Intelligent charging pile group cooperative control system based on dynamic load distribution

Abstract

The present application relates to the field of cooperative control, in particular to a smart charging pile group cooperative control system based on dynamic load distribution, which determines charging start aggregation, charging time length dispersion and charging network load mismatch by setting data acquisition module, data processing module, cooperative analysis module and cooperative control module, calculates charging law representation value of each monitoring time period to divide charging law tendency of each monitoring time period, calls cooperative control module, determines charging synchronization index in response to charging pile corresponding to monitoring time period with strong charging law tendency, locates loss load charging pile and load charging pile, performs dynamic load adjustment and completes control of charging pile group. The present application divides time period of network car charging station and identifies potential regularity of charging behavior, determines charging pile corresponding to regularity time period, and then regulates and controls specific charging pile group, thereby improving overall charging efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of cooperative control, in particular to an intelligent charging pile group cooperative control system based on dynamic load distribution. BACKGROUND

[0002] Under the background of accelerating the transformation of global energy structure to clean energy and the rapid development of new energy vehicle industry, the charging problem of online car-hailing is increasingly prominent as an important part of urban transportation. Online car-hailing charging areas often gather a large number of vehicles waiting to charge, and the charging demand has the characteristics of concentration and high intensity. The charging demand will concentrate and burst in some time periods, which will greatly increase the power demand of the charging pile cluster and the degree of load mutation. This is superimposed with the volatility of new energy grid connection, which brings great pressure to power supply.

[0003] Chinese patent publication No. CN110718947A discloses a charging pile group control power distribution time-sharing system, which includes a gateway, a connector connecting a cloud platform and a group control charging pile intelligent terminal, the gateway and the cloud platform are both provided with an external interface to receive dispatching instructions of an authentication third-party platform, the group control charging pile terminal is provided with a vehicle control guide function, power metering and charging, and a man-machine interface. The charging pile group control power distribution time-sharing system realizes the fusion of the functions of the electric meter and the charging pile through fusion design, so that the electric meter has the function of the charging pile, reduces repeated investment, reduces terminal manufacturing cost, guides time-sharing charging under the condition of ensuring safety, realizes load over-provisioning but not overloading, network control load, guides charging pile load to develop in a friendly direction to the power grid, realizes orderly charging and orderly power consumption, terminal device fusion, reduces social investment, improves efficiency, and realizes collaborative optimization of the system and the terminal two levels to realize low-cost and high-efficiency construction.

[0004] Chinese patent publication No. CN117621897A discloses a multi-pile networking negotiation charging management system and method. The system includes: a historical state data acquisition module that acquires historical state data of each group of charging piles in a target area; a charging pile state classification module that classifies charging piles according to historical state data; a distributed negotiation and decision-making module that uses a particle swarm optimization algorithm and a negotiation and cooperation mechanism according to the state of the charging piles; a negotiation and decision-making result is obtained by solving; and a charging execution module that generates a charging strategy for controlling each group of charging piles to execute charging actions. The invention collects historical state data of charging piles, uses a particle swarm optimization algorithm and a negotiation and cooperation mechanism, considers power grid load, user demand and charging pile state, and reasonably allocates power in the power grid. In addition, when a charging pile fails, it can timely warn and judge, achieving the effects of optimizing power grid power utilization, improving power system stability and reducing cost.

[0005] However, the prior art still has the following problems,

[0006] In actual conditions, the charging behavior of the online car-hailing is operation-driven, and presents the load characteristics of high intensity and weak regularity. According to the control method of the ordinary charging station, the online car-hailing dedicated charging station is directly managed. The disordered charging of the online car-hailing will cause the peak load pressure of the power grid, and reduce the charging efficiency. SUMMARY

[0007] Therefore, the application provides an intelligent charging pile group collaborative control system based on dynamic load distribution, to solve the problem that in actual conditions, the charging behavior of the online car-hailing is operation-driven, and presents the load characteristics of high intensity and weak regularity. According to the control method of the ordinary charging station, the online car-hailing dedicated charging station is directly managed. The disordered charging of the online car-hailing will cause the peak load pressure of the power grid, and reduce the charging efficiency.

[0008] To achieve the above-mentioned purpose, the application provides an intelligent charging pile group collaborative control system based on dynamic load distribution, which comprises:

[0009] A data acquisition module determines a plurality of monitoring time periods within a predetermined number of charging periods, acquires the charging start time points of the charging piles, the continuous charging time, the power load data and the power grid distribution data in each monitoring time period;

[0010] A data processing module connected with the data acquisition module, determines the charging start aggregation degree based on the charging start time points, determines the charging duration dispersion degree based on the continuous charging time, and determines the charging grid load mismatch degree based on the power load data and the power grid distribution data;

[0011] A collaborative analysis module connected with the data processing module, to calculate the charging regularity representation value of each monitoring time period by combining the charging start aggregation degree and the charging duration dispersion degree, to divide the charging regularity tendency of each monitoring time period, and to call a collaborative control module;

[0012] A collaborative control module connected with the data processing module and the collaborative analysis module, to determine the charging synchronization index based on the charging regularity representation value and the charging grid load mismatch degree in response to the charging pile corresponding to the monitoring time period with strong charging regularity tendency, to control the charging pile group, wherein,

[0013] The load charging pile and the load charging pile are positioned, the dynamic load is adjusted, and the control of the charging pile group is completed.

[0014] Further, the data processing module determines the charging start aggregation degree, which comprises,

[0015] To calculate a plurality of differences between each charging start time point and the adjacent charging start time point;

[0016] To determine the variance of each difference;

[0017] determining an average of the variance in a predetermined number of charging cycles as a charging start-up aggregation degree.

[0018] Further, the data processing module determines a charging duration dispersion degree, including,

[0019] determining a ratio of each of the duration charging time period and the corresponding monitoring time period;

[0020] determining an average of each of the ratios in a predetermined number of charging cycles as the charging duration dispersion degree.

[0021] Further, the data processing module determines a charging grid load mismatch degree, including,

[0022] constructing a power consumption load data time domain curve and a power grid power distribution time domain curve;

[0023] superimposing the power consumption load data time domain curve and the power grid power distribution time domain curve to determine a coincidence segment;

[0024] determining a ratio of a sum of corresponding times of each of the coincidence segments and the corresponding monitoring time period;

[0025] determining an inverse of an average of each of the ratios in a predetermined number of charging cycles as the charging grid load mismatch degree.

[0026] Further, the collaborative analysis module calculates a charging regularity representation value of each of the monitoring time periods, including,

[0027] determining a ratio of the charging start-up aggregation degree and a reference charging start-up aggregation degree as a first regularity influence factor;

[0028] determining a ratio of the charging duration dispersion degree and a reference charging duration dispersion degree as a second regularity influence factor;

[0029] determining a weighted sum of the first regularity influence factor and the second regularity influence factor as the charging regularity representation value.

[0030] Further, the collaborative analysis module divides a charging regularity tendency of each of the monitoring time periods, calling a collaborative control module, wherein,

[0031] if the charging regularity representation value is greater than a charging regularity representation value threshold, dividing the charging regularity tendency as a strong regularity tendency, calling the collaborative control module;

[0032] if the charging regularity representation value is less than or equal to the charging regularity representation value threshold, dividing the charging regularity tendency as a weak regularity tendency.

[0033] Furthermore, the collaborative control module determines the charging synchronization index, including,

[0034] The ratio of the charging network load mismatch to the reference charging network load mismatch is used to determine the first synchronization influence factor.

[0035] The ratio of the charging pattern characterization value to the benchmark charging pattern characterization value is used to determine the second synchronization influence factor.

[0036] The weighted sum of the first synchronization influence factor and the second synchronization influence factor is used to determine the charging synchronization index.

[0037] Furthermore, the collaborative control module controls the charging pile group, wherein,

[0038] If the charging synchronization index is greater than the charging synchronization index threshold, then the charging pile group will not be controlled.

[0039] If the charging synchronization index is less than or equal to the charging synchronization index threshold, then the underloaded charging piles and the overloaded charging piles are located, and dynamic load adjustment is performed to complete the control of the charging pile group.

[0040] Furthermore, the collaborative control module performs dynamic load adjustment, including:

[0041] Used to locate underloaded charging piles and overloaded charging piles;

[0042] Used to determine the load data corresponding to underloaded charging piles;

[0043] This is used to sort the aforementioned charging piles in descending order according to the missing values ​​of the load data;

[0044] Used to allocate the load data according to the order of arrangement;

[0045] The term "underloaded charging pile" refers to a charging pile with a load line less than the load line, while "loaded charging pile" refers to a charging pile with a load line greater than or equal to the load line.

[0046] Furthermore, the data acquisition module determines several monitoring time periods within a predetermined period, including:

[0047] Used to determine a charging cycle;

[0048] This is used to divide the charging cycle into a predetermined number of monitoring time periods.

[0049] Compared with existing technologies, this invention, by setting up a data acquisition module, a data processing module, a collaborative analysis module, and a collaborative control module, determines the charging initiation aggregation degree, charging duration dispersion, and charging network load mismatch degree, calculates the charging pattern characterization value for each monitoring time period, classifies the charging pattern tendency of each monitoring time period, calls the collaborative control module, responds to the charging piles corresponding to the monitoring time periods with strong charging pattern tendencies, determines the charging synchronization index, and controls the charging pile group, locates underloaded and overloaded charging piles, performs dynamic load adjustment, and completes the control of the charging pile group. This invention, by dividing ride-hailing charging stations into time periods and identifying potential patterns in charging behavior, identifies charging piles corresponding to regular time periods, and then precisely controls specific charging piles, thereby improving overall charging efficiency.

[0050] In particular, this invention identifies the charging patterns and tendencies within each monitoring time period, providing a theoretical basis for subsequent control of charging pile clusters. In practice, ride-hailing operations exhibit significant dynamic service characteristics, with charging behavior highly dependent on real-time driving demands. This typically manifests as a short-term charging mode, and the charging sequence is highly uncertain, lacking a fixed timeframe. This results in drastic load fluctuations and unclear charging patterns at dedicated ride-hailing charging stations. If dedicated charging stations are managed directly using methods applicable to ordinary charging stations, it would lead to uneven utilization of charging piles, excessively long waiting times in charging queues, and low charging efficiency. Therefore, this invention considers dividing the charging cycle of ride-hailing vehicles. By analyzing data from multiple monitoring time periods, it identifies and extracts some potential patterns and characteristic features within different charging cycles, determining some regularities in the charging cycle. Targeted collaborative control of the charging pile cluster is then implemented for monitoring time periods with different patterns, thereby improving charging efficiency.

[0051] In particular, this invention calculates the charging pattern characteristics of each monitoring time period by determining the charging initiation aggregation degree and the charging duration dispersion. This distinguishes between monitoring time periods that are "highly regular, predictable, and easy to control" and those that are "highly random and difficult to control," allowing subsequent control resources to be precisely allocated to the most effective targets. In practice, ride-hailing vehicles mostly use fragmented time (such as waiting for orders or eating) to charge, resulting in short charging durations. Charging piles need to adapt to charging various battery specifications within a limited time. In this case, frequent plugging and unplugging operations will exacerbate the slowdown in the charging pile's response performance, thereby affecting the accuracy and timeliness of the charging pile's dynamic power allocation to real-time charging demands, leading to reduced charging efficiency. Based on this, this invention obtains the charging initiation aggregation degree and charging duration dispersion of several monitoring time periods within a predetermined period. By comprehensively analyzing the charging duration and plugging / unplugging status, it identifies the charging pattern tendencies of charging piles during the monitoring time periods, providing a data foundation for subsequent coordinated control of charging pile groups and improving charging efficiency.

[0052] In particular, this invention addresses the issue of strong charging patterns by combining charging pattern characteristics with charging network load mismatch to calculate the charging synchronization index. In practice, ride-hailing vehicles, due to their unclear charging patterns, mostly use a combination of historical data analysis and real-time power compensation to allocate charging power to ensure the completion of charging tasks. However, for charging piles corresponding to monitoring periods with strong charging patterns (i.e., parameters such as vehicle arrival time, charging duration, and power demand show significant consistency and predictability during this period), relying solely on historical data analysis and real-time power compensation would lead to resource waste. Therefore, this invention considers regulating charging piles corresponding to monitoring periods with strong charging patterns by analyzing the load status of the charging pile group and implementing coordinated control to improve charging efficiency. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the intelligent charging pile group collaborative control system based on dynamic load distribution, as an embodiment of the invention.

[0054] Figure 2 To illustrate the charging pattern trends in the various monitoring time periods described in the embodiments of the invention, the logic block diagram of the collaborative control module is invoked.

[0055] Figure 3 This is a logic block diagram for controlling a group of charging piles, as an embodiment of the invention. Detailed Implementation

[0056] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0057] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0058] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0059] Please see Figure 1 , Figure 1This is a schematic diagram of the intelligent charging pile group collaborative control system based on dynamic load allocation according to an embodiment of the invention. The intelligent charging pile group collaborative control system based on dynamic load allocation of the present invention includes:

[0060] The data acquisition module determines several monitoring time periods within a predetermined charging cycle and acquires the charging start time, continuous charging time, power load data, and power grid distribution data of the charging pile within each monitoring time period.

[0061] A data processing module, which is connected to the data acquisition module, determines the charging start aggregation degree based on the charging start time point, determines the charging duration dispersion based on the continuous charging time, and determines the charging grid-load mismatch degree based on the electricity load data and the power grid distribution data.

[0062] The collaborative analysis module, which is connected to the data processing module, is used to calculate the charging pattern characterization value of each monitoring time period by combining the charging start aggregation degree and the charging duration dispersion, classify the charging pattern tendency of each monitoring time period, and call the collaborative control module.

[0063] A collaborative control module, connected to the data processing module and the collaborative analysis module, responds to charging piles exhibiting a strong charging pattern tendency. It determines a charging synchronization index based on the charging pattern characteristic value and the charging network load mismatch degree to control the charging pile group.

[0064] The system locates underloaded and overloaded charging piles, performs dynamic load adjustments, and controls the charging pile group.

[0065] Specifically, there is no limit to the predetermined number of charging cycles or the specific time of the charging cycle. In practice, the predetermined number is set to 30 units and the charging cycle is 1 day. Of course, those skilled in the art can also determine the number according to the actual situation, which will not be elaborated here.

[0066] Specifically, there are no restrictions on the specific methods for obtaining charging start time, continuous charging time, and power load data of charging piles. For example, the required data can be obtained by accessing an existing charging pile data management platform (such as an open-source platform, which can be used after registration). Of course, those skilled in the art can also use other feasible data collection methods (such as directly reading the local logs of the charging pile, using proprietary API interfaces, deploying sensor networks, etc.) to obtain the required data according to the actual situation, which will not be elaborated here.

[0067] Specifically, there are no restrictions on the specific methods for obtaining power grid distribution data. For example, it can be obtained by accessing the official data platform provided by the power grid company. Of course, those skilled in the art can also use other feasible data collection methods (such as subscribing to third-party energy data services, using open-source databases, etc.) to obtain the required data according to the actual situation, which will not be elaborated here.

[0068] Specifically, the data processing module determines the charging initiation aggregation degree, including,

[0069] Used to calculate several differences between each of the aforementioned charging start time points and adjacent charging start time points;

[0070] Used to determine the variance of each of the aforementioned differences;

[0071] The average of the variances over a predetermined number of charging cycles is used to determine the charging initiation aggregation degree.

[0072] Specifically, the calculation of charging start aggregation is based on a single charging pile. The adjacent charging start time point is the next charging start time point that is adjacent to the single charging pile in time. In practice, the sequence of charging start time points is [t1,t2,t3,t4,t5], then the difference is t2-t1,t3-t2,t4-t3,t5-t4.

[0073] It can be understood that variance measures the fluctuation of the time interval between consecutive charging start events. A small variance means that the time intervals are close, and the charging pile has high frequency of plugging and unplugging operations, which represents a high degree of aggregation and will lead to a decrease in charging efficiency.

[0074] Specifically, the data processing module determines the charging time dispersion, including,

[0075] Used to determine the ratio of each of the continuous charging time periods to the corresponding monitoring time periods;

[0076] The average value of each ratio within a predetermined number of charging cycles is used to determine the charging duration dispersion.

[0077] Specifically, this invention determines the charging initiation aggregation degree and charging duration dispersion, and calculates the charging pattern characterization value for each monitoring time period. This distinguishes between monitoring time periods that are "highly regular, predictable, and easy to control" and those that are "highly random and difficult to control," allowing subsequent control resources to be precisely allocated to the most effective targets. In reality, ride-hailing vehicles mostly use fragmented time (such as waiting for orders or eating) to charge, resulting in short charging durations. Charging piles need to adapt to charging various battery specifications within a limited time. In this case, frequent plugging and unplugging operations will exacerbate the slowdown in the charging pile's response performance, thereby affecting the accuracy and timeliness of the charging pile's dynamic power allocation to real-time charging demands, leading to reduced charging efficiency. Based on this, this invention obtains the charging initiation aggregation degree and charging duration dispersion for several monitoring time periods within a predetermined period. By comprehensively analyzing the charging duration and plugging / unplugging status, it reveals the charging pattern tendency of charging piles during the monitoring time periods, providing a data foundation for subsequent coordinated control of charging pile groups and improving charging efficiency.

[0078] Specifically, the data processing module determines the charging network load mismatch, including,

[0079] Used to construct time-domain curves of electricity load data and time-domain curves of power grid distribution;

[0080] This is used to overlay the time-domain curve of the electricity load data with the time-domain curve of the power grid distribution to determine the overlapping segment;

[0081] Used to determine the ratio of the sum of the times corresponding to each overlapping segment to the corresponding monitoring time period;

[0082] The reciprocal of the average of the ratios used to determine the predetermined number of charging cycles is the charging grid load mismatch.

[0083] Specifically, the collaborative analysis module calculates the charging pattern representation values ​​for each of the monitoring time periods, including:

[0084] The ratio of the charging start-up aggregation degree to the benchmark charging start-up aggregation degree is used as the first regularity influencing factor.

[0085] The ratio of the charging time dispersion to the reference charging time dispersion is used to determine the second regularity influence factor;

[0086] The weighted sum of the first and second regularity influence factors is used to determine the charging regularity characterization value.

[0087] Specifically, the benchmark charging start aggregation degree is calculated in advance. The historical charging start aggregation degrees of several charging piles during the same monitoring period are obtained in advance, and the average of each historical charging start aggregation degree is determined as the benchmark charging start aggregation degree.

[0088] Specifically, the baseline charging time dispersion is calculated in advance. The historical charging time dispersion of several charging piles during the same monitoring period is obtained in advance, and the mean of each historical charging time dispersion is determined as the baseline charging time dispersion.

[0089] Specifically, the sum of the weight coefficients of the first and second regularity influencing factors is 1. When conducting analysis of potential charging regularity, the charging start time directly reflects the time distribution characteristics of charging behavior, while the dispersion of charging duration is an indirect reflection and has a limited impact on the time rhythm of charging behavior. Therefore, when setting the weight coefficients, the weight coefficient of the first regularity influencing factor is assigned a higher value than that of the second regularity influencing factor. In practice, the weight coefficient of the first regularity influencing factor is set to 0.6, and the weight coefficient of the second regularity influencing factor is set to 0.4.

[0090] Specifically, the collaborative analysis module divides the charging pattern trends of each monitoring time period and calls the collaborative control module, wherein...

[0091] If the charging pattern representation value is greater than the charging pattern representation value threshold, the charging pattern tendency is classified as a strong pattern tendency, and the collaborative control module is invoked.

[0092] If the charging pattern characteristic value is less than or equal to the charging pattern characteristic value threshold, then the charging pattern tendency is classified as a weak pattern tendency.

[0093] Specifically, the purpose of setting a threshold for the charging pattern representation value is to determine a boundary for the regularity of the monitoring period. The value is obtained in advance by acquiring charging piles with regular charging behavior, determining several historical charging pattern representation values, and determining the product of the mean of each historical charging pattern representation value and the regularity coefficient as the charging pattern representation value threshold. In practice, in order to improve the accuracy of the division, the regularity coefficient is determined to be 1.2.

[0094] This invention identifies charging patterns and tendencies across different monitoring time periods, providing a theoretical basis for subsequent control of charging pile clusters. In practice, ride-hailing operations exhibit significant dynamic service characteristics, with charging behavior highly dependent on real-time driving demands. This typically manifests as a short-term charging mode, and the charging sequence is highly uncertain, lacking a fixed timeframe. This results in drastic load fluctuations and unclear charging patterns at dedicated ride-hailing charging stations. If dedicated charging stations are managed directly using methods applicable to ordinary charging stations, it leads to uneven utilization of charging equipment, excessively long waiting times in charging queues, and low charging efficiency. Therefore, this invention divides the charging cycle of ride-hailing vehicles. By analyzing data from multiple monitoring time periods, it identifies and extracts potential patterns and characteristics within different charging cycles, determining some regularities in the charging cycle. Targeted collaborative control of the charging pile cluster is then implemented for monitoring time periods with different patterns, improving charging efficiency.

[0095] Specifically, the collaborative control module determines the charging synchronization index, including...

[0096] The ratio of the charging network load mismatch to the reference charging network load mismatch is used to determine the first synchronization influence factor.

[0097] The ratio of the charging pattern characterization value to the benchmark charging pattern characterization value is used to determine the second synchronization influence factor.

[0098] The weighted sum of the first synchronization influence factor and the second synchronization influence factor is used to determine the charging synchronization index.

[0099] Specifically, the benchmark charging network load mismatch is calculated in advance. The historical charging network load mismatch of several charging piles during the same monitoring period is obtained in advance, and the average of each historical charging network load mismatch is determined as the benchmark charging network load mismatch.

[0100] Specifically, the baseline charging pattern characterization value is the charging pattern characterization value corresponding to the baseline charging start aggregation degree and the baseline charging duration dispersion.

[0101] Specifically, the sum of the weight coefficients of the first synchronization influence factor and the second synchronization influence factor is 1. When performing charging synchronization state analysis, the charging network load mismatch can more accurately determine the charging synchronization state. Therefore, when setting the weight coefficients, the weight coefficient of the first synchronization influence factor is assigned a higher value than that of the second synchronization influence factor. In practice, the weight coefficient of the first regular influence factor is set to 0.6, and the weight coefficient of the second regular influence factor is set to 0.4.

[0102] Specifically, the collaborative control module controls the charging pile group, wherein...

[0103] If the charging synchronization index is greater than the charging synchronization index threshold, then the charging pile group will not be controlled.

[0104] If the charging synchronization index is less than or equal to the charging synchronization index threshold, then the underloaded charging piles and the overloaded charging piles are located, and dynamic load adjustment is performed to complete the control of the charging pile group.

[0105] Specifically, the purpose of setting the charging synchronization index threshold is to establish a boundary for synchronizing the actual charging state with the grid distribution charging state. The threshold is a pre-calculated value obtained by acquiring several historical charging synchronization indices that have completed the charging behavior in advance. The product of the average value of each historical charging synchronization index and the synchronization coefficient is determined as the charging synchronization index threshold. In practice, to improve control accuracy, the regularity coefficient is set to 1.2.

[0106] Specifically, the collaborative control module performs dynamic load adjustments, including...

[0107] Used to locate underloaded charging piles and overloaded charging piles;

[0108] Used to determine the load data corresponding to underloaded charging piles;

[0109] This is used to sort the aforementioned charging piles in descending order according to the missing values ​​of the load data;

[0110] Used to allocate the load data according to the order of arrangement;

[0111] The term "underloaded charging pile" refers to a charging pile with a load line less than the load line, while "loaded charging pile" refers to a charging pile with a load line greater than or equal to the load line.

[0112] Specifically, the load line refers to the data corresponding to the power grid distribution, which will not be elaborated further here.

[0113] Understandably, in implementation, for example, there are three underloaded charging piles A, B, and C, with corresponding load data of 10KW, 6KW, and 5KW respectively, and two loaded charging piles A1 and B1, with corresponding load data missing values ​​of 15KW and 4KW respectively. In this case, A provides 10KW to A1, B provides 5KW to A1, B provides 1KW to B1, and C provides 3KW to B1, thus completing the dynamic adjustment of the load and realizing the coordinated control of the charging pile group.

[0114] Specifically, the data acquisition module determines several monitoring time periods within a predetermined period, including,

[0115] Used to determine a charging cycle;

[0116] This is used to divide the charging cycle into a predetermined number of monitoring time periods.

[0117] Specifically, there is no limit to the exact number of monitoring time periods. In practice, the 24-hour charging cycle is divided into 6 time periods starting from 0:00. Of course, those skilled in the art can also determine the exact number of monitoring time periods according to the actual situation, as long as it is reasonable. This will not be elaborated further.

[0118] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0119] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A collaborative control system for intelligent charging pile groups based on dynamic load distribution, characterized in that, include: The data acquisition module determines several monitoring time periods within a predetermined number of charging cycles, and acquires the charging start time, continuous charging time, power load data, and power grid distribution data of the charging pile within each monitoring time period. A data processing module, which is connected to the data acquisition module, determines the charging start aggregation degree based on the charging start time point, determines the charging duration dispersion based on the continuous charging time, and determines the charging grid-load mismatch degree based on the electrical load data and the power grid distribution data. The collaborative analysis module, which is connected to the data processing module, is used to calculate the charging pattern characterization value of each monitoring time period by combining the charging start aggregation degree and the charging duration dispersion, classify the charging pattern tendency of each monitoring time period, and call the collaborative control module. A collaborative control module, connected to the data processing module and the collaborative analysis module, responds to charging piles within monitoring time periods exhibiting strong charging patterns. It determines a charging synchronization index based on the charging pattern characteristic value and the charging network load mismatch degree to control the charging pile group. Locate underloaded and overloaded charging piles, perform dynamic load adjustment, and complete the control of the charging pile group; The data processing module determines the charging start aggregation degree, including, Used to calculate several differences between each of the aforementioned charging start time points and adjacent charging start time points; Used to determine the variance of each of the aforementioned differences; The average of the variance over a predetermined number of charging cycles is used to determine the charging initiation aggregation degree. The data processing module determines the charging time dispersion, including: Used to determine the ratio of each of the continuous charging time periods to the corresponding monitoring time periods; The average value of each ratio within a predetermined number of charging cycles is used to determine the charging duration dispersion. The data processing module determines the charging network load mismatch, including, Used to construct time-domain curves of electricity load data and time-domain curves of power grid distribution; This is used to overlay the time-domain curve of the electricity load data with the time-domain curve of the power grid distribution to determine the overlapping segment; Used to determine the ratio of the sum of the times corresponding to each overlapping segment to the corresponding monitoring time period; The reciprocal of the average of the ratios within a predetermined number of charging cycles is the charging grid load mismatch. The collaborative analysis module calculates the charging pattern characterization values ​​for each monitoring time period, including: The ratio of the charging start-up aggregation degree to the benchmark charging start-up aggregation degree is used as the first regularity influencing factor. The second regularity influence factor is used to determine the ratio of the dispersion of the charging time to the dispersion of the reference charging time. The weighted sum of the first and second regularity influence factors is used to determine the charging regularity characterization value.

2. The intelligent charging pile group collaborative control system based on dynamic load allocation according to claim 1, characterized in that, The collaborative analysis module divides the charging pattern trends of each monitoring time period and calls the collaborative control module, wherein... If the charging pattern representation value is greater than the charging pattern representation value threshold, the charging pattern tendency is classified as a strong pattern tendency, and the collaborative control module is invoked. If the charging pattern characteristic value is less than or equal to the charging pattern characteristic value threshold, then the charging pattern tendency is classified as a weak pattern tendency.

3. The intelligent charging pile group collaborative control system based on dynamic load allocation according to claim 1, characterized in that, The collaborative control module determines the charging synchronization index. include, The ratio of the charging network load mismatch to the reference charging network load mismatch is used to determine the first synchronization influence factor. The ratio of the charging pattern characterization value to the benchmark charging pattern characterization value is used to determine the second synchronization influence factor. The weighted sum of the first synchronization influence factor and the second synchronization influence factor is used to determine the charging synchronization index.

4. The intelligent charging pile group collaborative control system based on dynamic load allocation according to claim 1, characterized in that, The collaborative control module controls the charging pile group, wherein... If the charging synchronization index is greater than the charging synchronization index threshold, then the charging pile group will not be controlled. If the charging synchronization index is less than or equal to the charging synchronization index threshold, then the underloaded charging piles and the overloaded charging piles are located, and dynamic load adjustment is performed to complete the control of the charging pile group.

5. The intelligent charging pile group collaborative control system based on dynamic load allocation according to claim 1, characterized in that, The collaborative control module performs dynamic load adjustment, including: Used to locate underloaded charging piles and overloaded charging piles; Used to determine the load data corresponding to underloaded charging piles; This is used to sort the aforementioned charging piles in descending order according to the missing values ​​of the load data; Used to allocate the load data according to the order of arrangement; The term "underloaded charging pile" refers to a charging pile with a load line less than the load line, while "loaded charging pile" refers to a charging pile with a load line greater than or equal to the load line.

6. The intelligent charging pile group collaborative control system based on dynamic load allocation according to claim 1, characterized in that, The data acquisition module determines several monitoring time periods within a predetermined period, including: Used to determine a charging cycle; This is used to divide the charging cycle into a predetermined number of monitoring time periods.

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