Electric vehicle collaborative charging scheduling method oriented to user demands and charging emergency degree

Abstract

The invention discloses a user demand and charging emergency degree-oriented electric vehicle cooperative charging scheduling method, which comprises the following steps of: constructing a charging demand index, selecting an optimal electric vehicle charging mode, and establishing a cooperative charging scheduling optimization model to minimize the overall peak-valley load difference. Meanwhile, charging power of a slow-charging electric vehicle, charging power of a quick-charging electric vehicle and power grid operation node power flow constraints are considered, charging mode selection of an electric vehicle owner and peak regulation and valley value filling of power grid operation can be achieved, and more friendly power supply and demand interaction can be supported. Therefore, the method adapts to increasingly popularization of electric vehicles and rapid development of flexible power grids.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging, and in particular to a method, system, device and storage medium for coordinated charging scheduling of electric vehicles oriented to user needs and charging urgency. Background Art

[0002] With the development of battery technology and distributed energy storage technology, electric vehicles can greatly alleviate the shortage of non-renewable energy, promote sustainable economic development and maintain ecological balance. In addition, most electric vehicles use lithium batteries as energy storage media, which can be used as distributed energy to optimize grid resource allocation, improve grid power supply quality, reduce user electricity costs, and ensure the safety, reliability and economy of power supply. However, the traditional electric vehicle charging scheduling method does not consider the urgency of charging needs of different users, and only optimizes scheduling for a certain type (such as plug-and-play charging method), lacking consideration of constraints for different battery performance such as fast charging, slow charging and stable operation of the grid. Summary of the invention

[0003] The main purpose of the present invention is to provide an electric vehicle collaborative charging scheduling method, system, device and storage medium oriented to user needs and charging urgency, aiming to solve the above technical problems.

[0004] To achieve the above objectives, the present invention provides an electric vehicle collaborative charging scheduling method oriented to user needs and charging urgency.

[0005] The electric vehicle coordinated charging scheduling method based on user demand and charging urgency comprises the following steps:

[0006] After connecting electric vehicles to the grid, the framework of collaborative charging scheduling is determined by obtaining the electric energy information of electric vehicles;

[0007] Determine the hours during which electric vehicles can be dispatched;

[0008] When obtaining different charging requirements of multiple users, a charging urgency index is defined, and the charging mode of the user is determined according to the charging urgency index;

[0009] Determine the charging mode and charging status of electric vehicles;

[0010] Determine the electric vehicle collaborative optimization objective function according to the total load;

[0011] Determine constraints based on different charging requirements so that the charging demand is less than the total battery capacity.

[0012] Among them, the charging urgency indicator is positioned as:

[0013]

[0014] In the formula: is the charging power of slow-charging electric vehicles, η EV For charging efficiency, To end the battery charge level of the electric vehicle i when charging is completed, The battery charge level of the electric vehicle i when charging begins. is the battery capacity.

[0015] In one embodiment, the electric energy information of the electric vehicle includes:

[0016] The current remaining power of the electric vehicle battery, the maximum and minimum power of the battery charging, the user's expected charging start time and the expected charging time.

[0017] In one embodiment, the step of determining the dispatchable duration of the electric vehicle comprises:

[0018] Taking time gap as the scheduling time unit, the scheduling cycle is divided into several time intervals;

[0019] The time interval for connecting electric vehicle i to the grid can be expressed as:

[0020]

[0021] The time interval away from the grid can be expressed as:

[0022]

[0023] Where: i represents the electric vehicle index i=1,2,...,N, and is the time when the electric vehicle is connected to the grid and leaves the grid, symbol Indicates greater than The smallest integer, signed Indicates less than The largest integer, and

[0024] The dispatchable time for electric starter to access the grid is:

[0025]

[0026] Where T i rem is the number of time slots that the i-th electric vehicle remains connected to the grid.

[0027] In one embodiment, the charging mode includes a fast charging mode and a slow charging mode, and the charging state includes a charging state and a non-charging state.

[0028] In one embodiment, the step of determining the electric vehicle collaborative optimization objective function according to the total load is specifically as follows:

[0029] The total load includes the base load and electric vehicle charging load

[0030] The total load can be expressed as

[0031] The objective function is then defined as:

[0032]

[0033] Where: and Represent the maximum and minimum load demands of the power grid respectively.

[0034] In one embodiment, the step of determining the constraint conditions according to different charging requirements includes:

[0035] When the i-th electric vehicle is considered to be charging urgently, let x i,j It is equal to 1 in each time interval;

[0036] When the charging urgency of the fast-charging electric vehicle is low, the constraint condition is set so that the charging demand is less than the total power of the battery;

[0037] The electric vehicle constraints for emergency charging demand are expressed as:

[0038]

[0039] in is the time interval when the fast-charging electric vehicle stops charging, which can be expressed as:

[0040]

[0041] Where: For the largest SOC requirements, Indicates the total number of charging time slots required to meet the maximum SOC requirement.

[0042] In one embodiment, the electric vehicle coordinated charging scheduling method oriented to user demand and charging urgency further includes: adding a constraint condition for limiting the peak value of the power grid:

[0043]

[0044] Where: The maximum total load when the non-coordinated charging method meets the maximum SOC requirement of the electric vehicle

[0045] In addition, to achieve the above-mentioned objectives, the present invention also provides an electric vehicle collaborative charging scheduling device oriented to user needs and charging urgency, and the electric vehicle collaborative charging scheduling device oriented to user needs and charging urgency includes: a memory, a processor, and an electric vehicle collaborative charging scheduling program stored on the memory and executable on the processor, and when the electric vehicle collaborative charging scheduling program is executed by the processor, the steps of the electric vehicle collaborative charging scheduling method as described above are implemented.

[0046] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which an electric vehicle collaborative charging scheduling program is stored, and when the electric vehicle collaborative charging scheduling program is executed by a processor, the steps of the electric vehicle collaborative charging scheduling method oriented to user needs and charging urgency are implemented as described above.

[0047] Beneficial effects that can be achieved by the present invention: The embodiment of the present invention proposes a method for coordinated charging scheduling of electric vehicles oriented to user needs and charging urgency, which minimizes the overall peak-valley load difference by constructing charging demand indicators and selecting the optimal electric vehicle charging mode, and then establishing a coordinated charging scheduling optimization model. At the same time, the charging power of slow-charging electric vehicles, fast-charging electric vehicles and power grid operation node flow constraints are taken into account, which can realize the charging mode selection of electric vehicle owners and the peak-shaving and valley-filling of power grid operation, and can support more friendly power supply and demand interaction to adapt to the increasing popularity of electric vehicles and the rapid development of flexible power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a structural diagram of an electric vehicle cooperative charging scheduling device involved in an embodiment of the present invention;

[0049] Figure 2 It is a structural diagram of an electric vehicle cooperative charging scheduling system involved in an embodiment of the present invention;

[0050] Figure 3 The figure is a flow chart of the electric vehicle collaborative charging scheduling method according to the present invention based on user demand and charging urgency.

[0051] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0052] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0053] like Figure 1 As shown, Figure 1 It is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiment of the present invention.

[0054] The terminal of the embodiment of the present invention can be a PC, or it can be a smart phone, a tablet computer, an e-book reader, an MP3 (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Compression Standard Audio Layer 3) player, an MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Compression Standard Audio Layer 4) player, a portable computer, and other portable terminal devices with display function.

[0055] like Figure 1 As shown, the terminal may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0056] Optionally, the terminal may also include a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and the like. Among them, sensors include light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display screen according to the brightness of the ambient light, and the proximity sensor may turn off the display screen and / or backlight when the mobile terminal is moved to the ear. As a type of motion sensor, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile terminal (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; of course, the mobile terminal may also be equipped with other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., which will not be repeated here.

[0057] Those skilled in the art will understand that Figure 1The terminal structure shown in the figure does not constitute a limitation on the terminal, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0058] like Figure 1 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an electric vehicle cooperative charging scheduling program.

[0059] exist Figure 1 In the terminal shown, the network interface 1004 is mainly used to connect to the backend server and communicate data with the backend server; the user interface 1003 is mainly used to connect to the client (user end) and communicate data with the client; and the processor 1001 can be used to call the electric vehicle cooperative charging scheduling program stored in the memory 1005 and perform the following operations:

[0060] After connecting electric vehicles to the grid, the framework of collaborative charging scheduling is determined by obtaining the electric energy information of electric vehicles;

[0061] Determine the hours during which electric vehicles can be dispatched;

[0062] When obtaining different charging requirements of multiple users, a charging urgency index is defined, and the charging mode of the user is determined according to the charging urgency index;

[0063] Determine the charging mode and charging status of electric vehicles;

[0064] Determine the electric vehicle collaborative optimization objective function according to the total load;

[0065] Determine constraints based on different charging requirements so that the charging demand is less than the total battery capacity.

[0066] The specific embodiments of the present invention using the data storage device are basically the same as the following embodiments of the electric vehicle collaborative charging scheduling method based on user needs and charging urgency, and will not be described in detail here.

[0067] Reference Figure 2-3 The present invention provides an electric vehicle collaborative charging scheduling method based on user demand and charging urgency, and the electric vehicle collaborative charging scheduling method based on user demand and charging urgency includes:

[0068] Step 1: Determine the framework of collaborative charging scheduling. After the electric vehicle is connected to the power grid, the electric energy information sensor collects information such as the current remaining power of the electric vehicle battery, the maximum and minimum power of the battery charging, the user's expected charging start time and the expected charging duration, and sends the above information to the central controller for intelligent scheduling. At the same time, the scheduling results are received and sent to the electric vehicle charging interface for real-time charging control.

[0069] Step 2: Determine the duration of the electric vehicle dispatchable. Specifically, using time slots as dispatch time units can improve dispatch efficiency. The dispatch cycle of a day (24 hours) is divided into 96 time slots j = 1, 2, ..., 96, each time slot is 15 minutes. The time slot of electric vehicle i connecting to the grid is expressed as

[0070]

[0071] The time gap away from the grid is expressed as

[0072]

[0073] Where i represents the electric vehicle index i=1,2,...,N, and is the time when the electric vehicle is connected to the grid and leaves the grid, symbol Indicates greater than The smallest integer, signed Indicates less than The largest integer, and According to the definitions of (1) and (2), when the time when the electric vehicle is connected to the grid is between two time slots, the start time of its charging can be arranged at the next integer of the larger time slot; when the time when the electric vehicle is off the grid is between two time slots, the end time of its charging can be arranged at the previous integer of the smaller time slot.

[0074] End of count.

[0075] based on The dispatchable time for electric vehicles to access the grid is defined as:

[0076]

[0077] Where T i rem is the number of time slots that the ith electric vehicle maintains connection with the microgrid. i rem The response scheduling strategy can be optimized and allocated.

[0078] Step 3: Determine the charging urgency index. In response to the different charging needs of multiple users, the central controller makes intelligent decisions through the charging urgency indicator, gives the urgency ranking of the user's charging needs, and provides two charging modes for users to choose from by establishing a suitable optimization model: 1) Emergency charging needs: immediate charging, high charging power (charging prices fluctuate, charging costs are high, and charging time is short); 2) Non-emergency charging needs: collaborative charging, long charging time (charging prices are stable, charging costs are low, and charging power is average). The following charging urgency indicators are defined as:

[0079]

[0080] in is the charging power of slow-charging electric vehicles, η EV For charging efficiency, To end the battery charge level of the electric vehicle i when charging is completed, The battery charge level of the electric vehicle i when charging begins. is the battery capacity. i When CUI is less than 0, it means that the charging demand of electric vehicle i is in an emergency state. i When ≥0, it means that the charging demand of electric vehicle i is in a non-emergency state.

[0081] Step 4: Determine the charging mode of the electric vehicle. The present invention considers two charging modes: fast charging and slow charging:

[0082]

[0083] in Indicates the power during fast charging. Indicates the power during slow charging.

[0084] Step 5: Determine the charging state. In the optimization model of the present invention, the charging state variable x i,j is a binary variable. That is, when x i,j = 0, it indicates that electric vehicle i is not charged in the jth time interval; when x i,j =1, indicating that electric vehicle i is in the charging state during the jth time interval:

[0085]

[0086] Step 6: Determine the electric vehicle coordinated optimization objective function. This study aims to shift the grid load demand from the peak period to the valley period, thereby alleviating the peak load pressure of the microgrid, and the load of the microgrid includes the basic load and the electric vehicle charging load. The electric vehicle charging load includes the fast charging electric vehicle load and the slow charging electric vehicle load. Therefore, the total load in the coordinated charging scheduling model can be expressed as:

[0087]

[0088] in It represents the basic load of the power grid in the jth time interval (such as the superposition of household loads such as washing machines, refrigerators, and air conditioners); represents the power provided by the power grid to all electric vehicles in the jth time interval. If the i-th electric vehicle has an urgent charging demand, then otherwise In order to transfer the grid load demand from the peak period to the trough period and alleviate the peak load pressure of the microgrid, its objective function is defined as:

[0089]

[0090] in:. and Represent the maximum and minimum load demands of the power grid respectively

[0091] Step 7: Determine the constraints according to the optimization goal. When the i-th electric vehicle is considered as an emergency charger, let x i,j =1 in each time slot. Therefore, electric vehicles can charge from the time they are connected to the grid until they leave, ensuring that electric vehicles with urgent needs can charge more. In addition, when electric vehicles leave the grid, x i,j The value of is 0. However, when the charging urgency of the fast-charging electric vehicle is low, the continuous Charging will cause the battery to overcharge. In order to avoid overcharging of fast-charging electric vehicles, it is necessary to set constraints so that the charging demand must be less than the total power of the battery.

[0092] Therefore, the electric vehicle constraint condition for emergency charging demand is expressed as:

[0093]

[0094] in is the time interval when the fast-charging electric vehicle stops charging, which can be expressed as:

[0095]

[0096] in is the biggest SOC requirement, Indicates that the total number of charging time slots must meet the maximum SOC requirement, It is to prevent overcharging.

[0097] Electric vehicles have minimum and maximum charging requirements, and for slow-charging electric vehicles, only the minimum charging requirement needs to be met. Therefore, for slow-charging electric vehicles, there are:

[0098]

[0099] in is the battery SOC of the ith electric vehicle when it leaves the grid, which can be obtained by the following expression:

[0100]

[0101] in represents the battery SOC of the ith EV when it is connected to the grid. It is worth noting that the second term in (12) is the accumulation of all charging schedule durations, so x is only valid when the EV is within its schedule period. i.j =1; when the electric vehicle leaves the grid, the current dispatch ends x i.j =0.

[0102] The goal of the grid constraints is to reduce the peak-to-valley difference of the total load, but it cannot guarantee that the peak after coordinated scheduling is lower than the peak of the uncoordinated charging method that meets the maximum SOC demand of electric vehicles. In order to avoid the emergence of new microgrid charging peak loads in the coordinated scheduling scheme, the constraints for limiting the peak of the microgrid are added:

[0103]

[0104] in It represents the maximum total load when the non-coordinated charging method meets the maximum SOC requirement of the electric vehicle.

[0105] Step 8: Verify the effectiveness of the coordinated charging scheduling strategy. By solving the proposed optimization model, the charging scheduling results can be obtained. In addition, the effect of the coordinated charging scheduling method can be verified by comparing the coordinated charging method with the uncoordinated charging method.

[0106] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described device and each module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0107] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which an electric vehicle collaborative charging scheduling program is stored. When the electric vehicle collaborative charging scheduling program is executed by a processor, the following operations are implemented:

[0108] After connecting electric vehicles to the grid, the framework of collaborative charging scheduling is determined by obtaining the electric energy information of electric vehicles;

[0109] Determine the hours during which electric vehicles can be dispatched;

[0110] When obtaining different charging requirements of multiple users, a charging urgency index is defined, and the charging mode of the user is determined according to the charging urgency index;

[0111] Determine the charging mode and charging status of electric vehicles;

[0112] Determine the electric vehicle collaborative optimization objective function according to the total load;

[0113] Determine constraints based on different charging requirements so that the charging demand is less than the total battery capacity.

[0114] The specific embodiments of the computer-readable storage medium of the present invention are basically the same as the embodiments of the above-mentioned application software security vulnerability detection method, and will not be described in detail here.

[0115] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0116] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0117] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0118] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for coordinating charging of electric vehicles based on user needs and charging urgency, characterized in that: The electric vehicle coordinated charging scheduling method based on user demand and charging urgency comprises the following steps: After connecting electric vehicles to the grid, the framework of collaborative charging scheduling is determined by obtaining the electric energy information of electric vehicles; Determine the hours during which electric vehicles can be dispatched; When obtaining different charging requirements of multiple users, a charging urgency index is defined, and the charging mode of the user is determined according to the charging urgency index; Determine the charging mode and charging status of electric vehicles; Determine the electric vehicle collaborative optimization objective function according to the total load; Determine constraints based on different charging requirements so that the charging demand is less than the total battery capacity. Among them, the charging urgency indicator is positioned as: In the formula: is the charging power of slow-charging electric vehicles, η EV For charging efficiency, To end the battery charge level of the electric vehicle i when charging is completed, The battery charge level of the electric vehicle i when charging begins. is the battery capacity.

2. The electric vehicle collaborative charging scheduling method based on user demand and charging urgency as claimed in claim 1, characterized in that: The electric energy information of the electric vehicle includes: The current remaining power of the electric vehicle battery, the maximum and minimum power of the battery charging, the user's expected charging start time and the expected charging time.

3. The electric vehicle collaborative charging scheduling method based on user demand and charging urgency as claimed in claim 2, characterized in that: The step of determining the dispatchable duration of the electric vehicle comprises: Taking time gap as the scheduling time unit, the scheduling cycle is divided into several time intervals; The time interval for connecting electric vehicle i to the grid can be expressed as: The time interval away from the grid can be expressed as: Where: i represents the electric vehicle index i=1,2,...,N, and is the time when the electric vehicle is connected to the grid and leaves the grid, symbol Indicates greater than The smallest integer with a sign Indicates less than The largest integer, and The dispatchable time for electric starter to access the grid is: in is the number of time slots that the i-th electric vehicle remains connected to the grid.

4. The electric vehicle collaborative charging scheduling method based on user demand and charging urgency according to claim 3 is characterized in that: The charging mode includes a fast charging mode and a slow charging mode, and the charging state includes a charging state and a non-charging state.

5. The electric vehicle collaborative charging scheduling method based on user demand and charging urgency according to claim 4 is characterized in that: The step of determining the electric vehicle collaborative optimization objective function according to the total load is specifically as follows: The total load includes the base load and electric vehicle charging load The total load can be expressed as The objective function is then defined as: Where: and Represent the maximum and minimum load demands of the power grid respectively.

6. The electric vehicle collaborative charging scheduling method based on user demand and charging urgency according to claim 5 is characterized in that: The step of determining constraint conditions according to different charging requirements includes: When the i-th electric vehicle is considered to be charging urgently, let x i,j It is equal to 1 in each time interval; When the charging urgency of the fast-charging electric vehicle is low, the constraint condition is set so that the charging demand is less than the total power of the battery; The electric vehicle constraints for emergency charging demand are expressed as: in is the time interval when the fast-charging electric vehicle stops charging, which can be expressed as: Where: For the largest SOC requirements, Indicates the total number of charging time slots required to meet the maximum SOC requirement.

7. The electric vehicle collaborative charging scheduling method based on user demand and charging urgency according to claim 1 is characterized in that: The electric vehicle coordinated charging scheduling method for user demand and charging urgency also includes: adding a constraint condition for limiting the peak value of the power grid: Where: It represents the maximum total load when the non-coordinated charging method meets the maximum SOC requirement of the electric vehicle.

8. An electric vehicle collaborative charging scheduling system oriented to user needs and charging urgency, characterized in that: The electric vehicle collaborative charging scheduling system oriented to user needs and charging urgency includes: a control center and an information collector, wherein the information collector is used to collect charging information of electric vehicles and forward it to the control center, and the control center is used to send scheduling strategies to the information collector and then forward the scheduling strategies to the electric vehicles through the information collector.

9. An electric vehicle collaborative charging scheduling device based on user needs and charging urgency, characterized in that: The electric vehicle collaborative charging scheduling device oriented to user needs and charging urgency includes: a memory, a processor, and an electric vehicle collaborative charging scheduling program stored in the memory and executable on the processor. When the electric vehicle collaborative charging scheduling program is executed by the processor, the steps of the electric vehicle collaborative charging scheduling method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an electric vehicle collaborative charging scheduling program, which, when executed by a processor, implements the steps of the electric vehicle collaborative charging scheduling method according to any one of claims 1 to 7.

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