CN116579475B - Electric vehicle charging scheduling and charging station configuration joint optimization method considering charging randomness - Google Patents

Electric vehicle charging scheduling and charging station configuration joint optimization method considering charging randomness Download PDF

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CN116579475B
CN116579475B CN202310508768.9A CN202310508768A CN116579475B CN 116579475 B CN116579475 B CN 116579475B CN 202310508768 A CN202310508768 A CN 202310508768A CN 116579475 B CN116579475 B CN 116579475B
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朱家伟
黄科杰
章恩友
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Zhejiang University ZJU
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Abstract

The invention discloses a joint optimization method for electric vehicle charging scheduling and charging station configuration under consideration of charging randomness, which is characterized in that the method predicts the charging demand of an electric vehicle by modeling the battery capacity of the electric vehicle charged by the arrival, and establishes the electric vehicle scheduling problem as a linear programming model; adding constraint on electric vehicle charging continuity, taking minimized user charging cost and charging station load variance as objective functions, and solving a linear programming model to obtain electric vehicle charging scheduling optimization results; and carrying out user charging randomness simulation on the obtained result by adopting Monte Carlo simulation, and determining the suitable construction scale of the charging station and the capacity of the energy storage equipment. The method can realize ordered charging of the electric automobile, thereby reducing peak-valley difference of charging station load, reducing charging station construction cost, and determining charging station construction scale and energy storage equipment capacity by simulating randomness of user charging behavior.

Description

Electric vehicle charging scheduling and charging station configuration joint optimization method considering charging randomness
Technical Field
The invention belongs to the technical field of electricity service, and particularly relates to a joint optimization method for electric vehicle dispatching and charging station configuration by considering uncertainty of charging behavior of a user.
Background
The increasing greenhouse effect causes frequent occurrence of extreme weather, and reduction of carbon emissions has become a focus of attention for various countries. Development and popularization of electric automobiles are one of the effective means adopted in various countries. For example, in recent years, under the great popularization of the China government, the China electric automobile conservation amount is rapidly increasing. The rapid increase in the number of electric vehicles requires a mating electric vehicle charging station to meet the charging needs of the electric vehicle owners. In addition, along with the improvement of green energy permeability, the electric automobile charging station of planning construction needs supporting construction energy storage equipment in order to realize the make full use of green energy electricity generation. However, the existing electric vehicle charging station has the disadvantages of single function, unmatched scale, low charging efficiency and the like.
At present, research on electric vehicle charging station construction is mainly based on static charging demand simulation, and the construction scale of the charging station is determined by predicting the charging condition of an electric vehicle charged to the station. However, this method cannot reduce the peak-valley difference of the charging station load caused by disordered charging of the electric vehicle, resulting in low utilization rate of the charging station and further resulting in waste of construction cost of the charging station of the electric vehicle.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a joint optimization method for dispatching electric vehicles and configuring the charging stations by considering the randomness of charging of users so as to realize ordered charging of the electric vehicles, thereby reducing the peak-valley difference of the load of the charging stations, reducing the construction cost of the charging stations and determining the construction scale of the charging stations and the capacity of energy storage equipment by simulating the randomness of charging behaviors of the users.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a joint optimization method for electric vehicle charging scheduling and charging station configuration under consideration of charging randomness comprises the following steps:
s1: modeling the battery capacity of the electric automobile charged by the station, and predicting the charging requirement of the electric automobile on the basis. And combining the electric vehicle charging requirement and a time-of-use electricity price mechanism, and establishing the electric vehicle scheduling problem as a linear programming model.
S2: and (3) adding constraint on charging continuity of the electric vehicle by using the linear programming model obtained in the step (S1), and solving the linear programming model by taking the minimized user charging cost and the charging station load variance as objective functions to obtain an electric vehicle charging scheduling optimization result.
S3: and (3) carrying out user charging randomness simulation on the electric vehicle dispatching optimization result in the step S2 by adopting Monte Carlo simulation. And determining the suitable charging station construction scale and energy storage equipment capacity by calculating the satisfaction rate of different charging station construction powers on the charging condition Monte Carlo simulation sample.
In S1, the battery capacity of the electric vehicle conforms to gaussian distribution, and the battery capacity of the electric vehicle is modeled by the gaussian distribution, as shown in the following formula:
EVB E ＝[EVB 1 EVB 2 … EVB e ]，e∈E
charging demand EVC of electric automobile e The method comprises the following steps:
EVC e ＝EVB e ×η
wherein X is the battery capacity of the electric automobile, f (X) is the probability density function of Gaussian distribution of the battery capacity of the electric automobile, mu cap 、σ cap The average value and standard deviation of the battery capacity of the electric automobile are calculated according to the statistical data respectively; e is an electric automobile set charged by arriving at station, EVB e Represents battery capacity of electric automobile e, EVB E For all electric automobile battery capacity sets, eta is interval [0.5,0.9]]Is a random number of (a) in the memory.
The solution decision variables for electric vehicle scheduling are shown as follows:
wherein,the charge amount of the electric vehicle numbered e in the charging period t is represented. />Representing the charge schedule of the electric vehicle numbered e over time T, +.> And (5) a set of charging scheduling conditions for all the electric automobiles.
For all electric vehicles charged to the station, the charging scheduling result must meet the charging requirement, as shown in the following formula:
to represent the charging state of the electric automobile, charging state decision variables of the electric automobile are added as shown in the formula:
the charge condition is constrained by the following formula:
wherein, the charge amount of the electric automobile e in the t periodBy state of charge decision variables->Lower charging limit LC of electric automobile min Charged LC max And (5) jointly determining.
The continuity of the charging time of each electric vehicle in the electric vehicle charging schedule optimization is often ignored, and if the continuity of the electric vehicle charging is not considered, the fully charged time of the electric vehicle can be delayed, so that the charging comfort of the electric vehicle user is affected. In S2, the constraint of the user charging continuity is:
wherein the variables areDetermining whether the electric automobile e is charged in the t period, j and k represent the j and k decision variables +.>j is cycled from the 3 rd charging period to the T-th period, and k is cycled from the 1 st period to the j-1 st period. When electric automobile plans to be->And->The period between these two corresponding periods is charged, i.e. +.>By +.>Constraint is made by the equation ∈ ->Constraint ensures continuous charging.
In S2, the objective function of the linear programming is:
min[α 1 ·Cost c +α 2 ·Variance]
wherein,α 1 and alpha 2 Two parameters are used for adjusting the weight of each sub-objective function; />Representing the charge quantity of the electric automobile with the number e in the period t, Q t Charging electricity price for t period; />And (3) for the load of the charging station in the period T, wherein T is the length of the charging scheduling time.
S3 specifically comprises the following steps:
s3.1: setting the number of Monte Carlo simulation samples;
s3.2: generating different Monte Carlo simulation samples according to the dispatching optimization result and the charging randomness in the S2;
s3.3: counting the maximum load condition of the charging stations in each sample;
s3.4: initializing charging station construction power, setting iteration variables, and setting termination conditions and maximum iteration times;
s3.5: updating the charging station construction power according to the iteration variable;
s3.6: calculating the sample satisfaction rate;
s3.7: judging whether a termination condition is met or the maximum iteration number is reached, if so, performing S3.8, and if not, performing S3.5;
s3.8: and obtaining the proper construction scale of the charging station and the capacity of the energy storage equipment.
And S3.2, the randomness of the user charging is mainly reflected in that the user cannot completely charge according to the dispatching result solved by the linear programming. A common reason for deviating from the scheduled regime is that the user delays or reaches the charging station in advance, resulting in the electric vehicle charging not taking place within the scheduled time. The Monte Carlo simulation method is adopted herein, and different samples are generated according to the randomness of user charging. The charging condition of the electric automobile in the sample is shown in the following formula:
wherein S is E (N) represents the nth Monte Carlo simulation sample, N is the set of samples,the charge of EV numbered e in this sample during period t is represented.
To represent the case where the EV user advances or delays charging to the station, the change in sample charging time is as follows:
wherein phi is the interval [ phi ] 1 ，φ 2 ]A random integer number within the range of the random number,the charging start time and the charging end time of the electric automobile are respectively indicated.
In S3.3, the maximum load of the charging station in the different samples is calculated by:
s3.4, the iteration variable beta is an increment variable with a fixed step length in the interval of 0-1;
in S3.5, different samples in Monte Carlo simulation represent the load conditions of the EVCS under different user charging behaviors, and the Gaussian distribution of the maximum load of the EVCS under the uncertainty of user charging is obtained by calculating the samples, wherein the average standard deviation is mu respectively s ，σ s . In order to make the construction power of the EVCS meet different conditions caused by the charging randomness as much as possible, in S3.5, the charging station construction power update formula is as follows:
E evcs ＝(1-β)×Load(n) min +β×Load(n) max
wherein, for random variables subject to a gaussian distribution, their maximum deviation from the mean is bounded by a confidence level of no more than 99%. Thus, load is set min ＝μ S -3σ s 、Loαd max ＝μ S +3σ s 。
The sample satisfaction rate λ in S3.6 is calculated by:
wherein omega 1 For Monte Carlo simulation of the radix, ω of sample set N 2 E corresponding to maximum load of charging station less than beta at time in Monte Carlo simulation sample evcs Is a sample number of (a) in a sample. When lambda reaches the expected value, E is selected at that time evcs The maximum power for EVCS construction.
The energy storage device capacity determination formula in S3.8 is as follows:
wherein,the power generated in the period t is generated by photovoltaic power generation and wind power generation respectively, and epsilon is the energy conversion rate of the energy storage equipment during charging and discharging.
Compared with the prior art, the invention has the following beneficial effects:
compared with the research of configuration optimization of other charging stations, firstly, when the electric vehicle charging station is constructed and optimized, the charging stations are configured according to the charging demand simulation condition, and two problems of short-term charging schedule of the electric vehicle and long-term charging station planning are comprehensively considered. Through the scheduling of charging to electric automobile, the peak load and the valley load of balanced charging station operation in-process have reduced charging station construction cost and have improved charging station operation's stability simultaneously. Secondly, the invention considers the charging continuity when the electric vehicle is charged and scheduled, and when the electric vehicle entering the electric vehicle charging station is charged and scheduled, the electric vehicle can be continuously, but not discretely charged and scheduled, and the charging efficiency of the user can be improved, and the charging comfort of the user can be improved. Finally, the invention considers the possibility that the actual charging condition is inconsistent with the ideal result of the dispatching optimization due to the randomness of the charging behavior of the user, simulates the factor through Monte Carlo simulation, and calculates the more reliable charging station construction scale according to the simulation result.
Drawings
FIG. 1 is a workflow diagram of the charging station configuration joint optimization of the present invention;
FIG. 2 is a simulation of the battery capacity of an electric vehicle in an example of the invention;
FIG. 3 is a charging station load profile after charging an electric vehicle in an example of the invention;
FIG. 4 is a diagram illustrating the power interaction between an unoptimized charge schedule charging station and an external power grid in an example of the present invention;
FIG. 5 is an illustration of the interaction of the optimized charge schedule charging station with external grid power in an example of the present invention;
FIG. 6 is a charge schedule for an electric vehicle with charge continuity in an example of the present invention;
FIG. 7 is a charging schedule for an electric vehicle without charging continuity in an example of the present invention;
FIG. 8 is a comparison of charging time required for charging schedule with or without charging continuity in an embodiment of the present invention;
FIG. 9 is a diagram illustrating charging station loading for a sample in Monte Carlo simulation according to an embodiment of the present invention;
FIG. 10 is a diagram of a Monte Carlo simulation of the maximum load condition of all sample charging stations in an embodiment of the invention;
fig. 11 illustrates a charging station construction power and sample unsatisfied rate condition in an embodiment of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
The present invention will be described in further detail below with reference to the accompanying drawings, taking the construction of an electric vehicle charging station in a north place as an example under the assumption of the location determination of the electric vehicle charging station:
the electric vehicle charging station provided by the invention is integrated with an intelligent system, and can receive data of electric vehicle charging requirements and schedule electric vehicle charging. Electric car charging stations also house wind, photovoltaic power generation and energy storage equipment and purchase and sell electrical energy with an external grid. The relevant cost parameters for electric vehicle charging station construction are shown in table 1.
TABLE 1
Apparatus and method for controlling the operation of a device Price of
Charging pile unit power installation cost (Yuan/kW) 1000
Cost per unit capacity of energy storage device (Yuan/kWh) 500
Charging pile scale unit power maintenance cost (Yuan/kW year) 200
Maintenance cost per unit capacity of energy storage equipment (Yuan/kWh. Year) 10
Purchasing power costs (Yuan/kWh) to an external grid 0.8
To the outsideNetwork selling electric power cost (Yuan/kWh) 0.5
Firstly, simulating the charging requirement of a charging vehicle at a station, summarizing the charging scheduling problem of the electric vehicle into a linear programming problem model, and simultaneously establishing a charging station scale, energy storage equipment capacity and a renewable energy power generation model. And then, solving the electric vehicle dispatching line type planning model by taking the minimized user charging cost and the charging station load variance as objective functions. And finally, according to a linear programming result, simulating the charging randomness of the user by using Monte Carlo simulation, and calculating an approximate optimal solution of the charging station configuration and the energy storage capacity by using the sample satisfaction rate as a condition. The specific workflow is shown in fig. 1. The electric vehicle charging scheduling and charging station configuration joint optimization method considering charging randomness in the embodiment specifically comprises the following steps:
(1) Modeling the battery capacity of the electric automobile charged by the station, and predicting the charging requirement of the electric automobile on the basis. And combining the electric vehicle charging requirement and a time-of-use electricity price mechanism, and establishing the electric vehicle dispatching optimization problem as a linear programming problem model.
(2) And (3) adding continuous constraint on charging of the electric vehicle on the basis of the linear programming model in the step (1), and solving the linear programming model by taking minimized user charging cost and charging station load variance as objective functions to obtain an electric vehicle charging scheduling optimization result.
(3) And (3) carrying out user charging randomness simulation on the electric vehicle dispatching optimization result in the step (2) by adopting Monte Carlo simulation. And determining the suitable charging station construction scale and energy storage equipment capacity by calculating the satisfaction rate of different charging station construction powers on the charging condition Monte Carlo simulation sample.
Specifically, in the step (1), the battery capacity of the electric automobile accords with gaussian distribution, and modeling is performed on the battery capacity of the electric automobile through gaussian distribution, as shown in formulas (1) and (2):
EVB E ＝[EVB 1 EVB 2 … EVB e ]；e∈E (2)
the base number of the electric automobile set E is 50, namely 50 electric automobiles charged by the station. The average value and standard deviation of the battery capacity of the electric automobile are mu respectively cap ＝68.02kWh、σ cap The battery capacity simulation is shown in fig. 2, which is =17.98.
The charge amount of the electric vehicle has uncertainty, and when the electric vehicle charge demand is modeled, the electric vehicle charge demand is converted into a group of demand matrixes without random variables. The invention generates the charge demand EVC of the electric automobile through formulas (3) and (4) on the basis of the battery capacity generated by Gaussian distribution E ：
EVC e ＝EVB e ×η (3)
EVC E ＝[EVC 1 EVC 2 … EVC e ]；e∈E (4)
Eta is a random number in interval [0.5,0.9 ].
The time-of-use electricity price mechanism is shown in table 2.
TABLE 2
Time Price of electricity
23:00 to 6:00 the next day 0.32
7:00-10:00 and 15: 00-18:00 0.75
10:00～15:00 and 18: 00-23: 00 0.96
The solution variable of the electric automobile dispatching in the linear programming model is shown in a formula (5):
wherein,the charge amount of the electric vehicle numbered e in the charging period t is represented. />Representing the charge schedule of the electric vehicle numbered e over time T, +.>In this experiment the base of E was 50, the length of scheduling t=24 hours, +.>And (5) a set of charging scheduling conditions for all the electric automobiles.
For all electric vehicles charged to the station, the charging scheduling result must meet the charging requirement, as shown in the following formula:
to represent the state of charge of the electric vehicle, add a state of charge decision variable of the electric vehicle, as shown in formula (7):
and the charging condition of the electric automobile is controlled through a formula (8) and a formula (9):
wherein, the charge amount of the electric automobile e in the t periodBy state of charge decision variables->Lower charging limit LC of electric automobile min Upper charge limit LC max Co-determination, LC min 、LC max 5kw and 15kw, respectively.
Specifically, in the step (2), the continuity of the charging time of each electric vehicle in the electric vehicle charging schedule optimization is often ignored, and if the continuity of the electric vehicle charging is not considered, the fully charged time of the electric vehicle is delayed, so that the charging comfort of the electric vehicle user is affected. And constraining the charging continuity of the electric automobile as shown in a formula (10).
Wherein the variables areDetermining whether the electric automobile e is charged in the t period, j and k represent the j and k decision variables +.>j is cycled from the third charging period to the T-th period, and k is cycled from the 1 st period to the j-1 st period. When electric automobile gaugeDraw in->And->The period between these two periods is charged, i.e.>By all k < t < jConstraint is made by the equation ∈ ->Constraint ensures continuous charging.
Establishing minimized user charging cost and charging station load variance as objective functions:
min[α 1 ·Cost c +α 2 ·Variance] (11)
wherein:
alpha in formula (11) 1 And alpha 2 Two parameters are used for adjusting the weight of each sub-objective function; in the formulas (12) to (13),representing the charge amount of the electric vehicle numbered e in the charge period t, Q t Charging electricity price for t period; />For charging station in period tAnd the load, T, is the length of the charge scheduling time.
Specifically, in step (3), because the situation that the user cannot completely charge according to the scheduling result solved by the linear programming is considered, the invention carries out the random simulation of charging to the user through Monte Carlo simulation, counts different sample situations, and determines the construction power of the final charging station, and specifically comprises the following steps:
a. setting the number of Monte Carlo simulation samples;
b. the randomness of the user charging is mainly reflected in that the user cannot completely charge according to the dispatching result solved by the linear programming. A common reason for deviating from the scheduled regime is that the user delays or reaches the charging station in advance, resulting in the electric vehicle charging not taking place within the scheduled time. The Monte Carlo simulation method is adopted herein, and different samples are generated according to the randomness of user charging. The charging condition of the electric automobile in the sample is shown in the following formula:
wherein S is E (N) represents the nth Monte Carlo simulation sample, N is the set of samples,the charge of EV numbered e in this sample during period t is represented.
To represent the case where the EV user advances or delays charging to the station, the change in sample charging time is as follows:
wherein phi is the interval [ phi ] 1 ，φ 2 ]A random integer number within the range of the random number,the charging start time and the charging end time of the electric automobile are respectively indicated.
c. The maximum load of the charging stations in the different samples was calculated by:
d. the iteration variable is set to be beta, beta is E [0,1], the step length is 0.02, and the sample satisfaction rate condition is lambda less than or equal to 95%.
e. Updating charging station construction power by formula (17):
E evcs ＝(1-β)×Load(n) min +β×Load(n) max (17)
wherein, for random variables subject to a gaussian distribution, their maximum deviation from the mean is bounded by a confidence level of no more than 99%. Thus, load is set min ＝μ S -3σ s 、Load max ＝μ S +3σ s 。
f. The sample satisfaction rate λ is calculated by:
wherein omega 1 For Monte Carlo simulation of the radix, ω of sample set N 2 E corresponding to beta when maximum load of charging station in Monte Carlo simulation sample is smaller than or equal to evcs Is a sample number of (a) in a sample. When lambda reaches the expected value, E is selected at that time evcs The maximum power for EVCS construction.
g. And (3) judging whether the sample satisfaction rate reaches 95% or the maximum iteration number (50 times), if the sample satisfaction rate reaches the condition, executing the step h, and if the sample satisfaction rate does not reach the condition, executing the step e.
h. Determining the construction scale of the charging station and the capacity of the energy storage equipment, and calculating the capacity of the energy storage equipment through the following steps:
wherein,the power generated in the period t is generated by photovoltaic power generation and wind power generation respectively, and epsilon is the energy conversion rate of the energy storage equipment during charging and discharging.
The effects of the present invention are further described below with reference to the accompanying drawings of experimental results:
in this example, it is assumed that a charging station capable of accommodating 50 electric vehicles is constructed, and the charging station has photovoltaic power generation and wind power generation equipment, and the energy storage system is used for fully using green energy to generate power. As shown in fig. 3, by scheduling charging of the electric vehicle, the peak-valley difference of the charging station load during disordered charging is effectively reduced. Fig. 4 and fig. 5 compare the interaction situation between the charging station and the external power grid before and after the optimization, and as can be seen from fig. 4, although there is more vending energy income when the period from 1 point to 10 points is not optimized, the charging peak from 18 points to 22 points causes more purchase power expenditure, and finally the interaction cost with the external power grid will be higher when the period from 18 points to 22 points is not optimized. Meanwhile, the charging continuity will affect the charging schedule, fig. 6 and fig. 7 show that the electric vehicle has charging continuity charging schedule and the electric vehicle has no charging continuity charging schedule, fig. 8 compares the time required by charging the two, and it can be seen that the charging continuity has a significant effect on the charging time. Fig. 9 is a diagram showing charging station load conditions of a certain sample in monte carlo simulation, and it can be seen from the diagram that charging randomness of a user causes charging station load to fluctuate compared with a linear programming result. Fig. 10 is a charging station maximum load condition for all samples during monte carlo simulation. Fig. 11 is a plot of charging station construction power iteration versus sample unsatisfied rate.

Claims (8)

1. The combined optimization method for the electric vehicle charging schedule and the charging station configuration under the consideration of the charging randomness is characterized by comprising the following steps:
s1: modeling the battery capacity of the electric vehicle charged by the arrival, predicting the charging demand of the electric vehicle on the basis, and establishing the scheduling problem of the electric vehicle as a linear programming model by combining the charging demand of the electric vehicle and a time-of-use electricity price mechanism;
s2: adding constraint on charging continuity of the electric vehicle by using the linear programming model obtained in the step S1, and solving the linear programming model by taking minimized user charging cost and charging station load variance as objective functions to obtain an electric vehicle charging scheduling optimization result;
s3: carrying out user charging randomness simulation on the electric vehicle dispatching optimization result in the S2 by adopting Monte Carlo simulation; determining a proper charging station construction scale and energy storage equipment capacity by calculating the satisfaction rate of different charging station construction powers on a charging condition Monte Carlo simulation sample;
in S1, the battery capacity of the electric vehicle conforms to gaussian distribution, and the battery capacity of the electric vehicle is modeled by the gaussian distribution, as shown in the following formula:
EVB E ＝[EVB 1 EVB 2 … EVB e ]；e∈E
charging demand EVC of electric automobile e e The method comprises the following steps:
EVC e ＝EVB e ×η
wherein X is the battery capacity of the electric automobile, f (X) is the probability density function of Gaussian distribution of the battery capacity of the electric automobile, mu cap 、σ cap The average value and standard deviation of the battery capacity of the electric automobile are calculated according to the statistical data respectively; e is an electric automobile set charged by arriving at station, EVB e Represents battery capacity of electric automobile e, EVB E For all electric automobile battery capacity sets, eta is interval [0.5,0.9]]Random numbers of (a);
the solution decision variables for electric vehicle scheduling are shown as follows:
wherein,indicating the charge amount of the electric car numbered e in the period t, +.>Representing the charge schedule of the electric vehicle numbered e over time T, +.>A set of charging scheduling conditions for all electric vehicles;
for all electric vehicles charged to the station, the charging scheduling result must meet the charging requirement, as shown in the following formula:
to represent the state of charge of the electric vehicle, a state of charge decision variable of the electric vehicle is added, as shown in the following formula:
the charge condition is constrained by the following formula:
wherein, the charge amount of the electric automobile e in the t periodFrom state of charge decision variables/>Lower charging limit LC of electric automobile min Upper charge limit LC max Determining together;
in S2, the constraint of the user charging continuity is:
wherein the variables areDetermining whether the electric automobile e is charged in the t period, j and k represent the j and k period decision variables +.>j cycles from the third charging period to the T-th period, and k cycles from the 1-th period to the j-1 th period; when the electric automobile is plannedAnd->The period between these two corresponding periods is charged, i.e. +.>By for all k<t<Between jConstraint is made by the equation ∈ ->Constraint ensures continuous charging.
2. The method for joint optimization of electric vehicle charging schedule and charging station configuration in consideration of charging randomness according to claim 1, wherein in S2, an objective function of linear programming is:
min[α 1 ·Cost c +α 2 ·Variance]
wherein,α 1 and alpha 2 Two parameters are used for adjusting the weight of each sub-objective function; />Representing the charge quantity of the electric automobile with the number e in the period t, Q t Charging electricity price for t period; />And (3) for the load of the charging station in the period T, wherein T is the length of the charging scheduling time.
3. The method for jointly optimizing electric vehicle charging schedule and charging station configuration under consideration of charging randomness according to claim 2, wherein S3 specifically comprises the following steps:
s3.1: setting the number of Monte Carlo simulation samples;
s3.2: generating different Monte Carlo simulation samples according to the dispatching optimization result and the charging randomness in the S2;
s3.3: counting the maximum load condition of the charging station in each Monte Carlo simulation sample;
s3.4: initializing charging station construction power, setting iteration variables, and setting termination conditions and maximum iteration times;
s3.5: updating the charging station construction power according to the iteration variable;
s3.6: calculating the sample satisfaction rate;
s3.7: judging whether a termination condition is met or the maximum iteration number is reached, if not, carrying out S3.5, otherwise, carrying out S3.8;
s3.8: and determining the proper construction scale of the charging station and the capacity of the energy storage equipment.
4. The method for jointly optimizing electric vehicle charging scheduling and charging station configuration under consideration of charging randomness according to claim 3, wherein the charging randomness of the user in S3.2 is mainly reflected in that the user cannot completely charge according to a scheduling result of linear programming solution, so that the electric vehicle charging is not performed within a scheduling time, different samples are generated according to the user charging randomness by adopting a Monte Carlo simulation method, and the charging condition of the electric vehicle in the samples is shown in the following formula:
wherein S is E (N) represents the charge condition of the nth Monte Carlo simulation sample, N is the set of samples,indicating the charge of EV numbered e in the sample during period t;
to represent the case where the EV user advances or delays charging to the station, the change in sample charging time is as follows:
wherein phi is the interval [ phi ] 1 ,φ 2 ]A random integer number within the range of the random number,the charging start time and the charging end time of the electric automobile are respectively indicated.
5. The method for joint optimization of electric vehicle charging schedule and charging station configuration with consideration of charging randomness according to claim 4, wherein in S3.3, the maximum load of charging stations in different samples in the schedule time is calculated by the following formula:
6. the method for joint optimization of electric vehicle charging schedule and charging station configuration in consideration of charging randomness according to claim 5, wherein in S3.4, the iteration variable β is an increment variable with a fixed step size within interval 0.ltoreq.β.ltoreq.1.
7. The method for jointly optimizing charging schedule and charging station configuration of electric automobile under consideration of charging randomness as set forth in claim 6, wherein different samples in Monte Carlo simulation represent load conditions of EVCS under different user charging behaviors, and Gaussian distribution of maximum load of EVCS under user charging uncertainty is obtained through calculation of the samples, and average standard deviation is μ respectively S ，σ s In order to make the construction power of the EVCS meet different conditions caused by the charging randomness as much as possible, in S3.5, the charging station construction power update formula is as follows:
E evcs ＝(1-β)×Load(n) min +β×Load(n) max
wherein, for random variables subject to Gaussian distribution, the maximum deviation from the mean is bounded by a confidence of no more than 99%, thus, load is set min ＝μ S -3σ s 、Load max ＝μ S +3σ s ；
The sample satisfaction rate λ in S3.6 is calculated by:
wherein omega 1 For Monte Carlo simulation of the radix, ω of sample set N 2 E corresponding to maximum load of charging station less than beta at time in Monte Carlo simulation sample evcs Is a sample number of (a); when lambda reaches the expected value, E is selected at that time evcs The maximum power for EVCS construction.
8. The method for jointly optimizing electric vehicle charging schedule and charging station configuration under consideration of charging randomness according to claim 3, wherein the capacity determination formula of the energy storage device in S3.8 is as follows:
wherein,the power generated in the period t is generated by photovoltaic power generation and wind power generation respectively, and epsilon is the energy conversion rate of the energy storage equipment during charging and discharging.
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