Economic dispatching strategy based on space-time flexibility of charging of electric vehicles in traffic-power distribution network

Abstract

The invention provides an economic dispatching strategy based on the charging space-time flexibility of an electric vehicle in a traffic-power distribution network, and the strategy comprises the steps: designing an economic dispatching total target, and building a three-layer architecture model; power distribution network data, electric vehicle detailed data, electric vehicle charging requirements and traffic network data of a plurality of charging stations are collected; aggregating electric vehicle charging requirements and establishing an electric vehicle charging flexibility model; establishing a first-layer scheduling model according to the economic scheduling total target, the electric vehicle charging flexibility model and the power distribution network data; establishing a second-layer scheduling model according to the economic scheduling total target and the detailed data of the electric vehicle; establishing a third-layer scheduling model according to the economic scheduling total target, the third-layer scheduling model and the traffic network data; and providing a charging scheduling scheme for the electric vehicle user based on the first-layer scheduling model, the second-layer scheduling model and the third-layer scheduling model. The impact of the charging load on the main power grid is reduced, and the total cost of an electric vehicle user is also reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging scheduling, and in particular to an electric vehicle charging economic scheduling strategy. Background Art

[0002] In recent years, the rapid popularization of electric vehicles has brought significant changes to the transportation and energy industries. As the number of electric vehicles continues to increase, the integration of transportation-distribution networks provides new opportunities for optimizing electric vehicle charging plans. On the one hand, dispatching electric vehicles in transportation-distribution networks can reduce traffic congestion, manage the spatial distribution of charging loads, and reduce charging costs for electric vehicle users. On the other hand, electric vehicles can serve as flexible loads to help alleviate grid load fluctuations and solve the intermittent problem of renewable energy generation. Therefore, optimizing the charging plan for dispatching a large number of electric vehicles has become a focus of current research.

[0003] Since electric vehicles have both energy storage and transportation properties, considering the charging scheduling of electric vehicles in the transportation-distribution network is conducive to deep energy conservation. The scheduling method that only considers charging prices and transportation costs will seriously affect the operating costs of the distribution network. With the surge in the number of electric vehicles, focusing only on operating costs may affect its stability. The flexibility of the spatial distribution of electric vehicles has not been effectively utilized in scheduling. Most methods only focus on electric vehicles in charging stations, ignoring the spatial distribution and dynamic traffic conditions before electric vehicles initiate charging requests. Real-time scheduling can improve the efficiency of individual charging stations, but may not optimize the performance of the transportation-distribution network. This oversight may lead to inefficiencies in the entire system, such as increased travel time, increased energy consumption, traffic congestion, and reduced utilization of renewable energy.

[0004] In order to effectively utilize the spatial flexibility of electric vehicles in scheduling, it is necessary to accurately estimate their driving energy consumption and time based on the traffic conditions in the traffic network. Although machine learning-based energy consumption prediction frameworks can be quickly deployed without prior knowledge of road conditions, they often lack a clear physical interpretation and cannot adapt to different operating conditions. In contrast, traditional model-based methods are simpler and more applicable, estimating energy consumption by simulating the interaction between electric vehicle powertrain components. However, the dynamic changes in road conditions and traffic flow in the traffic network will affect the accuracy of dynamic energy estimation. However, these studies estimate the energy consumption of the entire road section and lack sufficient granularity to accurately capture the complex interaction between traffic dynamics and energy consumption. The current challenge is to integrate high-resolution data and traffic flow evolution reasoning, while considering macro traffic status and micro driving behavior to improve the accuracy of energy prediction.

[0005] The invention patent with the publication number CN105868942A discloses an orderly charging scheduling method for electric vehicles. The method calculates the energy consumption of the electric vehicle under the current working condition according to the pre-established electric vehicle driving range prediction model, and calculates the driving range of the electric vehicle according to the energy consumption of the electric vehicle under the current working condition and the current SOC; when the ratio between the driving range of the electric vehicle and the expected driving range before the next charge of the electric vehicle is less than the set threshold, the charging amount of the electric vehicle is calculated according to the pre-established electric vehicle power battery life model; and the charging process of the electric vehicle is scheduled according to the charging amount of the electric vehicle, the departure time and the current load condition of the distribution network, with the minimum peak-to-valley difference of the local distribution network as the optimization goal. This invention prolongs the service life of the electric vehicle power battery by reasonably guiding the charging and discharging of electric vehicles, can reduce the peak-to-valley difference of the power grid load, and ensure the stable operation of the power grid, but the invention does not consider the flexibility of the spatial distribution of electric vehicles. Summary of the invention

[0006] In response to the technical problem that the charging flexibility of electric vehicles has not been fully utilized, the present invention proposes an economic scheduling strategy based on the spatiotemporal flexibility of electric vehicle charging in the transportation-distribution network, which comprehensively considers various information of the transportation network and the distribution network, and simultaneously acts on the charging flexibility and scheduling process of electric vehicles, which can not only reduce the impact of charging load on the main power grid, but also utilize the energy properties of renewable energy and electric vehicles, and reduce the total cost of electric vehicle users.

[0007] In order to achieve the above object, the technical solution of the present invention is achieved as follows:

[0008] An economic dispatch strategy based on the spatiotemporal flexibility of electric vehicle charging in a transportation-distribution network includes the following steps:

[0009] S1: Design the overall goal of economic dispatch and establish a three-layer architecture model; collect distribution network data of multiple charging stations, detailed data of electric vehicles and charging requirements of electric vehicles, and transportation network data;

[0010] S2: In the first layer of the architecture model, aggregate EV charging requirements and establish an EV charging flexibility model;

[0011] S3: Establish the first-level dispatch model based on the overall economic dispatch target, electric vehicle charging flexibility model and distribution network data;

[0012] S4: In the second-level architecture model, the second-level dispatch model is established according to the overall economic dispatch target and detailed data of electric vehicles;

[0013] S5: In the third-layer architecture model, a third-layer dispatch model is established according to the overall economic dispatch target, the third-layer dispatch model and the transportation network data;

[0014] S6: Provide charging scheduling solutions for electric vehicle users based on the first-layer scheduling model, the second-layer scheduling model and the third-layer scheduling model.

[0015] The method for aggregating electric vehicle charging requirements and establishing an electric vehicle charging flexibility model in step S2 is:

[0016] 2.1 Based on the charging requirements of electric vehicles, electric vehicle users are classified into: electric vehicles without charging flexibility and electric vehicles with charging flexibility; electric vehicles with charging flexibility include: electric vehicles whose energy does not meet the minimum discharge requirements when the electric vehicle user arrives at the charging station and electric vehicles whose energy meets the minimum discharge requirements when the electric vehicle user arrives at the charging station;

[0017] 2.2: Calculate the energy boundary and effective charge and discharge power boundary of electric vehicles. Based on the electric vehicle user classification in step 2.1, aggregate the energy boundary and effective charge and discharge power boundary of electric vehicles, and finally obtain the electric vehicle charging flexibility model;

[0018] The first-layer scheduling model described in step S3 includes a first-layer scheduling objective function and a first-layer scheduling constraint condition;

[0019] The second-level scheduling model described in step S4 includes a second-level scheduling objective function and a second-level scheduling constraint condition; the second-level scheduling objective function includes a charging and discharging cost objective function and a battery degradation cost objective function; the second-level scheduling constraint condition includes a power balance constraint in the charging station and a constraint on satisfying the charging demand of electric vehicle users and the charging process;

[0020] The third-layer scheduling model described in step S5 includes a third-layer scheduling objective function and a third-layer scheduling constraint condition.

[0021] The calculation method of the energy boundary of the electric vehicle is:

[0022]

[0023] in, is the upper bound of the energy of the nth electric car at time t, is the lower bound of the energy of the nth electric car at time t, t n,a represents the time when the nth electric car arrives at the charging station, t n,d represents the time when the nth electric car leaves the charging station, p ev,n,c Represents the rated charging power of electric vehicles, η ev,c represents the charging efficiency of electric vehicles, p ev,n,d is the rated discharge power of the electric vehicle, η ev,d represents the discharge efficiency of electric vehicles, is the maximum energy of the nth electric car, is the minimum energy of the nth electric car, Δt represents the time interval of each calculation, and is the energy of the nth electric car when it leaves, is the energy demand of the nth electric car;

[0024] The calculation method of the effective charge and discharge power boundary of the electric vehicle is:

[0025]

[0026] in, represents the upper limit of the effective charging power of electric vehicles, It represents the lower bound of the effective discharge power of electric vehicles.

[0027] The electric vehicle charging flexibility model described in step S2 is:

[0028]

[0029] in, represents the effective charging power of the nth electric vehicle without charging flexibility, P ev,nf,t represents the effective charging power of all electric vehicles without charging flexibility, It represents the upper and lower bounds of the energy of all electric vehicles with charging flexibility when charging and discharging at time t. It represents the upper and lower bounds of the effective charging and discharging power of all electric vehicles with charging flexibility at time t; It represents the upper and lower bounds of the energy of the nth electric vehicle at time t; it represents the upper and lower bounds of the effective discharge power of the electric vehicle;

[0030] The first-level scheduling objective function is:

[0031]

[0032] Where f1 represents the target of minimizing the exchange power between the charging station and the grid in each time slot, P all,t represents the total power of the charging station at time t, E ev,t is the energy of all electric vehicles at time t, P pv,t represents the photovoltaic power generation at time t, P pv,t It means that at time t, P wind,t represents the wind turbine power generation at time t, P load,t represents the base load at time t;

[0033] The first-level scheduling constraints are:

[0034]

[0035] The charging and discharging cost objective function is:

[0036]

[0037] Among them, f2 is the minimum charging and discharging cost, b c The decision variables for selecting charging stations, N ev represents the total number of electric vehicles, P ev,n,t represents the effective charging and discharging power of the nth electric vehicle at time t, is the dynamic electricity price, t d is the unit time;

[0038] The battery degradation cost objective function is:

[0039]

[0040] Among them, f3 represents the minimum battery degradation cost;

[0041] The power balance constraint in the charging station is:

[0042]

[0043] Among them, P ess,t Indicates the charging and discharging power of the energy storage device;

[0044] The constraints for meeting the charging needs of electric vehicle users and the charging process are:

[0045]

[0046] t n,d =t n,now +t od,n ,

[0047]

[0048] in, represents the energy demand of the nth electric car, represents the state of charge required by the nth electric vehicle, Cap represents the initial state of charge when the nth electric vehicle makes a charging request. ev,n represents the battery capacity of the nth electric car; t n,now represents the current time of the nth electric vehicle at the charging station, t od,n Indicates the remaining time for the nth electric car to leave; Indicates the minimum state of charge of an electric vehicle, Indicates the maximum state of charge of an electric vehicle, e ev,n Indicates the charging amount of the nth electric vehicle during its stay at the charging station, SOC ev,n,trepresents the state of charge of the nth electric car at time t, Indicates the state of charge of the nth electric vehicle when it leaves the charging station.

[0049] The method for establishing the third-level scheduling objective function is as follows: based on the traffic network data, a spatiotemporal dynamic energy consumption estimation model is established according to the combination of macro and micro, fluid dynamics and the longitudinal dynamics of electric vehicles; a time cost calculation model is established based on the traffic network data; an energy consumption cost calculation model is established based on the spatiotemporal dynamic energy consumption estimation model, the time cost calculation model and the detailed data of electric vehicles; a traffic cost objective function is established based on the time cost calculation model and the energy consumption cost calculation model; and the third-level scheduling objective function is established by combining the charging and discharging cost objective function, the battery degradation cost objective function and the traffic cost objective function.

[0050] The method for establishing a spatiotemporal dynamic energy consumption estimation model is as follows: obtaining historical traffic flow information and map information in traffic network data, dividing the traffic network section into equal distances with a unit length of Δx, and obtaining at least one section microelement m; for the section microelement m, predicting the macro speed and acceleration of the electric vehicle in the future target section microelement m according to historical traffic flow information; using the macro speed and acceleration of the electric vehicle and combining the energy conversion process in the electric vehicle with the longitudinal dynamics equation of the electric vehicle to obtain the driving resistance of the electric vehicle; and obtaining a spatiotemporal dynamic energy consumption estimation model based on the driving resistance of the electric vehicle.

[0051] The macroscopic speed and acceleration are:

[0052]

[0053] Where A represents the vehicle macro acceleration, x m represents the distance, V represents the macroscopic speed of the vehicle, A local represents the local acceleration, A conv represents the acceleration of the convection term;

[0054] The driving resistance of the electric vehicle is:

[0055]

[0056] in, and They represent the driving resistance, slope resistance, rolling resistance, air resistance and acceleration resistance of the nth electric vehicle at time t respectively; M n is the total mass of the nth electric car; g, β t are gravity acceleration and road slope respectively; f r,n , C D,n , are the friction coefficient, drag coefficient and frontal area of ​​the nth electric car respectively; ρair is the air density; δ n is the rotational mass conversion coefficient of the nth electric vehicle.

[0057] The spatiotemporal dynamic energy consumption estimation model is expressed as:

[0058]

[0059] sta ij ∈{0,1},i,j∈Ω N

[0060] Among them, E od,n,t is the expected energy consumption of the nth electric vehicle on the od pair at time t, where the starting point of the od pair refers to the intersection where the electric vehicle sends the request and the destination is the charging station. ij is the length of the road section (i, j); a ij is the decision variable for selecting the road segment (i, j), δ represents the time conversion factor, Ω N Represents a node set in the transportation network model in the three-layer architecture model;

[0061] The time cost calculation model is:

[0062]

[0063] Among them, t od,n represents the time cost of the nth electric car on the od pair, R od represents the set of roads in the od pair, V tf (x m ,t) represents the speed of the nth electric car at the moment t on the road segment microelement m;

[0064] The energy consumption cost calculation model is:

[0065]

[0066] Among them, η e,n is the generator efficiency; η m,n is the mechanical power system efficiency, P aux It is the additional energy consumption caused by external factors such as weather.

[0067] The transportation cost objective function is:

[0068]

[0069] Among them, C TN is the minimum transportation cost, is the energy cost coefficient; is the time cost coefficient;

[0070] The third-level scheduling objective function is:

[0071]

[0072] Among them, C ev is the minimum total cost for electric vehicle users;

[0073] The third-layer scheduling constraints are:

[0074]

[0075] b c ∈{0,1},c∈Ω N

[0076] a ij b c =1,j=c

[0077] Among them, c is the charging station node, b c Decision variables for charging station selection.

[0078] The beneficial effects of the present invention are as follows: the present invention is applicable to the charging scheduling of a large number of electric vehicles in the transportation-distribution network, thereby improving the utilization rate of renewable energy, improving the ability to cope with the uncertainty of charging scheduling, utilizing the energy attributes of electric vehicles and reducing the total charging cost of users. The invention has three advantages. First, the aggregation of electric vehicle charging flexibility can reduce the impact of charging station charging load on the main power grid, thereby maintaining its stable operation; second, the power allocation mode of first totaling and then dividing reduces the computational complexity of the recursive multi-cycle problem, that is, the process of optimizing charging decisions in multiple time periods; finally, by considering various factors of the two networks at the same time, the total cost of users is reduced so that both networks benefit at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0080] Figure 1 It is a flow chart of the present invention.

[0081] Figure 2 It is a schematic diagram of the model structure of the present invention.

[0082] Figure 3 It is a spatiotemporal dynamic change diagram of vehicles in different motion states in the traffic flow of the present invention.

[0083] Figure 4 This is a diagram showing the power distribution results of the polymer electric vehicle of the present invention.

[0084] Figure 5 This is a diagram of the charging scheduling results of 100 electric vehicles distributed at different nodes according to the present invention.

[0085] Figure 6 This is a comparison chart of the number of times congestion occurs for different navigation methods of the present invention.

[0086] Figure 7 Comparison chart of cost comparison results of the present invention. DETAILED DESCRIPTION

[0087] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0088] like Figure 1 As shown, an economic dispatch strategy based on the spatiotemporal flexibility of electric vehicle charging in the transportation-distribution network includes the following steps:

[0089] S1: Design the overall goal of economic dispatch and establish a three-layer architecture model; collect distribution network data of multiple charging stations, detailed data of electric vehicles and charging requirements, and transportation network data.

[0090] The overall goal of economic dispatch is to minimize the exchange power between the charging station and the power grid in each time slot while minimizing the total cost of electric vehicle users. The total cost of electric vehicle users includes the charging cost of electric vehicle users and the transportation cost of electric vehicle users.

[0091] Establish a three-tier architecture model, such as Figure 2 shown.

[0092] The first and second layer architecture models are distribution network models. The first layer distribution network model consists of at least one charging station, which includes photovoltaic power generation equipment, wind power generation equipment, energy storage devices, charging facilities and basic power facilities. These devices are uniformly managed and dispatched by the control center.

[0093] The second layer of the distribution network model consists of charging facilities.

[0094] The third-layer architecture model is the traffic network model, which is a directed graph G TN =(Ω N ,Ω E ), where Ω Nis a node set, representing intersections and charging stations on urban roads; Ω E is an edge set, representing the roads between intersections and between intersections and charging stations; arc R i,j ∈Ω E ,i,j∈Ω N Represents the road from node i to node j.

[0095] Distribution network data includes: day-ahead forecasts of photovoltaic power generation, wind power generation and base load, energy storage equipment capacity and charging pile power limits, etc.

[0096] S2: In the first layer of the architecture model, EV charging requirements are aggregated and EV charging flexibility model is established.

[0097] Electric vehicle charging flexibility refers to the adjustability of when, where and how electric vehicles are charged; electric vehicle charging flexibility includes spatial flexibility, temporal flexibility and charging and discharging flexibility.

[0098] Spatial flexibility means that electric vehicles can choose to go to different charging stations and charge according to location convenience, time or price; temporal flexibility means that electric vehicles can choose to charge at different time periods to avoid peak hours or take advantage of low electricity price periods; charging and discharging flexibility means that the charging method (V2G) and speed can be adjusted according to battery needs or time constraints.

[0099] The method for aggregating electric vehicle charging requirements and establishing an electric vehicle charging flexibility model is:

[0100] 2.1 Based on the charging requirements of electric vehicles, electric vehicle users are classified into: electric vehicles (EV) without charging flexibility; electric vehicles with charging flexibility.

[0101] Electric vehicles with charging flexibility include: when the electric vehicle user arrives at the charging station, its energy does not meet the minimum discharge requirement; when the electric vehicle user arrives at the charging station, its energy meets the minimum discharge requirement.

[0102] 2.2: Calculate the energy boundary and effective charge and discharge power boundary of electric vehicles. Aggregate the energy boundary and effective charge and discharge power boundary of electric vehicles based on the electric vehicle user classification in step 2.1, and finally obtain the electric vehicle charging flexibility model.

[0103] The calculation method of the energy boundary of the electric vehicle is:

[0104]

[0105] in, is the upper bound of the energy of the nth electric car at time t, is the lower bound of the energy of the nth electric car at time t, t n,a represents the time when the nth electric car arrives at the charging station, t n,d represents the time when the nth electric car leaves the charging station, p ev,n,c Represents the rated charging power of electric vehicles, η ev,c represents the charging efficiency of electric vehicles, p ev,n,d is the rated discharge power of the electric vehicle, η ev,d represents the discharge efficiency of electric vehicles, is the maximum energy of the nth electric car, is the minimum energy of the nth electric car, Δt represents the time interval of each calculation, and is the energy of the nth electric car when it leaves, is the energy demand of the nth electric car.

[0106] The calculation method of the effective charge and discharge power boundary of the electric vehicle is:

[0107]

[0108] in, represents the upper limit of the effective charging power of electric vehicles, It represents the lower bound of the effective discharge power of electric vehicles.

[0109] The method for aggregating the energy boundary and effective charge and discharge power boundary of electric vehicles is:

[0110] For electric vehicles without charging flexibility as described:

[0111] Electric vehicle users do not stay long enough at charging stations to meet their charging needs, so they need to continue charging at the maximum effective charging power, which is the upper limit of the effective charging and discharging power of electric vehicles. The effective charging power of electric vehicles without charging flexibility is expressed as:

[0112]

[0113] in, Represents the effective charging power of the nth electric vehicle without charging flexibility.

[0114] The effective charging power of all electric vehicles without charging flexibility is expressed as:

[0115]

[0116] For the electric vehicles described as having charging flexibility:

[0117] The electric vehicle user's stay time at the charging station can meet his charging needs and is sufficient, so he can perform charging and discharging operations, while maintaining the stable operation of the power grid and reducing the charging cost. When the electric vehicle user arrives at the charging station and his energy does not meet the minimum discharge requirement, he needs to charge first and then perform a two-way energy exchange between the electric vehicle and the power grid (V2G, Vehicle-to-Grid). The present invention is mainly aimed at all electric vehicles with charging flexibility.

[0118] The energy boundaries and effective charging and discharging power boundaries of all electric vehicles with charging flexibility during charging and discharging are:

[0119]

[0120] in, represents the upper and lower bounds of the energy of all electric vehicles with charging flexibility when charging and discharging at time t, It represents the upper and lower bounds of the effective charging and discharging power of all electric vehicles with charging flexibility at time t.

[0121] The effective charging power formula of all electric vehicles without charging flexibility and the energy boundary and effective charging and discharging power boundary formula of all electric vehicles with charging flexibility during charging and discharging together constitute the electric vehicle charging flexibility model.

[0122] S3: Establish the first-level dispatch model based on the overall economic dispatch goal, electric vehicle charging flexibility model and distribution network data.

[0123] As shown in Question 1, the first-level scheduling objective function is established with the goal of minimizing the exchange power between the charging station and the power grid in each time slot. The energy boundaries and effective charging and discharging power boundaries of all electric vehicles with charging flexibility are used as constraints to establish the first-level scheduling constraints and obtain the first-level scheduling model.

[0124] The first-level scheduling objective function is:

[0125]

[0126] Where, f1 represents the target of minimizing the exchange power between the charging station and the grid in each time slot, P all,t represents the power to be allocated to all electric vehicles at time t, that is, the total power of the charging station at time t, P all,t =(E ev,t+1 -E ev,t ) / t0, t0 is the time interval, E ev,t is the energy of all electric vehicles at time t, P pv,t represents the photovoltaic power generation at time t, P pv,t It means that at time t, P wind,trepresents the wind turbine power generation at time t, P load,t represents the base load at time t, P pv,t , P pv,t , P wind,t Average P load,t All are day-ahead forecasts.

[0127] The first-level scheduling constraints include electric vehicle energy constraints and charging station total power constraints, which are:

[0128]

[0129] Among them, the energy constraint is to consider the capacity of energy storage equipment.

[0130] S4: In the second-level architecture model, a second-level scheduling model is established based on the overall economic scheduling goal and detailed electric vehicle data.

[0131] As shown in Question 1, with the goal of minimizing the charging cost of electric vehicle users, the second-level scheduling objective function and second-level scheduling constraints are established based on the detailed data of electric vehicles to obtain the second-level scheduling model.

[0132] The second-level scheduling objective function includes the charging and discharging cost objective function and the battery degradation cost objective function.

[0133] The charging and discharging cost objective function is:

[0134]

[0135] Among them, f2 is the charging and discharging cost target, that is, the minimum charging and discharging cost, b c The decision variables for selecting charging stations, N ev represents the total number of electric vehicles, P ev,n,t represents the effective charging and discharging power of the nth electric vehicle at time t, P ev,n,t Indicates the effective charging and discharging power of an electric vehicle with charging flexibility or the effective charging and discharging power of an electric vehicle without charging flexibility is the dynamic electricity price, t d As unit time.

[0136] Appropriate charging methods can reduce battery degradation and increase battery life. The degradation cost of a battery cell is related to the effective charge and discharge power P of an electric vehicle at time t. ev,n,t Its battery capacity Cap ev,n Related.

[0137] Battery degradation cost per electric vehicle Π n (P ev,n,t )for:

[0138]

[0139] Among them, π1 represents a parameter based on the price of the battery unit ($ / kWh), π2 represents the internal parameters of the battery (voltage, current), and π3 represents the state of charge.

[0140] The battery degradation cost objective function is:

[0141]

[0142] Among them, f3 represents the battery degradation cost target, that is, the minimum battery degradation cost.

[0143] The second level of scheduling constraints include: power balance constraints in charging stations, constraints on meeting the charging needs of electric vehicle users and the charging process.

[0144] During the effective charging and discharging power distribution process of electric vehicles, the power balance constraint in the charging station is:

[0145]

[0146] Among them, P ess,t Indicates the charging and discharging power of the energy storage device.

[0147] The constraints to meet the charging needs of electric vehicle users and the charging process are:

[0148]

[0149] t n,d =t n,now +t od,n ,

[0150]

[0151] in, represents the energy demand of the nth electric car, represents the state of charge required by the nth electric vehicle, Cap represents the initial state of charge when the nth electric vehicle makes a charging request. ev,n represents the battery capacity of the nth electric car; t n,now represents the current time of the nth electric vehicle at the charging station, t od,n Indicates the remaining time for the nth electric car to leave; Indicates the minimum state of charge of an electric vehicle, Indicates the maximum state of charge of an electric vehicle, e ev,n Indicates the charging amount of the nth electric vehicle during its stay at the charging station, SOC ev,n,t represents the state of charge of the nth electric car at time t, Indicates the state of charge of the nth electric vehicle when it leaves the charging station.

[0152] S5: In the third-layer architecture model, a third-layer scheduling model is established based on the overall economic scheduling goal, the third-layer scheduling model and the transportation network data.

[0153] As shown in Question 1, with the goal of minimizing the total cost of electric vehicle users, the third-level scheduling objective function and third-level scheduling constraints are established based on the traffic network data and the second-level scheduling objective function to obtain the third-level scheduling model.

[0154] The method for establishing the third-level scheduling objective function is as follows: based on the traffic network data, a spatiotemporal dynamic energy consumption estimation model is established according to the combination of macro and micro, fluid dynamics and the longitudinal dynamics of electric vehicles; a time cost calculation model is established based on the traffic network data; an energy consumption cost calculation model is established based on the spatiotemporal dynamic energy consumption estimation model, the time cost calculation model and the detailed data of electric vehicles; a traffic cost objective function is established based on the time cost calculation model and the energy consumption cost calculation model; and the third-level scheduling objective function is established by combining the charging and discharging cost objective function, the battery degradation cost objective function and the traffic cost objective function.

[0155] Specifically, the historical traffic flow information in the transportation network (TN) data is obtained, and the traffic network section is divided into equal distances with a unit length of Δx to obtain at least one section microelement m; for the section microelement m, the macro speed and acceleration of the electric vehicle in the future target section microelement m are predicted based on the historical traffic flow information. Figure 2 Here, RM represents the road segment element.

[0156] Since the macro traffic conditions can propagate in both upstream and downstream directions, the traffic state of the target road segment microelement m will be affected by the traffic state of the upstream and downstream adjacent road segments. Using fluid dynamics to analyze the changes in traffic flow on the road segment, the macro acceleration is divided into two parts:

[0157]

[0158] Where A represents the vehicle macro acceleration, x m represents the distance, V represents the macroscopic speed of the vehicle, A local represents the local acceleration, reflecting the change of traffic speed at a fixed position over time; A conv represents the acceleration of the convection term, reflecting the spatial changes of the traffic flow. Figure 3 As shown, where S local and S conv Represent the two states of the electric vehicle, S local Indicates the state of an electric vehicle transferring from one road segment microelement to another road segment microelement, which is represented by the local acceleration A localand the average speed of electric vehicles, S conv It represents the state change of electric vehicles in the same road section, which is represented by the convection acceleration A conv and the average speed of electric vehicles.

[0159] Local acceleration A local and the convection acceleration A conv The calculation formula is:

[0160]

[0161] Among them, the local acceleration A local and the convection acceleration A conv Corresponding to vehicles in different motion states, ΔT is the duration, V (m,t) It represents the average speed of the road segment microelement m at time t. According to the macro acceleration formula, the macro speed V of the electric car in the road segment microelement m at time t can be obtained. n,t and the macro acceleration A n,t .

[0162] Combining the energy conversion process in an electric vehicle with the longitudinal dynamics equation of the electric vehicle, the driving resistance of the electric vehicle is obtained:

[0163]

[0164] in, and They represent the driving resistance, slope resistance, rolling resistance, air resistance and acceleration resistance of the nth electric vehicle at time t respectively; M n is the total mass of the nth electric car; g, β t are gravity acceleration and road slope respectively; f r,n , C D,n , are the friction coefficient, drag coefficient and frontal area of ​​the nth electric car respectively; ρ air is the air density; δ n is the rotational mass conversion coefficient of the nth electric vehicle.

[0165] The spatiotemporal dynamic energy consumption estimation model of the nth electric vehicle at time t on the od pair (starting point to destination) is expressed as:

[0166]

[0167] sta ij ∈{0,1},i,j∈Ω N

[0168] Among them, E od,n,tis the expected energy consumption of the nth electric vehicle on the od pair at time t, where the starting point of the od pair refers to the intersection where the electric vehicle sends the request, and the destination is the charging station. ij is the length of the road section (i, j); a ij The decision variable for selecting the road section (i, j). When calculating the traffic cost in the three-layer scheduling model, different road section selections affect the traffic cost. The decision variable needs to be selected during the calculation process. δ represents the time conversion factor, which is a fixed value, indicating 3600s.

[0169] Establish a time cost calculation model based on traffic network data:

[0170]

[0171] Among them, t od,n represents the time cost of the nth electric car on the od pair, R od represents the set of roads in the od pair, V tf (x m ,t) represents the speed of the nth electric car on the road segment microelement m at time t.

[0172] Energy consumption cost is established based on spatiotemporal dynamic energy consumption estimation model, time cost calculation model and detailed data of electric vehicles Computational model:

[0173]

[0174] Among them, η e,n is the generator efficiency; η m,n is the mechanical power system efficiency, P aux It is the additional energy consumption caused by external factors such as weather.

[0175] Traffic cost objective function:

[0176]

[0177] Among them, C TN is the transportation cost target, i.e. the minimum transportation cost, is the energy cost coefficient; is the time cost coefficient.

[0178] The third-level scheduling objective function:

[0179]

[0180] Among them, C ev is the third-level scheduling objective, namely, the minimum total cost for electric vehicle users.

[0181] The third-level scheduling constraints are:

[0182]

[0183] b c ∈{0,1},c∈Ω N

[0184] a ij b c =1,j=c

[0185] Among them, c is the charging station node, b c Decision variables for charging station selection.

[0186] S6: Provide charging scheduling solutions for electric vehicle users based on the first-layer scheduling model, the second-layer scheduling model and the third-layer scheduling model.

[0187] Based on the first-level scheduling model, the total power P of the charging station that meets the first-level scheduling objective function is obtained. all,t , based on the second-layer scheduling model and the total power of the charging station P all,t Obtain the individual electric vehicle charging power P that satisfies the minimum charging and discharging cost f2 and the minimum battery degradation cost f3 ev,n , based on the traffic cost objective function, obtain the minimum traffic cost C TN , based on the minimum charging and discharging cost f2, the minimum battery degradation cost f3 and the minimum traffic cost C TN , calculate the minimum total cost of electric vehicle users through the third-layer scheduling model, and obtain the decision variables for charging station selection and road section selection that meet the minimum total cost target for electric vehicle users, select multiple charging stations and repeat step S6, and select the optimal solution that minimizes the exchange power between the charging station and the power grid in each time slot while minimizing the total cost of electric vehicle users, including providing charging station selection for electric vehicle users according to the decision variables for charging station selection, providing navigation path planning for electric vehicle users according to the decision variables for road section selection, and providing the optimal solution for the charging power P of individual electric vehicles according to the decision variables for road section selection. ev,n Provide electric vehicle users with charging and discharging solutions in charging stations.

[0188] In order to demonstrate the superiority of this economic dispatch strategy, an experiment based on the strategy of the present invention was conducted as follows:

[0189] Figure 4, shows the power distribution results of aggregated electric vehicles, showing the feasible areas of energy and power changes. The power and energy values ​​of the model remain within their limits, and the total electric vehicle charging power distribution curve in the figure closely follows the light blue curve (representing photovoltaic power generation + wind power generation - traditional basic load). In certain periods, it makes full use of renewable energy. Due to the intermittent nature of renewable energy, from 10 pm to 6 am, photovoltaic power generation is zero and wind power generation is insufficient to maintain conventional load. During this period, electric vehicles can perform V2G, thereby reducing the need for charging stations to purchase electricity from the main grid and alleviating the impact of charging load on the main grid. This not only ensures the safe operation of the main grid, but also improves the utilization rate of renewable energy.

[0190] Figure 5 The results of charging scheduling for 100 electric vehicles scattered at different nodes are shown to evaluate the impact of this method on the traffic network. Three navigation methods are compared: shortest path (Nm1), shortest time (Nm2) and minimum energy consumption (Nm3). The navigation methods Nm (1-3) in the figure usually concentrate traffic on certain sections of the road. In contrast, this method can distribute traffic more evenly, reduce peak traffic, and alleviate congestion. Figure 5 The number of congestion occurrences of different navigation methods was compared. Whenever the electric vehicle passed through a highly congested section (γ ≥ 86%), the congestion was counted. This method guided the user to avoid congested sections, while the electric vehicles of other navigation methods encountered congestion in multiple sections.

[0191] Figure 6 A comparison chart of the number of congestion occurrences for different navigation methods is provided. Whenever an electric vehicle passes through a highly congested section of road, the congestion is counted. This method guides users to avoid congested sections, while electric vehicles using other navigation methods encounter congestion on multiple sections of road.

[0192] Cost comparison results are as follows Figure 7 As shown in the figure, the benefits of this method for time-dependent distribution networks (T-DN) and electric vehicle users are further verified by comparing it with navigation method Nm4 (aims to minimize charging costs). Charging scheduling was performed for 10 electric vehicles from the same starting point, and the costs included charging, energy consumption, time, and battery degradation. Navigation method Nm 1 ignored traffic conditions and charging station availability, resulting in excessive time waste. Navigation method Nm2 and navigation method Nm3 considered traffic conditions with minimum energy consumption and shortest time, respectively, but did not consider charging costs, resulting in higher total costs. Navigation method Nm 4 ignored traffic conditions and also resulted in higher traffic costs. This method reduces the total cost of users during charging navigation.

[0193] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An economic dispatch strategy based on the spatiotemporal flexibility of electric vehicle charging in a transportation-distribution network, characterized in that: Includes steps: S1: Design the overall goal of economic dispatch and establish a three-layer architecture model; collect distribution network data of multiple charging stations, detailed data of electric vehicles and charging requirements of electric vehicles, and transportation network data; S2: In the first layer of the architecture model, aggregate EV charging requirements and establish an EV charging flexibility model; S3: Establish the first-level dispatch model based on the overall economic dispatch target, electric vehicle charging flexibility model and distribution network data; S4: In the second-level architecture model, the second-level dispatch model is established according to the overall economic dispatch target and detailed data of electric vehicles; S5: In the third-layer architecture model, a third-layer dispatch model is established according to the overall economic dispatch target, the third-layer dispatch model and the transportation network data; S6: Provide charging scheduling solutions for electric vehicle users based on the first-layer scheduling model, the second-layer scheduling model and the third-layer scheduling model.

2. The economic dispatch strategy based on the spatiotemporal flexibility of electric vehicle charging in the transportation-distribution network according to claim 1 is characterized in that: The method for aggregating electric vehicle charging requirements and establishing an electric vehicle charging flexibility model in step S2 is: 2.1 Based on the charging requirements of electric vehicles, electric vehicle users are classified into: electric vehicles without charging flexibility and electric vehicles with charging flexibility; electric vehicles with charging flexibility include: electric vehicles whose energy does not meet the minimum discharge requirements when the electric vehicle user arrives at the charging station and electric vehicles whose energy meets the minimum discharge requirements when the electric vehicle user arrives at the charging station; 2.2 Calculate the energy boundary and effective charge and discharge power boundary of electric vehicles, aggregate the energy boundary and effective charge and discharge power boundary of electric vehicles based on the electric vehicle user classification in step 2.1, and finally obtain the electric vehicle charging flexibility model; The first-layer scheduling model described in step S3 includes a first-layer scheduling objective function and a first-layer scheduling constraint condition; The second-level scheduling model described in step S4 includes a second-level scheduling objective function and a second-level scheduling constraint condition; the second-level scheduling objective function includes a charging and discharging cost objective function and a battery degradation cost objective function; the second-level scheduling constraint condition includes a power balance constraint in the charging station and a constraint on satisfying the charging demand of electric vehicle users and the charging process; The third-layer scheduling model described in step S5 includes a third-layer scheduling objective function and a third-layer scheduling constraint condition.

3. The economic dispatch strategy based on the spatiotemporal flexibility of electric vehicle charging in the transportation-distribution network according to claim 2 is characterized in that: The calculation method of the energy boundary of the electric vehicle is: in, is the upper bound of the energy of the nth electric car at time t, is the lower bound of the energy of the nth electric car at time t, t n,a represents the time when the nth electric car arrives at the charging station, t n,d represents the time when the nth electric car leaves the charging station, p ev,n,c Represents the rated charging power of electric vehicles, η ev,c represents the charging efficiency of electric vehicles, p ev,n,d is the rated discharge power of the electric vehicle, η ev,d represents the discharge efficiency of electric vehicles, is the maximum energy of the nth electric car, is the minimum energy of the nth electric car, Δt represents the time interval of each calculation, and is the energy of the nth electric car when it leaves, is the energy demand of the nth electric car; The calculation method of the effective charge and discharge power boundary of the electric vehicle is: in, represents the upper limit of the effective charging power of electric vehicles, It represents the lower bound of the effective discharge power of electric vehicles.

4. The economic dispatch strategy based on the spatiotemporal flexibility of electric vehicle charging in the transportation-distribution network according to claim 3 is characterized in that: The electric vehicle charging flexibility model described in step S2 is: in, represents the effective charging power of the nth electric vehicle without charging flexibility, P ev,nf,t represents the effective charging power of all electric vehicles without charging flexibility, It represents the upper and lower bounds of the energy of all electric vehicles with charging flexibility when charging and discharging at time t. It represents the upper and lower bounds of the effective charging and discharging power of all electric vehicles with charging flexibility at time t; It represents the upper and lower bounds of the energy of the nth electric vehicle at time t; it represents the upper and lower bounds of the effective discharge power of the electric vehicle; The first-level scheduling objective function is: Where f1 represents the target of minimizing the exchange power between the charging station and the grid in each time slot, P all,t represents the total power of the charging station at time t, E ev,t is the energy of all electric vehicles at time t, P pv,t represents the photovoltaic power generation at time t, P pv,t It means that at time t, P wind,t represents the wind turbine power generation at time t, P load,t represents the base load at time t; The first-level scheduling constraints are:

5. The economic dispatch strategy based on the spatiotemporal flexibility of electric vehicle charging in the transportation-distribution network according to claim 4 is characterized in that: The charging and discharging cost objective function is: Among them, f2 is the minimum charging and discharging cost, b c The decision variables for selecting charging stations, N ev represents the total number of electric vehicles, P ev,n,t represents the effective charging and discharging power of the nth electric vehicle at time t, is the dynamic electricity price, t d is the unit time; The battery degradation cost objective function is: Among them, f3 represents the minimum battery degradation cost; The power balance constraint in the charging station is: Among them, P ess,t Indicates the charging and discharging power of the energy storage device; The constraints for meeting the charging needs of electric vehicle users and the charging process are: t n,d =t n,now +t od,n , in, represents the energy demand of the nth electric car, represents the state of charge required by the nth electric vehicle, Cap represents the initial state of charge when the nth electric vehicle makes a charging request. ev,n represents the battery capacity of the nth electric car; t n,now represents the current time of the nth electric vehicle at the charging station, t od,n Indicates the remaining time for the nth electric car to leave; Indicates the minimum state of charge of an electric vehicle, Indicates the maximum state of charge of an electric vehicle, e ev,n Indicates the charge amount of the nth electric vehicle during its stay at the charging station, SOC ev,n,t represents the state of charge of the nth electric car at time t, Indicates the state of charge of the nth electric vehicle when it leaves the charging station.

6. The economic dispatch strategy based on the spatiotemporal flexibility of electric vehicle charging in the transportation-distribution network according to any one of claims 2 to 5, characterized in that: The method for establishing the third-level scheduling objective function is as follows: based on the traffic network data, a spatiotemporal dynamic energy consumption estimation model is established according to the combination of macro and micro, fluid dynamics and the longitudinal dynamics of electric vehicles; a time cost calculation model is established based on the traffic network data; an energy consumption cost calculation model is established based on the spatiotemporal dynamic energy consumption estimation model, the time cost calculation model and the detailed data of electric vehicles; a traffic cost objective function is established based on the time cost calculation model and the energy consumption cost calculation model; and the third-level scheduling objective function is established by combining the charging and discharging cost objective function, the battery degradation cost objective function and the traffic cost objective function.

7. The economic dispatch strategy based on the spatiotemporal flexibility of electric vehicle charging in the transportation-distribution network according to claim 6 is characterized in that: The method for establishing a spatiotemporal dynamic energy consumption estimation model is as follows: obtaining historical traffic flow information and map information in traffic network data, dividing the traffic network sections into equal distances with a unit length of Δx, and obtaining at least one section microelement m; For the road section micro-element m, the macroscopic speed and acceleration of the electric vehicle in the future target road section micro-element m are predicted based on the historical traffic flow information; the macroscopic speed and acceleration of the electric vehicle are used and the energy conversion process in the electric vehicle is combined with the longitudinal dynamic equation of the electric vehicle to obtain the driving resistance of the electric vehicle; based on the driving resistance of the electric vehicle, a spatiotemporal dynamic energy consumption estimation model is obtained.

8. The economic dispatch strategy based on the spatiotemporal flexibility of electric vehicle charging in the transportation-distribution network according to claim 7 is characterized in that: The macroscopic speed and acceleration are: Where A represents the vehicle macro acceleration, x m represents the distance, V represents the macroscopic speed of the vehicle, A local represents the local acceleration, A conv represents the acceleration of the convection term; The driving resistance of the electric vehicle is: in, and They represent the driving resistance, slope resistance, rolling resistance, air resistance and acceleration resistance of the nth electric vehicle at time t respectively; M n is the total mass of the nth electric car; g, β t are gravity acceleration and road slope respectively; f r,n , C D,n , are the friction coefficient, drag coefficient and frontal area of ​​the nth electric car respectively; ρ air is the air density; δ n is the rotational mass conversion coefficient of the nth electric vehicle.

9. The economic dispatch strategy based on the spatiotemporal flexibility of electric vehicle charging in the transportation-distribution network according to claim 8 is characterized in that: The spatiotemporal dynamic energy consumption estimation model is expressed as: s.t.a ij ∈{0,1},i,j∈Ω N ; Among them, E od,n,t is the expected energy consumption of the nth electric vehicle on the od pair at time t, where the starting point of the od pair refers to the intersection where the electric vehicle sends the request and the destination is the charging station. ij is the length of the road section (i, j); a ij is the decision variable for selecting the road segment (i, j), δ represents the time conversion factor, Ω N Represents a node set in the transportation network model in the three-layer architecture model; The time cost calculation model is: Among them, t od,n represents the time cost of the nth electric car on the od pair, R od represents the set of roads in the od pair, V tf (x m ,t) represents the speed of the nth electric car at the moment t on the road segment microelement m; The energy consumption cost calculation model is: Among them, η e,n is the generator efficiency; η m,n is the mechanical power system efficiency, P aux It is the additional energy consumption caused by external factors such as weather.

10. The economic dispatch strategy based on the spatiotemporal flexibility of electric vehicle charging in the transportation-distribution network according to claim 9 is characterized in that: The transportation cost objective function is: Among them, C TN is the minimum transportation cost, is the energy cost coefficient; is the time cost coefficient; The third-level scheduling objective function is: Among them, C ev is the minimum total cost for electric vehicle users; The third-layer scheduling constraints are: b c ∈{0,1},c∈Ω N ; a ij b c =1,j=c; Among them, c is the charging station node, b c Decision variables for charging station selection.

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