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CN114640133A - Urban power grid electric vehicle cooperative regulation and control method and system based on real-time information - Google Patents

Urban power grid electric vehicle cooperative regulation and control method and system based on real-time information Download PDF

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Publication number
CN114640133A
CN114640133A CN202210254577.XA CN202210254577A CN114640133A CN 114640133 A CN114640133 A CN 114640133A CN 202210254577 A CN202210254577 A CN 202210254577A CN 114640133 A CN114640133 A CN 114640133A
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electric vehicle
charging
electric automobile
time
real
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CN114640133B (en
Inventor
赵家庆
徐春雷
吕洋
赵奇
田江
张琦兵
潘琪
丁宏恩
李春
俞瑜
杨明
赵慧
孟雨庭
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State Grid Jiangsu Electric Power Co Ltd
Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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State Grid Jiangsu Electric Power Co Ltd
Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/14—Plug-in electric vehicles

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

A city power grid electric vehicle cooperative regulation and control method and system based on real-time information are disclosed, the method comprises the following steps: firstly, a parameterized aggregation EV charging model is developed, an energy boundary is used for representing charging flexibility, secondly, charging priorities of electric vehicles connected into a power grid are sequenced by using a good-bad solution distance method, and finally, a target function with minimum quadratic sum of deviation of active output correction in the day is established by taking a day-ahead scheduling result as reference, and the charging power distribution of the electric vehicles is realized based on the charging priorities. The urban power grid electric vehicle cooperative regulation and control method and system based on real-time information can analyze the charging demand and the energy boundary of the electric vehicle aggregation group, and reduce the influence of the traveling uncertainty of the electric vehicle on the day-ahead scheduling plan.

Description

Urban power grid electric vehicle cooperative regulation and control method and system based on real-time information
Technical Field
The invention belongs to the field of optimized operation of power systems, and particularly relates to a city power grid electric vehicle cooperative regulation and control method and system based on real-time information.
Background
The large-scale electric automobile can be used as a flexible load of a user side and a distributed energy storage resource, the power load of a power grid is adjusted in a coordinated mode, peak clipping and valley filling are achieved, and auxiliary service is provided for the power grid. The problems of changeable urban power grid operation modes, heavy local equipment loads, insufficient power supply capacity and the like caused by randomness, volatility and mobility of large-scale access of the electric automobile are increasingly prominent.
The traditional mode of original power grid centralized dispatching, direct control and day-time-before-real time is difficult to adapt to the requirement of large-scale electric automobile access, and a day-ahead plan of power grid dispatching needs to be corrected according to real-time information of electric automobiles so as to support city power grid electric automobiles to cooperatively regulate and control, so that the city power grid regulation and control capability is further improved, and the realization of a double-carbon target is promoted.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide a city power grid electric vehicle cooperative regulation and control method and system based on real-time information.
The invention adopts the following technical scheme:
a city power grid electric vehicle cooperative regulation and control method based on real-time information specifically comprises the following steps:
step 1, collecting energy changes of a historical electric vehicle during quick charging and slow charging;
step 2, generating an energy boundary of a single electric vehicle according to the forecast information of the electric vehicle in the day ahead, aggregating the energy boundaries of the single electric vehicles to obtain an energy boundary of an electric vehicle aggregation group, and obtaining an actual energy boundary of the electric vehicle aggregation group based on the collected real-time information of the electric vehicle;
step 3, according to the energy boundary of the electric vehicle aggregation group determined in the step 2, the fluctuation of the total load curve of the electric vehicle aggregation group is minimum to serve as an optimization target of day-ahead scheduling, and a day-ahead scheduling plan P is obtainedplan；
Step 4, establishing a target function by utilizing the aggregated charging power distribution and a day-ahead scheduling plan;
step 5, calculating a positive ideal solution of each attribute of the single electric automobile
Figure BDA0003548262450000021
Sum negative ideal solution
Figure BDA0003548262450000022
Wherein the attributes of the single electric vehicle include: remaining charge amount, elapsed time, remaining charge time;
and 6, calculating the charging priority by calculating the distance between the value of the attribute of the electric automobile and the positive and negative ideal solutions, and distributing the power of the electric automobile.
In the steps 1 and 3, the collected historical data and the real-time information comprise the battery capacity of the electric automobile and the efficiency of the charging pile in the charging time.
In step 2, the energy upper boundary of the single electric automobile in the quick charging process
Figure BDA0003548262450000023
Satisfies the following relation:
Figure BDA0003548262450000024
energy lower bound of slow charging process of single electric automobile
Figure BDA0003548262450000025
Satisfies the following relation:
Figure BDA0003548262450000026
in the formula,
Figure BDA0003548262450000027
indicates the battery capacity of the ith electric vehicle,
ηcthe efficiency of the charging pile is shown,
Pratedrepresents the rated power of a single electric vehicle,
at represents the duration of the charging process,
t represents the current time of day and,
Figure BDA0003548262450000028
represents the battery capacity required when the electric vehicle leaves,
Figure BDA0003548262450000029
the time of departure of the electric vehicle is indicated,
in the space between the upper energy boundary and the lower energy boundary of the single electric automobile, the charging solution of the single electric automobile is characterized by any monotone non-decreasing curve.
In step 2, the electric vehicles aggregate the energy boundaries of the population
Figure BDA00035482624500000210
Satisfies the following relation:
Figure BDA00035482624500000211
Figure BDA0003548262450000031
in the formula,
Figure BDA0003548262450000032
represents the upper energy boundary of the ith electric vehicle,
Figure BDA0003548262450000033
represents the energy lower bound of the ith electric vehicle,
ta,minwhich indicates the time interval, a-0, 1 … d,
td,maxwhich represents the d-th time interval,
Ntrepresents the total number of all electric vehicles;
the upper energy boundary E + of the electric automobile aggregation group is
Figure BDA0003548262450000034
The lower energy boundary E-is
Figure BDA0003548262450000035
In the space between the upper energy boundary and the lower energy boundary of the electric automobile aggregation group, the charging solution of the electric automobile aggregation group is characterized by any monotone non-decreasing curve.
In step 3, the charging solution of the electric automobile aggregation group is characterized by any monotone non-decreasing curve e (t) in the space between the energy upper boundary and the energy lower boundary of the electric automobile aggregation group, and the electric automobile aggregation group charging power distribution satisfies the following relational expression:
Figure BDA0003548262450000036
in the formula,
p (t) represents an electric vehicle aggregate population charging power distribution,
e (t) represents the value corresponding to the time t on the monotone non-decreasing curve,
t0indicating the initial moment of the aggregate charging,
t0+ H represents the end time of the aggregated charge.
In step 4, the objective function is:
Figure BDA0003548262450000041
and obtaining the charging power of the electric vehicle aggregation group by using the charging power distribution of the electric vehicle aggregation group in each time interval and the difference sum of squares of the day-ahead scheduling plan.
In the step 5, the process is carried out,
calculating the remaining charge according to the current SOC of the single electric automobile and the expected SOC when the electric automobile leaves and combining the battery capacity:
ai,1＝(SOCi,except-SOCi,now)×E
in the formula,
ai,1an index indicating a remaining amount of charge,
SOCi,exceptindicating the start time of the ith electric vehicleDesired SOC, SOCi,nowRepresents the current SOC of the ith electric vehicle,
e represents the energy power of the electric automobile;
calculating the staying time according to the current time and the arrival time of the single electric automobile:
ai,2＝t-tarrival
in the formula,
ai,2an indication of the elapsed time of residence is provided,
tarrivalrepresenting the arrival time of the electric vehicle;
calculating the remaining charging time according to the current time and the predicted leaving time of the single electric automobile:
ai,3＝tleave-t
in the formula,
ai,3an indication of the remaining charge time is shown,
tleaveindicating the expected departure time of the electric vehicle.
The positive ideal solution and the negative ideal solution respectively satisfy the following relational expressions:
Figure BDA0003548262450000051
Figure BDA0003548262450000052
Figure BDA0003548262450000053
in the formula,
Figure BDA0003548262450000054
represents a positive ideal solution
Figure BDA0003548262450000055
With the j-th attribute of a single electric vehicleThe maximum value is related to the maximum value,
Figure BDA0003548262450000056
represents a positive ideal solution
Figure BDA0003548262450000057
Relating to the minimum value of the jth attribute of the single electric vehicle; n represents the total number of values of any attribute;
c denotes a normalized weighting matrix which is,
C＝WB
and W is a weight vector, and assignment is carried out according to the importance degree of the index.
In step 6, respectively calculating a distance between the value of the ith attribute of the electric vehicle and the positive ideal solution and a distance between the ith attribute of the electric vehicle and the negative ideal solution:
Figure BDA0003548262450000058
Figure BDA0003548262450000059
in the formula,
Figure BDA00035482624500000510
representing the distance between the ith attribute of the electric automobile and the corresponding positive ideal solution;
Figure BDA00035482624500000511
representing the distance between the ith attribute of the electric automobile and the corresponding negative ideal solution;
m represents the total number of all attributes, and the value of M is 3 in the present invention.
Calculating the charging priority of the electric automobile according to the following relation:
Figure BDA00035482624500000512
power allocation based on charging priority in the following relationship:
Figure BDA0003548262450000061
the invention also discloses a city power grid electric vehicle cooperative regulation and control system based on real-time information, which comprises a data acquisition module, an energy boundary calculation module of an electric vehicle aggregation group, a day-ahead scheduling plan module, an objective function construction module, a positive and negative ideal solving module and a power distribution module;
the data acquired by the data acquisition module comprises the battery capacity of the electric automobile and the efficiency of the charging pile in the charging time, and the acquired data is input to an energy boundary calculation module of an electric automobile aggregation group;
the energy boundary calculation module of the electric automobile aggregation group calculates the energy boundary numerical value of a single electric automobile according to the received data, then calculates the energy boundary numerical value of the aggregation group, and inputs the calculation result to the day-ahead scheduling plan module;
the day-ahead scheduling plan module makes a day-ahead scheduling plan according to the boundary numerical value and inputs the made plan to the target function construction module;
the target function construction module constructs a target function according to the result input by the day-ahead scheduling plan module and inputs the target function to the positive and negative ideal solving module;
the positive and negative ideal solving module calculates positive and negative ideal solutions and inputs the solved solution values to the power distribution module;
the power distribution module calculates the charging priority of the electric automobile and charges the electric automobile according to the charging priority.
Compared with the prior art, the invention has the beneficial effects that a parameterized aggregation EV charging model based on real-time information is provided, and the charging flexibility is expressed by using an energy boundary; the charging priorities of the electric vehicles connected into the power grid are sequenced by using a good-bad solution distance method, so that the square sum of the deviations of the active output correction in the day is minimum while the influence of the traveling uncertainty of the electric vehicles on the day-ahead scheduling plan is reduced. The precision of the original day-ahead optimization is improved, the components which are optimized in real time in the day are added, the scheduling time is saved, and the real-time performance and the accuracy are improved.
Drawings
FIG. 1 is a block diagram of the steps of the city power grid electric vehicle cooperative regulation and control method based on real-time information according to the present invention;
FIG. 2 is a block diagram of a group energy boundary and dispatch plan for a previous electric vehicle in accordance with an embodiment of the present invention;
FIG. 3 is a flowchart of a dispatch plan modification based on real-time information in an implementation of the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1, the invention discloses a city power grid electric vehicle cooperative regulation and control method and system based on real-time information, and the specific method comprises the following steps:
obtaining a day-ahead scheduling plan by calculating a historical energy boundary, then obtaining an active output objective function of the electric automobile in the day again based on real-time information, calculating a positive and negative ideal solution, and finally performing power distribution;
the electric vehicle cooperative regulation and control method comprises the following steps:
step 1, collecting energy data changes of an electric automobile during quick charging and slow charging;
the collected energy comprises the battery capacity of the electric automobile and the efficiency of a charging pile in the charging time;
step 2, generating the energy boundary of a single electric vehicle and aggregating the energy boundaries of a group according to the forecast information of the electric vehicles in the day ahead
Figure BDA0003548262450000071
And based on the real-time information, obtaining the actual energy boundary (E) of the electric automobile group+，E-)：
In this embodiment, the electric vehicle prediction information is charging pile efficiency, charging process duration, and battery capacity required when the electric vehicle leaves the charging pile.
Figure BDA0003548262450000072
Energy upper bound representing a single electric vehicle fast charging process:
Figure BDA0003548262450000073
Figure BDA0003548262450000074
energy lower bound representing slow charging process of a single electric vehicle:
Figure BDA0003548262450000075
wherein
Figure BDA0003548262450000076
Represents the battery capacity, η, of the ith electric vehiclecIndicates the efficiency of the charging pile, PratedRepresents the rated power of a single electric vehicle, Δ T represents the duration of the charging process, T represents the current moment,
Figure BDA0003548262450000077
represents the battery capacity required when the electric automobile leaves the charging pile,
Figure BDA0003548262450000078
indicating the time of departure. The space between the upper and lower energy boundaries characterizes the flexibility of EV charging, and any monotonically non-decreasing curve in the middle represents a possible charging solution for the EV.
Generating energy boundaries for an electric vehicle aggregate population
Figure BDA0003548262450000081
Figure BDA0003548262450000082
Figure BDA0003548262450000083
Wherein,
Figure BDA0003548262450000084
represents the upper energy boundary of the ith electric vehicle,
Figure BDA0003548262450000085
represents the lower energy boundary, t, of the ith electric vehiclea,minRepresenting each time interval, a-0, 1 … d, td,maxDenotes the total number of all time intervals, NtRepresents the sum of all electric vehicles;
the upper energy boundary E + of the electric automobile aggregation group is
Figure BDA0003548262450000086
The lower energy boundary E-is
Figure BDA0003548262450000087
And step 3: and (3) according to the energy boundary determined in the step (2), taking the minimum fluctuation of the total load curve as an optimization target of the day-ahead scheduling:
Figure BDA0003548262450000088
wherein, PL(t) is the normal load value over the time period t; p (t) represents an aggregate charge power value; pavIs the daily average load value; t is ta,minRepresenting each time interval, a-0, 1 … d, td,maxRepresents the total number of all time intervals;
Figure BDA0003548262450000089
any monotonic non-decreasing curve E between the two represents a possible solution, and the aggregate charging power profile can be generalized as:
Figure BDA00035482624500000810
wherein, P (t) represents the aggregation charging power value, E (t) represents the corresponding value of the monotonic non-decreasing curve at the t moment; t is t0Denotes the initial time of the aggregate charging, t0+ H represents the end time of the aggregated charge.
Solving by adopting a gray wolf algorithm to obtain an optimal day-ahead scheduling plan Pplan。
Those skilled in the art can determine the calculation method of the aggregated charging power distribution according to practical situations, and this embodiment is only a preferred embodiment and should not necessarily limit the scope of the present invention.
And 4, step 4: establishing an objective function with the minimum square sum of the actual value of the active output and the deviation of the scheduling plan within a day:
Figure BDA0003548262450000091
thereby obtaining the charging power of the electric vehicle group in each time interval based on the actual information.
Wherein, ta,minRepresenting each time interval, a-0, 1 … d, td,maxRepresents the total number of all time intervals, p (t) represents the aggregate charging power profile;
and 5: for each time interval, based on real-time information, the following 3 attributes are calculated, specifically as follows:
from the current SOC, the desired SOC at departure, the battery capacity calculation remaining charge index a 1:
ai,1＝(SOCi,except-SOCi,now)×E
therein, SOCi,exceptExpected SOC, SOC representing the starting time of the ith electric vehiclei,nowRepresenting the expected SOC of the ith electric vehicle at the current moment; e represents the energy power of the electric automobile;
from the current time and arrival time, the stopped time index a2 is calculated:
ai,2＝t-tarrival
wherein, tarrivalRepresenting the arrival time of the electric automobile;
calculating a remaining charging time index a3 according to the current time and the estimated leaving time:
ai,3＝tleave-t
wherein, tleaveIndicating the departure time of the electric vehicle;
constructing attribute evaluation matrix A of N stopped electric vehicles
Figure BDA0003548262450000101
Carrying out standardization and normalization processing on the matrix A to obtain a matrix B
Figure BDA0003548262450000102
Determining a weight vector W according to the importance degree of different indexes
W＝{w1,w2,w3}
Thereby obtaining a normalized weighting matrix C
Figure BDA0003548262450000103
Calculating positive/negative ideal solutions of each attribute of the electric automobile, and determining the positive ideal solution of each attribute
Figure BDA0003548262450000104
Sum negative ideal solution
Figure BDA0003548262450000105
Those skilled in the art can determine the number and specific contents of the attributes according to actual situations, and the above description is only a specific embodiment and should not necessarily limit the scope of the present invention.
Figure BDA0003548262450000106
Figure BDA0003548262450000107
Figure BDA0003548262450000108
Wherein,
Figure BDA0003548262450000109
represents a positive ideal solution
Figure BDA00035482624500001010
Depending on the maximum value of the property,
Figure BDA00035482624500001011
represents a positive ideal solution
Figure BDA00035482624500001012
A minimum value that depends on the property; n represents the number of electric vehicles; j represents the number of attributes;
step 6: calculating a charging priority and distributing the power of the electric automobile;
the calculation is based on the distance between the charging electric vehicle and the positive ideal solution and the distance between the electric vehicle and the negative ideal solution:
Figure BDA0003548262450000111
m represents the total number of all attributes;
Figure BDA0003548262450000112
calculating the charging priority of the electric automobile:
Figure BDA0003548262450000113
power allocation based on charging priority:
Figure BDA0003548262450000114
the invention also discloses a city power grid electric vehicle cooperative regulation and control system based on real-time information, which comprises a data acquisition module, an energy boundary calculation module of an electric vehicle aggregation group, a day-ahead scheduling plan module, an objective function construction module, a positive and negative ideal solving module and a power distribution module;
the data acquired by the data acquisition module comprises the battery capacity of the electric automobile and the efficiency of the charging pile in the charging time, and the acquired data is input to an energy boundary calculation module of an electric automobile aggregation group;
the energy boundary calculation module of the electric automobile aggregation group calculates an energy boundary numerical value of the electric automobile aggregation group according to the received data, and inputs a calculation result to the day-ahead scheduling plan module;
the day-ahead scheduling plan module makes a day-ahead scheduling plan according to the boundary numerical value and inputs the made plan to the target function construction module;
the target function construction module constructs a target function according to the result input by the day-ahead scheduling plan module, and inputs the target function to the positive and negative ideal solving module;
the positive and negative ideal solving module calculates positive and negative ideal solutions and inputs the solved solution values to the power distribution module;
the power distribution module calculates the charging priority of the electric automobile and charges the electric automobile according to the charging priority.
Therefore, the method carries out day-ahead optimization to obtain a day-ahead scheduling plan, then obtains the electric vehicle aggregation curve again based on the real-time information, carries out day-interior optimization to adjust the scheduling curve, and finally carries out power distribution.
The invention aims to provide a city power grid electric vehicle cooperative regulation and control method based on real-time information, and aims to minimize the deviation of a daily optimization curve and a day-ahead scheduling curve in a charging station. As shown in fig. 2, data of 100 electric vehicles arriving in the simulated charging station is used as prediction information of day-ahead scheduling, and an energy curve is selected as an optimization result of the day-ahead scheduling. In the rolling optimization within a day, due to the error between the actual arrival information and the predicted information of the electric vehicle, the day-ahead scheduling plan cannot meet the requirements of the user, and therefore needs to be adjusted.
The dispatching plan after applying the cooperative regulation and control method provided by the invention is corrected as shown in fig. 3, the actual value of the active output in the day and the deviation square sum of the dispatching plan are the minimum as a target function, the charging power is adjusted based on the real-time information of the electric automobile, and the distribution of the charging power of the electric automobile is realized by using the charging priority.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are only preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for the purpose of limiting the scope of the present invention, and on the contrary, any modifications or modifications based on the spirit of the present invention should fall within the scope of the present invention.

Claims (11)

1. A city power grid electric vehicle cooperative regulation and control method based on real-time information is characterized by comprising the following steps:
the method specifically comprises the following steps:
step 1, collecting energy changes of a historical electric vehicle during quick charging and slow charging;
step 2, generating an energy boundary of a single electric vehicle according to the forecast information of the electric vehicle in the day ahead, aggregating the energy boundaries of the single electric vehicles to obtain an energy boundary of an electric vehicle aggregation group, and obtaining an actual energy boundary of the electric vehicle aggregation group based on the collected real-time information of the electric vehicle;
step 3, according to the energy boundary of the electric vehicle aggregation group determined in the step 2, the fluctuation of the total load curve of the electric vehicle aggregation group is minimum to serve as an optimization target of day-ahead scheduling, and a day-ahead scheduling plan P is obtainedplan；
Step 4, establishing a target function by utilizing the aggregated charging power distribution and a day-ahead scheduling plan;
step 5, calculating a positive ideal solution of each attribute of the single electric automobile
Figure FDA0003548262440000011
Sum negative ideal solution
Figure FDA0003548262440000012
Wherein the attributes of the single electric vehicle include: remaining charge amount, elapsed time, remaining charge time;
and 6, calculating the charging priority by calculating the distance between the value of the attribute of the electric automobile and the positive and negative ideal solutions, and distributing the power of the electric automobile.
2. The city power grid electric vehicle cooperative regulation and control method based on real-time information as claimed in claim 1,
in the steps 1 and 3, the collected historical data and the real-time information comprise the battery capacity of the electric automobile and the efficiency of the charging pile in the charging time.
3. The city power grid electric vehicle cooperative regulation and control method based on real-time information as claimed in claim 1, wherein,
in step 2, the energy upper boundary of the single electric automobile in the quick charging process
Figure FDA0003548262440000013
Satisfies the following relation:
Figure FDA0003548262440000014
energy lower bound of slow charging process of single electric automobile
Figure FDA0003548262440000015
Satisfies the following relation:
Figure FDA0003548262440000016
in the formula,
Figure FDA0003548262440000017
indicates the battery capacity of the ith electric vehicle,
ηcthe efficiency of the charging pile is shown,
Pratedrepresents the rated power of a single electric vehicle,
at represents the duration of the charging process,
t represents the current time of day and,
Figure FDA0003548262440000021
represents the battery capacity required when the electric vehicle leaves,
Figure FDA0003548262440000022
the time of departure of the electric vehicle is indicated,
in the space between the upper energy boundary and the lower energy boundary of the single electric automobile, the charging solution of the single electric automobile is characterized by any monotone non-decreasing curve.
4. The urban power grid electric vehicle cooperative regulation and control method based on real-time information according to claim 3, wherein,
in step 2, the energy boundaries of the electric automobile aggregation groups
Figure FDA0003548262440000023
Satisfies the following relation:
Figure FDA0003548262440000024
Figure FDA0003548262440000025
in the formula,
Figure FDA0003548262440000026
represents the upper energy boundary of the ith electric vehicle,
Figure FDA0003548262440000027
represents the energy lower bound of the ith electric vehicle,
ta,minwhich indicates the time interval, a-0, 1 … d,
td,maxwhich represents the d-th time interval,
Ntrepresents the total number of all electric vehicles;
the upper energy boundary E + of the electric automobile aggregation group is
Figure FDA0003548262440000028
The lower energy boundary E-is
Figure FDA0003548262440000029
In the space between the upper energy boundary and the lower energy boundary of the electric automobile aggregation group, the charging solution of the electric automobile aggregation group is characterized by any monotone non-decreasing curve.
5. The city power grid electric vehicle cooperative regulation and control method based on real-time information as claimed in claim 4,
in step 3, the charging solution of the electric automobile aggregation group is characterized by any monotone non-decreasing curve E (t) in the space between the upper energy boundary and the lower energy boundary of the electric automobile aggregation group, and the charging power distribution of the electric automobile aggregation group satisfies the following relational expression:
Figure FDA0003548262440000031
in the formula,
p (t) represents an electric vehicle aggregate population charging power distribution,
e (t) represents the value corresponding to the time t on the monotone non-decreasing curve,
t0indicating the initial moment of the aggregate charging,
t0+ H represents the end time of the aggregated charge.
6. The city power grid electric vehicle cooperative regulation and control method based on real-time information as claimed in claim 5,
in step 4, the objective function is:
Figure FDA0003548262440000032
and obtaining the charging power of the electric vehicle aggregation group by using the charging power distribution of the electric vehicle aggregation group in each time interval and the difference sum of squares of the day-ahead scheduling plan.
7. The city power grid electric vehicle cooperative regulation and control method based on real-time information as claimed in claim 6,
in the step 5, the process is carried out,
calculating the remaining charge according to the current SOC of the single electric automobile and the expected SOC when the electric automobile leaves and combining the battery capacity:
ai,1＝(SOCi,except-SOCi,now)×E
in the formula,
ai,1an index indicating a remaining amount of charge is indicated,
SOCi,exceptexpected SOC, SOC representing the starting time of the ith electric vehiclei,nowRepresents the current SOC of the ith electric vehicle,
e represents the energy power of the electric automobile;
calculating the staying time according to the current time and the arrival time of the single electric automobile:
ai,2＝t-tarrival
in the formula,
ai,2an indication of the elapsed time of residence is provided,
tarrivalrepresenting the arrival time of the electric vehicle;
calculating the remaining charging time according to the current time and the estimated leaving time of the single electric automobile:
ai,3＝tleave-t
in the formula,
ai,3an indication of the remaining charge time is shown,
tleaveindicating the expected departure time of the electric vehicle.
8. The city power grid electric vehicle cooperative regulation and control method based on real-time information as claimed in claim 7,
the positive ideal solution and the negative ideal solution respectively satisfy the following relational expressions:
Figure FDA0003548262440000041
Figure FDA0003548262440000042
Figure FDA0003548262440000043
in the formula,
Figure FDA0003548262440000051
represents a positive ideal solution
Figure FDA0003548262440000052
In relation to the maximum value of the jth attribute of a single electric vehicle,
Figure FDA0003548262440000053
represents a positive ideal solution
Figure FDA0003548262440000054
Relating to the minimum value of the jth attribute of the single electric vehicle; n represents the total number of values of any attribute;
c denotes a normalized weighting matrix which is,
C＝WB
and W is a weight vector, and assignment is carried out according to the importance degree of the index.
9. The city power grid electric vehicle cooperative regulation and control method based on real-time information as claimed in claim 8,
in step 6, respectively calculating a distance between the value of the ith attribute of the electric vehicle and the positive ideal solution and a distance between the ith attribute of the electric vehicle and the negative ideal solution:
Figure FDA0003548262440000055
Figure FDA0003548262440000056
in the formula,
Figure FDA0003548262440000057
representing the distance between the ith attribute of the electric automobile and the corresponding positive ideal solution;
Figure FDA0003548262440000058
representing the distance between the ith attribute of the electric automobile and the corresponding negative ideal solution;
m represents the total number of all attributes, and in the present invention M has a value of 3.
10. The city power grid electric vehicle cooperative regulation and control method based on real-time information of claim 9, characterized in that,
calculating the charging priority of the electric automobile according to the following relation:
Figure FDA0003548262440000059
power allocation based on charging priority in the following relationship:
Figure FDA00035482624400000510
。
11. the urban power grid electric vehicle cooperative regulation and control system based on the real-time information realized by the urban power grid electric vehicle cooperative regulation and control method based on the real-time information of any one of claims 1 to 10 is characterized by comprising a data acquisition module, an energy boundary calculation module of an electric vehicle aggregation group, a day-ahead scheduling plan module, an objective function construction module, a positive-negative ideal solution module and a power distribution module;
the data acquired by the data acquisition module comprises the battery capacity of the electric automobile and the efficiency of the charging pile in the charging time, and the acquired data is input to an energy boundary calculation module of an electric automobile aggregation group;
the energy boundary calculation module of the electric automobile aggregation group calculates the energy boundary numerical value of a single electric automobile according to the received data, then calculates the energy boundary numerical value of the aggregation group, and inputs the calculation result to the day-ahead scheduling plan module;
the day-ahead scheduling plan module makes a day-ahead scheduling plan according to the boundary numerical value and inputs the made plan to the target function construction module;
the target function construction module constructs a target function according to the result input by the day-ahead scheduling plan module, and inputs the target function to the positive and negative ideal solving module;
the positive and negative ideal solving module calculates positive and negative ideal solutions and inputs the solved solution values to the power distribution module;
the power distribution module calculates the charging priority of the electric automobile and charges the electric automobile according to the charging priority.
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