Power control method in case of abnormality of charging pile system

Abstract

The application is particularly a power control method for abnormal charging pile system, relates to electric vehicle charging control technical field, and comprises the following steps: obtaining the adjustment capability parameters of each end, constructing a multi-objective weighted objective function containing a power impact term and a severity weighted device stress term, taking the severity value of each end as the penalty coefficient of the corresponding end power adjustment amount, solving the distribution scheme of the power adjustment amount of each end under the power balance constraint and the device constraint of each end, and generating a power scheduling instruction. In the application, the heterogeneous abnormal information of the vehicle end, the charging pile end and the power grid end is uniformly quantized to generate a three-dimensional severity vector, and the severity value of each end is taken as the penalty weight of the power adjustment amount and is included in the collaborative optimization objective function, thereby solving the technical problems of power violent fluctuation caused by independent response of each end, insufficient adjustment capability of abnormal end and waste of adjustment resources in the prior art.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging control technology, and in particular to a power control method for charging pile systems in case of malfunction. Background Technology

[0002] In electric vehicle charging scenarios, charging stations involve three parties: the vehicle, the charging pile, and the power grid. Abnormal events may occur at any of these three ends during operation. On the power grid side, anomalies such as voltage dips, frequency shifts, and excessive harmonics may occur; on the charging pile side, anomalies such as power module overheating, reduced insulation, and excessive communication latency may occur; and on the vehicle side, anomalies such as communication interruptions in the battery management system, abnormal battery voltage fluctuations, and battery overheating may occur.

[0003] In existing technologies, each end typically employs an independent protection strategy to deal with anomalies at its own end. For example, a charging pile may directly reduce its rating or shut down after detecting that the power module is overheating, while the grid side may directly disconnect the grid connection point after detecting an abnormal voltage.

[0004] This single-end independent response method has the following technical problems: when an anomaly occurs at one end, that end still needs to independently undertake all power regulation tasks even when its own state is already limited, resulting in an unreasonable power regulation scheme. On the one hand, it may cause drastic fluctuations in the overall power of the charging station, and on the other hand, it cannot utilize the available regulation resources of other ends to share the power regulation burden, resulting in waste of regulation resources and interruption of charging tasks.

[0005] Therefore, a power control method for charging pile systems in case of abnormalities is proposed to address the aforementioned problems. Summary of the Invention

[0006] The purpose of this invention is to provide a power control method for charging pile systems in case of abnormalities, in order to solve the above-mentioned problems.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A power control method for a charging pile system in case of an anomaly includes: Operational data from the vehicle, charging pile, and power grid are collected. The current values ​​of various operational parameters are compared with corresponding preset thresholds. When the current value of a certain operational parameter exceeds the corresponding threshold, an abnormal event message containing an abnormal source identifier and an abnormal type identifier is generated. Based on the abnormal event message, the abnormal sub-indicators of each end are mapped to dimensionless values ​​between zero and one through a piecewise linear normalization function, and then weighted and fused to obtain the severity values ​​of the power grid end, the charging pile end, and the vehicle end, forming a three-dimensional severity vector. The regulation capability parameters of each end are obtained, and a multi-objective weighted objective function containing a power impact term and a severity-weighted equipment stress term is constructed. The severity-weighted equipment stress term uses the severity value of each end as the penalty coefficient for the corresponding end's power adjustment amount. Under the power balance constraint and the equipment constraints of each end, the allocation scheme of the power adjustment amount of each end is solved to generate a power scheduling command. The power scheduling command is distributed to the vehicle, charging pile, and power grid ends, and execution feedback data of each end is collected. The execution feedback data is compared with the power reference value to obtain the power deviation value.

[0009] Furthermore, the abnormal event message also includes the duration of the abnormality, an abnormality classification identifier, and a recoverability classification identifier. The abnormality classification identifier includes transient, persistent, and intermittent types, where anomalies with a duration less than a preset transient threshold are marked as transient, anomalies with a duration greater than or equal to the preset transient threshold are marked as persistent, and periodically occurring anomalies are marked as intermittent. The recoverability classification identifier includes self-recoverable, intervention-required, and unrecoverable types, where anomalies that automatically resume normal operation after being eliminated are marked as self-recoverable, anomalies that require control measures to recover are marked as intervention-required, and anomalies that require downtime for maintenance are marked as unrecoverable.

[0010] Furthermore, the severity value at the grid end is obtained by weighted fusion of four sub-indicators: voltage sag amplitude, frequency deviation, three-phase unbalance, and harmonic distortion rate. The sum of the weighting coefficients for the four sub-indicators is one. Specifically, the normalized function for voltage sag amplitude is the ratio of the voltage sag amplitude to the normalized upper limit determined by the rated voltage and the voltage sag sensitivity coefficient, taking the smaller of this ratio and one. The normalized function for frequency deviation is the ratio of the absolute value of the frequency deviation to the normalized upper limit of the frequency deviation. The ratio of the limit value is taken as the smaller of the ratio and one; the normalization function of the three-phase imbalance is the ratio of the three-phase imbalance to the normalized upper limit of the three-phase imbalance, and the smaller of the ratio and one; the normalization function of the harmonic distortion rate has a harmonic distortion rate reference value. When the harmonic distortion rate does not exceed the reference value, it is zero. After exceeding the reference value, it is linearly mapped by dividing the difference between the harmonic distortion rate and the reference value by the difference between the normalized upper limit of the harmonic distortion rate and the reference value, and the smaller of the mapped value and one is taken.

[0011] Furthermore, the severity value of the charging pile is obtained by weighted fusion of three sub-indicators: power module temperature, insulation resistance value, and communication latency. The sum of the weight coefficients corresponding to the three sub-indicators is one. Specifically, the normalization function for power module temperature has a lower safe temperature limit and a higher dangerous temperature limit. The normalization function is zero when the power module temperature is below the lower safe temperature limit, linearly mapped proportionally when it is between the lower safe temperature limit and the higher dangerous temperature limit, and is one when it reaches or exceeds the higher dangerous temperature limit. The normalization function for insulation resistance value has a lower dangerous insulation resistance limit and a safe insulation resistance limit. The normalization function is one when the insulation resistance value is below the lower dangerous limit, linearly mapped proportionally when it is between the lower dangerous limit and the upper safe limit, and is zero when it reaches or exceeds the upper safe limit. The normalization function for communication latency has a lower safe communication latency limit and a higher dangerous communication latency limit. The normalization function is zero when the communication latency is below the lower safe limit, linearly mapped proportionally when it is between the lower safe limit and the upper dangerous limit, and is one when it reaches or exceeds the upper dangerous limit.

[0012] Furthermore, the vehicle-side severity value is obtained by weighted fusion of three sub-indicators: battery management system communication anomaly, battery voltage anomaly, and battery temperature anomaly. The sum of the weight coefficients corresponding to the three sub-indicators is one. The battery management system communication anomaly is the ratio of the number of lost communication frames to the total number of frames to be received within a preset statistical time window. The battery voltage anomaly is the absolute value of the difference between the current battery voltage and the battery rated voltage divided by the battery rated voltage. The battery temperature anomaly is the current battery temperature measurement value. The three sub-indicators are mapped to dimensionless values ​​between zero and one through their respective piecewise linear normalization functions before participating in the weighted fusion.

[0013] Furthermore, after obtaining the base severity values ​​for each endpoint, time-varying correction processing is performed on the base severity values ​​for each endpoint. The corrected severity value is the result of multiplying the base severity value by a duration correction factor and then by a rate of change correction factor. The duration correction factor is the product of a duration correction coefficient and an exponential decay value minus the exponentially decreasing value calculated by dividing the anomaly duration by the time constant, such that the longer the anomaly duration, the higher the severity value. The rate of change correction factor is the product of a rate of change correction coefficient and the time rate of change of the base severity value. The time rate of change of the base severity value is the difference between the base severity values ​​of the current scheduling period and the previous scheduling period divided by the scheduling period duration. The corrected severity values ​​are truncated to the range of zero to one and replace the base severity values ​​to form the final three-dimensional severity vector.

[0014] Furthermore, the multi-objective weighted objective function also includes a charging task completion deviation term, which is defined as the sum of the squares of the differences between the target state of charge and the current state of charge of each connected vehicle; the power surge term is defined as the square of the L2 norm of the difference between the power reference value vector of the current scheduling cycle and the power reference value vector of the previous scheduling cycle; the severity-weighted equipment stress term is defined as the sum of the product of the grid-side severity value and the absolute value of the grid-side power adjustment, the product of the charging pile-side severity value and the sum of the absolute values ​​of the power adjustments of each charging pile, and the product of the vehicle-side severity value and the sum of the absolute values ​​of the power adjustments of each vehicle; the power balance constraint requires that the active power after grid-side adjustment is equal to the sum of the power after adjustment of all charging piles and the sum of the power loss within the charging station. When there are V2G discharging vehicles, the power balance constraint also includes the discharge power of the V2G vehicles.

[0015] Furthermore, the constraints also include severity-weighted adjustment constraints, which limit the absolute value of the power adjustment at each end to no more than the adjustment allowance obtained by subtracting the product of the severity value of that end and the maximum power adjustment capability of that end, plus the elasticity margin parameter; when the severity value of a certain end is one, the power adjustment at that end is limited to within the elasticity margin parameter; when the severity value of a certain end is zero, the upper limit of the power adjustment at that end is the sum of the maximum power adjustment capability and the elasticity margin parameter; the elasticity margin parameter takes a preset value greater than or equal to zero.

[0016] Furthermore, a hierarchical solution strategy is adopted when solving the power adjustment allocation scheme, including a scenario classification and strategy selection level, a simplified optimization solution level, and a real-time fine-tuning level. In the scenario classification and strategy selection level, the end with the largest severity value among the three ends is determined as the dominant anomaly source based on the three-dimensional severity vector, and the corresponding priority strategy is selected based on the comparison result of this severity value with the preset high severity threshold and medium severity threshold. In the simplified optimization solution level, the feasible region is reduced step by step in the order of safety hard constraints, priority strategy constraints, and charging task completion optimization for sequential decision-making. In the real-time fine-tuning level, the power scheduling scheme is iteratively corrected based on execution feedback data. The power scheduling instructions are divided into three levels according to their urgency: emergency instructions, fast instructions, and optimization instructions. When the communication link between the collaborative controller and a certain end is interrupted, that end operates according to the power reference value in the most recently received valid instruction, and other ends take over the adjustment task originally assigned to that end.

[0017] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention unifies the quantification of heterogeneous anomaly information from the vehicle end, charging pile end, and power grid end to generate a three-dimensional severity vector, and incorporates the severity values ​​of each end as penalty weights for power adjustment into the collaborative optimization objective function. This solves the technical problems in the prior art where independent responses from each end lead to drastic power fluctuations, insufficient adjustment capabilities of the abnormal ends, and waste of adjustment resources.

[0018] 2. This invention collects real-time operational data from vehicles, charging piles, and the power grid. When multiple anomalies are triggered, it generates event messages carrying anomaly source and type identifiers. A segmented, normalized, and fused assessment is used to construct a three-dimensional severity vector, enabling precise quantification and dynamic correction of multi-terminal anomalies. Based on this, a multi-objective optimization function is constructed with power impact minimization and severity-weighted equipment stress control as its core. Under the constraints of power balance and the adjustment capabilities of each terminal, power adjustment is collaboratively allocated, forming a flexible scheduling scheme that considers grid stability, charging infrastructure health, and vehicle charging demand. Closed-loop feedback and hierarchical solution strategies ensure the real-time performance and reliability of scheduling commands, maintaining orderly system operation even during communication interruptions or abnormal deterioration, effectively improving the safety margin and power adjustment flexibility of the charging network in the face of multi-source disturbances. Attached Figure Description

[0019] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0020] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0021] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0022] Example 1 Its specific implementation method is combined with the appendix Figure 1 Please provide a detailed explanation.

[0023] In this embodiment, it includes: Step 1: Collect multi-source operational data from the vehicle, charging pile, and power grid ends, identify abnormal events at each end, and generate abnormal event messages. The collaborative controller collects real-time operational data from three terminals through various communication interfaces. Vehicle-side operational data includes battery voltage, battery current, state of charge (SOC), battery management system communication status, battery temperature, and requested charging power. Charging pile-side operational data includes output voltage, output current, power module temperature, insulation resistance, and communication delay. Grid-side operational data includes three-phase voltage amplitude, grid frequency, harmonic content, and grid connection point switch status.

[0024] Based on the collected operational data from various terminals, anomalies are determined for each operational parameter. The current value of each operational parameter is compared with its corresponding preset threshold. When the current value of an operational parameter exceeds the corresponding threshold, an anomaly event message is generated for that operational parameter. The anomaly event message includes an anomaly source identifier, an anomaly type identifier, the current parameter value, the corresponding threshold, and the anomaly duration. The anomaly source identifier is marked according to the terminal to which the data belongs: the grid terminal is marked as a Class G anomaly, the charging pile terminal as a Class P anomaly, and the vehicle terminal as a Class V anomaly.

[0025] It should be noted that the threshold comparisons in the above anomaly detection methods have different meanings. In the processing of grid-side operational data, voltage anomaly identification refers to calculating the effective value (RMS) of the collected three-phase voltage data within a sliding time window. When the RMS value is lower than a preset percentage of the rated voltage (e.g., 0.9 times the rated voltage) and the duration exceeds a preset duration (e.g., 10ms), it is determined to be a voltage sag anomaly. Frequency anomaly identification refers to comparing the absolute value of the difference between the grid frequency and the rated frequency with a preset frequency deviation threshold. If the value exceeds the threshold, it is determined to be a frequency deviation anomaly. In the processing of charging pile-side operational data, power module overheating identification refers to comparing the current temperature of the power module with a temperature threshold, or comparing the rate of temperature change with a rate of change threshold. If either condition is met, it is determined to be an overheating anomaly. In the processing of vehicle-side operational data, battery management system communication interruption identification refers to performing timeout detection on the battery management system communication heartbeat signal. When the time without a response frame exceeds a preset timeout duration, it is determined to be a communication interruption anomaly.

[0026] It should be noted that the above-mentioned abnormal event messages also include abnormality nature classification identifiers based on the nature of the abnormality and recoverability classification identifiers based on recoverability. Abnormality nature classification identifiers include three types: transient, persistent, and intermittent. Abnormalities with a duration less than a preset transient threshold are marked as transient; abnormalities with a duration greater than or equal to the preset transient threshold are marked as persistent; and periodically occurring abnormalities are marked as intermittent. Recoverability classification identifiers include three types: self-recoverable, requiring intervention, and unrecoverable. Abnormalities that can automatically resume normal operation after the abnormality is eliminated are marked as self-recoverable; abnormalities requiring control measures to recover are marked as requiring intervention; and abnormalities requiring downtime for maintenance are marked as unrecoverable.

[0027] Step 2: Based on the abnormal event messages, calculate the abnormal severity value for each endpoint and generate a three-dimensional severity vector. Based on the abnormal event messages output in step 1, the abnormal information at each end is quantified, and the severity values ​​at the grid end, charging pile end, and vehicle end are calculated separately to form a three-dimensional severity vector. Before calculating the severity values ​​at each end, the raw operating parameters collected at each end are first preprocessed: the raw physical quantities of each sub-indicator (including voltage drop amplitude, frequency deviation, three-phase imbalance, harmonic distortion rate, power module temperature, insulation resistance value, communication delay, battery management system communication anomaly degree, battery voltage anomaly degree, and battery temperature anomaly degree) have different dimensions and numerical ranges, and cannot be directly weighted and fused. Therefore, each raw physical quantity is mapped to a dimensionless value between 0 and 1 by the piecewise linear normalization function corresponding to each sub-indicator before weighted and fused calculation.

[0028] The three-dimensional severity vector is represented as: ; in, for The overall severity value of the power grid at any given time. for The overall severity value of the charging station at any given time. for The overall severity value of the vehicle at any given time. The value indicates transpose. All three severity values ​​are dimensionless values ​​between 0 and 1, where 0 indicates no abnormality and 1 indicates the most severe abnormality.

[0029] Overall Severity Value of Power Grid The calculation formula is as follows: This is obtained by weighted fusion of multiple anomaly sub-indicators at the power grid end. ; in, This refers to the voltage drop. This is the frequency deviation value. For three-phase imbalance, Harmonic distortion rate, , , , The weight coefficients for each sub-indicator and satisfying the following conditions: . , , , These are the normalization functions corresponding to each sub-index, which map the original physical quantities of each sub-index to dimensionless values ​​between 0 and 1, specifically using a piecewise linear normalization method.

[0030] The voltage sag normalization function is calculated using the following formula: ; in, Rated voltage, This is the voltage drop sensitivity coefficient. The normalized upper limit of the voltage sag is the value at which the voltage sag is measured. When the normalization upper limit is reached or exceeded, If the value is 1, otherwise it is linearly mapped proportionally to the range of 0 to 1.

[0031] Furthermore, The physical meaning is: The larger the value, the higher the normalization upper limit. The smaller the value, that is, under the same voltage drop, The higher the severity value mapped, the more sensitive the device is to voltage dips. The smaller the value, the lower the sensitivity to voltage drops. The value of should satisfy ,and Not exceeding the rated voltage ,Right now This is to ensure that the normalization upper limit is physically reasonable.

[0032] The frequency deviation normalization function is calculated using the following formula: ; in, This is the upper limit of the normalized frequency deviation, when the absolute value of the frequency deviation... Reaching or exceeding the normalized upper limit of frequency deviation hour, If the value is 1, otherwise it is linearly mapped proportionally to the range of 0 to 1.

[0033] The normalized function for three-phase unbalance is calculated using the following formula: ; in, This is the normalized upper limit of the three-phase imbalance, when the three-phase imbalance... The normalized upper limit of the three-phase imbalance has been reached or exceeded. hour, If the value is 1, otherwise it is linearly mapped proportionally to the range of 0 to 1.

[0034] The normalized function for harmonic distortion rate is calculated using the following formula: ; in, This is the reference value for harmonic distortion rate. This is the normalized upper limit of the harmonic distortion rate, when Not exceeding the harmonic distortion rate benchmark value hour Take 0, when Exceeding the harmonic distortion rate benchmark value Then, a proportional linear mapping is performed to reach or exceed the normalized upper limit of the harmonic distortion rate. Take 1 at the time.

[0035] Furthermore, the harmonic distortion rate benchmark value The setting reflects the harmonic distortion rate below the harmonic distortion rate reference value. The physical meaning of a time when it does not have a significant impact on the system and does not need to be included in the severity calculation is... exist The value is always 0 within the interval; the normalized upper limit of the harmonic distortion rate. Should meet To ensure piecewise linear mapping intervals The length of the fraction should be greater than zero to avoid a denominator of zero.

[0036] Comprehensive severity value of charging pile The calculation formula is obtained by weighted fusion of multiple abnormal sub-indicators at the charging pile end: ; in, For the power module temperature, This is the insulation resistance value. For communication delay, , , The weight coefficients for each sub-indicator and satisfying the following conditions: . , , These are the normalization functions corresponding to each sub-index. They all adopt a piecewise linear mapping method to map each original physical quantity to a dimensionless value between 0 and 1 before participating in the weighted fusion calculation.

[0037] Temperature hazard normalization function The calculation formula is: ; in, This is the lower limit of temperature safety. This is the upper limit of the temperature danger zone, when the power module temperature... Below the temperature safety limit The danger level is 0 when the power module temperature is [not specified]. At the lower limit of temperature safety With upper limit of temperature danger A linear mapping is applied proportionally between the two values, depending on the power module temperature. Reaching or exceeding the upper limit of temperature danger The hazard level is 1. Upper limit of temperature hazard. Should meet This ensures that the length of the piecewise linear mapping interval is greater than zero, thus avoiding the case where the denominator is zero.

[0038] Insulation hazard normalization function The calculation formula is: ; in, This is the lower limit of the dangerous insulation resistance value. This refers to the upper limit of the safe insulation resistance value. The lower the value, the more dangerous the insulation condition. Below the dangerous lower limit of insulation resistance The danger level is 1 when the insulation resistance value is... At the dangerous lower limit of insulation resistance With the upper limit of insulation resistance The distance between them is linearly mapped proportionally, when the insulation resistance value Reaching or exceeding the upper limit of the safe insulation resistance value The risk level is 0. The upper limit of safe insulation resistance. Should meet This ensures that the length of the piecewise linear mapping interval is greater than zero, thus avoiding the case where the denominator is zero.

[0039] Communication risk normalization function The calculation formula is: ; in, This is the lower limit for communication delay safety. This is the upper limit of the communication delay risk; when the communication delay... Below the communication latency security limit The risk level is 0 when communication delay occurs. At the lower limit of communication latency With the upper limit of communication delay risk A linear mapping is applied proportionally between them, and when communication delay occurs... Reaching or exceeding the upper limit of communication delay risk The risk level is 1. Maximum risk level for communication delay. Should meet This ensures that the length of the piecewise linear mapping interval is greater than zero, thus avoiding the case where the denominator is zero.

[0040] Vehicle-side comprehensive severity value The calculation formula is as follows: This is obtained by weighted fusion of multiple abnormal sub-indicators from the vehicle side. ; in, For the communication anomaly parameter of the battery management system, This is a parameter for battery voltage anomaly. This refers to the battery temperature anomaly parameter. , , The weight coefficients for each sub-indicator and satisfying the following conditions: . , , These are the normalization functions corresponding to each sub-indicator. All of them employ the same piecewise linear mapping method as the normalization function at the charging pile end, mapping each original physical quantity to a dimensionless value between 0 and 1 before participating in the weighted fusion calculation. That is, each sub-indicator has a risk level of 0 when below the lower safety limit, a proportional linear mapping between the lower and upper safety limits, and a risk level of 1 when above the upper safety limit. , , These parameters correspond to the respective lower safety limit and upper danger limit for battery management system communication anomaly, battery voltage anomaly, and battery temperature anomaly.

[0041] Furthermore, the aforementioned battery management system communication anomaly parameters The method for obtaining the data is as follows: within a preset statistical time window, the ratio of the number of lost packets in the battery management system communication frames to the total number of frames to be received is calculated, and this ratio is used as the communication anomaly parameter of the battery management system. Current value; Battery voltage anomaly parameter The method for obtaining this value is as follows: divide the absolute value of the difference between the current battery voltage and the battery's rated voltage by the battery's rated voltage, and use this ratio as the battery voltage anomaly parameter. Current value; Battery temperature anomaly parameter The currently collected battery temperature measurement value is directly retrieved. All three parameters mentioned above are acquired in real time through the communication interface described in step 1.

[0042] In this embodiment, to ensure that the three-dimensional severity vector reflects the risk level of anomalies accumulating over time and the trend of anomaly deterioration, a time-varying correction is applied to the base severity values ​​for each end after calculation. Taking any end as an example, the corrected severity value... The calculation formula is: ; in, For this end in The baseline severity value at any given time. The duration of the anomaly. The time constant is a correction for the duration. This is a duration correction factor. This is the correction factor for the rate of change. The rate of change over time of the baseline severity value. Represents an exponential function. Duration correction factor. The longer the duration of the anomaly, the higher its severity; rate of change correction factor. The faster the severity increases, the higher the corrected severity value. The corrected severity values ​​at each endpoint replace the base severity value to form the final three-dimensional severity vector.

[0043] Furthermore, The calculation method is as follows: take the difference between the basic severity value of the current scheduling cycle and the previous scheduling cycle and divide it by the scheduling cycle duration. Use this difference quotient as the discrete approximation of the time change rate of the basic severity value, where the scheduling cycle duration is the time interval corresponding to one complete cycle of steps 1 to 4 executed by the collaborative controller.

[0044] Furthermore, to ensure the corrected severity value Physically reasonable, duration correction factor With the rate of change correction factor The value of should satisfy the following constraints: , , and when When, the rate of change correction factor That is, the correction factor is not less than 1, ensuring that time-varying corrections will only maintain or increase the severity value and will not decrease it; when When the abnormality is mitigating, to prevent the correction factor from becoming negative, the rate of change correction factor should be adjusted. A lower limit constraint is applied to ensure that the rate of change correction factor is not lower than a preset minimum positive value. Additionally, the corrected severity value... The final value should be truncated to Within the interval, to maintain consistency with the definition of the three-dimensional severity vector.

[0045] Step 3: Based on the three-dimensional severity vector and the adjustment capability parameters of each end, solve the collaborative optimization problem to generate power scheduling instructions for the vehicle end, charging pile end, and grid end. Based on the three-dimensional severity vector output in step 2 and the regulation capability parameters determined by the current operating status data of each terminal, with the main objective of minimizing power impact, the optimal allocation scheme of power regulation among the three terminals is solved, and a power scheduling instruction containing power reference values ​​of each terminal is generated.

[0046] First, obtain the regulation capacity parameters at each end. The grid-side regulation capacity parameters include the current active power. Adjustable range of active power Adjustable reactive power range and the upper limit of the rate of change of power The charging station's adjustable capability parameters include the current output power of each charging station. Rated power of each charging station and based on power module temperature Determined thermal limit power The maximum total power of the charging station is Among them, thermally limited power Determined according to a piecewise linear derating method: when the power module temperature... Below the temperature safety limit At that time, thermally limited power Equal to rated power When the power module temperature At the lower limit of temperature safety With upper limit of temperature danger Between, thermally limited power according to Linear derating; as the power module temperature... Reaching or exceeding the upper limit of temperature danger At that time, thermally limited power A value of 0 indicates that the charging station has stopped outputting power. The vehicle-side adjustment capability parameters include the current charging power of each vehicle. Based on state of charge and battery temperature Determined power adjustable range and the upper limit of the power change rate allowed by the battery management system. For vehicles supporting V2G functionality, the lower limit of the adjustable power range can be negative, representing the discharge capacity. The upper limit of the adjustable power range... Determined in the following manner: when the state of charge... Charging below the preset state of charge limit And battery temperature Below the dangerous upper limit of battery temperature At that time, the upper limit of the adjustable power range Take the maximum allowable charging power reported by the battery management system; when the state of charge... Reaching the maximum state of charge limit or battery temperature Reaching the upper limit of battery temperature danger At that time, the upper limit of the adjustable power range Setting it to 0 stops charging. Lower limit of adjustable power range. For vehicles that support V2G functionality, when the state of charge... The V2G discharge state limit is higher than the preset limit. At that time, the lower limit of the adjustable power range Take the negative value of the maximum allowable discharge power reported by the battery management system; otherwise, take the lower limit of the adjustable power range. Take 0.

[0047] Then, based on the three-dimensional severity vector and the adjustment capability parameters of each endpoint, the collaborative optimization problem is solved using a multi-objective weighted optimization approach. The decision variable for optimization is the power adjustment amount of each endpoint, expressed as: ; in, This refers to the active power adjustment at the grid end. This refers to the reactive power adjustment at the grid end. For the first Power adjustment amount for each charging station This represents the total number of charging stations. For the first The amount of adjustment in the vehicle's charging power. The total number of vehicles connected. This indicates transpose.

[0048] The objective function to be optimized is: ; in, , , These are the weighting coefficients for each sub-objective. The power impulse term is defined as follows: ,in This is the power reference value vector for the current calculation cycle. This is the power reference value vector from the previous calculation cycle. and These represent the time indices of two adjacent scheduling cycles, with the difference between them corresponding to the duration of one scheduling cycle, and the power surge term. Power abrupt changes are suppressed by minimizing the change in the power reference value vector between adjacent scheduling cycles. The deviation term for charging task completion is defined as follows: ,in The total number of vehicles connected. For the first The vehicle's target state of charge. For the first The vehicle's current state of charge and the deviation in charging task completion. This is used to ensure the completion of the charging task. It should be noted that the target state of charge... With current state of charge Both are dimensionless percentage values, and the square of the difference between them has the same dimension, so they can be directly summed. The severity-weighted equipment stress term is defined as follows: ,in , , All values ​​are dimensionless severity values ​​between 0 and 1, output from step 2. This represents the total number of charging stations. The total number of vehicles connected. , , All values ​​are absolute values ​​of the power adjustment, with consistent dimensions. The severity value is used as a dimensionless weighting coefficient to weight the power adjustment at each end. The severity-weighted equipment stress term... By using the severity values ​​of each endpoint as weighting coefficients for the corresponding power adjustment amounts, the endpoint with higher anomaly severity receives a smaller power adjustment amount. It should be noted that the power surge term... Deviance in charging task completion Severity-weighted equipment stress item Since the three sub-objectives have different dimensions, each sub-objective needs to be normalized before weighted summation. Specifically, the mean normalization method based on the range is used to map the values ​​of each sub-objective to the same dimensionless range, and then the values ​​are multiplied by the corresponding weight coefficients and summed.

[0049] The optimization constraints include power balance constraints, constraints on equipment at each end, and grid connection standard constraints. The power balance constraints are: ; in, This represents the total number of charging stations. This represents the power loss within the charging station. When a vehicle supporting V2G is discharging, the power balance constraint is corrected as follows: ; in This is a collection of V2G vehicles in discharge mode. For the first Discharge power of V2G vehicles ( A negative value indicates discharge.

[0050] The constraints for each terminal device are: the adjusted power of each charging pile must meet the following requirements. The adjusted power of each vehicle meets the requirements. The state of charge of each vehicle meets the requirements. Battery temperature meets requirements. The rate of change of power satisfies as well as .

[0051] The grid connection standard constraint is: the voltage at the grid connection point meets the following requirements. The power grid frequency meets The harmonic distortion rate satisfies .

[0052] It should be noted that the severity-weighted equipment stress term in the above optimization objective function... The allocation principle is as follows: the severity value of each terminal serves as a penalty weight for the power adjustment amount. Terminals with higher severity values ​​are assigned a larger penalty coefficient during the minimization of the objective function. Therefore, the optimization solution tends to allocate more power adjustment tasks to terminals with lower severity values. In other words, when the severity value of the grid terminal... At higher levels, the power regulation at the grid end The optimization results tend to be smaller, while the charging pile and vehicle end undertake more power regulation tasks.

[0053] In this embodiment of the application, in order to meet the real-time requirements of power scheduling, a hierarchical solution strategy is adopted when solving the above-mentioned collaborative optimization problem. The optimization solution process is divided into three levels: scenario classification and strategy selection, simplified optimization solution, and real-time fine-tuning.

[0054] In the scenario classification and strategy selection hierarchy, the dominant anomaly source and severity level are determined based on a three-dimensional severity vector, and the corresponding priority strategy is selected. The specific classification rule is: when the power grid severity value... When the severity value of the three endpoints is the maximum value and greater than or equal to the high severity threshold, the dominant anomaly source is the power grid. The priority strategy is to reduce the total power consumption of the charging station and schedule V2G vehicle discharge. When the severity value of the power grid endpoint is... When the severity value at the charging pile end is the maximum among the three ends and falls between the medium and high severity thresholds, the priority strategy is to prioritize adjusting the reactive power at the grid end, with active power adjustment secondary; when the severity value at the charging pile end... When the severity value is the maximum among the three endpoints and is greater than or equal to the high severity threshold, the preferred strategy is to perform load shifting within the charging station, transferring the charging tasks of the abnormal charging pile to the healthy charging pile; when the vehicle-side severity value... When the value of the three endpoints is greater than or equal to the high severity threshold, the priority strategy is to reduce the charging power of the vehicle and allocate the remaining charging tasks to other vehicles or charging stations.

[0055] Furthermore, the high severity threshold and moderate severity threshold mentioned in the above classification rules are both preset dimensionless values, with a range of values ​​within... Within the range, and with the high severity threshold being greater than the medium severity threshold, both together divide the severity level into three ranges: low, medium, and high, to correspond to different priority strategy selections.

[0056] In the simplified optimization solution hierarchy, based on the priority strategies determined by the scenario classification and strategy selection hierarchy, the above multi-objective optimization problem is simplified into a constraint satisfaction problem with priority. First, the hard safety constraints are satisfied, including equipment temperature limits, battery voltage limits, and grid voltage limits; second, the power regulation amount of each terminal is allocated according to the priority strategy; finally, the charging task completion rate is optimized within the remaining feasible region.

[0057] Furthermore, in the above simplified optimization solution hierarchy, the solution method for the constraint satisfaction problem with priority is as follows: the safety hard constraints, priority strategy constraints, and charging task completion optimization are processed in sequence. Each level is solved within the feasible region determined by the previous level. The allocation result of the power adjustment amount at each end is directly used as the input boundary condition of the next level. Thus, the original multi-objective optimization problem is transformed into a sequential decision-making process of gradually reducing the feasible region, avoiding the global solution of the complete optimization problem, so as to meet the real-time requirements.

[0058] In the real-time fine-tuning layer, after obtaining the initial power scheduling scheme by simplifying the optimization solution layer, iterative adjustments are made based on the subsequently received execution feedback data to correct the power reference values ​​at each end.

[0059] In this embodiment of the application, in order to further promote power grid stability under abnormal conditions, the above-mentioned optimization objective function also includes a power grid support term. When the grid side requires reactive power support, ,in This is a reference value for reactive power at the grid end, and a grid support item. A negative value indicates encouragement to provide reactive power support to the power grid. At this point, the optimization objective function becomes... , The weighting coefficients for the power grid support items.

[0060] In this embodiment of the application, in addition to the above constraints, a severity-weighted adjustment constraint is also included to limit the power adjustment amount of each terminal from not exceeding the adjustment tolerance of that terminal under the current severity: ; in, The power adjustment amount at one end. This represents the severity value at that end. This represents the maximum power regulation capability at this end. This is a flexibility margin parameter that allows for a moderate overshoot of the severity-weighted adjustment constraint in emergency situations. The severity-weighted adjustment constraint ensures that the power adjustment allocated to the end with higher anomaly severity is closer to zero.

[0061] Furthermore, in the aforementioned severity-weighted adjustment constraints, when At that time, the right-hand side of the severity-weighted adjustment constraint degenerates into an elasticity margin parameter. That is, the power adjustment at this end is limited to the elasticity margin parameter. within; when At that time, the right-hand side of the severity-weighted adjustment constraint is This means that this end can utilize additional flexibility parameters beyond its maximum power regulation capability. Elasticity margin parameter The preset value should be greater than or equal to zero; this is the elasticity margin parameter. The physical meaning is that in an emergency, each end is allowed to moderately exceed the adjustment tolerance determined by the severity weighting to ensure the feasibility of overall system power balance.

[0062] Step 4: Distribute power dispatch commands to the vehicle, charging pile, and power grid terminals, collect execution feedback data from each terminal, and generate system status update information. Based on the power dispatch command output in step 3, the cooperative controller distributes the power reference values ​​for each terminal contained in the power dispatch command to the corresponding execution terminals. The grid-side command includes active power reference values, reactive power reference values, and power change rate limits; the charging pile command includes the power reference value and operating status identifier (normal, derating, or standby) for each charging pile; and the vehicle-side command includes the power reference value and operating mode identifier (normal charging, derating charging, or V2G discharging) for each vehicle.

[0063] After the command distribution is completed, the collaborative controller collects execution feedback data from each end. This data includes the actual output power value, command execution status, and communication link health status of each end. The collaborative controller compares the execution feedback data with the corresponding power reference value to obtain the power deviation value for each end.

[0064] Based on the power deviation values ​​and communication link health status of each terminal, system status update information is generated. This information is input into the data acquisition process in step 1, updating the operational status data of each terminal and forming a closed-loop scheduling process from anomaly detection to instruction execution and status update. When the system status update information contains new abnormal events or the power deviation exceeds the allowable range, a new execution cycle from steps 1 to 4 is triggered.

[0065] It should be noted that the power scheduling instructions mentioned above are divided into three levels according to the urgency of the anomaly. The first level is an emergency instruction, requiring a response time of less than 10ms. All terminals must execute it immediately upon receipt; if execution is not completed within the response time, a local protection strategy is triggered. The second level is a fast instruction, requiring a response time of less than 100ms. All terminals execute it with priority; if execution is not completed within the response time, it operates according to a preset degradation reference value. The third level is an optimization instruction, requiring a response time of less than 1 second. All terminals execute it according to the recommended value; if execution is not completed within the response time, the original power state is maintained.

[0066] It should be noted that the power deviation value in the above execution feedback data is processed as follows: when the deviation ratio between the actual output power of a certain end and the power reference value exceeds the preset deviation threshold, it is determined whether the deviation direction is beneficial to system safety; if the deviation direction is beneficial to system safety (for example, the actual power of the abnormal end is lower than the power reference value), the current actual value is accepted and the status reference is updated; if the deviation direction is not beneficial to system safety, a correction instruction is generated to redistribute the power adjustment amount of other ends to compensate for the deviation.

[0067] In this embodiment, to maintain the system's basic operational capability under communication failure conditions, a communication failure degradation process is included during instruction distribution. When the communication link between the cooperative controller and a charging pile is interrupted, the charging pile operates according to the power reference value in the most recently received valid instruction, and other charging piles assume the adjustment tasks originally assigned to that charging pile. When the communication link between the charging pile and the vehicle battery management system is interrupted, the vehicle's V2G discharge function is stopped, and only the basic charging function controlled by the charging pile itself is maintained. When the communication link between the charging station and the grid-side monitoring terminal is interrupted, the charging station operates according to a preset fault-crossing operation curve. When all communication links are interrupted, each terminal executes its own preset independent safety protection strategy. After all communication links are restored, the cooperative controller resynchronizes the status data of each terminal and generates new power scheduling instructions through the optimization solution process in step 3, smoothly transitioning to the cooperative control mode.

[0068] In this embodiment, to improve the real-time performance and reliability of instruction distribution, the instruction distribution process is performed using an event-driven approach. The cooperative controller maintains an event queue arranged by priority, placing pending abnormal events into the corresponding queues from highest to lowest priority. The priority rules are as follows: power grid safety events have higher priority than equipment protection events, equipment protection events have higher priority than battery safety events, and battery safety events have higher priority than charging efficiency events. Within each scheduling cycle, the cooperative controller retrieves events from the highest priority queue for processing, triggering the calculations in steps 2 and 3, and then distributes the power scheduling instructions to each terminal in parallel. If the calculation result of step 3 is not obtained within a preset waiting time, the cooperative controller executes a preset safety protection strategy.

[0069] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A power control method for a charging pile system in case of an abnormality, characterized in that, include: The system collects operational data from the vehicle, charging pile, and power grid, compares the current values ​​of each operational parameter with the corresponding preset thresholds, and generates an abnormal event message containing an abnormal source identifier and an abnormal type identifier when the current value of a certain operational parameter exceeds the corresponding threshold. Based on the abnormal event message, the abnormal sub-indicators of each end are mapped to dimensionless values ​​between zero and one through a piecewise linear normalization function, and then weighted and fused to obtain the severity values ​​of the power grid end, the charging pile end, and the vehicle end, forming a three-dimensional severity vector. Obtain the regulation capability parameters of each end, construct a multi-objective weighted objective function including a power impulse term and a severity-weighted equipment stress term. The severity-weighted equipment stress term uses the severity value of each end as the penalty coefficient of the corresponding end power adjustment amount. Under the power balance constraint and the equipment constraint of each end, solve the allocation scheme of the power adjustment amount of each end, and generate a power scheduling instruction. The process involves distributing the power scheduling command to the vehicle, charging pile, and power grid, collecting execution feedback data from each end, comparing the execution feedback data with the power reference value to obtain a power deviation value, generating system status update information based on the power deviation value, and transmitting it back to the data collection process.

2. The power control method for a charging pile system in case of an abnormality according to claim 1, characterized in that, The abnormal event message also includes the duration of the abnormality, the classification identifier of the abnormality nature, and the classification identifier of recoverability. The classification identifier of the abnormality nature includes transient, continuous, and intermittent types, wherein anomalies with a duration less than a preset transient threshold are marked as transient, anomalies with a duration greater than or equal to the preset transient threshold are marked as continuous, and anomalies that occur periodically are marked as intermittent. The classification identifier of recoverability includes self-recoverable, intervention-required, and unrecoverable types, wherein anomalies that automatically resume normal operation after the anomaly is eliminated are marked as self-recoverable, anomalies that require control measures to recover are marked as intervention-required, and anomalies that require shutdown for maintenance are marked as unrecoverable.

3. The power control method for a charging pile system in case of an abnormality according to claim 1, characterized in that, The severity value at the grid end is obtained by weighted fusion of four sub-indicators: voltage sag amplitude, frequency deviation, three-phase imbalance, and harmonic distortion rate. The sum of the weighting coefficients for the four sub-indicators is one. Specifically, the normalized function for voltage sag amplitude is the ratio of voltage sag amplitude to a normalized upper limit determined by the rated voltage and the voltage sag sensitivity coefficient, taking the smaller of the ratio and one; the normalized function for frequency deviation is the ratio of the absolute value of frequency deviation to the normalized upper limit of frequency deviation, taking the smaller of the ratio and one; the normalized function for three-phase imbalance is the ratio of three-phase imbalance to the normalized upper limit of three-phase imbalance, taking the smaller of the ratio and one; the normalized function for harmonic distortion rate has a harmonic distortion rate benchmark value. When the harmonic distortion rate does not exceed the benchmark value, it is zero. When it exceeds the benchmark value, it is linearly mapped by dividing the difference between the harmonic distortion rate and the benchmark value by the difference between the normalized upper limit of harmonic distortion rate and the benchmark value, taking the smaller of the mapped value and one.

4. The power control method for a charging pile system in case of an abnormality according to claim 1, characterized in that, The severity value of the charging pile is obtained by weighted fusion of three sub-indicators: power module temperature, insulation resistance value, and communication latency. The sum of the weight coefficients corresponding to the three sub-indicators is one. Specifically, the normalization function for power module temperature has a lower limit for safe temperature and an upper limit for dangerous temperature. The normalization function is zero when the power module temperature is below the lower limit for safe temperature, linearly mapped proportionally when it is between the lower limit for safe temperature and the upper limit for dangerous temperature, and one when it reaches or exceeds the upper limit for dangerous temperature. The normalization function for insulation resistance value has a lower limit for dangerous insulation resistance and an upper limit for safe insulation resistance. The normalization function is one when the insulation resistance value is below the lower limit for dangerous insulation resistance, linearly mapped proportionally when it is between the lower limit for dangerous insulation resistance and the upper limit for safe insulation resistance, and zero when it reaches or exceeds the upper limit for safe insulation resistance. The normalization function for communication latency has a lower limit for safe communication latency and an upper limit for dangerous communication latency. The normalization function is zero when the communication latency is below the lower limit for safe communication latency, linearly mapped proportionally when it is between the lower limit for safe communication latency and the upper limit for dangerous communication latency, and one when it reaches or exceeds the upper limit for dangerous communication latency.

5. The power control method for a charging pile system in case of an abnormality according to claim 1, characterized in that, The vehicle-side severity value is obtained by weighted fusion of three sub-indicators: battery management system communication anomaly, battery voltage anomaly, and battery temperature anomaly. The sum of the weight coefficients corresponding to the three sub-indicators is one. The battery management system communication anomaly is the ratio of the number of lost communication frames to the total number of frames to be received within a preset statistical time window. The battery voltage anomaly is the absolute value of the difference between the current battery voltage and the battery rated voltage divided by the battery rated voltage. The battery temperature anomaly is the current battery temperature measurement value. The three sub-indicators are mapped to dimensionless values ​​between zero and one through their respective piecewise linear normalization functions before participating in the weighted fusion.

6. The power control method for a charging pile system in case of an abnormality according to claim 1, characterized in that, After obtaining the base severity values ​​for each endpoint, time-varying correction processing is performed on the base severity values ​​for each endpoint. The corrected severity value is the result of multiplying the base severity value by a duration correction factor and then by a rate of change correction factor. The duration correction factor is the product of a duration correction coefficient and an exponential decay value minus the exponentially decreasing value calculated by dividing the anomaly duration by the time constant, such that the longer the anomaly duration, the higher the severity value. The rate of change correction factor is the product of a rate of change correction coefficient and the time rate of change of the base severity value, where the time rate of change of the base severity value is the difference between the base severity values ​​of the current scheduling cycle and the previous scheduling cycle divided by the scheduling cycle duration. The corrected severity values ​​are truncated to the range of zero to one and replace the base severity values ​​to form the final three-dimensional severity vector.

7. The power control method for a charging pile system in case of an abnormality according to claim 1, characterized in that, The multi-objective weighted objective function also includes a charging task completion deviation term, which is defined as the sum of the squares of the differences between the target state of charge and the current state of charge of each connected vehicle; the power surge term is defined as the square of the L2 norm of the difference between the power reference value vector of the current scheduling cycle and the power reference value vector of the previous scheduling cycle; the severity-weighted equipment stress term is defined as the sum of the product of the grid-side severity value and the absolute value of the grid-side power adjustment, the product of the charging pile-side severity value and the sum of the absolute values ​​of the power adjustments of each charging pile, and the product of the vehicle-side severity value and the sum of the absolute values ​​of the power adjustments of each vehicle; the power balance constraint requires that the active power after grid-side adjustment is equal to the sum of the power after adjustment of all charging piles and the sum of the power loss within the charging station. When there are V2G discharging vehicles, the power balance constraint also includes the discharge power of V2G vehicles.

8. The power control method for a charging pile system in case of an abnormality according to claim 7, characterized in that, The constraints also include a severity-weighted adjustment constraint, which limits the absolute value of the power adjustment at each end to no more than the adjustment allowance obtained by subtracting the product of the end severity value and the end maximum power adjustment capability, plus the elasticity margin parameter. When the severity value of a certain end is one, the end power adjustment is limited to within the elasticity margin parameter; When the severity value of a certain end is zero, the upper limit of the end power adjustment is the sum of the maximum power adjustment capability and the elasticity margin parameter; the elasticity margin parameter takes a preset value that is greater than or equal to zero.

9. The power control method for a charging pile system in case of an abnormality according to claim 1, characterized in that, The power adjustment allocation scheme is solved using a hierarchical solution strategy, including a scenario classification and strategy selection level, a simplified optimization solution level, and a real-time fine-tuning level. In the scenario classification and strategy selection level, the end with the largest severity value among the three ends is identified as the dominant anomaly source based on the three-dimensional severity vector, and the corresponding priority strategy is selected based on the comparison results of the severity value with the preset high severity threshold and medium severity threshold. In the simplified optimization solution level, the feasible region is reduced step by step in the order of safety hard constraints, priority strategy constraints, and charging task completion optimization for sequential decision-making. In the real-time fine-tuning level, the power scheduling scheme is iteratively corrected based on execution feedback data. The power scheduling instructions are divided into three levels according to their urgency: emergency instructions, fast instructions, and optimization instructions. When the communication link between the collaborative controller and an end is interrupted, the end operates according to the power reference value in the most recently received valid instruction, and other ends take over the adjustment tasks originally assigned to the end.

Images