CN112418734A - Charging load distribution method for electric automobile - Google Patents
Charging load distribution method for electric automobile Download PDFInfo
- Publication number
- CN112418734A CN112418734A CN202011468781.9A CN202011468781A CN112418734A CN 112418734 A CN112418734 A CN 112418734A CN 202011468781 A CN202011468781 A CN 202011468781A CN 112418734 A CN112418734 A CN 112418734A
- Authority
- CN
- China
- Prior art keywords
- charging
- electric automobile
- electric
- electric vehicle
- period
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000013178 mathematical model Methods 0.000 claims description 7
- 238000005457 optimization Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 3
- 208000019901 Anxiety disease Diseases 0.000 abstract description 4
- 230000036506 anxiety Effects 0.000 abstract description 4
- 230000005611 electricity Effects 0.000 abstract description 3
- 230000001105 regulatory effect Effects 0.000 abstract description 3
- 230000004044 response Effects 0.000 abstract description 3
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 230000004931 aggregating effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
- G06Q10/06375—Prediction of business process outcome or impact based on a proposed change
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention discloses a charging load distribution method for an electric automobile, which comprises the steps of making an overall ordered charging plan for the electric automobile according to the electricity utilization characteristics of inflexible loads and the commuting rule of the electric automobile; furthermore, the information such as trip habits, charging preferences and mileage anxiety degree of each user are comprehensively considered, and charging load is fairly and reasonably distributed among each electric automobile. The invention can reduce the starting times of the charging process of the electric automobile and maintain the service life of the battery while finishing the charging requirement; under the condition of respecting the autonomous response of the electric automobile user, the charging load distribution strategy in each time period is closely linked with the intrinsic parameters and the power utilization characteristics of the electric automobile, and the ordered charging load of the electric automobile can be reasonably regulated and controlled.
Description
Technical Field
The invention relates to the technical field of intelligent control of electric automobiles, in particular to a charging load distribution method of an electric automobile.
Background
The generation of energy crisis and the development of each item of technique of electric automobile have promoted electric automobile's extensive popularization, and nowadays, each country increases the dynamics and carries out policy support to electric automobile, can foresee, will have a large amount of electric automobile to insert the electric wire netting in the future. After the large-scale electric automobile is connected to a power grid, economic benefit problems caused by the electric automobile and the influence of the electric automobile on the planning operation of a power system cannot be ignored.
If the charging behavior of the electric automobile user is not guided and controlled, the disordered charging of the electric automobile can cause a result of 'peak-to-peak' on the original load of the power grid, the safe and stable operation of the power grid is influenced, and the adverse effect on the economic benefit is also generated. Therefore, it is necessary to master the usage rule of the electric vehicle and control the charging load in the electric vehicle cluster.
However, the ordered charging of the large-scale electric vehicles is a group strategy formed by aggregating the charging behaviors of each electric vehicle individual, and when the overall charging strategy of the electric vehicles is prepared, how to extend and distribute the group strategy to each individual needs to be further researched, and the fairness and the rationality of the charging strategy are embodied by fully considering the self condition and the will of the individual.
At present, the research on orderly charging of electric automobiles at home and abroad mainly focuses on automobile centralized charging control, and the research on how to decompose the charging strategy result of the regional power grid level to each electric automobile involves less research. Therefore, how to reasonably distribute the ordered charging control requirements of the large-scale electric vehicles on the basis of fully considering the individual charging requirements of each electric vehicle and the wishes of users is an urgent problem to be solved.
Disclosure of Invention
The invention aims to solve the technical problem of providing an electric vehicle charging load distribution method so as to reasonably distribute the ordered charging control requirements of large-scale electric vehicles.
In order to solve the technical problem, the invention provides a method for distributing charging load of an electric vehicle, which comprises the following steps:
step S1, dividing one day into a plurality of time periods at set intervals according to the input original data of the distributed electric vehicle charging load, and obtaining the historical charging power of each time period;
step S2, determining the charging priority of the electric automobile according to the collected electric automobile data and the set data input by the user;
step S3, when the power grid has no dispatching requirement in the current time period, all electric vehicles are connected to the power grid for charging, otherwise, the step S4 is carried out;
step S4, traversing the information of each electric vehicle user, if the user does not want to accept the dispatching, immediately starting charging when the electric vehicle accesses the system, otherwise, turning to step S5;
step S5, if the battery capacity of the electric automobile reaches the total battery capacity in the current time period, the electric automobile does not participate in the optimization process, otherwise, the step S6 is carried out;
step S6, if the current time interval is judged to be the charging time of the electric automobile, immediately charging, otherwise, entering step S7;
step S7, if there is no charging arrangement needing to be distributed, then go to the next period, otherwise go to step S8;
step S8, a mathematical model of the charging load distribution is established based on the remaining electric vehicles, and the result is output.
Further, the step S1 is to divide one day into 96 time periods at intervals of 15 minutes.
Further, in step S2, the collected electric vehicle data includes driving mileage and historical driving mileage of the electric vehicle in the current state of charge; the setting data input by the user comprises whether the user is willing to participate in the dispatching plan or not, the expected residence time of the electric automobile, the expected electric quantity of the electric automobile after being charged and the planned trip on the next day.
Further, the step S6 is specifically: if tm,n<TjThen immediately charging, wherein tm,nM-th detection time node, T, representing the n-th vehiclejIndicating that the charging time is necessary, otherwise, the process proceeds to step S7.
Further, the mathematical model in step S8 takes the minimized number of times of charging the electric vehicle as an objective function, and satisfies the charging load arrangement in each time interval as a constraint condition, which is specifically expressed as:
the constraint conditions are as follows:
wherein n isjTotal number of electric vehicles, x, that can be scheduled for this periodn,jFor the charging decision of the nth electric vehicle in the present period, xn,j1, indicates that the vehicle is charging, xn,j0 indicates that the vehicle is not charged, xn,j-1For the charging decision of the nth electric vehicle in the previous period, Pn,jCharging power of the nth vehicle in this period, Pref,jCharging power, omega, schedulable for this periodnThe charging priority of the nth electric automobile.
Further, the charging priority ω of the nth electric vehiclenThe determination method comprises the following steps:
if the user sets the next day's mileage, ω isnComprises the following steps:
otherwise:
wherein L isn,tThe next day mileage, L, set for the usern,cIndicating the range that the nth electric vehicle can travel, L, in view of the battery state of the periodn,hAnd representing the historical trip mileage of the nth electric automobile.
The embodiment of the invention has the beneficial effects that: according to the method, on the premise of mileage anxiety of electric vehicle users, the charging frequency of each electric vehicle battery is minimum, the weighting coefficient of electric vehicle charging is determined by utilizing the historical trip mileage and the real-time battery state of the electric vehicles, the charging arrangement of the electric vehicle group is decomposed to each electric vehicle, and the reasonability and fairness of a large-scale electric vehicle ordered charging strategy are reflected; the charging requirement is completed, meanwhile, the charging start-stop times of the electric automobile can be reduced, and the service life of the battery is maintained; under the condition of respecting the autonomous response of the electric automobile user, the charging load distribution strategy in each time period is closely linked with the intrinsic parameters and the power utilization characteristics of the electric automobile, and the ordered charging load of the electric automobile can be reasonably regulated and controlled.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for distributing charging loads of an electric vehicle according to an embodiment of the present invention.
Fig. 2 is a curve diagram of the total load and the inflexible load of the power grid during the disordered charging and the ordered charging.
Fig. 3 is a schematic diagram of the number of times of charging the electric vehicle in consideration of the number of times of charging.
Fig. 4 is a schematic diagram of the number of cars charged in different time periods in two optimized charging modes.
Detailed Description
The following description of the embodiments refers to the accompanying drawings, which are included to illustrate specific embodiments in which the invention may be practiced.
Referring to fig. 1, an embodiment of the invention provides a method for distributing charging loads of an electric vehicle, including:
step S1, dividing one day into a plurality of time periods at set intervals according to the input original data of the distributed electric vehicle charging load, and obtaining the historical charging power of each time period;
step S2, determining the charging priority of the electric automobile according to the collected electric automobile data and the set data input by the user;
step S3, when the power grid has no dispatching requirement in the current time period, all electric vehicles are connected to the power grid for charging, otherwise, the step S4 is carried out;
step S4, traversing the information of each electric vehicle user, if the user does not want to accept the dispatching, immediately starting charging when the electric vehicle accesses the system, otherwise, turning to step S5;
step S5, if the battery capacity of the electric automobile reaches the total battery capacity in the current time period, the electric automobile does not participate in the optimization process, otherwise, the step S6 is carried out;
step S6, if the current time interval is judged to be the charging time of the electric automobile, immediately charging, otherwise, entering step S7;
step S7, if there is no charging arrangement needing to be distributed, then go to the next period, otherwise go to step S8;
step S8, a mathematical model of the charging load distribution is established based on the remaining electric vehicles, and the result is output.
Specifically, step S1 divides one day into 96 time periods specifically at 15 minute intervals.
In step S2, the collected electric vehicle data includes driving mileage and historical driving mileage of the electric vehicle under the current state of charge; the setting data input by the user comprises whether the user is willing to participate in the dispatching plan or not, the expected residence time of the electric automobile, the expected electric quantity of the electric automobile after being charged and the planned trip on the next day.
Step S6 is specifically executed if tm,n<TjThen immediately charging, wherein tm,nM-th detection time node, T, representing the n-th vehiclejIndicating that the charging time is necessary, otherwise, the process proceeds to step S7.
In step S8, the mathematical model takes the minimum number of times of charging the electric vehicle as an objective function, and satisfies the charging load arrangement in each time interval as a constraint condition, which is specifically expressed as:
the constraint conditions are as follows:
wherein n isjTotal number of electric vehicles, x, that can be scheduled for this periodn,jIs xn,j1, indicates that the vehicle is charging, x n,j0 indicates that the vehicle is not charged, xn,j-1For the charging decision of the nth electric vehicle in the previous period, Pn,jCharging power of the nth vehicle in this period, Pref,jCharging power, omega, schedulable for this periodnThe charging priority of the nth electric automobile.
Charging priority omega of nth electric automobilenThe determination method comprises the following steps:
if the user sets the next day's mileage, ω isnComprises the following steps:
otherwise:
wherein L isn,tThe next day mileage, L, set for the usern,cIndicating the range that the nth electric vehicle can travel, L, in view of the battery state of the periodn,hAnd representing the historical trip mileage of the nth electric automobile.
Firstly, making an overall ordered charging plan of the electric automobile according to load characteristics and the service condition of the electric automobile; furthermore, the intention of each user and the mileage anxiety degree are comprehensively considered, and the charging load is fairly and reasonably arranged to each electric automobile.
The embodiment of the invention carries out charging load distribution on the electric automobile based on the user will and the travel rule, and comprises the steps of dividing one day into 96 time periods, namely, 15min, predicting 96-point conventional load data and the condition that the electric automobile is connected to a power grid on the day by adopting the existing method according to historical conventional data, carrying out online real-time optimization on the conventional load data and the condition that the electric automobile is connected to the power grid on the day by a power company, making an overall ordered charging plan of the electric automobile, and obtaining the ideal charging power of each time period. Collecting electric vehicle data and relevant settings input by a user, wherein the electric vehicle data comprises driving mileage and historical traveling mileage of the electric vehicle in the current charge state; the user inputs relevant settings including whether the user is willing to participate in the dispatching plan, the expected residence time of the electric vehicle, the expected final state of charge of the electric vehicle and the next day planned trip.
Electric vehicles used by users in a residential area are taken as objects, the residential area is assumed to have 780 residents, each resident has one vehicle, wherein the number of the electric vehicles is 100, and the permeability of the electric vehicles is 12.8%; in the peak period of electricity utilization, the average electricity utilization of residents of each household is 4kW, namely, the highest peak of the total load of the residents is 3120 kW; 10% of electric vehicle users do not want to accept electric vehicle charging scheduling, and about 5% of users who are willing to accept electric vehicle scheduling set the trip mileage of the next day; charging the electric automobile by adopting a conventional charging mode, wherein the charging power is kept unchanged in the charging process, the charging power is 7kW, and each electric automobile is charged once a day; a plurality of required electric vehicle charging data are randomly generated by using the monte carlo method, and specific data are shown in table 1.
TABLE 1 electric vehicle charging data settings
The charging arrangement of the power system level of the region is known, and the ordered charging load of the electric automobile is shown in fig. 2, so that the distribution method of the charging load of the electric automobile effectively reduces the peak-valley difference of the load curve of the power grid, and plays a good role in regulation and control. As can be seen from fig. 3, the number of electric vehicles charged under regulation is significantly reduced, and the EV battery life is protected while the regulation requirement of the power grid is satisfied. As can be seen from fig. 4, the proposed load distribution strategy utilizes the time flexibility of electric vehicle charging to shift the charging period from the night period with more other electric loads to the early period with less other electric loads, which is beneficial to peak clipping and valley filling of the power grid.
The charging load distribution mathematical model takes the minimum charging times of the electric vehicle as an objective function, and takes the charging load arrangement in each period as a constraint condition. Determining a weighting coefficient by utilizing the historical trip mileage and the real-time battery state of the electric automobile: preferentially charging the electric automobile with longer historical trip mileage, and referring to the setting of the user if the user sets the next-day trip mileage, wherein the longer the mileage set by the user is, the higher the charging priority is; the smaller the remaining capacity of the current battery is, the higher the charging priority is. And finally, outputting the charging times of all the electric automobiles participating in the scheduling under the condition of considering the charging times and comparing the number of the electric automobiles charged in different periods in the unordered and ordered charging modes.
As can be seen from the above description, the embodiments of the present invention have the following beneficial effects: according to the method, on the premise that the mileage anxiety of the electric automobile user (namely the current vehicle charge amount and the willingness of the user to participate in regulation) is taken as a target, the charging times of each electric automobile battery are minimum, the weighting coefficient of the electric automobile charging is determined by utilizing the historical trip mileage and the battery real-time state of the electric automobile, the charging arrangement of the electric automobile group is decomposed to each electric automobile, and the rationality and the fairness of the large-scale electric automobile ordered charging strategy are reflected. The charging requirement is completed, meanwhile, the charging start-stop times of the electric automobile can be reduced, and the service life of the battery is maintained; under the condition of respecting the autonomous response of the electric automobile user, the charging load distribution strategy in each time period is closely linked with the intrinsic parameters and the power utilization characteristics of the electric automobile, and the ordered charging load of the electric automobile can be reasonably regulated and controlled.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (6)
1. A charging load distribution method for an electric vehicle is characterized by comprising the following steps:
step S1, dividing one day into a plurality of time periods at set intervals according to the input original data of the distributed electric vehicle charging load, and obtaining the historical charging power of each time period;
step S2, determining the charging priority of the electric automobile according to the collected electric automobile data and the set data input by the user;
step S3, when the power grid has no dispatching requirement in the current time period, all electric vehicles are connected to the power grid for charging, otherwise, the step S4 is carried out;
step S4, traversing the information of each electric vehicle user, if the user does not want to accept the dispatching, immediately starting charging when the electric vehicle accesses the system, otherwise, turning to step S5;
step S5, if the battery capacity of the electric automobile reaches the total battery capacity in the current time period, the electric automobile does not participate in the optimization process, otherwise, the step S6 is carried out;
step S6, if the current time interval is judged to be the charging time of the electric automobile, immediately charging, otherwise, entering step S7;
step S7, if there is no charging arrangement needing to be distributed, then go to the next period, otherwise go to step S8;
step S8, a mathematical model of the charging load distribution is established based on the remaining electric vehicles, and the result is output.
2. The electric vehicle charging load distribution method according to claim 1, wherein the step S1 is to divide a day into 96 time periods at intervals of 15 minutes.
3. The electric vehicle charging load distribution method according to claim 2, wherein in step S2, the collected electric vehicle data includes driving mileage and historical driving mileage of the electric vehicle under the current state of charge; the setting data input by the user comprises whether the user is willing to participate in the dispatching plan or not, the expected residence time of the electric automobile, the expected electric quantity of the electric automobile after being charged and the planned trip on the next day.
4. The electric vehicle charging load distribution method according to claim 3, wherein the step S6 is specifically: if tm,n<TjThen immediately charging, wherein tm,nM-th detection time node, T, representing the n-th vehiclejIndicating that the charging time is necessary, otherwise, the process proceeds to step S7.
5. The electric vehicle charging load distribution method according to claim 1, wherein the mathematical model in step S8 takes the minimum number of times of charging the electric vehicle as an objective function, and satisfies the charging load arrangement in each time interval as a constraint condition, which is specifically expressed as:
the constraint conditions are as follows:
wherein n isjTotal number of electric vehicles, x, that can be scheduled for this periodn,jFor the charging decision of the nth electric vehicle in the present period, xn,j1, indicates that the vehicle is charging, xn,j0 indicates that the vehicle is not charged, xn,j-1For the charging decision of the nth electric vehicle in the previous period, Pn,jCharging power of the nth vehicle in this period, Pref,jCharging power, omega, schedulable for this periodnThe charging priority of the nth electric automobile.
6. The electric vehicle charging load distribution method according to claim 5, wherein the charging priority ω of the nth electric vehiclenThe determination method comprises the following steps:
if the user sets the next day's mileage, ω isnComprises the following steps:
otherwise:
wherein L isn,tThe next day mileage, L, set for the usern,cIndicating the range that the nth electric vehicle can travel, L, in view of the battery state of the periodn,hAnd representing the historical trip mileage of the nth electric automobile.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011468781.9A CN112418734A (en) | 2020-12-15 | 2020-12-15 | Charging load distribution method for electric automobile |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011468781.9A CN112418734A (en) | 2020-12-15 | 2020-12-15 | Charging load distribution method for electric automobile |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112418734A true CN112418734A (en) | 2021-02-26 |
Family
ID=74776153
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011468781.9A Pending CN112418734A (en) | 2020-12-15 | 2020-12-15 | Charging load distribution method for electric automobile |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112418734A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113327035A (en) * | 2021-05-28 | 2021-08-31 | 兰州理工大学 | Electric quantity distribution method and device, electronic equipment and storage medium |
| CN113715669A (en) * | 2021-07-27 | 2021-11-30 | 西安交通大学 | Electric vehicle ordered charging control method, system, equipment and readable storage medium |
| CN113910963A (en) * | 2021-11-12 | 2022-01-11 | 集度科技有限公司 | Electric vehicle ordered charging control method, device and system and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103903090A (en) * | 2014-03-17 | 2014-07-02 | 东南大学 | Electric car charging load distribution method based on user will and out-going rule |
| CN105046371A (en) * | 2015-08-19 | 2015-11-11 | 东南大学 | Electric vehicle charge-discharge scheduling method based on demand side bidding |
| CN108944531A (en) * | 2018-07-24 | 2018-12-07 | 河海大学常州校区 | A kind of orderly charge control method of electric car |
-
2020
- 2020-12-15 CN CN202011468781.9A patent/CN112418734A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103903090A (en) * | 2014-03-17 | 2014-07-02 | 东南大学 | Electric car charging load distribution method based on user will and out-going rule |
| CN105046371A (en) * | 2015-08-19 | 2015-11-11 | 东南大学 | Electric vehicle charge-discharge scheduling method based on demand side bidding |
| CN108944531A (en) * | 2018-07-24 | 2018-12-07 | 河海大学常州校区 | A kind of orderly charge control method of electric car |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113327035A (en) * | 2021-05-28 | 2021-08-31 | 兰州理工大学 | Electric quantity distribution method and device, electronic equipment and storage medium |
| CN113327035B (en) * | 2021-05-28 | 2022-08-23 | 兰州理工大学 | Electric quantity distribution method and device, electronic equipment and storage medium |
| CN113715669A (en) * | 2021-07-27 | 2021-11-30 | 西安交通大学 | Electric vehicle ordered charging control method, system, equipment and readable storage medium |
| CN113715669B (en) * | 2021-07-27 | 2023-09-26 | 西安交通大学 | Ordered charging control method, system and equipment for electric automobile and readable storage medium |
| CN113910963A (en) * | 2021-11-12 | 2022-01-11 | 集度科技有限公司 | Electric vehicle ordered charging control method, device and system and storage medium |
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 | |
| CN112418734A (en) | Charging load distribution method for electric automobile | |
| CN111682567B (en) | Ordered charging and discharging control method considering user evaluation based on fuzzy control technology | |
| CN111762051B (en) | Electric automobile participating receiving-end power grid low-valley peak regulation demand response regulation and control method based on aggregators | |
| Yao et al. | A real-time charging scheme for demand response in electric vehicle parking station | |
| Mukherjee et al. | A review of charge scheduling of electric vehicles in smart grid | |
| Wu et al. | A hierarchical charging control of plug-in electric vehicles with simple flexibility model | |
| Han et al. | Ordered charge control considering the uncertainty of charging load of electric vehicles based on Markov chain | |
| Wang et al. | Two-stage optimal scheduling strategy for large-scale electric vehicles | |
| CN103903090B (en) | Electric car charging load distribution method based on user will and out-going rule | |
| Pournaras et al. | Socio-technical smart grid optimization via decentralized charge control of electric vehicles | |
| Yang et al. | Distributed approach for temporal–spatial charging coordination of plug-in electric taxi fleet | |
| CN116001624A (en) | Ordered charging method for one-pile multi-connected electric automobile based on deep reinforcement learning | |
| CN113580984A (en) | Electric vehicle ordered charging strategy and simulation method thereof | |
| CN110053508B (en) | Energy internet cluster operation scheduling method and system based on internet of vehicles platform | |
| CN111313437A (en) | Electric vehicle distributed frequency modulation control method considering charging plan optimization | |
| CN109447376A (en) | Residential block electric car charge and discharge Electric optimization based on user's comprehensive satisfaction | |
| Guo et al. | Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles | |
| CN114282821B (en) | Scheduling method, system and equipment for shared electric automobile | |
| Nie et al. | Multi-area self-adaptive pricing control in smart city with EV user participation | |
| CN113269372A (en) | Cluster electric vehicle schedulable capacity prediction evaluation method considering user will | |
| Krueger et al. | Multi-layer event-based vehicle-to-grid (V2G) scheduling with short term predictive capability within a modular aggregator control structure | |
| CN113487131B (en) | Station network interaction method based on charging demand and energy storage adjustment | |
| Patil et al. | An intelligent control strategy for vehicle-to-grid and grid-to-vehicle energy transfer | |
| CN113954680A (en) | Electric vehicle charging control method based on charging time interval optimization |
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 |