Electric vehicle space-time charging adjustable flexibility quantification method and device

Abstract

The invention discloses an electric vehicle space-time charging adjustable flexibility quantification method and device, and the method comprises the steps: building a charging space-time flexibility model based on a trip chain, and covering slow charging time and fast charging space flexibility modeling; a double-criterion random dynamic user balance model is constructed, and fast and slow charging loads in a balance state are obtained; and finally, quantitatively evaluating the slow charging time flexibility and the fast charging space flexibility potential by using the flexibility model. According to the method, traffic, price and user perception differences can be comprehensively considered, the charging load space-time schedulable characteristics are comprehensively reflected, and powerful support is provided for traffic-power system cooperative scheduling.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle energy management and power load flexibility control technology, specifically to a method and apparatus for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles. Background Technology

[0002] With the escalating energy crisis and increasingly prominent environmental issues, electric vehicles are widely considered to play a crucial role in future urban transportation systems. According to a report by the International Energy Agency, the global electric vehicle fleet has grown from approximately 5 million vehicles in 2018 to 40 million in 2023, and is projected to reach 500 million to 800 million by 2035. The large-scale integration of electric vehicles into the power grid will significantly increase electricity load, posing a series of challenges to the distribution system, including intensified peak loads, increased voltage fluctuations, and localized grid congestion. Against this backdrop, exploring the flexibility potential of electric vehicle charging behavior can not only help alleviate these grid pressures but also provide a new opportunity to enhance the absorption capacity of renewable energy.

[0003] In urban scenarios, electric vehicle (EV) users typically follow a travel chain pattern in their daily commutes. EVs can charge at slow charging stations in their parking lots at their destination (such as residences, workplaces, or leisure venues), or they can recharge at fast charging stations en route. Slow charging at the destination (hereinafter referred to as "slow charging") often involves parking time far exceeding charging time, demonstrating strong time flexibility. In contrast, fast charging at fast charging stations en route offers a more convenient recharge option for users unable to charge at their destination due to its higher charging power and significantly shorter charging time. To avoid delays to their subsequent journeys, most EV users begin charging immediately upon arriving at a fast charging station. Therefore, fast charging typically has lower time flexibility and is difficult to utilize through scheduling mechanisms. However, when EV users fast charge en route, multiple fast charging stations are often available within the remaining mileage coverage area. The EV user's route and choice of fast charging stations are influenced by factors such as traffic congestion, fast charging prices, and the perceived randomness of the user's choice. In this context, by introducing price incentives, users can be guided to change their charging decisions, thereby enabling the spatial redistribution of fast charging load among multiple fast charging stations, thus demonstrating the spatial flexibility of fast charging.

[0004] Therefore, it is necessary to conduct research on methods for quantifying the spatiotemporal charging adjustability of electric vehicles, so as to provide a theoretical basis and technical methodological support for electric vehicle charging load management and coordinated scheduling of transportation and power systems. Summary of the Invention

[0005] To address this, the present invention provides a method and apparatus for quantifying the spatiotemporal charging adjustability of electric vehicles. By constructing a dual-criteria stochastic dynamic user equilibrium model and combining it with a spatiotemporal flexibility quantification model, the invention solves the problem that existing technologies cannot comprehensively consider traffic congestion, price, and user perception differences, and cannot jointly quantify the time flexibility of slow charging and the spatial flexibility of fast charging, thus providing more comprehensive support for the coordinated scheduling of transportation-power systems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles, comprising:

[0007] Based on the electric vehicle travel chain, construct a spatiotemporal flexibility model for electric vehicle charging;

[0008] Based on the demand of electric vehicle travel chain, a dual-criteria stochastic dynamic user equilibrium model is constructed; the fast charging load and slow charging load of electric vehicles under the stochastic dynamic user equilibrium state are obtained through the dual-criteria stochastic dynamic user equilibrium model.

[0009] Based on the fast charging load and the slow charging load under the random dynamic user equilibrium state, the temporal flexibility potential of slow charging and the spatial flexibility potential of fast charging of electric vehicles are quantitatively evaluated through the spatiotemporal flexibility model of electric vehicle charging, and the quantitative evaluation results are obtained.

[0010] As a preferred embodiment of a method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles, the spatiotemporal flexibility model for electric vehicle charging includes: a slow charging time flexibility model and a fast charging space flexibility model;

[0011] The slow charging time flexibility model quantifies the potential of electric vehicle slow charging time flexibility by using delayed charge capacity, schedulable time margin, and energy accumulation boundary indicators.

[0012] The formula for calculating the delayable power is:

[0013]

[0014] In the formula, ta m,q The time when the electric vehicle arrives at its destination; td m+1,q The moment the electric vehicle leaves its location; This is the maximum output power of the slow charging station; The amount of electricity charged for electric vehicles; For power that can be delayed;

[0015] The formula for calculating the schedulable time margin is as follows:

[0016]

[0017]

[0018] In the formula, This represents the schedulable time margin; The latest charging start time;

[0019] The formula for calculating the energy accumulation boundary is:

[0020]

[0021] In the formula, The maximum amount of charge that can be transferred to the battery within time t; The minimum amount of charge that can be transferred to the battery within time t; This is the earliest time to end charging.

[0022] As a preferred method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles, the fast charging spatial flexibility model quantifies the potential of fast charging spatial flexibility by calculating the difference between the fast charging load curves of electric vehicles under scenarios with and without electricity price incentives.

[0023] The expression for the fast charging load curve is:

[0024]

[0025] In the formula, P k (t) is [T] h ,T h+1 The average fast charging load of FCS k during the time period; ΔT h For time period [T] h ,T h+1 The duration of Q; k,h For [T] h ,T h+1 The set of EV users connected to FCS k for charging within the specified time period; This is the maximum output power of the fast charging station; For [T] h ,T h+1 The effective charging time of EV user q at FCS k within a given time period is the charging time period for EV user q. and time period [T] h ,T h+1 The overlapping part of ] .

[0026] As a preferred method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles, the steps for obtaining the fast charging load and slow charging load of electric vehicles under stochastic dynamic user equilibrium state through the dual-criteria stochastic dynamic user equilibrium model are as follows:

[0027] An extended travel utility function is defined through a path selection model; based on the extended travel utility function, combined with the time-varying shortest path algorithm and stochastic utility theory, the user path and fast charging station selection are determined.

[0028] The traffic-charging simulation model is used to simulate users' driving, parking, and charging behaviors and obtain simulation data.

[0029] Based on the simulation data, the road passage time and charging price are iteratively updated until the termination criterion is met, the iteration stops, a stochastic dynamic user equilibrium state is reached, and the electric vehicle fast charging load and slow charging load under the stochastic dynamic user equilibrium state are obtained.

[0030] As a preferred embodiment of a method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles, the extended travel utility function is expressed as follows:

[0031]

[0032] In the formula, u q Let u be the sum of the utility of each trip in the travel chain of EV user q; m,q tt represents the utility of EV user q on the m-th trip; a represents the value per unit time; tt represents the utility of EV user q on the m-th trip. m,q For EV user q, the duration of the m-th trip; mc m,q The monetary cost incurred by EV user q during the mth trip due to fast charging.

[0033] Within the framework of utility maximization, the expression for the probability that a user chooses a fast-charging station that is accessible is:

[0034]

[0035] In the formula, K m,q For EV user q, the set of accessible fast charging stations during the m-th trip; β u Parameters reflecting the randomness of user perception. i and k are both accessible fast charging stations; Let q be the probability that user q will choose a fast charging station k that is accessible during the m-th trip; All are deterministic components, representing the objective in decision-making; ed m,q SOC at the point of departure from the starting point; E B This refers to the battery's capacity. For EV user q in the path The estimated power consumption; For EV user q in the path Estimated power consumption; min This is the minimum threshold for SOC.

[0036] As a preferred method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles, during the iterative updating of the road segment travel time and the charging price based on the simulation data, the expected road segment travel time and the charging price for the next iteration are calculated using the successive averaging method.

[0037] The formula for calculating the estimated travel time of the road segment in the next iteration is:

[0038]

[0039] In the formula, and These are the estimated road segment travel times used in the (n+1)th and nth iterations, respectively; The traffic congestion situation is calculated and recorded in the nth iteration; [T] h ,T h+1 [This represents a time period;]

[0040] The formula for calculating the charging price in the next iteration is:

[0041]

[0042] In the formula, and These are the charging prices at fast charging stations used in the (n+1)th and nth iterations, respectively. The charging price of the fast charging station is calculated and recorded in the nth iteration.

[0043] As a preferred embodiment of a method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles, during the iterative updating of the road segment travel time and charging price based on the simulation data, the iteration stops when the termination criterion is met; the expression for the termination criterion is:

[0044]

[0045] In the formula, U n Let n be the average travel utility of all EV users in the nth iteration; c ε represents the current iteration number; ε represents the simulation accuracy.

[0046] This invention also provides a device for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles, based on the above-mentioned method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles, comprising:

[0047] The electric vehicle charging spatiotemporal flexibility model construction module is used to construct an electric vehicle charging spatiotemporal flexibility model based on the electric vehicle travel chain.

[0048] The dual-criteria stochastic dynamic user equilibrium model construction and processing module is used to construct a dual-criteria stochastic dynamic user equilibrium model based on the electric vehicle travel chain demand; and to obtain the electric vehicle fast charging load and slow charging load under the stochastic dynamic user equilibrium state through the dual-criteria stochastic dynamic user equilibrium model.

[0049] The quantitative evaluation module is used to quantitatively evaluate the temporal flexibility potential of slow charging and the spatial flexibility potential of fast charging of electric vehicles based on the fast charging load and the slow charging load under the random dynamic user equilibrium state, and obtain the quantitative evaluation results.

[0050] As a preferred embodiment of a quantification device for the spatiotemporal charging adjustability flexibility of electric vehicles, the quantification evaluation module includes an electric vehicle charging spatiotemporal flexibility model comprising: a slow charging time flexibility model and a fast charging space flexibility model.

[0051] The slow charging time flexibility model quantifies the potential of electric vehicle slow charging time flexibility by using delayed charge capacity, schedulable time margin, and energy accumulation boundary indicators.

[0052] The formula for calculating the delayable power is:

[0053]

[0054] In the formula, ta m,q The time when the electric vehicle arrives at its destination; td m+1,q The moment the electric vehicle leaves its location; This is the maximum output power of the slow charging station; The amount of electricity charged for electric vehicles; For power that can be delayed;

[0055] The formula for calculating the schedulable time margin is as follows:

[0056]

[0057] In the formula, This represents the schedulable time margin; The latest charging start time;

[0058] The formula for calculating the energy accumulation boundary is:

[0059]

[0060] In the formula, The maximum amount of charge that can be transferred to the battery within time t; The minimum amount of charge that can be transferred to the battery within time t; This is the earliest time to end charging.

[0061] As a preferred embodiment of a quantification device for the spatial and temporal charging flexibility of electric vehicles, the quantification evaluation module uses a fast charging spatial flexibility model to quantify the potential of fast charging spatial flexibility by calculating the difference between the fast charging load curves of electric vehicles under scenarios with and without electricity price incentives.

[0062] The expression for the fast charging load curve is:

[0063]

[0064] In the formula, P k (t) is [T] h ,T h+1 The average fast charging load of FCS k during the time period; ΔT h For time period [T] h ,T h+1 The duration of Q; k,h For [T] h ,T h+1 The set of EV users connected to FCS k for charging within the specified time period; This is the maximum output power of the fast charging station; For [T] h ,T h+1 The effective charging time of EV user q at FCS k within a given time period is the charging time period for EV user q. and time period [T] h ,T h+1 The overlapping part of ] .

[0065] As a preferred embodiment of a quantification device for the adjustable flexibility of spatiotemporal charging of electric vehicles, the dual-criteria stochastic dynamic user equilibrium model construction and processing module includes the following sub-module in the process of obtaining the fast charging load and slow charging load of electric vehicles under the stochastic dynamic user equilibrium state through the dual-criteria stochastic dynamic user equilibrium model:

[0066] The user route determination and fast charging station selection submodule is used to define an extended travel utility function through a route selection model; based on the extended travel utility function, combined with the time-varying shortest path algorithm and stochastic utility theory, the user route and fast charging station selection are determined.

[0067] The traffic-charging simulation submodule is used to simulate users' driving, parking, and charging behaviors using a traffic-charging simulation model and obtain simulation data.

[0068] The road segment passage time and charging price iterative update submodule is used to iteratively update the road segment passage time and charging price based on the simulation data until the termination criterion is met, the iteration stops, the random dynamic user equilibrium state is reached, and the electric vehicle fast charging load and slow charging load under the random dynamic user equilibrium state are obtained.

[0069] As a preferred embodiment of a quantification device for the adjustable flexibility of spatiotemporal charging for electric vehicles, the extended travel utility function in the user path determination and fast charging station selection submodule of the dual-criteria stochastic dynamic user equilibrium model construction and processing module is expressed as follows:

[0070]

[0071] In the formula, u q Let u be the sum of the utility of each trip in the travel chain of EV user q; m,q tt represents the utility of EV user q on the m-th trip; a represents the value per unit time; tt represents the utility of EV user q on the m-th trip. m,q For EV user q, the duration of the m-th trip; mc m,q The monetary cost incurred by EV user q during the mth trip due to fast charging.

[0072] Within the framework of utility maximization, the expression for the probability that a user chooses a fast-charging station that is accessible is:

[0073]

[0074] In the formula, K m,q For EV user q, the set of accessible fast charging stations during the m-th trip; β u Parameters reflecting the randomness of user perception. i and k are both accessible fast charging stations; Let q be the probability that user q will choose a fast charging station k that is accessible during the m-th trip; All are deterministic components, representing the objective in decision-making; ed m,q SOC at the point of departure from the starting point; E B This refers to the battery's capacity. For EV user q in the path The estimated power consumption; For EV user q in the path Estimated power consumption; min This is the minimum threshold for SOC.

[0075] As a preferred solution for a time-space charging adjustable flexibility quantification device for electric vehicles, in the road segment travel time and charging price iterative update submodule of the dual-criteria stochastic dynamic user equilibrium model construction and processing module, during the iterative update of the road segment travel time and charging price based on the simulation data, the expected road segment travel time and charging price for the next iteration are calculated by the successive averaging method.

[0076] The formula for calculating the estimated travel time of the road segment in the next iteration is:

[0077]

[0078] In the formula, and These are the estimated road segment travel times used in the (n+1)th and nth iterations, respectively; The traffic congestion situation is calculated and recorded in the nth iteration; [T] h ,T h+1 [This represents a time period;]

[0079] The formula for calculating the charging price in the next iteration is:

[0080]

[0081] In the formula, and These are the charging prices at fast charging stations used in the (n+1)th and nth iterations, respectively. The charging price of the fast charging station is calculated and recorded in the nth iteration.

[0082] As a preferred embodiment of a quantification device for the spatiotemporal adjustable flexibility of electric vehicle charging, in the road segment travel time and charging price iterative update submodule of the dual-criteria stochastic dynamic user equilibrium model construction and processing module, during the iterative update of the road segment travel time and charging price based on the simulation data, the iteration stops when the termination criterion is met; the expression of the termination criterion is:

[0083]

[0084] In the formula, U n Let n be the average travel utility of all EV users in the nth iteration; c ε represents the current iteration number; ε represents the simulation accuracy.

[0085] This invention has the following advantages: Based on the electric vehicle (EV) travel chain, it constructs an EV charging spatiotemporal flexibility model; based on the EV travel chain demand, it constructs a dual-criteria stochastic dynamic user equilibrium model; through the dual-criteria stochastic dynamic user equilibrium model, it obtains the EV fast-charging load and slow-charging load under stochastic dynamic user equilibrium conditions; based on the fast-charging load and slow-charging load under stochastic dynamic user equilibrium conditions, it quantitatively evaluates the temporal flexibility potential of slow charging and the spatial flexibility potential of fast charging through the EV charging spatiotemporal flexibility model, obtaining quantitative evaluation results. This invention fully considers the impact of factors such as traffic congestion, fast-charging station pricing, and user perception differences on EV users' travel paths and their choice of fast-charging stations along the way, and uses a dual-criteria stochastic dynamic user equilibrium model to accurately characterize users' path selection, fast-charging, and slow-charging behaviors throughout the entire travel chain. This invention can jointly quantitatively evaluate the spatial flexibility potential of fast charging and the temporal flexibility potential of slow charging. Compared to flexibility analysis focusing only on a single charging type, it more comprehensively reflects the spatiotemporal schedulable characteristics of EV charging load, thus providing more accurate and powerful support for the coordinated scheduling of transportation and power systems. Attached Figure Description

[0086] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0087] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0088] Figure 1 This is a flowchart illustrating a method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles provided in Embodiment 1 of the present invention.

[0089] Figure 2 This is a schematic diagram of the specific implementation process of the method for quantifying the spatiotemporal charging adjustability of electric vehicles provided in Embodiment 1 of the present invention.

[0090] Figure 3 This is a schematic diagram of a travel chain including fast charging in transit, which is part of a method for quantifying the adjustable flexibility of spatiotemporal charging of electric vehicles provided in Embodiment 1 of the present invention.

[0091] Figure 4 This is a schematic diagram of relevant variables in the travel chain in a method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles provided in Embodiment 1 of the present invention.

[0092] Figure 5 This is a schematic diagram of possible charging scenarios for EV users at their destination in a spatiotemporal charging adjustability quantification method for electric vehicles provided in Embodiment 1 of the present invention;

[0093] Figure 6 This is a schematic diagram of the road network topology and the surrounding partitions of each node in one possible embodiment of Embodiment 1 of the present invention;

[0094] Figure 7 This is a schematic diagram illustrating the number of private cars and workplaces or other activity locations in each area in one possible embodiment of Embodiment 1 of the present invention;

[0095] Figure 8 This is a schematic diagram showing the distribution of vehicle numbers and departure times for each travel chain type in one possible embodiment of Embodiment 1 of the present invention;

[0096] Figure 9 This is a schematic diagram of other simulation parameters in one possible embodiment provided in Embodiment 1 of the present invention;

[0097] Figure 10 This is a schematic diagram of the charging load of each fast charging station in two scenarios in one possible embodiment of Embodiment 1 of the present invention; wherein, (a) is scenario 1; (b) is scenario 2;

[0098] Figure 11 This is a schematic diagram of the charging prices of each fast charging station in scenario 2 of a possible embodiment provided in Embodiment 1 of the present invention;

[0099] Figure 12 This is a schematic diagram of the spatial distribution of fast charging load during peak hours in two possible embodiments of the present invention, provided in Embodiment 1; wherein, (a) is 17:30-18:00; (b) is 18:00-18:30; and (c) is 18:30-19:00.

[0100] Figure 13 This is a schematic diagram illustrating the potential for spatial flexibility during peak fast charging periods in one possible embodiment of Embodiment 1 of the present invention; wherein, (a) is 17:30-18:00; (b) is 18:00-18:30; and (c) is 18:30-19:00.

[0101] Figure 14This is a schematic diagram of the daily charging volume of each area in two scenarios in a residence, workplace, and other locations in a possible embodiment of the present invention, provided in Embodiment 1 of the present invention; wherein, (a) is a residence; (b) is a workplace; and (c) is other locations;

[0102] Figure 15 This is a schematic diagram of the energy operation area of ​​an EV cluster parked in a residential area, workplace, and other locations in a possible embodiment of the present invention, provided in Embodiment 1; wherein, (a) is area 18: residential; (b) is area 16: workplace; and (c) is area 21: other locations;

[0103] Figure 16 This is a schematic diagram showing the distribution of the delayable power and the scheduleable time margin of an EV cluster parked at a residential area in area 18 at different arrival times in two possible scenarios provided in Embodiment 1 of the present invention; wherein, (a) is scenario 1; (b) is scenario 2;

[0104] Figure 17 This is a schematic diagram showing the distribution of the delayable charge and the schedulable time margin of an EV cluster parked at workplace 16 in area 16 at different arrival times in one possible embodiment of the present invention (Example 1).

[0105] Figure 18 This is a schematic diagram showing the distribution of the delayable charge and the schedulable time margin of an EV cluster parked in other locations in area 21 at different arrival times in one possible embodiment of Embodiment 1 of the present invention.

[0106] Figure 19 Different β values ​​are provided in one possible embodiment of the present invention, as shown in Embodiment 1. u A diagram illustrating the proportion of EV users selecting the optimal fast charging station under various value settings;

[0107] Figure 20 Different β values ​​are provided in one possible embodiment of the present invention, as shown in Embodiment 1. u A schematic diagram of the spatial distribution of fast charging load during peak hours (i.e., 18:00-18:30) under the current conditions;

[0108] Figure 21 Different β values ​​are provided in one possible embodiment of the present invention, as shown in Embodiment 1. u A diagram illustrating the potential flexibility of fast charging during peak hours (18:00-18:30).

[0109] Figure 22 This is a schematic diagram of the architecture of an adjustable flexibility quantification device for spatiotemporal charging of electric vehicles provided in Embodiment 2 of the present invention. Detailed Implementation

[0110] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0111] Example 1

[0112] See Figure 1 and Figure 2 Embodiment 1 of the present invention provides a method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles, comprising the following steps:

[0113] S1. Based on the electric vehicle travel chain, construct a spatiotemporal flexibility model for electric vehicle charging;

[0114] S2. Based on the electric vehicle travel chain demand, construct a dual-criteria stochastic dynamic user equilibrium model; obtain the fast charging load and slow charging load of electric vehicles under the stochastic dynamic user equilibrium state through the dual-criteria stochastic dynamic user equilibrium model.

[0115] S3. Based on the fast charging load and the slow charging load under the random dynamic user equilibrium state, the temporal flexibility potential of slow charging and the spatial flexibility potential of fast charging of electric vehicles are quantitatively evaluated through the spatiotemporal flexibility model of electric vehicle charging, and the quantitative evaluation results are obtained.

[0116] In this embodiment, in step S1, a spatiotemporal flexibility model for electric vehicle charging is constructed based on the electric vehicle travel chain;

[0117] Specifically, EV users' daily commutes typically follow a travel chain pattern, starting from home, traveling to multiple intermediate destinations, and finally returning to the starting point. Therefore, EV charging primarily occurs in two types of locations within this travel chain: first, charging at slow charging stations in the destination parking lot, demonstrating flexibility in charging time; and second, detouring to fast charging stations during the journey, demonstrating flexibility in charging location. For example... Figure 3 The diagram shows a trip chain involving a detour to FCS charging during the journey. Relevant variables for EV users in the trip chain are as follows: Figure 4 As shown.

[0118] The spatiotemporal flexibility model for electric vehicle charging includes a slow charging time flexibility model and a fast charging spatial flexibility model. The slow charging time flexibility model addresses the slow charging behavior of electric vehicles at the destination parking lot. Based on factors such as the battery state of charge when the user arrives and the parking duration, it quantifies the slow charging time flexibility potential of individual and clustered electric vehicles through indicators such as the deferable charge capacity, the schedulable time margin, and the energy accumulation boundary.

[0119] like Figure 5 As shown, the destination dl of EV user q is given. m,q Possible charging scenarios.

[0120] Delayable power consumption:

[0121]

[0122] If the parking time is insufficient to charge the EV, that is like Figure 5 As shown in (a), slow charging is inflexible, that is... exist Figure 5 (b) and Figure 5 In the two charging scenarios shown in (c), Slow charging is flexible. Specifically, in Figure 5 In (b), Only a portion of the battery charge is deferred. exist Figure 5 In (c), All battery charges are deferred.

[0123] Scheduled time margin, that is, the amount of actual charging that can be completed during parking time. Maximum allowed delayed charging time:

[0124]

[0125] In the formula, The latest charging start time.

[0126] exist Figure 5 In the charging scenario shown in (a), slow charging is inflexible. exist Figure 5 (b) and Figure 5 In the two charging scenarios shown in (c), slow charging is flexible.

[0127] Energy accumulation boundaries, which are the maximum and minimum accumulated charge that can be transferred to the battery within time t:

[0128] exist Figure 5 In the charging scenario shown in (a), slow charging is inflexible, and its upper and lower boundaries for energy accumulation overlap:

[0129]

[0130] exist Figure 5 (b) and Figure 5In the two charging scenarios shown in (c), slow charging is flexible, and the energy operating area is limited by the upper and lower boundaries of energy accumulation.

[0131]

[0132] In the formula, This is the earliest time to end charging.

[0133] In this embodiment, in order to analyze the slow charging time flexibility potential of a parked EV cluster in a certain area, the above-mentioned indicators of all individual EVs in the cluster are summed in time series to obtain the distribution of the delayable power and the schedulable time margin of the parked EV cluster at different arrival times and the energy operation area of ​​the EV cluster.

[0134] In this embodiment, the fast charging spatial flexibility model targets the fast charging behavior of electric vehicles at fast charging stations en route. By constructing the load curve of the fast charging station, the model calculates the difference between the load curves under scenarios with and without electricity price incentives, thereby quantifying the spatial flexibility potential of the fast charging station and the whole system.

[0135] Specifically, when EV user q begins their m-th trip, whether fast charging is needed during the trip depends on the initial State of Charge (SOC) and the required battery level for the trip. When an EV user chooses to fast charge during their trip, the FCS (Fast Charging Station) within the remaining range is usually not unique. Incentivizing users to choose different fast charging stations through price incentives can shift the fast charging load between multiple stations. The fast charging load for EV user q at FCS k is determined by the arrival time. Departure and departure times Characterization. The charging load curve of FCS k can be obtained by summing the charging amount and charging time of all EVs in the station. The expression of the fast charging load curve is:

[0136]

[0137] In the formula, P k (t) is [T] h ,T h+1 The average fast charging load of FCS k during the time period; ΔT h For time period [T] h ,T h+1 The duration of Q; k,h For [T] h ,T h+1 The set of EV users connected to FCS k for charging within the specified time period; This is the maximum output power of the fast charging station; For [T] h ,T h+1 The effective charging time of EV user q at FCS k within a given time period is the charging time period for EV user q. and time period [T] h ,T h+1 The overlapping part of ] .

[0138] The load flexibility potential of a fast charging station within a certain period can be quantified by calculating the difference between its load curves under scenarios with and without electricity price incentives. This difference reflects its responsiveness to price signals. The spatial distribution of the load flexibility potential of all fast charging stations within that period represents the spatial flexibility potential of fast charging within that period.

[0139] In this embodiment, in step S2, a dual-criteria stochastic dynamic user equilibrium model is constructed based on the electric vehicle travel chain demand; the fast charging load and slow charging load of electric vehicles under the stochastic dynamic user equilibrium state are obtained through the dual-criteria stochastic dynamic user equilibrium model.

[0140] Specifically, the steps for obtaining the fast-charging load and slow-charging load of electric vehicles under stochastic dynamic user equilibrium state through the dual-criteria stochastic dynamic user equilibrium model are as follows:

[0141] S21. Define an extended travel utility function through a path selection model; based on the extended travel utility function, combine the time-varying shortest path algorithm and stochastic utility theory to determine the user path and fast charging station selection;

[0142] Specifically, the extended travel utility function is defined as the sum of the time cost of the trip and the monetary cost incurred due to fast charging. The expression for the sum of the utility of each trip in the travel chain of EV user q is:

[0143]

[0144] In the formula, u q Let u be the sum of the utility of each trip in the travel chain of EV user q; m,q tt represents the utility of EV user q on the m-th trip; a represents the value per unit time; tt represents the utility of EV user q on the m-th trip. m,q For EV user q, the duration of the m-th trip; mc m,q The monetary cost incurred by EV user q during the mth trip due to fast charging.

[0145] A time-varying vector graph G = {V, E, W(t)} is used to describe the urban transportation network;

[0146] Where V = {1, 2, ..., V} represents all nodes in the urban transportation network; E = {(i, j) | i ≠ j, i, j ∈ V} represents the directed road segments between nodes; W(t) = {W ij (t)|(i,j)∈E} represents the weight of each road segment, i.e., the estimated travel time of each road segment; W ij (t) is a time-dependent function.

[0147] Given W(t), for EV user q, the path with the minimum estimated travel time from origin O to destination D. It can be obtained using the time-varying shortest path algorithm.

[0148] When EV user q begins their m-th trip, the system first determines whether fast charging is needed for the m-th trip. If fast charging is determined to be needed, the system evaluates each fast charging station based on stochastic utility theory:

[0149]

[0150] In the formula, This represents the perceived trip utility of EV user q when choosing to go to FCS k for charging on the m-th trip. This utility is composed of a definite component. and random components composition; Characterizing the goals in decision-making; It reflects the user's perceived error.

[0151] Within the framework of utility maximization, EV user q chooses from their optional FCS set K m,q The FCS k with the highest perceived utility is selected. The probability of selecting each reachable FCS k is determined by the following formula:

[0152]

[0153] In the formula, K m,q For EV user q, the set of accessible fast charging stations during the m-th trip; β u Parameters reflecting the randomness of user perception. i and k are both accessible fast charging stations; Let q be the probability that user q will choose a fast charging station k that is accessible during the m-th trip; All are deterministic components, representing the objective in decision-making; ed m,q SOC at the point of departure from the starting point; E B This refers to the battery's capacity. For EV user q in the path The estimated power consumption; For EV user q in the path Estimated power consumption; min This is the minimum threshold for SOC.

[0154] S22. Simulate users' driving, parking and charging behaviors using a traffic-charging simulation model to obtain simulation data;

[0155] Specifically, the traffic-charging simulation model simulates the driving, parking, fast charging, and slow charging behaviors of each EV user throughout the entire travel chain, based on the routes determined by the route selection model and the fast charging stations along the way. This traffic-charging simulation model includes two sub-models: a vehicle driving model and a vehicle parking-charging model.

[0156] The vehicle driving model can determine the driving speed and power consumption of each EV user during the driving process. First, the driving speed of EV user q when entering road segment (i,j) at time t is calculated through queue-based traffic micro-simulation. Furthermore, the speed of EV user q at a constant speed in road segment (i,j) is calculated. The energy consumption during driving. Finally, calculate the EV user q throughout the entire journey. The driving time and power consumption.

[0157] Based on driving speed and energy consumption during the journey, the vehicle stop-charge model can determine the parking and charging behavior of EV users at fast charging stations and their destinations. For parking and charging behavior at fast charging stations, the time to reach FCS k and the battery level upon arrival are calculated based on the EV user q's driving time and energy consumption during driving. When EV user q arrives at FCS k, charging is immediately initiated. Charging ends when the SOC reaches 80% of the battery's rated capacity, and EV user q then departs for their destination dl. m,q The fast charging load generated by EV user q at FCS k can be accounted for by the arrival time. Departure and departure times Characterization. For parking and charging behavior at the destination, the energy consumption of EV user q during driving time and driving is used to calculate their arrival time dl. m,q The time of arrival and the battery level upon arrival. Based on the battery level upon arrival and the distance from the destination (dl). m,q The time is used to calculate the actual amount of charging completed during the parking period. EV user q at destination dl m,q The slow charging load at the point can be reached at time ta m,q Departure time td m+1,q and the actual amount of charging completed during the parking period. Characterization.

[0158] S23. Based on the simulation data, iteratively update the road segment travel time and charging price until the termination criterion is met, stop the iteration, reach the stochastic dynamic user equilibrium state, and obtain the electric vehicle fast charging load and slow charging load under the stochastic dynamic user equilibrium state.

[0159] Specifically, the current iteration ends when all EV users complete their travel chain in the traffic-charging simulation model. Traffic congestion conditions generated during this iteration will be calculated and recorded using the following formula:

[0160]

[0161] In the formula, w ij (t) represents the time interval [T] h ,T h+1 The average driving time of all EV users entering road segment (i,j) within the [Q] area; ij,h Q represents the set of all EV users who enter road segment (i,j) during this time period; ij,h For set Q ij,h The number of EV users.

[0162] Each fast charging station calculates and records the charging price according to its own fast charging load using the following formula:

[0163]

[0164] In the formula, cp k (t) represents FCS k in the time interval [T] h ,T h+1 Charging prices within [the area]; cp nor θ is the base charging price; θ is the charging price adjustment coefficient; P k (t) represents FCS k in the time interval [T] h ,T h+1 Average fast charging load within [the specified range]; This indicates that FCS k is within the time period [T] h ,T h+1 The maximum fast charging load within [ ].

[0165] In this embodiment, the estimated road segment travel time and fast charging station charging price used for path selection in the next iteration are calculated using the successive averaging method;

[0166] The formula for calculating the estimated travel time of the road segment in the next iteration is:

[0167]

[0168] In the formula, and These are the estimated road segment travel times used in the (n+1)th and nth iterations, respectively; The traffic congestion situation is calculated and recorded in the nth iteration; [T] h ,T h+1 [This represents a time period;]

[0169] The formula for calculating the charging price in the next iteration is:

[0170]

[0171] In the formula, and These are the charging prices at fast charging stations used in the (n+1)th and nth iterations, respectively. The charging price of the fast charging station is calculated and recorded in the nth iteration.

[0172] In this embodiment, the average travel utility of all EV users is selected as the indicator to determine whether a stochastic dynamic user equilibrium state has been reached. The expression for the iteration termination criterion is:

[0173]

[0174] In the formula, U n Let n be the average travel utility of all EV users in the nth iteration; c ε represents the current iteration number; ε represents the simulation accuracy.

[0175] When the termination criterion is met, a stochastic dynamic user equilibrium state is considered to have been reached. At this point, no EV user can improve their perceived travel utility by unilaterally changing their route choice, thus obtaining the EV fast charging and slow charging load in equilibrium. If the termination criterion is not met, the estimated road travel time and fast charging station price are updated, and the next iteration begins. During the iteration process, EV users will adjust their route and fast charging station choices based on the updated estimated road travel time and fast charging station price.

[0176] In this embodiment, in step S3, based on the fast charging load and the slow charging load under the random dynamic user equilibrium state, the temporal flexibility potential of slow charging and the spatial flexibility potential of fast charging of electric vehicles are quantitatively evaluated through the electric vehicle charging spatiotemporal flexibility model to obtain the quantitative evaluation results.

[0177] Specifically, the fast charging load and slow charging load obtained in step S2 under the equilibrium state are substituted into the electric vehicle charging spatiotemporal flexibility model. For slow charging, the distribution of delayable power, the distribution of schedulable time margin, and the energy operation area of ​​the cluster of electric vehicles are calculated based on the slow charging load data to assess the time flexibility potential. For fast charging, the load change difference of each fast charging station under the electricity price incentive is calculated based on the fast charging load data to determine the distribution of spatial flexibility potential, and finally a comprehensive quantitative evaluation result is obtained.

[0178] In one possible embodiment, a specific example of the main arterial road network in Sioux Falls, USA, is provided below:

[0179] The road network comprises 24 nodes and 76 one-way road segments. The road network topology and the surrounding zoning of each node are as follows: Figure 6As shown, each zone is equipped with a fast charging station at a road network node. Based on the provided typical test scenario from Suffers, this invention sets up 65,758 private cars, encompassing three types of travel destinations: residences, workplaces, and other locations. The number of private cars and workplaces / other activity locations in each area are as follows: Figure 7 As shown in the figure. The types of travel chains for private cars, and the distribution of vehicle numbers and departure times for each type of travel chain are as follows. Figure 8 As shown. In this embodiment, a scenario with a future EV penetration rate of 100% is simulated, and 50% of travel destinations are equipped with slow charging stations. A Nissan Leaf model with a battery capacity of 24 kWh is selected as the research object. In the initial simulation settings, the initial SOC of all EVs is set to 80% of the battery capacity. To eliminate the influence of the initial SOC setting, this invention uses continuous simulation over multiple days. The initial SOC of the next day is determined by the SOC after the previous day's trip and the charging situation at home overnight, until the daily initial SOC distribution tends to stabilize. Finally, the results are analyzed using the stable daily initial SOC. Other simulation parameters are as follows: Figure 9 As shown.

[0180] To verify the effectiveness of the proposed model, the following two comparison scenarios are set up:

[0181] Scenario 1 (without considering price incentives): Assume that the charging price at all fast charging stations remains fixed at the base price cp during the iteration process. nor ;

[0182] Scenario 2 (Considering Price Incentives): The charging prices of all fast charging stations are continuously updated during the iteration process. To alleviate the problem of uneven load distribution among fast charging stations during peak charging periods, this invention sets a uniform fast charging load upper limit for all fast charging stations. This upper limit is set as the peak value of the average charging load curve of all fast charging stations obtained in Scenario 1.

[0183] First, the selection of fast charging stations in both scenarios is set to be fixed, i.e., β. u Approaching infinity, we verify that the proposed model can characterize EV users' response to fast charging station prices, and analyze the potential for fast charging space flexibility and the corresponding potential for slow charging time flexibility under price incentives. Then, we set the choice of fast charging stations in both scenarios to be uncertain, i.e., β. u By setting different finite values, the impact of this parameter on EV users' fast charging station selection behavior and its potential for fast charging space flexibility is analyzed.

[0184] I. Potential for EV charging flexibility under a defined fast charging station selection

[0185] The charging load of each fast charging station in the two scenarios, and the charging price of each fast charging station in scenario 2 are as follows: Figure 10 and Figure 11As shown, peak EV fast charging times are concentrated in the evening. When all fast charging stations adopt a uniform price, the load distribution among them is uneven. By incentivizing users through price reductions—specifically by setting lower electricity prices at fast charging stations with lower charging loads—users are encouraged to prioritize charging at these stations, thus significantly improving the uneven load distribution among fast charging stations.

[0186] Figure 12 and Figure 13 The spatial distribution of fast charging load and the potential for spatial flexibility of fast charging during peak hours are presented in two scenarios. Figure 13 The load flexibility potential of each fast charging station was calculated by subtracting the difference in charging load curves between each fast charging station in Scenario 1 and Scenario 2. Figure 13 In the graph, pink bars represent a decrease in charging load caused by price incentives, indicating a negative load flexibility potential for the fast charging station; green bars represent an increase in charging load caused by price incentives, indicating a positive load flexibility potential for the fast charging station. It can be seen that high-load fast charging stations (such as FCS22, FCS13, and FCS10) exhibit negative load flexibility potential, and the higher the load level, the greater the negative load flexibility potential. Furthermore, the absolute value of the maximum negative load flexibility potential that a single fast charging station can provide is greater than the maximum positive load flexibility potential it can provide. This is because the charging load of high-load fast charging stations is distributed and transferred to multiple surrounding low-load fast charging stations, effectively alleviating the problem of uneven load distribution among fast charging stations.

[0187] Figure 14 The daily charging volume for slow charging in various locations (residential, workplace, and other locations) is given in two scenarios. It can be seen that... Figure 14 Areas with higher daily slow charging rates in residential, workplace, and other locations correspond to Figure 7 Areas with a high number of private cars or where workplaces and other locations are relatively concentrated. Due to the long parking times at residences and workplaces, each charging cycle corresponds to... Figure 5 The charging scenario shown in (c) indicates that the delayed charge is equal to the actual charge completed during the parking period, demonstrating that slow charging in residential and workplace settings is "completely" flexible. However, for other locations with shorter parking times, the charging time corresponds to... Figure 5 The charging scenarios shown in (a) and 5(b) indicate that the amount of electricity delayed is less than the amount of electricity actually charged during the parking period, which means that slow charging in other locations is "partially" flexible.

[0188] from Figure 14As can be seen, the daily charging volume differs only at the residence in the two scenarios, while it remains almost the same at the workplace and other locations. This is because, compared to Scenario 1, EV users in Scenario 2 who require fast charging may change their chosen fast charging stations due to price incentives. Changes in fast charging station selection alter the entire travel route from the origin to the destination, thus affecting traffic conditions. This impact on traffic conditions is particularly evident during the evening peak charging period. EV users primarily return home in the evening and at night; the change in traffic conditions during this period affects their travel time and energy consumption, ultimately leading to differences in the time EV users arrive at their residences and the amount of energy they charge in the two scenarios. EV users, on the other hand, primarily arrive at their workplaces in the morning, with arrival times at other locations being more dispersed throughout the day. Therefore, the differences in the time EV users arrive at their workplaces / other locations and the amount of energy they charge in the two scenarios can be ignored. In the following analysis, only the differentiated results for slow charging at residences are presented in both scenarios. For slow charging at workplaces / other locations, since the results are almost identical in both scenarios, only the results for one scenario are given.

[0189] exist Figure 14 In (a), the daily charging volume at residence 18 in Area 2 increased. This is because the introduction of price incentives led to an increase in the fast-charging load at fast-charging stations in the surrounding area, further exacerbating traffic congestion. Consequently, the return journey time and energy consumption of EV users residing in Area 18 increased. It is precisely because of the increased travel time and energy consumption of EV users residing in Area 18 on their way home that the arrival time of EVs at their residences was delayed, accompanied by an increase in charging volume at residence 18. Figure 15 (a) shows the energy operating regions of the EV clusters parked at the residence in area 18 in two scenarios. For the reasons mentioned above, the upper energy boundary in scenario 2 has a smaller slope and a higher peak value.

[0190] Figure 16 The distribution of the deferable battery capacity and its scheduleable time margin for EV clusters parked at residential area 18 in two scenarios at different arrival times is presented. It can be seen that the peak of the deferable battery capacity occurs during the peak evening return journey of EV users. During this peak evening return journey, compared to scenario 1, the arrival of EV users in scenario 2 is significantly delayed. Specifically, in scenario 1, the total deferable battery capacity of EV users arriving between 17:30 and 18:30 is higher than in scenario 2; while in scenario 2, the total deferable battery capacity of EV users arriving between 18:30 and 20:30 is higher than in scenario 1.

[0191] In addition, from Figure 15(a) It can be observed that the flexibility potential of slow charging at residences begins to increase after the workday ends (around 5:00 PM) and gradually decreases in the early morning of the following day, exhibiting the greatest flexibility potential in the evening to nighttime hours. Figure 16 Different colors represent different schedulable time margins. It can be seen that all slow charging at the residence is deferred, with a schedulable time margin of more than 8 hours.

[0192] Figure 15 (b) and Figure 15 (c) The energy operating boundaries of EV clusters parked in a certain area of ​​workplace and other locations are given respectively. Figure 17 and Figure 18 The distribution of the deferable charge capacity and its schedulable time margin for the corresponding EV clusters at different arrival times is presented. For EV slow charging at the workplace, EV users arrive mainly between 7:00 and 10:00 AM, therefore its flexibility potential increases in the morning and decreases after get off work, with the highest flexibility potential during the day. Due to the long working hours, charging at the workplace is similar to slow charging at home, meaning that all slow charging at the workplace is deferable, with a schedulable time margin of 5-10 hours. However, for slow charging at other locations, due to insufficient parking time, the schedulable time margin is less than 1 hour, and 36% of charging behavior is not deferable. Therefore, the flexibility potential at other locations is significantly lower compared to homes and workplaces.

[0193] II. Impact Analysis of Uncertain Fast Charging Station Selection

[0194] For parameter β u This study analyzes the impact of EV users' fast charging station selection behavior and the potential for flexibility in fast charging space. β u The smaller the value, the stronger the randomness in the selection of fast charging stations; β u The higher the value, the more likely users are to choose the fast charging station with the highest efficiency. This invention addresses β. u Sensitivity analysis was performed using values ​​of 10, 20, 30, 40, 60, and 100, for example... Figure 19 As shown, different β values ​​are given. u The optimal proportion of EV users for each fast charging station is selected based on the given value. It can be seen that as β... u As the value increases, the proportion of users choosing the optimal FCS increases significantly.

[0195] Figure 20 and Figure 21 Different β values ​​were given respectively. u The spatial distribution of fast charging load and its corresponding spatial flexibility potential during peak hours (18:00-18:30) under the value. With β uAs the value increases, the perceived randomness decreases, meaning the probability of a user choosing the optimal fast charging station increases. This will lead to a more pronounced uneven load distribution among fast charging stations during peak hours. Furthermore, β... u The higher the value, the more significant the effect of price incentives on alleviating the uneven load distribution among fast charging stations, which means greater potential for spatial flexibility in fast charging. When β u As the range approaches infinity, under a given selection of fast charging stations, the uneven load distribution among fast charging stations reaches its maximum, simultaneously demonstrating the greatest potential for spatial flexibility in fast charging (e.g., Figure 12 (b) and Figure 13 (b) is shown.

[0196] In summary, this invention addresses the issue of coordinated scheduling of urban transportation and power systems by integrating electric vehicle charging load and traffic flow data. By quantifying the time flexibility of slow charging and the spatial flexibility of fast charging, it optimizes power grid load allocation and traffic network planning, alleviates the pressure of peak electricity demand and traffic congestion, and improves the overall operating efficiency of the system.

[0197] In terms of charging infrastructure operation and management: it provides decision-making basis for fast charging station and slow charging pile operators, dynamically adjusts charging prices, optimizes charging pile layout, balances the load of each site, improves equipment utilization and reduces operating costs by assessing the charging flexibility potential of different areas.

[0198] In terms of supporting renewable energy consumption: By combining the output characteristics of renewable energy sources such as wind power and photovoltaics, and utilizing the potential of quantitative charging flexibility, we can guide electric vehicles to charge during peak periods of new energy power generation, thereby increasing the consumption of renewable energy and promoting the transformation of the energy structure.

[0199] In terms of government energy and transportation policy-making: providing data support for government departments to formulate policies on electric vehicle promotion, charging subsidies, and road network construction; optimizing policy guidance by analyzing charging flexibility in different scenarios; and promoting the coordinated development of transportation and energy sectors.

[0200] Regarding electric vehicle user travel services: Based on the quantitative results of charging flexibility, personalized travel and charging suggestions are provided to users, such as recommending fast charging stations with reasonable prices and low load, or suggesting the best time for slow charging, thereby improving the user travel experience.

[0201] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0202] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0203] Example 2

[0204] See Figure 22 Embodiment 2 of the present invention also provides a device for quantifying the adjustable flexibility of spatiotemporal charging for electric vehicles, comprising:

[0205] Electric vehicle charging spatiotemporal flexibility model construction module 001 is used to construct an electric vehicle charging spatiotemporal flexibility model based on the electric vehicle travel chain;

[0206] The dual-criteria stochastic dynamic user equilibrium model construction and processing module 002 is used to construct a dual-criteria stochastic dynamic user equilibrium model based on the electric vehicle travel chain demand; and to obtain the electric vehicle fast charging load and slow charging load under the stochastic dynamic user equilibrium state through the dual-criteria stochastic dynamic user equilibrium model.

[0207] The quantitative evaluation module 003 is used to quantitatively evaluate the temporal flexibility potential of slow charging and the spatial flexibility potential of fast charging of electric vehicles based on the fast charging load and the slow charging load under the random dynamic user equilibrium state, and obtain the quantitative evaluation results.

[0208] In this embodiment, the electric vehicle charging spatiotemporal flexibility model in the quantitative evaluation module 003 includes: a slow charging time flexibility model and a fast charging spatial flexibility model;

[0209] The slow charging time flexibility model quantifies the potential of electric vehicle slow charging time flexibility by using delayed charge capacity, schedulable time margin, and energy accumulation boundary indicators.

[0210] The formula for calculating the delayable power is:

[0211]

[0212] In the formula, ta m,q The time when the electric vehicle arrives at its destination; td m+1,q The moment the electric vehicle leaves its location; This is the maximum output power of the slow charging station; The amount of electricity charged for electric vehicles; For power that can be delayed;

[0213] The formula for calculating the schedulable time margin is as follows:

[0214]

[0215] In the formula, This represents the schedulable time margin; The latest charging start time;

[0216] The formula for calculating the energy accumulation boundary is:

[0217]

[0218] In the formula, The maximum amount of charge that can be transferred to the battery within time t; The minimum amount of charge that can be transferred to the battery within time t; This is the earliest time to end charging.

[0219] In this embodiment, the fast charging spatial flexibility model in the quantitative evaluation module 003 calculates the difference between the fast charging load curves of electric vehicles under scenarios with and without electricity price incentives to quantitatively evaluate the spatial flexibility potential of fast charging.

[0220] The expression for the fast charging load curve is:

[0221]

[0222] In the formula, P k (t) is [T] h ,T h+1 The average fast charging load of FCS k during the time period; ΔT h For time period [T] h ,T h+1 The duration of Q; k,h For [T] h ,T h+1 The set of EV users connected to FCS k for charging within the specified time period; This is the maximum output power of the fast charging station; For [T] h ,T h+1 The effective charging time of EV user q at FCS k within a given time period is the charging time period for EV user q. and time period [T] h ,T h+1 The overlapping part of ] .

[0223] In this embodiment, the dual-criteria stochastic dynamic user equilibrium model construction and processing module 002, in the process of obtaining the electric vehicle fast charging load and slow charging load under the stochastic dynamic user equilibrium state through the dual-criteria stochastic dynamic user equilibrium model, includes the following sub-module:

[0224] The user route determination and fast charging station selection submodule 021 is used to define an extended travel utility function through a route selection model; based on the extended travel utility function, combined with the time-varying shortest path algorithm and stochastic utility theory, the user route and fast charging station selection are determined.

[0225] The traffic-charging simulation submodule 022 is used to simulate users' driving, parking and charging behaviors through a traffic-charging simulation model and obtain simulation data.

[0226] The road segment passage time and charging price iterative update submodule 023 is used to iteratively update the road segment passage time and charging price according to the simulation data until the termination criterion is met, the iteration stops, the random dynamic user equilibrium state is reached, and the electric vehicle fast charging load and slow charging load under the random dynamic user equilibrium state are obtained.

[0227] In this embodiment, in the user path determination and fast charging station selection submodule 021 of the dual-criteria stochastic dynamic user equilibrium model construction and processing module 002, the expression of the extended travel utility function is:

[0228]

[0229] In the formula, u q Let u be the sum of the utility of each trip in the travel chain of EV user q; m,q tt represents the utility of EV user q on the m-th trip; a represents the value per unit time; tt represents the utility of EV user q on the m-th trip. m,q For EV user q, the duration of the m-th trip; mc m,q The monetary cost incurred by EV user q during the mth trip due to fast charging.

[0230] Within the framework of utility maximization, the expression for the probability that a user chooses a fast-charging station that is accessible is:

[0231]

[0232] In the formula, K m,q For EV user q, the set of accessible fast charging stations during the m-th trip; β u Parameters reflecting the randomness of user perception. i and k are both accessible fast charging stations; Let q be the probability that user q will choose a fast charging station k that is accessible during the m-th trip; All are deterministic components, representing the objective in decision-making; edm,q SOC at the point of departure from the starting point; E B This refers to the battery's capacity. For EV user q in the path The estimated power consumption; For EV user q in the path Estimated power consumption; min This is the minimum threshold for SOC.

[0233] In this embodiment, in the road segment travel time and charging price iterative update submodule 023 of the dual-criteria stochastic dynamic user equilibrium model construction and processing module 002, during the iterative update of the road segment travel time and charging price based on the simulation data, the expected road segment travel time and charging price for the next iteration are calculated by the successive averaging method.

[0234] The formula for calculating the estimated travel time of the road segment in the next iteration is:

[0235]

[0236] In the formula, and These are the estimated road segment travel times used in the (n+1)th and nth iterations, respectively; The traffic congestion situation is calculated and recorded in the nth iteration; [T] h ,T h+1 [This represents a time period;]

[0237] The formula for calculating the charging price in the next iteration is:

[0238]

[0239] In the formula, and These are the charging prices at fast charging stations used in the (n+1)th and nth iterations, respectively. The charging price of the fast charging station is calculated and recorded in the nth iteration.

[0240] In this embodiment, in the road segment travel time and charging price iterative update submodule 023 of the dual-criteria stochastic dynamic user equilibrium model construction and processing module 002, during the iterative update of the road segment travel time and charging price based on the simulation data, the iteration stops when the termination criterion is met; the expression of the termination criterion is:

[0241]

[0242] In the formula, U nLet n be the average travel utility of all EV users in the nth iteration; c ε represents the current iteration number; ε represents the simulation accuracy.

[0243] It should be noted that the information interaction and execution process between the modules of the above system are based on the same concept as the method embodiment in Embodiment 1 of this application, and the resulting technical effects are the same as those in the method embodiment of this application. For details, please refer to the description in the method embodiment shown above in this application, and it will not be repeated here.

[0244] Example 3

[0245] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium storing program code for a method of quantifying the spatiotemporal charging adjustability flexibility of an electric vehicle. The program code includes instructions for executing the method of quantifying the spatiotemporal charging adjustability flexibility of an electric vehicle according to Embodiment 1 or any possible implementation thereof.

[0246] Computer-readable storage media can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives, SSDs).

[0247] Example 4

[0248] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0249] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor can execute the spatiotemporal charging adjustable flexibility quantification method for electric vehicles according to Embodiment 1 or any possible implementation thereof by calling the program instructions.

[0250] Specifically, a processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. This memory can be integrated into the processor or located outside the processor and exist independently.

[0251] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0252] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing systems. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Optionally, they can be implemented using program code executable by a computing system, thereby storing them in a storage system for execution by the computing system. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0253] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles, characterized in that, include: Based on the electric vehicle travel chain, construct a spatiotemporal flexibility model for electric vehicle charging; Based on the demand of electric vehicle travel chain, a dual-criteria stochastic dynamic user equilibrium model is constructed; the fast charging load and slow charging load of electric vehicles under the stochastic dynamic user equilibrium state are obtained through the dual-criteria stochastic dynamic user equilibrium model. Based on the fast charging load and the slow charging load under the random dynamic user equilibrium state, the temporal flexibility potential of slow charging and the spatial flexibility potential of fast charging of electric vehicles are quantitatively evaluated through the spatiotemporal flexibility model of electric vehicle charging, and the quantitative evaluation results are obtained.

2. The method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles according to claim 1, characterized in that, The spatiotemporal flexibility model for electric vehicle charging includes: a slow charging time flexibility model and a fast charging space flexibility model; The slow charging time flexibility model quantifies the potential of electric vehicle slow charging time flexibility by using delayed charge capacity, schedulable time margin, and energy accumulation boundary indicators. The formula for calculating the delayable power is: In the formula, ta m,q The time when the electric vehicle arrives at its destination; td m+1,q The moment the electric vehicle leaves its location; This is the maximum output power of the slow charging station; The amount of electricity charged for electric vehicles; For power that can be delayed; The formula for calculating the schedulable time margin is as follows: In the formula, This represents the schedulable time margin; The latest charging start time; The formula for calculating the energy accumulation boundary is: In the formula, The maximum amount of charge that can be transferred to the battery within time t; The minimum amount of charge that can be transferred to the battery within time t; This is the earliest time to end charging.

3. The method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles according to claim 2, characterized in that, The fast charging spatial flexibility model quantifies the potential of fast charging spatial flexibility by calculating the difference in fast charging load curves of electric vehicles under scenarios with and without electricity price incentives. The expression for the fast charging load curve is: In the formula, P k (t) is [T] h ,T h+1 The average fast charging load of FCS k during the time period; ΔT h For time period [T] h ,T h+1 The duration of Q; k,h For [T] h ,T h+1 The set of EV users connected to FCS k for charging within the specified time period; This is the maximum output power of the fast charging station; For [T] h ,T h+1 The effective charging time of EV user q at FCS k within a given time period is the charging time period for EV user q. and time period [T] h ,T h+1 The overlapping part of ] .

4. The method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles according to claim 3, characterized in that, In obtaining the fast-charging load and slow-charging load of electric vehicles under stochastic dynamic user equilibrium state through the aforementioned dual-criteria stochastic dynamic user equilibrium model, the following steps are taken: An extended travel utility function is defined through a path selection model; based on the extended travel utility function, combined with the time-varying shortest path algorithm and stochastic utility theory, the user path and fast charging station selection are determined. The traffic-charging simulation model is used to simulate users' driving, parking, and charging behaviors and obtain simulation data. Based on the simulation data, the road passage time and charging price are iteratively updated until the termination criterion is met, the iteration stops, a stochastic dynamic user equilibrium state is reached, and the electric vehicle fast charging load and slow charging load under the stochastic dynamic user equilibrium state are obtained.

5. The method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles according to claim 4, characterized in that, The expression for the extended travel utility function is: In the formula, u q Let u be the sum of the utility of each trip in the travel chain of EV user q; m,q tt represents the utility of EV user q on the m-th trip; a represents the value per unit time; tt represents the utility of EV user q on the m-th trip. m,q For EV user q, the duration of the m-th trip; mc m,q The monetary cost incurred by EV user q during the mth trip due to fast charging. Within the framework of utility maximization, the expression for the probability that a user chooses a fast-charging station that is accessible is: In the formula, K m,q For EV user q, the set of accessible fast charging stations during the m-th trip; β u Parameters reflecting the randomness of user perception. i and k are both accessible fast charging stations; Let q be the probability that user q will choose a fast charging station k that is accessible during the m-th trip; All are deterministic components, representing the objective in decision-making; ed m,q SOC at the point of departure from the starting point; E B This refers to the battery's capacity. For EV user q in the path The estimated power consumption; For EV user q in the path Estimated power consumption; min This is the minimum threshold for SOC.

6. The method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles according to claim 5, characterized in that, In the process of iteratively updating the road segment travel time and the charging price based on the simulation data, the expected road segment travel time and the charging price for the next iteration are calculated by the successive averaging method. The formula for calculating the estimated travel time of the road segment in the next iteration is: In the formula, and These are the estimated road segment travel times used in the (n+1)th and nth iterations, respectively; The traffic congestion situation is calculated and recorded in the nth iteration; [T] h ,T h+1 [This represents a time period;] The formula for calculating the charging price in the next iteration is: In the formula, and These are the charging prices at fast charging stations used in the (n+1)th and nth iterations, respectively. The charging price of the fast charging station is calculated and recorded in the nth iteration.

7. The method for quantifying the spatiotemporal charging adjustability flexibility of electric vehicles according to claim 6, characterized in that, During the iterative update of the road segment travel time and charging price based on the simulation data, the iteration stops when the termination criterion is met; the expression for the termination criterion is: In the formula, U n Let n be the average travel utility of all EV users in the nth iteration; c ε represents the current iteration number; ε represents the simulation accuracy.

8. A device for quantifying the spatiotemporal charging adjustability flexibility of an electric vehicle, employing the method for quantifying the spatiotemporal charging adjustability flexibility of an electric vehicle as described in any one of claims 1-7, characterized in that, include: The electric vehicle charging spatiotemporal flexibility model construction module is used to construct an electric vehicle charging spatiotemporal flexibility model based on the electric vehicle travel chain. The dual-criteria stochastic dynamic user equilibrium model construction and processing module is used to construct a dual-criteria stochastic dynamic user equilibrium model based on the electric vehicle travel chain demand; and to obtain the electric vehicle fast charging load and slow charging load under the stochastic dynamic user equilibrium state through the dual-criteria stochastic dynamic user equilibrium model. The quantitative evaluation module is used to quantitatively evaluate the temporal flexibility potential of slow charging and the spatial flexibility potential of fast charging of electric vehicles based on the fast charging load and the slow charging load under the random dynamic user equilibrium state, and obtain the quantitative evaluation results.

9. The adjustable flexibility quantification device for spatiotemporal charging of electric vehicles according to claim 8, characterized in that, In the quantitative evaluation module, the electric vehicle charging spatiotemporal flexibility model includes: a slow charging time flexibility model and a fast charging spatial flexibility model; The slow charging time flexibility model quantifies the potential of electric vehicle slow charging time flexibility by using delayed charge capacity, schedulable time margin, and energy accumulation boundary indicators. The formula for calculating the delayable power is: In the formula, ta m,q The time when the electric vehicle arrives at its destination; td m+1,q The moment the electric vehicle leaves its location; This is the maximum output power of the slow charging station; The amount of electricity charged for electric vehicles; For power that can be delayed; The formula for calculating the schedulable time margin is as follows: In the formula, This represents the schedulable time margin; The latest charging start time; The formula for calculating the energy accumulation boundary is: In the formula, The maximum amount of charge that can be transferred to the battery within time t; The minimum amount of charge that can be transferred to the battery within time t; This is the earliest time to end charging.

10. The quantification device for adjustable flexibility of spatiotemporal charging of electric vehicles according to claim 9, characterized in that, In the quantitative evaluation module, the fast charging spatial flexibility model quantifies the potential of fast charging spatial flexibility by calculating the difference between the fast charging load curves of electric vehicles under scenarios with and without electricity price incentives. The expression for the fast charging load curve is: In the formula, P k (t) is [T] h ,T h+1 The average fast charging load of FCS k during the time period; ΔT h For time period [T] h ,T h+1 The duration of Q; k,h For [T] h ,T h+1 The set of EV users connected to FCS k for charging within the specified time period; This is the maximum output power of the fast charging station; For [T] h ,T h+1 The effective charging time of EV user q at FCS k within a given time period is the charging time period for EV user q. and time period [T] h ,T h+1 The overlapping part of ] .

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