Electric vehicle adjustable potential evaluation method and system considering traffic flow impact

Abstract

The present invention belongs to the field of power system dispatching and control, and provides a method and system for evaluating the adjustable potential of electric vehicles taking into account the influence of traffic flow. The method uses a probability function of normal distribution to generate different travel demands of fuel vehicles in a traffic network; establishes a traffic flow scenario for fuel vehicles based on a traffic flow equilibrium model and a travel demand matrix; clusters the traffic flow scenarios generated by the traffic flow equilibrium model to obtain typical traffic flow scenarios, and reduces a large number of complex traffic flow scenarios, greatly reducing the amount of calculation of the patented method. The irrational factors of users are taken into account in the selection of charging paths for electric vehicle users, making the proposed method for evaluating the adjustable potential of electric vehicles more flexible. The minimum perceptible difference threshold theory is introduced to construct the relationship between traffic flow and user dispatch response willingness. The impact of traffic flow on the adjustable potential of electric vehicles is taken into consideration.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system dispatching and control, and in particular relates to a method and system for evaluating the adjustable potential of electric vehicles taking into account the influence of traffic flow. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The large-scale promotion of electric vehicles has brought about a large amount of concentrated load demand, and the large-scale access of new energy power generation to the power grid has caused significant fluctuations in power supply, all of which have brought challenges to the safe and stable operation of the power system.

[0004] In this context, strengthening the interaction between electric vehicles and new energy power systems and exploring a friendly interaction model that suits actual requirements, so that electric vehicles can be used as auxiliary adjustment settings for the power grid, has become a feasible option to enhance the stability of the power system. Prior to this, in order to provide a reference for establishing a friendly interaction model between electric vehicles and the power grid, it is necessary to accurately evaluate the adjustable potential of electric vehicles. However, the charging willingness of electric vehicle users is affected by traffic flow, and how to accurately and effectively evaluate the adjustable potential of electric vehicles has become an urgent problem to be solved. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a method and system for evaluating the adjustable potential of electric vehicles considering the influence of traffic flow. The present invention uses a probability function of normal distribution to generate different travel demands of fuel vehicles in the traffic network, and uses a traffic flow equilibrium model to establish a traffic flow model for fuel vehicles. The traffic flow scenes of fuel vehicles are clustered by the kmeans mean clustering method to obtain a typical traffic flow scene of fuel vehicles. A probability model and Monte Carlo simulation method are used to generate a large number of electric vehicle travel conditions. Finally, the chargeable and discharge amounts of electric vehicles under different travel conditions are calculated according to the preset charging strategy to evaluate the adjustable potential of electric vehicles.

[0006] According to some embodiments, a first solution of the present invention provides a method for evaluating the adjustable potential of electric vehicles considering the impact of traffic flow, using the following technical solutions:

[0007] The method for evaluating the adjustable potential of electric vehicles considering the impact of traffic flow includes:

[0008] Use the probability function of normal distribution to generate different travel demands of fuel vehicles in the traffic network, and obtain M travel demand matrices;

[0009] Establish a fuel vehicle traffic flow scenario based on the fuel vehicle traffic flow equilibrium model and travel demand matrix;

[0010] Cluster the traffic flow scenarios of fuel vehicles to obtain typical traffic flow scenarios of fuel vehicles;

[0011] Based on the electric vehicle travel characteristic model and typical fuel vehicle traffic flow scenarios, the Monte Carlo simulation method is used to generate a large number of electric vehicle travel conditions and power conditions;

[0012] Based on the minimum perceptible difference threshold theory, a user response willingness model considering the impact of traffic flow is established;

[0013] Based on the user response willingness model that takes into account the impact of traffic flow and the travel and power conditions of a large number of electric vehicles, the charging scheduling duration and the discharging scheduling duration of electric vehicles are calculated;

[0014] Based on the duration of electric vehicle charging scheduling and the duration of discharging scheduling, the charging adjustable potential and the discharging adjustable potential indicators of the electric vehicle are calculated to evaluate the adjustable potential of the electric vehicle.

[0015] Furthermore, the travel demand matrix represents the number of trips of fuel vehicles between different nodes.

[0016] Furthermore, the objective function of the traffic flow equilibrium model for fuel vehicles is to minimize the converted cost of time cost;

[0017] The constraints of this objective function are travel demand constraints, minimum traffic flow constraints, and path and road demand balance constraints.

[0018] Furthermore, the fuel vehicle traffic flow scenes are clustered to obtain typical fuel vehicle traffic flow scenes, specifically:

[0019] Set the target number of traffic flow scene features to ;

[0020] Select in the traffic flow scene feature The features are used as the initial cluster centers, and the distances from the traffic flow scene features to the cluster centers are calculated;

[0021] For a certain traffic flow scene feature, its category is the category of the cluster center closest to it, so that all traffic flow scene features can be classified and the first clustering is completed;

[0022] Reselect points as cluster centers until the traffic flow scene feature classification and cluster centers do not change;

[0023] get Cluster centers, representing A typical fuel vehicle traffic flow scenario.

[0024] Furthermore, based on the electric vehicle travel characteristic model and the typical fuel vehicle traffic flow scenario, the travel status and power status of a large number of electric vehicles are generated by Monte Carlo simulation method, specifically:

[0025] Establishing a travel characteristics model for electric vehicles;

[0026] Based on the electric vehicle travel characteristic model and typical fuel vehicle traffic flow scenarios, the Monte Carlo simulation method is used to generate a large number of electric vehicle travel conditions and power conditions.

[0027] Furthermore, the establishment of the electric vehicle travel characteristic model is specifically as follows:

[0028] Select the starting node of the electric vehicle from the traffic network nodes except the charging pile nodes;

[0029] Determine the end node of the electric vehicle based on the travel path with the minimum cost to reach the charging pile node;

[0030] Generate the start time of electric vehicles according to the probability model;

[0031] The power consumption of the electric vehicle during the next trip is used to determine the initial charging power requirement of the electric vehicle;

[0032] Determine the initial remaining power of the electric vehicle based on the initial mileage and the power consumption per kilometer of the electric vehicle;

[0033] Determine the starting charging time of the electric vehicle according to the sum of the starting time and the waiting time of the electric vehicle;

[0034] Based on the starting node, ending node, starting charging demand power, starting remaining power and starting charging time of the electric vehicle, a travel characteristic model of the electric vehicle is established.

[0035] Furthermore, based on the minimum perceptible difference threshold theory, a user response willingness model considering the impact of traffic flow is established, specifically:

[0036] The user's willingness to respond is affected by the congestion effect of traffic flow, that is, by the waiting time;

[0037] Based on the minimum perceptible difference threshold theory, when the waiting time is within the threshold, the shorter the waiting time, the higher the user's willingness to respond; when the waiting time is out of the threshold, the user's willingness to respond is saturated;

[0038] Among them, the user's response willingness is divided into charging response probability and discharging response probability.

[0039] Furthermore, the duration of the electric vehicle discharge scheduling is determined according to the state of charge of the electric vehicle required for the next trip, wherein the state of charge of the electric vehicle required for the next trip is specifically:

[0040] ;

[0041] In the formula, It is the state of charge of the electric vehicle battery necessary to meet the next trip; To get The required charging time; is the state of charge of the electric vehicle battery after discharge scheduling; is the maximum capacity of the battery; is the charging power of the electric vehicle; is the charging efficiency of electric vehicles; the charging power required for electric vehicles ; The initial remaining power of the electric vehicle ;

[0042] The duration of electric vehicle charging scheduling is determined according to the state of charge of the electric vehicle after it participates in the charging scheduling. The state of charge of the electric vehicle after it participates in the charging scheduling is specifically:

[0043] ;

[0044] In the formula, It is the state of charge of the electric vehicle after participating in charging scheduling; is the duration of the charging schedule.

[0045] Furthermore, the adjustable potential evaluation index is specifically:

[0046] Charging potential is the product of charging scheduling time and the charging power of the electric vehicle;

[0047] The discharge potential is the product of the discharge scheduling time and the discharge power of the electric vehicle.

[0048] According to some embodiments, a second solution of the present invention provides an electric vehicle adjustable potential assessment system considering the impact of traffic flow, which adopts the following technical solutions:

[0049] The electric vehicle adjustable potential assessment system considering the impact of traffic flow includes:

[0050] A travel demand determination module is configured to generate different travel demands of fuel vehicles in the traffic network using a probability function of a normal distribution, and obtain M travel demand matrices;

[0051] A traffic flow scenario determination module is configured to establish a fuel vehicle traffic flow scenario based on a fuel vehicle traffic flow equilibrium model and a travel demand matrix;

[0052] A typical traffic flow scenario determination module is configured to cluster fuel vehicle traffic flow scenarios to obtain typical fuel vehicle traffic flow scenarios;

[0053] A travel and power status determination module is configured to generate a large number of travel status and power status of electric vehicles through Monte Carlo simulation based on the travel characteristic model of electric vehicles and typical fuel vehicle traffic flow scenarios;

[0054] The user response willingness model building module is configured to build a user response willingness model considering the influence of traffic flow based on the minimum perceptible difference threshold theory;

[0055] A charging point and power scheduling time determination module is configured to calculate the charging scheduling duration and the discharging scheduling duration of the electric vehicle based on a user response willingness model that takes into account the impact of traffic flow and the travel conditions and power conditions of a large number of electric vehicles;

[0056] The adjustable potential evaluation module is configured to calculate the charging adjustable potential and the discharging adjustable potential index of the electric vehicle based on the charging scheduling duration and the discharging scheduling duration of the electric vehicle, and evaluate the adjustable potential of the electric vehicle.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The present invention proposes a method for evaluating the adjustable potential of electric vehicles that takes into account the impact of traffic flow, which is more in line with the path selection and charging selection of electric vehicle users in actual traffic network operation. The traffic flow scenes generated by the traffic flow equilibrium model are clustered using the kmeans mean clustering method to obtain typical traffic flow scenes, which reduces the massive complex traffic flow scenes and greatly reduces the calculation amount of the method of this patent. The irrational factors of users are taken into account in the selection of charging paths for electric vehicle users, making the electric vehicle adjustable potential evaluation method proposed in this patent more flexible. The minimum perceptible difference threshold theory is introduced to construct the relationship between traffic flow and user scheduling response willingness. The impact of traffic flow on the adjustable potential of electric vehicles is taken into consideration. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0060] Figure 1The present invention is a flowchart of an electric vehicle adjustable potential evaluation method considering the impact of traffic flow in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0062] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0063] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0064] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0065] Embodiment 1

[0066] The present embodiment provides a method for evaluating the adjustable potential of electric vehicles taking into account the influence of traffic flow. The present embodiment uses the method applied to a server as an example for illustration. It is understandable that the method can also be applied to a terminal, and can also be applied to a system including a terminal, a server, and a server, and is implemented through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this application. In the present embodiment, the method includes the following steps:

[0067] Use the probability function of normal distribution to generate different travel demands of fuel vehicles in the traffic network, and obtain M travel demand matrices;

[0068] Establish a fuel vehicle traffic flow scenario based on the fuel vehicle traffic flow equilibrium model and travel demand matrix;

[0069] Cluster the traffic flow scenarios of fuel vehicles to obtain typical traffic flow scenarios of fuel vehicles;

[0070] Based on the electric vehicle travel characteristic model and typical fuel vehicle traffic flow scenarios, the Monte Carlo simulation method is used to generate a large number of electric vehicle travel conditions and power conditions;

[0071] Based on the minimum perceptible difference threshold theory, a user response willingness model considering the impact of traffic flow is established;

[0072] Based on the user response willingness model that takes into account the impact of traffic flow and the travel and power conditions of a large number of electric vehicles, the charging scheduling duration and the discharging scheduling duration of electric vehicles are calculated;

[0073] Based on the duration of electric vehicle charging scheduling and the duration of discharging scheduling, the charging adjustable potential and the discharging adjustable potential indicators of the electric vehicle are calculated to evaluate the adjustable potential of the electric vehicle.

[0074] like Figure 1 As shown, the method of this embodiment specifically includes:

[0075] Step 1: Use the probability function of normal distribution to generate different travel demands of fuel vehicles in the traffic network. Travel Demand Matrix . Travel Demand Matrix Characterizes the number of trips of fuel vehicles between different nodes.

[0076] Step 2: Based on the generated Travel Demand Matrix and fuel vehicles, solving the traffic flow equilibrium model in Different travel demand matrices The traffic flow condition that meets the equilibrium condition of fuel vehicle traffic flow under A fuel vehicle traffic flow scenario.

[0077] Step 3: Based on The traffic flow scene of fuel vehicles is obtained by kmeans mean clustering method. A typical fuel vehicle traffic flow scenario.

[0078] Step 4: Establish an electric vehicle travel characteristics model including the starting node, the ending node, the starting time, the starting charging demand, the starting remaining power, and the starting charging time. A typical fuel vehicle traffic flow scenario is used to generate the travel status and power status of a large number of electric vehicles through the Monte Carlo simulation method.

[0079] Step 5: Based on the minimum perceptible difference threshold theory, a user response willingness model considering the impact of traffic flow is established. When the user has no response willingness, it means that the user's electric vehicle does not participate in the vehicle-grid interaction and the electric vehicle has no adjustable potential.

[0080] Step 6: Based on the user response willingness model considering the impact of traffic flow and the Monte Carlo simulation method, the The travel status and power status of a large number of electric vehicles under a typical fuel vehicle traffic flow scenario are calculated by establishing a discharge scheduling model and a charging scheduling model for electric vehicles to calculate the charging scheduling duration and discharge scheduling duration of a large number of electric vehicles.

[0081] Step 7: Based on the charging scheduling duration and discharging scheduling duration of electric vehicles under different fuel vehicle traffic flow scenarios, calculate the charging adjustable potential and discharging adjustable potential indicators of electric vehicles, and evaluate the adjustable potential of electric vehicles.

[0082] The step 1 is specifically as follows:

[0083] Travel Demand Matrix The following definitions can be made:

[0084] (1);

[0085] In the formula, the matrix Indicates that there is The generation of the traffic network of nodes The travel demand between nodes; Indicates In the travel demand matrix, from node To Node The number of fuel-powered vehicles.

[0086] and The following constraints should be met: ; .

[0087] for Travel Demand Matrix The Row, No. Elements of a column It can be generated using the following normal probability distribution function:

[0088] (2);

[0089] In the formula, is the expectation of travel demand. is the standard deviation of travel demand.

[0090] The step 2 is specifically as follows:

[0091] 1) The construction of the fuel vehicle traffic flow equilibrium model is as follows:

[0092] In order to obtain the traffic flow equilibrium model for fuel vehicles, the traffic network and related characteristic quantities need to be defined first.

[0093] The traffic network is represented as follows:

[0094] (3);

[0095] In the formula, is the set of nodes in the transportation network; is the set of roads in the transportation network.

[0096] the way Is a road collection The elements in can be expressed as follows:

[0097] (4);

[0098] In the formula, The starting point is The end point is road.

[0099] The nodes of the traffic network have different characteristics and can be divided into charging pile nodes and general road nodes. In this invention patent, the number of charging pile nodes is recorded as , the set of charging pile nodes is recorded as The number of general road nodes corresponding to this is recorded as , the set of general road nodes can be recorded as .

[0100] The travel demand matrix Recorded as a set .gather Characterizes the travel demand set of fuel vehicles.

[0101] yes The travel demand of fuel vehicles.

[0102] is the set of available paths for fuel vehicles. When all available paths are determined, the following constraints should be met:

[0103] (5);

[0104] In the formula, It is in In each travel scenario, from node To Node The actual number of fuel-powered vehicles on the road, i.e. Traffic flow on .

[0105] yes When, the path Traffic flow on

[0106] The relationship between the available paths for fuel vehicles and the roads in the transportation network is represented by the following formula:

[0107] (6);

[0108] In the formula, when the path Passing Road When Recorded as 1; when the path No road When Recorded as 0.

[0109] In the traffic flow equilibrium model of fuel vehicles, the road congestion effect is also considered. The road congestion effect is expressed by the following function:

[0110] (7);

[0111] In the formula, is the free travel time when the road has zero traffic; It's the road The traffic capacity; It's the road length; It's the road Traffic flow; When the road The traffic flow on When the car passes through the road The time required.

[0112] The final fuel vehicle traffic flow equilibrium model is as follows:

[0113] The objective function of the fuel vehicle traffic equilibrium model is to minimize the converted time cost:

[0114] (8);

[0115] in, When the road a The traffic flow on z When the car passes through the road a the time required; It is the traffic flow x a The integral of E It is the letter expressing the objective function, which represents the converted cost of fuel vehicle transportation time; It is the conversion factor between unit time and cost;

[0116] The fuel vehicle traffic flow equilibrium model has the following constraints:

[0117] Travel demand constraints:

[0118] (9);

[0119] Minimum traffic flow constraints:

[0120] (10);

[0121] Path and road demand balance constraints:

[0122] (11);

[0123] In the formula, It is the conversion factor between unit time and cost; is the path flow vector; is the road flow vector.

[0124] 2) Generation of fuel vehicle traffic flow scenarios:

[0125] For any travel demand matrix , the travel demand matrix Recorded as a set , we can get the following data:

[0126] yes The travel demand of fuel vehicles;

[0127] The travel demand is The set of available paths for fuel vehicles at time ;

[0128] yes When, the path Traffic flow on

[0129] The travel demand is The association between the available routes for fuel vehicles and the roads in the transportation network;

[0130] The travel demand is The passing roada Time required

[0131] Substituting the above data into the fuel vehicle traffic flow equilibrium model and constraints, we can get the road Traffic flow .

[0132] The step 3 is specifically as follows:

[0133] 1) Set the target number of traffic flow scene characteristics to . is the number of cluster centers for kmeans clustering.

[0134] 2) Select from traffic flow scene features Features as initial cluster centers .

[0135] The definition of traffic flow scene features is as follows:

[0136] (12);

[0137] In the formula, It's the road The number of fuel-powered vehicles in the market.

[0138] The distance from the traffic flow scene feature to the cluster center calculated by this method is as follows:

[0139] (13);

[0140] In the formula, Traffic flow scene characteristics To cluster center Manhattan distance.

[0141] 3) For a certain traffic flow scene feature, its category is the category of the cluster center closest to it. The category division of all traffic flow scene features is realized, and the first clustering is completed.

[0142] 4) Return to step 1) and select again points as cluster centers until the classification of sample data features and cluster centers do not change.

[0143] 5) Get Cluster Centers ,represent A typical traffic flow scenario.

[0144] The electric vehicle travel model in step 4 is specifically:

[0145] 1) Starting point of electric vehicles :

[0146] The starting point for electric vehicles It is selected from the traffic network nodes except the charging pile nodes. The selection should meet the following conditions:

[0147] (14);

[0148] In the formula, It is the starting point for electric vehicles In normal node The probability of N is the set of nodes in the transportation network. S It is a collection of charging pile nodes.

[0149] Using Monte Carlo simulation method: Generate random numbers uniformly distributed in the range [0,1] , the region [0,1] is based on Divide into The lengths of each part are , if the random number Belongs to the length , then the electric vehicle starts at the node In normal node .

[0150] 2) The end point of electric vehicles :

[0151] The end point of electric vehicles It is mainly based on the minimum path, that is, choosing the travel path with the minimum cost to reach the charging pile node.

[0152] The minimum path model is as follows:

[0153] The objective function of the minimum path model is to minimize the discounted time cost:

[0154] (15);

[0155] in, When travel needs W , the set of available paths for electric vehicles p The travel time cost here is y Represents traffic volume;

[0156] The travel demand constraints from the starting point to the end point should be met:

[0157] (16);

[0158] In the formula, yes The set of available paths for electric vehicles at that time; ; is the association matrix between nodes and roads. If The starting point of the road is ,but , if The end point of the road is ,but ,otherwise ; y p Is the path p Traffic flow on D w is the travel OD pair when the travel demand is w; It is used to characterize the association between electric vehicle paths and roads in the transportation network. Passing Road 1 if yes, 0 otherwise

[0159] In general, the end node Determined by the calculation result of the minimum path model. However, the end node The choice is also affected by other factors such as the user's subjective factors. The user's final choice of the end node It may not be completely consistent with the calculation result of the minimum path model, and there is a possibility of selecting other nodes as the end node.

[0160] Therefore, the end node The choice can be expressed as follows:

[0161] (17);

[0162] In the formula, is the end node selected according to the minimum path model; is generated in the uniform distribution probability model Values ​​within the interval; It is the end node that is not selected according to the minimum path model.

[0163] are other end nodes obtained by random sampling.

[0164] The sampling should satisfy the following probability model:

[0165] (18);

[0166] In the formula, is the number of charging pile nodes selected As an end node probability.

[0167] Using Monte Carlo simulation method: Generate random numbers uniformly distributed in the range [0,1] , the region [0,1] is based on Divide into The lengths of each part are , if the random number Belongs to the length ,but China Electric Vehicle Starting Point At the charging station node .

[0168] 3) Starting time of electric vehicles

[0169] The start of electric vehicles Generated by a probabilistic model.

[0170] The probability distribution function of the start time of an electric vehicle is as follows:

[0171] (19);

[0172] In the formula, is the starting time of the electric vehicle; is the expected start time; is the standard deviation of the start times.

[0173] Monte Carlo simulation method: Based on the probability distribution function of the starting time of electric vehicles , randomly generate the starting time of electric vehicles .

[0174] 4) Electric vehicle charging demand

[0175] The amount of electricity required for this charging of electric vehicles It is related to the power consumption of the next trip of the electric vehicle. The power consumption of the next trip is related to the next daily mileage. Related.

[0176] Daily mileage of electric vehicles in the present invention patent z Determined by the probability model.

[0177] The probability distribution function of daily mileage is as follows:

[0178] (20);

[0179] In the formula, is the daily mileage of the electric vehicle; is the daily mileage expectation; is the standard deviation of daily mileage.

[0180] Monte Carlo simulation method: Based on the probability distribution function of the daily mileage of electric vehicles , randomly generate daily mileage of electric vehicles .

[0181] 5) The initial remaining power of the electric vehicle :

[0182] The starting remaining power of the electric vehicle Mainly related to mileage, as shown in the following formula:

[0183] (twenty one);

[0184] In the formula, is the initial mileage of the electric vehicle; It is the electricity consumption per kilometer of the electric vehicle.

[0185] Initial mileage It is also generated by the probability distribution function, as shown below:

[0186] (twenty two);

[0187] In the formula, is the initial mileage expectation; is the standard deviation of the initial trip mileage.

[0188] Monte Carlo simulation method: Based on the probability distribution function of the initial mileage of electric vehicles , randomly generate the initial mileage of the electric vehicle .

[0189] 6) Initial charging time of electric vehicles :

[0190] Initial charging time for electric vehicles Affected by the congestion effect of traffic flow, it is shown in the following formula:

[0191] (twenty three);

[0192] In the formula, It is the time interval from the start time to the start charging time.

[0193] In the present invention, Defined as waiting time Therefore, the initial charging time of electric vehicles It can also be expressed as:

[0194] (twenty four).

[0195] The user response willingness model affected by traffic flow in step 5 is specifically:

[0196] User response willingness model:

[0197] The user's willingness to respond is affected by the congestion effect of traffic flow, that is, by the waiting time According to the theory of minimum perceptible difference threshold in psychology. When within the threshold, wait time The shorter the waiting time, the higher the user's willingness to respond. When the threshold is exceeded, users will be obviously unwilling to respond to the dispatch or their willingness to respond will be saturated.

[0198] The user's response willingness is divided into charging response probability and discharge response probability .

[0199] By analyzing historical user response willingness data, it can be found that users tend to respond to charging scheduling and are not inclined to respond to discharging scheduling, because discharging scheduling will affect the service life of electric vehicle batteries and the charging time.

[0200] Charge and discharge response probability and With waiting time The relationship is shown as follows:

[0201] (25);

[0202] In the formula, and The waiting time for electric vehicles to charge The upper and lower limits of the threshold, when the waiting time Less than the lower limit of the charging waiting time When the user's charging response willingness is saturated and reaches the maximum value, Greater than the upper limit of the charging waiting time When , the user's charging response willingness reaches the minimum value; is the minimum charging response probability; is the maximum charging response probability.

[0203] (26);

[0204] In the formula, and They are the waiting time when the electric vehicle is discharged The upper and lower limits of the threshold, when the waiting time Less than the lower limit of the discharge waiting time When the user's charging response willingness is saturated and reaches the maximum value, Greater than the upper threshold of the discharge waiting time When , the user's charging response willingness reaches the minimum value; is the minimum discharge response probability; is the maximum discharge response probability.

[0205] The charging and discharging scheduling model of step 6 is specifically:

[0206] 1) Electric vehicle charging behavior model:

[0207] The initial charging time of an electric vehicle is , the end time of charging of electric vehicles is the start time of electric vehicles , so the maximum charging and discharging time of electric vehicles is obtained ;

[0208] The charging power requirement of electric vehicles is , the initial remaining power is .

[0209] 2) Electric vehicle discharge scheduling model:

[0210] Electric cars have The probability of participating in discharge scheduling;

[0211] When electric vehicles participate in discharge scheduling, they should meet the following constraints:

[0212] (27);

[0213] In the formula, is the state of charge of the electric vehicle battery after discharge scheduling; is the maximum capacity of the battery; is the discharge power of the electric vehicle; is the discharge efficiency of the electric vehicle; is the discharge schedule duration; It is the minimum state of charge that an electric vehicle battery can accept.

[0214] (28);

[0215] In the formula, It is the state of charge of the electric vehicle battery necessary to meet the next trip; To get The required charging time, is the charging power, It is the charging efficiency.

[0216] 3) Electric vehicle charging scheduling model:

[0217] Electric cars have The probability of participating in charging scheduling;

[0218] When electric vehicles participate in charging scheduling, they should meet the following constraints:

[0219] (29);

[0220] In the formula, It is the state of charge of the electric vehicle after participating in charging scheduling; is the duration of the charging schedule.

[0221] 4) Charge and discharge time constraints:

[0222] Charge and discharge time and Should satisfy constraints.

[0223] The adjustable potential evaluation index in step 7 is specifically:

[0224] 1) Charging potential:

[0225] (30);

[0226] In the formula, is the charging schedule duration, It is the charging scheduling potential.

[0227] 2) Discharge potential:

[0228] (31);

[0229] In the formula, is the discharge schedule duration, is the discharge scheduling potential.

[0230] Embodiment 2

[0231] This embodiment provides an electric vehicle adjustable potential assessment system considering the impact of traffic flow, including:

[0232] A travel demand determination module is configured to generate different travel demands of fuel vehicles in the traffic network using a probability function of a normal distribution, and obtain M travel demand matrices;

[0233] A traffic flow scenario determination module is configured to establish a fuel vehicle traffic flow scenario based on a fuel vehicle traffic flow equilibrium model and a travel demand matrix;

[0234] A typical traffic flow scenario determination module is configured to cluster fuel vehicle traffic flow scenarios to obtain typical fuel vehicle traffic flow scenarios;

[0235] A travel and power status determination module is configured to generate a large number of travel status and power status of electric vehicles through Monte Carlo simulation based on the travel characteristic model of electric vehicles and typical fuel vehicle traffic flow scenarios;

[0236] The user response willingness model building module is configured to build a user response willingness model considering the influence of traffic flow based on the minimum perceptible difference threshold theory;

[0237] A charging point and power scheduling time determination module is configured to calculate the charging scheduling duration and the discharging scheduling duration of the electric vehicle based on a user response willingness model that takes into account the impact of traffic flow and the travel conditions and power conditions of a large number of electric vehicles;

[0238] The adjustable potential evaluation module is configured to calculate the charging adjustable potential and the discharging adjustable potential index of the electric vehicle based on the charging scheduling duration and the discharging scheduling duration of the electric vehicle, and evaluate the adjustable potential of the electric vehicle.

[0239] The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment 1. It should be noted that the above modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0240] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0241] The proposed system can be implemented in other ways. For example, the system embodiment described above is only illustrative. For example, the division of the above modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored. Although the above describes the specific implementation of the present invention in conjunction with the accompanying drawings, it is not a limitation on the protection scope of the present invention. Those skilled in the art should understand that on the basis of the technical solution of the present invention, various modifications or deformations that can be made by those skilled in the art without creative labor are still within the protection scope of the present invention.

Claims

1. A method for evaluating the adjustable potential of electric vehicles considering the impact of traffic flow, characterized in that: include: Use the probability function of normal distribution to generate different travel demands of fuel vehicles in the traffic network, and obtain M travel demand matrices; Based on the traffic flow equilibrium model of fuel vehicles and the travel demand matrix, a fuel vehicle traffic flow scenario is established, specifically: The objective function of the traffic flow equilibrium model for fuel vehicles is to minimize the converted cost of time cost: ； in, When the road a The traffic flow on z When the car passes through the road a the time required; It is the traffic flow x a The integral of E It is the letter expressing the objective function, which represents the converted cost of fuel vehicle transportation time; It is the conversion factor between unit time and cost; The traffic flow equilibrium model for fuel vehicles has the following constraints: Travel demand constraints: ； Minimum traffic flow constraints: ； Path and road demand balance constraints: ； In the formula, is the path flow vector; is the road flow vector; For any travel demand matrix , the travel demand matrix Recorded as a set , and get the following data: yes The travel demand of fuel vehicles; The travel demand is The set of available paths for fuel vehicles at time ; yes When, the path Traffic flow on The travel demand is The association between the available routes for fuel vehicles and the roads in the transportation network; The travel demand is When a fuel-powered car passes through the road a the time required; The data is introduced into the fuel vehicle traffic flow equilibrium model and constraints to obtain the road Traffic flow ; Cluster the traffic flow scenarios of fuel vehicles to obtain typical traffic flow scenarios of fuel vehicles; Based on the electric vehicle travel characteristic model and typical fuel vehicle traffic flow scenarios, the Monte Carlo simulation method is used to generate a large number of electric vehicle travel conditions and power conditions; The electric vehicle travel characteristic model is established, specifically: Select the starting node of the electric vehicle from the traffic network nodes except the charging pile nodes; Determine the end node of the electric vehicle based on the travel path with the minimum cost to reach the charging pile node; Generate the start time of electric vehicles according to the probability model; The power consumption of the electric vehicle during the next trip is used to determine the initial charging power requirement of the electric vehicle; Determine the initial remaining power of the electric vehicle based on the initial mileage and the power consumption per kilometer of the electric vehicle; Determine the starting charging time of the electric vehicle according to the sum of the starting time and the waiting time of the electric vehicle; Based on the starting node, ending node, starting charging demand power, starting remaining power and starting charging time of the electric vehicle, a travel characteristic model of the electric vehicle is established; Based on the minimum perceptible difference threshold theory, a user response willingness model considering the impact of traffic flow is established, specifically: The user's willingness to respond is affected by the congestion effect of traffic flow, that is, by the waiting time; Based on the minimum perceptible difference threshold theory, when the waiting time is within the threshold, the shorter the waiting time, the higher the user's willingness to respond; when the waiting time is out of the threshold, the user's willingness to respond is saturated; Among them, the user's response willingness is divided into charging response probability and discharging response probability; According to the user response willingness model considering the impact of traffic flow and the travel status and power status of a large number of electric vehicles, the charging scheduling duration and the discharging scheduling duration of electric vehicles are calculated, specifically: Electric vehicle charging behavior model: The initial charging time of an electric vehicle is , the end time of charging of electric vehicles is the start time of electric vehicles , get the maximum charge and discharge time of electric vehicle ; The charging power requirement of electric vehicles is , the initial remaining power is ; Electric vehicle discharge scheduling model: Electric cars have The probability of participating in discharge scheduling; When electric vehicles participate in discharge scheduling, they meet the following constraints: ； In the formula, is the state of charge of the electric vehicle battery after discharge scheduling; is the maximum capacity of the battery; is the discharge power of the electric vehicle; is the discharge efficiency of the electric vehicle; is the discharge schedule duration; It is the minimum state of charge that an electric vehicle battery can accept; ； In the formula, It is the state of charge of the electric vehicle battery necessary to meet the next trip; To get The required charging time, is the charging power, is the charging efficiency; Electric vehicle charging scheduling model: Electric cars have The probability of participating in charging scheduling; When electric vehicles participate in charging scheduling, they meet the following constraints: ； In the formula, It is the state of charge of the electric vehicle after participating in charging scheduling; is the duration of the charging schedule; Charge and discharge time constraints: Charge and discharge time and satisfy Constraints; Based on the duration of electric vehicle charging scheduling and the duration of discharging scheduling, the charging adjustable potential and the discharging adjustable potential indicators of the electric vehicle are calculated to evaluate the adjustable potential of the electric vehicle.

2. The electric vehicle adjustable potential evaluation method considering traffic flow impact as claimed in claim 1, characterized in that: The travel demand matrix represents the number of trips of fuel vehicles between different nodes.

3. The electric vehicle adjustable potential evaluation method considering traffic flow impact as claimed in claim 1, characterized in that: The objective function of the traffic flow equilibrium model for fuel vehicles is to minimize the converted cost of time cost; The constraints of this objective function are travel demand constraints, minimum traffic flow constraints, and path and road demand balance constraints.

4. The electric vehicle adjustable potential evaluation method considering traffic flow impact as claimed in claim 1, characterized in that: The fuel vehicle traffic flow scenes are clustered to obtain typical fuel vehicle traffic flow scenes, which are specifically: Set the number of traffic flow scene feature targets to ; Select in the traffic flow scene feature The features are used as the initial cluster centers, and the distances from the traffic flow scene features to the cluster centers are calculated; For a certain traffic flow scene feature, its category is the category of the cluster center closest to it, so that all traffic flow scene features can be classified and the first clustering is completed; Reselect points as cluster centers until the traffic flow scene feature classification and cluster centers do not change; get Cluster centers, representing A typical fuel vehicle traffic flow scenario.

5. The electric vehicle adjustable potential evaluation method considering traffic flow impact as claimed in claim 1, characterized in that: Based on the electric vehicle travel characteristic model and the typical fuel vehicle traffic flow scenario, the Monte Carlo simulation method is used to generate a large number of electric vehicle travel conditions and power conditions, specifically: Establish a travel characteristics model for electric vehicles; Based on the electric vehicle travel characteristic model and typical fuel vehicle traffic flow scenarios, the Monte Carlo simulation method is used to generate a large number of electric vehicle travel conditions and power conditions.

6. The electric vehicle adjustable potential evaluation method considering traffic flow impact as claimed in claim 1, characterized in that: The duration of the electric vehicle discharge scheduling is determined according to the state of charge of the electric vehicle required for the next trip, wherein the state of charge of the electric vehicle required for the next trip is specifically: ； In the formula, It is the state of charge of the electric vehicle battery necessary to meet the next trip; To get The required charging time; is the state of charge of the electric vehicle battery after discharge scheduling; is the maximum capacity of the battery; is the charging power of the electric vehicle; is the charging efficiency of electric vehicles; the charging power required for electric vehicles ; The initial remaining power of the electric vehicle ; The duration of electric vehicle charging scheduling is determined according to the state of charge of the electric vehicle after it participates in the charging scheduling. The state of charge of the electric vehicle after it participates in the charging scheduling is specifically: ； In the formula, It is the state of charge of the electric vehicle after participating in charging scheduling; is the duration of the charging schedule.

7. The electric vehicle adjustable potential assessment method considering traffic flow impact as claimed in claim 1, characterized in that: The adjustable potential evaluation index is specifically: Charging potential is the product of charging scheduling time and the charging power of the electric vehicle; The discharge potential is the product of the discharge scheduling time and the discharge power of the electric vehicle.

8. An electric vehicle adjustable potential assessment system considering the impact of traffic flow, characterized by comprising: A travel demand determination module is configured to generate different travel demands of fuel vehicles in the traffic network using a probability function of a normal distribution, and obtain M travel demand matrices; The traffic flow scenario determination module is configured to establish a fuel vehicle traffic flow scenario based on the fuel vehicle traffic flow equilibrium model and the travel demand matrix, specifically: The objective function of the traffic flow equilibrium model for fuel vehicles is to minimize the converted cost of time cost: ； in, When the road a The traffic flow on z When the car passes through the road a the time required; It is the traffic flow x a The integral of E It is the letter expressing the objective function, which represents the converted cost of fuel vehicle transportation time; It is the conversion factor between unit time and cost; The traffic flow equilibrium model for fuel vehicles has the following constraints: Travel demand constraints: ； Minimum traffic flow constraints: ； Path and road demand balance constraints: ； In the formula, is the path flow vector; is the road flow vector; For any travel demand matrix , the travel demand matrix Recorded as a set , and get the following data: yes The travel demand of fuel vehicles; The travel demand is The set of available paths for fuel vehicles at time ; yes When, the path Traffic flow on The travel demand is The association between the available routes for fuel vehicles and the roads in the transportation network; The travel demand is When a fuel-powered car passes through the road a the time required; The data is introduced into the fuel vehicle traffic flow equilibrium model and constraints to obtain the road Traffic flow ; A typical traffic flow scenario determination module is configured to cluster fuel vehicle traffic flow scenarios to obtain typical fuel vehicle traffic flow scenarios; A travel and power status determination module is configured to generate a large number of travel status and power status of electric vehicles through Monte Carlo simulation based on the travel characteristic model of electric vehicles and typical fuel vehicle traffic flow scenarios; The electric vehicle travel characteristic model is established, specifically: Select the starting node of the electric vehicle from the traffic network nodes except the charging pile nodes; Determine the end node of the electric vehicle based on the travel path with the minimum cost to reach the charging pile node; Generate the start time of electric vehicles according to the probability model; The power consumption of the electric vehicle during the next trip is used to determine the initial charging power requirement of the electric vehicle; Determine the initial remaining power of the electric vehicle based on the initial mileage and the power consumption per kilometer of the electric vehicle; Determine the starting charging time of the electric vehicle according to the sum of the starting time and the waiting time of the electric vehicle; Based on the starting node, ending node, starting charging demand power, starting remaining power and starting charging time of the electric vehicle, a travel characteristic model of the electric vehicle is established; The user response willingness model building module is configured to build a user response willingness model that takes into account the impact of traffic flow based on the minimum perceptible difference threshold theory, specifically: The user's willingness to respond is affected by the congestion effect of traffic flow, that is, by the waiting time; Based on the minimum perceptible difference threshold theory, when the waiting time is within the threshold, the shorter the waiting time, the higher the user's willingness to respond; when the waiting time is out of the threshold, the user's willingness to respond is saturated; Among them, the user's response willingness is divided into charging response probability and discharging response probability; The charging point scheduling time determination module is configured to calculate the charging scheduling duration and the discharging scheduling duration of the electric vehicle based on the user response willingness model considering the impact of traffic flow and the travel status and power status of a large number of electric vehicles, specifically: Electric vehicle charging behavior model: The initial charging time of an electric vehicle is , the end time of charging of electric vehicles is the start time of electric vehicles , get the maximum charge and discharge time of electric vehicle ; The charging power requirement of electric vehicles is , the initial remaining power is ; Electric vehicle discharge scheduling model: Electric cars have The probability of participating in discharge scheduling; When electric vehicles participate in discharge scheduling, they meet the following constraints: ； In the formula, is the state of charge of the electric vehicle battery after discharge scheduling; is the maximum capacity of the battery; is the discharge power of the electric vehicle; is the discharge efficiency of the electric vehicle; is the discharge schedule duration; It is the minimum state of charge that an electric vehicle battery can accept; ； In the formula, It is the state of charge of the electric vehicle battery necessary to meet the next trip; To get The required charging time, is the charging power, is the charging efficiency; Electric vehicle charging scheduling model: Electric cars have The probability of participating in charging scheduling; When electric vehicles participate in charging scheduling, they meet the following constraints: ； In the formula, It is the state of charge of the electric vehicle after participating in charging scheduling; is the duration of the charging schedule; Charge and discharge time constraints: Charge and discharge time and satisfy Constraints; The adjustable potential evaluation module is configured to calculate the charging adjustable potential and the discharging adjustable potential index of the electric vehicle based on the charging scheduling duration and the discharging scheduling duration of the electric vehicle, and evaluate the adjustable potential of the electric vehicle.

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