CN110991856B - Electric vehicle charging demand analysis method considering user limitation - Google Patents
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Abstract
The invention discloses an electric vehicle charging demand analysis method considering user finiteness, which comprises the following steps: s1, judging the probability of the activity under the current condition by analyzing the activity attribute, and establishing the transfer relationship between the activity and the trip chain of the traveler; s2, establishing a trip selection probability model on the activity-trip chain of the traveler; s3, obtaining a transfer rule of the traveler on the activity-trip chain and a charging rule of the electric automobile on the activity-trip chain; and S4, analyzing to obtain the daily trip times of the traveler, and combining the daily trip times of the traveler and the charging rule of the electric automobile on each activity-trip chain to obtain the daily charging requirement of the electric automobile. Compared with a statistical analysis method, the method provided by the invention not only can obtain a conclusion with more universal applicability, but also can reveal the internal reasons influencing the change of the charging demand of the electric automobile; compared with an electric vehicle behavior and charging demand analysis method based on complete rationality hypothesis, the method is more reasonable.
Description
Technical Field
The invention relates to the field of electric vehicle operation scheduling and power system load analysis, in particular to an electric vehicle charging demand analysis method considering user finiteness.
Background
As a green travel tool, the electric automobile, which is a fuel-alternative automobile, becomes the main vehicle in the future, and has become the trend of the times development. However, the travel, charging and discharging of large-scale electric vehicles inevitably brings great challenges to the coordinated development of urban traffic and power grids. Traffic jam can be caused, and influences such as deterioration of power quality and power grid stability can be caused. Therefore, the accurate grasp of the time-space behavior and the charging demand change of the electric automobile is the basis for meeting the travel demand of urban residents and ensuring the safe and economic operation of a power grid.
Preliminary research has been done at home and abroad on modeling the space-time behavior and the charging demand of the electric vehicle, and the method mainly focuses on integrating a statistical analysis method, a simulation analysis method and a complete rational hypothesis analysis. On the basis of a statistical analysis method, fitting analysis is carried out on characteristic quantities of travel and charging behaviors of the electric vehicle according to statistical survey data or tracking measurement data; in simulation analysis, dynamic evolution analysis is carried out on the space-time distribution of the charging requirement of the electric automobile by adopting an Agent-cellular automaton or fuzzy deduction-based method; on the basis of complete rational hypothesis analysis, an electric vehicle charging consumption decision model is provided based on the utility maximization principle. In the research work, incomplete rationality of behaviors such as charging, traveling and the like of electric vehicle users in actual situations is not considered, and accurate modeling of the charging load of the electric vehicle is influenced.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an electric vehicle charging demand analysis method considering user limitation aiming at the defects in the prior art, and compared with a statistical analysis method, the electric vehicle charging demand analysis method not only can obtain a conclusion with more universal applicability, but also can reveal the internal reasons influencing the change of the electric vehicle charging demand; compared with the electric vehicle behavior and charging demand analysis method based on complete rationality hypothesis, the method is more reasonable.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the invention provides an electric vehicle charging demand analysis method considering user finiteness, which comprises the following steps:
s1, dividing daily activities-trip behaviors of the traveler into a transfer relation between activity-trip chains and a transfer rule on the activity-trip chains by using an activity-analysis method in time geography, analyzing activity attributes, judging the probability of the activity under the current condition according to the activity attributes, and establishing the transfer relation between the activities and the trip chains of the traveler;
s2, modeling the perception utility of the limited traveler based on the accumulated prospect theory, and simultaneously considering the travel mode, the travel path and the reference point setting of the departure time to obtain a travel selection probability model on the activity-travel chain of the traveler;
s3, considering the dynamic characteristics of the traffic network, and establishing a balancing process of the dynamic traffic network by combining a trip selection probability model and a dynamic traffic balancing model of a rational traveler to obtain a transfer rule of the traveler on an activity-trip chain and a charging rule of the electric automobile on the activity-trip chain;
s4, obtaining daily trip times of the travelers by analyzing the transfer relation between daily activities of the travelers and the trip chains, and combining the daily trip times of the travelers and the charging rule of the electric vehicle on each activity-trip chain to obtain the daily charging demand of the electric vehicle.
Further, the rational limited traveler in step S2 of the present invention represents an actual traveler who cannot grasp all information of the traffic network due to limited cognitive ability and cannot completely find an optimal decision due to limited computing ability, thereby showing the rational limitation.
Further, the specific step of establishing the transition relationship between the activities and the travel chain of the traveler by analyzing the activity attributes and judging the probability of the activity under the current condition according to the activity attributes in step S1 of the present invention includes:
s11, connecting daily activities and trips of travelers by adopting an activity-trip chain based on an activity analysis method, reflecting internal relations between activities and trip arrangements of trips by a system, and describing interdependencies among all trips and space-time constraints of activity trips;
s12, according to daily activities and travel constraints of travelers, dividing the spatio-temporal transfer characteristics of the travelers into spatio-temporal transfer relations between the activity-travel chains and spatio-temporal transfer characteristics on the activity-travel chains;
and S13, dividing activities participated by individuals into two categories of necessary activities and flexible selection activities according to activity demand research of people, wherein the probability of the necessary activities is 1, and when the moment of the flexible selection activity traveler considering all travel modes and travel paths and reaching the activity place earliest under the current environmental condition is less than the expected moment of the activity start, the flexible selection activities occur according to certain conditional probability, otherwise, the activities do not occur.
Further, the specific step of obtaining the travel selection probability model on the activity-travel chain of the traveler in step S2 of the present invention includes:
s21, dividing the decision-making behavior of the traveler into an editing stage and an evaluation stage according to the accumulative foreground theory;
s22, in the editing stage, a traveler compiles based on the cognition of the current road network information, sets a reference point of the trip by integrating various constraint conditions, defines the reference point as 'income' and 'loss' according to the attributes of different trips, and forms subjective probability weight or decision weight about the 'income' and 'loss';
s23, in the evaluation stage, the traveler combines the income and the loss with the subjective probability weight to form the perception utility of different trip selections;
s24, the traveler obtains different decision perception utilities, and the selection behavior description that the optimal decision cannot be found completely in the travel selection is carried out by adopting a selection probability method.
Further, the step S22 of compiling the trip based on the knowledge of the current road network information and setting the reference point of the trip by integrating various constraint conditions includes:
according to analysis of a large amount of actual data, the 'activity starting time' and the 'acceptable earliest arrival time' exist in the activity trip of the traveler, the traveler will obtain 'income' when arriving at the activity place between the two times, and otherwise the traveler is 'lost', so the two times are used as reference points selected by the traveler at the departure time;
an expected optimal arrival time exists between the arrival time periods of the acquired gains, and the traveler arrives at the time of acquiring the maximum gains, so that the time is used as a reference point for path selection;
the most important criterion for the traveler to select the travel is the travel time, and the traveler is more inclined to select a travel mode with higher reliability, so that the travel time budget can be set as a reference point of the travel mode.
Further, the specific steps of obtaining the transition rule of the traveler on the active-travel chain and the charging rule of the electric vehicle on the active-travel chain in step S3 of the present invention include:
s31, considering that a public transportation line in an actual transportation system is fixed and has a special lane, respectively calculating the transfer rule of the public transportation line and the transfer rule of a private car, and obtaining the travel time of the public transportation by the distance and the average speed;
s32, dividing travel time of the private car into charging time and path travel time, wherein two charging scenes are considered during charging time calculation, charging is selected when the remaining capacity cannot meet the remaining mileage travel requirement and the remaining capacity of the electric car is lower than a threshold value in an activity place during travel, charging time of the electric car is obtained, and the path travel time of the private car is described by adopting a function related to road section flow and road section capacity;
s33, establishing a dynamic traffic balance model of the private cars, namely, the flow rate distributed to a certain path by a certain travel demand at any moment is equal to the total travel demand amount and is sequentially multiplied by the proportion of the private cars for traveling on the path at the moment, and meanwhile, the flow rate conservation constraint, the non-negative constraint, the mileage anxiety psychology of the electric car and the charging station charging pile quantity constraint are considered.
Further, the specific step of obtaining the daily charging requirement of the electric vehicle in step S4 of the present invention includes:
and (3) adopting typical activities participated in by local travelers on a daily basis, and sequentially calculating the charging demand of the electric vehicle on an activity-trip chain and the transfer relation between the activity and the trip chain from early work trip to work home-returning activity to obtain the daily participated trip times and daily charging demand of the electric vehicle.
Further, the transition relationship between the activities of the traveler and the travel chain established in step S1 of the present invention is specifically:
the number of activity schedule times per day of the individual i is n, activity-tripThe pattern is expressed as ATPiThe formula is as follows:
ATPi＝fin＝(gi1,gi2,…,gin)
wherein, gijA feature set for a jth activity engaged with an ith actor; f. ofinRepresents the activity-travel pattern for a whole day;
if the jth activity of the traveler is a necessary activity, the probability of the activity occurring is:
P[gij|fi,j-1]＝1
wherein f isi,j-1Historical activity before j activity for the traveler i-trip mode;
if the jth activity of the actor is an unnecessary activity, assume that the acceptable upper limit of the arrival time of the actor i for the activity to participate in is TjThe trip modes selected by the traveler are m types, the earliest time when the traveler reaches the activity place by using the kth type trip mode, and the upper limit of the confidence coefficient theta of the earliest time are respectively Tjk、Tjkθ；
P{Tjk≤Tjkθ}＝θ
The minimum value of the earliest upper limit of the arrival time of all travel modes of the traveler is TjminHaving a value of Tjmin＝min{TjkθK ∈ m }, then the probability of the activity occurring is:
the probability that the traveler participates in the j activity, i.e. the j activity-trip chain is generated as follows:
P(gij)＝P(fi,j-1)·P(gij|fi,j-1)。
further, the selecting a probability model in step S2 of the present invention specifically includes:
wherein the content of the first and second substances,
and muw(t) probability of selecting a trip mode k, a route p and a trip for the traveler at the moment t respectively; the prospect that the travel mode k is selected on the p path at the moment t under the condition that the travel time is distributed continuously
Prospect of selecting path p by kth travel mode at time t
Early arrival foreground
And late to foreground
Where the early-and late-arrival foreground is collectively referred to as the arrival foreground
Further, the dynamic traffic balancing model in step S3 of the present invention specifically indicates:
wherein, the first and the second end of the pipe are connected with each other,
for traffic on the available path p, DwThe trip demand is;
and muw(t) probability of selecting a trip mode k, a route p and a trip for the traveler at time t, respectively.
The invention has the following beneficial effects: according to the method for analyzing the charging requirement of the electric automobile considering the user rationality, 1, the rationality of a traveler in a travel decision process is considered, so that the method is more in accordance with an actual rule, and the obtained space-time behavior and the charging requirement of the electric automobile are more accurate. 2. Since the basic reason of the charging requirement of the electric automobile is taken as a starting point, the decision process and the driving and charging behaviors of a traveler are analyzed in principle, the internal factors influencing the charging requirement change of the electric automobile can be revealed, and effective measures for guiding the travel and the charging of the electric automobile are further made.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a flowchart of an electric vehicle charging demand analysis method and a technical implementation method considering user finiteness according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
As shown in fig. 1, a method for analyzing a charging requirement of an electric vehicle considering user finiteness according to an embodiment of the present invention includes:
and step S1, dividing daily activities-trip behaviors of the traveler into a transition relation between activity-trip chains and a transition rule on the activity-trip chains by using the idea of an activity-analysis method in time geography, and establishing the transition relation between the activities and the trip chains of the traveler by analyzing the activity attributes and judging the probability of the activity under the current condition according to the activity attributes.
The specific process is that according to the three-dimensional space-time expression of the activity behaviors of the traveler, the activity travel behaviors of the traveler are continuous in space-time, and the starting point and the activity time of each activity-travel chain are necessarily influenced by the historical activity-travel chain. For each actor, the behavior attributes describing the day of the actor comprise the times of participating in the activity, the type of the activity, the place of the activity, the departure time and the activityDuration of movement, travel time of the activity and travel mode. If the number of times of activity schedule of an individual i per day is n, the activity-travel pattern is expressed as ATPiThe method comprises the following steps:
ATPi＝fin＝(gi1,gi2,...,gin) (1)
wherein, gijA feature set for a jth activity engaged with an ith actor; f. ofinRepresenting an activity-travel pattern throughout the day.
According to the research of the activities of researchers, the activities participated by individuals are divided into two main categories of necessary activities and flexible selection activities, for example, work and travel are necessary activities, and shopping and friend making are flexible selection activities. Considering that the ATP of travelers varies every day, various patterns may occur with a certain probability. From a probabilistic perspective, the probability of each activity occurring is equal to the conditional probability of the previous activity occurring.
If the jth activity of the traveler is a first type of activity, the probability of the activity occurring is:
P[gij|fi,j-1]＝1 (2)
in the above formula fi,j-1Historical activity before j' th activity for the traveler i-trip mode.
If the jth activity of the actor is of the second type, assume that the acceptable upper limit of the arrival time of the actor i for the activity to participate in is TjThe trip modes selected by the traveler are m types, the earliest time when the traveler reaches the activity place by using the kth type trip mode, and the upper limit of the confidence coefficient theta of the earliest time are respectively Tjk、Tjkθ。
P{Tjk≤Tjkθ}＝θ (3)
The minimum value of the earliest upper limit of the arrival time of all the travel modes of the traveler is TjminHaving a value of Tjmin＝min{TjkθK ∈ m }, then the probability of the activity occurring is:
according to bayesian theory and equations (2) and (4), the probability of the traveler participating in the j-th activity (the j-th activity-trip chain generation) is:
P(gij)＝P(fi,j-1)·P(gij|fi,j-1) (5)
step S2, considering the limited rationality that the actual traveler cannot master all information of the traffic network due to limited cognitive ability and cannot completely find out the optimal decision due to limited computing ability, modeling the perception utility of the limited rational traveler based on the accumulated prospect theory to obtain a selection probability model of the limited rational traveler in the traveling mode, the traveling path and the departure time, and specifically comprising the following steps:
the spatial-temporal transition of the traveler on the activity-trip chain depends on the selection of the trip mode, departure time and trip path. The rationality means that on one hand, due to the limited cognitive ability of people, a decision maker cannot master all information and can only sense a result according to part of mastered information; on the other hand, decision makers tend to pursue "satisfaction" criteria in the decision making, rather than the optimization criteria, due to limited computational power.
The accumulative prospect theory considers the limited rationality of people and divides the selection decision-making behavior of a traveler into two stages of editing and evaluating. Compiling the current road network information by a traveler in an editing stage based on cognition, setting a reference point of the trip by integrating various constraint conditions, defining the reference point as 'income' and 'loss' relative to the reference point according to attributes of different trips, and forming subjective probability weights (or decision weights) about the 'income' and the 'loss'; in the evaluation phase, the traveler combines "profit" and "loss" with subjective probability weights to form perceptual utility for different travel choices. This process embodies the first aspect of user finiteness.
Let a selected foreground Pro appear as result xiHas a probability of piUser-defined reference point x0Has a probability of p0. Result x that will likely occuriArranged in ascending order as x-r≤x-(r-1)≤…≤x-1≤x0≤x1≤…≤xq-1≤xq. The model of cumulative foreground theory can be expressed as:
in the formula (6), VProG (x) is the perceived utility of the traveleri) As a result xiA cost function of (a); w is a+(﹒)，w-Coupled is a probabilistic weighting function of revenue and loss, respectively.
Taking into account the result xiAnd (3) introducing a probability distribution function F (x) of the selected result into the model (6) according to the continuity of the occurrence probability, wherein the perceptual utility of the traveler, namely the accumulated prospect is shown as the formula (7).
According to the research of an economic researcher on a value function and decision weight, the risk preference of people near a reference point is reversed, the risk is averted in the face of income, and the risk is pursued in the face of loss; in the face of equal-amount gains and losses, the avoidance degree of the losses is greater than the preference degree of the gains; the farther away from the reference point, the less the marginal change in revenue or loss will affect the psychology of the person; people tend to over-estimate small probability events and under-estimate large probability events. An 'S' -shaped cost function and an inverted 'S' -shaped decision function are obtained, which describe the rationality of the decision maker. And the cost function and decision weight can be expressed as:
in the above formula, α, β and λ (0)<α,β<1, lambda is not less than 1) inverseThe preference level of the performer for the risk is mapped, and the larger the value is, the less the sensitivity of the decision maker to the risk (income or loss) is reduced, and the more sensitive the decision maker to the risk is; parameters ζ and δ determine the degree of curvature of the inverted "S" type decision function; p is a radical of formulaobTo select an objective probability of occurrence of the result.
The perceptual utility of a decision maker for a selection can be described by equations (6) to (9).
The reference point selection is one of the important parameters affecting the perception utility, and actually, travelers are restricted in the selection of a travel mode, a departure time and a travel path, so that the influences of the three must be considered simultaneously when the reference point selection is carried out. When some researchers select the problem based on the departure time of the prospect theory research work trip, the acceptable earliest arrival time T is providedEAnd operation start time TW"two reference points as a function of value between which a traveler will get" revenue ", otherwise" loss "; between the two reference points there is an "expected optimal arrival time TO", the traveler is at TOThe maximum gain is reached.
Three reference point establishment rules are established on the basis of the method: 1) will TEAnd TWAs a reference point for the departure time selection; 2) will T0As a reference point for path selection, the arrival time is T away0The more recent the gain is larger; 3) as can be seen from a great deal of literature research, the most important criterion for trip selection by travelers is travel time, and travelers tend to select more reliable trip modes, so that the trip time budget can be set as a reference point of the trip mode:
wherein, the first and the second end of the pipe are connected with each other,
and
respectively representing travel time, travel time budget and a travel time reference point of the k travel mode selected on the path p at the time t;
and
are respectively as
Expectation and standard deviation of; w is the set of all Origin and Destination (OD) pairs; rho is a confidence coefficient; mu represents the preference degree of the active traveler for the risk.
And (4) carrying the three reference points into a formula (8) to obtain a cost function of travel mode selection, route selection, early arrival and late arrival. Further, the prospect that the travel time is continuously distributed according to the formula (7) and the travel mode k is selected on the p path at the time t
Prospect of selecting path p by kth travel mode at time t
Early arrival foreground
And late to foreground
Where the early-arriving and late-arriving foreground may be collectively referred to as the arriving foreground
The user will go out according to different going modes, departure time and going out in the course of going outAnd carrying out travel selection on the path foreground. In order to better embody the second aspect of the limited rationality of the travelers, a method for selecting probability is adopted to carry out travel selection description. Is provided with
And muw(t) the probability of selecting the trip mode k, the route p and the trip for the traveler at time t, respectively, the selection behavior of the traveler can be expressed as:
r in the formula (12)wIs aggregated for all available paths.
Step S3, the specific steps of taking into account the dynamic characteristics of the traffic network, and combining the travel selection probability model and the dynamic traffic balance model of the rational traveler to establish the balance process of the dynamic traffic network to obtain the transfer rule of the traveler on the activity-travel chain and the charging rule of the electric vehicle on the activity-travel chain are as follows:
by analyzing the travel selection value function and the decision weight function under the three reference points, the selection prospects of the travel mode, the route and the departure time are all functions of travel time, and therefore the travel time under different selections needs to be solved. In practice, public transportation, private cars and taxis are considered as main travel modes, and when a user selects a taxi, the decision-making behavior of the user is similar to that of the private car, so that the taxi can be classified as the private car for research.
The private car comprises a fuel private car and an electric private car, and 1 st, 2 nd and 3 rd travel modes are respectively set for the fuel private car, the electric private car and public transport. Because the public transportation route is fixed and has a special lane, the influence of other transportation tools is small, and the travel time can be obtained through the distance and the average speed.
The available path of the electric automobile is defined as follows: a path that does not include a loop and may reach a destination without charging or by charging halfway.
For a private car, the road section driving time is established according to a traffic field classical model:
in the formula, Tad,k(t) is the running time of the private car on the road section a at the moment t, wherein k is 1 and is a fuel automobile, and k is 2 and is an electric automobile;
is the free-run time for road segment a; caThe traffic capacity of the road section a; x is the number ofa(t) is the flow rate on segment a at time t.
Considering the charging time of the electric car, the private car path travel time (sum of travel time and charging time) can be expressed as:
in the formula (I), the compound is shown in the specification,
charging time of the electric vehicle e on the node i;
the charge correlation coefficient is 1 if the charge is positive, and 0 otherwise. Similarly, the expectation and variance of the path travel time may be expressed in terms of the expectation and variance of the travel time and the charging time.
Meanwhile, the variance of the driving time and the charging time of the road section is described by a variation coefficient:
in the formula, τad,k(t) and (. sigma.) ofad,k(t))2Respectively is the expected running time and variance of the private car on the road section a at the moment t;
and
respectively the expected charging time and the variance of the electric vehicle e on the node i;
is the coefficient of variation.
Generally, an electric vehicle generally produces a charging behavior in three cases: 1) in the process of travel, when the residual electric quantity can not meet the travel requirement of the residual mileage; 2) the residual electric quantity of the electric automobile is lower than a threshold value in an activity place; 3) and after the last day of activity is finished and the mobile phone returns home, the mobile phone is fully charged.
Then the charging time of the electric vehicle e at the node i on the path p at the time t is expected to be:
in the formula (I), the compound is shown in the specification,
the residual electric quantity of the electric automobile e at the moment t;
the electric quantity after the charging is finished; ceThe charging power can be regarded as a constant value P for the battery capacity of the electric vehicle ee。
Considering the mileage anxiety psychology of the electric vehicle user, on each charging node i, the charging capacity of the electric vehicle should meet the following requirements:
in the formula, meIs the anxiety mileage of the electric automobile e; c. CeThe power consumption rate of the electric automobile; on path p, the next charge node or path end point adjacent to node i is j,the distance between two nodes is di,j。
The quantity of the electric vehicles charged on the node i at the same time is not more than the quantity of the charging piles, and the quantity of the charging piles at the node i is CiThe method comprises the following steps:
the conditions for generating the charging demand of the electric automobile at the activity place are as follows:
where Φ is a threshold for the electric vehicle to generate a charging demand at the event location.
After charging at the activity site, the remaining capacity of the electric vehicle is:
in the formula (I), the compound is shown in the specification,
the time required for the electric vehicle to be fully charged; ATejThe duration of the user engagement activity j for electric vehicle e.
The travel time of the electric automobile comprises charging time and traveling time, and the traveling time is a function of the path flow, so that the flow distribution of private cars in a traffic network needs to be researched. Establishing a dynamic traffic balance model:
in formula (21) fp w(t) traffic on available path p, DwThe travel demand is. The following constraints need to be satisfied:
equations (22) and (23) are flow conservation and non-negative restriction conditions, respectively.
Space-time distribution models and charging demand models considering the limited selection of the active travelers in the travel modes, the travel paths and the departure moments are established through the formulas (12) to (23).
Step S4, the specific step of obtaining the daily charging demand of the electric vehicle by analyzing the daily trip times of the traveler and the charging rule of the electric vehicle on each activity-trip chain, which are obtained by analyzing the transfer relationship between the daily activities of the traveler and the trip chains, includes:
and (3) adopting typical activities participated in by local travelers on a daily basis, repeating the steps from early work trip to work home-returning activity in sequence to calculate the charging demand of the electric vehicle on an activity-trip chain and calculate the transfer relationship between the activity-trip chain, and obtaining the daily participated trip times and daily charging demand of the electric vehicle.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (2)
1. An electric vehicle charging demand analysis method considering user finiteness is characterized by comprising the following steps of:
s1, dividing daily activities-trip behaviors of the traveler into a transfer relation between activity-trip chains and a transfer rule on the activity-trip chains by using an activity-analysis method in time geography, analyzing activity attributes, judging the probability of the activity under the current condition according to the activity attributes, and establishing the transfer relation between the activity and the trip chains of the traveler;
s2, modeling the perception utility of the limited traveler based on the accumulated prospect theory, and simultaneously considering the travel mode, the travel path and the reference point setting of the departure time to obtain a travel selection probability model on the activity-travel chain of the traveler;
s3, considering the dynamic characteristics of the traffic network, and establishing a balancing process of the dynamic traffic network by combining a trip selection probability model and a dynamic traffic balancing model of a rational traveler to obtain a transfer rule of the traveler on an activity-trip chain and a charging rule of the electric automobile on the activity-trip chain;
s4, obtaining daily trip times of the travelers by analyzing the transfer relationship between daily activities of the travelers and trip chains, and combining the daily trip times of the travelers and the charging rule of the electric vehicle on each activity-trip chain to obtain the daily charging requirement of the electric vehicle;
in step S1, the specific steps of analyzing the activity attribute and determining the probability of the activity under the current condition according to the activity attribute to establish the transition relationship between the activity and the travel chain of the traveler include:
s11, connecting daily activities and trips of travelers by adopting an activity-trip chain based on an activity analysis method, reflecting internal relations between activities and trip arrangements of trips, and describing interdependencies among all trips and space-time constraints of activity trips;
s12, according to daily activities and travel constraints of travelers, dividing the spatio-temporal transfer characteristics of the travelers into spatio-temporal transfer relations between the activity-travel chains and spatio-temporal transfer characteristics on the activity-travel chains;
s13, dividing activities participated by individuals into two categories of necessary activities and flexible selection activities according to activity demand research of people, wherein the probability of the necessary activities is 1, when the moment of the flexible selection activity traveler considering all travel modes and travel paths and reaching the activity place earliest under the current environmental condition is less than the expected moment of the activity start, the flexible selection activities occur according to certain conditional probability, otherwise, the activities do not occur;
the specific step of obtaining the travel selection probability model on the activity-travel chain of the traveler in step S2 includes:
s21, dividing the decision-making behavior of the traveler into an editing stage and an evaluation stage according to the accumulative foreground theory;
s22, in the editing stage, a traveler compiles based on the cognition of the current road network information, sets a reference point of the trip by integrating various constraint conditions, defines the reference point as 'income' and 'loss' according to the attributes of different trips, and forms subjective probability weight about the 'income' and the 'loss';
s23, in the evaluation stage, the traveler combines the income and the loss with the subjective probability weight to form the perception utility of different trip selections;
s24, the traveler obtains different decision perception utilities, and a selection probability method is adopted to carry out selection behavior description that the optimal decision cannot be found completely in the travel selection;
the concrete steps of compiling the traveler based on the knowledge of the current road network information and setting the reference point of the trip by integrating various constraint conditions in step S22 include:
according to analysis of a large amount of actual data, the 'activity starting time' and the 'acceptable earliest arrival time' exist in the activity trip of the traveler, the traveler will obtain 'income' when arriving at the activity place between the two times, and otherwise the traveler is 'lost', so the two times are used as reference points selected by the traveler at the departure time;
an expected optimal arrival time exists between the arrival time periods of the acquired gains, and the traveler arrives at the time of acquiring the maximum gains, so that the time is used as a reference point for path selection;
the most important criterion for the traveler to select the trip is the travel time, and the traveler is more inclined to select a more reliable trip mode, so the trip time budget can be set as a reference point of the trip mode;
the specific steps of obtaining the transition rule of the traveler on the active-trip chain and the charging rule of the electric vehicle on the active-trip chain in step S3 include:
s31, considering that a public transportation line in an actual transportation system is fixed and has a special lane, respectively calculating the transfer rule of the public transportation line and the transfer rule of a private car, and obtaining the travel time of the public transportation by the distance and the average speed;
s32, dividing travel time of the private car into charging time and path travel time, wherein two charging scenes are considered during charging time calculation, charging is selected when the remaining capacity cannot meet the remaining mileage travel requirement and the remaining capacity of the electric car is lower than a threshold value in an activity place during travel, charging time of the electric car is obtained, and the path travel time of the private car is described by adopting a function related to road section flow and road section capacity;
s33, establishing a dynamic traffic balance model of the private cars, namely, the flow rate of a certain travel demand distributed to a certain path at any moment is equal to the total travel demand amount and is sequentially multiplied by the proportion of the private cars for travel on the path at the moment, and meanwhile, considering the path flow conservation constraint, the non-negative constraint, the anxiety psychology of the mileage of the electric cars and the charging station charging pile number constraint;
the specific step of obtaining the daily charging demand of the electric vehicle in step S4 includes:
the typical activities of daily participation of local travelers are adopted, charging demand calculation of the electric vehicle on an activity-trip chain and transfer relation calculation between the activity-trip chain are sequentially carried out from early work trip to work home activity, and daily participation trip times and daily charging demand of the electric vehicle are obtained;
the transition relationship between the activities of the travelers and the travel chain established in step S1 is specifically:
the number of times of activity schedule of individual i per day is n, and the activity-trip pattern is expressed as ATPiThe formula is as follows:
ATPi＝fin＝(gi1,gi2,...,gij,...,gin)
wherein, gijA feature set for a jth activity engaged by an ith actor; f. ofinRepresents an activity-travel pattern for an entire day;
if the jth activity of the traveler is a necessary activity, the probability of the activity occurring is:
P(gij|fi,j-1)＝1
wherein f isi,j-1Historical activity before j activity for the traveler i-trip mode;
if the jth activity of the traveler is an unnecessary activity, the acceptable upper limit of the arrival time of the traveler i for the activity to participate in is set as TjThe trip modes selected by the traveler are m types, the earliest time when the traveler reaches the activity place by using the kth type trip mode, and the upper limit of the confidence coefficient theta of the earliest time are respectively Tjk、Tjkθ；
P{Tjk≤Tjkθ}＝θ
The minimum value of the earliest upper limit of the arrival time of all travel modes of the traveler is TjminHaving a value of Tjmin＝min{TjkθK ∈ m }, then the probability of the activity occurring is:
the probability that the traveler participates in the j activity, i.e. the j activity-trip chain is generated as follows:
P(gij)＝P(fi,j-1)·P(gij|fi,j-1)
the selecting a probability model in step S2 specifically includes:
wherein the content of the first and second substances,
and muw(t) probability of selecting a trip mode k, a route p and a trip for the traveler at the moment t respectively; the prospect that the travel mode k is selected on the p path at the moment t under the condition that the travel time is distributed continuously
Prospect of selecting path p by kth travel mode at time t
The early and late arrivals foreground are collectively referred to as the arrival foreground
The dynamic traffic balancing model in step S3 specifically represents:
wherein f isp w(t) traffic on available path p, DwThe demand for travel is set;
and muw(t) probability of selecting a trip mode k, a route p and a trip for the traveler at time t, respectively.
2. The method for analyzing charging demand of an electric vehicle considering user finiteness as claimed in claim 1, wherein the finiteness traveler in the step S2 represents a real traveler who exhibits finiteness due to the limited cognitive ability not to grasp all information of the traffic network and due to the limited computing ability not to find the optimal decision.
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