A charging management system based on charging station load and charging control method thereof

Abstract

The present invention relates to a charging management system based on charging station load and a charging control method thereof, which includes an Internet of Things perception platform, a data processing platform and a data supervision platform connected in sequence, wherein the output end of the Internet of Things perception platform is connected to the input end of the data processing platform, and the output end of the data processing platform is connected to the input end of the data supervision platform; the Internet of Things perception platform includes an electric parameter measurement device, a battery vehicle charging pile and a car charging pile, collects the real-time status of the charging pile, the power data of the charging circuit, the full power parameters of the distribution system, and the execution results of the remote control instructions, and transmits them to the data processing platform. The present invention ensures the accuracy of the collected data by constructing an electric power data warehouse and model training, remotely and centrally monitors the daily status and charging process of charging stations scattered in various places, monitors the operating status of the transformer, and distributes and controls the charging power of the charger.

Description

Technical Field

[0001] The present invention relates to the technical field of charging management systems, and in particular to a charging management system based on charging station loads and a charging control method thereof. Background Art

[0002] The public has higher and higher requirements for green travel. Actively shortening the charging service circle and creating a safe and convenient car and battery vehicle charging environment for the public has become an important part of the "new infrastructure" and an important carrier for achieving the "carbon peak and carbon neutrality" goals. At the same time, with the gradual popularization of car charging piles and battery vehicle charging piles, the load pressure on the power grid is increasing. How to effectively reduce the load on the power grid has good economic benefits and safety significance for improving power distribution efficiency and ensuring charging safety.

[0003] The current charging control method is mainly based on the charging needs of end users. The charging power is identified by the terminal charging equipment to achieve power grading. However, this mechanism only controls the output power by the terminal charging equipment, and does not fully consider other loads in the entire charging station distribution system. It does not achieve scientific coordination and scheduling from the top level, which will cause the charging station transformer to overload, affect the safe operation of the power grid, and fail to meet the country's energy strategy requirements for energy conservation and consumption reduction. At the same time, since the data uploaded by the device is not screened, the accuracy of the data cannot be guaranteed, which will result in unsatisfactory charging control effects. Summary of the invention

[0004] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide a charging management system based on the charging station load and a charging control method thereof, which fully considers the load of the entire charging station and the accuracy of the data, realizes scientific coordination and scheduling from the top level, and monitors and controls the operation and charging behavior of the charging station distribution system in real time, so as to provide guarantee for the reliable, safe and economical operation of the charging station.

[0005] The object of the present invention is achieved in that:

[0006] A charging management system based on charging station load, comprising an Internet of Things perception platform, a data processing platform and a data supervision platform connected in sequence, wherein the output end of the Internet of Things perception platform is connected to the input end of the data processing platform, and the output end of the data processing platform is connected to the input end of the data supervision platform;

[0007] The IoT sensing platform includes a power parameter measurement device, a battery vehicle charging pile and a car charging pile. The power parameter measurement device includes a current transformer, a voltage transformer, a multi-function power meter, a full-network 4G communication module and a TF card standard slot. The real-time status of the charging pile, the power data of the charging circuit, the full power parameters of the distribution system, and the execution results of the remote control instructions are collected and transmitted to the data processing platform through the 4G network according to the data transmission standard;

[0008] The data processing platform includes a data receiving module, a data identification module, a data calculation module, a data analysis module, a data storage module, and a data interaction module. The output end of the Internet of Things perception platform is connected to the input end of the data receiving module, the output end of the data receiving module is connected to the input end of the data identification module, the output end of the data identification module is connected to the input end of the data calculation module, the output end of the data calculation module is connected to the input end of the data analysis module, the output end of the data analysis module is connected to the input end of the data storage module, and the output end of the data storage module is connected to the input end of the data interaction module;

[0009] The data supervision platform includes a data monitoring subsystem, a data analysis subsystem and an instruction management subsystem; the instruction management subsystem is used to create control instructions and send control instructions to the data processing platform. After the data receiving module receives the control instructions sent by the instruction management subsystem, it sends them to the Internet of Things perception platform via 4G. After the Internet of Things perception platform completes the execution, it sends the execution results back to the data processing platform via 4G. After receiving the control instruction execution results sent back by the Internet of Things perception platform, the data receiving module sends them to the data supervision platform.

[0010] Furthermore, the data receiving module is used to receive the real-time status of the charging pile, the charging circuit power data, the full power parameters of the distribution system and the remote control command execution results transmitted from the output end of the Internet of Things perception platform, and then transmit the data to the data identification module.

[0011] Furthermore, the data identification module is used to analyze the real-time status of the charging pile, the charging circuit power data, the full power parameters of the distribution system and the execution results of the remote control instructions, and incorporate the analyzed charging circuit power data and the full power parameters of the distribution system into the power data warehouse, check and identify the abnormal data in the real-time data based on the power data model, eliminate the abnormal data and erroneous data, estimate and correct the null value, missing data and negative data based on the data model, and then transmit the screened results to the data calculation module.

[0012] Furthermore, the data calculation module is used to calculate the energy consumption of the charging circuit, the average load factor and economic load factor of the charging station transformer, and the charging cost, and transmit the calculation results to the data analysis module.

[0013] Furthermore, the data analysis module is used to analyze whether the charging station transformer operates economically, whether the charging pile is abnormal, and the charging power distribution and control strategy, and then transmit the analysis results to the data storage module.

[0014] Furthermore, the data storage module is used to perform efficient multi-node concurrent and horizontally scalable processing on the analysis results formed by the data analysis module, including energy consumption data, full power parameters, charging order data and abnormal data, and then store the processed results in a database.

[0015] Furthermore, the data interaction module is used to extract data analysis results from the data storage module, including energy consumption data, full power parameters, charging order data and abnormal data, and the data supervision platform then extracts data from the data interaction module.

[0016] Furthermore, the data monitoring subsystem is used to monitor the real-time status of the charging pile, the charging circuit power data and the full power parameters of the distribution system in real time; the charging circuit power data includes phase A voltage, phase B voltage, phase C voltage, phase A current, phase B current, phase C current, total active power, charged amount, total active power, and battery state of charge; the full power parameters include phase voltage, line voltage, current, frequency, active power, reactive power, apparent power, active energy, reactive energy, power factor, three-phase imbalance, and harmonics; the power data model uses key power parameters such as power and current as dependent variables, and the remaining power parameters and charging power, charging time, and charging degree as independent variables, and establishes a multivariate linear regression model for each, and continuously adjusts and optimizes the independent variable coefficients through machine learning and training.

[0017] A charging control method based on a charging station load includes the following contents:

[0018] Step SS1: Calculate the average load factor β of the transformer, the calculation formula is

[0019] Among them, S is the average apparent power output of the transformer within a certain period of time, S N is the rated capacity of the transformer, which is the rated parameter of the transformer;

[0020] Step SS2: Calculate the transformer economic load factor β j , the calculation formula is

[0021] Among them, P0 is the no-load power loss of the transformer, PK is the rated load power loss of the transformer, both are rated parameters of the transformer;

[0022] K T is the load fluctuation loss coefficient, and the calculation formula is:

[0023] Among them, T is the statistical period time, the unit is hour, A i is the electricity per hour;

[0024] Step SS3: Calculate the transformer average load factor β obtained in step SS1 and the transformer economic load factor β obtained in step SS2 j , determine whether the transformer is operating economically;

[0025] Among them, if the average load factor β is less than 1.33β j 2 or greater than 0.75, it indicates that the transformer is not in economical operation state;

[0026] Step SS4: judging whether the charging pile status is charging according to the charging pile status collected in real time by the IoT sensing platform;

[0027] Step SS5: If the charging pile status is idle, that is, not charging, charging is not allowed to start; if the charging pile status is charging, the charging pile is controlled according to the charging power, the charged amount and the duration.

[0028] Furthermore, in step SS5, the method for controlling the charging pile includes: a. interrupting charging for a charging pile whose charging power exceeds the charging specification, b. interrupting charging for a charging pile whose charged amount and charging time have reached 80% of the charged amount and charging time specified when starting charging, c. implementing power grading for a charging pile whose charged amount and charging time have not reached 80% of the charged amount and charging time specified when starting charging, reducing the output power of one gradation of the charging pile, and recalculating and extending the charging time according to the required charging amount and charging time.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The present invention uses Internet of Things technology, wireless communication technology, power parameter sensing technology, cloud computing and big data analysis to monitor and control the operation and charging behavior of the charging station distribution system in real time, providing guarantee for the reliable, safe and economical operation of the charging station; it uses power parameter measurement devices, battery vehicle charging piles and car charging piles to communicate and interact with cloud servers through 4G, and collects data 24 hours a day. The platform ensures the accuracy of collected data by building a power data warehouse and model training, remotely and centrally monitors the daily status and charging process of charging stations scattered in various places, monitors the operating status of transformers, and distributes and controls the charging power of chargers.

[0031] The present invention uses big data analysis to build a power data warehouse and model training, adjust and optimize the model, eliminate abnormal data and erroneous data, estimate and correct data null values, data packet loss, data zero values ​​or near zero values, and data negative values, thereby ensuring the accuracy and usable value of the data, further improving the data quality and the effectiveness of the model, and providing scientific, reasonable and reliable data guarantee for business analysis.

[0032] The present invention uses a professional algorithm for the electric power industry, fully considers the load of the entire charging station distribution system, and automatically executes the charging power allocation and control strategy by judging the operating status of the transformer, thereby ensuring the safe and reliable operation of the charging station and the use of the charging pile. At the same time, under the premise of economical operation of the transformer, the active power loss of the entire power system is minimized, thereby reducing power loss and line loss, which plays an extremely important role in reducing energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the structure of the present invention.

[0034] Figure 2 It is a schematic diagram of the charging control process of the present invention.

[0035] in:

[0036] Internet of Things sensing platform 1, power parameter measuring device 11, battery vehicle charging pile 12, car charging pile 13, data processing platform 2, data receiving module 21, data identification module 22, data calculation module 23, data analysis module 24, data storage module 25, data interaction module 26, data supervision platform 3, data monitoring subsystem 31, data analysis subsystem 32, instruction management subsystem 33. DETAILED DESCRIPTION

[0037] In order to better understand the technical solution of the present invention, the following will be described in detail in conjunction with the relevant illustrations. It should be understood that the following specific embodiments are not intended to limit the specific implementation of the technical solution of the present invention, and are only implementations that can be adopted by the technical solution of the present invention. It should be noted that the description of the positional relationship of each component in this article, such as component A being located above component B, is based on the description of the relative positions of the components in the illustration, and is not intended to limit the actual positional relationship of the components.

[0038] Embodiment 1:

[0039] See also Figure 1 , Figure 1 The structural schematic diagram of the present invention is drawn. As shown in the figure, the present invention relates to a charging management system based on charging station load, which includes an Internet of Things perception platform 1, a data processing platform 2 and a data supervision platform 3 connected in sequence, the output end of the Internet of Things perception platform 1 is connected to the input end of the data processing platform 2, and the output end of the data processing platform 2 is connected to the input end of the data supervision platform 3; the Internet of Things perception platform 1 includes an electric parameter measurement device 11, a battery vehicle charging pile 12 and a car charging pile 13, the data processing platform 2 includes a data receiving module 21, a data identification module 22, a data calculation module 23, a data analysis module 24, a data storage module 25 and a data interaction module 26, and the data supervision platform 3 includes a data monitoring subsystem 31, a data analysis subsystem 32 and an instruction management subsystem 33.

[0040] The Internet of Things sensing platform 1 includes a power parameter measuring device 11, a battery vehicle charging pile 12 and a car charging pile 13. The power parameter measuring device 11 is mainly composed of hardware devices such as a current transformer, a voltage transformer, a multifunctional power meter, a full-network 4G communication module and a TF card standard slot. The real-time status of the charging pile, the power data of the charging circuit, the full power parameters of the distribution system, and the execution results of the remote control instructions are collected, and transmitted to the data processing platform 2 through the 4G network according to the data transmission standard. The output end of the full-network 4G communication module is connected to the input end of the data receiving module 21 of the data processing platform 2 through the 4G network;

[0041] The power data of the charging circuit include phase A voltage, phase B voltage, phase C voltage, phase A current, phase B current, phase C current, total active power, charged amount, total active power and battery charge state. The full power parameters of the distribution system include phase voltage, line voltage, current, frequency, active power, reactive power, apparent power, active energy, reactive energy, power factor, three-phase imbalance and harmonic parameters. The remote control instructions include equipment fault alarm instructions, energy consumption calculation instructions, transformer average load factor calculation instructions, transformer economic load factor calculation instructions, charging pile power control instructions, charging cost calculation instructions, overload protection instructions, no-load protection instructions, leakage protection instructions and over-temperature protection instructions.

[0042] The data processing platform 2 includes a data receiving module 21, a data identification module 22, a data calculation module 23, a data analysis module 24, a data storage module 25, and a data interaction module 26. The output end of the Internet of Things perception platform 1 is connected to the input end of the data receiving module 21, the output end of the data receiving module 21 is connected to the input end of the data identification module 22, the output end of the data identification module 22 is connected to the input end of the data calculation module 23, the output end of the data calculation module 23 is connected to the input end of the data analysis module 24, the output end of the data analysis module 24 is connected to the input end of the data storage module 25, and the output end of the data storage module 25 is connected to the input end of the data interaction module 26;

[0043] The data receiving module 21 is used to receive the real-time status of the charging pile, the power data of the charging circuit, the full power parameters of the power distribution system and the execution results of the remote control command transmitted from the output end of the Internet of Things perception platform, and then transmit the data to the data identification module 22;

[0044] The data identification module 22 is used to analyze the real-time status of the charging pile, the power data of the charging circuit, the full power parameters of the distribution system and the execution results of the remote control command, and incorporate the analyzed charging circuit power data and the full power parameters of the distribution system into the power data warehouse, check and identify the abnormal data in the real-time data based on the power data model, remove the abnormal data and erroneous data, estimate and correct the data null value, the missing data and the negative value of the data based on the data model, and then transmit the screened results to the data calculation module 23;

[0045] Abnormal data and erroneous data include abnormal data with large fluctuations in values ​​compared with other data, erroneous data exceeding the specifications of the transformer, and data with normal voltage and current and normal one-phase active power, one-phase active power close to 0, and one-phase active power negative value due to wrong phase sequence connection of the voltage line. Data null value includes data null value at a certain time point due to poor antenna installation or too far distance. Missing data includes data packet loss caused by network fluctuation. Negative data includes zero value and near-zero value of a certain data due to the fall-off of a certain transformer or poor contact of the needle, and negative data due to reverse connection of the transformer.

[0046] The data calculation module 23 is used to calculate the energy consumption of the charging circuit, the average load factor and economic load factor of the charging station transformer, and the charging cost, and transmit the calculation results to the data analysis module 24;

[0047] The data analysis module 24 is used to analyze whether the charging station transformer is operating economically, whether the charging pile is abnormal, and the charging power distribution and control strategy, and then transmit the analysis results to the data storage module 25;

[0048] The data storage module 25 is used to perform multi-node concurrent and horizontally scalable efficient processing on the analysis results formed by the data analysis module, including energy consumption data, full power parameters, charging order data and abnormal data, and then store the processed results in the database;

[0049] The data interaction module 26 is used to extract data analysis results from the data storage module 25 , including energy consumption data, full power parameters, charging order data and abnormal data. The data supervision platform 3 then extracts data from the data interaction module 26 .

[0050] The data supervision platform 3 includes a data monitoring subsystem 31, a data analysis subsystem 32 and an instruction management subsystem 33;

[0051] The data monitoring subsystem 31 is used to monitor the real-time status of the charging pile, the charging circuit power data and the full power parameters of the distribution system in real time; the charging circuit power data includes phase A voltage, phase B voltage, phase C voltage, phase A current, phase B current, phase C current, total active power, charged amount, total active power, and battery state of charge; the full power parameters include phase voltage, line voltage, current, frequency, active power, reactive power, apparent power, active electric energy, reactive electric energy, power factor, three-phase imbalance, and harmonics; the power data model uses key power parameters such as power and current as dependent variables, and the remaining power parameters and charging power, charging time, and charging degree as independent variables, and establishes each multivariate linear regression model, and continuously adjusts and optimizes the independent variable coefficients through machine learning and training.

[0052] The data analysis subsystem 32 is used to analyze whether the charging station transformer is operating economically, whether there are abnormalities in the power parameters of the charging circuit, and the charging power distribution and control strategy, and to distribute and control the charging power of the charger;

[0053] The instruction management subsystem 33 is used to create control instructions and send control instructions to the data processing platform 2. After receiving the control instructions sent by the instruction management subsystem 33, the data receiving module 21 sends them to the Internet of Things perception platform 1 through 4G. After the Internet of Things perception platform 1 completes the execution, it sends the execution result back to the data processing platform 2 through 4G. After receiving the control instruction execution result sent back by the Internet of Things perception platform 1, the data receiving module 21 sends it to the data supervision platform 3.

[0054] The control instructions include equipment fault alarm instructions, energy consumption calculation instructions, transformer average load factor calculation instructions, transformer economic load factor calculation instructions, charging pile power control instructions, charging cost calculation instructions, overload protection instructions, no-load protection instructions, leakage protection instructions, and over-temperature protection instructions.

[0055] See also Figure 2 , Figure 2 A schematic diagram of the charging control process of the present invention is drawn. As shown in the figure, the present invention relates to a charging control method based on a charging station load, including the following contents:

[0056] Step SS1: Calculate the average load factor β of the transformer, the calculation formula is

[0057] Among them, S is the average apparent power output of the transformer within a certain period of time, S N is the rated capacity of the transformer, which is the rated parameter of the transformer.

[0058] Step SS2: Calculate the transformer economic load factor β j , the calculation formula is

[0059] Among them, P0 is the no-load power loss of the transformer, P K is the rated load power loss of the transformer, both are rated parameters of the transformer;

[0060] K T is the load fluctuation loss coefficient, which needs to be calculated according to the following formula:

[0061] The calculation formula of load fluctuation loss coefficient is:

[0062] Among them, T is the statistical period time, the unit is hour, A i The electricity consumption per hour.

[0063] Step SS3: Calculate the transformer average load factor β obtained in step SS1 and the transformer economic load factor β obtained in step SS2 j , determine whether the transformer is operating economically;

[0064] Among them, if the average load factor β is less than 1.33β j 2 Or if it is greater than 0.75, it indicates that the transformer is not in economical operation state.

[0065] Step SS4: According to the charging pile status collected in real time by the Internet of Things sensing platform 1, determine whether the charging pile status is charging.

[0066] Step SS5: If the charging pile status is idle, that is, not charging, charging is not allowed to start; if the charging pile status is charging, the charging pile is controlled according to the charging power, the charged amount and the duration;

[0067] In step SS5, the strategy for controlling the charging pile includes interrupting charging for charging piles whose charging power exceeds the charging specification; interrupting charging for charging piles whose charged amount and charging time have reached 80% of the charged amount and charging time specified when starting charging; and implementing power grading for charging piles whose charged amount and charging time have not reached 80% of the charged amount and charging time specified when starting charging, reducing the output power of one gradation of the charging pile, and recalculating and extending the charging time according to the required charging amount and charging time.

[0068] The above are only specific application examples of the present invention and do not constitute any limitation on the protection scope of the present invention. Any technical solution formed by equivalent transformation or equivalent replacement shall fall within the protection scope of the present invention.

Claims

1. A charging management system based on charging station load, characterized by: It includes an Internet of Things perception platform, a data processing platform and a data supervision platform which are connected in sequence, wherein the output end of the Internet of Things perception platform is connected to the input end of the data processing platform, and the output end of the data processing platform is connected to the input end of the data supervision platform; The IoT sensing platform includes a power parameter measurement device, a battery vehicle charging pile and a car charging pile. The power parameter measurement device includes a current transformer, a voltage transformer, a multi-function power meter, a full-network 4G communication module and a TF card standard slot. The real-time status of the charging pile, the power data of the charging circuit, the full power parameters of the distribution system, and the execution results of the remote control instructions are collected and transmitted to the data processing platform through the 4G network according to the data transmission standard; The data processing platform includes a data receiving module, a data identification module, a data calculation module, a data analysis module, a data storage module, and a data interaction module; The data supervision platform includes a data monitoring subsystem, a data analysis subsystem and an instruction management subsystem; the instruction management subsystem is used to create control instructions and send control instructions to the data processing platform. After the data receiving module receives the control instructions sent by the instruction management subsystem, it sends them to the Internet of Things perception platform through 4G. After the Internet of Things perception platform completes the execution, it sends the execution result back to the data processing platform through 4G. After the data receiving module receives the control instruction execution result sent back by the Internet of Things perception platform, it sends it to the data supervision platform; The data receiving module is used to receive the real-time status of the charging pile, the power data of the charging circuit, the full power parameters of the power distribution system and the execution results of the remote control command transmitted from the output end of the Internet of Things perception platform, and then transmit the data to the data identification module; The data identification module is used to analyze the real-time status of the charging pile, the power data of the charging circuit, the full power parameters of the distribution system and the execution results of the remote control command, and incorporate the analyzed charging circuit power data and the full power parameters of the distribution system into the power data warehouse, check and identify the abnormal data in the real-time data based on the power data model, eliminate the abnormal data and erroneous data, estimate and correct the data null value, data missing and data negative value based on the data model, and then transmit the screened results to the data calculation module; The data calculation module is used to calculate the energy consumption of the charging circuit, the average load factor and economic load factor of the charging station transformer, and the charging cost, and transmit the calculation results to the data analysis module; The data analysis module is used to analyze whether the charging station transformer is operating economically, whether the charging pile is abnormal, and the charging power distribution and control strategy, and then transmit the analysis results to the data storage module; The data storage module is used to perform multi-node concurrent and horizontally scalable efficient processing on the analysis results formed by the data analysis module, including energy consumption data, full power parameters, charging order data and abnormal data, and then store the processed results in the database; The data interaction module is used to extract data analysis results from the data storage module, including energy consumption data, full power parameters, charging order data and abnormal data, and the data supervision platform then extracts data from the data interaction module; The data monitoring subsystem is used to monitor the real-time status of the charging pile, the charging circuit power data and the full power parameters of the distribution system in real time; the charging circuit power data includes phase A voltage, phase B voltage, phase C voltage, phase A current, phase B current, phase C current, total active power, charged amount, total active power, and battery state of charge; the full power parameters include phase voltage, line voltage, current, frequency, active power, reactive power, apparent power, active electric energy, reactive electric energy, power factor, three-phase imbalance, and harmonics; the power data model uses power and current as dependent variables, and the remaining power parameters and charging power, charging time, and charging degree as independent variables, and establishes a multivariate linear regression model for each, and continuously adjusts and optimizes the independent variable coefficients through machine learning and training.

2. A charging control method based on charging station load, characterized in that: Includes the following: Step SS1: Calculate the average load factor β of the transformer, the calculation formula is ; Among them, S is the average apparent power output of the transformer within a certain period of time, S N is the rated capacity of the transformer, which is the rated parameter of the transformer; Step SS2: Calculate the transformer economic load factor β j , the calculation formula is ; Among them, P0 is the no-load power loss of the transformer, P K is the rated load power loss of the transformer, both are rated parameters of the transformer; K T is the load fluctuation loss coefficient, and the calculation formula is: ; Among them, T is the statistical period time, the unit is hour, A i is the electricity consumption per hour; Step SS3: Calculate the transformer average load factor β obtained in step SS1 and the transformer economic load factor β obtained in step SS2 j , determine whether the transformer is operating economically; Among them, if the average load factor β is less than 1.33β j 2 or greater than 0.75, it indicates that the transformer is not in economical operation state; Step SS4: judging whether the charging pile status is charging according to the charging pile status collected in real time by the IoT sensing platform; Step SS5: If the charging pile status is idle, that is, not charging, charging is not allowed to start; if the charging pile status is charging, the charging pile is controlled according to the charging power, the charged amount and the duration; The method for controlling the charging piles includes: a. interrupting the charging of the charging piles whose charging power exceeds the charging specifications, b. interrupting the charging of the charging piles whose charged amount and charging time have reached 80% of the charged amount and charging time specified when starting the charging, c. implementing power grading for the charging piles whose charged amount and charging time have not reached 80% of the charged amount and charging time specified when starting the charging, reducing the output power of one gradation of the charging piles, and recalculating and extending the charging time according to the required charging amount and charging time.

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