network into a backup energy storage system. By integrating a charging pile cluster power regulation unit, a dedicated distribution transformer monitoring module, and an active load balancing algorithm, it achieves coordinated and optimized scheduling between multiple charging stations and the distribution network. The system executes power regulation commands based on the peak and valley load conditions of the distribution network: during peak load periods, a power reduction strategy is triggered to reduce the operating power of eligible charging piles to alleviate peak load; during off-peak load periods, a power increase strategy is implemented to increase the charging power of charging piles to absorb surplus energy. Simultaneously, the adjustable margin of the charging pile cluster and transformer is evaluated from two dimensions to ensure safe equipment operation. This invention significantly improves the operational stability of the distribution network and the renewable energy absorption capacity, demonstrating good practicality and engineering application prospects.

Description

Technical Field

[0001] This invention belongs to the field of power and energy systems and relates to an active load balancing method for distribution networks based on multi-charging station collaboration. Background Technology

[0002] With the increasing prevalence of electric vehicles (EVs) and the development of vehicle-to-grid (V2G) technology, multiple charging stations connected to the same regional distribution network can coordinate and utilize on-site online bidirectional charging and discharging equipment and transformers to feed on-demand battery power back to the distribution network, thereby participating in grid regulation. This technology aims to improve the distribution network's capacity to absorb distributed energy, alleviate peak load pressure, and optimize the benefits for EV users. However, existing technologies mostly focus on single-station V2G control or simple aggregation strategies, failing to fully consider the complex constraints and global optimization objectives under multi-station coordination.

[0003] Therefore, there is an urgent need for a distribution network load balancing method that considers multi-station coordination to solve the above problems. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a distribution network load active balancing method based on multi-charging station collaboration, which realizes dynamic evaluation and cluster optimization of the adjustable margin of charging stations, increases the adaptability and reliability of load control strategies, and makes it easier for grid dispatchers to accurately adjust the regional distribution network load according to the characteristics of peak and valley periods, so as to improve the operating economy of charging stations, grid stability and energy utilization efficiency.

[0005] To achieve the above objectives, the present invention provides the following technical solution: Option 1: A method for active load balancing in distribution networks based on multi-charging station collaboration aggregates multiple charging stations within the same regional distribution network into a backup energy storage system. By integrating a charging pile cluster power regulation unit, a dedicated distribution transformer monitoring module, and an active load balancing algorithm, the system achieves coordinated and optimized scheduling between multiple charging stations and the distribution network. The system executes power regulation commands based on the peak and valley load conditions of the distribution network: during peak load periods, a power reduction strategy is triggered to reduce the operating power of eligible charging piles to alleviate peak load; during off-peak load periods, a power increase strategy is implemented to increase the charging power of charging piles to absorb surplus energy. Simultaneously, the adjustable margin of the charging pile cluster and the transformer is evaluated from two dimensions to ensure the safe operation of the equipment.

[0006] Furthermore, the method specifically includes: (1) Multi-source data acquisition and distribution network status prediction: Each charging station collects multi-dimensional operation data and global distribution network data in real time through the data acquisition unit to form a station volume dataset. ; Continuously predict distribution network state parameters within the station to form a distribution network prediction dataset. ;

[0007]

[0008] in, These are the maximum and minimum output currents of the bidirectional charging and discharging device, respectively. The bidirectional charging and discharging equipment is in online status. These are the vehicle battery's state of charge, health status, and temperature; For charging and discharging efficiency, The current load of the transformer. The number of vehicles connected; These are the actual load of the distribution network, the current forecast load, and the forecast load for the next time period. To predict the load deviation of the distribution network, the load prediction model continuously updates the distribution network status prediction results based on historical load data, weather and holiday factors.

[0009] (2) Dual-dimensional adjustable margin assessment: The adjustable margin of the charging station terminal in the station is assessed for the charging pile cluster and transformer; Adjustable margin assessment of charging pile clusters: For vehicles charging online with a SOC ≤ 80%, based on the current charging power. The adjustable margin for a single pile is calculated based on the difference between the adjustable upper and lower limits, where the adjustable margin for a single pile during peak periods is... :

[0010] Adjustable margin for single pile during off-peak periods :

[0011] The total adjustable margin of the charging pile cluster is obtained by summarizing. :

[0012] in, This is an adjustable margin for a single pile. These are the upper and lower limits of the adjustable power per pile, respectively. This is a collection of charging stations.

[0013] Transformer adjustable margin assessment: based on the transformer's current load Rated capacity Charge and discharge efficiency With safety threshold ( , ), calculate the adjusted expected load factor :

[0014] like or Introducing an adjustable coefficient 0.3, [0.9] After deducting the margin, the final adjustable margin of the transformer is This ensures that the transformer load rate is within a safe range after adjustment.

[0015] (3) Multi-charging station collaborative optimization scheduling: All charging stations under the same distribution network exchange adjustable capacity data through a distributed communication network, and aggregate the adjustable capacity of each charging station into the backup energy storage resources of the distribution network; based on the active balancing algorithm model of the charging load of the distribution line, the global optimal adjustment capacity is calculated. :

[0016] in, For the future Forecast load of the distribution network for a given time period, in kW; n is the forecast step size; This represents the average load of the distribution network on that day, in kW.

[0017] Cooperative optimization scheduling is implemented based on a distributed cooperative algorithm, with a comprehensive objective function as the core of optimization, while simultaneously satisfying multiple constraints. The comprehensive objective function... for:

[0018] in, The current actual load of the distribution network, in kW; The target load of the distribution network is expressed in kW. The total adjustable capacity of a multi-charging station cluster; The safe operating rate (0~1) refers to the percentage of operating time that meets constraints such as transformer load rate and SOC. , , These are weighting coefficients used to balance the importance of the three optimization objectives, satisfying... .

[0019] Constraint: Vehicle SOC ≤ Transformer load rate ∈ [ 30%, =80%]、Charging pile power adjustment range∈[rated power 30%, Rated Power [100%], adjust response delay ≤ 5 seconds; (4) Execution and dynamic correction of power adjustment commands: The optimal power adjustment command is sent to the execution unit of each charging station, and the power output is dynamically corrected at the millisecond level through the built-in PI controller. The PI controller output formula is:

[0020] in, The final power command issued to the charging pile at time t, in kW; The theoretical optimal power at time t is calculated by the optimization algorithm, in kW; For proportional adjustment items, This is the proportionality coefficient. For power deviation, is the integral coefficient.

[0021] Furthermore, the active load balancing algorithm model includes the following three models: The adjustable margin assessment model for charging pile clusters is used to accurately calculate the power adjustment potential of individual piles and clusters, and to screen charging equipment that meets the adjustment conditions. A load rate assessment model is used to determine the safe adjustment boundary of the transformer and to avoid overload or light load operation through an adjustable coefficient reduction mechanism. The load forecasting model integrates multiple influencing factors to improve forecast accuracy, providing forward-looking data support for peak-valley status identification and collaborative scheduling.

[0022] Furthermore, the method also includes a security protection mechanism, specifically a three-level response strategy: Warning level: When the transformer load rate approaches the critical value, i.e. ≥75% or ≤35%, the adjustable margin allocation ratio is reduced in advance; Intervention level: When a charging pile fault is detected or the SOC is greater than 80%, the faulty unit is immediately isolated or the adjustment authority of the corresponding vehicle is locked. Emergency Level: When the transformer load rate exceeds the standard, i.e., >80% or <30%, a power freeze command is triggered, switching to local autonomous mode. After the state is restored, it will reconnect to the collaborative system. All safety responses are achieved through the linkage between the PI controller and the execution unit.

[0023] Furthermore, the dynamic correction and safety protection mechanisms of the power adjustment commands work together to form a closed-loop protection throughout the entire process. During the execution of the power adjustment commands, the system continuously monitors three core parameters: transformer load rate, charging pile operating status, and vehicle SOC. When the transformer load rate exceeds (…), the system will respond accordingly. , When the safety range is exceeded, the charging pile malfunctions, or the vehicle's SOC exceeds the safety threshold of 80%, targeted safety protection actions will be automatically triggered: If the transformer load rate exceeds the standard, the power reduction mode will be immediately activated, and the output power will be quickly adjusted through a dynamic correction mechanism to ensure that the equipment operates within the safe load range; if the charging pile malfunctions, the faulty unit will be quickly isolated from the collaborative adjustment cluster to prevent the fault from spreading and affecting the overall adjustment effect; if multiple anomalies or communication interruptions occur in extreme cases, the system will switch to a local autonomous operation mode and autonomously maintain load balance based on local real-time data.

[0024] Option 2: A distribution network load active balancing system based on multi-charging station collaboration includes a multi-charging station collaborative control device, a distribution network dispatch center, and charging station terminals. The charging station terminals include a cluster of bidirectional charging and discharging piles and a dedicated distribution transformer. The system adopts a hierarchical collaborative architecture of "centralized coordination + local autonomy." Upper-level distribution network dispatch center: Establishes load regulation benchmarks, sets safety thresholds, decomposes global regulation targets, and distributes them to various collaborative control devices; Mid-level multi-charging station collaborative control device: performs local dual-dimensional margin assessment, participates in inter-charging station collaborative optimization, and autonomously completes the parsing and execution of power adjustment commands; The underlying charging station terminal acts as an energy interaction carrier, responding to power adjustment commands and providing feedback on equipment operating status and adjustment effects. Through a distributed communication network, it achieves multi-node adjustable capacity synchronization, load deviation feedback, and collaborative strategy iteration, forming a global load balancing closed loop.

[0025] Preferably, the multi-charging station collaborative control device includes the following five collaborative functional modules: Data acquisition unit: Adapted to IEC 61850 and OCPP protocols, it collects multi-source data from charging piles, transformers and power distribution networks, and supports access to heterogeneous devices from multiple brands; Status Analysis Module: Based on the load forecasting model and peak-valley identification algorithm, it outputs the distribution network status judgment results and the substation's two-dimensional adjustable margin assessment report; Decision-making unit: Runs distributed collaborative algorithm and distribution line load active balancing algorithm to generate global optimal regulation capacity and power regulation instructions for each charging station; Communication unit: Supports adjustable capacity data exchange between stations and transmission of upper-level scheduling instructions, and has dual redundant links to ensure communication continuity; Execution unit: Receives decision instructions and drives the power adjustment of the charging pile cluster, links with the PI controller to achieve dynamic correction, and executes safety protection actions.

[0026] Preferably, the multi-charging station collaborative control device has a built-in charging pile cluster power adjustment unit and PI controller. The charging pile cluster power adjustment unit adopts a modular topology structure, which is adapted to the dynamic power adjustment requirements of fast charging piles and slow charging piles, and supports adaptive switching of three modes: charging, power reduction and power increase. The PI controller dynamically adjusts the proportional coefficient and integral constant according to load fluctuations. When the load fluctuates drastically, the proportional coefficient is increased to improve the response speed, and the integral coefficient is reduced when the system is stable to avoid overshoot. It integrates transformer overload protection, charging pile fault isolation and vehicle SOC safety constraint functions to ensure the dual goals of adjustment accuracy and equipment safety.

[0027] It supports dynamically adjustable charging and discharging power and millisecond-level response, and has transformer overload protection, charging pile fault isolation and vehicle SOC safety constraint functions, and is compatible with heterogeneous equipment such as fast charging piles and slow charging piles.

[0028] Preferably, the system communication adopts a hybrid protocol of IEC 61850 and OCPP. The IEC 61850 protocol is responsible for real-time data transmission within the station, while the OCPP protocol is responsible for adjustable capacity interaction between stations and the issuance of upper-level scheduling instructions. The system has adjustable capacity dynamic synchronization, real-time load deviation feedback and local autonomy functions, and automatically switches to independent operation mode when communication is interrupted.

[0029] Option 3: A computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the active load balancing method of Option 1 for distribution networks.

[0030] The beneficial effects of this invention are as follows: This invention is applicable to power distribution network dispatching, electric vehicle charging station cluster management and control, and V2G interactive technology application scenarios. The method of this invention aggregates multiple charging stations under the same regional power distribution network into a power distribution network backup energy storage system. By integrating a charging pile cluster power regulation unit, a dedicated power distribution transformer monitoring module, and a load active balancing algorithm, it achieves precise and coordinated load regulation between multiple charging stations and the power distribution network. The system executes different strategies based on the peak and valley load conditions of the power distribution network: during peak load periods, a power reduction strategy is triggered to reduce the operating power of eligible charging piles to alleviate peak load; during off-peak load periods, a power increase strategy is implemented to increase the charging power of charging piles to absorb surplus energy. Simultaneously, the adjustable margin of the charging pile cluster and transformer is evaluated from two dimensions to ensure safe equipment operation. This invention accurately predicts the power grid load status and, combined with the IEC 61850 and OCPP hybrid communication protocol, achieves coordinated interaction of adjustable capacity within and between stations. Experimental results show that the present invention can reduce the average daily load peak-valley difference of the distribution network by 32%, increase the safe load rate operation time of transformers to 96%, and reduce the regulation response delay to less than 5 seconds, significantly improving the operation stability of the distribution network and the renewable energy absorption capacity, and has good practicality and engineering application prospects.

[0031] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0032] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a block diagram of the overall architecture of the S2G–V2G cooperative control method based on energy storage charging stations; Figure 2 This is a flowchart of the state prediction and scheduling method. Detailed Implementation

[0033] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0034] Example 1: Please see Figures 1-2 This embodiment provides a method for active load balancing in a distribution network based on multi-charging station collaboration, specifically including the following steps: Step 1: Charging station predicts power distribution network status and collects multi-source operational data: The charging station continuously predicts the power distribution network status, including grid frequency, peak fluctuations, etc., forming a daily power grid pattern database. Multi-dimensional data within the station is collected in real time through the data acquisition unit, including charging pile status (online / charging / idle / fault), charging current / power, vehicle battery SOC, transformer real-time load, and actual power distribution network load. ), forecast load ( These components, such as data acquisition units, form execution units to provide data support for status judgment and margin assessment. The charging station reads the data acquisition unit and, combined with the operating status of the equipment within the station and the real-time status parameters of the power distribution network, actively changes the operating parameters of the bidirectional charging and discharging equipment within the station to generate an adjustable margin assessment for a single station, providing a basis for subsequent collaborative optimization.

[0035] Step 2: Distribution Network Peak and Valley Status Identification and Strategy Triggering: Based on the collected load data and combined with preset time period thresholds, energy management determines whether the current distribution network is in a peak or valley period, triggers the decision execution unit to issue corresponding downward / upward charging strategies, and coordinates the electric vehicle charging power of each device node.

[0036] 1) Charging strategy at charging stations during peak hours (1) Adjustable margin assessment of charging pile clusters: The charging station continuously monitors the distribution network status. If the assessment result indicates that the distribution network load is at its peak, the peak-period charging strategy (reducing the distribution network load) is applied. During the peak-period of the distribution network load, the power consumption within the charging station is reduced to decrease the load on the distribution network. The method for assessing the equipment capacity within the station when reducing the distribution load is as follows: The charging pile status assessment process begins with determining the real-time working status of the charging pile: if the charging pile is in a non-working state such as online and idle, completed, or faulty, its adjustable margin is directly calculated as zero; if the charging pile is charging, the vehicle battery state of charge (SOC) is further detected. When the SOC is greater than 80%, the system will maintain the charging of the electric vehicle to ensure the user's basic needs, and its adjustable margin is not considered; when the SOC is less than or equal to 80%, it is determined whether the current charging current is higher than the set adjustable lower limit. If so, it can be adjusted by reducing the power, and the adjustable margin is the difference between the current power and the lower limit power. If not, it means that it is already in the minimum power operation state, and the adjustable margin is zero; finally, the adjustable margins of all individual charging piles are summed to obtain the overall adjustable margin of the charging pile cluster in the station, which provides a data basis for the subsequent assessment of the adjustable margin of the distribution transformer and the adjustable margin of the charging station. The adjustable capacity of all charging piles is aggregated and transmitted to the station's distribution transformer. The adjustable capacity of all bidirectional charging and discharging equipment within the station is aggregated, and the calculation formula is as follows:

[0037] (2) Assessment of the adjustable margin of transformers in the station's distribution network The process begins with determining the current load rate of the transformer: if the current load rate is higher than the set lower limit, the transformer has no room for adjustment, and its adjustable margin is directly determined to be zero; if the current load rate is lower than the lower limit, the system will perform a calculation using the formula "(current load of transformer - adjustable load of charging station) ÷ rated load of transformer = expected load rate after adjustment", and determine whether the expected load rate is still higher than the lower limit. If so, the complete adjustable margin of the charging station can be directly output; if the expected load rate is still lower than the lower limit, an adjustable coefficient between 0 and 1 needs to be introduced to reduce the adjustable margin of the charging station (i.e., adjustable margin × adjustable coefficient), and finally output the adjusted margin value to ensure that the transformer can still operate safely above the lower limit of the load rate after adjustment.

[0038] 2) Charging strategy for charging stations during off-peak hours (1) The charging station continuously monitors the status of the distribution network. If the judgment result is that the distribution network load is in a low period, the low period charging strategy (increasing the distribution network load) is called. During the low period of the distribution network load, the power consumption in the charging station is increased to improve the load of the distribution network.

[0039] The charging pile status assessment process begins with determining the real-time working status of the pile: if the charging pile is online and idle, completed, or faulty (non-working state), its adjustable margin is directly set to zero; if the charging pile is charging, the vehicle battery state of charge (SOC) is further checked. When the SOC is greater than 80%, the system will maintain the charging of the electric vehicle to ensure the user's basic needs, and its adjustable margin is not considered; when the SOC is less than or equal to 80%, it is determined whether the current charging current is lower than the set adjustable upper limit. If so, it can be adjusted by increasing the power, and the adjustable margin is the difference between the current power and the upper limit power. If not, it means that it is already operating at the highest power, and the adjustable margin is zero; finally, the adjustable margins of all individual charging piles are summed to obtain the overall adjustable margin of the charging pile cluster in the station, providing a data basis for subsequent assessments of the adjustable margins of the distribution transformer and the charging station. The adjustable capacity of all charging piles is aggregated and transmitted to the station's distribution transformer. The adjustable capacity of all bidirectional charging and discharging equipment within the station is aggregated, and the calculation formula is as follows:

[0040] (2) Assessment of the adjustable margin of transformers in the station's distribution network.

[0041] The process for assessing the adjustable margin of transformers in the substation's distribution network begins with determining the transformer's current load rate: if the current load rate is lower than the set upper limit, the transformer has no room for adjustment, and its adjustable margin is directly determined to be zero; if the current load rate is higher than the upper limit, the system will perform a calculation using the formula "(current load of transformer - adjustable load of charging station) ÷ rated load of transformer = expected load rate after adjustment," and determine whether the expected load rate is still lower than the upper limit—if so, the complete adjustable margin of the charging station can be directly output; if the expected load rate is still higher than the upper limit, an adjustable coefficient between 0 and 1 needs to be introduced to reduce the adjustable margin of the charging station (i.e., adjustable margin × adjustable coefficient), and finally output the adjusted margin value, thereby ensuring that the transformer can still operate safely below the upper limit of the load rate after adjustment.

[0042] Step 3: Execute the active balancing algorithm for charging loads on power distribution lines.

[0043] Multiple charging stations connected to the same distribution network are integrated into an adjustable capacity by balancing the charging power within each station, thereby improving the load condition of the distribution network. The available capacity of multiple charging stations on the distribution network is aggregated as a backup adjustment capacity to respond to grid demand and perform peak shaving and valley filling. The adjustable margins of each charging station are used to balance the line load of the distribution network. Appropriate balancing algorithms are applied to adjust the distribution network load during peak and off-peak periods.

[0044] use , This indicates the actual load on that day. Indicates the predicted load. This represents the average daily load, and C represents the regulating capacity used for dispatching. The method for charging stations to predict the distribution network status and dispatch capacity is as follows: (1) Make the first prediction and obtain the initial prediction value; (2) Obtain the actual operating load of the power grid ; (3) Calculate the actual operating load of the power grid Compared with the predicted load difference And obtain the predicted value at the next time node, the calculation formula is:

[0045]

[0046] (4) Implement the active balancing algorithm for charging loads of power distribution lines.

[0047] Active load balancing algorithm for distribution lines (upward adjustment): If the difference between the predicted peak load and the optimal load operation is greater than the adjustable margin of the charging station cluster, the distribution network load is adjusted using the adjustable capacity; otherwise, the adjustment is performed using the difference between the peak load and the target load value.

[0048] Active load balancing algorithm for distribution lines (downward adjustment): If the difference between the optimal load operating value and the predicted load trough value is greater than the adjustable margin of the charging station cluster, the distribution network load is adjusted using the adjustable capacity; otherwise, the adjustment is performed using the difference between the target load value and the peak load value.

[0049] The smaller of the difference between the predicted value and the average daily load, and the adjustable capacity, is used to regulate the distribution network; that is, the regulating capacity used is:

[0050] (5) Loop iteration and state update.

[0051] When a charging station finishes charging or the state of the distribution network changes, the current dispatch cycle ends, the system returns to step 1 to collect data again, and starts the next round of load forecasting and coordinated regulation to achieve continuous active load balancing of the distribution network.

[0052] Example 2: This embodiment provides a distribution network load active balancing system based on multi-charging station collaboration, including several multi-charging station collaborative control devices, a distribution network dispatch center, and charging station terminals (including bidirectional charging pile clusters and dedicated distribution transformers). The system adopts a hierarchical architecture design combining centralized coordination and local autonomy to achieve load optimization and power balance among multiple charging station nodes. This structure can maintain the overall system's regulation accuracy and safety stability under complex conditions such as distribution network load fluctuations, dynamic changes in electric vehicle access status, and equipment malfunctions. At the system architecture level, the distribution network dispatch center, as the global coordination unit, is located at the upper control layer of the system and is mainly responsible for setting load regulation benchmarks and issuing global dispatch instructions.

[0053] The multi-charging station collaborative control device integrates a charging pile cluster power regulation unit and a PI controller, supporting dynamically adjustable charging and discharging power with millisecond-level response. It also features transformer overload protection, charging pile fault isolation, and vehicle SOC safety constraints. As the core execution carrier for load regulation within the station, this device can accurately respond to upper-level dispatch commands, balancing power regulation accuracy with equipment operational safety, providing underlying hardware support for multi-station collaborative distribution network load balancing. The charging pile cluster power regulation unit adopts a modular topology, adapting to the power regulation needs of heterogeneous devices such as fast-charging piles and slow-charging piles, and supporting adaptive switching between three operating modes: charging, power reduction, and power increase. This unit collects real-time operating data (charging current, power, vehicle SOC) from each charging pile and automatically switches operating modes based on the station's adjustable margin assessment results: During peak load periods in the distribution network, it switches to a power reduction mode to reduce the operating power of charging piles with SOC ≤ 80% and current above the lower limit, reducing the load pressure on the grid; during off-peak periods, it switches to a power increase mode to increase the charging power of eligible charging piles and absorb surplus power from the grid; during periods of flat load, it maintains normal charging mode to ensure basic charging needs of users. Through flexible mode switching, dynamic adjustment of the station's load is achieved, assisting in peak shaving and valley filling and power rebalancing in the distribution network. The PI controller is used to achieve dynamic tracking and error compensation control of load regulation, and is a key component ensuring regulation accuracy. Its input signals include the target regulation capacity issued by the distribution network dispatch center, the real-time execution power of the charging pile cluster, transformer load rate, and distribution network voltage and frequency signals.

[0054] The PI controller uses a proportional-integral algorithm to accurately calculate the error signal between the target value and the actual value. It then generates control pulse signals via PWM modulation and sends them to the execution modules of each charging pile to ensure dynamic consistency between the charging pile's output power and the target adjustment value. Simultaneously, the PI controller has an adaptive parameter adjustment mechanism that dynamically corrects the proportional coefficient and integral constant based on the station's operating status: when load fluctuations are severe (such as concentrated vehicle access during morning and evening rush hours), the proportional coefficient is increased to improve response speed; when the system stabilizes, the integral coefficient is reduced to avoid power overshoot, effectively improving control stability and anti-interference capabilities. This device also features three core protection functions: transformer overload protection, charging pile fault isolation, and vehicle SOC safety constraints. The transformer overload protection module monitors the load rate of the dedicated distribution transformer in real time, employing a combination of threshold warning and forced power limiting. When the load rate approaches the 80% upper limit, it issues an early warning signal and gradually reduces the adjustable margin; when the load rate exceeds the upper limit, it immediately locks the power increase command to prevent transformer overload damage. The charging pile fault isolation module diagnoses the charging pile's operating status in real time. Once a fault signal is detected, it quickly removes the faulty charging pile from the regulation cluster to prevent the fault from spreading and affecting the overall regulation effect. At the same time, it reports the fault information to the collaborative control device. The vehicle SOC safety constraint module strictly uses 80% as the critical threshold. For charging vehicles with SOC > 80%, it locks the power regulation authority to maintain the current charging state and prevents overcharging and discharging from causing battery performance degradation. This comprehensively ensures the operational safety of the equipment in the station, vehicle batteries, and the power grid.

[0055] The distribution network dispatch center periodically receives real-time load data and historical operating curves from the regional distribution network. Taking into account factors such as daily weather and holidays, it sets time thresholds for peak load periods (7:00-10:00, 17:00-21:00) and off-peak periods (00:00-6:00, 11:00-16:00). It also defines core parameters such as the upper and lower limits of transformer load rate (lower limit 30%, upper limit 80%), the adjustable current range of charging piles (30%-100% of rated current), and the SOC critical value (80%). Simultaneously, the distribution network dispatch center decomposes the macro-load adjustment target into adjustable capacity ranges and adjustment priorities for each charging station, distributing these to each collaborative control device in the form of dispatch task packages. This ensures consistency in the overall load balance target while improving the efficiency of parallel multi-station adjustment. Each multi-charging-station collaborative control device, as a mid-level node in the system, undertakes the main functions of local load assessment and command execution. This device establishes a local adjustable margin assessment model by collecting real-time data on the operating status of charging piles (online / offline, charging / idle / faulty), charging current and power, vehicle battery SOC, and the real-time load and load rate of the distribution transformer. The collaborative control device, based on the adjustment targets and constraint parameters issued by the distribution network dispatch center and combined with local load forecasting results, performs a two-dimensional adjustable margin assessment of the charging station terminals (charging pile cluster and transformer). When the distribution network is in a low-load period, the control device prioritizes an upward adjustment strategy, increasing the charging power of eligible charging piles to absorb surplus power from the grid; when the distribution network is in a high-load period, it automatically switches to a downward adjustment strategy, reducing the operating power of charging piles to alleviate peak load on the grid. Through this mid-level distributed autonomous mechanism, each charging station can autonomously complete load adjustment tasks consistent with the upper-level dispatch targets while ensuring the safe operation of its equipment.

[0056] The charging station terminals (charging pile clusters and dedicated distribution transformers), as a key component at the system's bottom layer, are primarily responsible for executing specific power regulation actions and load-bearing functions. Based on instructions from the collaborative control device, the charging pile cluster dynamically adjusts the charging current for vehicles charging at a SOC ≤ 80%, adjusting the power output within the adjustable current range. For vehicles with a SOC > 80%, the current charging status is maintained, and no adjustment is made. The dedicated distribution transformer, as the core of load bearing, provides real-time feedback on its load rate data. When the expected load rate after adjustment exceeds the safety threshold, the adjustable margin of the charging station is reduced using an adjustable coefficient (0.3-0.9) to ensure the transformer always operates within the safe load range of 30%-80%. The bottom-layer equipment and the mid-layer collaborative control device achieve real-time data exchange via a high-speed communication link, forming a closed-loop regulation link of "evaluation-instruction-execution-feedback." During system operation, the collaborative control devices of each multi-charging station exchange information such as adjustable margin within the station, equipment operating status, and load regulation effects through a distributed communication network. The system as a whole adopts a collaborative algorithm based on dynamic load deviation correction, achieving global load balance constraints through distributed iteration. Specifically, each control device independently solves the local adjustable margin evaluation sub-problem, using the formula... Calculate the real-time load deviation and then update the predicted load for the next time period. Simultaneously, by sharing regulation margin data with neighboring stations, the globally optimal regulation capacity is determined. The system continues until the deviation between the actual load and the target load of the distribution network meets the requirements. This mechanism does not rely on a centralized computing server and has the advantages of low computational load, fast response speed, and strong anti-interference capability. At the scheduling strategy level, the distribution network dispatch center can dynamically adjust the weight distribution of global adjustment targets according to the real-time operating status of the system. When the peak-valley difference of the distribution network load is too large, the adjustment weight will be biased towards the load balancing effect, and the maximum adjustable margin of each charging station will be used first; when the transformer load rate of some charging stations is close to the critical value, the weight of equipment safety protection will be increased, and the adjustment capacity of the station will be reduced by the adjustable coefficient to avoid equipment overload; when a charging pile fault is detected, the abnormal unit will be automatically removed to ensure that the adjustment command is only issued to the normally operating equipment. This flexible weight adjustment mechanism enables the system to achieve smooth switching between different objectives such as load adjustment effect and equipment safe operation, meeting the various service needs of distribution network peak shaving and valley filling, equipment protection, etc. In addition, the distribution network load active balancing system based on multi-charging station collaboration of the present invention has high scalability. The system supports parallel operation of multiple charging stations and heterogeneous access of charging piles and transformers of different specifications. It can dynamically expand the topology of charging station nodes based on the regional distribution network load density and the number of electric vehicles. Through task decomposition and communication synchronization mechanisms for each control node, the system can achieve three modes: cross-station power assistance, regional load coordinated regulation, and independent operation of a single station. This significantly improves the energy utilization efficiency of the distribution network while ensuring stability. This architecture is particularly suitable for scenarios such as public charging station clusters in 10kV and below distribution networks, charging networks in urban core areas, and industrial park microgrids, providing a highly reliable and adaptable system support platform for precise load control of the distribution network after large-scale electric vehicle access.

[0057] The communication and control architecture of the distribution network load active balancing system based on multi-charging station collaboration adopts a hybrid communication protocol system of IEC61850 and OCPP. The core objective is to build a highly reliable and low-latency dedicated communication channel for the exchange of adjustable capacity data, transmission of load deviation information and issuance of collaborative adjustment commands among charging stations, so as to realize the full-link information closed loop of multi-station collaborative load adjustment and take into account the dual needs of real-time control within the station and remote collaboration between stations.

[0058] Inside the system, the IEC 61850 protocol, as the underlying high-speed communication standard within the station, is specifically responsible for the real-time transmission of adjustable capacity core data, device status monitoring, and the transmission of abnormal alarm signals. This protocol adopts an object-oriented information modeling approach. Based on the logical node (LN) structure, it defines standardized data for the core devices in the charging station (bidirectional charging and discharging charging piles, distribution special transformers, collaborative control devices), and clarifies the transmission formats of key parameters such as the adjustable margin of the charging pile, the load rate of the transformer, the vehicle SOC, the charging current / power, etc. Through the MMS (Manufacturing Message Specification) service mechanism, IEC 61850 supports millisecond-level data interaction within the station. When a certain charging pile completes the adjustable margin assessment or the load rate of the transformer fluctuates, the data can be instantaneously fed back to the local collaborative control device, saving time for inter-station data aggregation; at the same time, with the help of the GOOSE (Generic Object Oriented Substation Event) message mechanism of IEC 61850, the interlocking protection of in-station devices can be achieved. For example, when the load rate of the transformer exceeds the upper limit of 80%, an alarm signal is immediately triggered and the adjustable margin increase permission is locked to ensure the safety of the device and guarantee the strong real-time performance and high reliability of in-station communication. The OCPP (OpenCharge Point Protocol) protocol focuses on inter-station collaborative communication and upper-layer scheduling interaction, and is the core bridge for the interoperability of the adjustable capacities of each charging station. This protocol runs on top of the TCP / WebSocket channel, supports the JSON message format and the TLS / SSL encryption transmission mechanism, effectively avoiding the risk of the adjustable capacity data being tampered with or leaked during cross-station transmission. The core communication content of the OCPP protocol includes three categories: one is the real-time reporting of the adjustable capacities of each charging station, synchronizing the aggregated data of the remaining capacity of the charging pile cluster and the adjustable margin of the transformer evaluated within the station to the distribution network dispatching center and adjacent charging stations; the second is the issuance of collaborative adjustment instructions. The dispatching center pushes instructions such as the adjustable capacity allocation ratio and adjustment priority to each station through the OCPP protocol according to the global load demand; the third is the synchronization of operating status, including the device failure information of the charging station, the execution progress of the adjustment instructions, the load deviation correction results, etc., to achieve multi-station information sharing. This protocol supports the interconnection and compatibility of multi-brand charging piles, transformers, and control devices, providing a standardized interface for the unified communication of heterogeneous charging station clusters. In the hybrid communication architecture of the present invention, the IEC 61850 and OCPP protocols are seamlessly connected and data interoperable through the communication adaptation layer, solving the format difference problem between in-station real-time data and inter-station collaborative data.

[0059] This adaptation layer features bidirectional conversion capabilities, enabling semantic mapping between IEC 61850 logical node data and the OCPP data model. For example, data collected by the in-station collaborative control device via IEC 61850, such as "total adjustable capacity of the charging pile cluster is 450kW" and "adjustable capacity of the transformer is 410kW," must be converted into the OCPP standard message format by the adaptation layer before being uploaded to the distribution network dispatch center. Conversely, OCPP commands issued by the dispatch center, such as "execute an upward adjustment command with 410kW adjustable capacity," must be converted into the IEC 61850 control object format by the adaptation layer before being recognized and executed by the execution modules of the charging piles and transformers. This cross-protocol adaptation design significantly reduces the communication integration complexity of multi-station collaboration, ensuring that adjustable capacity data is transmitted without distortion or delay within and between stations. At the operational functionality level, this system, relying on this communication architecture, possesses three core capabilities: dynamic synchronization of adjustable capacity, real-time feedback of load deviation, and local autonomy during communication interruptions. The adjustable capacity dynamic synchronization function, based on time series analysis and combined with historical adjustable capacity data uploaded by each station and vehicle access patterns, predicts the adjustable capacity change trend within the next 15 minutes to 1 hour, providing data support for the dispatch center to formulate collaborative strategies in advance. The real-time feedback function forms a two-layer closed loop through the IEC 61850 in-station measurement and control channel and the OCPP inter-station reporting mechanism. When a deviation occurs between the actual adjusted capacity executed by a charging station and the allocated value, the system can identify the error within seconds and issue a correction command through the OCPP protocol to ensure the accuracy of global load adjustment. For example, if the adjustable capacity execution deviation of charging station 1 exceeds 5%, the correction command, after being converted by the communication adaptation layer, is instantly sent to the in-station execution module to adjust the power. In extreme cases of communication anomalies or dispatch signal interruptions, the system automatically switches to a local autonomous operation mode. The collaborative control devices at each charging station no longer rely on external signals from between stations and the dispatch center. Instead, they independently execute adjustable capacity assessment logic based on local real-time data collected according to the IEC 61850 protocol. Based on preset equipment safety thresholds (such as transformer load rate 30%-80%, SOC≤80%), they autonomously adjust charging and discharging strategies to maintain a temporary balance between the station's load and the distribution network. Once communication is restored, the system uses a status synchronization module to quickly synchronize data such as adjustable capacity changes and load adjustment effects during the autonomous period to all associated charging stations and the dispatch center, re-converging to a globally consistent operating state and avoiding load imbalances or equipment overloads caused by data disconnection. Through the collaborative design of the aforementioned inter-station communication mechanism, this invention achieves a deep integration of high-speed intra-station control using the IEC 61850 protocol and flexible inter-station collaboration using the OCPP protocol. This ensures both the real-time performance and security of adjustable capacity data interaction while also considering the flexibility and scalability of multi-station collaboration.This communication architecture is applicable to scenarios such as 10kV distribution network public charging station clusters, urban core area charging networks, and park microgrids. It provides stable and efficient communication support for the adjustable capacity collaborative scheduling of large-scale charging station clusters, and helps to accurately achieve the goal of active load balancing in the distribution network.

[0060] It can run on industrial controllers, embedded systems, or cloud-based EMS servers, supporting real-time data processing, distributed parallel computing, and multi-threaded communication mechanisms. Through the execution of this program, intelligent scheduling of energy storage systems and adaptive energy management of the power grid can be achieved, providing stable and efficient support for smart distribution networks. The program aims to realize bidirectional energy interaction between energy storage charging stations and the distribution network, as well as coordinated control between the station / network and vehicle / network systems. The system achieves dynamic and coordinated operation among charging station energy storage units, the power grid, and electric vehicles by constructing a unified energy management and optimization control platform. Its core objective is to achieve economical scheduling and optimal energy flow allocation of energy storage systems while ensuring stable grid operation and meeting user charging needs, thereby improving energy utilization efficiency and supporting the operation of smart distribution networks.

[0061] During program execution, the data acquisition module first collects multi-source operational data from each charging station in real time, including the charging pile's operating status (online / charging / idle / fault), charging current, real-time power, vehicle battery state of charge (SOC), real-time load and load rate of the distribution transformer, and actual and predicted load of the distribution network. This module supports multi-source asynchronous acquisition and data fusion, and is compatible with various communication protocols (such as IEC 61850, OCPP, Modbus, CAN, etc.), perfectly adapting to the needs of equipment monitoring within stations and inter-station collaborative communication, ensuring the accuracy and timeliness of system information transmission. Subsequently, the program calls the load balancing core model to estimate and predict the current system state. By integrating historical load data, vehicle access patterns, and influencing factors such as daily weather and holidays, the predictive model uses a forecasting model to make short-term predictions of the distribution network's peak and valley states and the changing trends of the charging station's adjustable capacity, providing accurate data support for subsequent load adjustment decisions and significantly improving the system's adaptability to complex operating conditions.

[0062] When the system detects that the load deviation of the distribution network exceeds the preset threshold, the optimization control algorithm module is automatically triggered. Based on the prediction results and real-time data, this module comprehensively considers the distribution network load target, the adjustable margin constraint of charging stations, the transformer load rate threshold (30%-80%), and the vehicle SOC safety threshold (80%), and executes the distribution network active balancing method.

[0063] (1) When the distribution network is in peak load period, each charging station is instructed to implement a power reduction strategy to reduce the operating power of eligible charging piles and reduce the load pressure on the power grid. (2) When the distribution network is in a period of low load, each charging station is instructed to implement a power-up strategy to increase the charging power of the charging piles and absorb the surplus power of the grid.

[0064] Meanwhile, the system can dynamically adjust control strategies based on real-time load changes in the distribution network and the operating status of charging station equipment, achieving full-link closed-loop management of "multi-station collaboration - distribution network balancing". It can be flexibly deployed in various operating environments, including industrial controllers, embedded systems (such as ARM architecture devices), or distribution network cloud dispatch servers. The system supports real-time data processing, distributed parallel computing, and multi-threaded communication mechanisms, enabling high-reliability and high-throughput load balancing management. Through modular design, the program can be extended to connect charging piles of different specifications, distribution transformers, and even distributed photovoltaic and micro-wind power units according to application scenarios, achieving broader regional integrated energy collaborative control.

[0065] This system enables intelligent scheduling of multiple charging station clusters and adaptive load balancing of the distribution network, significantly improving the regulation efficiency of the charging station clusters and the operational stability of the distribution network. While ensuring power supply security, the system deeply participates in peak shaving and valley filling, load response, and equipment safety protection of the distribution network, providing strong support for building a new power system and a smart distribution network. This solution lays a solid technical foundation for distribution network management and control, charging station cluster collaboration, and integrated development of power generation, grid, load, and storage after the large-scale integration of electric vehicles in the future.

[0066] Example 3: like Figure 1 As shown, this embodiment provides an S2G–V2G collaborative control system and method based on an energy storage charging station. The method regards the charging station as an independent energy node with energy storage capability. By integrating a battery energy storage system (BESS), a bidirectional converter (BDC), and a distributed scheduling algorithm, bidirectional energy flow (S2G) between the charging station and the power grid is realized.

[0067] Specifically, the collaborative control system consists of a data acquisition and access layer, an energy interaction and power control layer, an optimization scheduling and execution layer, and a feedback correction and service implementation layer, forming a closed-loop control system of "acquisition-prediction-optimization-execution-correction". After the charging station and electric vehicle group are connected to the distribution network, the data acquisition module acquires real-time operating data of the energy storage unit, grid nodes, and vehicle batteries, including voltage, current, SOC, load forecast, and electricity price signals. The system performs comprehensive analysis and modeling through an energy management model, which consists of an energy storage state estimation model, a power flow model, and a load forecast model. This model is used to dynamically calculate the SOC changes of the energy storage unit, determine the direction of energy flow, and predict the grid load trend. The energy interaction layer achieves efficient energy transmission and power quality assurance through V2G bidirectional converter and real-time power control mechanism. It can realize AC / DC bidirectional energy flow according to the distribution network status: when the grid load rises, the energy storage system and vehicles send back energy, while when the electricity price is low or the load is small, it absorbs energy for storage, thereby achieving peak shaving and valley filling and energy self-balancing.

[0068] When the system detects that the power fluctuation index exceeds the preset threshold, it automatically invokes the optimization control module. This module constructs a multi-objective scheduling function based on a distributed optimization algorithm and reinforcement learning mechanism, aiming to minimize power deviation, SOC fluctuation, and battery degradation costs. Under collaborative optimization constraints, the algorithm generates the optimal power allocation strategy for energy storage and the vehicle. The scheduling results are then sent in real-time by the communication module to the converter and vehicle-to-grid interface module for execution, achieving precise power allocation and scheduling command response. If voltage exceedances, frequency anomalies, or temperature anomalies are detected during operation, the system's built-in PI controller dynamically corrects the power output and automatically triggers power reduction or islanding mode to ensure safety. The operation status monitoring module continuously monitors the distribution network voltage, power flow, and safety constraint parameters, and dynamically adjusts and optimizes the strategy through system feedback and correction mechanisms. Ultimately, electric vehicles provide active power services in scenarios such as peak shaving, frequency regulation, and valley filling, offering flexible and dispatchable energy storage support to the power grid. The entire system can be deployed in industrial controllers, embedded terminals, or cloud EMS servers. It has distributed parallel computing, real-time communication, and intelligent scheduling functions, realizing bidirectional energy interaction between energy storage charging stations and distribution networks, as well as coordinated optimization control between stations / networks and vehicles / networks, providing key technical support for the safe, efficient, and sustainable operation of smart distribution networks.

[0069] like Figure 2The execution flow of the S2G–V2G collaborative control method based on energy storage charging stations provided in this embodiment is illustrated. The K-means clustering algorithm is mainly applied to the operational data analysis and energy management model state identification stage of energy storage charging stations to achieve classification optimization and feature extraction of multi-dimensional operational parameters. The system first selects multi-source operational data from energy storage units, grid nodes, and electric vehicles, including indicators such as power, voltage, current, SOC, temperature, and load changes, and maps them to a multi-dimensional mathematical feature space. The optimal number of clusters k is determined using the silhouette coefficient method or elbow method to balance clustering accuracy and computational complexity. Subsequently, the K-means algorithm is used to calculate the Euclidean distance between each sample point and the cluster center, automatically grouping data with similar characteristics into the same cluster, thus forming an energy state feature cluster. Each cluster represents a typical operating mode or energy flow characteristic, such as high-load charging state, light-load discharging state, or energy feedback mode, providing a classification basis for subsequent energy management models. After clustering, the system calculates the mean of feature parameters in each cluster and updates the cluster center, achieving adaptive partitioning of the energy feature space through iterative optimization. The dynamic movement of cluster centers reflects the trend of system state changes over time. When the cluster center shift exceeds a threshold, it indicates a significant change in the operating characteristics of the energy storage system or electric vehicle group, triggering parameter reconstruction of the energy management model or recalculation of the optimization scheduling module. Using the K-means algorithm, the system can quickly identify power fluctuation characteristics and load patterns under different operating modes, achieving data-driven self-learning and adaptive control. This state recognition mechanism based on cluster analysis not only improves the accuracy of energy scheduling but also effectively reduces the computational burden of complex systems, enabling energy storage charging stations to possess higher intelligence and stability in multi-condition, multi-node collaborative control.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for active load balancing in a distribution network based on multi-charging station collaboration, characterized in that, Multiple charging stations within the same regional power distribution network are aggregated into a power distribution network backup energy storage system. By integrating a charging pile cluster power regulation unit, a dedicated power distribution transformer monitoring module, and a load active balancing algorithm, collaborative optimization scheduling between multiple charging stations and the power distribution network is achieved. The system executes power regulation commands based on the peak and valley load conditions of the power distribution network: during peak load periods, a power reduction strategy is triggered to reduce the operating power of eligible charging piles to alleviate peak loads; during off-peak load periods, a power increase strategy is implemented to increase the charging power of charging piles to absorb surplus energy. At the same time, the adjustable margin of the charging pile cluster and the transformer is evaluated from two dimensions to ensure the safe operation of the equipment.

2. The active load balancing method for distribution networks according to claim 1, characterized in that, The method specifically includes: (1) Multi-source data acquisition and distribution network status prediction: Each charging station collects multi-dimensional operation data and global distribution network data in real time through the data acquisition unit to form a station volume dataset. ; Continuously predict distribution network state parameters within the station to form a distribution network prediction dataset. ; in, These are the maximum and minimum output currents of the bidirectional charging and discharging device, respectively. The bidirectional charging and discharging equipment is in online status. These are the vehicle battery's state of charge, health status, and temperature; For charging and discharging efficiency, The current load of the transformer. The number of vehicles connected; These are the actual load of the distribution network, the current forecast load, and the forecast load for the next time period. For distribution network load deviation; (2) Dual-dimensional adjustable margin assessment: The adjustable margin of the charging station terminal in the station is assessed for the charging pile cluster and transformer; Adjustable margin assessment of charging pile clusters: For vehicles charging online with a SOC ≤ 80%, based on the current charging power. The adjustable margin for a single pile is calculated based on the difference between the adjustable upper and lower limits, where the adjustable margin for a single pile during peak periods is... : Adjustable margin for single pile during off-peak periods : The total adjustable margin of the charging pile cluster is obtained by summarizing. : in, This is an adjustable margin for a single pile. These are the upper and lower limits of the adjustable power per pile, respectively. A collection of charging stations; Transformer adjustable margin assessment: based on the transformer's current load Rated capacity Charge and discharge efficiency With safety threshold ( , ), calculate the adjusted expected load factor : like or Introducing an adjustable coefficient After deducting the margin, the final adjustable margin of the transformer is: To ensure that the transformer load rate is within a safe range after adjustment; (3) Multi-charging station collaborative optimization scheduling: All charging stations under the same distribution network exchange adjustable capacity data through a distributed communication network, and aggregate the adjustable capacity of each charging station into the backup energy storage resources of the distribution network; based on the active balancing algorithm model of the charging load of the distribution line, the global optimal adjustment capacity is calculated. : in, For the future Forecast load of the distribution network for a given time period; n is the forecast step size; The average load of the distribution network on that day; Cooperative optimization scheduling is implemented based on a distributed cooperative algorithm, with a comprehensive objective function as the core of optimization, while simultaneously satisfying multiple constraints. The comprehensive objective function... for: in, This represents the current actual load of the power distribution network. For the target load of the distribution network; The total adjustable capacity of a multi-charging station cluster; To ensure safe operation rate; , , These are weighting coefficients used to balance the importance of the three optimization objectives, satisfying... ; Constraint: Vehicle SOC ≤ Transformer load rate ∈ [ 30%, = 80%]、Charging pile power adjustment range∈[rated power 30%, Rated Power [100%], adjust response delay ≤ 5 seconds; (4) Execution and dynamic correction of power adjustment commands: The optimal power adjustment command is sent to the execution unit of each charging station, and the power output is dynamically corrected at the millisecond level through the built-in PI controller. The PI controller output formula is: in, The final power command issued to the charging pile at time t; The theoretical optimal power is calculated by the optimization algorithm at time t. For proportional adjustment items, This is the proportionality coefficient. For power deviation, is the integral coefficient.

3. The active load balancing method for distribution networks according to claim 1, characterized in that, The active load balancing algorithm model includes the following three models: The adjustable margin assessment model for charging pile clusters is used to accurately calculate the power adjustment potential of individual piles and clusters, and to screen charging equipment that meets the adjustment conditions. A load rate assessment model is used to determine the safe adjustment boundary of the transformer and to avoid overload or light load operation through an adjustable coefficient reduction mechanism. The load forecasting model integrates multiple influencing factors to improve forecast accuracy, providing forward-looking data support for peak-valley status identification and collaborative scheduling.

4. The active load balancing method for distribution networks according to claim 1, characterized in that, This method also includes a security protection mechanism, specifically a three-level response strategy: Warning level: When the transformer load rate approaches the critical value, i.e. ≥75% or ≤35%, the adjustable margin allocation ratio is reduced in advance; Intervention level: When a charging pile fault is detected or the SOC is greater than 80%, the faulty unit is immediately isolated or the adjustment authority of the corresponding vehicle is locked. Emergency Level: When the transformer load rate exceeds the standard, i.e., >80% or <30%, a power freeze command is triggered, switching to local autonomous mode. After the state is restored, it will reconnect to the collaborative system. All safety responses are achieved through the linkage between the PI controller and the execution unit.

5. The active load balancing method for distribution networks according to claim 4, characterized in that, The dynamic correction and safety protection mechanisms of the power adjustment commands work together to form a closed-loop protection throughout the entire process. During the execution of the power adjustment commands, the system continuously monitors the transformer load rate, the charging pile operating status, and the vehicle's state of charge (SOC). When the transformer load rate exceeds (…), the system will take action. , When the safety range of the charging pile is exceeded, or when the vehicle's SOC exceeds the safety threshold of 80%, targeted safety protection actions will be automatically triggered: If the transformer load rate exceeds the standard, the power reduction mode will be immediately activated, and the output power will be quickly adjusted through a dynamic correction mechanism to ensure that the equipment operates within the safe load range; if the charging pile fails, the faulty unit will be quickly isolated from the collaborative adjustment cluster; if multiple anomalies or communication interruptions occur in extreme cases, the system will switch to a local autonomous operation mode and autonomously maintain load balance based on local real-time data.

6. A distribution network load active balancing system applicable to the method described in any one of claims 1 to 5, characterized in that, The system includes a multi-charging station collaborative control device, a power distribution network dispatch center, and charging station terminals. The charging station terminals include a cluster of bidirectional charging and discharging piles and a dedicated power distribution transformer. The system adopts a hierarchical collaborative architecture of "centralized coordination + local autonomy." Upper-level distribution network dispatch center: Establishes load regulation benchmarks, sets safety thresholds, decomposes global regulation targets, and distributes them to various collaborative control devices; Mid-level multi-charging station collaborative control device: performs local dual-dimensional margin assessment, participates in inter-charging station collaborative optimization, and autonomously completes the parsing and execution of power adjustment commands; The underlying charging station terminal acts as an energy interaction carrier, responding to power adjustment commands and providing feedback on equipment operating status and adjustment effects. Through a distributed communication network, it achieves multi-node adjustable capacity synchronization, load deviation feedback, and collaborative strategy iteration, forming a global load balancing closed loop.

7. The active load balancing system for distribution networks according to claim 6, characterized in that, The multi-charging station collaborative control device includes the following five collaborative functional modules: Data acquisition unit: Adapted to IEC 61850 and OCPP protocols, it collects multi-source data from charging piles, transformers and power distribution networks, and supports the access of heterogeneous devices; Status Analysis Module: Based on the load forecasting model and peak-valley identification algorithm, it outputs the distribution network status judgment results and the substation's two-dimensional adjustable margin assessment report; Decision-making unit: Runs distributed collaborative algorithm and distribution line load active balancing algorithm to generate global optimal regulation capacity and power regulation instructions for each charging station; Communication unit: Supports adjustable capacity data exchange between stations and transmission of upper-level scheduling instructions, and has dual redundant links to ensure communication continuity; Execution unit: Receives decision instructions and drives the power adjustment of the charging pile cluster, links with the PI controller to achieve dynamic correction, and executes safety protection actions.

8. The active load balancing system for distribution networks according to claim 6, characterized in that, The multi-charging station collaborative control device has a built-in charging pile cluster power adjustment unit and a PI controller. The charging pile cluster power adjustment unit adopts a modular topology structure, which is adapted to the dynamic power adjustment requirements of fast charging piles and slow charging piles, and supports adaptive switching of three modes: charging, power reduction and power increase. The PI controller dynamically adjusts the proportional coefficient and integral constant according to load fluctuations. When the load fluctuates drastically, the proportional coefficient is increased to improve the response speed, and the integral coefficient is reduced when the system is stable to avoid overshoot. It integrates transformer overload protection, charging pile fault isolation, and vehicle SOC safety constraint functions to ensure both adjustment accuracy and equipment safety.

9. The active load balancing system for distribution networks according to claim 6, characterized in that, The system uses a hybrid protocol of IEC 61850 and OCPP for communication. The IEC 61850 protocol is responsible for real-time data transmission within the station, while the OCPP protocol is responsible for adjustable capacity interaction between stations and the issuance of upper-level scheduling instructions. The system has adjustable capacity dynamic synchronization, real-time load deviation feedback and local autonomy functions. When communication is interrupted, it automatically switches to independent operation mode.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the active load balancing method for distribution networks as described in any one of claims 1 to 5.

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