CN115230515A - Charging management system based on charging station load and charging control method thereof - Google Patents

Charging management system based on charging station load and charging control method thereof Download PDF

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CN115230515A
CN115230515A CN202210875781.3A CN202210875781A CN115230515A CN 115230515 A CN115230515 A CN 115230515A CN 202210875781 A CN202210875781 A CN 202210875781A CN 115230515 A CN115230515 A CN 115230515A
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data
charging
power
module
transformer
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张振
徐宇雷
何艳
王爱华
马楠
张锡磊
李平龙
孔亦坚
张彪
周晨煜
庞灏
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Jiangsu Ankeri Microgrid Research Institute Co ltd
Acrel Co Ltd
Jiangsu Acrel Electrical Manufacturing Co Ltd
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Jiangsu Ankeri Microgrid Research Institute Co ltd
Acrel Co Ltd
Jiangsu Acrel Electrical Manufacturing Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/68—Off-site monitoring or control, e.g. remote control
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2457—Query processing with adaptation to user needs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
    • H02J13/00022—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Power Engineering (AREA)
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  • Data Mining & Analysis (AREA)
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  • Transportation (AREA)
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Abstract

The invention relates to a charging management system based on charging station load and a charging control method thereof, which comprises an Internet of things sensing platform, a data processing platform and a data supervision platform which are connected in sequence, wherein the output end of the Internet of things sensing platform is connected with the input end of the data processing platform, and the output end of the data processing platform is connected with the input end of the data supervision platform; the Internet of things sensing platform comprises an electric parameter measuring device, a storage battery car charging pile and an automobile charging pile, collects the real-time state of the charging pile, the electric data of a charging loop, the full electric parameters of a power distribution system and the execution result of a remote control instruction, and transmits the result to the data processing platform. According to the invention, the accuracy of the acquired data is ensured by constructing an electric power data warehouse and model training, the daily states and the charging process of the charging stations scattered in various places are remotely and intensively monitored, the running state of the transformer is monitored, and the charging power of the charger is distributed and controlled.

Description

Charging management system based on charging station load and charging control method thereof
Technical Field
The invention relates to the technical field of charging management systems, in particular to a charging management system based on a charging station load and a charging control method thereof.
Background
People have higher and higher requirements on green traveling, the charging service circle is actively shortened, and an safe and convenient automobile and battery car charging environment is created for the people, so that the charging environment becomes an important content of new capital construction and an important carrier for assisting in realizing the targets of carbon peak reaching and carbon neutralization. Meanwhile, along with the gradual popularization of the construction of the automobile charging pile and the storage battery car charging pile, the load pressure of a power grid is larger and larger, and how to effectively reduce the load of the power grid has better economic benefits and safety significance for improving the power distribution efficiency and ensuring the charging safety.
The current charging control method is mainly based on the charging requirement of a terminal user, the charging power is identified through terminal charging equipment, and power grading is achieved, but the output power of the mechanism is only controlled by the terminal charging equipment, other loads in the power distribution system of the whole charging station are not fully considered, scientific overall planning and scheduling are not achieved from the top layer, overload of a transformer of the charging station can be formed, the safe operation of a power grid is affected, and the national energy strategic requirements for energy conservation and consumption reduction cannot be met. Meanwhile, since the data uploaded by the device is not screened, the accuracy of the data cannot be guaranteed, and the charging control effect is not ideal.
Disclosure of Invention
The invention aims to overcome the defects and provides a charging management system based on the load of a charging station and a charging control method thereof, which fully consider the accuracy of the load and data of the whole charging station, realize scientific overall planning and scheduling from the top layer, monitor and control the operation and charging behavior of a power distribution system of the charging station in real time and provide guarantee for the reliable, safe and economic operation of the charging station.
The purpose of the invention is realized by the following steps:
a charging management system based on charging station load comprises an Internet of things sensing platform, a data processing platform and a data supervision platform which are connected in sequence, wherein the output end of the Internet of things sensing platform is connected with the input end of the data processing platform, and the output end of the data processing platform is connected with the input end of the data supervision platform;
the Internet of things perception platform comprises a power parameter measuring device, a storage battery car charging pile and an automobile charging pile, wherein the power parameter measuring device comprises a current transformer, a voltage transformer, a multifunctional power instrument, a full-network-through 4G communication module and a TF card standard slot, collects the real-time state of the charging pile, charging loop power data, full power parameters of a power distribution system and a remote control instruction execution result, and transmits the charging pile real-time state, the charging loop power data, the full power parameters of the power distribution system and the remote control instruction execution result to a data processing platform through a 4G network according to a data transmission standard;
the data processing platform comprises a data receiving module, a data screening module, a data computing module, a data analyzing module, a data storage module and a data interaction module, wherein the output end of the sensing platform of the Internet of things is connected with the input end of the data receiving module, the output end of the data receiving module is connected with the input end of the data screening module, the output end of the data screening module is connected with the input end of the data computing module, the output end of the data computing module is connected with the input end of the data analyzing module, the output end of the data analyzing module is connected with the input end of the data storage module, and the output end of the data storage module is connected with the input end of the data interaction module;
the data supervision platform comprises a data monitoring subsystem, a data analysis subsystem and a command management subsystem; the instruction management subsystem is used for creating a control instruction and sending the control instruction to the data processing platform, the data receiving module receives the control instruction sent by the instruction management subsystem and then sends the control instruction to the Internet of things sensing platform through 4G, the Internet of things sensing platform sends an execution result back to the data processing platform through 4G after execution is completed, and the data receiving module receives the control instruction execution result sent back by the Internet of things sensing platform and then sends the control instruction execution result to the data supervision platform.
Furthermore, the data receiving module is used for receiving a real-time state of the charging pile, charging loop electric power data, full electric power parameters of a power distribution system and a remote control instruction execution result transmitted from the output end of the sensing platform of the internet of things, and then transmitting the data to the data screening module.
Furthermore, the data screening module is used for analyzing the charging pile real-time state, the charging loop electric power data, the distribution system full electric power parameters and the remote control instruction execution result, bringing the analyzed charging loop electric power data and the analyzed distribution system full electric power parameters into an electric power data warehouse, checking and screening abnormal data existing in the real-time data based on an electric power data model, eliminating the abnormal data and the error data, estimating and correcting data null values, data missing and data negative values based on the data model, and transmitting the screened result to the data calculation module.
Further, the data calculation module is used for calculating the energy consumption of the charging loop, the average load coefficient and the economic load coefficient of the charging station transformer and the charging cost, and transmitting the calculation result to the data analysis module.
Further, the data analysis module is used for analyzing whether the charging station transformer runs economically, whether the charging pile is abnormal, charging power distribution and control strategies, and then transmitting analysis results to the data storage module.
Furthermore, the data storage module is used for performing multi-node concurrent high-efficiency processing capable of being horizontally expanded on the analysis results formed by the data analysis module, including the energy consumption data, the full power parameters, the charging order data and the abnormal data, and then storing the processed results into the database.
Further, the data interaction module is used for extracting data analysis results from the data storage module, wherein the data analysis results comprise energy consumption data, full power parameters, charging order data and abnormal data, and the data supervision platform extracts data from the data interaction module.
Furthermore, the data monitoring subsystem is used for monitoring the real-time state of the charging pile, the electric power data of the charging loop and the full electric power parameters of the power distribution system in real time; the charging loop power data comprise A phase voltage, B phase voltage, C phase voltage, A phase current, B phase current, C phase current, total active power, charged amount, total active power and battery state of charge; the full power parameters comprise phase voltage, line voltage, current, frequency, active power, reactive power, apparent power, active electric energy, reactive electric energy, power factors, three-phase unbalance degrees and harmonic waves; the electric power data model takes key electric power parameters such as power, current and the like as dependent variables, and residual electric power parameters, charging power, charging duration and charging degree are taken as independent variables, so that a multiple linear regression model is respectively established, and independent variable coefficients are adjusted and optimized through machine learning and training.
A charging control method based on charging station loads comprises the following steps:
step SS1: calculating the average load coefficient beta of the transformer according to the formula
Figure BDA0003762430790000031
Wherein S is the apparent power of the average output of the transformer in a certain time, S N The rated capacity of the transformer is the rated parameter of the transformer;
step SS2: calculating the economic load coefficient beta of the transformer j The calculation formula is
Figure BDA0003762430790000032
Wherein, P 0 For no-load power loss of the transformer, P K Rated load power loss for the transformer, both of which are rated parameters of the transformer;
K T for the load fluctuation loss coefficient, the calculation formula is
Figure BDA0003762430790000033
Wherein T is statistical period time in hours, A i The electricity quantity per hour;
step SS3: according to the average load coefficient beta of the transformer calculated in the step SS1 and the economic load coefficient beta of the transformer calculated in the step SS2 j Judging whether the transformer runs economically or not;
wherein, if the average load factor beta is less than 1.33 beta j 2 Or greater than 0.75, indicating that the transformer is not in an economic operating state;
and step SS4: judging whether the charging pile state is in charge or not according to the charging pile state acquired by the sensing platform of the Internet of things in real time;
and step SS5: if the charging pile is in an idle state, namely not in charging, the charging is not allowed to be started; and if the charging pile state is in charging, controlling the charging pile according to the charging power, the charged amount and the charging time length.
Further, in step SS5, the method for controlling the charging pile includes: a. the method comprises the steps of carrying out charging interruption on charging piles of which the charging power exceeds the charging specification, b, carrying out charging interruption on the charging piles of which the charged amount and the charged time reach 80% of the specified charging amount and the specified charging time when charging is started, c, carrying out power grading on the charging piles of which the charged amount and the charged time do not reach 80% of the specified charging amount and the specified charging time when charging is started, reducing the output power of the charging pile of the first grade, and recalculating and prolonging the charging time according to the required charging amount and charging time.
Compared with the prior art, the invention has the beneficial effects that:
the charging station power distribution system real-time monitoring and controlling system utilizes the internet of things technology, the wireless communication technology, the power parameter sensing technology, the cloud computing and the big data analysis to monitor and control the operation and the charging behavior of the charging station power distribution system in real time, and provides guarantee for the reliable, safe and economic operation of the charging station; adopt electric power parameter measurement device, storage battery car to fill electric pile and car and fill electric pile and pass through 4G and carry out communication and data interaction with high in the clouds server, the incessant data collection of 24 hours. The platform guarantees the accuracy of the acquired data by constructing an electric power data warehouse and model training, remotely and centrally monitors the daily states and the charging processes of charging stations scattered in various places, monitors the running state of a transformer, and distributes and controls the charging power of a charger.
According to the method, big data analysis is used, an electric power data warehouse and model training are constructed, the model is adjusted and optimized, abnormal data and error data are removed, and null data, packet loss data, zero data values or values close to the zero values and negative data values are estimated and corrected, so that the accuracy and the usable value of the data are guaranteed, the data quality and the effectiveness of the model are further improved, and scientific, reasonable and reliable data guarantee is provided for business analysis.
The invention uses a professional algorithm in the power industry, fully considers the load of the power distribution system of the whole charging station, and automatically executes the charging power distribution and control strategy by judging the running state of the transformer, thereby ensuring the safety and reliability of the running of the charging station and the use of the charging pile. Meanwhile, on the premise that the transformer operates economically, the active loss of the whole power system is minimized, so that the electric energy loss and the line loss are reduced, and the method has an extremely important effect on reducing energy waste.
Drawings
FIG. 1 is a schematic view of the present invention.
Fig. 2 is a schematic diagram of a charging control flow according to the present invention.
Wherein:
the system comprises an internet of things sensing platform 1, an electric power parameter measuring device 11, an electric vehicle charging pile 12, an automobile charging pile 13, a data processing platform 2, a data receiving module 21, a data screening module 22, a data calculating module 23, a data analyzing module 24, a data storage module 25, a data interaction module 26, a data monitoring platform 3, a data monitoring subsystem 31, a data analyzing subsystem 32 and an instruction management subsystem 33.
Detailed Description
For a better understanding of the technical aspects of the present invention, reference will now be made in detail to the accompanying drawings. It should be understood that the following specific examples are not intended to limit the embodiments of the present invention, but are merely exemplary embodiments of the present invention. It should be noted that the description of the positional relationship of the components, such as the component a is located above the component B, is based on the description of the relative positions of the components in the drawings, and is not intended to limit the actual positional relationship of the components.
Example 1:
referring to fig. 1, fig. 1 depicts a schematic structural diagram of the present invention. As shown in the figure, the charging management system based on the charging station load comprises an internet of things sensing platform 1, a data processing platform 2 and a data supervision platform 3 which are connected in sequence, wherein the output end of the internet of things sensing platform 1 is connected with the input end of the data processing platform 2, and the output end of the data processing platform 2 is connected with the input end of the data supervision platform 3; the internet of things perception platform 1 comprises an electric power parameter measuring device 11, an electric storage vehicle charging pile 12 and an automobile charging pile 13, the data processing platform 2 comprises a data receiving module 21, a data screening module 22, a data calculating module 23, a data analyzing module 24, a data storage module 25 and a data interaction module 26, and the data supervision platform 3 comprises a data monitoring subsystem 31, a data analyzing subsystem 32 and an instruction management subsystem 33.
The Internet of things sensing platform 1 comprises an electric parameter measuring device 11, an electromobile charging pile 12 and an automobile charging pile 13, wherein the electric parameter measuring device 11 mainly comprises hardware equipment such as a current transformer, a voltage transformer, a multifunctional electric instrument, a whole network communication 4G communication module, a TF card standard slot and the like, the real-time state of the charging pile, charging loop electric data, the whole electric parameters of a power distribution system and a remote control instruction execution result are collected and transmitted to the data processing platform 2 through a 4G network according to a data transmission standard, and the output end of the whole network communication 4G communication module is connected with the input end of a data receiving module 21 of the data processing platform 2 through the 4G network;
the charging loop power data comprise A phase voltage, B phase voltage, C phase voltage, A phase current, B phase current, C phase current, total active power, charged amount, total active power and battery state of charge, all power parameters of the power distribution system comprise phase voltage, line voltage, current, frequency, active power, reactive power, apparent power, active electric energy, reactive electric energy, power factors, three-phase unbalance degree and harmonic parameters, and the remote control instructions comprise an equipment fault alarm instruction, an energy consumption calculation instruction, a transformer average load coefficient calculation instruction, a transformer economic load coefficient calculation instruction, a charging pile power control instruction, a charging expense calculation instruction, an overload protection instruction, a no-load protection instruction, an electric leakage protection instruction and an over-temperature protection instruction.
The data processing platform 2 comprises a data receiving module 21, a data screening module 22, a data calculating module 23, a data analyzing module 24, a data storing module 25 and a data interaction module 26, wherein the output end of the internet of things sensing platform 1 is connected with the input end of the data receiving module 21, the output end of the data receiving module 21 is connected with the input end of the data screening module 22, the output end of the data screening module 22 is connected with the input end of the data calculating module 23, the output end of the data calculating module 23 is connected with the input end of the data analyzing module 24, the output end of the data analyzing module 24 is connected with the input end of the data storing module 25, and the output end of the data storing module 25 is connected with the input end of the data interaction module 26;
the data receiving module 21 is configured to receive a real-time state of the charging pile, charging loop power data, full power parameters of the power distribution system, and a remote control instruction execution result transmitted from the output end of the internet of things sensing platform, and then transmit the data to the data screening module 22;
the data screening module 22 is configured to analyze a charging pile real-time state, charging loop power data, distribution system full power parameters, and a remote control instruction execution result, bring the analyzed charging loop power data and distribution system full power parameters into a power data warehouse, check and screen abnormal data existing in the real-time data based on a power data model, remove abnormal data and error data, estimate and correct a data null value, a certain data deficiency, and a certain data negative value based on the data model, and transmit a screened result to the data calculation module 23;
the abnormal data and the error data comprise abnormal data with large numerical value fluctuation difference compared with other data, error data exceeding the specification of the transformer, and data with normal voltage and current and normal active power of one phase, close to 0 active power of one phase and negative active power of one phase caused by wrong connection of a voltage line phase sequence, the data null value comprises a data null value at a certain time point caused by poor antenna installation or too far distance, certain data loss comprises data packet loss caused by network fluctuation, certain data negative value comprises a certain data zero value and a data near zero value caused by falling of a certain mutual sensor or poor contact of a needle, and certain data negative value caused by reverse connection of the transformer;
the data calculation module 23 is configured to calculate energy consumption of the charging loop, an average load coefficient and an economic load coefficient of the transformer of the charging station, and charging cost, and transmit a calculation result to the data analysis module 24;
the data analysis module 24 is configured to analyze whether the charging station transformer operates economically, whether the charging pile is abnormal, and a charging power distribution and control strategy, and then transmit an analysis result to the data storage module 25;
the data storage module 25 is configured to perform multi-node concurrent high-efficiency processing capable of horizontal expansion on 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;
the data interaction module 26 is configured to extract data analysis results from the data storage module 25, where the data analysis results include energy consumption data, full power parameters, charging order data, and abnormal data, and the data supervision platform 3 extracts data from the data interaction module 26.
The data supervision platform 3 comprises a data monitoring subsystem 31, a data analysis subsystem 32 and an instruction management subsystem 33;
the data monitoring subsystem 31 is used for monitoring the real-time state of the charging pile, the electric power data of the charging loop and the full electric power parameters of the power distribution system in real time; the charging loop power data comprises A phase voltage, B phase voltage, C phase voltage, A phase current, B phase current, C phase current, total active power, charged amount, total active power and battery state of charge; the full power parameters comprise phase voltage, line voltage, current, frequency, active power, reactive power, apparent power, active electric energy, reactive electric energy, power factors, three-phase unbalance and harmonic waves; the electric power data model takes key electric power parameters such as power, current and the like as dependent variables, and residual electric power parameters, charging power, charging duration and charging degree as independent variables, respectively establishes a multiple linear regression model, and continuously adjusts and optimizes independent variable coefficients through machine learning and training.
The data analysis subsystem 32 is used for analyzing whether a charging station transformer runs economically, whether charging loop power parameters are abnormal, and charging power distribution and control strategies, and distributing and controlling the charging power of the charger;
the instruction management subsystem 33 is used for creating a control instruction and sending the control instruction to the data processing platform 2, the data receiving module 21 receives the control instruction sent by the instruction management subsystem 33 and then sends the control instruction to the internet of things sensing platform 1 through 4G, the internet of things sensing platform 1 sends an execution result back to the data processing platform 2 through 4G after execution is completed, and the data receiving module 21 receives the execution result of the control instruction sent back by the internet of things sensing platform 1 and then sends the control instruction to the data monitoring platform 3;
the control instructions comprise an equipment fault alarm instruction, an energy consumption calculation instruction, a transformer average load coefficient calculation instruction, a transformer economic load coefficient calculation instruction, a charging pile power control instruction, a charging expense calculation instruction, an overload protection instruction, a no-load protection instruction, an electric leakage protection instruction and an over-temperature protection instruction.
Referring to fig. 2, fig. 2 depicts a charge control flow diagram of the present invention. As shown in the figure, the charging control method based on the charging station load according to the present invention includes the following steps:
step SS1: calculating the average load coefficient beta of the transformer by the formula
Figure BDA0003762430790000071
Wherein S is the apparent power of the average output of the transformer in a certain time, S N The rated capacity of the transformer is the rated parameter of the transformer.
Step SS2: calculating the economic load coefficient beta of the transformer j The calculation formula is
Figure BDA0003762430790000081
Wherein, P 0 For no-load power loss of the transformer, P K Rated load power loss for the transformer, both of which are rated parameters of the transformer;
K T for the load fluctuation loss coefficient, it is necessary to calculate according to the following formulaTo obtain:
the load fluctuation loss coefficient is calculated by the formula
Figure BDA0003762430790000082
Wherein T is statistical period time in hours, A i The electricity per hour.
Step SS3: according to the average load coefficient beta of the transformer calculated in the step SS1 and the economic load coefficient beta of the transformer calculated in the step SS2 j Judging whether the transformer runs economically or not;
wherein if the average load factor beta is less than 1.33 beta j 2 Or greater than 0.75, this indicates that the transformer is not operating economically.
And step SS4: and judging whether the charging pile state is in charging according to the charging pile state acquired by the Internet of things sensing platform 1 in real time.
Step SS5: if the charging pile state is idle, namely not charging, the charging is not allowed to be started; if the charging pile state is in charging, controlling the charging pile according to the charging power, the charged amount and the charging duration;
in the step SS5, the strategy for controlling the charging piles comprises the step of implementing charging interruption on the charging piles of which the charging power exceeds the charging specification; the charging method comprises the steps that charging interruption is carried out on charging piles of which the charged amount and the charged time length reach 80% of the specified charging amount and the specified charging time length when charging is started; and performing power grading on charging piles of which the charged quantity and the charged time length do not reach 80% of the specified charging quantity and the specified charging time length when charging is started, reducing the output power of the first-grade charging piles, and recalculating and prolonging the charging time length according to the required charging quantity and the required charging time length.
The above is only a specific application example of the present invention, and the protection scope of the present invention is not limited in any way. All the technical solutions formed by equivalent transformation or equivalent replacement fall within the protection scope of the present invention.

Claims (10)

1. A charging management system based on charging station load, its characterized in that: the system comprises an Internet of things sensing platform, a data processing platform and a data supervision platform which are sequentially connected, wherein the output end of the Internet of things sensing platform is connected with the input end of the data processing platform, and the output end of the data processing platform is connected with the input end of the data supervision platform;
the Internet of things perception platform comprises a power parameter measuring device, a storage battery car charging pile and an automobile charging pile, wherein the power parameter measuring device comprises a current transformer, a voltage transformer, a multifunctional power instrument, a full-network-through 4G communication module and a TF card standard slot, collects the real-time state of the charging pile, charging loop power data, full power parameters of a power distribution system and a remote control instruction execution result, and transmits the charging pile real-time state, the charging loop power data, the full power parameters of the power distribution system and the remote control instruction execution result to a data processing platform through a 4G network according to a data transmission standard;
the data processing platform comprises a data receiving module, a data screening module, a data calculating module, a data analyzing module, a data storing module and a data interaction module;
the data supervision platform comprises a data monitoring subsystem, a data analysis subsystem and an instruction management subsystem; the instruction management subsystem is used for creating a control instruction and sending the control instruction to the data processing platform, the data receiving module receives the control instruction sent by the instruction management subsystem and then sends the control instruction to the Internet of things sensing platform through 4G, the Internet of things sensing platform sends an execution result back to the data processing platform through 4G after execution is completed, and the data receiving module receives the control instruction execution result sent back by the Internet of things sensing platform and then sends the control instruction execution result to the data supervision platform.
2. The charging management system according to claim 1, wherein: the data receiving module is used for receiving a charging pile real-time state, charging loop electric power data, full electric power parameters of a power distribution system and a remote control instruction execution result transmitted from the output end of the sensing platform of the Internet of things, and then transmitting the data to the data screening module.
3. The charging management system according to claim 1, wherein: the data screening module is used for analyzing the charging pile real-time state, charging loop electric power data, distribution system full electric power parameters and remote control instruction execution results, bringing the analyzed charging loop electric power data and distribution system full electric power parameters into an electric power data warehouse, checking and screening abnormal data existing in the real-time data based on an electric power data model, eliminating the abnormal data and error data, estimating and correcting data null values, data missing values and data negative values based on the data model, and transmitting screened results to the data computing module.
4. The charging-station-load-based charging management system of claim 1, wherein: the data calculation module is used for calculating the energy consumption of the charging loop, the average load coefficient and the economic load coefficient of the charging station transformer and the charging cost, and transmitting the calculation result to the data analysis module.
5. The charging management system according to claim 1, wherein: the data analysis module is used for analyzing whether the charging station transformer operates economically, whether the charging pile is abnormal, charging power distribution and control strategies and then transmitting analysis results to the data storage module.
6. The charging management system according to claim 1, wherein: the data storage module is used for performing multi-node concurrent high-efficiency processing capable of horizontally expanding on analysis results formed by the data analysis module, including energy consumption data, full power parameters, charging order data and abnormal data, and then storing the processed results into a database.
7. The charging management system according to claim 1, wherein: the data interaction module is used for extracting data analysis results from the data storage module, wherein the data analysis results comprise energy consumption data, full power parameters, charging order data and abnormal data, and the data supervision platform extracts data from the data interaction module.
8. The charging-station-load-based charging management system of claim 1, wherein: the data monitoring subsystem is used for monitoring the real-time state of the charging pile, the electric power data of the charging loop and the full electric power parameters of the power distribution system in real time; the charging loop power data comprise A phase voltage, B phase voltage, C phase voltage, A phase current, B phase current, C phase current, total active power, charged amount, total active power and battery state of charge; the full power parameters comprise phase voltage, line voltage, current, frequency, active power, reactive power, apparent power, active electric energy, reactive electric energy, power factors, three-phase unbalance and harmonic waves; the electric power data model takes key electric power parameters such as power, current and the like as dependent variables, and residual electric power parameters, charging power, charging duration and charging degree are taken as independent variables, so that a multiple linear regression model is respectively established, and independent variable coefficients are adjusted and optimized through machine learning and training.
9. A charging control method based on a charging station load is characterized by comprising the following steps:
step SS1: calculating the average load coefficient beta of the transformer by the formula
Figure DEST_PATH_IMAGE001
Wherein S is the apparent power of the average output of the transformer in a certain time, S N The rated capacity of the transformer is the rated parameter of the transformer;
step SS2: calculating the economic load coefficient beta of the transformer j The calculation formula is
Figure 99240DEST_PATH_IMAGE002
Wherein, P 0 For no-load power loss of the transformer, P K Rated load power loss for transformerBoth are rated parameters of the transformer;
K T for the load fluctuation loss coefficient, the calculation formula is
Figure DEST_PATH_IMAGE003
Wherein T is statistical period time in hours, A i The electricity quantity per hour;
step SS3: according to the average load coefficient beta of the transformer calculated in the step SS1 and the economic load coefficient beta of the transformer calculated in the step SS2 j Judging whether the transformer runs economically or not;
wherein if the average load factor beta is less than 1.33 beta j 2 Or greater than 0.75, indicating that the transformer is not in an economic operating state;
and step SS4: judging whether the charging pile state is in charge or not according to the charging pile state acquired by the sensing platform of the Internet of things in real time;
step SS5: if the charging pile is in an idle state, namely not in charging, the charging is not allowed to be started; and if the charging pile state is in charging, controlling the charging pile according to the charging power, the charged amount and the charging time length.
10. A charging station load-based charging control method according to claim 9, characterized in that: in the step SS5, the method for controlling the charging pile comprises the following steps: a. the method comprises the steps of carrying out charging interruption on charging piles of which the charging power exceeds a charging specification, b, carrying out charging interruption on the charging piles of which the charged amount and the charged time reach 80% of the specified charged amount and the specified charged time when charging is started, c, carrying out power grading on the charging piles of which the charged amount and the charged time do not reach 80% of the specified charged amount and the specified charged time when charging is started, reducing the output power of the first-grade charging piles, and recalculating and prolonging the charging time according to the required charged amount and the required charged time.
CN202210875781.3A 2022-07-25 2022-07-25 Charging management system based on charging station load and charging control method thereof Pending CN115230515A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117565728A (en) * 2024-01-17 2024-02-20 新汽有限公司 New energy automobile battery charging outfit data acquisition system
CN117584790A (en) * 2023-11-23 2024-02-23 北京海蓝云联技术有限公司 Capacity-free charging pile control system

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117584790A (en) * 2023-11-23 2024-02-23 北京海蓝云联技术有限公司 Capacity-free charging pile control system
CN117565728A (en) * 2024-01-17 2024-02-20 新汽有限公司 New energy automobile battery charging outfit data acquisition system
CN117565728B (en) * 2024-01-17 2024-03-22 新汽有限公司 New energy automobile battery charging outfit data acquisition system

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