CN114640133B - Urban power grid electric automobile cooperative regulation and control method and system based on real-time information - Google Patents
Urban power grid electric automobile cooperative regulation and control method and system based on real-time information Download PDFInfo
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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
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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
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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
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- 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
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- 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
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- 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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Abstract
A method and a system for collaborative regulation and control of electric vehicles in an urban power grid based on real-time information, wherein the method comprises the following steps: firstly, a parameterized aggregate EV charging model is developed, an energy boundary is used for representing charging flexibility, secondly, a good-bad solution distance method is used for sequencing charging priorities of electric vehicles connected to a power grid, finally, a daily scheduling result is used as a reference, an objective function with the smallest sum of squares of deviation of active output correction amounts in the day is established, and charging power distribution of the electric vehicles is realized based on the charging priorities. The urban power grid electric vehicle collaborative regulation and control method and system based on the real-time information can analyze the charging demand and the energy boundary of the electric vehicle aggregation group, and reduce the influence of the electric vehicle travel uncertainty on the day-ahead dispatching plan.
Description
Technical Field
The invention belongs to the field of power system optimization operation, and particularly relates to a method and a system for collaborative regulation and control of an electric automobile of an urban power grid based on real-time information.
Background
The large-scale electric automobile can be used as a flexible load on a user side and a distributed energy storage resource, and can cooperatively adjust the power grid power load, cut peaks and fill valleys, thereby providing auxiliary services for the power grid. The problems of variable running modes of the urban power network, heavy local equipment load, insufficient power supply capacity and the like caused by randomness, volatility and mobility of the large-scale access of the electric automobile are increasingly outstanding.
The traditional mode of centralized dispatching and direct control of the original power grid and 'day-time-real-time' is difficult to adapt to the requirement of large-scale electric vehicle access, and the power grid dispatching day-ahead plan needs to be corrected according to the electric vehicle real-time information so as to support the electric vehicles of the urban power grid to cooperatively regulate and control, thereby further improving the regulation capacity of the urban power grid and promoting the realization of a 'double carbon' target.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a method and a system for collaborative regulation and control of an electric automobile of an urban power grid based on real-time information.
The invention adopts the following technical scheme:
the urban power grid electric automobile cooperative regulation and control method based on the real-time information specifically comprises the following steps of:
step 1, collecting energy changes during quick charge and slow charge of a historical electric automobile;
step 2, generating an energy boundary of a single electric automobile according to the prediction information of the electric automobile in the day-ahead, polymerizing the energy boundary of the single electric automobile to obtain an energy boundary of an electric automobile polymerization group, and obtaining an actual energy boundary of the electric automobile group based on the acquired real-time information of the electric automobile;
step 3, according to the energy boundary of the electric vehicle aggregation group determined in the step 2, taking the minimum fluctuation of the electric vehicle group total load curve as an optimization target of day-ahead scheduling to obtain a day-ahead scheduling plan P plan ；
Step 4, establishing an objective function by utilizing the aggregate charging power distribution and the day-ahead dispatch plan;
step 5, calculating the positive ideal solution of each attribute of the single electric automobileAnd negative ideal solution->Wherein, the attribute of single electric automobile includes: a remaining charge amount, a stay time, and a remaining charge time;
and 6, calculating the charging priority by calculating the distance between the value of the electric vehicle attribute and the positive and negative ideal solutions, and carrying out electric vehicle power distribution.
In the steps 1 and 3, the collected historical data and real-time information include the battery capacity of the electric vehicle, and the efficiency of the charging pile in the charging time.
In step 2, the upper energy boundary of the rapid charging process of a single electric vehicleThe following relation is satisfied:
single electric automobile slow chargingLower boundary of energy of processThe following relation is satisfied:
in the method, in the process of the invention,
represents the battery capacity of the i-th electric automobile,
η c indicating the efficiency of the charging pile,
P rated indicating the rated power of a single electric car,
Δt represents the duration of the charging process,
t represents the current time of day and,
indicating the battery capacity required when the electric vehicle leaves,
the departure time of the electric automobile is indicated,
in the space between the upper and lower energy boundaries of a single electric vehicle, the charging solution of the single electric vehicle is characterized by any monotonically non-decreasing curve.
In step 2, energy boundaries of the electric vehicle aggregation populationThe following relation is satisfied:
in the method, in the process of the invention,
represents the upper energy boundary of the ith electric car,
represents the lower energy boundary of the ith electric car,
t a,min each time interval, a=0, 1 … d,
t d,max the d-th time interval is indicated,
N t representing the total number of all electric vehicles;
the energy upper boundary E+ of the electric automobile aggregation group isThe lower energy boundary E-is
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 represented by any monotonically non-decreasing curve.
In step 3, the charging solution of the electric vehicle aggregation group is characterized by any monotonically non-decreasing curve E (t) in the space between the upper energy boundary and the lower energy boundary of the electric vehicle aggregation group, and the electric vehicle aggregation group charging power distribution satisfies the following relation:
in the method, in the process of the invention,
p (t) represents the electric automobile aggregate group charging power distribution,
e (t) represents a value corresponding to the time t on a monotonically non-decreasing curve,
t 0 indicating the initial moment of the aggregate charge,
t 0 +h represents the end time of the aggregate charge.
In step 4, the objective function is:
and obtaining the charging power of the electric automobile aggregation group by using the square sum of the differences of the electric automobile aggregation group charging power distribution and the day-ahead scheduling plan in each time interval.
In the step 5 of the process, the process is carried out,
calculating a remaining charge amount according to a current SOC of the single electric vehicle and a desired SOC when the electric vehicle leaves in combination with a battery capacity:
a i,1 ＝(SOC i,except -SOC i,now )×E
in the method, in the process of the invention,
a i,1 an index indicating the amount of charge remaining,
SOC i,except desired SOC indicating start time of ith electric vehicle, SOC i,now Indicating the current SOC of the i-th electric automobile,
e represents the energy power of the electric automobile;
calculating the retention time according to the current time and the arrival time of the single electric automobile:
a i,2 ＝t-t arrival
in the method, in the process of the invention,
a i,2 indicating the indication of the time of residence,
t arrival indicating the arrival time of the electric automobile;
calculating the remaining charging time according to the current time and the expected departure time of the single electric automobile:
a i,3 ＝t leave -t
in the method, in the process of the invention,
a i,3 indicating an index of the remaining charge time and,
t leave indicating the estimated departure time of the electric vehicle.
The positive ideal solution and the negative ideal solution respectively satisfy the following relation:
in the method, in the process of the invention,
representing an ideal solution->Regarding the maximum value of the j-th attribute of a single electric car>Representing a positive ideal solutionA minimum value of a j-th attribute of the single electric automobile; n represents the total number of values of any attribute;
c represents a canonical weighting matrix,
C＝WB
and W is a weight vector, and assignment is carried out according to the importance degree of the index.
In step 6, the distance between the value of the i-th attribute of the electric vehicle and the positive ideal solution and the distance between the i-th attribute of the electric vehicle and the negative ideal solution are calculated respectively:
in the method, in the process of the invention,
representing the distance between the ith attribute of the electric automobile and the corresponding positive ideal solution;
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, the value of M is 3.
The charging priority of the electric automobile is calculated by the following relation:
power allocation based on charging priority in the following relation:
the invention also discloses an urban power grid electric vehicle collaborative regulation and control system based on real-time information, which comprises a data acquisition module, an electric vehicle aggregation group energy boundary calculation module, a day-ahead scheduling 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 into the energy boundary calculation module of the electric automobile aggregation group;
the energy boundary calculation module of the electric automobile aggregation group calculates the energy boundary value of a single electric automobile according to the received data, then calculates the energy boundary value of the aggregation group, and inputs the calculation result to the day-ahead scheduling planning module;
the day-ahead scheduling plan module formulates a day-ahead scheduling plan according to the boundary value, and inputs the formulated plan to the objective function construction module;
the objective function construction module constructs an objective function according to the result input by the day-ahead scheduling planning module, and inputs the objective function to the positive and negative ideal solving module;
the positive and negative ideal solution module calculates positive and negative ideal solutions, and inputs the obtained solution values to the power distribution module;
and 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 provides a parameterized aggregate EV charging model based on real-time information, and the energy boundary is used for representing the charging flexibility; and sequencing the charging priority of the electric automobile connected to the power grid by using a good-bad solution distance method, and realizing that the square sum of the deviation of the daily active output correction quantity is minimum while reducing the influence of the travel uncertainty of the electric automobile on the daily scheduling plan. The precision of 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 instantaneity and the accuracy are improved.
Drawings
FIG. 1 is a block diagram of steps of a method for collaborative regulation and control of an electric vehicle in an urban power grid based on real-time information;
FIG. 2 is a schematic illustration of a population energy boundary and schedule for a day-ahead electric vehicle in accordance with one embodiment of the present invention;
FIG. 3 is a diagram illustrating a scheduling plan modification based on real-time information in accordance with an embodiment 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 more clearly illustrating the technical solutions of the present invention and are not intended to limit the scope of protection of the present application.
As shown in fig. 1, the invention discloses a method and a system for collaborative regulation and control of an electric car of an urban power grid based on real-time information, wherein the method specifically comprises the following steps:
firstly, a day-ahead dispatching plan is obtained by calculating a historical energy boundary, then an active power output objective function in the electric automobile day is obtained again based on real-time information, positive and negative ideal solutions are calculated, and finally power distribution is carried out;
the electric automobile cooperative regulation and control method comprises the following steps:
step 1, collecting energy data change during quick charge and slow charge of an electric automobile;
the collected energy comprises the battery capacity of the electric automobile and the efficiency of the charging pile in the charging time;
step 2, generating an energy boundary of a single electric automobile according to the prediction information of the electric automobile in the day-ahead, and aggregating the energy boundaries of groupsAnd based on the real-time information, obtaining the actual energy boundary (E + ，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.
An upper energy boundary representing a single electric vehicle rapid charging process:
the energy lower boundary representing the slow charging process of a single electric car:
wherein the method comprises the steps ofRepresents the battery capacity, eta of the ith electric vehicle c Representing the efficiency of the charging pile, P rated Represents the rated power of a single electric vehicle, deltaT represents the duration of the charging process, T represents the current time,/-at>Indicating the battery capacity required when the electric vehicle leaves the charging post,/->Indicating the departure time. The space between the upper and lower energy boundaries characterizes the flexibility of EV charging, and any monotonically non-decreasing curve in between represents the EV possible charging solution.
Generating energy boundaries for electric vehicle aggregation populations
Wherein,indicating the upper energy boundary of the ith electric car,/->Represents the lower energy boundary, t, of the ith electric automobile a,min Representing each time interval, a=0, 1 … d, t d,max Representing the total number of all time intervals, N t Representing the sum of all electric vehicles;
the energy upper boundary E+ of the electric automobile aggregation group isThe energy lower boundary E-is +.>
Step 3: and (3) taking the minimum fluctuation of the total load curve as an optimization target of the day-ahead scheduling according to the energy boundary determined in the step (2):
wherein P is L (t) is a conventional load value over a period of time t; p (t) represents an aggregate charge power value; p (P) av Mean daily load values; t is t a,min Representing each time interval, a=0, 1 … d, t d,max Representing the total number of all time intervals;
any monotonic non-decreasing curve E between the two represents a possible solution, and the aggregate charging power distribution can be generalized as:
wherein P (t) represents an aggregate charging power value, E (t) represents a value corresponding to the moment t on a monotonically non-decreasing curve; t is t 0 Indicating the initial time of aggregation charging, t 0 +h represents the end time of the aggregate charge.
Solving by adopting a gray wolf algorithm to obtain an optimal day-ahead scheduling plan P plan 。
The method for determining the aggregate charging power distribution according to the actual situation can be determined by a person skilled in the art, and the present embodiment is only a preferred mode and is not necessarily limited to the protection scope of the present invention.
Step 4: establishing an objective function with minimum sum of actual daily active output value and scheduling plan deviation square:
thereby obtaining the charging power of the electric automobile group in each time interval based on the actual information.
Wherein t is a,min Representing each time interval, a=0, 1 … d, t d,max Representing the total number of all time intervals, P (t) representing the aggregate charging power distribution;
step 5: for each time interval, based on the real-time information, the following 3 attributes are calculated, and the specific process is as follows:
based on the current SOC, the desired SOC at the time of departure, the battery capacity calculates a remaining charge index a1:
a i,1 ＝(SOC i,except -SOC i,now )×E
wherein SOC is i,except Desired SOC indicating start time of ith electric vehicle, SOC i,now Representing the expected SOC of the ith electric automobile at the current moment; e represents the energy power of the electric automobile;
calculating a stayed time index a2 according to the current time and the arrival time:
a i,2 ＝t-t arrival
wherein t is arrival Indicating the arrival time of the electric automobile;
calculating a residual charging time index a3 according to the current time and the predicted departure time:
a i,3 ＝t leave -t
wherein t is leave Indicating the departure time of the electric automobile;
constructing an attribute evaluation matrix A of N stopped electric vehicles
The matrix A is standardized and normalized to obtain a matrix B
According to the importance degree of different indexes, determining a weight vector W
W＝{w 1 ,w 2 ,w 3 }
Thereby obtaining the canonical weighting matrix C
Calculating positive/negative ideal solutions of each attribute of the electric automobile, and determining positive ideal solutions of each attributeAnd negative ideal solution
Those skilled in the art can determine the number and specific content of the attributes according to the actual situation, and the above description is only given of a specific embodiment and should not be taken as necessarily limiting the scope of the invention.
Wherein,representing an ideal solution->Depending on the maximum value of the property, < >>Representing an ideal solution->A minimum value dependent on the attribute; n represents the number of electric vehicles; j represents the number of attributes;
step 6: calculating charging priority and distributing power of the electric automobile;
calculating a distance between the charged electric vehicle and the positive ideal solution and a distance between the electric vehicle and the negative ideal solution:
m represents the total number of all attributes;
calculating the charging priority of the electric automobile:
charging priority based power allocation:
the invention also discloses an urban power grid electric vehicle collaborative regulation and control system based on real-time information, which comprises a data acquisition module, an electric vehicle aggregation group energy boundary calculation module, a day-ahead scheduling 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 into the energy boundary calculation module of the electric automobile aggregation group;
the energy boundary calculation module of the electric automobile aggregation group calculates the energy boundary value of the electric automobile aggregation group according to the received data, and inputs the calculation result to the day-ahead scheduling planning module;
the day-ahead scheduling plan module formulates a day-ahead scheduling plan according to the boundary value, and inputs the formulated plan to the objective function construction module;
the objective function construction module constructs an objective function according to the result input by the day-ahead scheduling planning module, and inputs the objective function to the positive and negative ideal solving module;
the positive and negative ideal solution module calculates positive and negative ideal solutions, and inputs the obtained 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 invention carries out day-ahead optimization to obtain a day-ahead scheduling plan, then obtains an electric automobile aggregation curve again based on real-time information, carries out day-ahead optimization to adjust the scheduling curve, and finally carries out power distribution.
The invention discloses a real-time information-based urban power grid electric vehicle cooperative regulation and control method, and aims to minimize deviation of a day optimization curve and a day-ahead dispatching curve in a charging station. As shown in fig. 2, the data of 100 electric vehicles arriving in the simulated charging station are used as the prediction information of the day-ahead schedule, and an energy curve is selected as the optimization result of the day-ahead schedule. When the rolling optimization is carried out in the day, the day-ahead scheduling plan cannot meet the requirements of users due to errors between actual arrival information and prediction information of the electric automobile, so that the electric automobile needs to be adjusted.
The scheduling plan correction after the collaborative regulation and control method is applied is shown in fig. 3, the sum of squares of the deviation of the actual value of the daily active force and the scheduling plan is taken as an objective function, the charging power is adjusted based on the real-time information of the electric automobile, and the charging priority is utilized to realize the distribution of the charging power of the electric automobile.
While the applicant has described and illustrated the embodiments of the present invention in detail with reference to the drawings, 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 to limit the scope of the present invention, but any improvements or modifications based on the spirit of the present invention should fall within the scope of the present invention.
Claims (11)
1. The urban power grid electric automobile cooperative regulation and control method based on real-time information is characterized by comprising the following steps of:
the method specifically comprises the following steps:
step 1, collecting energy changes during quick charge and slow charge of a historical electric automobile;
step 2, generating an energy boundary of a single electric automobile according to the prediction information of the electric automobile in the day-ahead, polymerizing the energy boundary of the single electric automobile to obtain an energy boundary of an electric automobile polymerization group, and obtaining an actual energy boundary of the electric automobile group based on the acquired real-time information of the electric automobile;
energy upper boundary of single electric automobile quick charging processThe following relation is satisfied:
energy lower boundary of single electric automobile slow charging processThe following relation is satisfied:
in the method, in the process of the invention,
represents the battery capacity, eta of the ith electric vehicle c Representing the efficiency of the charging pile, P rated Represents the rated power of a single electric vehicle, deltaT represents the duration of the charging process, T represents the current time,/-at>Indicating the battery capacity required when the electric vehicle is away, < >>Indicating the departure time of the electric automobile;
step 3, according to the energy boundary of the electric vehicle aggregation group determined in the step 2, taking the minimum fluctuation of the electric vehicle group total load curve as an optimization target of day-ahead scheduling to obtain a day-ahead scheduling plan P plan ；
Step 4, establishing an objective function by utilizing the aggregate charging power distribution and the day-ahead dispatch plan;
step 5, calculating the positive ideal solution of each attribute of the single electric automobileAnd negative ideal solution->Wherein, the attribute of single electric automobile includes: a remaining charge amount, a stay time, and a remaining charge time;
and 6, calculating the charging priority by calculating the distance between the value of the electric vehicle attribute and the positive and negative ideal solutions, and carrying out electric vehicle power distribution.
2. The urban power grid electric vehicle cooperative regulation and control method based on real-time information according to claim 1, wherein,
in the steps 1 and 3, the collected historical data and real-time information include the battery capacity of the electric vehicle, and the efficiency of the charging pile in the charging time.
3. The urban power grid electric vehicle cooperative regulation and control method based on real-time information according to claim 1, wherein,
in step 2, the charging solution of the single electric automobile is characterized by any monotonically non-decreasing curve in the space between the upper energy boundary and the lower energy boundary of the single electric automobile.
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, energy boundaries of the electric vehicle aggregation populationThe following relation is satisfied:
in the method, in the process of the invention,
represents the upper energy boundary of the ith electric car,
represents the lower energy boundary of the ith electric car,
t a,min each time interval, a=0, 1 … d,
t d,max the d-th time interval is indicated,
N t representing the total number of all electric vehicles;
the energy upper boundary E+ of the electric automobile aggregation group isThe lower energy boundary E-is
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 represented by any monotonically non-decreasing curve.
5. The urban power grid electric vehicle cooperative regulation and control method based on real-time information according to claim 4, wherein,
in step 3, the charging solution of the electric vehicle aggregation group is characterized by any monotonically non-decreasing curve E (t) in the space between the upper energy boundary and the lower energy boundary of the electric vehicle aggregation group, and the electric vehicle aggregation group charging power distribution satisfies the following relation:
in the method, in the process of the invention,
p (t) represents the electric automobile aggregate group charging power distribution,
e (t) represents a value corresponding to the time t on a monotonically non-decreasing curve,
t 0 indicating the initial moment of the aggregate charge,
t 0 +h represents the end time of the aggregate charge.
6. The urban power grid electric vehicle cooperative regulation and control method based on real-time information according to claim 5, wherein,
in step 4, the objective function is:
and obtaining the charging power of the electric automobile aggregation group by using the square sum of the differences of the electric automobile aggregation group charging power distribution and the day-ahead scheduling plan in each time interval.
7. The urban power grid electric vehicle cooperative regulation and control method based on real-time information according to claim 6, wherein,
in the step 5 of the process, the process is carried out,
calculating a remaining charge amount according to a current SOC of the single electric vehicle and a desired SOC when the electric vehicle leaves in combination with a battery capacity:
a i，1 ＝(SOC i，except -tSOC i，now )×E
in the method, in the process of the invention,
a i,1 an index indicating the amount of charge remaining,
SOC i,except desired SOC indicating start time of ith electric vehicle, SOC i,now Indicating the current SOC of the i-th electric automobile,
e represents the energy power of the electric automobile;
calculating the retention time according to the current time and the arrival time of the single electric automobile:
a i，2 ＝t-t arrival
in the method, in the process of the invention,
a i,2 indicating the indication of the time of residence,
t arrival indicating the arrival time of the electric automobile;
calculating the remaining charging time according to the current time and the expected departure time of the single electric automobile:
a i，3 ＝t leave -t
in the method, in the process of the invention,
a i,3 indicating an index of the remaining charge time and,
t leave indicating the estimated departure time of the electric vehicle.
8. The urban power grid electric vehicle cooperative regulation and control method based on real-time information according to claim 7, wherein,
the positive ideal solution and the negative ideal solution respectively satisfy the following relation:
in the method, in the process of the invention,
representing an ideal solution->J-th to single electric carMaximum value of attribute related ∈ ->Representing an ideal solution->A minimum value of a j-th attribute of the single electric automobile; n represents the total number of values of any attribute; ci j The value of the j-th attribute of the i-th electric automobile is represented;
c represents a canonical weighting matrix,
C＝WB
w is a weight vector, and assignment is carried out according to the importance degree of the index; and B represents a matrix obtained after the attribute evaluation of N electric vehicles is standardized and normalized.
9. The urban power grid electric vehicle cooperative regulation and control method based on real-time information according to claim 8, wherein,
in step 6, the distance between the value of the i-th attribute of the electric vehicle and the positive ideal solution and the distance between the i-th attribute of the electric vehicle and the negative ideal solution are calculated respectively:
in the method, in the process of the invention,
representing the distance between the ith attribute of the electric automobile and the corresponding positive ideal solution;
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, the value of M is 3.
10. The urban power grid electric vehicle cooperative regulation and control method based on real-time information according to claim 9, wherein,
the charging priority of the electric automobile is calculated by the following relation:
power allocation based on charging priority in the following relation:
11. the urban power grid electric vehicle cooperative regulation and control system based on real-time information and realized by the urban power grid electric vehicle cooperative regulation and control method based on real-time information according to any one of claims 1 to 10 is characterized in that the cooperative regulation and control system comprises a data acquisition module, an energy boundary calculation module of an electric vehicle aggregation group, a day-ahead scheduling 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 value of a single electric automobile according to the received data, then calculates the energy boundary value of the aggregation group, and inputs the calculation result to the day-ahead scheduling planning module;
the day-ahead scheduling plan module formulates a day-ahead scheduling plan according to the boundary value, and inputs the formulated plan to the objective function construction module;
the objective function construction module constructs an objective function according to the result input by the day-ahead scheduling planning module, and inputs the objective function to the positive and negative ideal solving module;
the positive and negative ideal solution module calculates positive and negative ideal solutions, and inputs the obtained solution values to the power distribution module;
and 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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