A multi-vehicle charging transmission control method with limited power capacity based on power parameter measurement

Abstract

The present invention relates to a limited power capacity multi-vehicle charging transmission control method based on power parameter measurement, which belongs to the field of new energy charging control technology. The method includes: setting a wireless power switch in front of a new energy vehicle charger, obtaining the state execution information of the wireless power switch and uploading it to a user charging platform, measuring the load power and charging current of the vehicle charging line through the wireless power switch; obtaining available power resource information and line carrying range by monitoring the remaining power capacity of the community substation and the remaining current carrying capacity of the power node line; obtaining user charging application information through the user charging platform; establishing a power resource scheduling planning model to calculate the decision control amount of the wireless power switch; switching the wireless power switch according to the decision control amount and updating the state execution information to the user charging platform. Realize multi-vehicle charging under the condition of limited community power capacity and power node line carrying capacity.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy charging control, and in particular relates to a limited power capacity multi-vehicle charging transmission control method based on power parameter measurement. Background Art

[0002] At present, the power capacity of community substations in old residential areas cannot meet the power demand of family new energy vehicle owners in the community for the installation of charging piles. This is mainly reflected in the fact that the line carrying capacity of the community line power-taking node cannot meet the power demand of family new energy vehicle owners involved in the line for the installation of charging piles. Since most users' demand for charging time is concentrated in the same time period, the charging interface is not released in time after charging is completed, which prolongs the waiting time of the charging queue sequence and leads to a decrease in the utilization rate of power resources. The scheduling algorithm of existing smart charging piles is too complicated and costly, and the pricing method of priority charging services will affect the user experience. Summary of the invention

[0003] In order to solve the above problems existing in the prior art, the present invention provides a limited power capacity multi-vehicle charging transmission control system based on power parameter measurement;

[0004] The purpose of the present invention can be achieved by the following technical solutions:

[0005] A method for controlling multi-vehicle charging transmission with limited power capacity based on power parameter measurement is characterized by comprising:

[0006] S1: A wireless power switch is set in front of the new energy vehicle charger, and the state execution information of the wireless power switch is obtained and uploaded to the user charging platform, and the load power and charging current of the vehicle charging line connected to the user charging platform are measured through the wireless power switch;

[0007] S2: Obtain available power resource information and line carrying range by monitoring the remaining power capacity of the community substation and the remaining current carrying capacity of the power-taking node line; obtain user charging application information through the user charging platform;

[0008] S3: establishing a power resource scheduling planning model according to the load power and charging current of the charging line, the user charging application information, the available power resource information, and the line carrying range, and calculating the decision control amount of the wireless power switch through the power resource scheduling planning model;

[0009] S4: Preset a queue update cycle, control the wireless power switch according to the decision control amount, and regularly update the state execution information to the user charging platform according to the queue update cycle.

[0010] Specifically, the load power and charging current are measured by disconnecting the wireless power switches of all charging circuits during the measurement period, closing the individual wireless power switches one by one, and disconnecting the wireless power switches after recording the load power and charging current.

[0011] Specifically, the user charging application information includes the floating rate submitted by the user when applying for charging and the expected charging time parameters.

[0012] Specifically, the power resource scheduling planning model includes an optimization objective function, a power capacity constraint, a line load balance constraint, and a charging demand constraint; the maximum resource utilization of the optimization objective function is calculated through the power capacity constraint, the line load balance constraint, and the charging demand constraint, and the decision control amount is obtained according to the wireless power switch control state corresponding to the maximum resource utilization;

[0013] The optimization objective function is:

[0014]

[0015] Where Pro is the resource utilization value, m is the total number of wireless power switches, i is the node count of the wireless power switch, and A i is the expected charging amount corresponding to the wireless power switch, and the expected charging amount is the charging amount set by the user on the user charging platform, k i is the actual charging amount of the i-th wireless power switch node, f(i) is the queue priority weight corresponding to the i-th wireless power switch node, and a is the heterogeneous optimization parameter, which is a standard conversion parameter between units set according to the remaining carrying capacity of each charging node, so that the load power is equal to The calculated results are kept in numerical unit synchronization; S(j) is the load power of the node expected to perform the charging task, and j is the count of the node performing the charging task; It means that the current charging vehicle still needs to perform charging tasks and keep the single charging line within the required resource utilization value under the load state at a unified time scale. It represents the resource utilization value of each charging node when the original charging task is not optimized for resource scheduling strategy.

[0016] Specifically, the power capacity constraint is: the total charging capacity corresponding to the node queue executing the charging task is less than or equal to the remaining power capacity of the community substation.

[0017] Specifically, the line load balancing constraint is: the load power and charging current corresponding to the node queue executing the charging task are within the carrying range of the line.

[0018] Specifically, the charging demand constraint is: obtaining the user's ideal power demand according to the user's charging application information, setting a user priority queue according to the user's ideal power demand, and obtaining a charging task execution node queue by extracting an executable set of the user priority queue; wherein, the method for setting the user priority queue is: setting the queue priority weight of the user priority queue in each charging period through the charging time preference, charging frequency and ideal charging amount selected for each charging feedback collected by the user charging platform.

[0019] The beneficial effects of the present invention are:

[0020] The load power and charging current of the vehicle charging line connected to the user charging platform are measured through a wireless power switch, providing a data entry point for the multi-vehicle charging transmission control method; by monitoring the remaining power capacity of the community substation and the remaining carrying current capacity of the power-taking node line, the available power resource information and line carrying range are obtained, and a power resource scheduling and planning model is established, so that power scheduling can respond to actual conditions more flexibly and promptly, and power scheduling can be planned more accurately to avoid overload or waste of resources, thereby saving costs and improving resource utilization efficiency; the decision control amount of the wireless power switch calculated according to the power resource scheduling and planning model is used to control the switch of the wireless power switch, which helps to reasonably allocate power resources, improve the power supply reliability of the power system, and reduce power failures caused by overload or imbalance. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0022] Figure 1 It is a flow chart of a method for controlling charging and transmission of multiple vehicles with limited power capacity based on power parameter measurement of the present invention;

[0023] Figure 2 It is a topological diagram of the limited power capacity multi-vehicle charging transmission control method in the present invention. DETAILED DESCRIPTION

[0024] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0025] See also Figure 1-2 , a multi-vehicle charging transmission control method with limited power capacity based on power parameter measurement:

[0026] S1: A wireless power switch is set in front of the new energy vehicle charger, and the state execution information of the wireless power switch is obtained and uploaded to the user charging platform, and the load power and charging current of the vehicle charging line connected to the user charging platform are measured through the wireless power switch;

[0027] S2: Obtain available power resource information and line carrying range by monitoring the remaining power capacity of the community substation and the remaining current carrying capacity of the power-taking node line; obtain user charging application information through the user charging platform;

[0028] S3: establishing a power resource scheduling and planning model according to the load power and charging current of the charging line, the user charging application information, the available power resource information, and the line carrying range, and calculating the decision control amount of the wireless power switch through the power resource scheduling and planning model;

[0029] S4: Preset a queue update cycle, control the wireless power switch according to the decision control amount, and regularly update the state execution information to the user charging platform according to the queue update cycle.

[0030] In this embodiment, a wireless power switch is added to the front end of each car owner's home charger, and the power parameters of each charging line are measured in real time. A user charging platform is built, which can obtain the remaining power capacity of the community substation and the remaining current carrying capacity of the power node line in real time, and comprehensively calculate the part of the wireless power switches that can be closed in combination with the floating rate submitted by the user when applying for charging and the expected charging time parameters. A wireless rechargeable sensor network is used to realize the user charging platform, and the charging current is collected by a Hall sensor to improve the reading accuracy of the current data. A constant voltage and constant current dual-loop PI algorithm is used to control the drive output of each power device of the system.

[0031] Specifically, the load power and charging current are measured by disconnecting the wireless power switches of all charging circuits during the measurement period, closing the individual wireless power switches one by one, and disconnecting the wireless power switches after recording the load power and charging current.

[0032] Specifically, the user charging application information includes the floating rate submitted by the user when applying for charging and the expected charging time parameters.

[0033] Specifically, the power resource scheduling planning model includes an optimization objective function, a power capacity constraint, a line load balance constraint, and a charging demand constraint; the maximum resource utilization of the optimization objective function is calculated through the power capacity constraint, the line load balance constraint, and the charging demand constraint, and the decision control amount is obtained according to the wireless power switch control state corresponding to the maximum resource utilization;

[0034] The optimization objective function is:

[0035]

[0036] Where Pro is the resource utilization value, m is the total number of wireless power switches, i is the node count of the wireless power switch, and A i is the expected charging amount corresponding to the wireless power switch, and the expected charging amount is the charging amount set by the user on the user charging platform, k i is the actual charging amount of the i-th wireless power switch node, f(i) is the queue priority weight corresponding to the i-th wireless power switch node, and a is the heterogeneous optimization parameter, which is a standard conversion parameter between units set according to the remaining carrying capacity of each charging node, so that the load power is equal to The calculated results are kept in numerical unit synchronization; S(j) is the load power of the node expected to perform the charging task, and j is the count of the node performing the charging task; It means that the current charging vehicle still needs to perform charging tasks and keep the single charging line within the required resource utilization value under the load state at a unified time scale. It represents the resource utilization value of each charging node when the original charging task is not optimized for resource scheduling strategy.

[0037] Specifically, the power capacity constraint is: the total charging capacity corresponding to the node queue executing the charging task is less than or equal to the remaining power capacity of the community substation.

[0038] Specifically, the line load balancing constraint is: the load power and charging current corresponding to the node queue executing the charging task are within the carrying range of the line.

[0039] Specifically, the charging demand constraint is: obtaining the user's ideal power demand according to the user's charging application information, setting a user priority queue according to the user's ideal power demand, and obtaining a charging task execution node queue by extracting an executable set of the user priority queue; wherein, the method for setting the user priority queue is: setting the queue priority weight of the user priority queue in each charging period through the charging time preference, charging frequency and ideal charging amount selected for each charging feedback collected by the user charging platform.

[0040] In this embodiment, the user's ideal demand for electricity is based on the floating rate and expected charging time parameters submitted by the user when applying for charging. The floating rate divides the fixed electricity price range according to the baseline load. According to the travel pattern of the electric vehicle and the charging power type of the charging pile, the reservation information of the electric vehicle is initialized, and then the expected charging time parameters are pre-processed to extract the electric vehicle charging scheduling combination that meets the fixed power scenario, and set the priority according to the reservation time.

[0041] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.

[0042] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0043] The program code included on the computer readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0044] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A limited power capacity multi-vehicle charging transmission control method based on power parameter measurement, characterized in that: include: S1: A wireless power switch is set in front of the new energy vehicle charger, and the state execution information of the wireless power switch is obtained and uploaded to the user charging platform, and the load power and charging current of the vehicle charging line connected to the user charging platform are measured through the wireless power switch; S2: Obtain available power resource information and line carrying range by monitoring the remaining power capacity of the community substation and the remaining current carrying capacity of the power-taking node line; obtain user charging application information through the user charging platform; S3: establishing a power resource scheduling planning model according to the load power and charging current of the charging line, the user charging application information, the available power resource information, and the line carrying range, and calculating the decision control amount of the wireless power switch through the power resource scheduling planning model; The power resource scheduling planning model includes an optimization objective function, a power capacity constraint, a line load balance constraint, and a charging demand constraint; the maximum resource utilization of the optimization objective function is calculated through the power capacity constraint, the line load balance constraint, and the charging demand constraint, and the decision control amount is obtained according to the wireless power switch control state corresponding to the maximum resource utilization; The optimization objective function is: Where Pro is the resource utilization value, m is the total number of wireless power switches, i is the node count of the wireless power switch, and A i is the expected charging amount corresponding to the wireless power switch, and the expected charging amount is the charging amount set by the user on the user charging platform, k i is the actual charging amount of the i-th wireless power switch node, f(i) is the queue priority weight corresponding to the i-th wireless power switch node, and a is the heterogeneous optimization parameter, which is a standard conversion parameter between units set according to the remaining carrying capacity of each charging node, so that the load power is equal to The calculated results are kept in numerical unit synchronization; S(j) is the load power of the node expected to perform the charging task, and j is the count of the node performing the charging task; It means that the current charging vehicle still needs to perform charging tasks and keep the single charging line within the required resource utilization value under the load state at a unified time scale. It represents the resource utilization value of each charging node when the original charging task is not optimized for resource scheduling strategy; S4: Preset a queue update cycle, control the wireless power switch according to the decision control amount, and regularly update the state execution information to the user charging platform according to the queue update cycle.

2. The method for controlling charging transmission of multiple vehicles with limited power capacity based on power parameter measurement according to claim 1 is characterized in that: The load power and charging current are measured by disconnecting the wireless power switches of all charging circuits during the measurement period, closing the individual wireless power switches one by one, and disconnecting the wireless power switches after recording the load power and charging current.

3. The method for controlling multi-vehicle charging transmission with limited power capacity based on power parameter measurement according to claim 1, characterized in that: The user charging application information includes the floating rate submitted by the user when applying for charging and the expected charging time parameters.

4. The method for controlling multi-vehicle charging transmission with limited power capacity based on power parameter measurement according to claim 1, characterized in that: The power capacity constraint is: the total charging capacity corresponding to the node queue executing the charging task is less than or equal to the remaining power capacity of the community substation.

5. The method for controlling multi-vehicle charging transmission with limited power capacity based on power parameter measurement according to claim 1, characterized in that: The line load balancing constraint is that the load power and charging current corresponding to the node queue executing the charging task are within the carrying range of the line.

6. The method for controlling multi-vehicle charging transmission with limited power capacity based on power parameter measurement according to claim 1, characterized in that: The charging demand constraint is: obtaining the user's ideal power demand according to the user's charging application information, setting a user priority queue according to the user's ideal power demand, and obtaining a charging task execution node queue by extracting the executable set of the user priority queue; wherein, the method for setting the user priority queue is: setting the queue priority weight of the user priority queue in each charging period through the charging time preference, charging frequency and ideal charging amount selected for each charging feedback collected by the user charging platform.

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