CN112238781B - Electric automobile ordered charging control method based on layered architecture - Google Patents

Electric automobile ordered charging control method based on layered architecture Download PDF

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CN112238781B
CN112238781B CN202011058985.5A CN202011058985A CN112238781B CN 112238781 B CN112238781 B CN 112238781B CN 202011058985 A CN202011058985 A CN 202011058985A CN 112238781 B CN112238781 B CN 112238781B
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charging
load
electric vehicle
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time
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CN112238781A (en
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张琳娟
许长清
王利利
席乐
秦楠
张平
卢丹
周楠
郭璞
郑征
李锰
李景丽
陈星�
杨旭晨
时永凯
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Zhengzhou University
Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
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    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/63—Monitoring or controlling charging stations in response to network capacity
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/66—Data transfer between charging stations and vehicles
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Abstract

The invention discloses an electric automobile ordered charging control method based on a layered architecture, which comprises the following steps: establishing a probability model influencing the charging load factors of the electric automobile by using a Monte Carlo method; calculating the total load of the electric automobile under the condition of orderly charging according to the probability model; establishing an ordered charging control model based on a layered architecture, wherein the ordered charging control model comprises an upper charging control center and a lower charging station area which are connected, the upper charging control center optimizes the charging process of the electric automobile by utilizing a particle swarm algorithm according to the total load to obtain a power guide curve, the lower charging station area utilizes the particle swarm algorithm to solve the optimal charging time interval according to the power guide curve, and the ordered charging load demand is calculated according to the optimal charging time interval; and calculating and outputting an ordered charging load curve according to the ordered charging load demand and the base load. The invention can shift the peak period of the charging load of the electric automobile, and reduce the impact of the charging load of the electric automobile on a power grid.

Description

Electric automobile ordered charging control method based on layered architecture
Technical Field
The invention belongs to the technical field of electric automobile charging, and particularly relates to an electric automobile ordered charging control method based on a layered architecture.
Background
With the rapid development of global industrialization process, the consumption of non-renewable resources such as petroleum shows a gradually increasing trend, so that energy resources face a serious shortage problem; on the other hand, the natural environment is seriously tested due to the emission problem of carbon dioxide and the like, and people pay more and more attention to environmental protection due to the frequent occurrence of extremely severe weather. In order to alleviate problems such as insufficient natural resources and environmental pollution, electric vehicles have been favored in recent years. However, in the future, the large-scale electric vehicle is charged disorderly, which inevitably causes great influence on the stable and safe operation of the power grid, and the charging load of the disorderly electric vehicle can bring a series of problems of voltage drop, line overload, power grid load peak value increase, network loss and the like to the power distribution network. Therefore, it is necessary to research an ordered charging strategy of the electric vehicle, and to guide and control charging and discharging of the electric vehicle, so that the electric grid, the user and the electric vehicle company can obtain the maximum benefit.
At present, a large number of scholars develop related research work on the ordered charging control strategy of the electric automobile, and the obtained results are remarkable. The method comprises the following steps that a multi-objective optimization model of the charging station is established by adopting a particle swarm optimization with the aim of stabilizing load fluctuation and minimizing load peak-valley difference as targets and the charging power of each time period of the charging station as an optimization object; some charging and replacing stations are taken as research objects, aiming at the problems of load peak-valley difference increase and higher charging cost, the lowest all-day cost of the charging stations is taken as an optimization target, the initial charging time is taken as an optimization object, and an economic operation model of the charging and replacing stations under the condition of time-of-use electricity price is established; some centralized optimization problems which are usually adopted are decomposed into a plurality of sub-problems to be solved and optimized, the income of a charging station is maximized as an optimization target, and under time-of-use electricity price and fixed electricity price strategies, centralized optimization strategies and distributed optimization strategies are adopted to carry out simulation respectively, so that load prediction results under the conditions of disordered and ordered charging of the electric vehicle are obtained.
Disclosure of Invention
The invention provides an electric vehicle ordered charging control method based on a layered architecture, aiming at the problems that the disordered charging brings great load to a power grid and causes line overload and network loss, the capacity limit of a charging station is considered, an electric vehicle double-layer optimized control model is established, the electricity consumption cost is reduced, the charging time requirement of a user is met, the load variance is reduced, and peak clipping and valley filling are taken as targets, the ordered charging control of an electric vehicle is realized, the peak period of the electric vehicle charging load is shifted backwards, and the peak period of the electricity consumption at night is shifted to the low valley period of the electricity consumption in the next morning.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
an electric automobile ordered charging control method based on a layered architecture comprises the following steps:
s1, establishing a probability model influencing the charging load factors of the electric automobile by using a Monte Carlo method;
s2, calculating the total load of the electric automobile under the ordered charging according to the probability model obtained in the step S1;
s3, establishing an ordered charging control model based on a layered architecture, wherein the ordered charging control model comprises an upper charging control center and a lower charging station, the upper charging control center and the lower charging station are connected, the upper charging control center optimizes the charging process of the electric vehicle by using a particle swarm algorithm according to the total load under ordered charging obtained in the step S2 to obtain a power guidance curve, the lower charging station solves the optimal charging time interval of the electric vehicle by using the particle swarm algorithm according to the power guidance curve, and the ordered charging load demand is calculated according to the optimal charging time interval;
and S4, calculating an ordered charging load curve according to the ordered charging load demand obtained in the step S3 and the corresponding basic load, and outputting a power guide curve and an ordered charging load curve of the electric automobile.
In step S1, the probabilistic models include a start state of charge probabilistic model, a start time of charge probabilistic model, and a charge duration probabilistic model.
The initial state of charge probability model is:
Figure BDA0002711677150000021
in the formula ISOCRepresents the initial state of charge of the electric vehicle, f (I)SOC) Indicating the initial state of charge of an electric vehicle ISOCOf a probability function of1Indicating the initial state of charge of an electric vehicle ISOCMean value of (a)1Indicating the initial state of charge of an electric vehicle ISOCStandard deviation of (d);
the probability model of the initial charging time is as follows:
Figure BDA0002711677150000022
wherein t represents the initial charging time of the electric vehicle, f (t) represents the probability function of the initial charging time t, mu2Denotes the mean value, σ, of the initial charging time t2Represents the standard deviation of the initial charging time t;
the charging duration probability model is as follows:
Figure BDA0002711677150000023
in the formula, TcIndicating the charging period of the electric vehicle, EsocRepresenting a desired charging target state of charge of the electric vehicle; e represents the battery capacity of the electric vehicle, PcThe charging power of the electric vehicle is indicated, and η indicates the charging efficiency.
In step S2, the calculating the total load under the ordered charging of the electric vehicle includes the following steps:
s2.1, initializing basic parameters of the electric automobile under the ordered charging;
s2.2, initializing the initial charge states of all the electric vehicles and the initial charging time of each electric vehicle by using a Monte Carlo method according to the initial charge state probability model and the initial charging time probability model obtained in the step S1;
s2.3, setting the expected charging target state of charge of the electric automobile to be 1, and calculating the charging time of each electric automobile according to the initial state of charge of the electric automobile obtained in the step S2.2 and the charging time probability model obtained in the step S1;
s2.4, dividing one day into L time intervals, and counting the number of the electric vehicles charged in each time interval according to the initial charging time and the charging time of each electric vehicle;
and S2.5, calculating the total load of each time period in the next day of ordered charging according to the number of the electric vehicles which are charged in each time period obtained in the step S2.4 and the base load of the corresponding time period.
In step S2.5, the calculation formula of the total load of each time slot in the next day of the ordered charging is:
Phm,j＝Pc*a(j)+Pb,j；
in the formula, Phm,jRepresenting the total load at the jth time interval of the next day of ordered charging, a (j) representing the number of electric vehicles charged at the jth time interval of the day, Pb,jRepresenting the base load at the jth time of day.
In step S3, the upper charging control center optimizes the charging process of the electric vehicle by using a particle swarm algorithm according to the total load under the ordered charging obtained in step S2 to obtain a power guidance curve, and the lower charging station uses the particle swarm algorithm to solve the optimal charging time period of the electric vehicle according to the power guidance curve and calculate the ordered charging load demand according to the optimal charging time period, including the following steps:
s3.1, setting the maximum simulation times Num and initializing the current simulation times m.
S3.2, the upper charging control center adopts a particle swarm algorithm to optimize the charging process of the electric automobile to obtain a power guide curve according to the total load of each time interval in the next day of ordered charging by setting a target function with the minimum total load variance and a constraint condition that the maximum power limit of a lower platform charging station is not exceeded;
s3.3, the lower-layer platform area charging station sets an optimization objective function according to the power guide curve obtained in the step S3.2 and the minimum load peak-valley difference, sets a constraint condition that the maximum power limit value of the lower-layer platform area charging station is not exceeded, the charging time period meets the actual condition and the battery state continuity, and utilizes a particle swarm algorithm to solve the optimal charging time period of the electric vehicle by changing the charging time period of the electric vehicle;
s3.4, the charging station of the lower-layer platform area calculates the charging quantity of the electric vehicles at each time period in one day according to the optimal charging time period of the electric vehicles obtained in the step S3.3, and calculates the charging load requirements of the electric vehicles at each time period in one day according to the charging quantity of the electric vehicles;
and S3.5, comparing the current simulation frequency m with the maximum simulation frequency Num, if m is less than Num, making m equal to m +1, respectively and correspondingly updating the total load of each time interval in the next day of ordered charging according to the charging load requirements of each time interval in the day obtained in the step S3.4, and returning to the step S3.2, otherwise, executing the step S4.
The objective function with the minimum total load variance is as follows:
Figure BDA0002711677150000031
in the formula, F1Representing the objective function, P, with minimum variance of the total loadb,jRepresents the base load of the electric vehicle at the jth time of day, Ps,jIndicating the guidance load corresponding to the jth time interval in the power guidance curve issued by the upper charging control center,
Figure BDA0002711677150000041
representing the average of the expected total loads at the jth time of day;
average expected total load at jth time of day
Figure BDA0002711677150000042
The corresponding formula is:
Figure BDA0002711677150000043
the constraint condition not exceeding the maximum power limit of the lower-layer platform area charging station is as follows:
P(j)≤Pmax,j∈(1,2,...,L)；
wherein P (j) represents the total power in the j-th time period of the day, PmaxRepresenting the maximum power limit of the lower-floor platform charging station.
The optimization objective function is:
F2＝λ1min(PEV,j)+λ2ρ(Ps,j,PEV,j)；
in the formula, F2Representing an optimization objective function, PEV,jThe charging load requirement of the electric automobile in the jth time period in one day is represented, rho represents a correlation coefficient, and lambda represents1And λ2All represent a weight coefficient, Ps,jIndicating a guidance load corresponding to a jth time interval in a power guidance curve issued by an upper charging control center;
the constraint conditions that the maximum power limit value and the charging time period of the charging station not exceeding the lower-layer transformer area accord with the actual situation and the battery state continuity are respectively as follows:
the maximum power limit value of a lower-layer platform area charging station is not exceeded:
P(j)≤Pmax,j∈(1,2,...,L)；
the charging time period conforms to the practical situation constraint:
tin,i≤ton,i≤t≤min{tout,i,ton,i+Tc}；
in the formula: t is tin,iAnd tout,iRespectively representing the time of the ith electric vehicle accessing and leaving the charging station of the lower platform area; t is ton,iIndicating the time when the ith electric automobile starts to be charged; t iscIs the charging period.
Third, constraint of continuity of battery state:
Figure BDA0002711677150000044
in the formula: sj+1Represents the state of charge of the electric vehicle in the j +1 th time period, SjRepresents the state of charge, T, of the electric vehicle in the jth time intervalc,jRepresents the charging time of the electric vehicle in the jth time period, eta represents the charging efficiency, E represents the battery capacity of the electric vehicle, PcRepresents the charging power of the electric vehicle.
The invention has the beneficial effects that: the method establishes a corresponding probability model for factors influencing the charging load of the electric automobile, establishes a double-layer ordered charging control model, and optimizes the ordered charging control model by adopting a particle swarm algorithm, so that the peak period of the charging load of the electric automobile is shifted backwards, the peak period of the basic power load is avoided, and compared with disordered charging, the method has the advantages of reducing the impact of the charging load of the electric automobile on a power grid, stabilizing a total load curve and reducing the cost of users.
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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 flowchart of an orderly charging control strategy for an electric vehicle based on a layered architecture according to the present invention.
Fig. 2 is a schematic view of a charging load without a base load under the disordered charging and under the ordered charging.
Fig. 3 is a schematic view of a charging load including a base load under the disordered charging and under the ordered charging.
Fig. 4 is a diagram showing an update method of the particle position of each generation of the particle swarm algorithm.
FIG. 5 is a flow chart of a particle swarm algorithm.
FIG. 6 is a schematic diagram of an ordered charge control model.
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.
To facilitate an understanding of the present application, the methods used in the present application will now be explained:
the Monte Carlo method (Monte Carlo method) is a statistical simulation method guided by the probability statistical theory. The basic idea is to establish a probability model for relevant factors according to the random characteristics of practical problems, generate a large number of random number sequences through the probability distribution of variables, thereby obtaining a series of experimental samples, and take the result obtained by simulation as the practical approximate solution, wherein the more the test times are, the more accurate the obtained solution is.
Particle Swarm Optimization (PSO) is a method for solving an optimization problem by simulating the foraging behavior of a bird swarm. The adaptive value of each particle can be obtained according to the parameters of the position, the speed and the like of the particle, and the optimal result is obtained by continuously comparing the individual optimal value with the global optimal value.
As shown in fig. 4, x represents the position of the particle, and the position of the particle is influenced by the flight speed of the particle, the self-memory of the particle, and the population during the process of finding the optimal solution, and the particle is continuously updated by tracking two extrema, namely an individual extremum and a global extremum. Assuming that the dimension of the target search space is D, the size of the population is N, wherein the vector dimension of the ith particle is D
Xi＝(xi1,xi2,…,xiD)；(1)
In the formula, xiDRepresents the D-dimensional coordinate corresponding to the position of the ith particle, and i is 1,2, …, N, XiRepresenting the coordinate position of the ith particle with the space dimension of D dimension;
the flight velocity of the ith particle is recorded as:
Vi＝(vi1,vi2,…,viD)；(2)
in the formula, ViRepresenting the flight velocity, v, of the ith particle in the D dimensioniDRepresenting the D-dimension flight speed corresponding to the ith particle;
the individual extremum of the ith particle is noted as:
Pbest＝(pi1,pi2,…,piD)；(3)
in the formula, PbestRepresenting individual extrema, p, of the ith particle resulting from synthesis of function values of the D-spaceiDA function value representing the ith particle in the D-dimensional space;
the global extremum of the whole particle swarm is recorded as:
gbest＝(pg1,pg2,…,pgD)；(4)
in the formula, gbestRepresenting the individual extremum of the particle, i.e. the global extremum, p, which is optimal for the individual extremum in the entire particle swarmgDAn objective function value representing a dimension D of particles having an optimal individual extremum throughout the population of particles;
after finding the individual extrema and the global extrema, the particles update the velocity and position according to the following two formulas:
vid＝w*vid+c1r1(pid-xid)+c2r2(pgd-xid)；(5)
in the formula, vidRepresenting the D-dimension flight speed corresponding to the ith particle after updating, w represents the inertia weight, pidRepresenting the D-coordinate position, x, of the corresponding particle when the ith particle obtains the individual extremumidIndicating that the ith particle currently corresponds to the D-coordinate position, pgdRepresenting the position of the D-coordinate of the particle corresponding to the global extremum, c1,c2All represent a learning factor, r1,r2All represent uniform random numbers within 0-1;
xid＝xid+vid。(6)
as shown in fig. 5, a flow chart of the particle swarm algorithm includes the following steps:
(1) initializing parameters such as positions, speeds and the like of all particles in a population;
(2) calculating the adaptive value Fit [ i ] of each particle according to an optimization problem;
(3) fit value Fit [ i ] of current particle]Historical extreme value p of current particlebest(i) Making a comparison if Fit [ i ]]>pbest(i) Then use Fit [ i]Replacement of pbest(i)；
(4) By comparing the adaptation value Fit [ i ] of the current particle]Global extreme g with current particlebest(i) Finding and obtaining a global optimal solution of the population if Fit [ i]>gbest(i) Then use Fit [ i]G is replaced bybest(i)；
(5) Updating the velocity v of the particle according to equation (5) and equation (6)iAnd position xi；
(6) And (4) judging whether a loop iteration ending condition is met, if so, exiting the loop iteration, and otherwise, returning to the step (2).
An electric automobile ordered charging control method based on a layered architecture comprises the following steps:
and S1, establishing a probability model influencing the charging load factors of the electric automobile by using a Monte Carlo method.
The charging load is greatly influenced by factors such as initial charge state, initial charging time, charging time and the like among the factors influencing the charging load of the electric automobile, and the probability model comprises an initial charge state probability model, an initial charging time probability model and a charging time probability model.
The initial state of charge probability model is:
Figure BDA0002711677150000071
in the formula ISOCRepresents the initial state of charge of the electric vehicle, f (I)SOC) Indicating the initial state of charge of an electric vehicle ISOCOf a probability function of1Indicating the initial state of charge of an electric vehicle ISOCMean value of (a)1Indicating the initial state of charge of an electric vehicle ISOCStandard deviation of (d);
the initial charge state is the percentage of the battery capacity in the total capacity of the battery at the initial charging moment of the electric automobile, and is the premise for obtaining the charging time of the electric automobile.
The probability model of the initial charging time is as follows:
Figure BDA0002711677150000072
wherein t represents the initial charging time of the electric vehicle, f (t) represents the probability function of the initial charging time t, mu2Show standMean value, σ, of the initial charging time t2Represents the standard deviation of the initial charging time t;
the initial charging time is a description of when the electric vehicle is connected to a power grid for charging, a general user selects a trip ending time as the charging initial time, and the initial charging time is a precondition for obtaining a charging period of the electric vehicle.
The charging duration probability model is as follows:
Figure BDA0002711677150000073
in the formula, TcIndicating the charging period of the electric vehicle, ESOCRepresenting a desired charging target state of charge of the electric vehicle; e represents the battery capacity of the electric vehicle, PcThe charging power of the electric vehicle is indicated, and η indicates the charging efficiency.
The charging time duration is a description of the charging time required for meeting the user requirements of the electric automobile, and is a premise for obtaining the charging time period of the electric automobile.
The initial charging time and the initial state of charge of the electric vehicle are both obtained from the National Household Travel Survey (NHTS) as a data source.
S2, calculating the total load of the electric automobile under the ordered charging according to the initial state of charge probability model obtained in the step S1, and the method comprises the following steps:
and S2.1, initializing basic parameters of the electric automobile under the ordered charging.
The basic parameters comprise the number N of the electric automobiles and the charging power PcCharging efficiency η and battery capacity E; in the present embodiment, the number N of electric vehicles is set to 200, the battery capacity E is set to 82kWh, and the charging power P is setc7kW, the charging efficiency eta is 0.9.
S2.2, utilizing the Monte Carlo method to carry out initial charge state I on all the electric vehicles according to the initial charge state probability model and the initial charge time probability model obtained in the step S1SOCAnd initializing the initial charging time t of each electric automobile.
The initial state of charge ISOCThe initial charging time t of the electric vehicle is randomly extracted by the probability distribution function which meets the normal distribution N (0.5, 0.01), the probability distribution function which meets the normal distribution N (17.6, 11.56) is randomly extracted by the probability distribution function which meets the normal distribution N (0.5, 0.01), and the probability distribution function which meets the normal distribution N (17.6, 11.56) are respectively obtained by the expectation and the variance which correspond to the initial charging state probability model and the initial charging time probability model.
S2.3, setting the expected charging target state of charge E of the electric automobile SOC1, the initial state of charge I of the electric vehicle obtained according to step S2.2SOCCalculating the charging time length T of each electric automobile by the basic parameters initialized in the step S2.1 and the charging time length probability model obtained in the step S1c。
S2.4, dividing one day into L time intervals, and according to the initial charging time T and the charging time T of each electric automobilecAnd counting the number of the electric vehicles which are charged in each time interval.
And S2.5, calculating the total load of each time period in the next day of ordered charging according to the number of the electric vehicles which are charged in each time period obtained in the step S2.4 and the base load of the corresponding time period.
The calculation formula of the total load is as follows:
Phm,j＝Pc*a(j)+Pb,j；
in the formula, Phm,jRepresenting the total load at the jth time interval of the next day of ordered charging, a (j) representing the number of electric vehicles charged at the jth time interval of the day, Pb,jRepresenting the base load at the jth time of day;
in this embodiment, L is 1440, which means that 24 hours are divided into 1440 periods at intervals of 1 min.
And S3, as shown in FIG. 6, establishing an ordered charging control model based on a layered architecture, wherein the ordered charging control model comprises an upper charging control center and a lower charging station, the upper charging control center is connected with the lower charging station, the upper charging control center optimizes the charging process of the electric vehicle according to the total load under ordered charging obtained in the step S2 by using a particle swarm algorithm to obtain a power guidance curve, and the lower charging station solves the optimal charging time period of the electric vehicle by using the particle swarm algorithm according to the power guidance curve and calculates the ordered charging load demand according to the optimal charging time period.
The upper charging control center optimizes the charging process of the electric vehicle by using a particle swarm algorithm according to the total load of each time interval in the next day of the ordered charging obtained in the step S2 to obtain a power guide curve, the lower charging station uses the particle swarm algorithm to solve the optimal charging time interval of the electric vehicle according to the power guide curve, and calculates the ordered charging load demand according to the optimal charging time interval, and the method comprises the following steps:
and S3.1, setting the maximum simulation time Num to be 4, and initializing the current simulation time m to be 1.
And S3.2, the upper charging control center adopts a particle swarm algorithm to optimize the charging process of the electric automobile to obtain a power guide curve according to the total load of each time interval in the next day of ordered charging by setting a target function with the minimum total load variance and a constraint condition that the maximum power limit of the lower charging station is not exceeded.
The objective function with the minimum total load variance is as follows:
Figure BDA0002711677150000091
in the formula, F1Representing the objective function, P, with minimum variance of the total loadb,jRepresents the base load of the electric vehicle at the jth time of day, Ps,jIndicating the guidance load corresponding to the jth time interval in the power guidance curve issued by the upper charging control center,
Figure BDA0002711677150000092
representing the average of the expected total loads at the jth time of day;
average expected total load at jth time of day
Figure BDA0002711677150000093
The corresponding formula is:
Figure BDA0002711677150000094
the constraint condition not exceeding the maximum power limit of the lower-layer platform area charging station is as follows:
P(j)≤Pmax,j∈(1,2,...,L)；
in the formula: p (j) represents the total power in the j-th time period of the day, PmaxThe power limit of the lower floor platform charging station is shown, and is generally 2400 kW.
And S3.3, the lower-layer platform area charging station sets an optimization objective function according to the power guidance curve obtained in the step S3.2 and the minimum load peak-valley difference, sets constraint conditions that the maximum power limit value of the lower-layer platform area charging station is not exceeded, the charging time period accords with the actual condition and the battery state continuity, and solves the optimal charging time period of the electric vehicle by changing the charging time period of the electric vehicle through a particle swarm algorithm.
The power guidance curve is one of important reference factors of the optimization target of the lower-layer platform charging station, and in order to reduce impact caused by overlarge load peak-valley difference caused by charging of the electric vehicle, the actual charging load of the lower-layer platform charging station is required to follow the power guidance curve as much as possible, so that the load peak-valley difference of the lower-layer platform charging station is required to be considered.
The optimization objective function is:
F2＝λ1min(PEV,j)+λ2ρ(Ps,j,PEV,j)；
in the formula, F2Representing an optimization objective function, PEV,jRepresents the charging load demand of the electric automobile in the j-th time period of the day, and when m is 1, PEV,j＝Phm,jWhen m is>1 time, PEV,jOptimized according to an optimization objective function F2, wherein rho represents a correlation coefficient, and lambda represents the correlation coefficient1And λ2All represent a weight coefficient, Ps,jIn a power guide curve issued by an upper charging control centerCorresponding to the instructional load for the jth time period.
The constraint conditions that the maximum power limit value and the charging time period of the charging station not exceeding the lower-layer transformer area accord with the actual situation and the battery state continuity are respectively as follows:
the maximum power limit value of a lower-layer platform area charging station is not exceeded:
P(j)≤Pmax,j∈(1,2,...,L)；
the charging time period conforms to the practical situation constraint:
tin,i≤ton,i≤t≤min{tout,i,ton,i+Tc}；
in the formula, tin,iAnd tout,iRespectively representing the time of the ith electric vehicle accessing and leaving the charging station of the lower platform area; t is ton,iIndicates the time T for charging the ith electric vehiclecIndicating the charging period.
Third, constraint of continuity of battery state:
Figure BDA0002711677150000101
in the formula, Sj+1Represents the state of charge of the electric vehicle in the j +1 th time period, SjRepresents the state of charge, T, of the electric vehicle in the jth time intervalc,jRepresents the charging time of the electric vehicle in the jth time period, eta represents the charging efficiency, E represents the battery capacity of the electric vehicle, PcRepresents the charging power of the electric vehicle.
And S3.4, calculating the charging quantity of the electric vehicles at each time period in one day by the charging station of the lower-layer platform area according to the optimal charging time period of the electric vehicles obtained in the step S3.3, and calculating the charging load requirements of the electric vehicles at each time period in one day according to the charging quantity of the electric vehicles.
And S3.5, comparing the current simulation frequency m with the maximum simulation frequency Num, if m is less than Num, making m equal to m +1, respectively and correspondingly updating the total load of each time interval in the next day of ordered charging according to the charging load requirements of each time interval obtained in the step S3.4, returning to the step S3.2, otherwise, executing the step S4.
And optimizing the corresponding objective function value more than the last time through simulation, wherein the obtained charging load requirement is the final charging load requirement when the maximum simulation times are reached. In the ordered charging control model, the electric automobile is charged through the lower-layer platform region charging stations respectively, the lower-layer platform region charging stations receive the charging requirements of the electric automobile and upload the charging requirements to the upper-layer charging control center, the upper-layer charging control center issues a power guidance curve to charge the lower-layer platform region charging stations, and the lower-layer platform region charging stations obtain ordered charging load curves according to the power guidance curve and the optimization objective function.
And S4, the upper charging control center calculates an ordered charging load curve according to the basic load obtained in the step S3 and corresponding to the ordered charging load demand superposition, and outputs a power guide curve and an ordered charging load curve of the electric automobile.
The ordered charging load curve can also be understood as a power following curve, the obtained ordered charging load curve is compared with the power guide curve, and whether the power guide curve is effectively executed or not can be verified by comparing the correlation between the ordered charging load curve and the power guide curve; the ordered charging load curve is compared with the unordered charging load curve, so that the effect of the ordered charging control method on stabilizing load fluctuation and eliminating peaks and valleys can be verified.
In order to verify the above effect, the following charging loads of all electric vehicles in a day under the disordered charging are calculated based on the monte carlo method according to the probability model established in step S1, as shown in fig. 1, including the following steps:
a, initializing basic parameters and a current simulation number M' of the disordered charging electric automobile, and setting a maximum simulation number M under disordered charging;
the basic parameters comprise the number N of the electric automobiles and the charging power PcBattery capacity E, charging efficiency eta and maximum simulation times M; in the present embodiment, the number N of electric vehicles charged in order is 200, the maximum simulation number M is 10, the battery capacity E of the electric vehicle is 82kWh, and the charging power P of the electric vehicle is setc7kW, charging efficiency eta is 0.9, and the current simulation number m' is 1.
b, utilizing a Monte Carlo method to carry out initial charge state I on all electric vehicles according to the initial charge state probability model and the initial charge time probability modelSOCInitializing the initial charging time t of each electric automobile;
initial state of charge ISOCRandomly extracting probability distribution functions meeting normal distribution N (0.5, 0.01) and meeting the probability distribution functions of normal distribution N (0.5, 0.01); the initial charging time t of the electric vehicle is randomly extracted by a probability distribution function satisfying a normal distribution N (17.6, 11.56).
c, setting the expected charging target state of charge E of the electric automobileSOCThe initial state of charge I of the electric automobile obtained according to the step b is 1SOCCalculating the charging time length T of each electric automobile by the charging time length probability modelc；
d, calculating the charging time length T according to the initial charging time T obtained in the step b and the charging time length T calculated in the step ccCounting the number of the electric vehicles which are charged at each time interval in one day;
e, calculating the total charging load of all electric vehicles in each time period in one day under the disordered charging according to the charging quantity of the electric vehicles which are charged in each time period obtained in the step d and the basic load of the corresponding time period;
the calculation formula of the total charging load is as follows:
Ph'm',j＝Pc*a'(j)+Pb,j；
in formula (II), Ph'm',jRepresents the total charging load of all electric vehicles in the j th time period of the day under the unordered charging calculated in the m 'th simulation, a' (j) represents the number of the electric vehicles in the j th time period of the day under the unordered charging, and Pb,jRepresenting the base load at the jth time of day.
f, comparing the current simulation number M 'with the maximum simulation number M, if M' is less than M, making M equal to M +1, returning to the step b, otherwise, executing the step g;
g, calculating the total charging of all electric vehicles in each time period in one day under the condition of disordered charging according to each simulationLoad calculation total charging load mean value of corresponding time interval
Figure BDA0002711677150000121
Namely the total load of the electric automobile charged in disorder;
Figure BDA0002711677150000122
in the formula (I), the compound is shown in the specification,
Figure BDA0002711677150000123
represents the average value of the total charging load of all the electric automobiles in the j-th time period in the day under the disordered charging.
In order to verify the effect of the invention, taking a certain city as an example, basic parameters of the electric vehicle are reasonably set, and as shown in table 1, the ordered charging load curve and other related simulation results of the electric vehicle are calculated based on the ordered charging control strategy of the electric vehicle with a layered framework.
TABLE 1 ordered charging example parameters
Figure BDA0002711677150000124
Through simulation calculation, the charging load conditions of the electric automobile after disorder and ordered control are obtained, as shown in fig. 2. It can be seen that if no guiding control is added to the charging, the peak period of the charging load of the electric automobile is positioned in the period of 17:00-23:00 at night, and is concentrated in the end period of one-day driving of the user and is coincided with the peak period of the power consumption of the basic load; under the condition of orderly charging, the peak value of the charging load of the electric automobile moves backwards and is transferred to the next morning in the period of 00:00-05:00, so that the impact of the charging load of the electric automobile on a power grid in the peak period of power utilization is relieved.
Fig. 3 shows the total load curve before and after the electric vehicle orderly control, i.e. the base load and the charging load of the electric vehicle. It can be seen that the electricity consumption peak period of the disordered charging coincides with the base load peak period, and the peak-valley difference is 2016.7 kW; the load peak value is transferred by the ordered charging, so that the load in the low valley period is filled, the whole charging load is more stable, and the difference between the ordered charging peak and the ordered charging valley is 1029.1 kW; the peak-valley difference of ordered charging is reduced by 43.9% compared with that of unordered charging.
In addition, the ordered charging strategy also has a great effect on reducing the charging cost of the user. The expenses for each period of peak-valley and total expenses in both the disordered and ordered charging cases as shown in table 2 can be calculated by setting the price of electricity according to the zheng state city policy. It can be seen that after the sequential charging optimization is carried out, the total cost is reduced by 28.96%, and the electricity consumption cost is obviously reduced.
TABLE 2 electric vehicle cost during peak-to-valley electricity price period
Figure BDA0002711677150000131
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (5)

1. A control method for orderly charging an electric automobile based on a layered architecture is characterized by comprising the following steps:
s1, establishing a probability model influencing the charging load factors of the electric automobile by using a Monte Carlo method;
s2, calculating the total load of the electric automobile under the ordered charging according to the probability model obtained in the step S1;
s3, establishing an ordered charging control model based on a layered architecture, wherein the ordered charging control model comprises an upper charging control center and a lower charging station, the upper charging control center and the lower charging station are connected, the upper charging control center optimizes the charging process of the electric vehicle by using a particle swarm algorithm according to the total load under ordered charging obtained in the step S2 to obtain a power guidance curve, the lower charging station solves the optimal charging time interval of the electric vehicle by using the particle swarm algorithm according to the power guidance curve, and the ordered charging load demand is calculated according to the optimal charging time interval;
s4, calculating an ordered charging load curve according to the ordered charging load demand obtained in the step S3 and the corresponding basic load, and outputting a power guide curve and an ordered charging load curve of the electric automobile;
in step S1, the probability models include an initial state of charge probability model, an initial charging time probability model, and a charging duration probability model;
the initial state of charge probability model is:
Figure FDA0003250346150000011
in the formula ISOCRepresents the initial state of charge of the electric vehicle, f (I)SOC) Indicating the initial state of charge of an electric vehicle ISOCOf a probability function of1Indicating the initial state of charge of an electric vehicle ISOCMean value of (a)1Indicating the initial state of charge of an electric vehicle ISOCStandard deviation of (d);
the probability model of the initial charging time is as follows:
Figure FDA0003250346150000012
wherein t represents the initial charging time of the electric vehicle, f (t) represents the probability function of the initial charging time t, mu2Denotes the mean value, σ, of the initial charging time t2Represents the standard deviation of the initial charging time t;
the charging duration probability model is as follows:
Figure FDA0003250346150000013
in the formula, TcIndicating the charging period of the electric vehicle, EsocRepresenting a desired charging target state of charge of the electric vehicle; e represents the electricity of an electric automobilePool volume, PcThe charging power of the electric vehicle is represented, and eta represents the charging efficiency;
in step S2, the calculating the total load under the ordered charging of the electric vehicle includes the following steps:
s2.1, initializing basic parameters of the electric automobile under the ordered charging;
s2.2, initializing the initial charge states of all the electric vehicles and the initial charging time of each electric vehicle by using a Monte Carlo method according to the initial charge state probability model and the initial charging time probability model obtained in the step S1;
s2.3, setting the expected charging target state of charge of the electric automobile to be 1, and calculating the charging time of each electric automobile according to the initial state of charge of the electric automobile obtained in the step S2.2 and the charging time probability model obtained in the step S1;
s2.4, dividing one day into L time intervals, and counting the number of the electric vehicles charged in each time interval according to the initial charging time and the charging time of each electric vehicle;
and S2.5, calculating the total load of each time period in the next day of ordered charging according to the number of the electric vehicles which are charged in each time period obtained in the step S2.4 and the base load of the corresponding time period.
2. The method for controlling ordered charging of an electric vehicle based on a layered architecture as claimed in claim 1, wherein in step S2.5, the calculation formula of the total load of each time interval in the next day of ordered charging is:
Phm,j＝Pc*a(j)+Pb,j；
in the formula, Phm,jRepresenting the total load at the jth time interval of the next day of ordered charging, a (j) representing the number of electric vehicles charged at the jth time interval of the day, Pb,jRepresenting the base load at the jth time of day.
3. The method for controlling ordered charging of electric vehicles according to claim 2, wherein in step S3, the upper charging control center optimizes the charging process of the electric vehicle by using a particle swarm algorithm according to the total load under ordered charging obtained in step S2 to obtain a power guidance curve, and the lower charging station in the platform area solves the optimal charging time period of the electric vehicle by using the particle swarm algorithm according to the power guidance curve, and calculates the ordered charging load demand according to the optimal charging time period, comprising the following steps:
s3.1, setting a maximum simulation time Num and initializing a current simulation time m;
s3.2, the upper charging control center adopts a particle swarm algorithm to optimize the charging process of the electric automobile to obtain a power guide curve according to the total load of each time interval in the next day of ordered charging by setting a target function with the minimum total load variance and a constraint condition that the maximum power limit of a lower platform charging station is not exceeded;
s3.3, the lower-layer platform area charging station sets an optimization objective function according to the power guide curve obtained in the step S3.2 and the minimum load peak-valley difference, sets a constraint condition that the maximum power limit value of the lower-layer platform area charging station is not exceeded, the charging time period meets the actual condition and the battery state continuity, and utilizes a particle swarm algorithm to solve the optimal charging time period of the electric vehicle by changing the charging time period of the electric vehicle;
s3.4, the charging station of the lower-layer platform area calculates the charging quantity of the electric vehicles at each time period in one day according to the optimal charging time period of the electric vehicles obtained in the step S3.3, and calculates the charging load requirements of the electric vehicles at each time period in one day according to the charging quantity of the electric vehicles;
and S3.5, comparing the current simulation frequency m with the maximum simulation frequency Num, if m is less than Num, making m equal to m +1, respectively and correspondingly updating the total load of each time interval in the next day of ordered charging according to the charging load requirements of each time interval in the day obtained in the step S3.4, returning to the step S3.2, otherwise, executing the step S4.
4. The method for controlling orderly charging of the electric vehicle based on the hierarchical architecture according to claim 3, wherein the objective function with the minimum total load variance is as follows:
Figure FDA0003250346150000031
in the formula, F1Representing the objective function, P, with minimum variance of the total loadb,jRepresents the base load of the electric vehicle at the jth time of day, Ps,jIndicating the guidance load corresponding to the jth time interval in the power guidance curve issued by the upper charging control center,
Figure FDA0003250346150000033
representing the average of the expected total loads at the jth time of day;
average expected total load at jth time of day
Figure FDA0003250346150000034
The corresponding formula is:
Figure FDA0003250346150000032
the constraint condition not exceeding the maximum power limit of the lower-layer platform area charging station is as follows:
P(j)≤Pmax,j∈(1,2,...,L)；
wherein P (j) represents the total power in the j-th time period of the day, PmaxRepresenting the maximum power limit of the lower-floor platform charging station.
5. The method for controlling ordered charging of the electric vehicle based on the hierarchical architecture as claimed in claim 4, wherein the optimization objective function is:
F2＝λ1min(PEV,j)+λ2ρ(Ps,j,PEV,j)；
in the formula, F2Representing an optimization objective function, PEV,jRepresents the charging load requirement of the electric automobile in the jth time period of a day, and rho represents the phaseCoefficient of correlation, λ1And λ2All represent a weight coefficient, Ps,jIndicating a guidance load corresponding to a jth time interval in a power guidance curve issued by an upper charging control center;
the constraint conditions that the maximum power limit value and the charging time period of the charging station not exceeding the lower-layer transformer area accord with the actual situation and the battery state continuity are respectively as follows:
the maximum power limit value of a lower-layer platform area charging station is not exceeded:
P(j)≤Pmax,j∈(1,2,...,L)；
the charging time period conforms to the practical situation constraint:
tin,i≤ton,i≤t≤min{tout,i,ton,i+Tc}；
in the formula: t is tin,iAnd tout,iRespectively representing the time of the ith electric vehicle accessing and leaving the charging station of the lower platform area; t is ton,iIndicating the time when the ith electric automobile starts to be charged; t iscIs the charging time;
third, constraint of continuity of battery state:
Figure FDA0003250346150000041
in the formula: sj+1Represents the state of charge of the electric vehicle in the j +1 th time period, SjRepresents the state of charge, T, of the electric vehicle in the jth time intervalc,jRepresents the charging time of the electric vehicle in the jth time period, eta represents the charging efficiency, E represents the battery capacity of the electric vehicle, PcRepresents the charging power of the electric vehicle.
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