Power distribution network source and network load cooperative scheduling method and system for electric vehicle

Abstract

The invention is suitable for the technical field of electric vehicle charging, and provides a power distribution network source and network load cooperative scheduling method and system for an electric vehicle. The method comprises the steps of determining peak and valley time period information by acquiring source network power supply data of a power distribution network; charging access direct monitoring and load charging cooperative monitoring are carried out; constructing a load adjustment model of the target charging area; constructing a plurality of adjustment objective functions; and calculating adjustment variable data, and performing charging adjustment control according to the priority sequence and the adjustment variable data. The method can construct a load adjustment model, carry out load adjustment planning based on the load adjustment model, construct a plurality of adjustment objective functions, calculate adjustment variable data, and carry out charging adjustment control according to a priority sequence and the adjustment variable data, thereby achieving the intelligent and automatic control of the charging of the electric vehicle. The problems of line loss, voltage deviation and instable frequency are avoided, and potential threats to the electric energy quality, the operation efficiency and the stability of the power distribution network are reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle charging, and in particular relates to a distribution network source-grid-load coordinated dispatching method and system for electric vehicles. Background Art

[0002] The coordinated dispatching of sources, grids and loads in the distribution network for electric vehicles is a technology that uses communication, computing and control technologies in the power system to achieve real-time information interaction and coordinated optimization dispatching between the power source and the power grid for the specific load of electric vehicle charging.

[0003] The traditional electric vehicle charging control mechanism is relatively simple. It mainly starts charging directly after the user connects the electric vehicle to the charger, or selects the off-peak period to charge the electric vehicle according to the user's manual settings.

[0004] However, this charging mechanism lacks intelligence and automation and cannot achieve effective coordination and optimized scheduling of electric vehicle charging activities. Specifically, uncoordinated large-scale electric vehicle charging behavior will significantly increase line losses in the distribution network. The sharp increase in charging load may also cause grid voltage deviation and easily lead to grid frequency instability. Therefore, it poses a potential threat to the power quality, operating efficiency and stability of the distribution network. Summary of the invention

[0005] The purpose of the embodiments of the present invention is to provide a distribution network source-grid-load coordinated scheduling method and system for electric vehicles, aiming to solve the technical problems existing in the prior art mentioned in the background technology.

[0006] The embodiment of the present invention is implemented as follows: A distribution network source-grid-load coordinated dispatching method for electric vehicles, the method specifically comprising the following steps: Acquire the source network power supply data of the distribution network, perform dynamic peak and valley analysis on the source network power supply data, and determine the peak and valley time period information; Determine the target charging area, conduct direct monitoring of charging access and coordinated monitoring of load charging according to the preset coordinated scheduling cycle, and obtain charging access data and comprehensive charging data; Constructing a load adjustment model for a target charging area according to the peak and valley period information, the charging access data, and the comprehensive charging data; Based on the load adjustment model, load adjustment planning is performed to construct multiple adjustment objective functions; According to multiple adjustment objective functions and preset adjustment constraint data, the adjustment variable data is calculated, and priority analysis is performed to generate a priority order. Charging adjustment control is performed according to the priority order and the adjustment variable data.

[0007] As a further limitation of the technical solution of the embodiment of the present invention, the obtaining of the source network power supply data of the distribution network, performing dynamic peak-valley analysis on the source network power supply data, and determining the peak-valley period information specifically includes the following steps: Obtain source network power supply data of the distribution network; Cleaning and sorting the source network power supply data to generate time series power supply data; According to the preset peak-valley threshold data, dynamic peak-valley analysis is performed on the sequential power supply data to determine the peak-valley period information.

[0008] As a further limitation of the technical solution of the embodiment of the present invention, the determining of the target charging area, performing direct monitoring of charging access and coordinated monitoring of load charging according to a preset coordinated scheduling cycle, and obtaining charging access data and comprehensive charging data specifically include the following steps: Determine the target charging area; According to the preset collaborative scheduling cycle, access monitoring instructions and comprehensive monitoring instructions are periodically generated; According to the access monitoring instruction, charging access monitoring of electric vehicles is performed in the target charging area to obtain charging access data; According to the comprehensive monitoring instruction, comprehensive charging monitoring of electric vehicles is performed on the target charging area to obtain a plurality of first comprehensive data; Performing identity recognition on a plurality of the first comprehensive data to determine a plurality of directly monitored vehicles; generating and sending a collaborative monitoring request to a plurality of the directly monitored vehicles; By using the plurality of directly monitored vehicles, the plurality of indirectly monitored vehicles are cooperatively connected, and second comprehensive data of the plurality of indirectly monitored vehicles is obtained; The plurality of the first comprehensive data and the plurality of the second comprehensive data are sorted to obtain comprehensive charging data.

[0009] As a further limitation of the technical solution of the embodiment of the present invention, the construction of the load adjustment model of the target charging area according to the peak and valley period information, the charging access data and the comprehensive charging data specifically includes the following steps: Performing common load monitoring on the target charging area to obtain common load data; Performing real-time analysis on the comprehensive charging data to obtain average charging load data; The load adjustment model of the target charging area is constructed by integrating the common load data, the average charging load data, the peak and valley time period information and the charging access data.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the expression of the load adjustment model is: ; in, Represents the valley period, Represents the flat period, Represents the peak period, for The normal load at the time, is the average charging load, is the current time in the collaborative scheduling cycle, is the total number of vehicles in the target charging area, for The proportion of charging at the beginning of the time, and is the preset charging scheduling parameter, , and Adjust variables for charging.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the load adjustment planning based on the load adjustment model and the construction of multiple adjustment objective functions specifically include the following steps: Based on the load adjustment model, a load adjustment plan with minimum peak value is performed to construct a load peak minimum function; Based on the load adjustment model, a load adjustment plan with minimum load difference is carried out, and a load difference minimum function is constructed.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the load peak minimum function is: ; The load difference minimum function is: .

[0013] As a further limitation of the technical solution of the embodiment of the present invention, the calculation of adjustment variable data according to multiple adjustment objective functions and preset adjustment constraint data, and priority analysis to generate a priority order, and the charging adjustment control according to the priority order and the adjustment variable data specifically includes the following steps: Calculating adjustment variable data according to a plurality of adjustment objective functions and preset adjustment constraint data; Extracting historical access duration data from the comprehensive charging data; Comparing the access durations according to the historical access duration data, and performing relevant processing with the priorities to generate a priority order; According to the priority order and the adjustment variable data, charging adjustment control of the electric vehicle is performed in the target charging area.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the expression for adjusting the constraint data is: ; in, It corresponds to the lower limit of the charging adjustment variable during valley, flat and peak periods. It corresponds to the upper limit of the charging adjustment variable during valley, flat and peak periods.

[0015] A distribution network source-grid-load coordinated dispatching system for electric vehicles, the system comprising a dynamic peak-valley analysis module, a charging coordinated monitoring module, an adjustment model building module, a load adjustment planning module and a charging adjustment processing module, wherein: A dynamic peak-valley analysis module is used to obtain the source network power supply data of the distribution network, perform dynamic peak-valley analysis on the source network power supply data, and determine the peak-valley period information; The charging collaborative monitoring module is used to determine the target charging area, conduct direct monitoring of charging access and collaborative monitoring of load charging according to the preset collaborative scheduling cycle, and obtain charging access data and comprehensive charging data; An adjustment model building module, used to build a load adjustment model of a target charging area according to the peak and valley period information, the charging access data and the comprehensive charging data; A load adjustment planning module, used to perform load adjustment planning based on the load adjustment model and construct multiple adjustment objective functions; The charging adjustment processing module is used to calculate the adjustment variable data according to multiple adjustment objective functions and preset adjustment constraint data, and perform priority analysis to generate a priority order, and perform charging adjustment control according to the priority order and the adjustment variable data.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The embodiment of the present invention obtains the source network power supply data of the distribution network to determine the peak and valley time period information; performs direct monitoring of charging access and coordinated monitoring of load charging; constructs a load adjustment model for the target charging area; constructs multiple adjustment objective functions; calculates adjustment variable data, and performs charging adjustment control according to the priority order and adjustment variable data. It is possible to construct a load adjustment model, perform load adjustment planning based on the load adjustment model, construct multiple adjustment objective functions, calculate adjustment variable data, and perform charging adjustment control according to the priority order and adjustment variable data, thereby realizing intelligent and automated control of electric vehicle charging, avoiding line loss, voltage offset and frequency instability, and reducing potential threats to the power quality, operating efficiency and stability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of a distribution network source-grid-load coordinated dispatching method for electric vehicles provided by an embodiment of the present invention is shown; Figure 2 A flow chart showing dynamic peak-valley analysis in a method provided by an embodiment of the present invention is shown; Figure 3 A flow chart showing direct monitoring of charging access and coordinated monitoring of load charging in the method provided by an embodiment of the present invention is shown; Figure 4 A flow chart of constructing a load adjustment model in the method provided by an embodiment of the present invention is shown; Figure 5 A flowchart of constructing multiple adjustment objective functions in the method provided by an embodiment of the present invention is shown; Figure 6 A flowchart of charging adjustment control in the method provided by an embodiment of the present invention is shown; Figure 7 An application architecture diagram of a distribution network source-grid-load coordinated dispatching system for electric vehicles provided in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] It is understandable that in the prior art, the charging control mechanism of electric vehicles is relatively simple. It mainly starts charging directly after the user connects the electric vehicle to the charger, or chooses the off-peak period to charge the electric vehicle according to the user's manual settings. It lacks an intelligent and automated charging mechanism and cannot achieve effective coordination and optimized scheduling of electric vehicle charging activities. Specifically, uncoordinated large-scale electric vehicle charging behavior will significantly increase the line loss of the distribution network. The sharp increase in charging load may also cause the grid voltage to shift and easily lead to grid frequency instability. Therefore, it poses a potential threat to the power quality, operating efficiency and stability of the distribution network.

[0020] In order to solve the above problems, the embodiment of the present invention discloses a distribution network source-grid-load coordinated dispatching method and system for electric vehicles, which obtains the source-grid power supply data of the distribution network, performs dynamic peak-valley analysis on the source-grid power supply data, and determines the peak-valley period information; determines the target charging area, and performs direct monitoring of charging access and coordinated monitoring of load charging according to the preset coordinated dispatching cycle, and obtains charging access data and comprehensive charging data; constructs a load adjustment model for the target charging area according to the peak-valley period information, charging access data and comprehensive charging data; performs load adjustment planning based on the load adjustment model, and constructs multiple adjustment objective functions; calculates adjustment variable data according to multiple adjustment objective functions and preset adjustment constraint data, and performs priority analysis to generate a priority order, and performs charging adjustment control according to the priority order and adjustment variable data. It is possible to construct a load adjustment model, perform load adjustment planning based on the load adjustment model, construct multiple adjustment objective functions, calculate adjustment variable data, and perform charging adjustment control according to the priority order and adjustment variable data, thereby realizing intelligent and automatic control of electric vehicle charging, avoiding problems such as line loss, voltage offset and frequency instability, and reducing potential threats to the power quality, operation efficiency and stability of the distribution network.

[0021] Specifically, Figure 1 A flow chart of a distribution network source-grid-load coordinated dispatching method for electric vehicles provided in an embodiment of the present invention is shown.

[0022] In a preferred embodiment of the present invention, a distribution network source-grid-load coordinated dispatching method for electric vehicles is provided, and the method specifically comprises the following steps: Step S101: Acquire power supply data of a distribution network, perform dynamic peak-valley analysis on the power supply data of the distribution network, and determine peak-valley time period information.

[0023] In an embodiment of the present invention, according to a preset historical time period, source network power supply data corresponding to the historical time period is extracted from the historical record data of the distribution network, and the source network power supply data is cleaned by removing duplications, invalidity and anomalies, and then the data is sorted according to the time series of the historical time period to generate time series power supply data, and then the peak and valley analysis of the power supply is performed according to the time series power supply of different power sources and the total time series power supply, and the power supply is compared according to the preset peak and valley threshold data, and then the peak and valley time period information is determined based on the comparison result.

[0024] It can be understood that the source-grid power supply data refers to the data on the power supply provided by different power sources (including thermal power sources, wind power sources, hydropower sources, solar power sources, etc.) through the distribution network at different times within a historical period.

[0025] It can be understood that in the embodiment of the present invention, the determination of the peak and valley time periods is not only related to the total sequential power supply, but also to the sequential power supply of different power sources, which can ensure the subsequent charging adjustment of electric vehicles, which is related to the power supply of different power sources, thereby realizing the coordinated scheduling of source, grid and load.

[0026] Specifically, Figure 2 A flow chart of performing dynamic peak-valley analysis in the method provided in an embodiment of the present invention is shown.

[0027] Among them, in the preferred embodiment provided by the present invention, the obtaining of the source network power supply data of the distribution network, performing dynamic peak-valley analysis on the source network power supply data, and determining the peak-valley period information specifically includes the following steps: Step S1011, obtaining source network power supply data of the distribution network.

[0028] Step S1012: Clean and organize the source network power supply data to generate time-series power supply data.

[0029] Step S1013: Perform dynamic peak-valley analysis on the sequential power supply data according to preset peak-valley threshold data to determine peak-valley period information.

[0030] Furthermore, the distribution network source-grid-load coordinated dispatching method for electric vehicles further includes the following steps: Step S102: determine the target charging area, perform direct charging access monitoring and load charging collaborative monitoring according to a preset collaborative scheduling cycle, and obtain charging access data and comprehensive charging data.

[0031] In an embodiment of the present invention, by determining the target charging area, and then according to the preset collaborative scheduling cycle, periodically generating access monitoring instructions and comprehensive monitoring instructions, and then according to the access monitoring instructions, performing charging access monitoring of electric vehicles on the target charging area to obtain charging access data, and according to the comprehensive monitoring instructions, performing comprehensive charging monitoring of electric vehicles on the target charging area to obtain multiple first comprehensive data, by performing identity identification on the multiple first comprehensive data, determining multiple direct monitoring vehicles that send corresponding first comprehensive data, and other charging access electric vehicles are marked as indirect monitoring vehicles, by generating a collaborative monitoring request, sending the collaborative monitoring request to the multiple direct monitoring vehicles, and then through the multiple direct monitoring vehicles, establishing a collaboratively connected communication relationship with the multiple indirect monitoring vehicles, and through the multiple direct monitoring vehicles, assisting in obtaining the second comprehensive data of the multiple indirect monitoring vehicles, and then comprehensively sorting the multiple first comprehensive data and the multiple second comprehensive data to obtain comprehensive charging data.

[0032] It is understandable that the collaborative scheduling period can be set to 24 hours.

[0033] It can be understood that the charging access data includes both the current number of charging accesses for electric vehicles in the target charging area and the total number of electric vehicles in the target charging area.

[0034] It can be understood that the comprehensive charging data is composed of a plurality of first comprehensive data and a plurality of second comprehensive data; the first comprehensive data and the second comprehensive data both contain the charging time and charging power of the corresponding electric vehicle.

[0035] It can be understood that the communication relationship of the collaborative connection is between the direct monitoring vehicle and the corresponding indirect monitoring vehicle, which is realized through wireless communication technologies such as Bluetooth, ZigBee, WiFi, etc.

[0036] Specifically, Figure 3 A flow chart of direct monitoring of charging access and coordinated monitoring of load charging in the method provided in an embodiment of the present invention is shown.

[0037] Among them, in the preferred embodiment provided by the present invention, the determination of the target charging area, performing direct monitoring of charging access and coordinated monitoring of load charging according to a preset coordinated scheduling cycle, and obtaining charging access data and comprehensive charging data specifically include the following steps: Step S1021: Determine the target charging area.

[0038] Step S1022: periodically generate access monitoring instructions and comprehensive monitoring instructions according to a preset collaborative scheduling period.

[0039] Step S1023: According to the access monitoring instruction, the charging access of the electric vehicle is monitored in the target charging area to obtain charging access data.

[0040] Step S1024: Perform comprehensive charging monitoring of the electric vehicle in the target charging area according to the comprehensive monitoring instruction to obtain a plurality of first comprehensive data.

[0041] Step S1025: perform identity recognition on the plurality of the first comprehensive data to determine a plurality of directly monitored vehicles.

[0042] Step S1026: Generate and send a collaborative monitoring request to multiple directly monitored vehicles.

[0043] Step S1027: through the multiple directly monitored vehicles, coordinately connect the multiple indirectly monitored vehicles and obtain the second comprehensive data of the multiple indirectly monitored vehicles.

[0044] Step S1028: sort the plurality of the first comprehensive data and the plurality of the second comprehensive data to obtain comprehensive charging data.

[0045] Furthermore, the distribution network source-grid-load coordinated dispatching method for electric vehicles further includes the following steps: Step S103: construct a load adjustment model for the target charging area according to the peak and valley period information, the charging access data and the comprehensive charging data.

[0046] In the embodiment of the present invention, common loads such as lighting and air conditioning are monitored in the target charging area to obtain common load data, and the comprehensive charging data is analyzed in real time to obtain average charging load data of electric vehicles. Then, the common load data, average charging load data, peak and valley time period information and charging access data are integrated to construct a load adjustment model for the target charging area. Specifically, the expression of the load adjustment model for charging electric vehicles in the target charging area is: ; in, Represents the valley period, Represents the flat period, Represents the peak period, for The normal load at the time, is the average charging load, is the current time in the collaborative scheduling cycle, is the total number of vehicles in the target charging area, for The proportion of charging at the beginning of the time, and is the preset charging scheduling parameter, , and Adjust variables for charging.

[0047] Understandably, , and The charging adjustment variables correspond to adjusting the charging quantity of electric vehicles during valley, flat and peak periods respectively.

[0048] Specifically, Figure 4 A flow chart of constructing a load adjustment model in the method provided in an embodiment of the present invention is shown.

[0049] Among them, in the preferred embodiment provided by the present invention, the construction of the load adjustment model of the target charging area according to the peak and valley period information, the charging access data and the comprehensive charging data specifically includes the following steps: Step S1031: perform common load monitoring on the target charging area to obtain common load data.

[0050] Step S1032: Analyze the comprehensive charging data in real time to obtain average charging load data.

[0051] Step S1033: construct a load adjustment model for the target charging area by integrating the common load data, the average charging load data, the peak and valley time period information, and the charging access data.

[0052] Furthermore, the distribution network source-grid-load coordinated dispatching method for electric vehicles further includes the following steps: Step S104: Based on the load adjustment model, load adjustment planning is performed to construct a plurality of adjustment objective functions.

[0053] In the embodiment of the present invention, based on the load adjustment model, a load adjustment plan with minimum peak value and minimum load difference is performed, and then a load peak minimum function and a load difference minimum function are constructed. Specifically, the load peak minimum function is: ; The load difference minimum function is: .

[0054] Specifically, Figure 5 A flow chart of constructing multiple adjustment objective functions in the method provided by an embodiment of the present invention is shown.

[0055] Among them, in the preferred implementation mode provided by the present invention, the load adjustment planning is performed based on the load adjustment model, and the construction of multiple adjustment objective functions specifically includes the following steps: Step S1041: Based on the load adjustment model, a load adjustment plan with minimum peak value is performed to construct a load peak minimum function.

[0056] Step S1042: Based on the load adjustment model, a load adjustment plan with minimum load difference is performed, and a load difference minimum function is constructed.

[0057] Furthermore, the distribution network source-grid-load coordinated dispatching method for electric vehicles further includes the following steps: Step S105: Calculate adjustment variable data according to multiple adjustment objective functions and preset adjustment constraint data, perform priority analysis, generate a priority sequence, and perform charging adjustment control according to the priority sequence and the adjustment variable data.

[0058] In an embodiment of the present invention, adjustment constraint data is obtained, the adjustment constraint data is combined with a plurality of adjustment objective functions, adjustment variable data is calculated, and historical access time data is extracted from comprehensive charging data. The historical access time data is analyzed, and the access time of a plurality of electric vehicles connected for charging is compared. Then, according to the comparison result of the access time, the priority is processed and a priority order is generated. Then, according to the priority order and the adjustment variable data, the charging of the electric vehicle in the target charging area is adjusted and controlled, so that the charging quantity of the electric vehicle is adjusted accordingly during the valley period, the flat period and the peak period, ensuring that both the adjustment constraint data and the requirements corresponding to the plurality of adjustment objective functions are met, thereby realizing intelligent and automatic control of the charging of the electric vehicle, avoiding the problems of line loss, voltage offset and frequency instability, and reducing the potential threat to the power quality, operation efficiency and stability of the distribution network. Specifically, the expression of the adjustment constraint data is: ; in, It corresponds to the lower limit of the charging adjustment variable during valley, flat and peak periods. It corresponds to the upper limit of the charging adjustment variable during valley, flat and peak periods.

[0059] It can be understood that the priority order is related to the charging time of each electric vehicle. Specifically: the longer the charging time, the lower the priority; the shorter the charging time, the higher the priority.

[0060] It is understandable that adjusting variable data is , or The specific value corresponding to the charging adjustment variable is: when the current moment in the coordinated scheduling cycle is the valley period, the corresponding value is The specific value of; when the current time in the coordinated scheduling cycle is a flat period, the corresponding The specific value of; when the current time in the coordinated scheduling cycle is the peak period, the corresponding The specific value of .

[0061] Specifically, Figure 6 A flow chart of charging adjustment control in the method provided by an embodiment of the present invention is shown.

[0062] Among them, in the preferred embodiment provided by the present invention, the calculation of adjustment variable data according to multiple adjustment objective functions and preset adjustment constraint data, and the priority analysis to generate a priority order, and the charging adjustment control according to the priority order and the adjustment variable data specifically includes the following steps: Step S1051 : Calculate adjustment variable data according to a plurality of adjustment objective functions and preset adjustment constraint data.

[0063] Step S1052: extract historical access duration data from the comprehensive charging data.

[0064] Step S1053: Compare the access durations according to the historical access duration data, and perform related processing with the priorities to generate a priority order.

[0065] Step S1054: performing charging adjustment control on the electric vehicle in the target charging area according to the priority order and the adjustment variable data.

[0066] Furthermore, Figure 7 An application architecture diagram of a distribution network source-grid-load coordinated dispatching system for electric vehicles provided in an embodiment of the present invention is shown.

[0067] Among them, in another preferred embodiment provided by the present invention, a distribution network source-grid-load coordinated dispatching system for electric vehicles includes: The dynamic peak-valley analysis module 101 is used to obtain the source network power supply data of the distribution network, perform dynamic peak-valley analysis on the source network power supply data, and determine the peak-valley period information.

[0068] In an embodiment of the present invention, the dynamic peak-valley analysis module 101 extracts the source network power supply data corresponding to the historical time period from the historical record data of the distribution network according to a preset historical time period, performs data cleaning processing on the source network power supply data by removing duplications, invalidity and anomalies, and then organizes the data according to the time series of the historical time period to generate time series power supply data, and then performs power supply peak-valley analysis according to the time series power supply of different power sources and the total time series power supply, compares the power supply according to the preset peak-valley threshold data, and then determines the peak-valley time period information based on the comparison result.

[0069] The charging collaborative monitoring module 102 is used to determine the target charging area, perform charging access direct monitoring and load charging collaborative monitoring according to a preset collaborative scheduling cycle, and obtain charging access data and comprehensive charging data.

[0070] In an embodiment of the present invention, the charging collaborative monitoring module 102 determines the target charging area, and then periodically generates access monitoring instructions and comprehensive monitoring instructions according to a preset collaborative scheduling period, and then performs charging access monitoring of electric vehicles in the target charging area according to the access monitoring instructions to obtain charging access data, and performs comprehensive charging monitoring of electric vehicles in the target charging area according to the comprehensive monitoring instructions to obtain multiple first comprehensive data, and identifies multiple direct monitoring vehicles that send corresponding first comprehensive data by performing identity identification on the multiple first comprehensive data, while other charging-accessed electric vehicles are marked as indirect monitoring vehicles, and generates a collaborative monitoring request, sends the collaborative monitoring request to multiple direct monitoring vehicles, and then establishes a collaboratively connected communication relationship with multiple indirect monitoring vehicles through multiple direct monitoring vehicles, and assists in obtaining second comprehensive data of multiple indirect monitoring vehicles through multiple direct monitoring vehicles, and then comprehensively organizes the multiple first comprehensive data and multiple second comprehensive data to obtain comprehensive charging data.

[0071] The adjustment model building module 103 is used to build a load adjustment model of the target charging area according to the peak and valley period information, the charging access data and the comprehensive charging data.

[0072] In the embodiment of the present invention, the adjustment model building module 103 monitors the common loads such as lighting and air conditioning in the target charging area to obtain common load data, and performs real-time analysis on the comprehensive charging data to obtain the average charging load data of the electric vehicle, and then integrates the common load data, the average charging load data, the peak and valley time period information and the charging access data to build a load adjustment model for the target charging area. Specifically, the expression of the load adjustment model for charging electric vehicles in the target charging area is: ; in, Represents the valley period, Represents the flat period, Represents the peak period, for The normal load at the time, is the average charging load, is the current time in the collaborative scheduling cycle, is the total number of vehicles in the target charging area, for The proportion of charging at the beginning of the time, and is the preset charging scheduling parameter, , and Adjust variables for charging.

[0073] The load adjustment planning module 104 is used to perform load adjustment planning based on the load adjustment model and construct multiple adjustment objective functions.

[0074] In the embodiment of the present invention, the load adjustment planning module 104 performs load adjustment planning with minimum peak value and minimum load difference based on the load adjustment model, and then constructs a load peak minimum function and a load difference minimum function. Specifically, the load peak minimum function is: ; The load difference minimum function is: .

[0075] The charging adjustment processing module 105 is used to calculate the adjustment variable data according to multiple adjustment objective functions and preset adjustment constraint data, and perform priority analysis to generate a priority sequence, and perform charging adjustment control according to the priority sequence and the adjustment variable data.

[0076] In the embodiment of the present invention, the charging adjustment processing module 105 obtains adjustment constraint data, combines the adjustment constraint data with multiple adjustment objective functions, calculates adjustment variable data, and extracts historical access time data from the comprehensive charging data. By analyzing the historical access time data, the access time of multiple electric vehicles connected for charging is compared, and then the comparison result of the access time is correlated with the priority to generate a priority order, and then the charging adjustment control of the electric vehicle in the target charging area is performed according to the priority order and the adjustment variable data, so that the charging quantity of the electric vehicle is adjusted accordingly during the valley period, the flat period and the peak period, ensuring that both the adjustment constraint data and the corresponding requirements of the multiple adjustment objective functions are met, thereby realizing intelligent and automatic control of electric vehicle charging, avoiding line loss, voltage offset and frequency instability, and reducing the potential threat to the power quality, operation efficiency and stability of the distribution network. Specifically, the expression of the adjustment constraint data is: ; in, It corresponds to the lower limit of the charging adjustment variable during valley, flat and peak periods. It corresponds to the upper limit of the charging adjustment variable during valley, flat and peak periods.

[0077] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0078] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0079] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A distribution network source-grid-load coordinated dispatching method for electric vehicles, characterized in that: The method specifically comprises the following steps: Acquire the source network power supply data of the distribution network, perform dynamic peak and valley analysis on the source network power supply data, and determine the peak and valley time period information; Determine the target charging area, conduct direct monitoring of charging access and coordinated monitoring of load charging according to the preset coordinated scheduling cycle, and obtain charging access data and comprehensive charging data; Constructing a load adjustment model for a target charging area according to the peak and valley period information, the charging access data, and the comprehensive charging data; Based on the load adjustment model, load adjustment planning is performed to construct multiple adjustment objective functions; According to multiple adjustment objective functions and preset adjustment constraint data, the adjustment variable data is calculated, and priority analysis is performed to generate a priority order. Charging adjustment control is performed according to the priority order and the adjustment variable data.

2. The method for coordinated dispatching of distribution network sources, grids and loads for electric vehicles according to claim 1, characterized in that: The step of obtaining the source network power supply data of the distribution network, performing dynamic peak-valley analysis on the source network power supply data, and determining the peak-valley period information specifically includes the following steps: Obtain source network power supply data of the distribution network; Cleaning and sorting the source network power supply data to generate time series power supply data; According to the preset peak-valley threshold data, dynamic peak-valley analysis is performed on the sequential power supply data to determine the peak-valley period information.

3. The method for coordinated dispatching of distribution network sources, grids and loads for electric vehicles according to claim 1, characterized in that: Determining the target charging area, performing direct monitoring of charging access and coordinated monitoring of load charging according to a preset coordinated scheduling cycle, and obtaining charging access data and comprehensive charging data specifically include the following steps: Determine the target charging area; According to the preset collaborative scheduling cycle, access monitoring instructions and comprehensive monitoring instructions are periodically generated; According to the access monitoring instruction, charging access monitoring of electric vehicles is performed in the target charging area to obtain charging access data; According to the comprehensive monitoring instruction, comprehensive charging monitoring of electric vehicles is performed on the target charging area to obtain a plurality of first comprehensive data; Performing identity recognition on a plurality of the first comprehensive data to determine a plurality of directly monitored vehicles; generating and sending a collaborative monitoring request to a plurality of the directly monitored vehicles; By using the plurality of directly monitored vehicles, the plurality of indirectly monitored vehicles are cooperatively connected, and second comprehensive data of the plurality of indirectly monitored vehicles is obtained; The plurality of the first comprehensive data and the plurality of the second comprehensive data are sorted to obtain comprehensive charging data.

4. The method for coordinated dispatching of distribution network sources, grids and loads for electric vehicles according to claim 1, characterized in that: The constructing of a load adjustment model for a target charging area according to the peak and valley period information, the charging access data and the comprehensive charging data specifically comprises the following steps: Performing common load monitoring on the target charging area to obtain common load data; Performing real-time analysis on the comprehensive charging data to obtain average charging load data; The load adjustment model of the target charging area is constructed by integrating the common load data, the average charging load data, the peak and valley time period information and the charging access data.

5. The method for coordinated dispatching of distribution network sources, grids and loads for electric vehicles according to claim 4, characterized in that: The expression of the load adjustment model is: ； in, Represents the valley period, Represents the flat period, Represents the peak period, for The normal load at the time, is the average charging load, is the current time in the collaborative scheduling cycle, is the total number of vehicles in the target charging area, for The proportion of charging at the beginning of the time, and is the preset charging scheduling parameter, , and Adjust variables for charging.

6. The method for coordinated dispatching of distribution network sources, grids and loads for electric vehicles according to claim 5, characterized in that: The load adjustment planning based on the load adjustment model and the construction of multiple adjustment objective functions specifically include the following steps: Based on the load adjustment model, a load adjustment plan with minimum peak value is performed to construct a load peak minimum function; Based on the load adjustment model, a load adjustment plan with minimum load difference is carried out, and a load difference minimum function is constructed.

7. The method for coordinated dispatching of distribution network sources, grids and loads for electric vehicles according to claim 6, characterized in that: The load peak minimum function is: ； The load difference minimum function is: 。 8. The method for coordinated dispatching of distribution network sources, grids and loads for electric vehicles according to claim 7, characterized in that: The method of calculating the adjustment variable data according to the plurality of adjustment objective functions and the preset adjustment constraint data, performing priority analysis, generating a priority sequence, and performing charging adjustment control according to the priority sequence and the adjustment variable data specifically includes the following steps: Calculating adjustment variable data according to a plurality of adjustment objective functions and preset adjustment constraint data; Extracting historical access duration data from the comprehensive charging data; Comparing the access durations according to the historical access duration data, and performing relevant processing with the priorities to generate a priority order; According to the priority order and the adjustment variable data, charging adjustment control of the electric vehicle is performed in the target charging area.

9. The method for coordinated dispatching of distribution network sources, grids and loads for electric vehicles according to claim 8, characterized in that: The expression for adjusting the constraint data is: ； in, It corresponds to the lower limit of the charging adjustment variable during valley, flat and peak periods. It corresponds to the upper limit of the charging adjustment variable during valley, flat and peak periods.

10. A distribution network source-grid-load coordinated dispatching system for electric vehicles, characterized in that: The system includes a dynamic peak-valley analysis module, a charging collaborative monitoring module, an adjustment model building module, a load adjustment planning module and a charging adjustment processing module, wherein: A dynamic peak-valley analysis module is used to obtain the source network power supply data of the distribution network, perform dynamic peak-valley analysis on the source network power supply data, and determine the peak-valley period information; The charging collaborative monitoring module is used to determine the target charging area, conduct direct monitoring of charging access and collaborative monitoring of load charging according to the preset collaborative scheduling cycle, and obtain charging access data and comprehensive charging data; An adjustment model building module, used to build a load adjustment model of a target charging area according to the peak and valley period information, the charging access data and the comprehensive charging data; A load adjustment planning module, used to perform load adjustment planning based on the load adjustment model and construct multiple adjustment objective functions; The charging adjustment processing module is used to calculate the adjustment variable data according to multiple adjustment objective functions and preset adjustment constraint data, and perform priority analysis to generate a priority order, and perform charging adjustment control according to the priority order and the adjustment variable data.

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