CN109740825A - A kind of electric car charging/discharging thereof considered under traffic congestion factor - Google Patents
A kind of electric car charging/discharging thereof considered under traffic congestion factor Download PDFInfo
- Publication number
- CN109740825A CN109740825A CN201910079583.4A CN201910079583A CN109740825A CN 109740825 A CN109740825 A CN 109740825A CN 201910079583 A CN201910079583 A CN 201910079583A CN 109740825 A CN109740825 A CN 109740825A
- Authority
- CN
- China
- Prior art keywords
- electric car
- user
- charge
- indicate
- electric
- 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
Landscapes
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The present invention relates to the electric car charging/discharging thereofs under a kind of consideration traffic congestion factor.In order to more be close to the users, real scene progress electric car charge and discharge is electrically optimized, the present invention is based on this actual conditions of traffic congestion factor, traffic route photo is handled in real time using the convolutional neural networks in deep learning, judge the congestion level of Current traffic road, influence by the congestion status of traffic route to user's travel time, electric car is started to charge and is modeled constantly, on this basis, comprehensively consider energy content of battery demand and user's trip requirements, establish the multiple target objective function of grid side peak-valley ratio and user power utilization totle drilling cost, it solves to obtain the charge and discharge scheme of electric car cluster using genetic algorithm, demonstrate its validity, model is close to real scene, with very real directive significance.
Description
Technical field
The invention belongs to technical field of electric power, and in particular to consider that the electric car cluster timesharing under traffic congestion factor is fixed
Valence charge and discharge Optimization Scheduling.
Background technique
In global energy transformation process, historic change is occurring for Demand-side, and electric car is since it is in energy conservation
The advantages of in terms of emission reduction, important impetus will be played in terms of energy transition.Because the behavior of automobile user has
Randomness, uncertainty, if not controling effectively to electric car cluster, the unordered charging row of large-scale electric car networking
For negative effect will be brought to the stable operation of power grid, aggravate the operation burden of power grid.In order to encourage automobile user
The United Dispatching for participating in power grid, as can flexible dispatching Demand Side Response resource, fixed a price this excitation hand by timesharing
Section is alleviated power grid pressure, is filled in network load trough period for guiding user to discharge in network load peak period
Electricity fills up low ebb.However, the electrically optimized constraint and limitation by all many conditions of the charge and discharge of electric car, electric car itself
Energy content of battery constraint is the factor that consider of standing in the breach, but only considers that the energy constraint of battery is still very unilateral, later
Also new constraint condition, the trip requirements constraint of user, battery charging and discharging power constraint are proposed successively, but they do not examine
Consider the serious problems that current automobile user is faced in trip: the traffic congestion status got worse, and traffic is gathered around
Blocking up this constraint condition is that we are carrying out an important factor for cannot ignoring when electric car charge and discharge is electrically optimized, the system of traffic congestion
The travel time of user will be about directly affected, the planning to electric car charge and discharge is further influenced.
Summary of the invention
In view of the above-mentioned deficiencies in the prior art, it is an object of the present invention to provide electric car under a kind of consideration traffic congestion factor
Cluster timesharing price charge and discharge Optimization Scheduling.
To achieve the goals above, technical scheme is as follows:
A kind of electric car cluster timesharing price charge and discharge Optimization Scheduling considered under traffic congestion factor is proposed,
Traffic route photo is handled in real time using the convolutional neural networks in deep learning, judges the congestion of Current traffic road
Degree, the influence by the congestion status of traffic route to user's travel time, starts to charge electric car and builds constantly
Mould based on the incentive policy of timesharing price, comprehensively considers energy content of battery demand and user's trip requirements on this basis, establishes
The multiple target objective function of grid side peak-valley ratio and user power utilization totle drilling cost, using the something lost of the non-dominated ranking with elitism strategy
Propagation algorithm is solved to obtain the charge and discharge scheme of electric car cluster.Specifically includes the following steps:
(1) the Current traffic road degree of crowding is obtained using convolutional neural networks algorithm:
(1-1) acquires traffic route picture sample, and sample is classified according to the road degree of crowding, establishes convolutional Neural
Sample set and label needed for network algorithm.
(1-2) constructs the convolutional neural networks containing input layer, convolutional layer, pond layer and full articulamentum.
Sample set is training set and test set according to 7:3 ratio cut partition by (1-3).
(1-4) makes a gift to someone sample set convolutional neural networks, successively carries out convolution, pondization operates, the advanced features extracted
It as the input of full articulamentum, is exported finally by softmax function, with the intersection between prediction result and sample label
Iterative calculation is repeated as loss function, using gradient descent method in entropy, until algorithmic statement, obtains trained convolution mind
Through network model.
Test sample is input to the convolutional neural networks trained and carries out cross validation by (1-5), verifies classifying quality
Accuracy determines the final parameter of convolutional neural networks.
(1-6) predicts new picture using trained convolutional neural networks, judges that the road in current photo is gathered around
Stifled degree.
(2) user established under the influence of traffic congestion terminates stroke time model:
(2-1) traffic congestion factor terminates the influence of stroke to user: when road is in unimpeded state, user can be by
It is reached according to normal time；When road is in congestion status, the time that user reaches will have more 0.6 times than normal arrival time；
When road is in blocked state, the time that user reaches will have more 1.2 times than normal arrival time.
(2-2) terminates stroke time model according to traffic congestion influence value under Current traffic congestion level, user；It is specific public
Formula is as follows:
Here λ is traffic congestion influence value, μcIndicate that user starts to charge the mean value of time model, scIndicate that user opens
The variance of beginning charging time model, takes μc=15.6, sc=3.2.
(3) establish batteries of electric automobile state of charge model: batteries of electric automobile state of charge is expressed as present battery electricity
The percentage of lotus；Calculation formula is as follows:
Here t is discrete sampling instant, and t=1,2 ..., 24, S (t) indicate the battery charge shape of t moment electric car
State, CmaxIndicating batteries of electric automobile total capacity, S (t) indicates electric car in the state of charge of t moment, and Δ t is time interval,
(t+ Δ t) indicates state of charge of the electric car in moment t+ Δ t, η to Sc、ηfThe respectively charge-discharge electric power of electric car.
(4) it establishes with the multi-goal optimizing function of power grid peak-valley ratio and user power utilization totle drilling cost:
(4-1) establishes power grid peak-valley ratio objective function: the power grid sometime maximum load and minimum load in the period
Difference be network load peak-valley difference, the ratio of peak-valley difference and maximum load is the peak-valley ratio of network load, in the present invention with
Day is time cycle, referred to as day peak-valley ratio；
Power grid day total load are as follows:
Power grid net peak-valley ratio are as follows:
Here Pn,iIndicate the basic load of i moment power grid, Pc,k,iIndicate jth electric car in the charging function at i moment
Rate is positive, Pf,k,iIndicate that jth electric car in the discharge power at i moment, is negative, P indicates power grid day total load, PiIt indicates
Under all electric cars participate in, the total load at i moment, f1Indicate that power grid day peak-valley ratio, i are the time, k is of electric car
Number, min indicate minimum value, and max indicates maximum value.
(4-2) user power utilization totle drilling cost: the charging totle drilling cost of user subtracts the total revenue of user's electric discharge, electric car electricity consumption
Cost is lower, and the enthusiasm responsiveness that user participates in network load regulation is also higher；
User power utilization totle drilling cost are as follows:
Here i is time, Pc,iIndicate i moment electric car charging general power, Pf,iIndicate the electric discharge of i moment electric car
General power, Ec,iIndicate i moment electric car charging price, Ef,iIndicate i moment electric car electric discharge price, f2Indicate that user uses
Electric totle drilling cost.
(5) electric car charge and discharge constraint condition is established:
(5-1) batteries of electric automobile state constraint: the trip requirements in order to guarantee user itself, and also to electronic vapour
The service life of vehicle battery and loss, batteries of electric automobile state are necessarily less than the upper limit of the battery status equal to setting, it is necessary to be greater than
Equal to the lower limit of the battery status of setting.
The constraint of (5-2) electric car charge-discharge electric power limit value: electric car charge-discharge electric power is no more than its rated value.
(5-3) participates in regulation electric car total amount constraint: the electric car total amount of charge and discharge is no more than participation regulation
Total number of users；
The constraint of (5-4) user's trip requirements: the remaining capacity of electric car should be able to meet the basic daily demand of user；It is public
Formula is as follows:
Send≥Sneed (6)
Here SendIndicate the remaining capacity of electric car, SendIndicate user's daily demand electricity.
(5-5) charge and discharge time-constrain: the time that electric car is charged and discharged should be after user arrives at the destination；Tool
Body formula are as follows:
Here Tc,startIndicate that electric car starts to charge time, Tf,startIndicate that electric car starts discharge time,
TarriveIndicate user's arrival time.
(6) multiple target of with constraint conditions is carried out using the genetic algorithm NSGA-II of the non-dominated ranking with elitism strategy
Optimization, obtaining Pareto optimal solution set is optimal charge and discharge electric work of all electric cars for participating in regulation in each period
Rate.
Invention has the beneficial effects that:
The present invention considers the traffic congestion factor in electric car charge and discharge process, passes through convolution in deep learning method
Neural network handles traffic route photo in real time, judges the congestion level of Current traffic road, is compared to traditional
Modeling method avoids cumbersome manual features construction process, is easier to adapt to complicated scene.
The invention proposes traffic congestion influence degree values, for indicating influence journey of the traffic congestion to user's travel time
Degree.
The present invention optimizes electric car charge and discharge under the influence of traffic congestion factor, model more close to
The real-life and application scenarios of user has extraordinary realistic meaning.
Detailed description of the invention
Fig. 1 is algorithm implementation flow chart.
Fig. 2 is all kinds of state examples of traffic route data set.
Fig. 3 is each layer schematic diagram of convolutional neural networks.
Specific embodiment
Below in conjunction with attached drawing, specific embodiments of the present invention will be further explained.
The invention proposes the electric car cluster timesharing price electrically optimized sides of charge and discharge under a kind of consideration traffic congestion factor
Method handles traffic route photo in real time using the convolutional neural networks in deep learning, judges Current traffic road
Congestion level, the influence by the congestion status of traffic route to user's travel time, proposes traffic congestion influence degree value,
Electric car is started to charge with this and is modeled constantly, on this basis, based on the incentive policy of timesharing price, is comprehensively considered
Energy content of battery demand and user's trip requirements establish the multiple target target letter of grid side peak-valley ratio and user power utilization totle drilling cost
Number, is solved to obtain the charge and discharge scheme of electric car cluster using the genetic algorithm of the non-dominated ranking with elitism strategy.
Flow diagram of the present invention is as shown in Figure 1, the specific steps are as follows:
(1) the Current traffic road degree of crowding is obtained using convolutional neural networks algorithm:
(1-1) acquires traffic route picture sample, and sample is classified according to the road degree of crowding, establishes convolutional Neural
Sample set and label needed for network algorithm.
(1-2) constructs the convolutional neural networks containing input layer, convolutional layer, pond layer and full articulamentum.
Sample set is training set and test set according to 7:3 ratio cut partition by (1-3).
(1-4) makes a gift to someone sample set convolutional neural networks, successively carries out convolution, pondization operates, by convolutional layer to image
The advanced features extracted can be known another characteristic as machine and be input to full articulamentum, finally led to by the extraction for carrying out feature
It crosses softmax function to be exported, the cross entropy between prediction result and sample label is declined as loss function using gradient
Iterative calculation is repeated in method, until algorithmic statement, obtains trained convolutional neural networks model；
Test sample is input to the convolutional neural networks trained and carries out cross validation by (1-5), verifies classifying quality
Accuracy determines the final parameter of convolutional neural networks.
(1-6) predicts new picture using trained convolutional neural networks, judges that the road in current photo is gathered around
Stifled degree.
Fig. 2 is convolutional neural networks flow chart, and Fig. 3 is traffic route data set example；
Corresponding relationship such as table 1 between classification results and congestion level:
Table is corresponded between 1 classification results of table and congestion level
| Classification results | 0 | 1 | 2 |
| Congestion level | It is unimpeded | Congestion | Blocking |
(2) user established under the influence of traffic congestion terminates stroke time model:
(2-1) traffic congestion factor terminates the influence of stroke to user: when road is in unimpeded state, user can be by
It is reached according to normal time；When road is in congestion status, the time that user reaches will have more 0.6 times than normal arrival time；
When road is in blocked state, the time that user reaches will have more 1.2 times than normal arrival time.
(2-2) terminates stroke time model according to traffic congestion influence value under Current traffic congestion level, user；It is specific public
Formula is as follows:
Here λ is traffic congestion influence value, μcIndicate that user starts to charge the mean value of time model, σcIndicate that user opens
The variance of beginning charging time model, takes μc=15.6, σc=3.2.
Corresponding relationship such as table 2 between congestion influence degree value and congestion level:
2 congestion influence degree value of table and congestion level correspond to table
| Congestion level | It is unimpeded | Congestion | Blocking |
| Congestion influence degree value | 1 | 1.6 | 2.2 |
(3) establish batteries of electric automobile state of charge model: batteries of electric automobile state of charge is expressed as present battery electricity
The percentage of lotus；Calculation formula is as follows:
Here t is discrete sampling instant, and t=1,2 ..., 24, S (t) indicate the battery charge shape of t moment electric car
State, CmaxIndicating batteries of electric automobile total capacity, S (t) indicates electric car in the state of charge of t moment, and Δ t is time interval,
(t+ Δ t) indicates state of charge of the electric car in moment t+ Δ t, η to Sc、ηfThe respectively charge-discharge electric power of electric car.
(4) it establishes with the multi-goal optimizing function of power grid peak-valley ratio and user power utilization totle drilling cost:
(4-1) establishes power grid peak-valley ratio objective function: the power grid sometime maximum load and minimum load in the period
Difference be network load peak-valley difference, the ratio of peak-valley difference and maximum load is the peak-valley ratio of network load, in the present invention with
Day is time cycle, referred to as day peak-valley ratio；
Power grid day total load are as follows:
Power grid net peak-valley ratio are as follows:
Here Pn,iIndicate the basic load of i moment power grid, Pc,k,iIndicate jth electric car in the charging function at i moment
Rate is positive, Pf,k,iIndicate that jth electric car in the discharge power at i moment, is negative, P indicates power grid day total load, PiIt indicates
Under all electric cars participate in, the total load at i moment, f1Indicate that power grid day peak-valley ratio, i are the time, k is of electric car
Number, min indicate minimum value, and max indicates maximum value.
(4-2) user power utilization totle drilling cost: the charging totle drilling cost of user subtracts the total revenue of user's electric discharge, electric car electricity consumption
Cost is lower, and the enthusiasm responsiveness that user participates in network load regulation is also higher；
User power utilization totle drilling cost are as follows:
Here i is time, Pc,iIndicate i moment electric car charging general power, Pf,iIndicate the electric discharge of i moment electric car
General power, Ec,iIndicate i moment electric car charging price, Ef,iIndicate i moment electric car electric discharge price, f2Indicate that user uses
Electric totle drilling cost.
(5) electric car charge and discharge constraint condition is established:
(5-1) batteries of electric automobile state constraint: the trip requirements in order to guarantee user itself, and also to electronic vapour
The service life of vehicle battery and loss, batteries of electric automobile state are necessarily less than the upper limit of the battery status equal to setting, it is necessary to be greater than
Equal to the lower limit of the battery status of setting.
The constraint of (5-2) electric car charge-discharge electric power limit value: electric car charge-discharge electric power is no more than its rated value.
(5-3) participates in regulation electric car total amount constraint: the electric car total amount of charge and discharge is no more than participation regulation
Total number of users,
The constraint of (5-4) user's trip requirements: the remaining capacity of electric car should be able to meet the basic daily demand of user；It is public
Formula is as follows:
Send≥Sneed (6)
Here SendIndicate the remaining capacity of electric car, SendIndicate user's daily demand electricity.
(5-5) charge and discharge time-constrain: the time that electric car is charged and discharged should be after user arrives at the destination；Tool
Body formula are as follows:
Here Tc,startIndicate that electric car starts to charge time, Tf,startIndicate that electric car starts discharge time,
TarriveIndicate user's arrival time.
(6) multiple target of with constraint conditions is carried out using the genetic algorithm NSGA-II of the non-dominated ranking with elitism strategy
Optimization, obtaining Pareto optimal solution set is optimal charge and discharge electric work of all electric cars for participating in regulation in each period
Rate.
Claims (3)
1. a kind of electric car charging/discharging thereof considered under traffic congestion factor, it is characterised in that this method specifically includes following
Each step:
Step (1) obtains the Current traffic road degree of crowding using convolutional neural networks algorithm；
The user that step (2) is established under the influence of traffic congestion terminates stroke time model；
Step (3) establishes batteries of electric automobile state of charge model: batteries of electric automobile state of charge is expressed as present battery electricity
The percentage of lotus；Calculation formula is as follows:
Here t is discrete sampling instant, and t=1,2 ..., 24, S (t) indicate the battery state of charge of t moment electric car,
CmaxIndicate batteries of electric automobile total capacity, S (t) indicates electric car in the state of charge of t moment, and Δ t is time interval, S (t
+ Δ t) indicates state of charge of the electric car in moment t+ Δ t, ηc、ηfThe respectively charge-discharge electric power of electric car；
Step (4) is established with the multi-goal optimizing function of power grid peak-valley ratio and user power utilization totle drilling cost:
Step (4-1) establishes power grid peak-valley ratio objective function: the power grid sometime maximum load and minimum load in the period
Difference be network load peak-valley difference, the ratio of peak-valley difference and maximum load is the peak-valley ratio of network load, using day as the time
Period, referred to as day peak-valley ratio；
Power grid day total load are as follows:
Power grid net peak-valley ratio are as follows:
Here Pn,iIndicate the basic load of i moment power grid, Pc,k,iIndicate charge power of the jth electric car at the i moment,
It is positive, Pf,k,iIndicate that jth electric car in the discharge power at i moment, is negative, P indicates power grid day total load, PiIndicate all
Under electric car participates in, the total load at i moment, f1Indicating that power grid day peak-valley ratio, i are the time, k is the number of electric car,
Min indicates minimum value, and max indicates maximum value；
Step (4-2) user power utilization totle drilling cost: the charging totle drilling cost of user subtracts the total revenue of user's electric discharge, electric car electricity consumption
Cost is lower, and the enthusiasm responsiveness that user participates in network load regulation is also higher；
User power utilization totle drilling cost are as follows:
Here i is time, Pc,iIndicate i moment electric car charging general power, Pf,iIndicate i moment electric car electric discharge total work
Rate, Ec,iIndicate i moment electric car charging price, Ef,iIndicate i moment electric car electric discharge price, f2Indicate that user power utilization is total
Cost,；
Step (5) establishes electric car charge and discharge constraint condition:
Step (5-1) batteries of electric automobile state constraint: batteries of electric automobile state is necessarily less than the battery status equal to setting
The upper limit, it is necessary to more than or equal to the lower limit of the battery status of setting；
The constraint of step (5-2) electric car charge-discharge electric power limit value: electric car charge-discharge electric power is no more than its rated value；
Step (5-3) participates in regulation electric car total amount constraint: the electric car total amount of charge and discharge is no more than participation regulation
Total number of users；
The constraint of step (5-4) user's trip requirements: the remaining capacity of electric car should be able to meet the basic daily demand of user；It is public
Formula is as follows:
Send≥Sneed (6)
Here SendIndicate the remaining capacity of electric car, SendIndicate user's daily demand electricity；
Step (5-5) charge and discharge time-constrain: the time that electric car is charged and discharged should be after user arrives at the destination；It is public
Formula are as follows:
Here Tc,startIndicate that electric car starts to charge time, Tf,startIndicate that electric car starts discharge time, Tarrive
Indicate user's arrival time；
Step (6) carries out the multiple target of with constraint conditions using the genetic algorithm NSGA-II of the non-dominated ranking with elitism strategy
Optimization, obtaining Pareto optimal solution set is optimal charge and discharge electric work of all electric cars for participating in regulation in each period
Rate.
2. a kind of electric car charging/discharging thereof considered under traffic congestion factor according to claim 1, feature exist
In: step (1) is specifically:
Step (1-1) acquires traffic route picture sample, and sample is classified according to the road degree of crowding, establishes convolutional Neural
Sample set and label needed for network algorithm；
Step (1-2) constructs the convolutional neural networks containing input layer, convolutional layer, pond layer and full articulamentum；
Sample set is training set and test set according to 7:3 ratio cut partition by step (1-3)；
Step (1-4) makes a gift to someone sample set convolutional neural networks, successively carries out convolution, pondization operates, the advanced features extracted
It as the input of full articulamentum, is exported finally by softmax function, with the intersection between prediction result and sample label
Iterative calculation is repeated as loss function, using gradient descent method in entropy, until algorithmic statement, obtains trained convolution mind
Through network model；
Test sample is input to the convolutional neural networks trained and carries out cross validation by step (1-5), verifies classifying quality
Accuracy determines the final parameter of convolutional neural networks；
Step (1-6) predicts new picture using trained convolutional neural networks, judges that the road in current photo is gathered around
Stifled degree.
3. a kind of electric car charging/discharging thereof considered under traffic congestion factor according to claim 1, feature exist
In: step (2) is specifically:
Step (2-1) traffic congestion factor terminates the influence of stroke to user:
When road is in unimpeded state, user can reach according to normal time；When road is in congestion status, user is arrived
The time reached will have more 0.6 times than normal time；When road is in blocked state, the time that user reaches will compare normal time
Have more 1.2 times；
Step (2-2) terminates stroke time model according to traffic congestion influence value under Current traffic congestion level, user:
Here λ is traffic congestion influence value, μcIndicate that user starts to charge the mean value of time model, σcIndicate that user starts to fill
The variance of electric time model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910079583.4A CN109740825A (en) | 2019-01-28 | 2019-01-28 | A kind of electric car charging/discharging thereof considered under traffic congestion factor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910079583.4A CN109740825A (en) | 2019-01-28 | 2019-01-28 | A kind of electric car charging/discharging thereof considered under traffic congestion factor |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109740825A true CN109740825A (en) | 2019-05-10 |
Family
ID=66366373
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910079583.4A Pending CN109740825A (en) | 2019-01-28 | 2019-01-28 | A kind of electric car charging/discharging thereof considered under traffic congestion factor |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109740825A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111564861A (en) * | 2020-06-03 | 2020-08-21 | 厦门理工学院 | Method, device and equipment for solving charge and discharge time period and storage medium |
| CN112345956A (en) * | 2020-11-24 | 2021-02-09 | 广州橙行智动汽车科技有限公司 | Battery pack charge state detection method and device |
| CN114037177A (en) * | 2021-11-22 | 2022-02-11 | 山东德佑电气股份有限公司 | Bus charging load optimization method in crowded traffic state based on train number chain |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016106373A1 (en) * | 2014-12-22 | 2016-06-30 | Robert Bosch Gmbh | Method for adaptive demand charge reduction |
| CN108510128A (en) * | 2018-04-11 | 2018-09-07 | 华南理工大学广州学院 | A kind of region electric vehicle charging load spatial and temporal distributions prediction technique |
-
2019
- 2019-01-28 CN CN201910079583.4A patent/CN109740825A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016106373A1 (en) * | 2014-12-22 | 2016-06-30 | Robert Bosch Gmbh | Method for adaptive demand charge reduction |
| CN108510128A (en) * | 2018-04-11 | 2018-09-07 | 华南理工大学广州学院 | A kind of region electric vehicle charging load spatial and temporal distributions prediction technique |
Non-Patent Citations (3)
| Title |
|---|
| 崔华: ""基于CNN深度学习模型的交通图像拥堵识别"", 《科技创新与应用》 * |
| 李伟生: ""基于V2G的电动汽车充放电优化调度策略研究"", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
| 王斐: ""电动汽车充电对电网的影响及其优化调度研究述评"", 《南方电网技术》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111564861A (en) * | 2020-06-03 | 2020-08-21 | 厦门理工学院 | Method, device and equipment for solving charge and discharge time period and storage medium |
| CN112345956A (en) * | 2020-11-24 | 2021-02-09 | 广州橙行智动汽车科技有限公司 | Battery pack charge state detection method and device |
| CN114037177A (en) * | 2021-11-22 | 2022-02-11 | 山东德佑电气股份有限公司 | Bus charging load optimization method in crowded traffic state based on train number chain |
| CN114037177B (en) * | 2021-11-22 | 2024-05-14 | 山东德佑电气股份有限公司 | Bus charging load optimization method based on train number chain in crowded traffic state |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021098352A1 (en) | Active power distribution network planning model establishment method taking into consideration site selection and capacity determination of electric vehicle charging stations | |
| CN111178619B (en) | Multi-objective optimization method considering distributed power supply and charging station joint planning | |
| CN105868942B (en) | The orderly charging schedule method of electric car | |
| CN110570050B (en) | Road-network-vehicle-related electric vehicle charging guiding method | |
| CN106651059B (en) | Optimal configuration method for electric vehicle charging station | |
| CN104485727B (en) | A kind of off-network photovoltaic generation energy-storage system and its energy dispatching method | |
| CN108407633B (en) | A kind of electric bus electric charging station optimizing operation method | |
| CN111311122B (en) | Charging station construction method for smart city | |
| CN109740825A (en) | A kind of electric car charging/discharging thereof considered under traffic congestion factor | |
| CN103241130A (en) | Energy management method and system for electric bus charging and swap station | |
| CN112467722A (en) | Active power distribution network source-network-load-storage coordination planning method considering electric vehicle charging station | |
| CN108596667B (en) | Electric automobile real-time charging electricity price calculation method based on Internet of vehicles | |
| CN108320064A (en) | A kind of electric vehicle cooperates with charging dual-layer optimization dispatching method with wind-powered electricity generation | |
| CN108062619B (en) | Rail vehicle-ground integrated capacity configuration method and device | |
| CN108269008B (en) | Charging facility optimization planning method considering user satisfaction and distribution network reliability | |
| CN107919675B (en) | Charging station load scheduling model comprehensively considering benefits of vehicle owners and operators | |
| CN110138006A (en) | Consider more micro electric network coordination Optimization Schedulings containing New-energy electric vehicle | |
| CN112183882B (en) | Intelligent charging station charging optimization method based on electric vehicle quick charging requirement | |
| CN112001598A (en) | Energy storage configuration evaluation and operation optimization method for different users based on energy storage type selection | |
| CN110189025A (en) | Consider the electric automobile charging station programme acquisition methods that different load increases | |
| CN107832891A (en) | A kind of charging station planing method of city expressway mouth | |
| CN112097783B (en) | Electric taxi charging navigation path planning method based on deep reinforcement learning | |
| CN113326467A (en) | Multi-station fusion comprehensive energy system multi-target optimization method based on multiple uncertainties, storage medium and optimization system | |
| CN103345663A (en) | Combinatorial optimization method of electric power system set considering creep speed constraints | |
| CN115239032B (en) | Highway service area microgrid planning method and system considering energy self-consistency rate |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190510 |
|
| RJ01 | Rejection of invention patent application after publication |