Method and device for evaluating ability of electric vehicle to participate in vehicle network interaction

Abstract

The invention provides a method and device for evaluating the ability of an electric vehicle to participate in vehicle network interaction, and belongs to the field of electric vehicle and power network interaction. The method comprises the following steps: dividing an activity area and a state of an electric automobile fleet to establish a key state set; based on the key state set, the number of the electric vehicles in each state and the vehicle-mounted battery energy in each region in each time period are calculated in combination with travel historical data of the electric vehicle fleet, and then a space-time distribution evaluation optimization model of the electric vehicles participating in the vehicle network interaction capability is established and solved; and obtaining an optimization result of the number upper limit of the electric vehicles and the vehicle-mounted battery energy upper limit in each state in each region in each time period, and completing evaluation. The method can give consideration to the flexibility of the electric vehicle in time and space dimensions, and can effectively evaluate the spatial and temporal distribution of the ability of the electric vehicle fleet to participate in the vehicle network interaction.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle and power grid interaction, and specifically relates to a method and device for evaluating the ability of electric vehicles to participate in vehicle-grid interaction. Background Technology

[0002] In recent years, with the advancement of transportation electrification and the increasing penetration rate of electric vehicles, electric vehicles, as a mobile energy storage resource, possess inherent flexibility that can provide services such as peak shaving and valley filling, peak and frequency regulation, and demand-side response for grid operation, and even support for emergency control in special circumstances. Against this backdrop, further application of vehicle-to-grid (V2G) technology can unlock the ability of electric vehicles to support the grid in reverse.

[0003] A crucial foundation for electric vehicle (EV) interaction with the power grid is the accurate and reasonable assessment of EVs' ability to participate in vehicle-to-grid (V2G) interaction. This assessment supports subsequent stages such as application and regulation for V2G participation. Currently, most published literature on V2G participation capability assessment focuses on the temporal dimension of EV charging load transfer or orderly charging, rarely considering the spatial differences in EV charging flexibility between different regions. It lacks effective assessment methods for adjustable capabilities across both temporal and spatial dimensions. Furthermore, most published literature focuses on scenarios where operators have dispatch authority over vehicles (such as buses), with limited consideration of the capability assessment and regulation optimization for private cars, taxis, and ride-hailing vehicles—EV fleets that cannot be directly dispatched—to participate in V2G interaction.

[0004] In the prior art, patent application number CN202110345166.7 proposes a method and system for evaluating the response capability of electric vehicles participating in grid interaction. It assesses the response capability of electric vehicles by obtaining the index values ​​and corresponding weights of various indicators of the electric vehicles participating in grid interaction. However, the assessed capability range is not intuitive enough, and it does not consider the distribution of electric vehicle participation capability in vehicle-grid interaction over a longer time scale and spatial dimension. Patent application number CN202510174405.5 proposes a precise evaluation method for the aggregated adjustable capability of electric vehicles interacting with the distribution network. It evaluates the feasible domain of a single electric vehicle based on an energy storage-like model and obtains the aggregated adjustable capability, but it does not consider the spatial distribution of electric vehicle participation capability in vehicle-grid interaction. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a method and apparatus for evaluating the ability of electric vehicles to participate in vehicle-to-grid interaction. This invention establishes a key state set of electric vehicles with time and space dual-dimensional indices, effectively taking into account the flexibility of electric vehicles in both time and space dimensions. By establishing and solving an evaluation optimization model for the ability of electric vehicles to participate in vehicle-to-grid interaction, corresponding capability evaluation results can be obtained for different forms of vehicle-to-grid interaction and conservative requirements, which helps support the safe and stable operation of the power grid.

[0006] A first aspect of this invention provides a method for evaluating the ability of electric vehicles to participate in vehicle-to-grid interaction, comprising:

[0007] The activity areas of the electric vehicle fleet are divided;

[0008] Based on the activity area division results, the states of the electric vehicle fleet are divided, and a key state set is established;

[0009] Based on the key state set and combined with the travel history data of the electric vehicle fleet, the spatiotemporal distribution of the state quantities of the electric vehicle fleet is calculated, including: the number of electric vehicles in each state of the key state set and the on-board battery energy in each time period and region.

[0010] Based on the spatiotemporal distribution of the state variables of the electric vehicle fleet, a spatiotemporal distribution evaluation and optimization model for the ability of electric vehicles to participate in vehicle-to-grid interaction is established and solved. The optimization results of the upper limit of the number of electric vehicles and the upper limit of the on-board battery energy in each state of the key state set in each time period and region are obtained, and the evaluation is completed.

[0011] In one specific embodiment of the present invention, the classification of the electric vehicle fleet status takes into account the driving status of the electric vehicles, the passenger-carrying status, and the state of charge of the on-board batteries.

[0012] In one specific embodiment of the present invention, the key state set includes the following states:

[0013] 1) NA unavailable: This means the electric vehicle is not working at this time; in the NA state, the SoC of the electric vehicle remains stable; when the electric vehicle is about to start working, the next state of NA is Start Working SW;

[0014] 2) Passenger-carrying IT: This means the electric vehicle is carrying passengers. In the IT state, the SoC of the electric vehicle will decrease due to driving. If the destination of the electric vehicle is located in another area, the next state of IT is inter-area transfer TR; otherwise, the next state of IT is trip end ET.

[0015] 3) Charging in progress (CG): This means the electric vehicle is being charged. In the CG state, the SoC of the electric vehicle will increase until the charging process ends or the battery is fully charged. When the charging process ends, the next state of CG is End of charging (EC).

[0016] 4) Idle ID: This means that the electric vehicle is not carrying passengers and is waiting for the next order. In the state ID, the SoC of the electric vehicle decreases due to driving. When the SoC drops below the range anxiety threshold of the driver user, the next state of the ID is Start Charging SC. If the SoC does not drop below the range anxiety threshold and a new order arrives, the next state of the ID is Start Trip ST.

[0017] 5) Start working SW: This means the electric vehicle starts working and is ready to carry passengers; in the SW state, the electric vehicle's SoC remains stable; if a new order arrives, the next state of SW is ST; otherwise, the next state of SW should be ID.

[0018] 6) End of Work (EW): This indicates that the electric vehicle is about to end its work. In the EW state, the SoC of the electric vehicle remains stable. The next state after EW is NA.

[0019] 7) Start of Trip ST: This indicates the start of a new order for the electric vehicle; in state ST, the SoC of the electric vehicle will decrease due to driving; the next state after ST is IT.

[0020] 8) End of Trip ET: This means the electric vehicle is about to reach its destination and the trip is about to end. In state ET, the SoC of the electric vehicle decreases due to driving. If the SoC does not drop below the range anxiety threshold, the next state of ET is ID. If the SoC drops below the range anxiety threshold, the next state of ET is Start Charging SC.

[0021] 9) Start Charging (SC): This indicates that the electric vehicle has started charging. In the SC state, the electric vehicle's SoC increases due to charging. The next state after SC is CG.

[0022] 10) End of Charge EC: The electric vehicle is about to end charging; in the EC state, the SoC of the electric vehicle will increase; if there is no upcoming order, the next state of EC is ID; if there is a new order, the next state of EC is ST; if the electric vehicle is about to finish working, the next state of EC is EW.

[0023] 11) Inter-regional transfer TR: This means that the electric vehicle is about to finish its trip and transfer to another region; under the state TR, the taxi's SoC decreases; if the SoC does not drop below the range anxiety threshold and there is no upcoming trip demand, the next state of TR is ID; if a new trip begins, the next state of TR is ST; if the SoC drops below the range anxiety threshold, the next state of TR is SC.

[0024] In a specific embodiment of the present invention, the spatiotemporal distribution evaluation and optimization model for the ability of electric vehicles to participate in vehicle-to-grid interaction includes: an optimization model for the upper limit of the number of electric vehicles and an optimization model for the upper limit of the energy of electric vehicles;

[0025] For any region, the objective function of the electric vehicle quantity upper limit optimization model is:

[0026]

[0027] The objective function of the electric vehicle energy ceiling optimization model is:

[0028]

[0029] Where, x t e represents the number of vehicles participating in vehicle-to-everything (V2X) interaction in this region during time period t. t The energy stored in the onboard batteries of vehicles participating in vehicle-to-grid interaction in this region during time period t;

[0030] For time period t, the weighting factor is applied to the upper limit of the number of vehicles. For time period t, the weighting factor is applied to the upper limit of the vehicle battery energy.

[0031] The constraints of the electric vehicle quantity ceiling optimization model and the electric vehicle energy ceiling optimization model are the same, including:

[0032]

[0033]

[0034] in, These represent the number of electric vehicles in region i during time period t that are in states ID, EC, SC, and CG, respectively. These represent the number of electric vehicles in time period t region i that are in the ET, ST, EW, and SW states, respectively. The number of vehicles transferred from region j to region i due to order reasons during time period t; and These represent the onboard battery energy of an electric vehicle in region i during time period t under the states ID, EC, SC, CG, ET, ST, EW, and SW. The onboard battery energy transferred from region j to region i during time period t; Let ΔE be the duration of participation in grid regulation, ΔE be the average energy consumption per time period when participating in grid regulation via V2G, and λ be the energy consumption per unit time period. ID λ represents the energy consumption of idle vehicles during a single time period. CG For the charging energy within a single time period, λ IT The energy consumed by a vehicle during a single period of time; β EW,min β SW,min β EC,min β SC,min β ET,min β ST,min These represent the minimum states of charge that an electric vehicle's onboard battery should have under EW, SW, EC, SC, ET, and ST states, respectively; β EW,max β SW,max β EC,max β SC,max β ET,max β ST,max These represent the maximum state of charge that an electric vehicle's onboard battery should have under the EW, SW, EC, SC, ET, and ST states, respectively. Energy consumed by vehicles transferred from region j to region i due to order reasons during time period t;

[0035] Solving the optimization model for the upper limit of the number of electric vehicles with equation (1) as the objective function and equations (3)-(13) as constraints, we obtain x. t The optimal solution is to set x t The optimal solution is denoted as the upper limit of the number of electric vehicles in each region that can respond to V2G and support grid regulation in each time period, and is called .

[0036] Solving the electric vehicle energy ceiling optimization model with equation (2) as the objective function and equations (3)-(13) as constraints, we obtain e t The optimal solution is to e t The optimal solution is denoted as the upper limit of the on-board battery energy in each region during each time period.

[0037] In one specific embodiment of the present invention, it further includes:

[0038] Based on the solution results of the spatiotemporal distribution evaluation optimization model for electric vehicles' participation in vehicle-to-grid interaction, a two-stage optimization model for scheduling control of electric vehicles' participation in vehicle-to-grid interaction is established. The specific steps are as follows:

[0039] 1) Construct the first-stage pre-scheduling optimization model;

[0040] The objective function of the pre-scheduling optimization model in the first stage is to minimize the total cost, as expressed below;

[0041]

[0042] in, Represents the spatial pre-transfer decision of electric vehicles; k i,j,t and e i,j,t Let f(z) and z(j) represent the number of electric vehicles transferred from region i to region j during time period t, and the cumulative on-board battery energy, respectively; f(z) and the expected operating cost are the electric vehicle dispatch and transfer costs.

[0043] z is the decision variable of the first-stage pre-scheduling optimization model, u = {u i,t} represents the uncertainty of V2G demand in different regions and time periods; Ξ represents the typical scenario set, and ω represents the scenario index. It is the operating cost under the vehicle pre-scheduling decision z and the power grid regulation demand u; This represents the unit cost of dispatching electric vehicles from region i to region j.

[0044] The constraints of the first-stage pre-scheduling optimization model include:

[0045]

[0046] in, This represents the upper limit of the number of available electric vehicles in region i during time period t. Indicates the maximum onboard battery energy that can be adjusted; u i,ω,t P represents the V2G demand proposed by the power grid regulation in region i during time period t under scenario ω. v2g E is the rated discharge power. bat The rated capacity of the vehicle battery, δ i,ω,t and μ i,ω,t These are intermediate auxiliary variables introduced for the number of pre-scheduled electric vehicles and the on-board battery energy, respectively; Δt is the length of a single time period.

[0047] 2) Construct a temporary scheduling and charging / discharging optimization model for the second stage;

[0048] The objective function of the second-stage temporary scheduling and charging / discharging optimization model is to minimize the total operating cost, as expressed below:

[0049]

[0050] Where y is the decision variable of the second-stage temporary scheduling and charge / discharge optimization model; and These represent the charging and discharging power of vehicles participating in V2G in time period ti under scenario ω; SD, V2G, and ED are newly introduced states, where:

[0051] Start of Discharge SD: This means that the electric vehicle begins the vehicle-to-grid (V2G) interactive discharge process at the electric vehicle charging and discharging station; the next state of SD is V2G.

[0052] V2G during discharge: This means that the electric vehicle is undergoing vehicle-to-grid (V2G) interactive discharge at the electric vehicle charging and discharging station. In the V2G state, the SoC of the electric vehicle will decrease as it participates in the V2G interactive discharge until the discharge process ends or the battery energy reaches the lower limit. The next state of V2G is ED.

[0053] End of discharge (ED): This means the electric vehicle is about to terminate the vehicle-to-grid (V2G) discharge; the next state of ED is ID.

[0054] The constraints of the second-stage temporary scheduling and charge / discharge optimization model include:

[0055] Constraints related to participation in demand response:

[0056]

[0057] Among them, u i,ω,t To meet the needs of V2G participation in grid regulation in region i during time period t under scenario ω;

[0058] State transition relationship constraints:

[0059]

[0060] Among them, e i,j,t Let λ be the energy transferred from region i to region j in time period t; ID It represents the energy consumption of a vehicle in ID state within a single time period;

[0061] Electric vehicle power and energy boundary constraints:

[0062]

[0063] Where, β V2G,min The minimum permissible state of charge of the vehicle battery when providing V2G services;

[0064] Other constraints:

[0065]

[0066]

[0067] in, and These represent the capacities of electric vehicle charging stations and electric vehicle charging / discharging stations in region i, respectively.

[0068] In one specific embodiment of the present invention, it further includes:

[0069] The two-stage optimization model is simplified as follows:

[0070] 1) The number of pre-scheduled transfer vehicles k in the two-stage optimization model i,j,t Relaxing integer variables to continuous variables;

[0071] 2) In the constraint conditions, equations (16) and (42) are mutually exclusive constraints and are removed from the two-stage optimization model.

[0072] In one specific embodiment of the present invention, it further includes:

[0073] The simplified two-stage optimization model has an objective function consisting of equations (14) and (21), and constraints including equations (15), (17)-(20), and (22)-(41). Solving this model yields the number of vehicles pre-scheduled and the energy value k. i,j,t e i,j,t This is for use in the pre-scheduling of vehicles across spaces.

[0074] A second aspect of the present invention provides a device for evaluating the ability of an electric vehicle to participate in vehicle-to-grid interaction, comprising:

[0075] The area division module is used to divide the activity area of ​​the electric vehicle fleet;

[0076] The key state set construction module is used to divide the state of the electric vehicle fleet based on the activity area division results and establish a key state set;

[0077] The spatiotemporal distribution calculation module is used to calculate the spatiotemporal distribution of the state quantities of the electric vehicle fleet based on the key state set and the travel history data of the electric vehicle fleet, including: the number of electric vehicles in each state of the key state set and the on-board battery energy in each time period and region.

[0078] The evaluation module is used to establish and solve an evaluation optimization model for the spatiotemporal distribution of the electric vehicle fleet's state variables, thereby obtaining the optimization results of the upper limit of the number of electric vehicles in each state of the key state set and the upper limit of the on-board battery energy in each time period and region. The evaluation is then completed.

[0079] A third aspect of the present invention provides an electronic device comprising:

[0080] At least one processor; and a memory communicatively connected to said at least one processor;

[0081] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to perform the above-described method for evaluating the ability of an electric vehicle to participate in vehicle-to-grid interaction.

[0082] A fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described method for evaluating the ability of an electric vehicle to participate in vehicle-to-grid interaction.

[0083] The features and beneficial effects of this invention are as follows:

[0084] To better describe the spatiotemporal distribution of the potential of electric vehicle fleets to participate in vehicle-to-grid (V2G) interaction while meeting travel order demands, this invention identifies the key states of the electric vehicle fleet and establishes state transition relationships. Based on this, an optimization model is established to assess the ability of each spatial region and each temporal time period to participate in V2G interaction regulation. By solving this model, the maximum number of vehicles and the maximum energy that can be provided for each region and time period to participate in grid regulation while meeting travel order demands are obtained. Furthermore, this invention establishes a vehicle optimization strategy comprising two stages: pre-scheduling and temporary scheduling. In the pre-scheduling stage, considering the uncertainty of grid V2G regulation demands, vehicles in the fleet are pre-scheduled across regions to meet grid regulation demands as much as possible. In the real-time scheduling stage, temporary spatial scheduling and charging / discharging arrangements are made for electric vehicles to ensure the fulfillment of travel order demands.

[0085] The proposed assessment method for electric vehicles' participation in vehicle-to-grid (V2G) interaction capabilities establishes a key state set for electric vehicles with both temporal and spatial subscripts. Compared to traditional flexibility assessment methods, this method better considers the flexibility of electric vehicles in both time and space dimensions, and can effectively assess the spatiotemporal distribution of the ability of electric vehicle fleets to participate in V2G interaction. By establishing and solving an optimization model for assessing the ability of electric vehicles to participate in V2G interaction, corresponding capability assessment results can be obtained for different forms of V2G interaction and conservative requirements, thus more effectively characterizing their regulatory capabilities.

[0086] The scheduling and control strategy for electric vehicles participating in vehicle-grid interaction proposed in this invention addresses the problem of electric vehicle reallocation and charging / discharging scheduling by constructing a stochastic optimization model that includes two stages: pre-scheduling and temporary scheduling. This strategy can effectively optimize cross-regional vehicle scheduling and charging / discharging operation based on the grid regulation needs of different regions and time periods, thus supporting the safe and stable operation of the power grid. Attached Figure Description

[0087] Figure 1 This is an overall flowchart of a method for evaluating the ability of an electric vehicle to participate in vehicle-to-grid interaction, according to an embodiment of the present invention.

[0088] Figure 2 This is a schematic diagram of the state transition relationship of an electric vehicle in a specific embodiment of the present invention.

[0089] Figure 3 This is a schematic diagram of the state transition relationship of an electric vehicle with a newly added state in a specific embodiment of the present invention. Detailed Implementation

[0090] This invention proposes a method and apparatus for evaluating the ability of electric vehicles to participate in vehicle-to-grid interaction. The technical solution of this invention will be described in more detail below with reference to the accompanying drawings and specific embodiments.

[0091] A first aspect of this invention provides a method for evaluating the ability of electric vehicles to participate in vehicle-to-grid interaction, comprising:

[0092] The activity areas of the electric vehicle fleet are divided;

[0093] Based on the activity area division results, the states of the electric vehicle fleet are divided, and a key state set is established;

[0094] Based on the key state set and combined with the travel history data of the electric vehicle fleet, the spatiotemporal distribution of the state quantities of the electric vehicle fleet is calculated, including: the number of electric vehicles in each state of the key state set and the on-board battery energy in each time period and region.

[0095] Based on the spatiotemporal distribution of the state variables of the electric vehicle fleet, a spatiotemporal distribution evaluation and optimization model for the ability of electric vehicles to participate in vehicle-to-grid interaction is established and solved. The optimization results of the upper limit of the number of electric vehicles and the upper limit of the on-board battery energy in each state of the key state set in each time period and region are obtained, and the evaluation is completed.

[0096] In one specific embodiment of the present invention, the electric vehicles considered in the method for evaluating the ability of electric vehicles to participate in vehicle-to-grid interaction can be electric vehicle fleets such as taxis, ride-hailing vehicles, and autonomous vehicles. In this embodiment, an electric taxi fleet is used as the electric vehicle fleet, and the overall process of the method is as follows: Figure 1 As shown, it includes the following steps:

[0097] 1) Divide the activity range of the electric vehicle fleet into spatial regions.

[0098] In this embodiment, the activity range of the electric vehicle fleet is divided into spatial regions according to the latitude and longitude coordinate range of the spatial region and the number of sub-regions that need to be divided, and the subscript number and latitude and longitude coordinate coverage of each region are determined.

[0099] 2) Establish a key state set by dividing the state of the electric vehicle fleet.

[0100] In this embodiment, for region i and time period t, all vehicles currently located in that region can be classified into states based on their driving status, whether they are carrying passengers, and the remaining energy or state of charge (SoC) of their onboard batteries. In a specific embodiment of the present invention, for an electric taxi fleet, there are 11 typical states and their descriptions as follows:

[0101] 2-1) Not Available (NA): The taxi is not working at this time. In the NA state, the taxi's SoC remains stable. The next state of NA before starting to work is SW.

[0102] 2-2) In Trip (IT): The taxi is currently carrying passengers. In state IT, the taxi's SoC decreases as it travels. If the destination is in another area, the next state of IT is TR; otherwise, the next state of IT is ET.

[0103] 2-3) Charging (CG): The taxi is charging at an electric vehicle charging station or charging / discharging station. In state CG, the taxi's SoC increases until the charging process ends or the battery is fully charged. When the charging process is complete and the taxi is about to leave the charging station or charging / discharging station, the next state of CG is EC.

[0104] 2-4) Idle (ID): The taxi is not carrying any passengers and is waiting for the next order. In state ID, the taxi's SoC will also decrease due to driving. When the SoC drops below the driver's range anxiety threshold, i.e., the taxi needs to be charged, the next state of ID is SC; or, if the SoC is still sufficient (i.e., not dropping below the range anxiety threshold) and a new passenger order arrives, the next state of ID is ST.

[0105] 2-5) Start Working (SW): The taxi has just started working and is ready to pick up passengers. In the SW state, it is assumed that the taxi's SoC remains stable. If a new order arrives, the next state of SW should be ST; otherwise, the next state of SW should be ID.

[0106] 2-6) End Work (EW): The taxi is about to finish its shift. In the EW state, it is assumed that the taxi's SoC remains stable. The next state after EW is NA.

[0107] 2-7) Start Trip (ST): A new taxi order begins. In state ST, the taxi's SoC decreases due to driving. The next state after ST is IT.

[0108] 2-8) End Trip (ET): The taxi is about to reach its destination, and the trip is about to end. In state ET, the taxi's SoC will also decrease due to driving. If the SoC is sufficient, the next state of ET is ID. If the SoC drops below the range anxiety threshold, the next state of ET is SC, indicating that the taxi needs to charge, or the taxi is about to finish its shift and wants to charge before handing it over.

[0109] 2-9) Start Charging (SC): The taxi begins the charging process at an electric vehicle charging station or charging / discharging station. In the SC state, the taxi's SoC increases due to charging. The next state after SC is CG.

[0110] 2-10) End Charging (EC): The taxi is about to end charging and leave the charging station. In EC state, the taxi's SoC will increase unless the battery is fully charged. If there are no upcoming orders, the next state of EC will be ID; if there are new orders, the next state of EC will be ST; if the taxi is going off duty, the next state of EC will be EW.

[0111] 2-11) Inter-regional Transfer (TR): The taxi is about to end its trip and transfer to another region. If the destination is in a different region than the origin, TR can be considered a special state. In state TR, the taxi's SoC decreases. If the SoC is sufficient and there is no upcoming trip demand, the next state of TR is ID; if a new trip begins, it is ST; and if the SoC drops below the mileage anxiety threshold, it is SC, indicating that the taxi needs to charge, or the taxi is about to finish its shift and wants to charge before handover.

[0112] Figure 2 This is a schematic diagram illustrating the state transition relationships of an electric vehicle in a specific embodiment of the present invention. Among all states, NA, IT, CG, and ID are primary states, while the other states are transitional states. It should be noted that the differences between different regions have not been mentioned in the previous descriptions of the states. To reflect this, a subscript 'i' is added to the abbreviation of each state to indicate that it is a state of region i. Specifically, the subscripts for state TR are i and j, indicating that the taxi is transferring from region i to another region j. Figure 2 In the diagram, the highlighted TR boxes and dashed arrows represent the corresponding states transitioning from region i to all other regions, and the states transitioning from all other regions to region i. Based on the TR states, the states of two different regions can be connected to form a complete state transition diagram considering all regions. Since the complete state transition diagram is quite large, Figure 2 Only one area is shown for explanation. Figure 2 In the diagram, solid arrows indicate transitions between different states belonging to the same region, meaning that a state at a certain time period can be transformed into the state pointed to by the arrow in the next time period along the direction of the arrow; dashed arrows indicate transitions between states involving different regions. Since the state transition of a vehicle across regions must pass through state TR, dashed arrows all point to or originate from state TR.

[0113] It should be noted that the state of each region may differ at different time periods t. Therefore, in this embodiment, each state can also be described by introducing a subscript t to represent the state value at different time periods. The above method can also be used for state division for ride-hailing vehicles and unmanned vehicles.

[0114] 3) Based on the results of step 2), calculate the spatiotemporal distribution of the state variables of the electric vehicle fleet.

[0115] In this embodiment, the travel records, battery monitoring, coordinates and other accessible historical data of electric vehicles are used to calculate the number of vehicles in each state and the on-board battery energy in each state in each time period and region, based on the key state set established in step 2).

[0116] 4) Based on the results of step 3), establish and solve the spatiotemporal distribution evaluation optimization model for electric vehicles' ability to participate in vehicle-to-grid interaction.

[0117] In this embodiment, assessing the flexibility of electric vehicles (EVs) in participating in grid regulation and vehicle-to-grid interaction within a specific region essentially involves quantifying the number of EVs available for regulation and the energy stored in their onboard batteries for each time period. Even under a given travel schedule, EVs within a region will exhibit varying levels of flexibility due to different schedules. In other words, flexibility over a given period should vary within a certain range. Therefore, a crucial task in assessing their adjustability is to provide an upper limit on the number of vehicles that can participate in regulation and an upper limit on their stored energy for each time period.

[0118] Considering the various forms and scenarios of electric vehicles participating in vehicle-to-grid (V2G) interaction, the conservative requirements for flexibility assessment also vary. Rather than directly describing the adjustable range by defining the upper and lower limits of the power and energy trajectory envelope, obtaining corresponding upper and lower limits for different V2G interaction forms and requirements is more valuable, allowing for a more effective characterization of its regulation capabilities and the embedding and generation of further optimization solutions. In practice, the assessment of the number and energy of electric vehicles capable of responding to grid regulation demands is often influenced by many factors. For example, response prices and deviation penalty costs may differ at different times, and the prediction errors of grid regulation demands or the driving and charging sequence of electric vehicles may also be time-varying. The minimum number of adjustable vehicles and the available onboard battery energy at any given time is, of course, zero; therefore, this embodiment only assesses the upper limit of the number of vehicles and the upper limit of onboard battery energy at any given time. The upper limit of the adjustable range can be obtained by constructing and solving the corresponding optimization model.

[0119] In this embodiment, for any region, an optimization model for the upper limit of the number of electric vehicles and an optimization model for the upper limit of the energy of electric vehicles are established respectively.

[0120] The objective function of the electric vehicle quantity limit optimization model is:

[0121]

[0122] The objective function of the electric vehicle energy ceiling optimization model is:

[0123]

[0124] Where, x t e represents the number of vehicles participating in vehicle-to-everything (V2X) interaction in this region during time period t. t x represents the energy stored in the onboard batteries of vehicles participating in vehicle-to-grid interaction in this region during time period t. t and e t These are the decision variables output by the two optimization models, respectively.

[0125] For time period t, the weighting factor is applied to the upper limit of the number of vehicles. For time period t, there is a weighting factor for the upper limit of the vehicle battery energy.

[0126] In this embodiment, the value of the weighting factor can be set considering factors such as electricity market prices and prediction errors; typically, the weighting factor value is not negative. Taking electricity market prices as an example, the weighting factors related to the number of electric vehicles in each region and time period can be used. Set the values ​​of the electricity price curve for the electricity demand response market for each time period on a given day, so as to obtain a higher number of vehicles that can participate in vehicle-to-grid interaction during periods when market prices are higher.

[0127] The constraints of the electric vehicle quantity limit optimization model and the electric vehicle energy limit optimization model are the same. Specifically, based on the transition relationships between states in the key state set, the state variables in each time period within any region should satisfy the following constraints:

[0128]

[0129] in, These represent the number of electric vehicles in region i during time period t that are in states ID, EC, SC, and CG, respectively. These represent the number of electric vehicles in time period t region i that are in the ET, ST, EW, and SW states, respectively. Let be the number of vehicles transferred from region j to region i during time period t due to order reasons. It should be noted that the lowercase letter 'n' is used to identify decision variables, while the uppercase letter 'N' is used to identify parameters determined by orders and fleet work arrangements. and The values ​​represent the onboard battery energy of an electric vehicle in region i during time period t under the states ID, EC, SC, CG, ET, ST, EW, and SW. Let t be the onboard battery energy transferred from region j to region i during time period t. Let ΔE be the duration of participation in grid regulation, ΔE be the average energy consumption per time period when participating in grid regulation via V2G, and λ be the energy consumption per unit time period. ID λ represents the energy consumption of idle vehicles during a single time period. CG For the charging energy within a single time period, λ IT This refers to the energy consumed by a vehicle during a single period of time. β EW,min β SW,min β EC,min β SC,min β ET,min β ST,min These represent the minimum state of charge (SOC) that an electric vehicle's onboard battery should have under EW, SW, EC, SC, ET, and ST states, respectively; that is, the ratio of the onboard battery's energy to its rated energy. β EW,max β SW,max β EC,max β SC,max β ET,max β ST,max These represent the maximum state of charge that an electric vehicle's onboard battery should have under the EW, SW, EC, SC, ET, and ST states, respectively. This refers to the energy consumed by vehicles transferred from region j to region i during time period t due to order reasons.

[0130] In this embodiment, equations (3) and (4) respectively show the relationship between the number of vehicles and the stored energy and other state variables, while also considering the following... The time period adjustment retains a portion of the vehicles. Equations (5) and (6) describe the relationship between variables of several states related to charging, respectively. Equations (7)-(13) give the upper and lower limits of the stored energy in the vehicle under states EW / SW / EC / SC / ET / ST, respectively. Among them, the vehicle energy under state TR also considers the energy consumption of the journey, adding a term.

[0131] Furthermore, the electric vehicle quantity limit optimization model and the electric vehicle energy limit optimization model can be solved using solvers that support mixed-integer linear programming, such as CPLEX, Gurobi, and COPT.

[0132] Specifically, by solving the optimization model for the upper limit of the number of electric vehicles with equation (1) as the objective function and equations (3)-(13) as constraints, x is obtained. t The optimal solution is to set x t The optimal solution is denoted as the upper limit of the number of electric vehicles in each region that can respond to V2G and support grid regulation in each time period, and is called .

[0133] Solving the electric vehicle energy ceiling optimization model with equation (2) as the objective function and equations (3)-(13) as constraints, we obtain e t The optimal solution is to e t The optimal solution is denoted as the upper limit of the on-board battery energy in each region during each time period.

[0134] Furthermore, the method described in this embodiment also includes:

[0135] Based on the assessment of the capability of electric vehicles to participate in vehicle-to-grid interaction, the method described in this embodiment can perform scheduling control of electric vehicles participating in vehicle-to-grid interaction, specifically:

[0136] 5) Based on the results of step 4), establish a two-stage optimization model for scheduling and control of electric vehicles participating in vehicle-network interaction.

[0137] In this embodiment, different areas within a city may have different demand response needs. Furthermore, flexible resources such as electric vehicles not only have time-based adjustability but can also be spatially adjusted through vehicle scheduling. Electric vehicle fleet operators can, while ensuring travel demand, transfer some electric vehicles (such as taxis, ride-hailing vehicles, and autonomous vehicles) to different areas at appropriate times to meet the reverse discharge demand proposed by the power system operator. Illustratively, this embodiment constructs a two-stage stochastic optimization model to handle the electric vehicle reallocation and charging / discharging scheduling problem. In the first stage of this optimization model, the upper limit of the number of electric vehicles obtained from the previous step is applied. and the upper limit of vehicle battery energy The specific steps are as follows:

[0138] 5-1) Construct the first-stage pre-scheduling optimization model.

[0139] Electric vehicle fleet managers typically develop pre-schedule plans for their vehicles one day or several hours in advance. Illustratively, taking day-ahead pre-schedule as an example, the first stage establishes an economically optimal pre-schedule optimization model, considering the uncertainties of the next day's grid regulation demand and the flexibility of electric vehicles.

[0140] In this embodiment, the spatial pre-transfer decision of the electric vehicle is calculated in the objective function of the first-stage pre-scheduling optimization model. The goal is to minimize the total cost, where k i,j,t and e i,j,t Let f(z) represent the number of electric vehicles transferred from region i to region j during time period t, and z represent the cumulative onboard battery energy, respectively. The objective function also considers the electric vehicle dispatching and transfer cost f(z) and the expected operating cost, as shown below:

[0141]

[0142] Where z is the decision variable of the first-stage pre-scheduling optimization model, u = {u i,t} refers to the uncertainty of V2G demand in different regions and time periods. Taking into account the uncertainty of grid demand and the differences in different regions, this embodiment divides grid regulation demand into several typical scenarios, where Ξ is the set of typical scenarios and ω is the scenario index. This is the operating cost under the vehicle pre-scheduling decision z and the power grid regulation demand u, which is also the optimization target value of the second stage problem. This represents the unit cost of dispatching electric vehicles from region i to region j.

[0143] The constraints of the first-stage pre-scheduling optimization model include:

[0144]

[0145] in, This represents the upper limit of the number of available electric vehicles in region i during time period t. This represents the maximum onboard battery energy that can be adjusted, obtained from the evaluation in step 4). i,ω,t P represents the V2G demand proposed by the power grid regulation in region i during time period t under scenario ω. v2g E is the rated discharge power. bat The rated capacity of the vehicle battery, δ i,ω,t and μ i,ω,tThese are intermediate auxiliary variables introduced for the number of pre-scheduled electric vehicles and their onboard battery energy, respectively. In other words, these auxiliary variables correspond to the number of vehicles and their onboard battery energy in the second phase of temporary scheduling. Δt is the length of a single time period, typically 0.25 hours (or 15 minutes).

[0146] In this embodiment, equation (15) restricts the number of pre-scheduled vehicles to be non-negative, and equation (16) indicates that the number of bidirectional vehicle transfers between any two areas cannot be positive simultaneously. Equation (17) ensures that the original number of vehicles that can participate in regulation and the number of vehicles entering through pre-scheduling can meet the needs of grid regulation. Equation (18) ensures that the adjustable range of electricity can also meet the needs of grid regulation, wherein... The term refers to the electricity demand regulated by the V2G grid. Equations (19)-(20) are non-negativity constraints for auxiliary variables.

[0147] 5-2) Construct a temporary scheduling and charging / discharging optimization model for the second stage.

[0148] In this embodiment, the second stage involves temporary vehicle dispatching and optimized charging and discharging arrangements. Based on the pre-dispatch arrangements made in the first stage, temporary vehicle dispatching will be conducted on the second day according to the actual grid regulation requirements and vehicle flexibility, and vehicle charging and discharging will be arranged accordingly.

[0149] The objective function of the second-stage temporary dispatch and charging / discharging optimization model is to minimize the total operating cost, which includes penalties for unmet grid regulation needs, temporary vehicle dispatching transfer costs, and V2G costs, as shown in the following equation:

[0150]

[0151] Where y is the decision variable of the second-stage temporary scheduling and charge / discharge optimization model. and These represent the charging and discharging power of the vehicle participating in V2G during time period t in scenario ω. Here, based on the aforementioned state relationships, a new state V2G is introduced. i SD i ED i V2G refers to vehicles in region i that participate in demand response. i Region i has a self-circulating main state, while SD i and ED i It is a transitional state, and its specific meaning is as follows:

[0152] Start Discharging (SD): The taxi begins the vehicle-to-grid (V2G) discharge process at the electric vehicle charging and discharging station. The next state after SD is V2G.

[0153] Discharging (Vehicle to Grid, V2G): The taxi is undergoing vehicle-to-grid (V2G) discharge at an electric vehicle charging / discharging station. In the V2G state, the taxi's System-on-Grid (SoC) decreases as it participates in V2G discharge, until the discharge process ends or the battery energy reaches its lower limit. The next state after V2G is ED (Electronic Discharge).

[0154] End Discharging (ED): The taxi is about to terminate the vehicle-to-network (V2N) interaction discharge. The next state after ED is ID.

[0155] Figure 3 This is a schematic diagram illustrating the state transition relationship of an electric vehicle with a newly added state in a specific embodiment of the present invention. For simplicity, Figure 3 The middle overlooked Figure 2 Except ID i Other states besides those mentioned above. Figure 3 In the diagram, arrows represent transitions between different states; that is, the state at a certain time period can be transformed into the state pointed to by the arrow in the next time period by following the direction of the arrow. and These are the unmet grid regulation demands in region i during time period t under scenario ω and the penalty rate corresponding to time period t, π. rmd This is the unit cost of reverse discharge. k′ j,i,t,ω It represents the number of electric vehicles that temporarily move from region j to region i during time period t in scenario ω.

[0156] The constraints of the second-stage temporary scheduling and charge / discharge optimization model include:

[0157] Constraints related to participation in demand response:

[0158]

[0159] Among them, u i,ω,t The demand for V2G participation in grid regulation in region i during time period t under scenario ω is given. Equations (22)-(23) are used to calculate the unmet grid regulation demand.

[0160] State transition relationship constraints;

[0161] The constraints on state transition relationships are very similar to those on the optimization model in the previous vehicle-to-network interaction capability assessment. However, due to the introduction of discharge-related states and two-stage fleet scheduling, the state transition relationship constraints here should be slightly modified, as shown below:

[0162]

[0163]

[0164] Among them, e i,j,tThe energy transferred from region i to region j in time period t can be simply expressed by E a k i,j,t To estimate, where E a λ is the average onboard energy transferred from the electric vehicle. ID This represents the energy consumption of a vehicle in state ID within a single time period. Equations (24)-(25) show the temporal coupling relationship between the number of vehicles and onboard energy in state ID, respectively. The tilde ~ represents a random variable with uncertainty. Equations (26)-(27) show the constraints for states CG and IT, respectively, where λ CG It is the charging capacity within a single time period, λ IT This is the energy consumed by a vehicle during a single time period. Equations (28)-(29) represent the relationship between the number of vehicles participating in V2G demand response and their onboard energy.

[0165] Power and energy boundary constraints for electric vehicles;

[0166] The onboard energy for each state should meet the upper and lower limits provided by the relevant number of vehicles and some threshold energy parameters, as shown below:

[0167]

[0168] Where, β V2G,min The minimum permissible state of charge (SoC) of the vehicle battery when providing V2G services. Equation (30) indicates that the SoC of a V2G vehicle should be no less than a given lower limit or greater than 100%. Similarly, the vehicle SoC for each state SD, ED, SC, TR, ID, IT, ST, ET, SW and EW should be no less than the corresponding SoC lower limit given by equations (33)-(39). As shown in equations (31)-(32), the charging and discharging power of an electric vehicle is subject to the rated charging and discharging capacity P, respectively. rt,c and P rt,d Restrictions.

[0169] Other constraints include:

[0170]

[0171] in, and These are the capacities (or the maximum number of vehicles served simultaneously) of the electric vehicle charging stations and electric vehicle charging / discharging stations in region i, respectively. Equations (40)-(41) ensure that the number of vehicles in the charging stations and charging / discharging stations will not exceed the capacity limit. Equation (42) ensures that the charging and discharging power cannot be positive simultaneously within the same region and time period.

[0172] 6) Simplify and solve the two-stage optimization model established in step 5); the specific steps are as follows:

[0173] 6-1) Considering that the number of regions, states, and time periods determines the size of the variables in the complete model above, in order to avoid too many integer variables making the model difficult to solve, the number of pre-scheduled transfer vehicles k in the model of step 5) is reduced. i,j,t Relax integer variables to continuous variables.

[0174] 6-2) For the mutual exclusion constraint in the model of step 5) where two variables do not simultaneously take positive values, i.e., equations (16) and (42), when the cross-regional transfer cost of vehicles is satisfied... For positive and V2G discharge cost π d When the condition is positive, the corresponding constraints are naturally met, so the related mutual exclusion constraints can be ignored.

[0175] 6-3) Solve the optimization model after simplification in steps 6-1) and 6-2).

[0176] In this embodiment, the simplified optimization model after conversion is summarized as follows: the objective function consists of equations (14) and (21), and the constraints include equations (15), (17)-(20), and (22)-(41). The model can be solved using solvers that support linear programming, such as CPLEX, Gurobi, and COPT, to obtain the number of vehicles pre-scheduled and the energy value k. i,j,t e i,j,t This is for use in the pre-scheduling of vehicles across spaces.

[0177] To achieve the above embodiments, a second aspect of the present invention provides an electric vehicle participation in vehicle-to-grid interaction capability assessment device, comprising:

[0178] The area division module is used to divide the activity area of ​​the electric vehicle fleet;

[0179] The key state set construction module is used to divide the state of the electric vehicle fleet based on the activity area division results and establish a key state set;

[0180] The spatiotemporal distribution calculation module is used to calculate the spatiotemporal distribution of the state quantities of the electric vehicle fleet based on the key state set and the travel history data of the electric vehicle fleet, including: the number of electric vehicles in each state of the key state set and the on-board battery energy in each time period and region.

[0181] The evaluation module is used to establish and solve an evaluation optimization model for the spatiotemporal distribution of the electric vehicle fleet's state variables, thereby obtaining the optimization results of the upper limit of the number of electric vehicles in each state of the key state set and the upper limit of the on-board battery energy in each time period and region. The evaluation is then completed.

[0182] It should be noted that the foregoing explanation of an embodiment of an electric vehicle's ability to participate in vehicle-to-grid interaction assessment method also applies to an electric vehicle's ability to participate in vehicle-to-grid interaction assessment device in this embodiment, and will not be repeated here. According to an embodiment of the present invention, an electric vehicle's ability to participate in vehicle-to-grid interaction assessment device divides the activity area of ​​an electric vehicle fleet; based on the activity area division results, it divides the states of the electric vehicle fleet and establishes a key state set; based on the key state set, combined with the travel history data of the electric vehicle fleet, it calculates the spatiotemporal distribution of the state quantities of the electric vehicle fleet, including: the number of electric vehicles in each state of the key state set and the onboard battery energy in each time period and region; based on the spatiotemporal distribution of the state quantities of the electric vehicle fleet, it establishes and solves a spatiotemporal distribution assessment optimization model for the ability to participate in vehicle-to-grid interaction, obtaining the optimized results of the upper limit of the number of electric vehicles in each state of the key state set and the upper limit of the onboard battery energy in each time period and region, thus completing the assessment.

[0183] In one specific embodiment of the present invention, the classification of the electric vehicle fleet status takes into account the driving status of the electric vehicles, the passenger-carrying status, and the state of charge of the on-board batteries.

[0184] In one specific embodiment of the present invention, the key state set includes the following states:

[0185] 1) NA unavailable: This means the electric vehicle is not working at this time; in the NA state, the SoC of the electric vehicle remains stable; when the electric vehicle is about to start working, the next state of NA is Start Working SW;

[0186] 2) Passenger-carrying IT: This means the electric vehicle is carrying passengers. In the IT state, the SoC of the electric vehicle will decrease due to driving. If the destination of the electric vehicle is located in another area, the next state of IT is inter-area transfer TR; otherwise, the next state of IT is trip end ET.

[0187] 3) Charging in progress (CG): This means the electric vehicle is being charged. In the CG state, the SoC of the electric vehicle will increase until the charging process ends or the battery is fully charged. When the charging process ends, the next state of CG is End of charging (EC).

[0188] 4) Idle ID: This means that the electric vehicle is not carrying passengers and is waiting for the next order. In the state ID, the SoC of the electric vehicle decreases due to driving. When the SoC drops below the range anxiety threshold of the driver user, the next state of the ID is Start Charging SC. If the SoC does not drop below the range anxiety threshold and a new order arrives, the next state of the ID is Start Trip ST.

[0189] 5) Start working SW: This means the electric vehicle starts working and is ready to carry passengers; in the SW state, the electric vehicle's SoC remains stable; if a new order arrives, the next state of SW is ST; otherwise, the next state of SW should be ID.

[0190] 6) End of Work (EW): This indicates that the electric vehicle is about to end its work. In the EW state, the SoC of the electric vehicle remains stable. The next state after EW is NA.

[0191] 7) Start of Trip ST: This indicates the start of a new order for the electric vehicle; in state ST, the SoC of the electric vehicle will decrease due to driving; the next state after ST is IT.

[0192] 8) End of Trip ET: This means the electric vehicle is about to reach its destination and the trip is about to end. In state ET, the SoC of the electric vehicle decreases due to driving. If the SoC does not drop below the range anxiety threshold, the next state of ET is ID. If the SoC drops below the range anxiety threshold, the next state of ET is Start Charging SC.

[0193] 9) Start Charging (SC): This indicates that the electric vehicle has started charging. In the SC state, the electric vehicle's SoC increases due to charging. The next state after SC is CG.

[0194] 10) End of Charge EC: The electric vehicle is about to end charging; in the EC state, the SoC of the electric vehicle will increase; if there is no upcoming order, the next state of EC is ID; if there is a new order, the next state of EC is ST; if the electric vehicle is about to finish working, the next state of EC is EW.

[0195] 11) Inter-regional transfer TR: This means that the electric vehicle is about to finish its trip and transfer to another region; under the state TR, the taxi's SoC decreases; if the SoC does not drop below the range anxiety threshold and there is no upcoming trip demand, the next state of TR is ID; if a new trip begins, the next state of TR is ST; if the SoC drops below the range anxiety threshold, the next state of TR is SC.

[0196] In a specific embodiment of the present invention, the spatiotemporal distribution evaluation and optimization model for the ability of electric vehicles to participate in vehicle-to-grid interaction includes: an optimization model for the upper limit of the number of electric vehicles and an optimization model for the upper limit of the energy of electric vehicles;

[0197] For any region, the objective function of the electric vehicle quantity upper limit optimization model is:

[0198]

[0199] The objective function of the electric vehicle energy ceiling optimization model is:

[0200]

[0201] Where, x t e represents the number of vehicles participating in vehicle-to-everything (V2X) interaction in this region during time period t. t The energy stored in the onboard batteries of vehicles participating in vehicle-to-grid interaction in this region during time period t;

[0202] For time period t, the weighting factor is applied to the upper limit of the number of vehicles. For time period t, the weighting factor is applied to the upper limit of the vehicle battery energy.

[0203] The constraints of the electric vehicle quantity ceiling optimization model and the electric vehicle energy ceiling optimization model are the same, including:

[0204]

[0205] in, These represent the number of electric vehicles in region i during time period t that are in states ID, EC, SC, and CG, respectively. These represent the number of electric vehicles in time period t region i that are in the ET, ST, EW, and SW states, respectively. The number of vehicles transferred from region j to region i due to order reasons during time period t; and These represent the onboard battery energy of an electric vehicle in region i during time period t under the states ID, EC, SC, CG, ET, ST, EW, and SW. The onboard battery energy transferred from region j to region i during time period t; Let ΔE be the duration of participation in grid regulation, ΔE be the average energy consumption per time period when participating in grid regulation via V2G, and λ be the energy consumption per unit time period. ID λ represents the energy consumption of idle vehicles during a single time period. CG For the charging energy within a single time period, λ IT The energy consumed by a vehicle during a single period of time; β EW,min β SW,min β EC,min β SC,min β ET,min β ST,min These represent the minimum states of charge that an electric vehicle's onboard battery should have under EW, SW, EC, SC, ET, and ST states, respectively; β EW,max β SW,max β EC,max β SC,max β ET,max β ST,maxThese represent the maximum state of charge that an electric vehicle's onboard battery should have under the EW, SW, EC, SC, ET, and ST states, respectively. Energy consumed by vehicles transferred from region j to region i due to order reasons during time period t;

[0206] Solving the optimization model for the upper limit of the number of electric vehicles with equation (1) as the objective function and equations (3)-(13) as constraints, we obtain x. t The optimal solution is to set x t The optimal solution is denoted as the upper limit of the number of electric vehicles in each region that can respond to V2G and support grid regulation in each time period, and is called .

[0207] Solving the electric vehicle energy ceiling optimization model with equation (2) as the objective function and equations (3)-(13) as constraints, we obtain e t The optimal solution is to e t The optimal solution is denoted as the upper limit of the on-board battery energy in each region during each time period.

[0208] In one specific embodiment of the present invention, it further includes:

[0209] Based on the solution results of the spatiotemporal distribution evaluation optimization model for electric vehicles' participation in vehicle-to-grid interaction, a two-stage optimization model for scheduling control of electric vehicles' participation in vehicle-to-grid interaction is established. The specific steps are as follows:

[0210] 1) Construct the first-stage pre-scheduling optimization model;

[0211] The objective function of the pre-scheduling optimization model in the first stage is to minimize the total cost, as expressed below;

[0212]

[0213] in, Represents the spatial pre-transfer decision of electric vehicles; k i,j,t and e i,j,t Let f(z) and z(j) represent the number of electric vehicles transferred from region i to region j during time period t, and the cumulative on-board battery energy, respectively; f(z) and the expected operating cost are the electric vehicle dispatch and transfer costs.

[0214] z is the decision variable of the first-stage pre-scheduling optimization model, u = {u i,t} represents the uncertainty of V2G demand in different regions and time periods; Ξ represents the typical scenario set, and ω represents the scenario index. It is the operating cost under the vehicle pre-scheduling decision z and the power grid regulation demand u; This represents the unit cost of dispatching electric vehicles from region i to region j.

[0215] The constraints of the first-stage pre-scheduling optimization model include:

[0216]

[0217]

[0218] in, This represents the upper limit of the number of available electric vehicles in region i during time period t. Indicates the maximum onboard battery energy that can be adjusted; u i,ω,t P represents the V2G demand proposed by the power grid regulation in region i during time period t under scenario ω. v2g E is the rated discharge power. bat The rated capacity of the vehicle battery, δ i,ω,t and μ i,ω,t These are intermediate auxiliary variables introduced for the number of pre-scheduled electric vehicles and the on-board battery energy, respectively; Δt is the length of a single time period.

[0219] 2) Construct a temporary scheduling and charging / discharging optimization model for the second stage;

[0220] The objective function of the second-stage temporary scheduling and charging / discharging optimization model is to minimize the total operating cost, as expressed below:

[0221]

[0222] Where y is the decision variable of the second-stage temporary scheduling and charge / discharge optimization model; and These represent the charging and discharging power of vehicles participating in V2G in time period ti under scenario ω; SD, V2G, and ED are newly introduced states, where:

[0223] Start of Discharge SD: This means that the electric vehicle begins the vehicle-to-grid (V2G) interactive discharge process at the electric vehicle charging and discharging station; the next state of SD is V2G.

[0224] V2G during discharge: This means that the electric vehicle is undergoing vehicle-to-grid (V2G) interactive discharge at the electric vehicle charging and discharging station. In the V2G state, the SoC of the electric vehicle will decrease as it participates in the V2G interactive discharge until the discharge process ends or the battery energy reaches the lower limit. The next state of V2G is ED.

[0225] End of discharge (ED): This means the electric vehicle is about to terminate the vehicle-to-grid (V2G) discharge; the next state of ED is ID.

[0226] The constraints of the second-stage temporary scheduling and charge / discharge optimization model include:

[0227] Constraints related to participation in demand response:

[0228]

[0229] Among them, u i,ω,t To meet the needs of V2G participation in grid regulation in region i during time period t under scenario ω;

[0230] State transition relationship constraints:

[0231]

[0232]

[0233] Among them, e i,j,t Let λ be the energy transferred from region i to region j in time period t; ID It represents the energy consumption of a vehicle in ID state within a single time period;

[0234] Electric vehicle power and energy boundary constraints:

[0235]

[0236] Where, β V2G,min The minimum permissible state of charge of the vehicle battery when providing V2G services;

[0237] Other constraints:

[0238]

[0239] in, and These represent the capacities of electric vehicle charging stations and electric vehicle charging / discharging stations in region i, respectively.

[0240] In one specific embodiment of the present invention, it further includes:

[0241] The two-stage optimization model is simplified as follows:

[0242] 1) The number of pre-scheduled transfer vehicles k in the two-stage optimization model i,j,t Relaxing integer variables to continuous variables;

[0243] 2) In the constraint conditions, equations (16) and (42) are mutually exclusive constraints and are removed from the two-stage optimization model.

[0244] In one specific embodiment of the present invention, it further includes:

[0245] The simplified two-stage optimization model has an objective function consisting of equations (14) and (21), and constraints including equations (15), (17)-(20), and (22)-(41). Solving this model yields the number of vehicles pre-scheduled and the energy value k. i,j,t ei,j,t This is for use in the pre-scheduling of vehicles across spaces.

[0246] This allows for the establishment of a key state set for electric vehicles with both time and space subscripts, effectively balancing the flexibility of electric vehicles in both time and space dimensions. By establishing and solving an evaluation and optimization model for the ability of electric vehicles to participate in vehicle-grid interaction, corresponding capability evaluation results can be obtained for different forms of vehicle-grid interaction and conservative requirements, which helps support the safe and stable operation of the power grid.

[0247] To implement the above embodiments, a third aspect of the present invention provides an electronic device, comprising:

[0248] At least one processor; and a memory communicatively connected to said at least one processor;

[0249] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to perform the above-described method for evaluating the ability of an electric vehicle to participate in vehicle-to-grid interaction.

[0250] To implement the above embodiments, a fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described method for evaluating the ability of an electric vehicle to participate in vehicle-to-grid interaction.

[0251] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0252] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform a method for evaluating the vehicle-to-grid interaction capability of an electric vehicle according to the above embodiments.

[0253] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0254] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0255] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0256] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0257] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0258] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0259] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0260] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0261] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for evaluating the ability of electric vehicles to participate in vehicle-to-grid interaction, characterized in that, include: The activity areas of the electric vehicle fleet are divided; Based on the activity area division results, the states of the electric vehicle fleet are divided, and a key state set is established; Based on the key state set and combined with the travel history data of the electric vehicle fleet, the spatiotemporal distribution of the state quantities of the electric vehicle fleet is calculated, including: the number of electric vehicles in each state of the key state set and the on-board battery energy in each time period and region. Based on the spatiotemporal distribution of the state variables of the electric vehicle fleet, a spatiotemporal distribution evaluation and optimization model for the ability of electric vehicles to participate in vehicle-to-grid interaction is established and solved. The optimization results of the upper limit of the number of electric vehicles and the upper limit of the on-board battery energy in each state of the key state set in each time period and region are obtained, and the evaluation is completed.

2. The method according to claim 1, characterized in that, The classification of the electric vehicle fleet's status takes into account the electric vehicles' driving status, passenger-carrying status, and the state of charge of the onboard batteries.

3. The method according to claim 2, characterized in that, The key state set includes the following states: 1) NA unavailable: This means the electric vehicle is not working at this time; in the NA state, the SoC of the electric vehicle remains stable; when the electric vehicle is about to start working, the next state of NA is Start Working SW; 2) Passenger-carrying IT: This means the electric vehicle is carrying passengers. In the IT state, the SoC of the electric vehicle will decrease due to driving. If the destination of the electric vehicle is located in another area, the next state of IT is inter-area transfer TR; otherwise, the next state of IT is trip end ET. 3) Charging in progress (CG): This means the electric vehicle is being charged. In the CG state, the SoC of the electric vehicle will increase until the charging process ends or the battery is fully charged. When the charging process ends, the next state of CG is End of charging (EC). 4) Idle ID: This indicates that the electric vehicle is not carrying passengers and is waiting for the next order; in the status ID, the SoC of the electric vehicle decreases due to driving. When the SoC drops below the driver's range anxiety threshold, the next state of ID is Start Charging SC; if the SoC does not drop below the range anxiety threshold and a new order arrives, the next state of ID is Start Trip ST. 5) Start working SW: This means the electric vehicle starts working and is ready to carry passengers; in the SW state, the electric vehicle's SoC remains stable; if a new order arrives, the next state of SW is ST; otherwise, the next state of SW should be ID. 6) End of Work (EW): This indicates that the electric vehicle is about to end its work. In the EW state, the SoC of the electric vehicle remains stable. The next state after EW is NA. 7) Start of Trip ST: This indicates the start of a new order for the electric vehicle; in state ST, the SoC of the electric vehicle will decrease due to driving; the next state after ST is IT. 8) End of Trip ET: This means the electric vehicle is about to reach its destination and the trip is about to end; in state ET, the electric vehicle's SoC decreases due to driving. If the SoC does not drop below the range anxiety threshold, the next state of ET is ID; if the SoC drops below the range anxiety threshold, the next state of ET is Start Charging SC. 9) Start Charging (SC): This indicates that the electric vehicle has started charging. In the SC state, the electric vehicle's SoC increases due to charging. The next state after SC is CG. 10) End of Charge EC: The electric vehicle is about to end charging; in the EC state, the SoC of the electric vehicle will increase; if there is no upcoming order, the next state of EC is ID; if there is a new order, the next state of EC is ST; if the electric vehicle is about to finish working, the next state of EC is EW. 11) Inter-regional transfer (TR): This means that the electric vehicle is about to finish its trip and transfer to another region; under the TR status, the taxi's SoC decreases; If the SoC does not drop below the range anxiety threshold and there is no upcoming trip requirement, the next state of TR is ID; if a new trip begins, the next state of TR is ST; if the SoC drops below the range anxiety threshold, the next state of TR is SC.

4. The method according to claim 3, characterized in that, The spatiotemporal distribution evaluation and optimization model for electric vehicles' ability to participate in vehicle-to-grid interaction includes: an optimization model for the upper limit of the number of electric vehicles and an optimization model for the upper limit of the energy of electric vehicles. For any region, the objective function of the electric vehicle quantity upper limit optimization model is: The objective function of the electric vehicle energy ceiling optimization model is: Where, x t e represents the number of vehicles participating in vehicle-to-everything (V2X) interaction in this region during time period t. t The energy stored in the onboard batteries of vehicles participating in vehicle-to-grid interaction in this region during time period t; For time period t, the weighting factor is applied to the upper limit of the number of vehicles. For time period t, the weighting factor is applied to the upper limit of the vehicle battery energy. The constraints of the electric vehicle quantity ceiling optimization model and the electric vehicle energy ceiling optimization model are the same, including: in, These represent the number of electric vehicles in region i during time period t that are in states ID, EC, SC, and CG, respectively. These represent the number of electric vehicles in time period t region i that are in the ET, ST, EW, and SW states, respectively. The number of vehicles transferred from region j to region i due to order reasons during time period t; and These represent the onboard battery energy of an electric vehicle in region i during time period t under the states ID, EC, SC, CG, ET, ST, EW, and SW. The onboard battery energy transferred from region j to region i during time period t; Let ΔE be the duration of participation in grid regulation, ΔE be the average energy consumption per time period when participating in grid regulation via V2G, and λ be the energy consumption per unit time period. ID λ represents the energy consumption of idle vehicles during a single time period. CG For the charging energy within a single time period, λ IT The energy consumed by a vehicle during a single period of time; β EW,min β SW,min β EC,min β SC,min β ET,min β ST,min These represent the minimum states of charge that an electric vehicle's onboard battery should have under EW, SW, EC, SC, ET, and ST states, respectively; β EW,max β SW,max β EC,max β SC,max β ET,max β ST,max These represent the maximum state of charge that an electric vehicle's onboard battery should have under the EW, SW, EC, SC, ET, and ST states, respectively. Energy consumed by vehicles transferred from region j to region i due to order reasons during time period t; Solving the optimization model for the upper limit of the number of electric vehicles with equation (1) as the objective function and equations (3)-(13) as constraints, we obtain x. t The optimal solution is to set x t The optimal solution is denoted as the upper limit of the number of electric vehicles in each region that can respond to V2G and support grid regulation in each time period, and is called . Solving the electric vehicle energy ceiling optimization model with equation (2) as the objective function and equations (3)-(13) as constraints, we obtain e t The optimal solution is to e t The optimal solution is denoted as the upper limit of the on-board battery energy in each region during each time period.

5. The method according to claim 4, characterized in that, Also includes: Based on the solution results of the spatiotemporal distribution evaluation optimization model for electric vehicles' participation in vehicle-to-grid interaction, a two-stage optimization model for scheduling control of electric vehicles' participation in vehicle-to-grid interaction is established. The specific steps are as follows: 1) Construct the first-stage pre-scheduling optimization model; The objective function of the pre-scheduling optimization model in the first stage is to minimize the total cost, as expressed below; in, Represents the spatial pre-transfer decision of electric vehicles; k i,j,t and e i,j,t Let f(z) and z(j) represent the number of electric vehicles transferred from region i to region j during time period t, and the cumulative on-board battery energy, respectively; f(z) and the expected operating cost are the electric vehicle dispatch and transfer costs. z is the decision variable of the first-stage pre-scheduling optimization model, u = {u i,t } represents the uncertainty of V2G demand in different regions and time periods; Ξ represents the typical scenario set, and ω represents the scenario index. It is the operating cost under the vehicle pre-scheduling decision z and the power grid regulation demand u; This represents the unit cost of dispatching electric vehicles from region i to region j. The constraints of the first-stage pre-scheduling optimization model include: in, This represents the upper limit of the number of available electric vehicles in region i during time period t. Indicates the maximum onboard battery energy that can be adjusted; u i,ω,t P represents the V2G demand proposed by the power grid regulation in region i during time period t under scenario ω. v2g E is the rated discharge power. bat The rated capacity of the vehicle battery, δ i,ω,t and μ i,ω,t These are intermediate auxiliary variables introduced for the number of pre-scheduled electric vehicles and the on-board battery energy, respectively; Δt is the length of a single time period. 2) Construct a temporary scheduling and charging / discharging optimization model for the second stage; The objective function of the second-stage temporary scheduling and charging / discharging optimization model is to minimize the total operating cost, as expressed below: Where y is the decision variable of the second-stage temporary scheduling and charge / discharge optimization model; and These represent the charging and discharging power of vehicles participating in V2G in time period ti under scenario ω; SD, V2G, and ED are newly introduced states, where: Start of Discharge SD: This means that the electric vehicle begins the vehicle-to-grid (V2G) interactive discharge process at the electric vehicle charging and discharging station; the next state of SD is V2G. V2G during discharge: This means that the electric vehicle is undergoing vehicle-to-grid (V2G) interactive discharge at the electric vehicle charging and discharging station. In the V2G state, the SoC of the electric vehicle will decrease as it participates in the V2G interactive discharge until the discharge process ends or the battery energy reaches the lower limit. The next state of V2G is ED. End of discharge (ED): This means the electric vehicle is about to terminate the vehicle-to-grid (V2G) discharge; the next state of ED is ID. The constraints of the second-stage temporary scheduling and charge / discharge optimization model include: Constraints related to participation in demand response: Among them, u i,ω,t To meet the needs of V2G participation in grid regulation in region i during time period t under scenario ω; State transition relationship constraints: Among them, e i,j,t Let λ be the energy transferred from region i to region j in time period t; ID It represents the energy consumption of a vehicle in ID state within a single time period; Electric vehicle power and energy boundary constraints: Where, β V2G,min The minimum permissible state of charge of the vehicle battery when providing V2G services; Other constraints: in, and These represent the capacities of electric vehicle charging stations and electric vehicle charging / discharging stations in region i, respectively.

6. The method according to claim 5, characterized in that, Also includes: The two-stage optimization model is simplified as follows: 1) The number of pre-scheduled transfer vehicles k in the two-stage optimization model i,j,t Relaxing integer variables to continuous variables; 2) In the constraint conditions, equations (16) and (42) are mutually exclusive constraints and are removed from the two-stage optimization model.

7. The method according to claim 5, characterized in that, Also includes: The simplified two-stage optimization model has an objective function consisting of equations (14) and (21), and constraints including equations (15), (17)-(20), and (22)-(41). Solving this model yields the number of vehicles pre-scheduled and the energy value k. i,j,t e i,j,t This is for use in the pre-scheduling of vehicles across spaces.

8. A device for evaluating the ability of electric vehicles to participate in vehicle-to-grid interaction, characterized in that, include: The area division module is used to divide the activity area of ​​the electric vehicle fleet; The key state set construction module is used to divide the state of the electric vehicle fleet based on the activity area division results and establish a key state set; The spatiotemporal distribution calculation module is used to calculate the spatiotemporal distribution of the state quantities of the electric vehicle fleet based on the key state set and the travel history data of the electric vehicle fleet, including: the number of electric vehicles in each state of the key state set and the on-board battery energy in each time period and region. The evaluation module is used to establish and solve an evaluation optimization model for the spatiotemporal distribution of the electric vehicle fleet's state variables, thereby obtaining the optimization results of the upper limit of the number of electric vehicles in each state of the key state set and the upper limit of the on-board battery energy in each time period and region. The evaluation is then completed.

9. An electronic device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, the instructions being configured to perform the method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method according to any one of claims 1-7.

Images