CN112348387B - Dynamic power dispatching method for assisting user in traveling through charging and discharging strategies - Google Patents

Dynamic power dispatching method for assisting user in traveling through charging and discharging strategies Download PDF

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CN112348387B
CN112348387B CN202011275579.4A CN202011275579A CN112348387B CN 112348387 B CN112348387 B CN 112348387B CN 202011275579 A CN202011275579 A CN 202011275579A CN 112348387 B CN112348387 B CN 112348387B
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charging
time
parking
automobile
power
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朱永胜
冯超
肖俊明
董燕
巫付专
冯洁
杨亮
郑志帅
魏翱龙
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Zhongyuan University of Technology
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Abstract

The invention provides a dynamic power dispatching method for assisting users in traveling through a charging and discharging strategy, which comprises the following steps: constructing a half Markov probability trip model; preprocessing the data to obtain the probability distribution of the characteristic quantity; relevant data are extracted by using Monte Carlo, and the random user travel behavior based on the space-time coupling characteristic is preliminarily simulated; the method comprises the following steps of adding a charging and discharging strategy during the period by utilizing the tolerance of half Markov to the parking time, and considering factors such as electricity price guide, charging and discharging modes, charging and discharging threshold factors and the like related to places; the electric automobile is connected to the grid, and multi-target power dispatching processing is carried out; and (4) performing simulation calculation by adopting a regional power grid, and explaining the rationality and effectiveness of the proposed model. The invention aims at the uncertain behavior of large-scale electric vehicles accessing the power grid, considers the influence of the uncertain behavior on the economy of users and the safety of the power grid, formulates a dynamic power dispatching method and provides practical value for researching the influence of the random behavior of the electric vehicles on the dispatching of the power grid.

Description

Dynamic power dispatching method for assisting user in traveling through charging and discharging strategies
Technical Field
The invention belongs to the field of new energy system optimization and scheduling, and particularly relates to a dynamic power scheduling method for assisting users in going out by using a charging and discharging strategy, wherein uncertain behaviors of a single electric vehicle are used as energy storage and load to participate in a power grid charging and discharging decision.
Background
Electric Vehicles (EVs) have the characteristics of high energy utilization rate, no mobile waste discharge and the like, have become one of strategic emerging industries which are key supports in China, and the increasingly advanced battery equipment and related charging technology level also promote the continuous popularization of the electric vehicles. In recent 3 years, the holding speed of electric automobiles in China market is respectively 282%, 210% and 190%, which exceed those in the United states, Europe and other regions. While the traveling of the large-scale electric vehicle in the urban intranet inevitably has the behavior of energy interaction with the power grid in the traveling area, so that the distribution of the electric vehicle load has great uncertainty in both time and space. Therefore, the establishment of the electric vehicle charging and discharging strategy based on the random trip of the user is the premise and the basis for analyzing the regional power grid dynamic power dispatching problem.
The existing analysis method for the travel behavior of the electric vehicle can be divided into the following steps: directly limiting travel time, utilizing Monte Carlo to perform travel simulation, analyzing and controlling a travel chain based on statistical data and the like. In the methods, the space-time coupling randomness of the user travel behaviors is weakened; while the complexity of data processing is increased, the charging and discharging selection and the travel assistance which maximize the benefit of a user are ignored. And rarely relates to the discharge of the electric automobile to the power grid under the background of random behaviors, and the EVs are used as peak-valley differences of the load-stabilizing power grid. In terms of optimizing scheduling on both sides of the user and the power grid, only one-side target is generally considered, and the benefit of the user and the power grid is unbalanced.
Disclosure of Invention
Aiming at the defects in the background technology, the invention provides a dynamic power dispatching method for assisting a user in traveling by a charging and discharging strategy, and solves the problem that the benefit of the user and a power grid is unbalanced due to weakening of space-time coupling randomness of user traveling behaviors in the prior art.
The technical scheme of the invention is realized as follows:
a dynamic power dispatching method for assisting users in traveling through a charging and discharging strategy comprises the following steps:
the method comprises the following steps: counting the basic load, the wind power output and the automobile remaining capacity of the cell A;
step two: acquiring a data set from an NHTS (NHTS) database, and fitting automobile state quantities in the data set to obtain a probability distribution function, wherein the automobile state quantities comprise start time, end time and parking duration;
step three: performing state transition probability calculation on the parking destinations in the data set by using the half Markov probability to obtain a parking destination probability matrix, and constructing a mileage matrix according to the parking destinations in the data set;
step four: randomly sampling the probability distribution function in the second step by using Monte Carlo to obtain the starting time of the automobile B, and setting the starting place as home and the initial electric quantity as full power;
step five: obtaining the parking place of the next state j according to the parking destination probability matrix, obtaining the corresponding driving mileage from the mileage matrix by the parking place of the current state i and the parking place of the next state j, and obtaining the current time and the current electric quantity load SOC on the basis of the given speed and the power consumption per houriAnd judging the current electric quantity load SOCiIf the charging time is less than the minimum value of the safe electric quantity, 0.2, if so, charging the automobile B, otherwise, randomly taking a number as the charging and discharging quantity in the charging and discharging range, wherein the charging time is less than the parking time, and the automobile B belongs to the cell A;
step six: adding the charging and discharging decision of the automobile B into the model, and constructing a multi-target function according to the charging and discharging decision of the automobile B, the basic load of the cell A and the wind-power output;
step seven: optimizing the multi-objective function by using a multi-objective intelligent optimization algorithm, and adjusting the charge and discharge electric quantity;
step eight: and when the charging and discharging time length is equal to the parking time length, updating the current time by using the parking time length, judging whether the current time is equal to the ending time or not, if so, judging that the ending place is home, returning to the step six, and otherwise, executing the step five.
The docking destinations include docking at home, docking at work, and docking at other locations; the parking time length is divided into a parking at home time length, a parking at work place time length and a parking at other places according to different parking destinations.
The probability distribution function corresponding to the stay at home time length is as follows:
Figure GDA0003579747700000021
the probability distribution function corresponding to the duration of parking at the working place is as follows:
Figure GDA0003579747700000022
the probability distribution function corresponding to the duration of parking at other places is as follows:
SO(x)＝1-SH(x)-SW(x)
where x denotes the data set sample, SH(x) A probability distribution function representing a parking area as a residential area, and a represents the formula SH(x) Fitting coefficient of (1), SW(x) Probability distribution function representing parking area as residential area, am'、bm'、cm'Are all represented by formula SW(x) Fitting coefficient of (1), SO(x) The probability distribution function corresponding to the time length of parking at other places is shown, and m' is 1,2, … and 8 is expressed by the formula SW(x) The number of the fitting coefficients.
The parking destination probability matrix is:
Figure GDA0003579747700000023
wherein, PtA probability matrix of the parking destination is represented,
Figure GDA0003579747700000024
t represents time, m is 1,2 … … n represents a departure point at a certain time, and q is 1,2 … … n represents a destination at a certain time.
The charge and discharge decision u*Comprises the following steps:
Figure GDA0003579747700000031
wherein the content of the first and second substances,
Figure GDA0003579747700000032
the charging pile is selected to be used for fast charging at the kth time period in the ith journey of the automobile,
Figure GDA0003579747700000033
the charging pile is selected to be used for slow charging at the kth time period in the ith journey of the automobile,
Figure GDA0003579747700000034
represents a charging and discharging selection strategy under the interaction between the automobile and the power grid at the kth time interval in the ith journey, alpha' is a charging and discharging threshold factor,
Figure GDA0003579747700000035
indicating that the electric automobile charges the power grid for the kth period of the ith journey,
Figure GDA0003579747700000036
and the discharging price of the electric vehicle to the power grid in the kth period of the ith journey is shown.
The multi-objective function comprises a user-side economy objective function and a power grid-side safety objective function, wherein the user-side economy objective function is as follows:
Figure GDA0003579747700000037
the grid side safety objective function is as follows:
Figure GDA0003579747700000038
wherein l represents the number of mileage sections, h (i) represents all time sections included in the ith section of travel, epsilon represents the power grid conversion efficiency, and epsilonexRepresents the discharge efficiency of the power battery of the electric automobile, epsilonimThe charging efficiency of the power battery of the electric automobile is shown,
Figure GDA0003579747700000039
indicating that the electric automobile discharges electricity to the power grid in the kth period of the ith strokeThe price of the mixture is higher than the standard value,
Figure GDA00035797477000000310
indicating that the electric automobile charges the power grid for the kth period of the ith journey,
Figure GDA00035797477000000311
represents the electric power discharged by the electric automobile,
Figure GDA00035797477000000312
represents the electric power for charging the electric vehicle,
Figure GDA00035797477000000313
indicating the discharge time at the kth period of the i-segment stroke,
Figure GDA00035797477000000314
representing the charging time in the kth period of the i-segment of the journey, CdRepresents the consumption rate of the power battery, thetaimRepresenting the coefficient of charge, thetaexRepresents the coefficient of charge discharged from the V2G battery,
Figure GDA00035797477000000315
representing the load demand of the system during the kth period of the ith trip,
Figure GDA00035797477000000316
represents the charging power of the automobile in the kth period of the ith journey,
Figure GDA00035797477000000317
indicating the discharge power of the automobile in the kth period of the ith journey,
Figure GDA00035797477000000318
represents the average daily load of the system.
Average daily load of the system
Figure GDA00035797477000000319
Comprises the following steps:
Figure GDA00035797477000000320
where T denotes a scheduling period.
The constraint conditions corresponding to the multi-objective function comprise electric vehicle power constraint, battery SOC constraint, electric vehicle travel constraint and charging and discharging time constraint.
The power constraint of the electric automobile is as follows:
Figure GDA00035797477000000321
Figure GDA00035797477000000322
Figure GDA0003579747700000041
Figure GDA0003579747700000042
wherein the content of the first and second substances,
Figure GDA0003579747700000043
represents the electric power for charging the electric vehicle,
Figure GDA0003579747700000044
represents the electric power discharged by the electric automobile,
Figure GDA0003579747700000045
Figure GDA0003579747700000046
indicating that the electric vehicle is in a charging state;
Figure GDA0003579747700000047
indicating that the electric vehicle is in dischargeA state; i isi,kIs a binary number, i.e. Ii,k1 is in charge-discharge state, Ii,k0 is the other state; q is the power consumption per kilometer of the battery, diThe driving mileage of the electric automobile in the ith section of the journey is obtained;
the battery SOC constraint is:
Figure GDA0003579747700000048
Figure GDA0003579747700000049
wherein Q is0Is the battery capacity, SOCmaxIs the maximum value of the SOC of the battery, SOCminIs the minimum value of the SOC of the battery, SOCi,kRepresents the battery SOC at the kth time in the ith trip,
Figure GDA00035797477000000410
represents the charging electric power at the kth moment in the ith journey of the electric vehicle,
Figure GDA00035797477000000411
the charging time of the k +1 th time in the ith journey of the electric automobile is shown,
Figure GDA00035797477000000412
represents the discharge electric power of the electric automobile at the kth moment in the ith stroke,
Figure GDA00035797477000000413
the discharging time represents the k +1 th time in the ith stroke of the electric automobile;
the travel constraint of the electric automobile is as follows:
Figure GDA00035797477000000414
wherein, b is 1,2, …, m represents different stroke sections;
the charge and discharge time constraints are as follows:
Figure GDA00035797477000000415
Figure GDA00035797477000000416
Figure GDA00035797477000000417
Figure GDA00035797477000000418
wherein, ti,k+1For the electric automobile at the (k + 1) th moment in the ith section of the travel every day, ti,kFor the electric automobile at the kth moment in the ith journey every day,
Figure GDA00035797477000000419
at the moment when the electric vehicle is charged during driving,
Figure GDA00035797477000000420
the moment when the electric vehicle is in driving to discharge V2G is shown.
The beneficial effect that this technical scheme can produce:
(1) the method establishes the NHSMP probabilistic decision uncertainty behavior model, considers the correlation between time and space, ensures that each vehicle behavior is independent, maximizes the randomness of the travel behavior of the vehicle owner, and reduces the calculation amount of path search.
(2) According to the invention, the parking time lengths of different places are fitted by using the inclusion of the NHSMP to the parking time, and meanwhile, factors such as electricity price guide, a charging and discharging mode and a charging and discharging threshold factor related to the places are considered in a grid-connected charging and discharging strategy, so that the model is closer to a real scene.
(3) And connecting the random trip vehicle into a power grid, changing the EV between the stored energy and the load through the influence on the economy of a user side and the safety of a power supply side, charging and discharging to the power grid, smoothing the energy fluctuation of the power grid, and realizing a multi-target dynamic power dispatching model.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the dynamic power scheduling of the present invention;
FIG. 2 is a schematic diagram of a random trip of an electric vehicle based on NHSMP according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a charge/discharge selection method according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of threshold factors according to an embodiment of the present invention;
fig. 5 shows the actual electricity prices of a certain electric vehicle at different locations according to the embodiment of the present invention.
FIG. 6 is an iterative flow diagram of the multi-objective algorithm SPEA2-SDE employed by an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, when considering a dynamic power scheduling model for assisting a user in traveling through an NHSMP charging and discharging strategy, the dynamic power scheduling model includes three major parts, namely an NHSMP probability traveling space-time coupling model, and a charging and discharging decision assists electric vehicle traveling and electric vehicle network entry to perform power scheduling. The model is based on a NHSMP probability trip model as a framework, and after the prepared data are fitted, data are extracted by using Monte Carlo, so that the NHSMP probability trip is perfected. By utilizing the inclusion of the NHSMP to the parking time, a charging and discharging strategy is added in the parking period to assist the user in going out, and factors such as electricity price guidance, charging and discharging modes, charging and discharging threshold factors and the like related to the place are considered. By adjusting the charge and discharge power, the influence of the EV on the economic efficiency of the user side and the safety scheduling of the power supply side is researched. The embodiment of the invention provides a dynamic power dispatching method for assisting a user in traveling through a charging and discharging strategy, which comprises the following steps:
the method comprises the following steps: counting the basic load, the wind power output and the automobile remaining capacity of the cell A;
step two: acquiring a data set from an NHTS (NHTS) database, and fitting automobile state quantities in the data set to obtain a probability distribution function, wherein the automobile state quantities comprise start time, end time and parking duration; the docking destinations include docking at home, docking at work, and docking at other locations; the parking time length is divided into a parking at home time length, a parking at work place time length and a parking at other places according to different parking destinations.
As shown in fig. 2, the state quantities in the EV random trip diagram based on NHSMP include: the space state quantity: parking destination, charging place and driving distance di-1,i(ii) a The time state quantity comprises sunrise starting time, sunrise ending time and running duration
Figure GDA0003579747700000063
Length of dwell
Figure GDA0003579747700000064
Where i denotes the i-th state quantity from the starting point.
Based on the inclusion of the NHSMP decision on the parking duration, it is no longer constrained to lie in an exponential distribution. Fitting for dwell duration for trip area, wherein SH、SWThe probability distribution function of the parking area as a residential area and a working area is respectively represented.
The probability distribution function corresponding to the stay at home time length is as follows:
Figure GDA0003579747700000061
the probability distribution function corresponding to the duration of parking at the working place is as follows:
Figure GDA0003579747700000062
the probability distribution function corresponding to the duration of parking at other places is as follows:
SO(x)＝1-SH(x)-SW(x) (3)
where x denotes the data set sample, SH(x) A probability distribution function representing the parking area as a residential area, and alpha represents the formula SH(x) Fitting coefficient of (1), SW(x) Probability distribution function representing parking area as residential area, am'、bm'、cm'Are all represented by formula SW(x) Fitting coefficient of (1), SO(x) The probability distribution function corresponding to the time length of parking at other places is shown, and m' is 1,2, … and 8 is expressed by the formula SW(x) The number of the fitting coefficients.
Step three: performing state transition probability calculation on the parking destinations in the data set by using the half Markov probability to obtain a parking destination probability matrix, and constructing a mileage matrix according to the parking destinations in the data set; from the markov property, the probability of a transition from one state to another depends only on the current state, and not on the previous state. However, the Markov chain itself only describes sequential transitions between states, not temporal transitions; the markov process, while taking into account the time dependence, ignores the reality that parking times are generally non-exponentially distributed. For the method, NHSMP is used, and the driving probability and the parking duration in the random process are determined by considering the time correlation. The strategy is used for modeling the non-exponentially distributed parking time and the non-exponentially distributed driving probability and researching the uncertain travel behavior and the charging and discharging decision of the electric automobile.
In general, a NHSMP decision may be described in two ways. On one hand, the time state sequence of the model is displayed by the Markov process, and the study is carried out by adopting a discrete sequence by taking the hour as a unit. Assume that the current time corresponds to the state of i. The state transition probability is expressed as a probability of transition to the next state under the condition of the current state. The state transition probabilities satisfy the following relationship:
Figure GDA0003579747700000071
pij≥0 (5)
wherein, t0≤t1≤…tn≤tn+1. The Markov process characteristic shows that a certain future state is only related to the current state and is related to the previous state xn-1,…x1,x0Irrelevant, i.e. the ineffectiveness of the markov process.
Another aspect presents a dwell time conditional distribution function of the system from one state to another over a period of time:
Sij(t)＝P(Tn≤t|Xn(tn)＝i,Xn+1(tn+1)＝j) (6)
in the formula, t is the state transition time from the state to the actual parking time.
Qij(t)＝P(Xn+1＝j,Tn≤t|Xn＝i,Tn-1)＝pijSij(t) (7)
The above probabilities, that is, the probability of the system being in the state and being converted into the state within a period of time t, are obtained by integrating the formula (4) and the formula (6).
And (3) extracting travel data in the probability density by using Monte Carlo, and simultaneously considering the NHSMP transition probability to provide probability selection for places, behaviors and the like under different states. Wherein, the transition probability P of the vehicle purpose at different starting points and different timet(parking destination probability momentMatrix) as follows:
Figure GDA0003579747700000072
wherein, PtA probability matrix of the parking destination is represented,
Figure GDA0003579747700000073
t represents time, m is 1,2 … … n represents a departure point at a certain time, and q is 1,2 … … n represents a destination at a certain time.
Step four: randomly sampling the probability distribution function in the second step by using Monte Carlo to obtain the starting time of the automobile B, and specifying the starting place as home and the initial electric quantity as full-charge SOC0；
Step five: obtaining the parking place of the next state j according to the parking destination probability matrix, obtaining the corresponding driving mileage from the mileage matrix by the parking place of the current state i and the parking place of the next state j, and obtaining the current time and the current electric quantity load SOC on the basis of the given speed and the power consumption per houriAnd judging the current electric quantity load SOCiIf the charging time is less than the minimum value of the safe electric quantity, 0.2, if so, charging the automobile B, otherwise, randomly taking a number as the charging and discharging quantity in the charging and discharging range, wherein the charging time is less than the parking time, and the automobile B belongs to the cell A;
step six: calculating a charging and discharging decision of the automobile B according to the data set, and constructing a multi-target function according to the charging and discharging decision of the automobile B, the basic load of the cell A and the wind and electricity output; the charge and discharge decision u*Comprises the following steps:
Figure GDA0003579747700000081
wherein the content of the first and second substances,
Figure GDA0003579747700000082
the charging pile is selected to be used for fast charging at the kth time period in the ith journey of the automobile,
Figure GDA0003579747700000083
the charging pile is selected to be used for slow charging at the kth time period in the ith journey of the automobile,
Figure GDA0003579747700000084
shows a charging and discharging selection strategy (see the attached figure 3) under the interaction between the vehicle and the power grid at the kth time interval in the ith journey, alpha' is a charging and discharging threshold factor,
Figure GDA0003579747700000085
indicating that the electric automobile charges the power grid for the kth period of the ith journey,
Figure GDA0003579747700000086
and the discharging price of the electric vehicle to the power grid in the kth period of the ith journey is shown.
The charge and discharge modes are divided into two cases: one is to carry out grid-connected charging and discharging at home; and the other mode is that in the EV driving process, quick charging or slow charging is selected on the charging pile. See figure 3 for details.
The charging time for this trip satisfies the following condition:
Figure GDA0003579747700000087
in the formula: t is tc,ijRepresents the charging time between state i and state j; k represents the starting time of the parking stage; t is tjRepresents the start time of the j state; SOCtRepresenting the battery capacity at each moment of the parking stage; p represents a normal charging power, and is a constant value; t isijRepresenting the length of the parking time between state i and state j. The charging decision on the charging pile is represented by charging time, and the expression is as follows:
Figure GDA0003579747700000088
Figure GDA0003579747700000089
wherein, tc,i,kRepresenting the charging time of the charging pile under different charging strategies; pfIndicating the value of the power of the boost, PmIndicating the amount of power for the trickle charge.
Different battery depletion costs also cause charge and discharge revenue fluctuation. And introducing a charging and discharging threshold factor, controlling the charging and discharging times in the parking time, and indirectly controlling the charging duration. As shown in fig. 4. Under the condition that the threshold factor is proper, the charging and discharging time length meets the following condition.
tj-k≥tc,ij+tf,ij≥ταij (13)
In the formula: t is tf,ijRepresents the discharge time period between state i and state j; τ represents a unit time, i.e., 1 h. Tau alphaijAnd the lowest threshold represents the sum of the charging and discharging time lengths in the parking stage between the state i and the state j.
And aiming at the actual scenes of different routes on the demand side, the electricity price making schemes are different. In order to simulate the user's electricity price response specification as realistic as possible: when charging at home, the real-time electricity price charged by the user participating in the power grid is directly adopted
Figure GDA0003579747700000091
When the charging pile is used on a driving road, the price of electricity is set as d (t) according to the average price of charging fees in a certain area, wherein
Figure GDA0003579747700000092
The discharge electricity price is
Figure GDA0003579747700000093
The three electricity rates are shown in fig. 5.
The method comprises the steps that a random trip vehicle is connected into a power grid, the influence of an EV on the economy of a user side and the safety of a power supply side is discussed, a multi-objective function comprises an economy objective function of the user side and a safety objective function of the power grid side, a user connects an electric vehicle into the power grid, the cost of charge of a single vehicle and the cost of battery loss degradation are assumed to be positive, the discharge income is negative, and the cost of a demand side is minimized, namely the user income is maximized. The user-side economic objective function is:
Figure GDA0003579747700000094
wherein l represents the number of mileage sections, h (i) represents all time sections included in the ith section of travel, epsilon represents the power grid conversion efficiency, and the dimension is 1, epsilonexThe discharge efficiency of the power battery of the electric automobile is expressed by a dimension of 1, epsilonimThe dimension of the charging efficiency of the power battery of the electric automobile is 1,
Figure GDA0003579747700000095
indicating that the electric automobile discharges the electricity price to the power grid in the kth period of the ith journey,
Figure GDA0003579747700000096
indicating that the electric automobile charges the power grid for the kth period of the ith journey,
Figure GDA0003579747700000097
represents the electric power discharged by the electric automobile,
Figure GDA0003579747700000098
represents the electric power for charging the electric vehicle,
Figure GDA0003579747700000099
indicating the discharge time at the kth period of the i-segment stroke,
Figure GDA00035797477000000910
representing the charging time in the kth period of the i-segment of the journey, CdRepresents the consumption rate of the power battery, thetaimRepresenting coefficient of charge capacity in dimension 1, thetaexThe coefficient of the discharged electric quantity of the V2G battery is shown, and the dimension is 1.
The daily load variance is used as a safety index of a power grid, namely the objective function describes the capacity of the electric automobile for stabilizing the system load fluctuation, and the smaller the function value is, the stronger the capacity is. The grid side safety objective function is as follows:
Figure GDA00035797477000000911
Figure GDA00035797477000000912
representing the load demand of the system during the kth period of the ith trip,
Figure GDA00035797477000000913
represents the charging power of the automobile in the kth period of the ith journey,
Figure GDA00035797477000000914
indicating the discharge power of the automobile in the kth period of the ith journey,
Figure GDA00035797477000000915
represents the average daily load of the system.
Average daily load of the system
Figure GDA00035797477000000916
Comprises the following steps:
Figure GDA00035797477000000917
where T denotes a scheduling period, i.e. 24 hours a day.
The constraint conditions corresponding to the multi-objective function comprise electric vehicle power constraint, battery SOC constraint, electric vehicle travel constraint and charging and discharging time constraint.
The charging and discharging power of the electric automobile is between an upper limit and a lower limit, and simultaneously, a mutual exclusion principle of the charging and discharging states is met. The power constraint of the electric automobile is as follows:
Figure GDA00035797477000000918
Figure GDA0003579747700000101
Figure GDA0003579747700000102
Figure GDA0003579747700000103
wherein the content of the first and second substances,
Figure GDA0003579747700000104
represents the electric power for charging the electric vehicle,
Figure GDA0003579747700000105
represents the electric power discharged by the electric automobile,
Figure GDA0003579747700000106
Figure GDA0003579747700000107
indicating that the electric vehicle is in a charging state;
Figure GDA0003579747700000108
indicating that the electric vehicle is in a discharging state; i isi,kIs a binary number, i.e. Ii,k1 is in charge-discharge state, Ii,k0 is the other state; q is the power consumption per kilometer of the battery, diThe driving mileage of the electric automobile in the ith section of the journey is obtained;
the upper and lower limits of the SOC are constrained in consideration of the fact that the battery loss has a certain influence on the user economy. The battery SOC constraint is:
Figure GDA0003579747700000109
Figure GDA00035797477000001010
wherein Q is0Is the battery capacity, SOCmaxIs the maximum value of the SOC of the battery, SOCminIs the minimum value of the SOC of the battery, SOCi,kRepresents the battery SOC at the kth time in the ith trip,
Figure GDA00035797477000001011
represents the charging electric power at the kth moment in the ith journey of the electric vehicle,
Figure GDA00035797477000001012
the charging time of the k +1 th time in the ith journey of the electric automobile is shown,
Figure GDA00035797477000001013
represents the discharge electric power of the electric automobile at the kth moment in the ith stroke,
Figure GDA00035797477000001014
the discharging time represents the k +1 th time in the ith stroke of the electric automobile;
in consideration of the common problem of mileage anxiety of electric automobiles. The travel constraint of the electric vehicle is as follows:
Figure GDA00035797477000001015
wherein, b is 1,2, …, m represents different stroke sections; SOC0The dimension is 1 for the SOC value of the battery at the beginning; the above equation describes the initial battery charge SOC0Q0The electric quantity consumed by running in the b +1 section of travel is subtracted and still stays at the upper and lower limits of the electric quantity of the battery,
that is, the current remaining capacity can satisfy the capacity consumed by the next trip.
In each time interval, the charging and discharging duration of the electric automobile does not exceed the time difference between two adjacent time intervals, and the charging and discharging time of each stroke is not negative; mutually exclusive charge and discharge time at the same time; the electric vehicle cannot be charged and V2G discharged during running. The charge and discharge time constraints are as follows:
Figure GDA00035797477000001016
Figure GDA0003579747700000111
Figure GDA0003579747700000112
Figure GDA0003579747700000113
wherein, ti,k+1For the electric automobile at the (k + 1) th moment in the ith section of the travel every day, ti,kFor the electric automobile at the kth moment in the ith journey every day,
Figure GDA0003579747700000114
at the moment when the electric vehicle is charged during driving,
Figure GDA0003579747700000115
the moment when the electric vehicle is in driving to discharge V2G.
Step seven: optimizing the multi-objective function by using a multi-objective intelligent optimization algorithm, and adjusting the charge and discharge electric quantity; compared with the traditional scheduling model, the dimensionality is high, and the coupling degree between the variables and the constraints is high. Therefore, the multi-target algorithm SPEA2-SDE based on the transfer density estimation strategy is applied to the solution of the dynamic multi-target scheduling problem, and finally effective approximation of the optimal leading edge is achieved through multiple iterations, as shown in FIG. 6.
The specific implementation steps of the algorithm SPEA2-SDE are as follows:
initializing charge and discharge power: evolving population XtAnd an external population
Figure GDA0003579747700000116
And adding SDE to calculate the fitness value of the individuals in the corresponding population. According to the environment selection mechanism, X is selectedtAnd
Figure GDA0003579747700000117
all non-dominant individuals in the population are stored in an external population
Figure GDA0003579747700000118
If the population evolution condition can be reached, outputting
Figure GDA0003579747700000119
Otherwise, adopting championship match selection method to select external population
Figure GDA00035797477000001110
Selecting individuals as parent population to enter a mating pool, performing crossing and mutation operations on the parent population, and selecting a new generation of evolved population X by adopting density estimationt+1。
Step eight: and when the charging and discharging time length is equal to the parking time length, updating the current time by using the parking time length, judging whether the current time is equal to the ending time or not, if so, judging that the ending place is home, returning to the step six, and otherwise, executing the step five.
And finally, carrying out simulation calculation by adopting a local area network, and carrying out simulation calculation by adopting the cell of 1000 user population. According to investigation, with the increase of public charging infrastructure and the improvement of subsidy policy of the pure electric vehicles, the permeability of the pure electric vehicles in China gradually increases but slowly increases, and in 2020, the permeability of the pure electric vehicles in China reaches about 5%, so that simulation calculation is mainly performed by 50 vehicles, and the rationality and the effectiveness of the provided model are explained.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.

Claims (7)

1. A dynamic power dispatching method for assisting users in traveling through a charging and discharging strategy is characterized by comprising the following steps:
the method comprises the following steps: counting the basic load, the wind power output and the automobile remaining capacity of the cell A;
step two: acquiring a data set from an NHTS (NHTS) database, and fitting automobile state quantities in the data set to obtain a probability distribution function, wherein the automobile state quantities comprise start time, end time and parking duration;
step three: performing state transition probability calculation on the parking destinations in the data set by using the half Markov probability to obtain a parking destination probability matrix, and constructing a mileage matrix according to the parking destinations in the data set;
step four: randomly sampling the probability distribution function in the second step by using Monte Carlo to obtain the starting time of the automobile B, and setting the starting place as home and the initial electric quantity as full power;
step five: obtaining the parking place of the next state j according to the parking destination probability matrix, obtaining the corresponding driving mileage from the mileage matrix by the parking place of the current state i and the parking place of the next state j, and obtaining the current time and the current electric quantity load SOC on the basis of the given speed and the power consumption per houriAnd judging the current electric quantity load SOCiIf the charging time is less than the minimum value of the safe electric quantity, 0.2, if so, charging the automobile B, otherwise, randomly taking a number as the charging and discharging quantity in the charging and discharging range, wherein the charging time is less than the parking time, and the automobile B belongs to the cell A;
step six: adding the charging and discharging decision of the automobile B into the model, and constructing a multi-target function according to the charging and discharging decision of the automobile B, the basic load of the cell A and the wind-power output;
the charge and discharge decision u*Comprises the following steps:
Figure FDA0003560393000000011
wherein the content of the first and second substances,
Figure FDA0003560393000000012
the charging pile is selected to be used for fast charging at the kth time period in the ith journey of the automobile,
Figure FDA0003560393000000013
the charging pile is selected to be used for slow charging at the kth time period in the ith journey of the automobile,
Figure FDA0003560393000000014
represents a charging and discharging selection strategy under the interaction between the automobile and the power grid at the kth time interval in the ith journey, alpha' is a charging and discharging threshold factor,
Figure FDA0003560393000000015
indicating that the electric automobile charges the power grid for the kth period of the ith journey,
Figure FDA0003560393000000016
indicating that the electric vehicle discharges electricity price to the power grid in the kth period of the ith journey;
the multi-objective function comprises a user-side economy objective function and a power grid-side safety objective function, wherein the user-side economy objective function is as follows:
Figure FDA0003560393000000017
the grid side safety objective function is:
Figure FDA0003560393000000018
wherein l represents the number of mileage steps per day, h (i) represents all time periods included in the ith trip, and epsilon represents electricityNetwork conversion efficiency, eexRepresents the discharge efficiency of the power battery of the electric automobile, epsilonimThe charging efficiency of the power battery of the electric automobile is shown,
Figure FDA0003560393000000019
indicating that the electric automobile discharges the electricity price to the power grid in the kth period of the ith journey,
Figure FDA00035603930000000110
indicating that the electric automobile charges the power grid for the kth period of the ith journey,
Figure FDA0003560393000000021
represents the electric power discharged by the electric automobile,
Figure FDA0003560393000000022
represents the electric power for charging the electric vehicle,
Figure FDA0003560393000000023
indicating the discharge time at the kth period of the i-segment stroke,
Figure FDA0003560393000000024
representing the charging time in the kth period of the i-segment of the journey, CdRepresents the consumption rate of the power battery, thetaimRepresenting the coefficient of charge, thetaexRepresents the coefficient of charge discharged from the V2G battery,
Figure FDA0003560393000000025
representing the load demand of the system during the kth period of the ith trip,
Figure FDA0003560393000000026
represents the charging power of the automobile in the kth period of the ith journey,
Figure FDA0003560393000000027
indicating that the automobile is placed at the kth time interval of the ith journeyThe electric power is supplied to the electric motor,
Figure FDA0003560393000000028
represents the average daily load of the system;
step seven: optimizing the multi-objective function by using a multi-objective intelligent optimization algorithm, and adjusting the charge and discharge electric quantity;
step eight: and when the charging and discharging time length is equal to the parking time length, updating the current time by using the parking time length, judging whether the current time is equal to the ending time or not, if so, judging that the ending place is home, returning to the step six, and otherwise, executing the step five.
2. The dynamic power scheduling method for user trip assisted by charge and discharge strategy according to claim 1, characterized in that the parking destination comprises parking at home, parking at work and parking at other places; the parking time length is divided into a parking at home time length, a parking at work place time length and a parking at other places according to different parking destinations.
3. The dynamic power scheduling method for assisting users in traveling according to the charge and discharge strategy of claim 2, wherein the probability distribution function corresponding to the stay at home time length is as follows:
Figure FDA0003560393000000029
the probability distribution function corresponding to the duration of parking at the working place is as follows:
Figure FDA00035603930000000210
the probability distribution function corresponding to the duration of parking at other places is as follows:
SO(x)＝1-SH(x)-SW(x)
where x denotes the data set sample, SH(x) Indicating parking areasAlpha is a probability distribution function of a residential area and represents the formula SH(x) Fitting coefficient of (1), SW(x) Probability distribution function representing parking area as work area, am'、bm'、cm'Are all represented by formula SW(x) Fitting coefficient of (1), SO(x) The probability distribution function corresponding to the time length of parking at other places is shown, and m' is 1,2, … and 8 is expressed by the formula SW(x) The number of the fitting coefficients.
4. The dynamic power scheduling method for assisting users in traveling according to the charge and discharge strategy of claim 1, wherein the parking destination probability matrix is:
Figure FDA00035603930000000211
wherein, PtA probability matrix of the parking destination is represented,
Figure FDA00035603930000000212
t represents time, m is 1,2 … … n represents a departure point at a certain time, and q is 1,2 … … n represents a destination at a certain time.
5. The dynamic power dispatching method for assisting user travel according to charge-discharge strategy of claim 1, wherein average daily load of the system is
Figure FDA0003560393000000031
Comprises the following steps:
Figure FDA0003560393000000032
where T denotes a scheduling period.
6. The dynamic power dispatching method for assisting user travel according to the charge and discharge strategy of claim 5, wherein the constraint conditions corresponding to the multi-objective function comprise electric vehicle power constraint, battery SOC constraint, electric vehicle travel constraint and charge and discharge time constraint.
7. The dynamic power dispatching method for assisting user travel according to the charge-discharge strategy of claim 6, wherein the power constraint of the electric vehicle is as follows:
Figure FDA0003560393000000033
Figure FDA0003560393000000034
Figure FDA0003560393000000035
Figure FDA0003560393000000036
wherein the content of the first and second substances,
Figure FDA0003560393000000037
represents the electric power for charging the electric vehicle,
Figure FDA0003560393000000038
represents the electric power discharged by the electric automobile,
Figure FDA0003560393000000039
Figure FDA00035603930000000310
indicating that the electric vehicle is in a charging state;
Figure FDA00035603930000000311
indicating that the electric vehicle is in a discharging state; i isi,kIs a binary number, i.e. Ii,k1 is in charge-discharge state, Ii,k0 is the other state; q is the power consumption per kilometer of the battery, diThe driving mileage of the electric automobile in the ith section of the journey is obtained;
the battery SOC constraint is:
Figure FDA00035603930000000312
Figure FDA00035603930000000313
wherein Q is0Is the battery capacity, SOCmaxIs the maximum value of the SOC of the battery, SOCminIs the minimum value of the SOC of the battery, SOCi,kRepresents the battery SOC at the kth time in the ith trip,
Figure FDA00035603930000000314
represents the charging electric power at the kth moment in the ith journey of the electric vehicle,
Figure FDA00035603930000000315
the charging time of the k +1 th time in the ith journey of the electric automobile is shown,
Figure FDA00035603930000000316
represents the discharge electric power of the electric automobile at the kth moment in the ith stroke,
Figure FDA00035603930000000317
the discharging time represents the k +1 th time in the ith stroke of the electric automobile;
the travel constraint of the electric automobile is as follows:
Figure FDA0003560393000000041
wherein, b is 1,2, …, m represents different stroke sections;
the charge and discharge time constraints are as follows:
Figure FDA0003560393000000042
Figure FDA0003560393000000043
Figure FDA0003560393000000044
Figure FDA0003560393000000045
wherein, ti,k+1For the electric automobile at the (k + 1) th moment in the ith section of the travel every day, ti,kFor the electric automobile at the kth moment in the ith journey every day,
Figure FDA0003560393000000046
at the moment when the electric vehicle is charged during driving,
Figure FDA0003560393000000047
the moment when the electric vehicle is in driving to discharge V2G is shown.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107392462A (en) * 2017-07-18 2017-11-24 上海电力学院 A kind of grid-connected dispatching method of electric automobile for considering sort feature
CN109034648A (en) * 2018-08-13 2018-12-18 华南理工大学广州学院 A kind of electric car cluster demand response potential evaluation method
CN110674575A (en) * 2019-09-20 2020-01-10 华北电力大学（保定） Electric vehicle cluster charging demand and discharging capacity model modeling method based on time sequence traveling set
CN111428946A (en) * 2020-04-28 2020-07-17 三峡大学 Distributed optimal scheduling method for supply side of charging and storage station

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2014039389A (en) * 2012-08-15 2014-02-27 Honda Motor Co Ltd Renewable energy storing/charging management system
CN107133415B (en) * 2017-05-22 2019-07-23 河海大学 It is a kind of to consider the satisfied electric car charge and discharge Electric optimization with distribution safety of user
CN107104454A (en) * 2017-06-06 2017-08-29 重庆大学 Meter and the optimal load flow node electricity price computational methods in electric automobile power adjustable control domain
CN108288112B (en) * 2018-01-30 2020-08-21 广西电网有限责任公司柳州供电局 Regional electric vehicle charging station load prediction method based on user travel simulation
CN111178619B (en) * 2019-12-25 2023-11-07 东南大学 Multi-objective optimization method considering distributed power supply and charging station joint planning
CN114004450A (en) * 2021-09-28 2022-02-01 国网山东省电力公司烟台供电公司 Ordered charging model guided by electric vehicle charging load interactive real-time pricing strategy

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107392462A (en) * 2017-07-18 2017-11-24 上海电力学院 A kind of grid-connected dispatching method of electric automobile for considering sort feature
CN109034648A (en) * 2018-08-13 2018-12-18 华南理工大学广州学院 A kind of electric car cluster demand response potential evaluation method
CN110674575A (en) * 2019-09-20 2020-01-10 华北电力大学（保定） Electric vehicle cluster charging demand and discharging capacity model modeling method based on time sequence traveling set
CN111428946A (en) * 2020-04-28 2020-07-17 三峡大学 Distributed optimal scheduling method for supply side of charging and storage station

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