An electric vehicle cluster aggregation charging and discharging control method and device
By classifying electric vehicles and modeling energy accumulation boundaries, the computational and communication burden in traditional electric vehicle group charging and discharging optimization methods is solved, and efficient electric vehicle group aggregation charging and discharging control is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- STATE GRID ELECTRIC VEHICLE SERVICE CO LTD
- Filing Date
- 2023-10-30
- Publication Date
- 2026-07-03
AI Technical Summary
Traditional electric vehicle (EV) swarm charging and discharging optimization methods have heavy computational and communication burdens, making them difficult to apply to large-scale EV swarms, and they do not consider energy loss.
Electric vehicles are categorized, and their corresponding energy accumulation boundaries and charge/discharge power boundaries are determined. These are then substituted into a pre-built aggregation model for constraint control, thus establishing an electric vehicle group aggregation model from the perspective of energy accumulation.
It reduces computational and communication burdens, improves computational efficiency for electric vehicle fleet optimization, reduces computational complexity independent of the number of electric vehicles, and supports the determination of charging and discharging behavior of individual electric vehicles.
Smart Images
Figure CN117698505B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of electric vehicle dispatchability potential quantification technology, specifically to a method and apparatus for controlling the aggregated charging and discharging of electric vehicle groups. Background Technology
[0002] The traditional electric vehicle (EV) EV group charging and discharging optimization method refers to the method that optimizes the charging and discharging behavior of individual EVs as the decision variable. This traditional method optimizes the charging and discharging behavior of each EV in the EV group, then sums the charging and discharging power curves of all EVs over time to obtain the aggregated charging and discharging power of the EV group. This can be abstracted as a processing approach from small (individual EVs) to large (EV group). However, the traditional EV group charging and discharging optimization method incurs certain computational and communication burdens, which increase rapidly with the number of EVs. In particular, when dealing with large-scale EV groups, the EV group charging and discharging optimization model based on the traditional method becomes difficult to solve, or even infeasible.
[0003] This paper adopts a process from large (electric vehicle fleet) to small (individual electric vehicle) – that is, first optimizing the aggregated charging and discharging behavior of the electric vehicle fleet, and then using charging and discharging power allocation technology to determine the charging and discharging behavior of individual electric vehicles. This approach has good computational efficiency and provides a novel method for the centralized charging and discharging optimization scheduling of electric vehicle fleets. Traditional electric vehicle fleet modeling methods establish the electric vehicle fleet aggregation model from the perspective of charging and discharging power, i.e., the aggregated charging and discharging power boundary. However, this boundary continuously changes with the charging and discharging actions of electric vehicles, meaning that the charging / discharging power boundary of the current time period is directly related to the charging and discharging actions performed by each electric vehicle in the previous time period. Therefore, it is not suitable for the optimization scheduling problem of aggregated charging and discharging behavior of electric vehicle fleets. Some studies have characterized the aggregated charging and discharging behavior of electric vehicle fleets from the perspective of energy accumulation; however, they have not considered the energy loss during the charging and discharging process of the electric vehicle fleet. Summary of the Invention
[0004] To overcome the above-mentioned defects, the present invention proposes a method and device for controlling the aggregated charging and discharging of electric vehicle groups.
[0005] Firstly, a method for controlling the aggregated charging and discharging of electric vehicle groups is provided, the method comprising:
[0006] Electric vehicles are classified, and the energy accumulation boundary and charge / discharge power boundary corresponding to the type of electric vehicle are determined;
[0007] The energy accumulation boundary and charge / discharge power boundary corresponding to the type of electric vehicle are substituted into a pre-built aggregation model to constrain and control the charge and discharge power of the electric vehicle group.
[0008] Preferably, the electric vehicle belongs to the following types: Class I electric vehicle, Class II electric vehicle, and Class III electric vehicle.
[0009] Furthermore, the classification of electric vehicles includes:
[0010] Electric vehicles that are parked in the community for less than a preset threshold and are charged at rated power immediately after being connected to a charging station will be classified as Category I electric vehicles.
[0011] Electric vehicles that have been parked in the community for a longer period than a preset threshold and whose remaining SOC upon arrival in the community is higher than the minimum battery SOC allowed during charging and discharging will be classified as Category II electric vehicles.
[0012] Electric vehicles that arrive at the community with a remaining SOC lower than the minimum allowable SOC during charging and discharging, and whose accumulated energy after connecting to the charging pile reaches the minimum chargeable energy of their battery before participating in charge and discharge control scheduling, are classified as Category III electric vehicles.
[0013] Furthermore, the energy accumulation boundary and charge / discharge power boundary corresponding to the first type of electric vehicle are as follows:
[0014]
[0015]
[0016] In the above formula, t end,i and t next,i These represent the times when the i-th electric vehicle returns to and leaves the community, respectively. and Let be the charging power boundary and discharging power boundary of the i-th electric vehicle during time period t, respectively. and Let be the upper and lower boundaries of the energy accumulation of the i-th electric vehicle during time period t, respectively. The rated charging power of the i-th electric vehicle. Let be the charging efficiency of the i-th electric vehicle, Δt be the scale of a unit time interval, and t be the current time period. The charging power for the i-th electric vehicle. The specified energy requirement for the i-th electric vehicle. Let be the upper boundary of the energy accumulation of the i-th electric vehicle during time period t-1.
[0017] Furthermore, the energy accumulation boundary and charge / discharge power boundary corresponding to the second type of electric vehicle are as follows:
[0018]
[0019]
[0020]
[0021]
[0022] In the above formula, This represents the rated discharge power of the i-th electric vehicle; Let represent the discharge efficiency of the i-th electric vehicle. and Let be the maximum and minimum rechargeable energy of the battery of the i-th electric vehicle, respectively. Let be the lower boundary of the energy accumulation of the i-th electric vehicle during time period t-1. Let i be the rated discharge power of the i-th electric vehicle. Let be the discharge efficiency of the i-th electric vehicle.
[0023] Furthermore, when t∈[t end,i ,t c,i When [the third type of electric vehicle is mentioned], the energy accumulation boundary and charge / discharge power boundary are as follows:
[0024]
[0025]
[0026] When t∈[t c,i ,t next,i When [the third type of electric vehicle is mentioned], the energy accumulation boundary and charge / discharge power boundary are as follows:
[0027]
[0028]
[0029]
[0030]
[0031] In the above formula, t c,i To extend the time,
[0032] Furthermore, the specified energy requirements, maximum rechargeable energy of the battery, and minimum rechargeable energy of the i-th electric vehicle are as follows:
[0033]
[0034]
[0035]
[0036] In the above formula, and Let S and B be the minimum and maximum battery SOC allowed for the i-th electric vehicle during charging and discharging, respectively. and Let Q be the remaining battery SOC when the i-th electric vehicle returns to the community and the SOC demand when it leaves the community next time. i Let be the rated battery capacity of the i-th electric vehicle.
[0037] Furthermore, the pre-built aggregation model is as follows:
[0038]
[0039] P t c +|P t d |≤max{P t c ,|P t d |}
[0040]
[0041]
[0042]
[0043] In the above formula, P t c and P t d These represent the charging power and discharging power of the electric vehicle group during time period t, respectively. end and T next These are the earliest and latest times for scheduling charging and discharging of the electric vehicle fleet, respectively. and P represents the upper and lower boundaries of the energy accumulation of the electric vehicle group at time t. t max+ and P t max- These represent the charging power boundary and discharging power boundary of the electric vehicle group during time period t, respectively. and These are the charging and discharging loss coefficients for the electric vehicle fleet, respectively. The energy accumulation level of the electric vehicle group during time period t. This represents the cumulative energy level of the electric vehicle group during time period t-1.
[0044] Furthermore, the earliest and latest times for the charging and discharging scheduling of the electric vehicle fleet are as follows:
[0045]
[0046] The upper and lower boundaries of the energy accumulation of the electric vehicle group during time period t are as follows:
[0047]
[0048] The charging power boundary and discharging power boundary of the electric vehicle group during time period t are as follows:
[0049]
[0050] The charging and discharging loss coefficients of the electric vehicle fleet are as follows:
[0051]
[0052] In the above formula, Ω 1 Ω 2 and Ω 3 They are respectively groups of electric vehicles of categories one, two, and three. Z represents the access status of the i-th electric vehicle during time period t, and Z represents the total number of electric vehicles.
[0053] Furthermore, the access status of the i-th electric vehicle during time period t is as follows:
[0054]
[0055] Secondly, a charging and discharging control device for a fleet of electric vehicles is provided, the charging and discharging control device for a fleet of electric vehicles comprising:
[0056] The analysis module is used to classify electric vehicles and determine the energy accumulation boundary and charge / discharge power boundary corresponding to the type of electric vehicle.
[0057] The control module substitutes the energy accumulation boundary and charge / discharge power boundary corresponding to the type of electric vehicle into a pre-built aggregation model to constrain and control the charge and discharge power of the electric vehicle group.
[0058] Preferably, the electric vehicle belongs to the following types: Class I electric vehicle, Class II electric vehicle, and Class III electric vehicle.
[0059] Furthermore, the analysis module is specifically used for:
[0060] Electric vehicles that are parked in the community for less than a preset threshold and are charged at rated power immediately after being connected to a charging station will be classified as Category I electric vehicles.
[0061] Electric vehicles that have been parked in the community for a longer period than a preset threshold and whose remaining SOC upon arrival in the community is higher than the minimum battery SOC allowed during charging and discharging will be classified as Category II electric vehicles.
[0062] Electric vehicles that arrive at the community with a remaining SOC lower than the minimum allowable SOC during charging and discharging, and whose accumulated energy after connecting to the charging pile reaches the minimum chargeable energy of their battery before participating in charge and discharge control scheduling, are classified as Category III electric vehicles.
[0063] Furthermore, the energy accumulation boundary and charge / discharge power boundary corresponding to the first type of electric vehicle are as follows:
[0064]
[0065]
[0066] In the above formula, t end,i and t next,i These represent the times when the i-th electric vehicle returns to and leaves the community, respectively. and Let be the charging power boundary and discharging power boundary of the i-th electric vehicle during time period t, respectively. and Let be the upper and lower boundaries of the energy accumulation of the i-th electric vehicle during time period t, respectively. The rated charging power of the i-th electric vehicle. Let be the charging efficiency of the i-th electric vehicle, Δt be the scale of a unit time interval, and t be the current time period. The charging power for the i-th electric vehicle. The specified energy requirement for the i-th electric vehicle. Let be the upper boundary of the energy accumulation of the i-th electric vehicle during time period t-1.
[0067] Furthermore, the energy accumulation boundary and charge / discharge power boundary corresponding to the second type of electric vehicle are as follows:
[0068]
[0069]
[0070]
[0071]
[0072] In the above formula, This represents the rated discharge power of the i-th electric vehicle; Let represent the discharge efficiency of the i-th electric vehicle. and Let be the maximum and minimum rechargeable energy of the battery of the i-th electric vehicle, respectively. Let be the lower boundary of the energy accumulation of the i-th electric vehicle during time period t-1. Let i be the rated discharge power of the i-th electric vehicle. Let be the discharge efficiency of the i-th electric vehicle.
[0073] Furthermore, when t∈[t end,i ,t c,i When [the third type of electric vehicle is mentioned], the energy accumulation boundary and charge / discharge power boundary are as follows:
[0074]
[0075]
[0076] When t∈[t c,i ,t next,i When [the third type of electric vehicle is mentioned], the energy accumulation boundary and charge / discharge power boundary are as follows:
[0077]
[0078]
[0079]
[0080]
[0081] In the above formula, t c,i To extend the time,
[0082] Furthermore, the specified energy requirements, maximum rechargeable energy of the battery, and minimum rechargeable energy of the i-th electric vehicle are as follows:
[0083]
[0084]
[0085]
[0086] In the above formula, and Let S and B be the minimum and maximum battery SOC allowed for the i-th electric vehicle during charging and discharging, respectively. and Let Q be the remaining battery SOC when the i-th electric vehicle returns to the community and the SOC demand when it leaves the community next time. i Let be the rated battery capacity of the i-th electric vehicle.
[0087] Furthermore, the pre-built aggregation model is as follows:
[0088]
[0089] P t c +|P t d |≤max{P t c ,|P t d |}
[0090]
[0091]
[0092]
[0093] In the above formula, P t c and P t d These represent the charging power and discharging power of the electric vehicle group during time period t, respectively. end and T next These are the earliest and latest times for scheduling charging and discharging of the electric vehicle fleet, respectively. and P represents the upper and lower boundaries of the energy accumulation of the electric vehicle group at time t. t max+ and P t max- These represent the charging power boundary and discharging power boundary of the electric vehicle group during time period t, respectively. and These are the charging and discharging loss coefficients for the electric vehicle fleet, respectively. The energy accumulation level of the electric vehicle group during time period t. This represents the cumulative energy level of the electric vehicle group during time period t-1.
[0094] Furthermore, the earliest and latest times for the charging and discharging scheduling of the electric vehicle fleet are as follows:
[0095]
[0096] The upper and lower boundaries of the energy accumulation of the electric vehicle group during time period t are as follows:
[0097]
[0098] The charging power boundary and discharging power boundary of the electric vehicle group during time period t are as follows:
[0099]
[0100] The charging and discharging loss coefficients of the electric vehicle fleet are as follows:
[0101]
[0102] In the above formula, Ω 1 Ω 2 and Ω 3 They are respectively groups of electric vehicles of categories one, two, and three. Z represents the access status of the i-th electric vehicle during time period t, and Z represents the total number of electric vehicles.
[0103] Furthermore, the access status of the i-th electric vehicle during time period t is as follows:
[0104]
[0105] Thirdly, a computer device is provided, comprising: one or more processors;
[0106] The processor is used to store one or more programs;
[0107] When the one or more programs are executed by the one or more processors, the electric vehicle group aggregation charging and discharging control method is implemented.
[0108] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, wherein when the computer program is executed, the electric vehicle group aggregation charging and discharging control method is implemented.
[0109] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects:
[0110] This invention provides a method and apparatus for controlling the charging and discharging of an electric vehicle (EV) group, comprising: classifying EVs and determining the energy accumulation boundary and charging / discharging power boundary corresponding to each EV type; and substituting the energy accumulation boundary and charging / discharging power boundary corresponding to each EV type into a pre-constructed aggregation model to constrain and control the charging and discharging power of the EV group. The technical solution provided by this invention establishes an EV group aggregation model considering charging and discharging energy losses from an energy accumulation perspective, thereby characterizing the schedulable charging / discharging power boundary and energy accumulation boundary of the EV group aggregation model. Compared with traditional EV group charging and discharging optimization methods, EV group charging and discharging optimization only needs to optimize and control the charging and discharging behavior of one EV group aggregation model, making the computational complexity of EV group optimization independent of the number of EVs. Furthermore, it does not require frequent parameter transfer with each EV during the optimization process, thus greatly reducing the computational and communication burden and supporting corresponding charging and discharging control algorithms to determine the charging and discharging behavior of individual EVs. Attached Figure Description
[0111] Figure 1 This is a schematic flowchart of the main steps of the electric vehicle group aggregation charging and discharging control method according to an embodiment of the present invention;
[0112] Figure 2 This is a schematic diagram of the energy operating area and charging / discharging power boundary of three types of electric vehicles according to an embodiment of the present invention;
[0113] Figure 3 This is a schematic diagram of the energy operation area and charging / discharging control of an electric vehicle fleet according to an embodiment of the present invention;
[0114] Figure 4 This is a schematic diagram of the charging and discharging power boundary of the electric vehicle group aggregation model according to an embodiment of the present invention;
[0115] Figure 5 This is a schematic diagram illustrating the changes in the upper and lower boundaries of energy accumulation during the charging and discharging process of the electric vehicle group aggregation model according to an embodiment of the present invention.
[0116] Figure 6 This is a main structural block diagram of the electric vehicle group aggregation charging and discharging control device according to an embodiment of the present invention. Detailed Implementation
[0117] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.
[0118] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0119] As disclosed in the background section, the electric vehicle (EV) group charging and discharging optimization method that uses the charging and discharging behavior of a single EV as the decision variable is called the traditional EV group charging and discharging optimization method. The traditional method optimizes the charging and discharging behavior of each EV in the group, and then sums the charging and discharging power curves of all EVs over time to obtain the aggregated charging and discharging power of the EV group. This can be abstracted as a processing approach from small (individual EV) to large (EV group). However, the traditional EV group charging and discharging optimization method incurs certain computational and communication burdens, which increase rapidly with the number of EVs. In particular, when dealing with large-scale EV groups, the EV group charging and discharging optimization model based on the traditional method becomes difficult to solve, or even infeasible.
[0120] This paper adopts a process from large (electric vehicle fleet) to small (individual electric vehicle) – that is, first optimizing the aggregated charging and discharging behavior of the electric vehicle fleet, and then using charging and discharging power allocation technology to determine the charging and discharging behavior of individual electric vehicles. This approach has good computational efficiency and provides a novel method for the centralized charging and discharging optimization scheduling of electric vehicle fleets. Traditional electric vehicle fleet modeling methods establish the electric vehicle fleet aggregation model from the perspective of charging and discharging power, i.e., the aggregated charging and discharging power boundary. However, this boundary continuously changes with the charging and discharging actions of electric vehicles, meaning that the charging / discharging power boundary of the current time period is directly related to the charging and discharging actions performed by each electric vehicle in the previous time period. Therefore, it is not suitable for the optimization scheduling problem of aggregated charging and discharging behavior of electric vehicle fleets. Some studies have characterized the aggregated charging and discharging behavior of electric vehicle fleets from the perspective of energy accumulation; however, they have not considered the energy loss during the charging and discharging process of the electric vehicle fleet.
[0121] To address the aforementioned issues, this invention provides a method and apparatus for controlling the charging and discharging of an electric vehicle (EV) group. The method includes: classifying EVs and determining the energy accumulation boundary and charging / discharging power boundary corresponding to each EV type; and substituting the energy accumulation boundary and charging / discharging power boundary corresponding to each EV type into a pre-constructed aggregation model to constrain and control the charging and discharging power of the EV group. The technical solution provided by this invention establishes an EV group aggregation model considering charging and discharging energy losses from an energy accumulation perspective, thereby characterizing the schedulable charging / discharging power boundary and energy accumulation boundary of the EV group aggregation model. Compared to traditional EV group charging and discharging optimization methods, EV group charging and discharging optimization only requires optimizing and controlling the charging and discharging behavior of one EV group aggregation model. This makes the computational complexity of EV group optimization independent of the number of EVs, and eliminates the need for frequent parameter transfers between EVs during the optimization process. Therefore, it significantly reduces the computational and communication burden and can support corresponding charging and discharging control algorithms to determine the charging and discharging behavior of individual EVs.
[0122] The above plan will be explained in detail below.
[0123] Example 1
[0124] See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of an electric vehicle group aggregation charging and discharging control method according to an embodiment of the present invention. Figure 1 As shown, the electric vehicle group aggregation charging and discharging control method in this embodiment of the invention mainly includes the following steps:
[0125] Step S101: Classify electric vehicles and determine the energy accumulation boundary and charging / discharging power boundary corresponding to the type of electric vehicle;
[0126] Step S102: Substitute the energy accumulation boundary and charging / discharging power boundary corresponding to the type of electric vehicle into the pre-built aggregation model to constrain and control the charging and discharging power of the electric vehicle group.
[0127] In this embodiment, the types of electric vehicles include: Class I electric vehicles, Class II electric vehicles, and Class III electric vehicles.
[0128] In one implementation, the classification of electric vehicles includes:
[0129] Electric vehicles that are parked in the community for less than a preset threshold and are charged at rated power immediately after being connected to a charging station will be classified as Category I electric vehicles.
[0130] Electric vehicles that have been parked in the community for a longer period than a preset threshold and whose remaining SOC upon arrival in the community is higher than the minimum battery SOC allowed during charging and discharging will be classified as Category II electric vehicles.
[0131] Electric vehicles that arrive at the community with a remaining SOC lower than the minimum allowable SOC during charging and discharging, and whose accumulated energy after connecting to the charging pile reaches the minimum chargeable energy of their battery before participating in charge and discharge control scheduling, are classified as Category III electric vehicles.
[0132] In classifying electric vehicles, three parameters are first introduced: the specified energy requirement of the i-th electric vehicle, the maximum rechargeable energy of the battery, and the minimum rechargeable energy of the battery, as follows:
[0133]
[0134]
[0135]
[0136] In the above formula, and Let S and B be the minimum and maximum battery SOC allowed for the i-th electric vehicle during charging and discharging, respectively. and Let Q be the remaining battery SOC when the i-th electric vehicle returns to the community and the SOC demand when it leaves the community next time. i Let be the rated battery capacity of the i-th electric vehicle.
[0137] Then, based on whether the remaining battery power and time of the electric vehicles returning to the community meet the controllable conditions, the electric vehicle groups are divided into three categories;
[0138] Based on the three defined parameters and the three categories of electric vehicles, Figure 2A schematic diagram of the energy operating range and charge / discharge power boundaries for three types of electric vehicles is given. In the diagram, the solid lines... The dashed line represents the fastest energy accumulation path (upper boundary) for an electric vehicle. The solid line represents the slowest energy accumulation path (lower boundary) for an electric vehicle. The fastest and slowest energy accumulation paths together constitute the energy operating region of the electric vehicle; The dashed line represents the charging power boundary of an electric vehicle. This represents the discharge power boundary of an electric vehicle, which together constitute the adjustable range of the electric vehicle's charging / discharging power. From... Figure 2 It can be observed that the upper and lower boundaries of energy accumulation and charging power coincide for the first type of electric vehicle, indicating that this type of electric vehicle does not possess controllable conditions. For the second type of electric vehicle, A negative value, according to the formula for the three parameters, physically means that the remaining SOC of this type of electric vehicle upon returning to the community is higher than the minimum SOC limit of the battery. It has charging and discharging capabilities. For the third type of electric vehicle, If it is a positive value, according to the formula It can be seen that the remaining SOC of this type of electric vehicle when it returns to the community is lower than the lower limit of battery SOC. Therefore, these types of electric vehicles do not have the ability to discharge electricity. According to... Figure 2 As shown in (c), this type of electric vehicle must first be charged at the rated charging power, and when the battery energy accumulation level reaches It has charging and discharging capabilities.
[0139] Next, we will model the energy accumulation boundary and charge / discharge power boundary of the above three types of electric vehicles:
[0140] In one embodiment, the energy accumulation boundary and charge / discharge power boundary corresponding to the first type of electric vehicle are as follows:
[0141]
[0142]
[0143] In the above formula, t end,i and t next,i These represent the times when the i-th electric vehicle returns to and leaves the community, respectively. and Let be the charging power boundary and discharging power boundary of the i-th electric vehicle during time period t, respectively. and Let be the upper and lower boundaries of the energy accumulation of the i-th electric vehicle during time period t, respectively. The rated charging power of the i-th electric vehicle. Let be the charging efficiency of the i-th electric vehicle, Δt be the scale of a unit time interval, and t be the current time period. The charging power for the i-th electric vehicle. The specified energy requirement for the i-th electric vehicle. Let be the upper boundary of the energy accumulation of the i-th electric vehicle during time period t-1.
[0144] The energy accumulation boundary and charge / discharge power boundary corresponding to the second type of electric vehicle are as follows:
[0145]
[0146]
[0147]
[0148]
[0149] In the above formula, This represents the rated discharge power of the i-th electric vehicle; Let represent the discharge efficiency of the i-th electric vehicle. and Let be the maximum and minimum rechargeable energy of the battery of the i-th electric vehicle, respectively. Let be the lower boundary of the energy accumulation of the i-th electric vehicle during time period t-1. Let i be the rated discharge power of the i-th electric vehicle. Let be the discharge efficiency of the i-th electric vehicle.
[0150] When t∈[t end,i ,t c,i When [the third type of electric vehicle is mentioned], the energy accumulation boundary and charge / discharge power boundary are as follows:
[0151]
[0152]
[0153] When t∈[t c,i ,t next,i When [the third type of electric vehicle is mentioned], the energy accumulation boundary and charge / discharge power boundary are as follows:
[0154]
[0155]
[0156]
[0157]
[0158] In the above formula, t c,i To extend the time,
[0159] The energy accumulation boundary and charge / discharge power boundary of each individual electric vehicle provide key parameters for establishing an electric vehicle swarm aggregation model. The energy accumulation boundary of the electric vehicle swarm can be obtained by summing the time-series energy accumulation boundaries of the individual electric vehicles; similarly, the charge / discharge power boundary of the electric vehicle swarm can be obtained by summing the time-series charge / discharge power boundaries of the individual electric vehicles. This forms a flexible and adjustable charging / discharging region for the electric vehicle swarm.
[0160] Figure 2 The energy operation region diagram and charge / discharge control diagram of the electric vehicle fleet are presented. It can be seen that the upper and lower boundaries of the energy accumulation of the electric vehicle fleet will become smooth curves, and the charging / discharging power boundary will also become a smooth curve. Figure 3 (b) provides a charging and discharging power curve for a group of electric vehicles, and its corresponding energy accumulation curve is shown below. Figure 3 As shown in (a). Different charging and discharging behaviors (or electricity consumption behaviors) will... Figure 3 (a) corresponds to a different energy accumulation curve, which brings considerable flexibility and adjustability potential for the optimized operation of electric vehicle fleet charging and discharging. According to Figure 3 As shown, the aggregation model of the electric vehicle group is represented as a 10-tuple vector, i.e.
[0161] Specifically, the pre-built aggregation model is as follows:
[0162]
[0163] P t c +|P t d |≤max{P t c ,|P t d |}
[0164]
[0165]
[0166]
[0167] In the above formula, P t c and P t d These represent the charging power and discharging power of the electric vehicle group during time period t, respectively.end and T next These are the earliest and latest times for scheduling charging and discharging of the electric vehicle fleet, respectively. and P represents the upper and lower boundaries of the energy accumulation of the electric vehicle group at time t. t max+ and P t max- These represent the charging power boundary and discharging power boundary of the electric vehicle group during time period t, respectively. and These are the charging and discharging loss coefficients for the electric vehicle fleet, respectively. The energy accumulation level of the electric vehicle group during time period t. This represents the cumulative energy level of the electric vehicle group during time period t-1.
[0168] The earliest and latest times for the charging and discharging scheduling of the electric vehicle fleet are as follows:
[0169]
[0170] The upper and lower boundaries of the energy accumulation of the electric vehicle group during time period t are as follows:
[0171]
[0172] The charging power boundary and discharging power boundary of the electric vehicle group during time period t are as follows:
[0173]
[0174] The charging and discharging loss coefficients of the electric vehicle fleet are as follows:
[0175]
[0176] In the above formula, Ω 1 Ω 2 and Ω 3 They are respectively groups of electric vehicles of categories one, two, and three. Z represents the access status of the i-th electric vehicle during time period t, and Z represents the total number of electric vehicles.
[0177] Furthermore, the access status of the i-th electric vehicle during time period t is as follows:
[0178]
[0179] Combination Figure 3It can be observed that the established electric vehicle (EV) swarm aggregation model is equivalent to aggregating a large number of individual EVs into a rechargeable and optimizable energy storage unit from an energy accumulation perspective, exhibiting similar charging and discharging operation characteristics to individual EVs. The difference lies in the smoothing of the energy accumulation boundary and the charging / discharging power boundary of the EV swarm aggregation model. This is primarily because its calculation process is closely related to the entry and exit of individual EVs within the swarm. Compared to traditional EV swarm charging and discharging optimization methods, EV swarm charging and discharging optimization only requires optimizing the charging and discharging behavior of an EV swarm aggregation model. This makes the computational complexity of EV swarm optimization independent of the number of EVs, and eliminates the need for frequent parameter transfers between EVs during the optimization process. Therefore, it significantly reduces the computational and communication burden, supporting corresponding charging and discharging control algorithms to determine the charging and discharging behavior of individual EVs.
[0180] To verify the rationality of the proposed method in calculating system carbon flow, a community of 100 electric vehicles with V2G functionality was used for validation. The rationality and effectiveness of the method were analyzed and verified. For simulation purposes, it was assumed that the rated charging and discharging power were 6.6kW and -6.6kW, respectively, the charging and discharging efficiency was 0.95, and the rated battery capacity was 35kWh. and We take values of 0.1 and 0.9 respectively. To simulate the grid connection and disconnection process of electric vehicles, we assume that the remaining battery power of the electric vehicle returns to the community. Following a normal distribution N(0.6, 0.1), the expected charge of an electric vehicle leaving the community is... It follows a uniform distribution in the range [0.8, 0.9]. In the electric vehicle charging and discharging decision layer, L... min Set the value to 0, and divide the electric vehicles into 10 groups in the Laxity-SOC plane.
[0181] Figure 4 This demonstrates the charge / discharge power boundaries of the electric vehicle (V electric vehicle) swarm aggregation model. Corresponding to... Figure 5 The changes in the upper and lower boundaries of energy accumulation during the charging and discharging process of the electric vehicle swarm aggregation model are presented. The two graphs depict the schedulable boundary of charging / discharging power and the boundary of energy accumulation in the electric vehicle swarm aggregation model.
[0182] Example 2
[0183] Based on the same inventive concept, this invention also provides a charging and discharging control device for a fleet of electric vehicles, such as... Figure 6 As shown, the electric vehicle group aggregation charging and discharging control device includes:
[0184] The analysis module is used to classify electric vehicles and determine the energy accumulation boundary and charge / discharge power boundary corresponding to the type of electric vehicle.
[0185] The control module substitutes the energy accumulation boundary and charge / discharge power boundary corresponding to the type of electric vehicle into a pre-built aggregation model to constrain and control the charge and discharge power of the electric vehicle group.
[0186] Preferably, the electric vehicle belongs to the following types: Class I electric vehicle, Class II electric vehicle, and Class III electric vehicle.
[0187] Furthermore, the analysis module is specifically used for:
[0188] Electric vehicles that are parked in the community for less than a preset threshold and are charged at rated power immediately after being connected to a charging station will be classified as Category I electric vehicles.
[0189] Electric vehicles that have been parked in the community for a longer period than a preset threshold and whose remaining SOC upon arrival in the community is higher than the minimum battery SOC allowed during charging and discharging will be classified as Category II electric vehicles.
[0190] Electric vehicles that arrive at the community with a remaining SOC lower than the minimum allowable SOC during charging and discharging, and whose accumulated energy after connecting to the charging pile reaches the minimum chargeable energy of their battery before participating in charge and discharge control scheduling, are classified as Category III electric vehicles.
[0191] Furthermore, the energy accumulation boundary and charge / discharge power boundary corresponding to the first type of electric vehicle are as follows:
[0192]
[0193]
[0194] In the above formula, t end,i and t next,i These represent the times when the i-th electric vehicle returns to and leaves the community, respectively. and Let be the charging power boundary and discharging power boundary of the i-th electric vehicle during time period t, respectively. and Let be the upper and lower boundaries of the energy accumulation of the i-th electric vehicle during time period t, respectively. The rated charging power of the i-th electric vehicle. Let be the charging efficiency of the i-th electric vehicle, Δt be the scale of a unit time interval, and t be the current time period. The charging power for the i-th electric vehicle. The specified energy requirement for the i-th electric vehicle. Let be the upper boundary of the energy accumulation of the i-th electric vehicle during time period t-1.
[0195] Furthermore, the energy accumulation boundary and charge / discharge power boundary corresponding to the second type of electric vehicle are as follows:
[0196]
[0197]
[0198]
[0199]
[0200] In the above formula, This represents the rated discharge power of the i-th electric vehicle; Let represent the discharge efficiency of the i-th electric vehicle. and Let be the maximum and minimum rechargeable energy of the battery of the i-th electric vehicle, respectively. Let be the lower boundary of the energy accumulation of the i-th electric vehicle during time period t-1. Let i be the rated discharge power of the i-th electric vehicle. Let be the discharge efficiency of the i-th electric vehicle.
[0201] Furthermore, when t∈[t end,i ,t c,i When [the third type of electric vehicle is mentioned], the energy accumulation boundary and charge / discharge power boundary are as follows:
[0202]
[0203]
[0204] When t∈[t c,i ,t next,i When [the third type of electric vehicle is mentioned], the energy accumulation boundary and charge / discharge power boundary are as follows:
[0205]
[0206]
[0207]
[0208]
[0209] In the above formula, t c,i To extend the time,
[0210] Furthermore, the specified energy requirements, maximum rechargeable energy of the battery, and minimum rechargeable energy of the i-th electric vehicle are as follows:
[0211]
[0212]
[0213]
[0214] In the above formula, and Let S and B be the minimum and maximum battery SOC allowed for the i-th electric vehicle during charging and discharging, respectively. and Let Q be the remaining battery SOC when the i-th electric vehicle returns to the community and the SOC demand when it leaves the community next time. i Let be the rated battery capacity of the i-th electric vehicle.
[0215] Furthermore, the pre-built aggregation model is as follows:
[0216]
[0217] P t c +|P t d |≤max{P t c ,|P t d |}
[0218]
[0219]
[0220]
[0221] In the above formula, P t c and P t d These represent the charging power and discharging power of the electric vehicle group during time period t, respectively. end and T next These are the earliest and latest times for scheduling charging and discharging of the electric vehicle fleet, respectively. and P represents the upper and lower boundaries of the energy accumulation of the electric vehicle group at time t. t max+ and P t max- These represent the charging power boundary and discharging power boundary of the electric vehicle group during time period t, respectively. and These are the charging and discharging loss coefficients for the electric vehicle fleet, respectively. The energy accumulation level of the electric vehicle group during time period t. This represents the cumulative energy level of the electric vehicle group during time period t-1.
[0222] Furthermore, the earliest and latest times for the charging and discharging scheduling of the electric vehicle fleet are as follows:
[0223]
[0224] The upper and lower boundaries of the energy accumulation of the electric vehicle group during time period t are as follows:
[0225]
[0226] The charging power boundary and discharging power boundary of the electric vehicle group during time period t are as follows:
[0227]
[0228] The charging and discharging loss coefficients of the electric vehicle fleet are as follows:
[0229]
[0230] In the above formula, Ω 1 Ω 2 and Ω 3 They are respectively groups of electric vehicles of categories one, two, and three. Z represents the access status of the i-th electric vehicle during time period t, and Z represents the total number of electric vehicles.
[0231] Furthermore, the access status of the i-th electric vehicle during time period t is as follows:
[0232]
[0233] Example 3
[0234] Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement corresponding method flows or corresponding functions, thereby implementing the steps of the electric vehicle group aggregation charging and discharging control method in the above embodiments.
[0235] Example 4
[0236] Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the electric vehicle group aggregation charging and discharging control method in the above embodiments.
[0237] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0238] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0239] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0240] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0241] 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 it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A method for controlling the aggregated charging and discharging of electric vehicle groups, characterized in that, The method includes: Electric vehicles are classified, and the energy accumulation boundary and charge / discharge power boundary corresponding to the type of electric vehicle are determined; The energy accumulation boundary and charging / discharging power boundary corresponding to the type of electric vehicle are substituted into a pre-constructed aggregation model to constrain and control the charging and discharging power of the electric vehicle group. The electric vehicles are classified into three types: Class I electric vehicles, Class II electric vehicles, and Class III electric vehicles. The classification of electric vehicles includes: Electric vehicles that are parked in the community for less than a preset threshold and are charged at rated power immediately after being connected to a charging station will be classified as Category I electric vehicles. Electric vehicles that have been parked in the community for a longer period than a preset threshold and whose remaining SOC upon arrival in the community is higher than the minimum battery SOC allowed during charging and discharging will be classified as Category II electric vehicles. Electric vehicles that arrive at the community with a remaining SOC lower than the minimum allowed battery SOC during charging and discharging, and whose accumulated energy after connecting to the charging pile reaches the minimum chargeable energy of their battery before participating in charging and discharging control scheduling, are classified as the third type of electric vehicles. The pre-built aggregation model is as follows: In the above formula, and electric vehicle groups Charging and discharging power during the time period and These are the earliest and latest times for scheduling charging and discharging of the electric vehicle fleet, respectively. and electric vehicle groups The upper and lower boundaries of energy accumulation over a period of time. and electric vehicle groups The charging power boundary and discharging power boundary of the time period. and These are the charging and discharging loss coefficients for the electric vehicle fleet, respectively. For electric vehicle groups Energy accumulation level over a period of time For electric vehicle groups The cumulative energy level over a period of time.
2. The method as described in claim 1, characterized in that, The energy accumulation boundary and charge / discharge power boundary corresponding to the first type of electric vehicle are as follows: In the above formula, and The first i The return and departure times of Taiwanese electric vehicles to communities. and The first i Taiwan Electric Vehicles The charging power boundary and discharging power boundary of the time period. and The first i Taiwan Electric Vehicles The upper and lower boundaries of energy accumulation over a period of time. For the first i The rated charging power of the electric vehicle in Taiwan For the first i The charging efficiency of electric vehicles in Taiwan. A scale for a unit time interval. For the current time period, For the first i The charging power of Taiwan's electric vehicles, For the first i The specified energy requirements for electric vehicles in Taiwan For the first i Taiwan Electric Vehicles The upper limit of energy accumulation over a period of time.
3. The method as described in claim 2, characterized in that, The energy accumulation boundary and charge / discharge power boundary corresponding to the second type of electric vehicle are as follows: In the above formula, Indicates the first i The rated discharge power of the electric vehicle; Indicates the first i The discharge efficiency of Taiwanese electric vehicles. and The first i The maximum and minimum rechargeable energy of the batteries in Taiwanese electric vehicles. For the first i Taiwan Electric Vehicles The lower boundary of energy accumulation over a period of time. For the first i The rated discharge power of the electric vehicle For the first i The discharge efficiency of the electric vehicle.
4. The method as described in claim 3, characterized in that, when ∈[ , When [the third type of electric vehicle is mentioned], the energy accumulation boundary and charge / discharge power boundary are as follows: when ∈[ , When [the third type of electric vehicle is mentioned], the energy accumulation boundary and charge / discharge power boundary are as follows: In the above formula, To extend the time, .
5. The method as described in claim 4, characterized in that, The first i The specified energy requirements, maximum rechargeable battery energy, and minimum rechargeable battery energy for electric vehicles in Taiwan are as follows: In the above formula, and The first i The minimum and maximum battery SOC allowed during charging and discharging of electric vehicles in Taiwan. and The first i The remaining battery SOC of an electric vehicle returning to the community and the SOC demand when it leaves the community next time. For the first i The rated battery capacity of the electric vehicle.
6. The method as described in claim 5, characterized in that, The earliest and latest times for the charging and discharging scheduling of the electric vehicle fleet are as follows: The electric vehicle group The upper and lower boundaries of energy accumulation over a given period are as follows: The electric vehicle group The charging power boundary and discharging power boundary for each time period are as follows: The charging and discharging loss coefficients of the electric vehicle fleet are as follows: In the above formula, , and They are respectively groups of electric vehicles of categories one, two, and three. For the first i Taiwan Electric Vehicles The access status for a given time period, where Z represents the total number of electric vehicles.
7. The method as described in claim 6, characterized in that, The first i Taiwan Electric Vehicles The access status for each time period is as follows: 。 8. A charging and discharging control device for a fleet of electric vehicles, characterized in that, The device includes: The analysis module is used to classify electric vehicles and determine the energy accumulation boundary and charge / discharge power boundary corresponding to the type of electric vehicle. The control module substitutes the energy accumulation boundary and charging / discharging power boundary corresponding to the type of electric vehicle into a pre-built aggregation model to constrain and control the charging and discharging power of the electric vehicle group. The electric vehicles are classified into three types: Class I electric vehicles, Class II electric vehicles, and Class III electric vehicles. The analysis module is specifically used for: Electric vehicles that are parked in the community for less than a preset threshold and are charged at rated power immediately after being connected to a charging station will be classified as Category I electric vehicles. Electric vehicles that have been parked in the community for a longer period than a preset threshold and whose remaining SOC upon arrival in the community is higher than the minimum battery SOC allowed during charging and discharging will be classified as Category II electric vehicles. Electric vehicles that arrive at the community with a remaining SOC lower than the minimum allowed battery SOC during charging and discharging, and whose accumulated energy after connecting to the charging pile reaches the minimum chargeable energy of their battery before participating in charging and discharging control scheduling, are classified as the third type of electric vehicles. The pre-built aggregation model is as follows: In the above formula, and electric vehicle groups Charging and discharging power during the time period and These are the earliest and latest times for scheduling charging and discharging of the electric vehicle fleet, respectively. and electric vehicle groups The upper and lower boundaries of energy accumulation over a period of time. and electric vehicle groups The charging power boundary and discharging power boundary of the time period. and These are the charging and discharging loss coefficients for the electric vehicle fleet, respectively. For electric vehicle groups Energy accumulation level over a period of time For electric vehicle groups The cumulative energy level over a period of time.
9. The apparatus as claimed in claim 8, characterized in that, The energy accumulation boundary and charge / discharge power boundary corresponding to the first type of electric vehicle are as follows: In the above formula, and The first i The return and departure times of Taiwanese electric vehicles to communities. and The first i Taiwan Electric Vehicles The charging power boundary and discharging power boundary of the time period. and The first i Taiwan Electric Vehicles The upper and lower boundaries of energy accumulation over a period of time. For the first i The rated charging power of the electric vehicle in Taiwan For the first i The charging efficiency of electric vehicles in Taiwan. A scale for a unit time interval. For the current time period, For the first i The charging power of Taiwan's electric vehicles, For the first i The specified energy requirements for electric vehicles in Taiwan For the first i Taiwan Electric Vehicles The upper limit of energy accumulation over a period of time.
10. The apparatus as claimed in claim 9, characterized in that, The energy accumulation boundary and charge / discharge power boundary corresponding to the second type of electric vehicle are as follows: In the above formula, Indicates the first i The rated discharge power of the electric vehicle; Indicates the first i The discharge efficiency of Taiwanese electric vehicles. and The first i The maximum and minimum rechargeable energy of the batteries in Taiwanese electric vehicles. For the first i Taiwan Electric Vehicles The lower boundary of energy accumulation over a period of time. For the first i The rated discharge power of the electric vehicle For the first i The discharge efficiency of the electric vehicle.
11. The apparatus as claimed in claim 10, characterized in that, when ∈[ , When [the third type of electric vehicle is mentioned], the energy accumulation boundary and charge / discharge power boundary are as follows: when ∈[ , When [the third type of electric vehicle is mentioned], the energy accumulation boundary and charge / discharge power boundary are as follows: In the above formula, To extend the time, .
12. The apparatus as claimed in claim 11, characterized in that, The first i The specified energy requirements, maximum rechargeable battery energy, and minimum rechargeable battery energy for electric vehicles in Taiwan are as follows: In the above formula, and The first i The minimum and maximum battery SOC allowed during charging and discharging of electric vehicles in Taiwan. and The first i The remaining battery SOC of an electric vehicle returning to the community and the SOC demand when it leaves the community next time. For the first i The rated battery capacity of the electric vehicle.
13. The apparatus as claimed in claim 12, characterized in that, The earliest and latest times for the charging and discharging scheduling of the electric vehicle fleet are as follows: The electric vehicle group The upper and lower boundaries of energy accumulation over a given period are as follows: The electric vehicle group The charging power boundary and discharging power boundary for each time period are as follows: The charging and discharging loss coefficients of the electric vehicle fleet are as follows: In the above formula, , and They are respectively groups of electric vehicles of categories one, two, and three. For the first i Taiwan Electric Vehicles The access status for a given time period, where Z represents the total number of electric vehicles.
14. The apparatus as claimed in claim 13, characterized in that, The first i Taiwan Electric Vehicles The access status for each time period is as follows: 。 15. A computer device, characterized in that, include: One or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, the electric vehicle group aggregation charging and discharging control method as described in any one of claims 1 to 7 is implemented.
16. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the electric vehicle group aggregation charging and discharging control method as described in any one of claims 1 to 7.