Electric vehicle charging load dispatching system

Abstract

The invention relates to the technical field of electric vehicle charging operation, and particularly provides an electric vehicle charging load scheduling system, which comprises a regulation and control execution channel, a service operation channel and a data acquisition channel which are respectively provided with a special encryption machine, the regulation and control execution channel and the business operation channel are connected with the MQTT gateway through the communication agent service module; according to the technical scheme provided by the invention, the service instruction and the acquisition task are ensured not to interfere with each other by configuring the special encryption machine, so that the concurrent acquisition processing capability is improved; the regulation and control task processing priority is determined by calculating and evaluating the adjustable potentials of different operators and the power regulation capability of the charging pile, so that the total quantity of instructions can be greatly reduced, and the regulation and control task concurrent execution capability of the high-concurrency data dedicated channel is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging operation, and particularly relates to an electric vehicle charging load scheduling system. Background Art

[0002] Currently, new energy vehicles are showing a rapid development trend in the passenger vehicle market. Therefore, it is particularly important to give full play to the load storage characteristics of electric vehicles and accelerate the large-scale application of vehicle-grid interaction.

[0003] To meet the high concurrency and high performance requirements for massive charging and discharging resources to access the cloud system for rapid aggregation and regulation, it is necessary to carry out research on optimizing the key technologies of real-time information interaction and efficient cluster regulation that meet large-scale vehicle-pile-network interaction, and optimize the system architecture design that meets the minute-level regulation requirements.

[0004] However, in the scenario of upstream data collection service, the current encryption machine does not have the ability to concurrently collect and process a large number of devices, resulting in the accumulation of collected data, causing problems such as data processing delay and inconsistent device online status. In the scenario of downstream data interaction, due to the lack of task processing priority setting or dedicated encryption machines for device upstream and downstream channels, the concurrent execution ability of regulation tasks with high downstream priority is poor, making it difficult to support the implementation of the whole network's second-level vehicle-grid interaction regulation service. Summary of the Invention

[0005] In order to overcome the above defects, the present invention proposes an electric vehicle charging load scheduling system, which includes:

[0006] A regulation execution channel, a service operation channel, and a data collection channel respectively provided with dedicated encryption machines;

[0007] The regulation execution channel and the service operation channel are connected to the MQTT Internet of Things gateway through a communication proxy service module;

[0008] The regulation execution channel is used to send the regulation commands and operation instructions generated by the regulation strategy center to the regulation object through the communication proxy service module and the MQTT Internet of Things gateway, and receive the command result feedback and operation instruction execution result feedback from the regulation object;

[0009] The service operation channel is used to send the service query instructions generated by the service center to the regulation object through the communication proxy service module and the MQTT Internet of Things gateway;

[0010] The data collection channel is used to obtain the real-time query data corresponding to the service query instructions through the MQTT Internet of Things gateway, and upload the real-time query data to the service middle platform / big data platform;

[0011] Among them, the objects to be regulated include: operators and / or charging piles.

[0012] Preferably, the process of the regulation strategy center generating regulation commands and operation instructions includes:

[0013] Set the operator load regulation priority from high to low according to the adjustable capacity of the charging load of the operator;

[0014] Use the entropy weight method to calculate the comprehensive evaluation score of the power regulation of the charging piles corresponding to the operator, and set the load regulation priority of the charging piles corresponding to the operator in the order from high to low according to the scores;

[0015] Generate operation instructions based on the operator load regulation priority and the load regulation priority of the charging piles corresponding to the operator.

[0016] Preferably, the adjustable capacity of the charging load of the operator is as follows:

[0017]

[0018] In the above formula, is the adjustable capacity of the charging load of operator m at time t, is the charging power of charging vehicle n accessed by operator m at time t, is the judgment coefficient of charging vehicle n participating in charging load regulation at time t, and N is the total number of charging vehicles accessed by operator m.

[0019] Furthermore, the judgment coefficient of charging vehicle n participating in charging load regulation at time t is as follows:

[0020]

[0021] In the above formula, is the battery power of charging vehicle n at time t, is the minimum set value of the battery power of charging vehicle n.

[0022] Furthermore, in the process of using the entropy weight method to calculate the comprehensive evaluation score of the power regulation of the charging piles corresponding to the operator, the information entropy E of the j-th preset index is determined according to the following formula j :

[0023]

[0024] In the above formula, I is the number of charging piles corresponding to the operator, and Y ij is the index value of the j-th preset index of charging pile i corresponding to the operator. The weight W of the j-th preset index is determined according to the following formula j :

[0025]

[0026] In the above formula, J is the preset number of indicators.

[0027] Furthermore, the preset indicators include: the charging power and charging energy of the electric vehicle.

[0028] Furthermore, the operation instruction is: decompose and regulate the power with reference to the maximum rated power of the charging pile, and select charging piles to participate in load regulation in turn according to the load regulation priority of the operator and the load regulation priority of the corresponding charging pile of the operator until the decomposed power of the selected charging pile reaches the regulation command.

[0029] One or more of the above technical solutions of the present invention have at least one or more of the following beneficial effects:

[0030] The present invention relates to the technical field of electric vehicle charging operation, and specifically provides an electric vehicle charging load scheduling system, including: a regulation execution channel, a service operation channel, and a data acquisition channel respectively provided with dedicated encryption machines; the regulation execution channel and the service operation channel are connected to the MQTT Internet of Things gateway through a communication proxy service module; the regulation execution channel and the service operation channel are connected to the MQTT Internet of Things gateway through a communication proxy service module; the regulation execution channel is used to send the regulation command and operation instruction generated by the regulation strategy center to the regulation object through the communication proxy service module and the MQTT Internet of Things gateway, and receive the command result feedback and operation instruction execution result feedback fed back by the regulation object; the service operation channel is used to send the service query instruction generated by the service center to the regulation object through the communication proxy service module and the MQTT Internet of Things gateway; the data acquisition channel is used to obtain the real-time query data corresponding to the service query instruction through the MQTT Internet of Things gateway and upload the real-time query data to the service middle platform / big data platform; wherein, the regulation object includes: operators and / or charging piles. The technical solution provided by the present invention ensures that the service instruction and the acquisition task do not interfere with each other by configuring a dedicated encryption machine, improving the concurrent acquisition and processing ability; by calculating and evaluating the adjustable potential of different operators and the power adjustment ability of charging piles, the regulation task processing priority is established, which can greatly reduce the total amount of instructions and improve the concurrent execution ability of the regulation tasks of the high-concurrency data dedicated channel. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is the main structural block diagram of the electric vehicle charging load scheduling system according to the embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0032] The following further describes in detail the specific embodiments of the present invention with reference to the accompanying drawings.

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0034] Embodiment 1

[0035] Refer to the attached Figure 1 , Figure 1 which is a schematic diagram of the main steps of an electric vehicle charging load scheduling system according to an embodiment of the present invention. As Figure 1 shown, the electric vehicle charging load scheduling system in the embodiment of the present invention mainly includes: a regulation execution channel, a service operation channel, and a data acquisition channel, each provided with a dedicated encryption machine;

[0036] The regulation execution channel and the service operation channel are connected to the MQTT Internet of Things gateway through a communication proxy service module;

[0037] The regulation execution channel is used to send the regulation commands and operation instructions generated by the regulation strategy center to the regulation object through the communication proxy service module and the MQTT Internet of Things gateway, and receive the feedback of the command result feedback and the operation instruction execution result feedback from the regulation object;

[0038] The service operation channel is used to send the service query instructions generated by the service center to the regulation object through the communication proxy service module and the MQTT Internet of Things gateway;

[0039] The data acquisition channel is used to obtain the real-time query data corresponding to the service query instructions through the MQTT Internet of Things gateway and upload the real-time query data to the service middle platform / big data platform;

[0040] Among them, the regulation object includes: operators and / or charging piles.

[0041] In this embodiment, the process of the regulation strategy center generating regulation commands and operation instructions includes:

[0042] Set the operator load regulation priority from high to low according to the adjustable charging load capacity of the operator;

[0043] Use the entropy weight method to calculate the comprehensive evaluation score of the power adjustment of the charging piles corresponding to the operator, and set the load regulation priority of the charging piles corresponding to the operator from high to low according to the scores;

[0044] Generate operation instructions based on the operator load regulation priority and the load regulation priority of the charging piles corresponding to the operator.

[0045] In this embodiment, the adjustable charging load capacity of the operator is as follows:

[0046]

[0047] In the above formula, is the adjustable charging load capacity of operator m at time t, is the charging power of charging vehicle n accessed by operator m at time t, is the judgment coefficient of charging vehicle n participating in charging load regulation at time t, and N is the total number of charging vehicles accessed by operator m.

[0048] In one embodiment, the judgment coefficient of charging vehicle n participating in charging load regulation at time t is as follows:

[0049]

[0050] In the above formula, is the battery power of charging vehicle n at time t, is the minimum set value of the battery power of charging vehicle n.

[0051] In one embodiment, in the process of calculating the comprehensive evaluation score of the power regulation of the charging piles corresponding to the operator by using the entropy weight method, the information entropy E of the j-th preset index is determined according to the following formula j :

[0052]

[0053] In the above formula, I is the number of charging piles corresponding to the operator, and Y ij is the index value of the j-th preset index of charging pile i corresponding to the operator. The weight W of the j-th preset index is determined according to the following formula j :

[0054]

[0055] In the above formula, J is the number of preset indexes.

[0056] In one embodiment, the preset indexes include: the charging power and charging electricity of electric vehicles.

[0057] In one embodiment, the operation instruction is: decompose the regulation power with reference to the maximum rated power of the charging pile, and select the charging piles to participate in load regulation in turn according to the load regulation priority of the operator and the load regulation priority of the charging piles corresponding to the operator until the decomposed power of the selected charging piles reaches the regulation command.

[0058] In a specific embodiment, the data acquisition channel has high-speed parallel decoding capabilities. The data acquisition channel is responsible for reporting real-time data collected from the TCU and the SDK pile, including real-time monitoring data during the charging process, charging models, charging transaction records, and other data. The regulation execution channel is a dedicated channel that supports operations such as load regulation instructions, orderly charging strategies, and charging pile startup and shutdown. The service operation channel is the execution channel for all downstream operation instructions except for regulation execution, and it supports the execution of downstream service operations such as issuing charging models to all TCUs (control and management modules inside the charging piles) and SDK (charging pile development toolkits), charging service fee charging models, charging pile parameter settings, and charging authentication.

[0059] In a specific embodiment, an example of calculating the comprehensive evaluation score for power regulation of a charging pile and the sorting vector of charging pile load decomposition at a certain moment is given. An example of the original vehicle data at a certain moment is shown in Table 1 below:

[0060] Table 1

[0061]

[0062] After vector normalization and the evaluation method proposed by the present invention, the calculation results are shown in Table 2 below:

[0063] Table 2

[0064]

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that it is still possible to modify the specific embodiments of the present invention or make equivalent replacements. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. An electric vehicle charging load scheduling system, characterized in that, The system includes: a regulation execution channel, a service operation channel, and a data acquisition channel, each provided with a dedicated encryption machine; The regulation execution channel and the service operation channel are connected to the MQTT Internet of Things gateway through a communication proxy service module; The regulation execution channel is used to send the regulation commands and operation instructions generated by the regulation strategy center to the regulation object through the communication proxy service module and the MQTT Internet of Things gateway, and receive the feedback of the command result feedback and the operation instruction execution result feedback from the regulation object; The service operation channel is used to send the service query instructions generated by the service center to the regulation object through the communication proxy service module and the MQTT Internet of Things gateway; The data acquisition channel is used to obtain the real-time query data corresponding to the service query instructions through the MQTT Internet of Things gateway and upload the real-time query data to the service middle platform / big data platform; Among them, the regulation object includes: operators and / or charging piles.

2. The system according to claim 1, wherein The process of the regulation strategy center generating regulation commands and operation instructions includes: Setting the operator load regulation priority from high to low according to the adjustable charging load capacity of the operator; Calculating the comprehensive evaluation score of the power regulation of the charging piles corresponding to the operator by using the entropy weight method, and setting the load regulation priority of the charging piles corresponding to the operator from high to low according to the score; Generating operation instructions based on the operator load regulation priority and the load regulation priority of the charging piles corresponding to the operator.

3. The system according to claim 1, characterized in that, The adjustable charging load capacity of the operator is as follows: In the above formula, is the adjustable capacity of the charging load of operator m at time t, is the charging power of charging vehicle n connected to operator m at time t, is the judgment coefficient of charging vehicle n participating in charging load regulation at time t, and N is the total number of charging vehicles connected to operator m.

4. The system according to claim 3, wherein The judgment coefficient of the charging vehicle n participating in the charging load regulation at time t is as follows: In the above formula, is the battery power of the charging vehicle n at time t, is the minimum set value of the battery power of the charging vehicle n.

5. The system according to claim 3, wherein In the process of calculating the comprehensive evaluation score of the power regulation of the charging piles corresponding to the operators by using the entropy weight method, the information entropy E of the j-th preset index is determined according to the following formula j : In the above formula, I is the number of charging piles corresponding to the operator, and Y ij is the index value of the j-th preset index of the i-th charging pile corresponding to the operator. The weight W of the j-th preset index is determined by the following formula j : In the above formula, J is the number of preset indicators.

6. The system according to claim 5, wherein The preset indicators include: the charging power and the charging power of the electric vehicle.

7. The system according to claim 2, wherein The operation instruction is: decomposing the regulation power with reference to the maximum rated power of the charging pile, and sequentially selecting the charging piles to participate in the load regulation according to the operator load regulation priority and the load regulation priority of the charging piles corresponding to the operator until the decomposed power of the selected charging piles reaches the regulation command.

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