Method and device for calculating load space regulation capability of electric vehicle fast charging station

Abstract

The application provides a kind of electric vehicle fast charging station load space regulation ability calculation method and device, belong to electric vehicle fast charging station regulation and control field.Therein, the method includes: based on vehicle classification result, traffic flow-power flow mapping model is established;Through the traffic equilibrium constraint of mapping model and the power-traffic coupling constraint conversion on capacity scale, the space regulation range constraint of electric vehicle fast charging station load is obtained;Based on the power-traffic coupling constraint and total cost constraint on the time scale of mapping model, the estimation model of response time and regulation cost of electric vehicle fast charging station load is established;Based on space regulation range constraint and estimation model, the space regulation ability result of electric vehicle fast charging station load is obtained.The application takes fast charging station as granularity to react the spatial dimension aggregation flexibility of electric vehicle load group, so that power system can fully utilize the spatial regulation ability of electric vehicle fast charging station load, and provide regulation ability support for new type power system.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle fast charging station control technology, specifically relating to a method and device for calculating the load space adjustment capacity of an electric vehicle fast charging station. Background Technology

[0002] Under the dual-carbon goals and dual-control of energy, the power system's regulation capacity is insufficient. There is an urgent need to transcend the boundaries of the power supply side and tap into the enormous flexibility inherent in the demand side to improve the power system's regulation capacity and the utilization rate of new energy sources, ensuring the energy supply and safe and stable operation of the new power system. On the demand side, electric vehicle (EV) loads have grown rapidly in recent years. By guiding / controlling the charging time of EVs using slow charging, EV load groups can be aggregated into large-scale time-transferable flexibility resources. This time-dimensional aggregation flexibility is mainly reflected in the considerable time regulation capacity of the charging communities and slow charging stations. Simultaneously, by guiding EVs using fast charging to choose fast charging stations in different geographical locations, EV load groups can be aggregated into large-scale spatially transferable flexibility resources. This spatial-dimensional aggregation flexibility is mainly reflected in the considerable spatial regulation capacity of fast charging station loads. However, the spatiotemporal distribution of EV loads is constrained by vehicle user intentions and the complex physical dynamics of the transportation network. How to reasonably characterize the aggregation flexibility of EV load groups is a significant technical challenge in fully utilizing their spatiotemporal regulation capabilities to provide regulation capacity support for the new power system.

[0003] Existing methods for characterizing and utilizing the aggregation flexibility of electric vehicle load groups mainly focus on exploring their temporal adjustment potential, while their exploration of spatial adjustment potential, impact assessment, and utilization is relatively insufficient. A Chinese patent application (CN202111253252.1) entitled "Method, Apparatus, Electronic Device, and Storage Medium for Calculating the Flexibility of Electric Vehicle Groups" proposes a method for calculating the flexibility range of fast-charging load power and energy of electric vehicles within an aggregation area. This method, after obtaining the fast-charging demand forecast results of electric vehicles based on an electric vehicle travel chain model, uses the substation distribution area within the electric vehicle's activity range as the aggregation area. It comprehensively considers the spatial coupling constraints between fast-charging load power in different aggregation areas. However, this method 1) characterizes the aggregation flexibility at the substation's distribution area, which typically includes multiple fast charging stations; 2) does not consider the effect of price incentive signals on users' route selection intentions; and 3) does not consider the time delay in the traffic flow-power flow mapping. Therefore, this method 1) cannot effectively utilize the spatial adjustment capability of each fast charging station's load, 2) cannot effectively utilize the price sensitivity factor of the spatial dimension flexibility of the electric vehicle load group, and 3) cannot effectively utilize the response time factor of the spatial dimension flexibility of the electric vehicle load group. Thus, it fails to achieve effective spatial adjustment of the electric vehicle load group by fast charging stations. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a method and apparatus for calculating the spatial regulation capacity of electric vehicle fast charging stations. This invention uses fast charging stations as the granularity to reflect the spatial dimensional aggregation flexibility of electric vehicle load groups, enabling the power system to fully utilize the spatial regulation capacity of electric vehicle fast charging station loads, thereby providing regulation capacity support for new power systems.

[0005] A first aspect of this invention provides a method for calculating the load space adjustment capacity of an electric vehicle fast charging station, comprising:

[0006] Based on vehicle classification results, a traffic flow-electric flow mapping model is established for any time period. The traffic flow-electric flow mapping model considers the effect of price incentive signals on vehicle users' route selection intentions and the time delay in the traffic flow-electric flow mapping. The traffic flow-electric flow mapping model includes: traffic equilibrium constraints, electric-traffic coupling constraints at the capacity scale, electric-traffic coupling constraints at the time scale, and total cost constraints.

[0007] By converting the traffic equilibrium constraint and the power-traffic coupling constraint on the capacity scale, the spatial adjustment range constraint of the load of electric vehicle fast charging stations is obtained.

[0008] Based on the power-transportation coupling constraints and the total cost constraints at the time scale, an estimation model for the response time and adjustment cost of the electric vehicle fast charging station load during the time period is established.

[0009] Based on the spatial adjustment range constraints and the estimation models for response time and adjustment cost, the spatial adjustment capability of the electric vehicle fast charging station load is obtained.

[0010] In one specific embodiment of the present invention, the input variables of the traffic flow-electricity flow mapping model include ;in, These are the travel origin-destination pairs (OD pairs) during time period t. Traffic flow with charging demand and traffic flow without charging demand between the starting point r and the ending point s; These represent the trip origin-destination pairs (OD pairs) without price incentives during time period t. The The traffic flow with charging demand and the traffic flow without charging demand on the route; These represent the price incentives for vehicles with charging needs and vehicles without charging needs during time period t, respectively. , These are the upper limits for adjusting prices for vehicles with charging needs and vehicles without charging needs, respectively.

[0011] The control variables of the traffic flow-electricity flow mapping model include ;in, These are the trip origin-destination pairs (OD pairs) with price incentives during time period t. The REV and RGV traffic flow on the route;

[0012] The dependent variables of the traffic flow-electricity flow mapping model include , , , , , ;in, Let be the load value of fast charging station i during time period t; It is the weighted sum of the RGV and NGV traffic flows passing through fast charging station i during time period t; Let t be the traffic flow on road a during time period t; This is an estimate of the travel time on road a during time period t; Let be the longest travel time from the starting point to fast charging station i among all feasible paths that pass through fast charging station i during time period t. The total operating cost of all traffic flow in time period t;

[0013] The input parameters of the traffic flow-electricity flow mapping model include , , , , , , , , ;

[0014] in, For fast charging station-path association parameters, if fast charging station i is in the travel OD pair The On the path, then =1, otherwise =0;

[0015] For road-path association parameters, if road a is in the trip OD pair The On the path, then =1, otherwise =0;

[0016] For the road-fast charging station-route association parameters, if both road a and fast charging station i are in the trip OD pair The If a is on a path and road a is between the starting point of the path and fast charging station i, then =1, otherwise =0;

[0017] Among them, in the travel origin-destination pair The There can be at most one fast charging station on this route;

[0018] Estimating relevant empirical constants for road travel time, This represents the number of vehicles that fast charging station i can serve per unit of time. The power consumption of electrical equipment in the fast charging station, excluding charging piles. The average charging power consumption of an electric vehicle. The average economic value of a unit of time spent for a single vehicle user;

[0019] Among them, vehicles with charging needs and vehicles without charging needs include: REV vehicles that have charging needs and are willing to be relocated to fast charging stations on other feasible routes; NEV vehicles that have charging needs and are unwilling to be relocated to fast charging stations on other feasible routes; RGV vehicles that have no charging needs and are willing to be relocated to other feasible routes; and NGV vehicles that have no charging needs and are unwilling to be relocated to other feasible routes.

[0020] In a specific embodiment of the present invention, the traffic equilibrium constraint is expressed as follows:

[0021] (1)

[0022] (2)

[0023] (3)

[0024] (4)

[0025] The power-traffic coupling constraint at the capacity scale is expressed as follows:

[0026] (5)

[0027] (6)

[0028] (7)

[0029] The power-traffic coupling constraint at the aforementioned time scale is expressed as follows:

[0030] (8)

[0031] (9)

[0032] (10)

[0033] The total cost constraint is expressed as follows:

[0034] (11).

[0035] In a specific embodiment of the present invention, the conversion between the traffic equilibrium constraint and the power-traffic coupling constraint at the capacity scale includes:

[0036] 1) By introducing simultaneously on both sides of the equal sign in equation (1) And simultaneously introduce on both sides of the equal sign in (6) In equation (1) Replace with Then equation (1) can be rewritten as equation (12) as shown below:

[0037] (12)

[0038] 2) By introducing simultaneously on both sides of the equal sign in equation (2) And simultaneously introduce on both sides of the equal sign in equation (7) In equation (2) Replace with Then equation (2) can be rewritten as equation (13) as shown below:

[0039] (13)

[0040] 3) By introducing simultaneously on both sides of the equal sign in equation (3) and And according to equation (6), the part in equation (3) Replace with Then equation (3) can be rewritten as equation (14) as shown below:

[0041] (14)

[0042] 4) By introducing simultaneously on both sides of the equal sign in equation (4) and And according to equation (7), the part in equation (4) Replace with Then equation (4) can be rewritten as equation (15) as shown below:

[0043] (15)

[0044] 5) According to equation (6), in equation (5) Replace with Then equation (5) can be rewritten as equation (16) as shown below:

[0045] (16).

[0046] In a specific embodiment of the present invention, obtaining the spatial adjustment range constraint of the electric vehicle fast charging station load includes:

[0047] 1) Establish benchmark values;

[0048] make for The baseline value, express The values ​​of traffic flow distribution without price incentives ;

[0049] make for The baseline value, express The values ​​of traffic flow distribution without price incentives ;

[0050] 2) Establish the adjustment quantity and its corresponding constraints, including:

[0051] make Let i be the adjustment amount of the load of electric vehicle fast charging station i during time period t. and and The relation is constrained by the following formula (17) shows the load regulation constraint of the fast charging station;

[0052] make Let be the weighted value of the RGV traffic flow regulation of electric vehicle fast charging station i passing through any time period t. and and The relation is constrained by equation (18). The constraints on the weighted values ​​of the RGV traffic flow regulation amount are shown below;

[0053] (17)

[0054] (18)

[0055] 3) According to the formula (17) In equation (12) Replace with ,

[0056] Equation (12) can then be rewritten as equation (19) as shown below. :

[0057] (19)

[0058] 4) According to formula (18) In equation (13) Replace with ,

[0059] Equation (13) can then be rewritten as equation (20) as shown below. :

[0060] (20)

[0061] 5) According to the formula (17) The formula in (14) Replace with ,

[0062] Then equation (14) can be rewritten as shown below. (twenty one):

[0063] (twenty one)

[0064] in, Is it with and The relevant packaging parameters, and ;

[0065] 6) According to formula (18) In equation (15) Replace with ,

[0066] Equation (15) can then be rewritten as equation (22) as shown below. :

[0067] (twenty two)

[0068] in, Is it with and The relevant packaging parameters, and ;

[0069] 7) According to the formula (17) The formula in (16) Replace with ,

[0070] Then equation (16) can be rewritten as shown below. (twenty three):

[0071] (twenty three)

[0072] in, Is it with The relevant packaging parameters, and ;

[0073] Equations (19)-(23) constitute the spatial adjustment range constraint of the load of electric vehicle fast charging station.

[0074] In a specific embodiment of the present invention, the step of establishing an estimation model for the response time and adjustment cost of the electric vehicle fast charging station load during the time period, based on the power-traffic coupling constraint and the total cost constraint at the time scale, includes:

[0075] 1) Establish The baseline value is denoted as , express The values ​​taken under traffic flow distribution without price incentives;

[0076] 2) Order Let t be the total adjustment cost of the electric vehicle fast charging station load during time period t. and and The relationship is constrained by the total adjustment cost constraint of the fast charging station load shown in equation (24):

[0077] (twenty four)

[0078] 3) Based on the time-scale power-traffic coupling constraints and total cost constraints of the traffic flow-power flow mapping model, establish an estimation model for the response time and adjustment cost of the electric vehicle fast charging station load in time period t, including:

[0079] Establish a set of pairs of any , The feasible solution for de-aggregating the scheduling results is expressed as follows:

[0080] (25)

[0081] in, This represents the traffic flow regulation amount on road a during time period t;

[0082] This indicates that travel origin-destination (OD) without price incentives... The The allocation factor of traffic flow with charging demand on each route; This indicates that travel origin-destination (OD) without price incentives... The The allocation factor for traffic flow without charging demand on the route; eps is a decimal.

[0083] An estimation model for the response time of the electric vehicle fast charging station load during time period t is established, and the expression is as follows:

[0084] (26)

[0085] in, Is it with The relevant packaging parameters, and ;

[0086] An estimation model for the total adjustment cost of the electric vehicle fast charging station load during time period t is established, and the expression is as follows:

[0087] (27)

[0088] in, Is it with The relevant packaging parameters, and .

[0089] In a specific embodiment of the present invention, obtaining the spatial regulation capability result of the electric vehicle fast charging station load based on the estimation model of the spatial regulation range constraint and the response time and regulation cost includes:

[0090] The input parameters for the estimation models of the spatial adjustment range constraint and the response time and adjustment cost are: , , , Input variables are , , , , , The control variable is , The dependent variable is , ;

[0091] Based on the spatial adjustment range constraint and the estimation model of response time and adjustment cost, the spatial adjustment capability of any electric vehicle fast charging station load is obtained, and its adjustment range is constrained by equations (19)-(23). Any adjustment result , The corresponding response time estimate is expressed by equation (26). The response cost estimate is expressed by equation (27). .

[0092] A second aspect of the present invention provides a device for calculating the load space adjustment capacity of an electric vehicle fast charging station, comprising:

[0093] The mapping model construction module is used to establish a traffic flow-electric flow mapping model for any time period based on vehicle classification results. The traffic flow-electric flow mapping model considers the effect of price incentive signals on vehicle users' route selection intentions and the time delay in the traffic flow-electric flow mapping. The traffic flow-electric flow mapping model includes: traffic equilibrium constraints, electric-traffic coupling constraints at the capacity scale, electric-traffic coupling constraints at the time scale, and total cost constraints.

[0094] The spatial adjustment range constraint acquisition module is used to obtain the spatial adjustment range constraint of the electric vehicle fast charging station load by converting the traffic balance constraint and the power-traffic coupling constraint on the capacity scale.

[0095] The response time and adjustment cost estimation model building module is used to establish an estimation model for the response time and adjustment cost of the electric vehicle fast charging station load during the time period based on the power-traffic coupling constraint and the total cost constraint at the time scale.

[0096] The spatial adjustment module is used to obtain the spatial adjustment capability result of the electric vehicle fast charging station load based on the spatial adjustment range constraint and the estimation model of the response time and adjustment cost.

[0097] A third aspect of the present invention provides an electronic device comprising:

[0098] At least one processor; and a memory communicatively connected to said at least one processor;

[0099] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to perform the above-described method for calculating the load space adjustment capacity of a fast charging station for electric vehicles.

[0100] A fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described method for calculating the load space adjustment capacity of a fast charging station for electric vehicles.

[0101] The features and beneficial effects of this invention are as follows:

[0102] This invention comprehensively considers the effect of price incentive signals on vehicle users' route selection intentions and the time delay in the traffic flow-electricity flow mapping, and establishes a traffic flow-electricity flow mapping model. Based on the constraints of the traffic flow-electricity flow mapping model, it then obtains the spatial adjustment range constraints of electric vehicle fast charging station load and the estimation models for the response time and adjustment cost of electric vehicle fast charging station load, ultimately realizing the estimation of the spatial adjustment capability of electric vehicle fast charging station load. This invention achieves the spatial dimensional aggregation flexibility of electric vehicle load groups reasonably characterized at the granularity of fast charging stations, enabling the power system to fully utilize the spatial adjustment capability of electric vehicle fast charging station load, thereby providing adjustment capability support for the new power system. Attached Figure Description

[0103] Figure 1 This is an overall flowchart of a method for calculating the load space adjustment capacity of a fast charging station for electric vehicles in an embodiment of the present invention. Detailed Implementation

[0104] This invention proposes a method and device for calculating the load space adjustment capacity of electric vehicle fast charging stations, which will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0105] A first aspect of this invention provides a method for calculating the load space adjustment capacity of an electric vehicle fast charging station, comprising:

[0106] Based on vehicle classification results, a traffic flow-electric flow mapping model is established for any time period. The traffic flow-electric flow mapping model considers the effect of price incentive signals on vehicle users' route selection intentions and the time delay in the traffic flow-electric flow mapping. The traffic flow-electric flow mapping model includes: traffic equilibrium constraints, electric-traffic coupling constraints at the capacity scale, electric-traffic coupling constraints at the time scale, and total cost constraints.

[0107] By converting the traffic equilibrium constraint and the power-traffic coupling constraint on the capacity scale, the spatial adjustment range constraint of the load of electric vehicle fast charging stations is obtained.

[0108] Based on the power-transportation coupling constraints and the total cost constraints at the time scale, an estimation model for the response time and adjustment cost of the electric vehicle fast charging station load during the time period is established.

[0109] Based on the spatial adjustment range constraints and the estimation models for response time and adjustment cost, the spatial adjustment capability of the electric vehicle fast charging station load is obtained.

[0110] In a specific embodiment of the present invention, the overall process of the method for calculating the load space adjustment capacity of an electric vehicle fast charging station is as follows: Figure 1 As shown, it includes the following steps:

[0111] 1) Establish a traffic flow-electricity flow mapping model for any time period t. The specific steps are as follows:

[0112] 1-1) Determine the model's input variables, control variables, dependent variable, and input parameters. Specifically:

[0113] 1-1-1) Determine the input variables, including The details are as follows:

[0114] In this embodiment, all vehicles in the traffic flow are divided into four categories: REV (Vehicles with charging demand and with regulation willingness) are defined as vehicles with charging demand and willing to be redirected to fast-charging stations on other feasible routes; NEV (Vehicles with charging demand and without path regulation willingness) are defined as vehicles with charging demand but unwilling to be redirected to fast-charging stations on other feasible routes; RGV (Vehicles without charging demand and with path regulation willingness) are defined as vehicles without charging demand and willing to be redirected to other feasible routes; and NGV (Vehicles without charging demand and without path regulation willingness) are defined as vehicles without charging demand but unwilling to be redirected to other feasible routes. REV and NEV must be electric vehicles; RGV and NGV may include gasoline-powered vehicles.

[0115] make These are the travel origin-destination pairs (OD pairs) during the time period t (1 hour in this example). Traffic flow with and without charging demand between the starting point r and the ending point s, in units of vehicles / hour; These represent the trip origin-destination pairs (OD pairs) without price incentives during time period t. The The traffic flow with and without charging demand on each route, in units of vehicles / hour, can be taken as the distribution value under the Wardrop User Equilibrium principle; These represent the adjusted price incentives for vehicles with charging needs and vehicles without charging needs during time period t, respectively, in yuan / vehicle. ,in, These are the upper limits of the price incentive adjustment for vehicles with charging needs and vehicles without charging needs, respectively. They are all empirical parameters, and the unit is yuan / vehicle.

[0116] 1-1-2) Determine the control variables for the model, including .

[0117] in, These are the trip origin-destination pairs (OD pairs) with price incentives during time period t. The REV and RGV traffic flow on the route, unit: vehicles / hour.

[0118] 1-1-3) Determine the dependent variables of the model, including , , , , , .

[0119] Among them, the dependent variable closely related to the load of electric vehicle fast charging stations is , , The details are as follows:

[0120] The load value of fast charging station i during time period t, in kilowatts; The weighted sum of RGV and NGV vehicle traffic passing through fast charging station i during time period t, in kilowatts; Traffic flow on road a during time period t, in vehicles per hour; This is an estimate of the travel time on road a during time period t, in hours. It represents the longest travel time from the starting point to fast charging station i among all feasible paths passing through fast charging station i during time period t, in hours. This represents the total operating cost of all traffic flow during time period t, expressed in yuan per hour.

[0121] 1-1-4) Determine the input parameters of the model, including , , , , , , , , .

[0122] The correlation parameters between fast charging stations and the road network in the transportation system include:

[0123] Fast charging station - path association parameter (value 0 or 1). If fast charging station i is in the travel OD pair The On this path, =1, otherwise =0.

[0124] : Road-path association parameter (value 0 or 1), if road a is in the trip OD pair The On the path, then =1, otherwise =0.

[0125] : Road-Fast Charging Station-Route Association Parameter (value 0 or 1). If both road a and fast charging station i are in the trip OD pair The On a given path, where road a is between the starting point of the path and fast charging station i, then... =1, otherwise =0.

[0126] Among them, in the travel origin-destination pair The There can be at most one fast charging station on this route.

[0127] Empirical constants related to road travel time are estimated, where, Unit: hour (vehicles / hour) -n , Unit: hour It is usually a small positive integer, for example, it takes the value of 4 in the Bureau of Public Roads function published by the U.S. Department of Transportation. The service capacity of fast charging station i is the number of vehicles that fast charging station i can serve per unit of time, expressed in vehicles per hour. The power consumption of other equipment in the fast charging station (i.e., electrical equipment other than the charging pile, such as lighting equipment) is expressed in kilowatts. Average charging power consumption of an electric vehicle, in kilowatt-hours per vehicle. The average economic value per unit of time spent by a single vehicle user, expressed in yuan per hour per vehicle.

[0128] 1-2) Establish a traffic flow-electricity flow mapping model for time period t. The specific steps are as follows:

[0129] 1-2-1) Construct the traffic equilibrium constraint for time period t, as shown in the following expression:

[0130] (1)

[0131] (2)

[0132] (3)

[0133] (4)

[0134] Equation (1) represents the network equilibrium constraint of REV traffic flow; Equation (2) represents the network equilibrium constraint of RGV traffic flow; Equations (3) and (4) respectively indicate that the traffic flow distribution of REV and RGV is non-negative.

[0135] 1-2-2) Construct the power-traffic coupling constraints on the capacity scale for time period t, as shown in the following expression:

[0136] (5)

[0137] (6)

[0138] (7)

[0139] Equation (5) represents the total sum of REV and NEV traffic flow to fast charging station i, which is constrained by the service capacity of fast charging station i; Equation (6) represents the load composition of fast charging station i, including: the power consumption of other equipment (i.e. electrical equipment other than charging piles, such as lighting equipment) and the charging power consumption of REV and NEV; Equation (7) represents the weighted value of the sum of RGV and NGV traffic flow passing through fast charging station i.

[0140] 1-2-3) Construct the power-transportation coupling constraints on the time scale of time period t, as shown in the following expression:

[0141] (8)

[0142] (9)

[0143] (10)

[0144] Equation (8) represents the traffic flow on each road; Equation (9) represents the estimated travel time on each road. Equation (10) represents the longest travel time from the starting point to the fast charging station i among all feasible paths passing through the fast charging station i in time period t, which is defined in this example as the response time required for load adjustment of the electric vehicle fast charging station i in time period t.

[0145] 1-2-4) Construct the total cost constraint for time period t, as shown in the following expression:

[0146] (11)

[0147] Equation (11) represents the total operating cost of all traffic flow in any time period t, including:

[0148] Total travel time cost of all traffic flow ( ) and the total incentive cost for REV and RGV ( ).

[0149] 2) Using a bottom-up model equivalence transformation method, the traffic equilibrium constraint and capacity-scale power-traffic coupling constraint model established in step 1) are transformed into a spatial regulation characteristic model of the electric vehicle fast charging station load over time period t, so as to obtain the spatial regulation range constraint of the electric vehicle fast charging station load. The specific steps are as follows:

[0150] 2-1) Perform an equivalent transformation on the traffic equilibrium constraints and the power-traffic coupling constraints at the capacity scale established in step 1). The specific steps are as follows:

[0151] 2-1-1) By introducing simultaneously on both sides of the equal sign in equation (1) And simultaneously introduce on both sides of the equal sign in (6) The formula (1) can be used to... Replace with Then equation (1) is rewritten as equation (12). Equation (12) represents the expression... The network equilibrium constraints of the REV traffic flow are described. Equation (12) can characterize the spatial coupling characteristics of the load of electric vehicle fast charging stations in terms of characterizing the external characteristics of the load.

[0152] (12)

[0153] 2-1-2) By introducing simultaneously on both sides of the equal sign in equation (2) And simultaneously introduce on both sides of the equal sign in equation (7) The formula (2) can be used to... Replace with Then equation (2) is rewritten as equation (13). Equation (13) represents the expression... The network equilibrium constraints for RGV traffic flow are described.

[0154] (13)

[0155] 2-1-3) By introducing simultaneously on both sides of the equal sign in equation (3) and And simultaneously, according to equation (6), the equation in equation (3) can be... Replace with Then equation (3) is rewritten as equation (14). Equation (14) represents the expression... The described REV traffic flow distribution is non-negative. Equation (14) characterizes the lower limit of the load of electric vehicle fast charging stations in terms of the external characteristics of the load.

[0156] (14)

[0157] 2-1-4) By introducing simultaneously on both sides of the equal sign in equation (4) and And simultaneously, according to equation (7), the equation (4) can be... Replace with Then equation (4) is rewritten as equation (15). Equation (15) represents the expression... The RGV traffic flow distribution described is non-negative.

[0158] (15)

[0159] 2-1-5) According to equation (6), the equation in equation (5) can be... Replace with Then equation (5) is rewritten as equation (16). Equation (16) represents the expression... The total sum of REV and NEV traffic flows described is constrained by the service capacity of fast charging station i. Equation (16) characterizes the upper limit of the load of electric vehicle fast charging station in terms of the external characteristics of the load.

[0160] (16)

[0161] 2-2) By introducing a benchmark value, and based on the traffic equilibrium constraints and power-traffic coupling constraints at the capacity scale after the transformation in step 2-1), a spatial regulation characteristic model of the electric vehicle fast charging station load in time period t is established to obtain the spatial regulation range constraint of the electric vehicle fast charging station load. The specific steps are as follows:

[0162] 2-2-1) Establish a baseline value. Specifically:

[0163] definition for The benchmark value is in kilowatts. express The values ​​of traffic flow distribution without price incentives .

[0164] definition for The benchmark value is in kilowatts. express The values ​​of traffic flow distribution without price incentives .

[0165] 2.2.2) Establish the adjustment quantity and its corresponding constraints. Specifically:

[0166] definition The adjustment amount of the load of electric vehicle fast charging station i during time period t, in kilowatts. and and The relation is constrained by the following formula (17) shows the fast charging station load regulation constraint.

[0167] definition The weighted value of the RGV traffic flow regulation of electric vehicle fast charging station i passing through any time period t, in kilowatts. and and The relation is constrained by equation (18). The constraints on the weighted values ​​of the RGV traffic flow regulation amount are shown.

[0168] (17)

[0169] (18)

[0170] 2-2-3) According to the formula (17) The formula in (12) Replace with Then equation (12) is rewritten as equation (19). .Mode (19) indicates that with The spatial coupling characteristics of the load at electric vehicle fast charging stations are described. Equation [1] characterizes the spatial regulation characteristics of the load at electric vehicle fast charging stations. (19) Spatial coupling characteristics that can characterize the load regulation capability of electric vehicle fast charging stations.

[0171] (19)

[0172] 2-2-4) According to formula (18) In equation (13) Replace with Then equation (13) is rewritten as equation (20). .Mode (20) indicates that with The network equilibrium constraints for RGV traffic flow are described.

[0173] (20)

[0174] 2-2-5) According to formula (17) The formula In Replace with Then equation (14) is rewritten as equation (21). Equation (21) Characterization with The lower limit of the load of a fast charging station for electric vehicles is described. Among them, Is it with and The relevant packaging parameters, and have Regarding the spatial regulation characteristics of the load on electric vehicle fast charging stations, the formula... (21) It can characterize the upper limit of the load adjustment capability of electric vehicle fast charging stations.

[0175] (twenty one)

[0176] 2-2-6) According to formula (18) In equation (15) Replace with Then equation (15) is rewritten as equation (22). .Mode (22) indicates that with The described RGV traffic flow distribution is non-negative. Wherein, Is it with and The relevant packaging parameters, and have .

[0177] (twenty two)

[0178] 2-2-7) According to the formula (17) can be derived from equation (16). Replace with , then the formula Rewritten as a formula (23). Formula (23) indicates that with The formula describes the upper limit of the load on electric vehicle fast charging stations. Regarding the spatial regulation characteristics of the load on electric vehicle fast charging stations, the equation... (23) It can characterize the upper limit of the load adjustment capability of electric vehicle fast charging stations. Is it with The relevant packaging parameters, and have .

[0179] (twenty three)

[0180] 3) Based on the power-traffic coupling constraints and total cost constraints at the time scale in step 1), an estimation model for the response time and adjustment cost of the electric vehicle fast charging station load in time period t is established. The specific steps are as follows:

[0181] 3-1) Establish a baseline value. Details are as follows:

[0182] Defined as The benchmark value is expressed in yuan per hour. express The values ​​are taken under the traffic flow distribution without price incentives.

[0183] 3-2) Establish the adjustment amount and its corresponding constraints. Specifically:

[0184] Defined as the total adjustment cost of the electric vehicle fast charging station load in any time period t, in yuan / hour. and and The relationship is constrained by the total adjustment cost constraint of the fast charging station load shown in equation (24).

[0185] (twenty four)

[0186] 3-3) Based on the power-transportation coupling constraints and total cost constraints established in step 1), an estimation model for the response time and adjustment cost of electric vehicle fast-charging station load in time period t is established. Specifically:

[0187] 3-3-1) Establish a set of pairs of any , The feasible solution for de-aggregating the scheduling results is expressed as follows:

[0188] (25)

[0189] Equation (25) represents the traffic flow regulation amount on each road. Wherein, This represents the traffic flow regulation on road a during time period t, in units of vehicles per hour. Essentially, It is a set of any , The scheduling results are used to de-aggregate feasible solutions. This indicates that travel origin-destination (OD) without price incentives... The The allocation factor of traffic flow with charging demand on each route. This indicates that travel origin-destination (OD) without price incentives... The The allocation factor for traffic flow without charging demand on a given route. eps is a decimal (it can be the smallest positive number in MATLAB, e.g., eps=2.22044604925031e-016) to effectively handle numerical errors caused by division by zero.

[0190] 3-3-2) Establish an estimation model for the response time of the electric vehicle fast charging station load during time period t, with the following expression:

[0191] (26)

[0192] Based on equations (8)-(10) Equation (26) represents the estimated response time required for load adjustment of electric vehicle fast charging stations. Essentially, it represents the longest travel time from the starting point to fast charging station i among all feasible paths passing through fast charging station i within time period t during the adjustment process. , The upper boundary after that. Among them, Is it with The relevant packaging parameters, and have .

[0193] 3-3-3) Establish an estimation model for the total adjustment cost of the electric vehicle fast charging station load during time period t, as shown in the following expression:

[0194] (27)

[0195] Based on equations (11) and (24), equation (27) represents the estimated total adjustment cost of the electric vehicle fast charging station load. Essentially, it represents the sum of the total traffic cost and total incentive cost behind the electric vehicle fast charging station load during time period t in the adjustment process. , The upper bound of the subsequent change. Wherein, Is it with The relevant packaging parameters, and have .

[0196] 4) Based on the results of steps 2) and 3), the calculation results of the load space adjustment capacity of the electric vehicle fast charging station are obtained.

[0197] In this embodiment, the input parameter is , , , Input variables are , , , , , The control variable is , The dependent variable is , .

[0198] Based on the spatial adjustment range constraint of the electric vehicle fast charging station load obtained in step 2) and the estimation model of the response time and adjustment cost of the electric vehicle fast charging station load obtained in step 3), the spatial adjustment characteristics of any electric vehicle fast charging station load are obtained, and its adjustment range is constrained by equations (19)-(23). Any adjustment result , The corresponding response time estimate is expressed by equation (26). The adjustment cost is estimated as expressed by equation (27). .

[0199] In this embodiment, under any power system dispatching scenario that considers spatial flexibility, the power system operator can directly transfer (19)- , , By embedding this into existing power system dispatch models, the spatial regulation capability of electric vehicle fast charging station loads can be fully utilized, resulting in more optimized power system dispatch outcomes. The control variables related to the electric vehicle fast charging station loads are: , .

[0200] To achieve the above embodiments, a second aspect of the present invention provides a device for calculating the load space adjustment capacity of an electric vehicle fast charging station, comprising:

[0201] The mapping model construction module is used to establish a traffic flow-electric flow mapping model for any time period based on vehicle classification results. The traffic flow-electric flow mapping model considers the effect of price incentive signals on vehicle users' route selection intentions and the time delay in the traffic flow-electric flow mapping. The traffic flow-electric flow mapping model includes: traffic equilibrium constraints, electric-traffic coupling constraints at the capacity scale, electric-traffic coupling constraints at the time scale, and total cost constraints.

[0202] The spatial adjustment range constraint acquisition module is used to obtain the spatial adjustment range constraint of the electric vehicle fast charging station load by converting the traffic balance constraint and the power-traffic coupling constraint on the capacity scale.

[0203] The response time and adjustment cost estimation model building module is used to establish an estimation model for the response time and adjustment cost of the electric vehicle fast charging station load during the time period based on the power-traffic coupling constraint and the total cost constraint at the time scale.

[0204] The spatial adjustment module is used to obtain the spatial adjustment capability result of the electric vehicle fast charging station load based on the spatial adjustment range constraint and the estimation model of the response time and adjustment cost.

[0205] It should be noted that the foregoing explanation of the embodiment of a method for calculating the spatial adjustment capacity of electric vehicle fast charging station load also applies to the electric vehicle fast charging station load spatial adjustment capacity calculation device of this embodiment, and will not be repeated here. According to the embodiment of the present invention, an electric vehicle fast charging station load spatial adjustment capacity calculation device establishes a traffic flow-electricity flow mapping model for any time period based on vehicle classification results. The traffic flow-electricity flow mapping model considers the effect of price incentive signals on vehicle users' route selection intentions and the time delay in the traffic flow-electricity flow mapping. The traffic flow-electricity flow mapping model includes: traffic equilibrium constraints, power-traffic coupling constraints at the capacity scale, power-traffic coupling constraints at the time scale, and total cost constraints. By transforming the traffic equilibrium constraints and the power-traffic coupling constraints at the capacity scale, a spatial adjustment range constraint for the electric vehicle fast charging station load is obtained. Based on the power-traffic coupling constraints at the time scale and the total cost constraint, an estimation model for the response time and adjustment cost of the electric vehicle fast charging station load for the time period is established. Based on the spatial adjustment range constraint and the estimation models for response time and adjustment cost, the spatial adjustment capacity result of the electric vehicle fast charging station load is obtained. This enables the spatial aggregation flexibility of electric vehicle load groups, with fast charging stations as the granularity, allowing the power system to fully utilize the spatial regulation capabilities of electric vehicle fast charging station loads, thus providing a foundation for the regulation capabilities of new power systems.

[0206] To implement the above embodiments, a third aspect of the present invention provides an electronic device, comprising:

[0207] At least one processor; and a memory communicatively connected to said at least one processor;

[0208] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to perform the above-described method for calculating the load space adjustment capacity of a fast charging station for electric vehicles.

[0209] To implement the above embodiments, a fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described method for calculating the load space adjustment capacity of a fast charging station for electric vehicles.

[0210] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0211] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform a method for calculating the load space adjustment capacity of an electric vehicle fast charging station according to the above embodiments.

[0212] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0213] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0214] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0215] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0216] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0217] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0218] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0219] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0220] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for calculating the load spatial adjustment capacity of an electric vehicle fast charging station, characterized in that, include: Based on the vehicle classification results, a traffic flow-electricity flow mapping model is established for any time period. The traffic flow-electricity flow mapping model considers the effect of price incentive signals on vehicle users' route selection intentions and the time delay in the traffic flow-electricity flow mapping. The traffic flow-electricity flow mapping model includes: traffic equilibrium constraints, electric-traffic coupling constraints at the capacity scale, electric-traffic coupling constraints at the time scale, and total cost constraints. By converting the traffic equilibrium constraint and the power-traffic coupling constraint on the capacity scale, the spatial adjustment range constraint of the load of electric vehicle fast charging stations is obtained. Based on the power-transportation coupling constraints and the total cost constraints at the time scale, an estimation model for the response time and adjustment cost of the electric vehicle fast charging station load during the time period is established. Based on the spatial adjustment range constraints and the estimation models of response time and adjustment cost, the spatial adjustment capability of the electric vehicle fast charging station load is obtained. The input variables of the traffic flow-electricity flow mapping model include: ;in, These are the travel origin-destination pairs (OD pairs) during time period t. Traffic flow with charging demand and traffic flow without charging demand between the starting point r and the ending point s; These represent the trip origin-destination pairs (OD pairs) without price incentives during time period t. The The traffic flow with charging demand and the traffic flow without charging demand on the route; These represent the price incentives for vehicles with charging needs and vehicles without charging needs during time period t, respectively. , These are the upper limits for adjusting prices for vehicles with charging needs and vehicles without charging needs, respectively. The control variables of the traffic flow-electricity flow mapping model include ;in, These are the trip origin-destination pairs (OD pairs) with price incentives during time period t. The REV and RGV traffic flow on the route; The dependent variables of the traffic flow-electricity flow mapping model include , , , , , ;in, Let be the load value of fast charging station i during time period t; It is the weighted sum of the RGV and NGV traffic flows passing through fast charging station i during time period t; The traffic flow on road a during time period t; This is an estimate of the travel time on road a during time period t; Let be the longest travel time from the starting point to fast charging station i among all feasible paths that pass through fast charging station i during time period t. The total operating cost of all traffic flow in time period t; The input parameters of the traffic flow-electricity flow mapping model include , , , , , , , , ; in, For fast charging station-path association parameters, if fast charging station i is in the travel OD pair The On the path, then =1, otherwise =0; For road-path association parameters, if road a is in the trip OD pair The On the path, then =1, otherwise =0; For the road-fast charging station-route association parameters, if both road a and fast charging station i are in the trip OD pair The If a is on a path and road a is between the starting point of the path and fast charging station i, then =1, otherwise =0; Among them, in the travel origin-destination pair The There can be at most one fast charging station on this route; Estimating relevant empirical constants for road travel time, This refers to the number of vehicles that fast charging station i can serve per unit of time. The power consumption of electrical equipment in the fast charging station, excluding charging piles. The average charging power consumption of an electric vehicle. The average economic value of a unit of time spent for a single vehicle user; Among them, vehicles with charging needs and vehicles without charging needs include: REV vehicles that have charging needs and are willing to be relocated to fast charging stations on other feasible routes; NEV vehicles that have charging needs and are unwilling to be relocated to fast charging stations on other feasible routes; RGV vehicles that have no charging needs and are willing to be relocated to other feasible routes; and NGV vehicles that have no charging needs and are unwilling to be relocated to other feasible routes. The traffic equilibrium constraint is expressed as follows: （1） （2） （3） （4） The power-traffic coupling constraint at the capacity scale is expressed as follows: （5） （6） （7） The power-traffic coupling constraint at the aforementioned time scale is expressed as follows: （8） （9） （10） The total cost constraint is expressed as follows: （11）。 2. The method according to claim 1, characterized in that, The conversion between the traffic equilibrium constraint and the power-traffic coupling constraint at the capacity scale includes: 1) By introducing simultaneously on both sides of the equal sign in equation (1) And simultaneously introduce on both sides of the equal sign in (6) , in equation (1) Replace with Then equation (1) can be rewritten as equation (12) as shown below: （12） 2) By introducing simultaneously on both sides of the equal sign in equation (2) And simultaneously introduce on both sides of the equal sign in equation (7) In equation (2) Replace with Then equation (2) can be rewritten as equation (13) as shown below: （13） 3) By introducing simultaneously on both sides of the equal sign in equation (3) and And according to equation (6), the part in equation (3) Replace with Then equation (3) can be rewritten as equation (14) as shown below: （14） 4) By introducing simultaneously on both sides of the equal sign in equation (4) and And according to equation (7), the part in equation (4) Replace with Then equation (4) can be rewritten as equation (15) as shown below: （15） 5) According to equation (6), in equation (5) Replace with Then equation (5) can be rewritten as equation (16) as shown below: （16）。 3. The method according to claim 2, characterized in that, The spatial adjustment range constraint for the load of electric vehicle fast charging stations includes: 1) Establish benchmark values; make for The baseline value, express The values ​​of traffic flow distribution without price incentives ; make for The baseline value, express The values ​​of traffic flow distribution without price incentives ; 2) Establish the adjustment quantity and its corresponding constraints, including: make Let i be the adjustment amount of the load of electric vehicle fast charging station i during time period t. and and The relational constraints are as follows: (17) shows the load regulation constraint of the fast charging station; make Let be the weighted value of the RGV traffic flow regulation of electric vehicle fast charging station i passing through any time period t. and and The relation is constrained by equation (18). The constraints on the weighted values ​​of the RGV traffic flow regulation amount are shown below; （17） （18） 3) According to the formula (17) In equation (12) Replace with , Equation (12) can then be rewritten as equation (19) as shown below. : （19） 4) According to formula (18) In equation (13) Replace with , Equation (13) can then be rewritten as equation (20) as shown below. : （20） 5) According to the formula (17) The formula in (14) Replace with , Then equation (14) can be rewritten as shown below. (twenty one): （21） in, Is it with and The relevant packaging parameters, and ; 6) According to formula (18) In equation (15) Replace with , Equation (15) can then be rewritten as equation (22) as shown below. : （22） in, Is it with and The relevant packaging parameters, and ; 7) According to the formula (17) The formula in (16) Replace with , Then equation (16) can be rewritten as shown below. (twenty three): （23） in, Is it with The relevant packaging parameters, and ; Equations (19)-(23) constitute the spatial adjustment range constraint of the load of electric vehicle fast charging station.

4. The method according to claim 3, characterized in that, The estimation model for the response time and adjustment cost of electric vehicle fast charging station load during the time period, based on the power-traffic coupling constraint and the total cost constraint at the time scale, includes: 1) Establish The baseline value is denoted as , express The values ​​taken under traffic flow distribution without price incentives; 2) Order Let t be the total adjustment cost of the electric vehicle fast charging station load during time period t. and and The relationship is constrained by the total adjustment cost constraint of the fast charging station load shown in equation (24): （24） 3) Based on the time-scale power-traffic coupling constraints and total cost constraints of the traffic flow-power flow mapping model, establish an estimation model for the response time and adjustment cost of the electric vehicle fast charging station load in time period t, including: Establish a set of pairs of any , The feasible solution for de-aggregating the scheduling results is expressed as follows: （25） in, This represents the traffic flow regulation amount on road a during time period t; This indicates that travel origin-destination (OD) without price incentives... The The allocation factor of traffic flow with charging demand on each route; This indicates that travel origin-destination (OD) without price incentives... The The allocation factor for traffic flow without charging demand on the route; eps is a decimal. An estimation model for the response time of the electric vehicle fast charging station load during time period t is established, and the expression is as follows: （26） in, Is it with The relevant packaging parameters, and ; An estimation model for the total adjustment cost of the electric vehicle fast charging station load during time period t is established, and the expression is as follows: （27） in, Is it with The relevant packaging parameters, and .

5. The method according to claim 4, characterized in that, The estimation model based on the spatial adjustment range constraint and the response time and adjustment cost yields the spatial adjustment capability result of the electric vehicle fast charging station load, including: The input parameters for the estimation models of the spatial adjustment range constraint and the response time and adjustment cost are: , , , Input variables are , , , , , The control variable is , The dependent variable is , ; Based on the spatial adjustment range constraint and the estimation model of response time and adjustment cost, the spatial adjustment capability of any electric vehicle fast charging station load is obtained, and its adjustment range is constrained by equations (19)-(23). Any adjustment result , The corresponding response time estimate is expressed by equation (26). The response cost estimate is expressed by equation (27). .

6. A device for calculating the load space adjustment capacity of an electric vehicle fast charging station, characterized in that, include: The mapping model construction module is used to establish a traffic flow-electric flow mapping model for any time period based on vehicle classification results. The traffic flow-electric flow mapping model considers the effect of price incentive signals on vehicle users' route selection intentions and the time delay in the traffic flow-electric flow mapping. The traffic flow-electricity flow mapping model includes: traffic equilibrium constraints, electric-traffic coupling constraints at the capacity scale, electric-traffic coupling constraints at the time scale, and total cost constraints. The spatial adjustment range constraint acquisition module is used to obtain the spatial adjustment range constraint of the electric vehicle fast charging station load by converting the traffic balance constraint and the power-traffic coupling constraint on the capacity scale. The response time and adjustment cost estimation model building module is used to establish an estimation model for the response time and adjustment cost of the electric vehicle fast charging station load during the time period based on the power-traffic coupling constraint and the total cost constraint at the time scale. The spatial adjustment module is used to obtain the spatial adjustment capability result of the electric vehicle fast charging station load based on the spatial adjustment range constraint and the estimation model of the response time and adjustment cost. The input variables of the traffic flow-electricity flow mapping model include: ;in, These are the travel origin-destination pairs (OD pairs) during time period t. Traffic flow with charging demand and traffic flow without charging demand between the starting point r and the ending point s; These represent the trip origin-destination pairs (OD pairs) without price incentives during time period t. The The traffic flow with charging demand and the traffic flow without charging demand on the route; These represent the price incentives for vehicles with charging needs and vehicles without charging needs during time period t, respectively. , These are the upper limits for adjusting prices for vehicles with charging needs and vehicles without charging needs, respectively. The control variables of the traffic flow-electricity flow mapping model include ;in, These are the trip origin-destination pairs (OD pairs) with price incentives during time period t. The REV and RGV traffic flow on the route; The dependent variables of the traffic flow-electricity flow mapping model include , , , , , ;in, Let be the load value of fast charging station i during time period t; It is the weighted sum of the RGV and NGV traffic flows passing through fast charging station i during time period t; The traffic flow on road a during time period t; This is an estimate of the travel time on road a during time period t; Let be the longest travel time from the starting point to fast charging station i among all feasible paths passing through fast charging station i during time period t. The total operating cost of all traffic flow in time period t; The input parameters of the traffic flow-electricity flow mapping model include , , , , , , , , ; in, For fast charging station-path association parameters, if fast charging station i is in the travel OD pair The On this path, =1, otherwise =0; For road-path association parameters, if road a is in the trip OD pair The On this path, =1, otherwise =0; For the road-fast charging station-route association parameters, if both road a and fast charging station i are in the trip OD pair The If a is on a path and road a is between the starting point of the path and fast charging station i, then =1, otherwise =0; Among them, in the travel origin-destination pair The There can be at most one fast charging station on this route; Estimating relevant empirical constants for road travel time, This refers to the number of vehicles that fast charging station i can serve per unit of time. The power consumption of electrical equipment in the fast charging station, excluding charging piles. The average charging power consumption of an electric vehicle. The average economic value of a unit of time spent for a single vehicle user; Among them, vehicles with charging needs and vehicles without charging needs include: REV vehicles that have charging needs and are willing to be relocated to fast charging stations on other feasible routes; NEV vehicles that have charging needs and are unwilling to be relocated to fast charging stations on other feasible routes; RGV vehicles that have no charging needs and are willing to be relocated to other feasible routes; and NGV vehicles that have no charging needs and are unwilling to be relocated to other feasible routes. The traffic equilibrium constraint is expressed as follows: （1） （2） （3） （4） The power-traffic coupling constraint at the capacity scale is expressed as follows: （5） （6） （7） The power-traffic coupling constraint at the aforementioned time scale is expressed as follows: （8） （9） （10） The total cost constraint is expressed as follows: （11）。 7. An electronic device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, the instructions being configured to perform the method described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method according to any one of claims 1-5.

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