CN110861508A - Charging control method and system shared by residential area direct current chargers and storage medium - Google Patents

Charging control method and system shared by residential area direct current chargers and storage medium Download PDF

Info

Publication number
CN110861508A
CN110861508A CN201910980851.XA CN201910980851A CN110861508A CN 110861508 A CN110861508 A CN 110861508A CN 201910980851 A CN201910980851 A CN 201910980851A CN 110861508 A CN110861508 A CN 110861508A
Authority
CN
China
Prior art keywords
charging
electric
private car
taxi
residential area
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201910980851.XA
Other languages
Chinese (zh)
Other versions
CN110861508B (en
Inventor
陈良亮
李骆
徐石明
杨凤坤
郑红娟
宋杰
李化
周材
邵军军
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
State Grid Shandong Electric Power Co Ltd
NARI Group Corp
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
State Grid Electric Power Research Institute
Original Assignee
State Grid Corp of China SGCC
State Grid Shandong Electric Power Co Ltd
NARI Group Corp
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
State Grid Electric Power Research Institute
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, State Grid Shandong Electric Power Co Ltd, NARI Group Corp, Nari Technology Co Ltd, NARI Nanjing Control System Co Ltd, State Grid Electric Power Research Institute filed Critical State Grid Corp of China SGCC
Priority to CN201910980851.XA priority Critical patent/CN110861508B/en
Publication of CN110861508A publication Critical patent/CN110861508A/en
Application granted granted Critical
Publication of CN110861508B publication Critical patent/CN110861508B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/64—Optimising energy costs, e.g. responding to electricity rates
    • 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
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • 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
    • Y02T90/12—Electric charging stations
    • 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

Abstract

The invention discloses a shared charging control method for a residential area direct-current charger, which comprises the steps of establishing an electric private car charging load model; establishing an electric taxi charging load model; establishing a charging scheduling multi-objective double-layer optimization model according to the charging load model of the electric private car and the charging load model of the electric taxi; according to the charging dispatching multi-objective double-layer optimization model, charging load curves of electric private cars and electric taxis in residential areas are calculated, and vehicle charging is arranged according to the curves, so that the peak-valley difference of the total power consumption load of the residential areas is minimum, the satisfaction degree of users is maximum, the utilization rate of a charger can be improved, and the load fluctuation of a power distribution network in the residential areas can be reduced.

Description

Charging control method and system shared by residential area direct current chargers and storage medium
Technical Field
The invention belongs to the technical field of electric vehicle charging, and particularly relates to a shared charging control method for a residential area direct-current charger.
Background
At present, the development of electric vehicles is in an early application stage, the utilization rate of a direct-current charger is low, the construction cost is high, the construction scale is limited, and therefore queuing phenomena can occur in public charging stations during the charging peak period. Although the charging facilities in the residential area are relatively abundant, the residential area is not open to the outside, the charging facilities are generally alternating current charging piles, the charging time is long, and even if the charging facilities are open to the outside, users of electric taxis in the society do not want to charge. In order to alleviate the charging queuing problem of the public charging station and improve the utilization rate of charging facilities in a residential area, a residential area direct current charger sharing charging mode is provided. The shared charging mode has three advantages, and firstly, the utilization rate of the charging facility can be improved by one pile of multi-gun type direct current charger, so that the income of operators of the charging facility is increased. Secondly, the driving mileage of the conventional electric vehicle is short, and if the vehicle owner drives the vehicle for a long distance, the driving mileage is not enough or no charging facility can be found during driving.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a shared charging control method for a residential area direct-current charger, which can improve the utilization rate of the charger and reduce the load fluctuation of a power distribution network in a residential area.
The technical scheme adopted by the invention is as follows:
in a first aspect, a shared charging control method for a residential area direct current charger is provided, and the method includes the following steps:
establishing an electric private car charging load model;
establishing an electric taxi charging load model;
establishing a charging scheduling multi-objective double-layer optimization model according to the charging load model of the electric private car and the charging load model of the electric taxi;
and calculating charging load curves of the electric private car and the electric taxi in the residential area according to the charging scheduling multi-objective double-layer optimization model.
With reference to the first aspect, further, the establishing of the electric private car charging load model specifically includes:
setting:
the charging starting time of each electric private car is independent;
the daily driving mileage of each electric private car is independent;
when the electric private car is charged, the electric quantity of the battery is charged to the maximum allowable value of the SOC value every time;
on the basis of the above assumptions:
probability density function of electric private car driving end time t:
Figure RE-GDA0002370862680000021
wherein, musExpected value, sigma, of a probability density function representing the end of a private car runsThe standard deviation of the probability density function at the running end time of the private car is obtained;
probability density function of daily average driving mileage of electric private car:
Figure RE-GDA0002370862680000022
wherein, muDIs the average number, sigma, of lnr in the probability density function of the daily mileage of a private carDIs the standard deviation of lnr in the probability density function of mileage of private car driving per day;
the probability density function of the SOC value when the electric private car is initially charged is as follows:
Figure RE-GDA0002370862680000023
wherein the content of the first and second substances,
Figure RE-GDA0002370862680000024
and obtaining the probability density function of the SOC value when the electric private car is initially charged.
With reference to the first aspect, further, the establishing of the electric taxi charging load model specifically includes:
setting:
the charging starting time of each electric taxi is independent;
the SOC values of each electric taxi are independent when the electric taxi starts to be charged;
the electric taxi charges the battery electric quantity to the maximum allowable value of the SOC value every time the electric taxi is charged;
on the basis of the above assumptions:
probability density function of charging starting time t of the electric taxi:
Figure RE-GDA0002370862680000025
wherein, muciAnd σciRespectively representing the expectation and the standard deviation of the probability density function at the moment when the electric taxi starts to be charged, wherein i represents the charging time interval;
the probability density function of the daily driving mileage r of the electric taxi is as follows:
Figure RE-GDA0002370862680000031
wherein, muTAnd deltaTRespectively representing the expected value and the standard deviation of the daily average driving mileage probability density function of the electric taxi;
the probability density function of the SOC value when the electric taxi starts to be charged is as follows:
Figure RE-GDA0002370862680000032
wherein the content of the first and second substances,
Figure RE-GDA0002370862680000033
the SOC value of the electric taxi is a probability density function when the electric taxi starts to be charged,
Figure RE-GDA0002370862680000034
and charging the electric taxi with the SOC value.
With reference to the first aspect, further, the establishing a charging scheduling multi-objective double-layer optimization model specifically includes:
performing first-layer optimization by taking the minimum difference between the peak and valley of the total power consumption load of the residential area and the deviation of the charging plan of the electric automobile as optimization targets; and performing second-layer optimization with the maximum satisfaction degree of the electric vehicle user as an optimization target.
With reference to the first aspect, further, the objective function of the first layer optimization is:
Figure RE-GDA0002370862680000035
wherein f is1,1、f1,2And f1,3Respectively representing the standard deviation of the total electricity consumption load of a residential area, the charging load of an electric private car and the charging load of an electric taxi in one day during ordered charging; pz,std、Ps,stdAnd Pc,stdRespectively representing the standard deviation of the total electricity consumption load of a residential area, the charging load of an electric private car and the charging load of an electric taxi in one day when the electric taxi is charged disorderly; a is1,1、a1,2、a1,3Respectively, representing the corresponding weight coefficients.
With reference to the first aspect, further, the objective function of the second layer optimization is:
Figure RE-GDA0002370862680000036
Figure RE-GDA0002370862680000037
Figure RE-GDA0002370862680000038
Figure RE-GDA0002370862680000041
Figure RE-GDA0002370862680000042
wherein the content of the first and second substances,
Figure RE-GDA0002370862680000043
and
Figure RE-GDA0002370862680000044
respectively representCost satisfaction with electric private cars and electric taxis,
Figure RE-GDA0002370862680000045
and
Figure RE-GDA0002370862680000046
respectively representing the traveling satisfaction degrees of the electric private car and the electric taxi;
Figure RE-GDA0002370862680000047
and
Figure RE-GDA0002370862680000048
respectively representing the lowest charging expense and the highest charging expense of the ith electric taxi;
Figure RE-GDA0002370862680000049
and
Figure RE-GDA00023708626800000410
respectively representing the charging power of the ith electric private car and the electric taxi in a time period t under the condition of charging with the maximum travel satisfaction; ctRepresents the electricity rate for time period t;
Figure RE-GDA00023708626800000411
and
Figure RE-GDA00023708626800000412
respectively representing the charging power of the ith electric private car and the electric taxi in the time period t when the charging is disordered;
Figure RE-GDA00023708626800000413
and
Figure RE-GDA00023708626800000414
respectively representing the charging power of the ith electric private car and the electric taxi in the time period t when the charging is orderly carried out; a is2,1And a2,2Respectively representing the corresponding weight coefficients; m is1And m2Respectively indicating the charging of electric private car and electric taxi in one dayThe number of electricity times, T, is the total number of time segments, and delta T represents the simulation step length.
In a second aspect, a shared charging control system for a residential area direct current charger is provided, which includes:
a modeling module: establishing an electric private car charging load model;
establishing an electric taxi charging load model;
establishing a charging scheduling multi-objective double-layer optimization model according to the charging load model of the electric private car and the charging load model of the electric taxi;
a calculation module: and calculating charging load curves of the electric private car and the electric taxi in the residential area according to the charging scheduling multi-objective double-layer optimization model.
In a third aspect, a shared charging control system for a residential area direct-current charger is provided, which comprises a memory and a processor;
the memory is to store instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method of any of the first aspects.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of the first aspect.
Has the advantages that: according to the invention, the charging conformity model of the electric private car and the electric taxi is established, then the charging dispatching multi-objective double-layer optimization model is established, the benefits of a power grid company and the safe and stable operation of a power grid are ensured through one layer of optimization, and the benefits of electric car users are ensured by performing the second optimization on the basis of the first layer of optimization, so that more electric car users are attracted to participate in the charging dispatching activity. The charging coordination scheduling control of the electric private car and the electric taxi can be effectively realized through the obtained charging load curves of the electric private car and the electric taxi, the peak-valley difference of the total electricity utilization load of a residential area can be effectively reduced while the charging of the car is ensured, and the running safety of a power distribution network of the residential area is improved; in addition, through the user satisfaction model, the travel satisfaction and the cost satisfaction of the user are improved while the load fluctuation of the power distribution network in the residential area is reduced, and therefore the enthusiasm of the electric vehicle user for participating in charging scheduling is improved.
Drawings
FIG. 1 is a schematic diagram of a residential area DC charger sharing charging;
FIG. 2 is a schematic diagram of a charging management system of a residential area DC charger;
FIG. 3 is a graph of the electrical load of the residential base;
FIG. 4 is a graph of total load of electricity consumption in residential areas before and after electric vehicle charging scheduling;
FIG. 5 is a graph of charging load of an electric private car before and after an electric car charging schedule;
FIG. 6 is a graph of charging load for an electric taxi before and after an electric vehicle charge schedule;
fig. 7 is a satisfaction histogram for electric private cars and electric taxis before and after a charging schedule.
Detailed Description
To further describe the technical features and effects of the present invention, the present invention will be further described with reference to the accompanying drawings and detailed description.
The invention provides a shared charging control method for a residential area direct current charger, which is characterized in that a shared charging mode is implemented in a residential area by utilizing a pile of multi-gun type direct current chargers, the utilization rate of charging facilities can be improved, the charging queuing phenomenon of a public charging station can be relieved, the load fluctuation of a residential area power distribution network can be reduced through a multi-objective double-layer optimization control strategy, the operation safety of a power grid is improved, and the enthusiasm of electric vehicle users for participating in charging scheduling is improved.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
the residential area direct current charger sharing mode is that a pile of multi-gun direct current chargers are equivalent to a high-power direct current charger of a public charging station in daytime, and rapid charging service is mainly provided for external electric taxi users. In order to meet the night charging requirements of original electric private car users in residential areas, a part of one multi-gun type direct current charger provides charging services for the electric private car users in a low-power direct current slow charging mode at night, and the other part of one multi-gun type direct current charger still provides charging services for foreign taxis in a high-power direct current fast charging mode. The one-pile multi-gun type direct current charger has the function of free switching, when an electric vehicle user selects a high-power direct current quick charging mode, the one-pile multi-gun type direct current charger combines the output power of a plurality of charging modules onto one charging gun through a selector switch, and no power is output on other charging guns; when an electric vehicle user adopts a low-power direct-current slow charging mode, the maximum output power of each charging gun is a fixed value, and the charging behaviors among the charging guns are not interfered with each other.
The shared charging control method of the residential area direct current charger is based on a shared charging mode and aims at minimizing peak-valley difference of electricity load of the residential area, minimizing deviation of actual charging power and required charging power and maximizing user satisfaction, and the direct current charger is controlled through the energy router, so that the satisfaction of electric vehicle users and the income of facility operators are increased, and meanwhile, the running safety of a power distribution network is improved. The method comprises the following specific steps:
step one, modeling is carried out on the charging load of the electric private car according to the charging behavior characteristics of the electric private car.
Setting:
1) the charging starting time of each electric private car is independent;
2) the daily driving mileage of each electric private car is independent;
3) when the electric private car is charged, the battery electric quantity is charged to the maximum allowable value of SOC (State of charge) value every time;
on the basis of the above assumptions:
the initial charging time of the electric private car is determined by the SOC value of the electric car and the charging habit of a user, and the probability density function of the driving end time t of the electric private car is as follows:
Figure RE-GDA0002370862680000061
wherein, musExpected value, sigma, of a probability density function representing the end of a private car runsThe standard deviation of the probability density function at the running end time of the private car is obtained;
the daily driving mileage of the electric private car is determined by the daily working distance of a car owner, the daily average daily driving mileage r of the electric private car meets logarithm positive power distribution, and the probability density function is as follows:
Figure RE-GDA0002370862680000062
wherein, muDIs the average number, sigma, of lnr in the probability density function of the daily mileage of a private carDIs the standard deviation of lnr in the probability density function of mileage of private car driving per day;
the SOC value of the electric private car during initial charging is subjected to sectional distribution, and the probability density function is as follows:
Figure RE-GDA0002370862680000063
wherein the content of the first and second substances,
Figure RE-GDA0002370862680000064
and obtaining the probability density function of the SOC value when the electric private car is initially charged.
The charging load of the electric private car can be obtained according to the probability density function.
And step two, modeling the charging load of the electric taxi according to the charging behavior characteristics of the taxi user.
Setting:
1) the charging starting time of each electric taxi is independent;
2) the SOC values of each electric taxi are independent when the electric taxi starts to be charged;
3) the electric taxi charges the battery electric quantity to the maximum allowable value of the SOC value every time the electric taxi is charged;
on the basis of the above assumptions:
the charging starting time t of the electric taxi meets the characteristic of sectional probability distribution in one day, and each section is subjected to positive distribution, wherein the probability density function is as follows:
Figure RE-GDA0002370862680000071
wherein, muciAnd σciRespectively representing the expectation and the standard deviation of the probability density function at the moment when the electric taxi starts to be charged, wherein i represents the charging time interval; the charging time interval of the electric taxi is divided into 4, and when i is equal to 1, t belongs to (0: 00-9: 00)]Taking out muc1＝4.5，σc13; when i is 2, t is belonged to (9: 00-15: 00)]Taking out muc2＝12，σc22.45; when i is 3, t is belonged to (15: 00-19: 00)]Taking out muc3＝16，σc33.16; when i is 4, t is belonged to (19: 00-24: 00)]Taking out muc4＝21，σc4＝2.65。
The daily average driving mileage of the electric taxi meets the characteristic of sectional probability distribution in one day, each section is subjected to positive distribution, and the probability density function of the daily average driving mileage r of the electric taxi is as follows:
Figure RE-GDA0002370862680000072
wherein, muTAnd deltaTRespectively representing the expected value and the standard deviation of the daily average driving mileage probability density function of the electric taxi;
the probability density function of the SOC value when the electric taxi starts to be charged is as follows:
Figure RE-GDA0002370862680000073
wherein the content of the first and second substances,
Figure RE-GDA0002370862680000074
the SOC value of the electric taxi is a probability density function when the electric taxi starts to be charged,
Figure RE-GDA0002370862680000075
is electricityAnd the SOC value when the movable taxi starts to charge.
And obtaining the charging load of the electric taxi according to the probability density function.
Step three, establishing a charging scheduling multi-objective double-layer optimization model according to the charging load model of the electric private car and the charging load model of the electric taxi; the first layer takes the minimum difference between the peak and valley of the total load of electricity consumption of residential areas and the deviation of the charging plan of the electric automobile as optimization targets, and the second layer takes the maximum satisfaction degree of users of the electric automobile as the optimization target.
The objective function of the first layer optimization is:
Figure RE-GDA0002370862680000081
wherein f is1Showing that the total load peak-valley difference of electricity utilization of the residential area and the deviation of the charging plan of the electric automobile are minimum, f1,1、f1,2And f1,3Respectively representing the standard deviation of the total power consumption load of a residential area, the charging load of an electric private car and the charging load of an electric taxi in one day when orderly charging (the orderly charging refers to the control of the charging process of the electric car, including the consideration of the satisfaction degree of a user and the non-consideration of the satisfaction degree of the user); pz,std、Ps,stdAnd Pc,stdRespectively representing the standard deviation (unit: KW) of the total power consumption load, the charging load of the electric private car and the charging load of the electric taxi in a residential area in one day when the electric taxi is charged disorderly (the disorderly charging refers to the charging process of the electric car, the electric car is not controlled, and the electric taxi is charged randomly); a is1,1、a1,2、a1,3Respectively, representing the corresponding weight coefficients.
The first layer of optimization contains 4 constraints, which are specifically as follows:
1) and the SOC value continuity and the maximum value constraint of the power batteries of the electric private car and the electric taxi.
Figure RE-GDA0002370862680000082
Figure RE-GDA0002370862680000083
Figure RE-GDA0002370862680000084
Figure RE-GDA0002370862680000085
Wherein, ηchRepresenting the charging efficiency of the electric automobile (assuming that the charging efficiency of all electric private cars and electric taxis are equal); b isiRepresents the battery capacity of the electric car (assuming that the battery capacities of all electric private cars and electric taxis are equal), (kW · h);
Figure RE-GDA0002370862680000086
and
Figure RE-GDA0002370862680000087
respectively representing the charge states of the ith electric private car and the electric taxi at the time t;
Figure RE-GDA0002370862680000088
represents the maximum allowable SOC value for the electric vehicle (assuming equal maximum allowable SOC values for all electric private cars and electric taxis).
2) And the charging electric quantity of the power battery of the electric private car and the electric taxi is restricted.
Figure RE-GDA0002370862680000089
Figure RE-GDA0002370862680000091
Wherein the content of the first and second substances,
Figure RE-GDA0002370862680000092
and
Figure RE-GDA0002370862680000093
the initial charge states of the ith electric private car and the ith electric taxi are respectively shown, T is the number of time segments, in the embodiment, 24 hours a day is divided into 48 segments, and delta T is a simulation step length, namely, each segment is 30 minutes.
3) And the charging power of the electric private car and the electric taxi is restricted.
Figure RE-GDA0002370862680000094
Figure RE-GDA0002370862680000095
Wherein the content of the first and second substances,
Figure RE-GDA0002370862680000096
and
Figure RE-GDA0002370862680000097
respectively representing the maximum charging power (unit: kW) of the electric private car and the electric taxi;
Figure RE-GDA0002370862680000098
and
Figure RE-GDA0002370862680000099
respectively showing the charging power (unit: kW) of the ith electric private car and the ith electric taxi at the time of t when orderly charging is carried out
4) And (4) power constraint of interconnection and interworking platform.
Figure RE-GDA00023708626800000910
Wherein the content of the first and second substances,
Figure RE-GDA00023708626800000911
representing the resident basic electric load for the period t,
Figure RE-GDA00023708626800000912
indicating time t-interval interworkingPower limit (kW), m, issued by the platform to the energy controller1And m2Respectively representing the charging times of the electric private car and the electric taxi in one day.
The objective function of the second layer optimization is:
Figure RE-GDA00023708626800000913
Figure RE-GDA00023708626800000914
Figure RE-GDA00023708626800000915
Figure RE-GDA00023708626800000916
Figure RE-GDA00023708626800000917
wherein f is2The overall degree of satisfaction is represented as,
Figure RE-GDA00023708626800000918
and
Figure RE-GDA00023708626800000919
respectively represents the satisfaction degree of the electric private car and the electric taxi,
Figure RE-GDA00023708626800000920
and
Figure RE-GDA00023708626800000921
respectively represents the travel satisfaction of the electric private car and the electric taxi.
Figure RE-GDA00023708626800000922
And
Figure RE-GDA00023708626800000923
respectively representing the lowest and highest charging fees (yuan) of the ith electric private car;
Figure RE-GDA00023708626800000924
and
Figure RE-GDA00023708626800000925
respectively representing the lowest and highest charging fees (yuan) of the ith electric taxi;
Figure RE-GDA00023708626800000926
and
Figure RE-GDA00023708626800000927
respectively representing the charging power (unit: kW) of the ith electric private car and the ith electric taxi in the charging situation with the maximum travel satisfaction; ctRepresents the electricity rate (unit: element) of the time period t;
Figure RE-GDA0002370862680000101
and
Figure RE-GDA0002370862680000102
respectively representing the charging power (unit: kW) of the ith electric private car and the electric taxi in the time period t when the electric private car and the electric taxi are charged out of order; a is2,1And a2,2Respectively representing the corresponding weight coefficients; m is1And m2Respectively representing the charging times of the electric private car and the electric taxi in one day.
The constraints of the second layer optimization are as follows:
1) second layer optimization time f1Equal to the first layer optimization time f1Is measured.
Figure RE-GDA0002370862680000103
Wherein the content of the first and second substances,
Figure RE-GDA0002370862680000104
to representAnd optimizing the obtained peak-valley difference of the total electricity consumption load of the residential area and the minimum deviation of the actual charging power and the total required charging power of the electric automobile in the first layer.
2) Other constraints are the same as for layer 1 optimization.
And step four, calculating charging load curves of the electric private car and the electric taxi in the residential area according to the charging scheduling multi-objective double-layer optimization model.
According to chebyshev's law, the following formula is satisfied for an arbitrarily small positive number epsilon:
Figure RE-GDA0002370862680000105
wherein x isi,t1, denotes that the i-th private car is being charged at time t, xi,t0, the private car is not charged at the time t; n is a radical of1Indicating the number of existing electric private cars in the residential area, E (x)t) And D (x)t) The expectation and variance of the number of private cars charged by the access power grid at the moment t are shown.
The number of private cars which are connected into the power grid for charging at the equivalent public charging station of the residential area at the moment t is equal to N of the charging probability of a single private car at the moment t1Doubling; n is a radical of1The average value of the SOC values when the private car starts to charge is equal to the expected value of the SOC value when the private car starts to charge; if the number of taxis outside the residential area is N2If the number of taxis which are accessed to the power grid for charging by the equivalent public charging station in the residential area at the moment t is equal to N of the charging probability of a single taxi at the moment t2Multiple, N2The average value of the SOC values of the taxies when the taxies start to charge is equal to the expected value of the SOC value of the single taxi when the taxies start to charge.
Calculating the charging load of the electric automobile in the residential area by adopting a Monte Carlo method according to the charging load model of the electric private car and the charging load model of the electric taxi; the system firstly extracts the number of private cars and taxis which are connected to a power grid at each time point, then extracts SOC values when the private cars and the taxis start to be charged, the private cars and the taxis leave only if all the private cars and the taxis are fully charged (namely the battery charge is charged to the maximum allowable SOC value), calculates the charging time required by each private car and the taxis according to the charging requirements of each private car and the taxis (the charging electric quantity of each electric car can be calculated according to the SOC value when the charging is started and the SOC value when the charging is finished, the charging time length of each electric car is calculated according to the charging power of the electric car), further obtains a charging load curve of 24 hours all day by calculating the total charging load at each time point, then superposes the charging load curves obtained by each time simulation and calculates the average value of the charging load curves of the electric cars (the charging load curves of the electric cars are calculated by multiple times of simulation, each time is a simulated charging load curve), finally outputting a charging load curve of the equivalent public charging station of the residential area, and arranging vehicle charging according to the curve so that the peak-valley difference of the total power consumption load of the residential area is minimum and the satisfaction degree of users is maximum.
The embodiment of the invention provides a shared charging control system of a residential area direct current charger, which can be used for loading the shared charging control method of the residential area direct current charger, and comprises the following steps:
a modeling module: establishing an electric private car charging load model;
establishing an electric taxi charging load model;
establishing a charging scheduling multi-objective double-layer optimization model according to the charging load model of the electric private car and the charging load model of the electric taxi;
a calculation module: and calculating charging load curves of the electric private car and the electric taxi in the residential area according to the charging scheduling multi-objective double-layer optimization model.
The embodiment of the invention also provides a charging control system shared by the residential area direct-current chargers, which can also be as follows: comprising a memory and a processor; the memory is to store instructions;
the processor is used for operating according to the instruction to execute the step of the shared charging control method of the residential area direct current charger.
The invention also provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the aforementioned community direct current charger shared charging control method.
Fig. 2 shows a charging management system of a residential area dc charger. In fig. 2, the charging management system of the residential area direct current charger mainly comprises an interconnection platform, an electric vehicle, an energy router, an energy controller, the direct current charger, a mobile phone APP and the like. The energy source controller is a novel concentrator with functions of data acquisition, storage, communication, charging strategy formulation and the like, and the energy source router is a novel intelligent electric meter with functions of data acquisition, storage, communication, power control, shared charging strategy execution and the like.
After the electric vehicles enter a residential area charging station, each multi-gun type direct current charger corresponds to one or more electric vehicles, and the energy source router acquires the related information of the electric vehicles through the direct current charger and then transmits the information to the energy source controller. The collected information comprises information such as an initial SOC value of the electric automobile, a maximum SOC value (the maximum SOC value is set to be 0.9), estimated leaving time of a user, rated capacity of a power battery of the electric automobile and the like; in addition, the energy router can also collect basic electric loads of residential users (daily electric loads of residential residents in a community, such as air conditioners, electric lamps and the like) and transmit the basic electric loads to the energy controller. The energy controller interacts with the interconnection platform to obtain prediction information (reference past experience value) of the basic load of the residential area users and prediction information of the charging load of the electric vehicles, and obtains the real-time charging power of each electric vehicle of the residential area equivalent public charging station through optimization calculation by combining real-time data uploaded by the energy router. And the energy controller refreshes data every 30 minutes, and if the predicted value of the electricity utilization basic load of the residential area user changes, the predicted value of the charging load of the electric automobile changes, and the electric automobile is detected to be accessed into the power grid or the charging is finished, the energy router updates the database of the energy router to carry out optimization calculation again to obtain the new real-time charging power of each electric automobile.
And establishing a residential area charging station and an operation scene thereof by using the residential area parking lot and the area thereof, and performing simulation analysis on the charging conditions of the original electric private car and the external electric taxi in the residential area by using a Monte Carlo simulation method in combination with the charging load models of the electric private car and the electric taxi. Wherein, the simulation period is 0: 00-24: 00, simulation step length of 30 min.
Assuming that the reserved quantity of electric private cars in a residential area is 1200, the daily average driving mileage of the private cars is 60km, the number of external electric taxis is 200, the daily average driving mileage of the electric taxis is 400 km., taking Biandee 6 as an example, the driving mileage of the electric automobile is 400km, the battery capacity is 82 kW.h, and the electricity consumption per kilometer is 19.5 kWh.h, and assuming that the charging efficiency is ηch0.92, and the maximum allowable SOC value is 0.9. According to the relevant national regulations, the configuration ratio of electric automobiles to charging facilities in residential areas is 1: 1; known by the quantity of the original electronic private car of residential area, if according to the traditional facility construction scheme that charges of residential area then need install 1200 exchange charging stake of 7 kW. If the charging facility in the residential area adopts a shared direct-current charger, and if each direct-current charger is configured with 4 charging guns, 300 multi-gun direct-current chargers need to be installed in the residential area, and the maximum charging power of each multi-gun direct-current charging node is 28 kW. The utilization rate of the charging facility needs to be considered in the operation process of the charging facility, and the calculation formula of the capacity utilization rate of the charging facility is as follows:
Figure RE-GDA0002370862680000121
Figure RE-GDA0002370862680000122
wherein N isCPThe number of the shared direct current chargers is as follows; pCP,maxThe maximum output power (kW) of each shared direct current charger is obtained; qCSThe total charging capacity (one day) of all electric vehicles in the equivalent public charging station of the residential area (unit: kW.h); t is the number of time segments in a simulation cycle (one simulation cycle is averagely divided into T time segments); pCS,run(t) is the total charging power (kW) of all electric vehicles of the residential area equivalent charging station at the moment t; Δ t is the simulation step size.
The total residential electricity load includes two parts, one is the electric vehicle charging load, and the other is the residential basic electricity load, which is shown in fig. 3. The total charging cost of the electric automobile in the charging station in the residential area comprises two parts of electricity cost and service cost, wherein the price of the service cost per hour is a fixed value, the electricity cost adopts peak-valley electricity price, and a peak-valley electricity price table is shown in a table 1.
TABLE 1 Peak and Valley electricity price Table for residential area
Type (B) Time period Electricity price (Yuan/kWh)
Peak(s) 10：00～15：00、18：00～21：00 1.2282
Grain 23：00～7：00 0.3518
Flat plate 7：00～10：00、15：00～18：00、21：00～23：00 0.8495
After the residential area charging station adopts the multi-objective double-layer optimization algorithm, a residential area electricity consumption total load curve after electric vehicle charging scheduling is shown in fig. 4. As shown in fig. 4, the charging load of the electric vehicle is 21: near can form the peak of charging (unordered charging) near 00 time, superpose with original residential area basic power load's peak value mutually, and electric automobile charges less in residential area basic power load low ebb section, if residential area equivalent public charging station adopts unordered charging (electric automobile begins to charge immediately after arriving equivalent public charging station, until being full of), can lead to the poor grow of residential area power consumption total load peak valley, the undulant aggravation of power consumption total load to influence the security of residential area distribution network operation. After private cars and taxis are charged and scheduled at equivalent public charging stations in residential areas according to charging curves, under the condition that user satisfaction is not considered and the user satisfaction is considered, the energy routers rarely arrange electric cars to be charged at the peak time of basic load, the electric cars are arranged to be charged at the valley time of basic load as much as possible (forcibly arranged through a calculated charging strategy), the peak-valley difference of the total power consumption load of the residential areas is effectively reduced, and therefore the operation safety of a power distribution network in the residential areas is improved. In addition, in the case of considering the user satisfaction, in order to improve the user travel satisfaction and the cost satisfaction when the energy controller makes a charging strategy, a part of electric vehicle charging is required to be preferentially carried out in the time periods of 15: 00-18: 00 and 22: 00-0: 30, so that the user travel satisfaction and the cost satisfaction of the electric vehicle are improved.
As shown in fig. 5 and 6, the peak time of the basic electrical load of the residential area is 18: 00-22: 00, and the valley time is 1: 00-6: 00; in order to reduce the peak-valley difference of the total electricity consumption load of the residential area, under the condition of optimizing charging and considering the satisfaction degree of users, the electric private car is hardly charged in the time interval of 18: 00-22: 00, and the electric private car is mainly charged in the time interval of 0: 00-6: 00. Meanwhile, the price of the electric charge is higher in the time interval of 18: 00-22: 00, and is lower in the time interval of 0: 00-6: 00, so that the charging cost of the electric private car is lower under the conditions of optimizing charging and considering the satisfaction degree of a user. In addition, in the case of considering the user satisfaction, the energy controller is preferentially arranged to charge the electric private car during the time periods of 15: 00-18: 00 and 22: 00-24: 00 in order to increase the satisfaction of the electric private car user when the charging policy is formulated; in the time period of 6: 00-10: 00, the electric private car is arranged to be charged less, so that the traveling satisfaction and the expense satisfaction of a private car user are improved.
As shown in fig. 6, compared to the electric private car, the change of the charging load of the electric taxi before and after the charging schedule is relatively small, and the electric taxi is scheduled to be charged less in the time interval of 18: 00-22: 00, mainly to avoid the charging load peak value overlapping with the residential area basic electricity load peak value. In the case of considering the user satisfaction, the electric taxi is arranged to be charged more in the time period of 22: 00-24: 00, mainly to improve the cost satisfaction of the electric taxi user. In addition, as can be seen from fig. 5 and 6, since the electric private car adopts a slow charging mode and the charging scheduling range is large, the charging load curve of the electric private car changes greatly before and after the charging scheduling; and the electric taxi adopts a quick charging mode and has a smaller charging scheduling range, so that the charging load curve of the electric taxi changes less before and after the charging scheduling.
The travel satisfaction and cost satisfaction ratio before and after the electric vehicle user participates in the charging schedule is shown in fig. 7. As shown in fig. 7, in the case of the disordered charging, the satisfaction degrees of the electric private car and the electric taxi are both low, and the satisfaction degrees of the electric private car and the electric taxi both reach the maximum value of 1, so that the overall satisfaction degrees of the electric private car and the electric taxi are still high. Under the condition of not considering the user satisfaction, the travel satisfaction of the electric private car user is very low, and the cost satisfaction is higher; the traveling satisfaction of the electric taxi user is high, the cost satisfaction is low, the overall satisfaction of the electric private car user is low, and the overall satisfaction of the electric taxi user is general. Under the condition of considering the user satisfaction, the travel satisfaction of the electric private car user is low, and the cost satisfaction is high; the travel satisfaction of the electric taxi user is high, and the cost satisfaction is also high; compared with the situation that the user satisfaction degree is not considered for optimizing charging, under the situation that the user satisfaction degree is considered for optimizing charging, the travel satisfaction degree and the cost satisfaction degree of the electric private car are both improved, the travel satisfaction degree of the electric taxi user is improved, and the cost satisfaction degree is almost unchanged.
Table 2 shows data pairs before and after optimization of charging of all electric vehicles in the residential charging station.
Table 2 comparison of data of each equivalent public charging station before and after charge scheduling
Figure RE-GDA0002370862680000141
As shown in table 2, the total peak-to-valley load difference and the total peak-to-valley load difference rate of the total power consumption load in the residential area are both greatly reduced under the condition that the user satisfaction degree is not considered and the user satisfaction degree is considered; when the user satisfaction situation is considered, because the travel satisfaction and the cost satisfaction of the electric vehicle user need to be improved, the peak-to-valley difference of the total power consumption load and the peak-to-valley difference rate of the total power consumption load of the residential area are slightly larger than those of the situation without considering the user satisfaction. In the aspect of charging cost of the electric automobile, compared with disordered charging, under the condition that the user satisfaction degree is not considered and the user satisfaction degree is considered, the total charging cost of the electric private car is greatly reduced, and the total charging cost of the electric taxi is only slightly reduced; compared with the situation that the user satisfaction degree is not considered, under the situation that the user satisfaction degree is considered, the total charging cost of the electric private car is reduced slightly, and the total charging cost of the electric taxi is almost unchanged. In addition, in the aspect of utilization rate of charging facilities in a residential area, when an external electric taxi is not introduced, the utilization rate of an alternating-current charging pile in the residential area is 7.6%; after an external electric taxi is introduced, the utilization rate of the shared direct-current charger in the residential area is 15.78%. Therefore, after the outside electric taxi is introduced into the residential area, the utilization rate of the charging facility of the residential area is remarkably improved.
In conclusion, the utilization rate of charging facilities in the residential area can be improved by sharing the charging mode through the residential area direct current charger, so that the income of operators of the charging facilities is improved; through the charge coordinated scheduling of the private car and the taxi, the peak-valley difference of the total electricity utilization load of the residential area can be effectively reduced, so that the operation safety of the power distribution network of the residential area is improved; in addition, through the user satisfaction model, the travel satisfaction and the cost satisfaction of the user are improved while the load fluctuation of the power distribution network in the residential area is reduced, and therefore the enthusiasm of the electric vehicle user for participating in charging scheduling is improved.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above embodiments do not limit the present invention in any way, and all technical solutions obtained by taking equivalent substitutions or equivalent changes fall within the scope of the present invention.

Claims (9)

1. A shared charging control method for residential area direct current chargers is characterized by comprising the following steps:
establishing an electric private car charging load model;
establishing an electric taxi charging load model;
establishing a charging scheduling multi-objective double-layer optimization model according to the charging load model of the electric private car and the charging load model of the electric taxi;
and calculating charging load curves of the electric private car and the electric taxi in the residential area according to the charging scheduling multi-objective double-layer optimization model.
2. The shared charging control method for the residential area direct-current charger according to claim 1, characterized in that:
the establishment of the electric private car charging load model specifically comprises the following steps:
setting:
the charging starting time of each electric private car is independent;
the daily driving mileage of each electric private car is independent;
when the electric private car is charged, the electric quantity of the battery is charged to the maximum allowable value of the SOC value every time;
on the basis of the above assumptions:
probability density function of electric private car driving end time t:
Figure FDA0002235136260000011
wherein, musExpected value, sigma, of a probability density function representing the end of a private car runsThe standard deviation of the probability density function at the running end time of the private car is obtained;
probability density function of daily average driving mileage of electric private car:
Figure FDA0002235136260000012
wherein, muDIs the average number of ln r in the probability density function of daily driving mileage of the private car, sigmaDThe standard deviation of ln r in the daily driving mileage probability density function of the private car is shown;
the probability density function of the SOC value when the electric private car is initially charged is as follows:
Figure FDA0002235136260000021
wherein the content of the first and second substances,
Figure FDA0002235136260000022
and obtaining the probability density function of the SOC value when the electric private car is initially charged.
3. The shared charging control method for the residential area direct-current charger according to claim 1, characterized in that:
the establishment of the charging load model of the electric taxi specifically comprises the following steps:
setting:
the charging starting time of each electric taxi is independent;
the SOC values of each electric taxi are independent when the electric taxi starts to be charged;
the electric taxi charges the battery electric quantity to the maximum allowable value of the SOC value every time the electric taxi is charged;
on the basis of the above assumptions:
probability density function of charging starting time t of the electric taxi:
Figure FDA0002235136260000023
wherein, muciAnd σciRespectively representing the expectation and the standard deviation of the probability density function at the moment when the electric taxi starts to be charged, wherein i represents the charging time interval;
the probability density function of the daily driving mileage r of the electric taxi is as follows:
Figure FDA0002235136260000024
wherein, muTAnd deltaTRespectively representing the expected value and the standard deviation of the daily average driving mileage probability density function of the electric taxi;
the probability density function of the SOC value when the electric taxi starts to be charged is as follows:
Figure FDA0002235136260000025
wherein the content of the first and second substances,
Figure FDA0002235136260000031
the SOC value of the electric taxi is a probability density function when the electric taxi starts to be charged,
Figure FDA0002235136260000032
and charging the electric taxi with the SOC value.
4. The shared charging control method for the residential area direct-current charger according to claim 1, wherein the establishment of the multi-objective double-layer optimization model for the charging schedule specifically comprises:
performing first-layer optimization by taking the minimum difference between the peak and valley of the total power consumption load of the residential area and the deviation of the charging plan of the electric automobile as optimization targets; and performing second-layer optimization with the maximum satisfaction degree of the electric vehicle user as an optimization target.
5. The shared charging control method for the residential area direct-current charger according to claim 4, characterized in that: the objective function of the first layer optimization is:
Figure FDA0002235136260000033
wherein f is1,1、f1,2And f1,3Respectively representThe standard deviation of the total power consumption load of residential areas, the charging load of electric private cars and the charging load of electric taxis in one day during sequential charging; pz,std、Ps,stdAnd Pc,stdRespectively representing the standard deviation of the total electricity consumption load of a residential area, the charging load of an electric private car and the charging load of an electric taxi in one day when the electric taxi is charged disorderly; a is1,1、a1,2、a1,3Respectively, representing the corresponding weight coefficients.
6. The shared charging control method for the residential area direct-current charger according to claim 5, characterized in that: the objective function of the second layer optimization is:
Figure FDA0002235136260000034
Figure FDA0002235136260000035
Figure FDA0002235136260000036
Figure FDA0002235136260000037
Figure FDA0002235136260000038
wherein the content of the first and second substances,
Figure FDA0002235136260000039
and
Figure FDA00022351362600000310
respectively represents the satisfaction degree of the electric private car and the electric taxi,
Figure FDA00022351362600000311
and
Figure FDA00022351362600000312
respectively representing the traveling satisfaction degrees of the electric private car and the electric taxi;
Figure FDA00022351362600000313
and
Figure FDA00022351362600000314
respectively representing the lowest charging expense and the highest charging expense of the ith electric taxi;
Figure FDA00022351362600000315
and
Figure FDA00022351362600000316
respectively representing the charging power of the ith electric private car and the electric taxi in a time period t under the condition of charging with the maximum travel satisfaction; ctRepresents the electricity rate for time period t;
Figure FDA00022351362600000317
and
Figure FDA00022351362600000318
respectively representing the charging power of the ith electric private car and the electric taxi in the time period t when the charging is disordered;
Figure FDA0002235136260000041
and
Figure FDA0002235136260000042
respectively representing the charging power of the ith electric private car and the electric taxi in the time period t when the charging is orderly carried out; a is2,1And a2,2Respectively representing the corresponding weight coefficients; m is1And m2The charging times of the electric private car and the electric taxi in one day are respectively represented, T is the total time segment number, and delta T represents the simulation step length.
7. The utility model provides a residential area direct current charger sharing control system that charges which characterized in that includes:
a modeling module: establishing an electric private car charging load model;
establishing an electric taxi charging load model;
establishing a charging scheduling multi-objective double-layer optimization model according to the charging load model of the electric private car and the charging load model of the electric taxi;
a calculation module: and calculating charging load curves of the electric private car and the electric taxi in the residential area according to the charging scheduling multi-objective double-layer optimization model.
8. The utility model provides a residential area direct current charger sharing control system that charges which characterized in that includes: a memory and a processor;
the memory is to store instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the steps of the method of any one of claims 1 to 6.
CN201910980851.XA 2019-10-16 2019-10-16 Charging control method and system shared by residential area direct current chargers and storage medium Active CN110861508B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910980851.XA CN110861508B (en) 2019-10-16 2019-10-16 Charging control method and system shared by residential area direct current chargers and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910980851.XA CN110861508B (en) 2019-10-16 2019-10-16 Charging control method and system shared by residential area direct current chargers and storage medium

Publications (2)

Publication Number Publication Date
CN110861508A true CN110861508A (en) 2020-03-06
CN110861508B CN110861508B (en) 2021-07-06

Family

ID=69652781

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910980851.XA Active CN110861508B (en) 2019-10-16 2019-10-16 Charging control method and system shared by residential area direct current chargers and storage medium

Country Status (1)

Country Link
CN (1) CN110861508B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111137162A (en) * 2020-03-09 2020-05-12 贵州电网有限责任公司 Fill electric pile through energy internet energy supply
CN112124135A (en) * 2020-08-19 2020-12-25 国电南瑞科技股份有限公司 Electric vehicle shared charging demand analysis method and device

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008199780A (en) * 2007-02-13 2008-08-28 Toyota Motor Corp Power supply control device, and electric vehicle
CN106295942A (en) * 2015-06-24 2017-01-04 中国电力科学研究院 City electric car public charging network service ability evaluation methodology and the system of evaluation
CN106786556A (en) * 2017-01-20 2017-05-31 国网江苏省电力公司泰州供电公司 A kind of charging electric vehicle load forecasting method based on blind number theory
CN107745650A (en) * 2017-10-26 2018-03-02 电子科技大学 A kind of orderly charge control method of electric automobile based on Peak-valley TOU power price
CN109193727A (en) * 2018-10-30 2019-01-11 国网江苏省电力有限公司南通供电分公司 A kind of intelligent distribution network probability coordinated planning method containing electric car

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008199780A (en) * 2007-02-13 2008-08-28 Toyota Motor Corp Power supply control device, and electric vehicle
CN106295942A (en) * 2015-06-24 2017-01-04 中国电力科学研究院 City electric car public charging network service ability evaluation methodology and the system of evaluation
CN106786556A (en) * 2017-01-20 2017-05-31 国网江苏省电力公司泰州供电公司 A kind of charging electric vehicle load forecasting method based on blind number theory
CN107745650A (en) * 2017-10-26 2018-03-02 电子科技大学 A kind of orderly charge control method of electric automobile based on Peak-valley TOU power price
CN109193727A (en) * 2018-10-30 2019-01-11 国网江苏省电力有限公司南通供电分公司 A kind of intelligent distribution network probability coordinated planning method containing electric car

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111137162A (en) * 2020-03-09 2020-05-12 贵州电网有限责任公司 Fill electric pile through energy internet energy supply
CN111137162B (en) * 2020-03-09 2020-12-04 贵州电网有限责任公司 Fill electric pile through energy internet energy supply
CN112124135A (en) * 2020-08-19 2020-12-25 国电南瑞科技股份有限公司 Electric vehicle shared charging demand analysis method and device

Also Published As

Publication number Publication date
CN110861508B (en) 2021-07-06

Similar Documents

Publication Publication Date Title
Jiang et al. A real-time EV charging scheduling for parking lots with PV system and energy store system
CN112186809B (en) Virtual power plant optimization cooperative scheduling method based on V2G mode of electric vehicle
CN105160451B (en) A kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle
CN103241130B (en) Energy management method and system for electric bus charging and swap station
Erol-Kantarci et al. Prediction-based charging of PHEVs from the smart grid with dynamic pricing
CN107719180B (en) Hybrid parking lot multi-source complementary charging method based on electric automobile flexible charging
CN109599856B (en) Electric vehicle charging and discharging management optimization method and device in micro-grid multi-building
CN104966127B (en) A kind of electric vehicle economic load dispatching method based on demand response
CN108573317B (en) Method for optimally controlling charging and discharging strategies of power change station
CN109193718B (en) Selective electric vehicle network access regulation and control method suitable for V2G
CN105322559A (en) V2G technology-based power distribution scheduling control method for electric vehicles
CN103679299A (en) Electric automobile optimal peak-valley time-of-use pricing method giving consideration to owner satisfaction degree
CN103605079B (en) Public Electric Vehicles and echelon thereof utilize the V2G active volume appraisal procedure of battery cluster
CN111310966A (en) Micro-grid site selection and optimal configuration method containing electric vehicle charging station
Jiang et al. A coordinated charging strategy for electric vehicles based on multi-objective optimization
CN110861508B (en) Charging control method and system shared by residential area direct current chargers and storage medium
Yu et al. The impact of charging battery electric vehicles on the load profile in the presence of renewable energy
CN114914923B (en) Grid method based variable-time-length two-stage electric vehicle scheduling method and system
CN109672199B (en) Method for estimating peak clipping and valley filling capacity of electric vehicle based on energy balance
CN116029453A (en) Electric automobile charging pile configuration method, recording medium and system
CN109693573B (en) Electric automobile cluster charging power optimization method for promoting consumption of renewable energy
Li et al. Optimal dispatch for PV-assisted charging station of electric vehicles
Li et al. Multi-objective optimal dispatching of electric vehicle cluster considering user demand response
He et al. Optimal charging strategy of electric vehicles customers in a smart electrical car park
CN110334903B (en) Electric automobile charging scheduling method based on knapsack algorithm

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant