CN108573322A - One kind sharing electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type - Google Patents
One kind sharing electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type Download PDFInfo
- Publication number
- CN108573322A CN108573322A CN201810205208.5A CN201810205208A CN108573322A CN 108573322 A CN108573322 A CN 108573322A CN 201810205208 A CN201810205208 A CN 201810205208A CN 108573322 A CN108573322 A CN 108573322A
- Authority
- CN
- China
- Prior art keywords
- state
- battery
- electrical changing
- station
- electricity demanding
- 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
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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The present invention discloses one kind and sharing electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type, includes the following steps：(1) state probability for changing electricity demanding of electric charging station is predicted using markoff process；(2) inbound battery history data are counted, the discretization of inbound battery charge state is completed；(3) initial battery status and charging pile parameter for reading in electric charging station, obtain electric charging station battery charge class set；(4) battery charge and discharge number of units is optimized by two-stage charge and discharge system optimizing control, obtains battery charge and discharge control result.The present invention uses markoff process trend prediction method, probabilistic forecasting is carried out to the electricity demanding of changing of electrical changing station, policymaker is set to be best understood from the risk for changing electricity demanding range and its faced of electrical changing station, to provide necessary data basis to make more rational charging and conversion electric decision.
Description
Technical field
The invention belongs to electric automobile charging station technical fields, and in particular to one kind sharing electrical changing station dynamic based on multi-vehicle-type
Load forecasting method.
Background technology
In recent years, wideling popularize with new energy technology, electric vehicle, the application of electric bus are more and more, change
Power station change electric load by electric automobile during traveling time and spatial distribution it is probabilistic influence to show it is stronger random uncertain
Property, and electrical changing station changes electric load and is solely dependent upon present status in the state of future time instance, it is unrelated with the state before system.
Electric bus running time and mileage relatively determine that the more difficult prediction of running time and mileage travelled of private savings electric vehicle is electric
Electric bus changes electric load prediction technique and is not suitable for.Therefore, how effectively and quickly to be predicted using a kind of simple method
Electrical changing station changes electric load, it has also become is badly in need of the critical issue solved in this field.
Invention content
Goal of the invention：In view of the deficienciess of the prior art, changing electricity to electrical changing station the object of the present invention is to provide a kind of
Demand carry out probabilistic forecasting, the risk for changing electricity demanding range and its faced of electrical changing station can be best understood from, to for
That makes that more rational charging and conversion electric decision provides necessary data basis shares electrical changing station Dynamic Load Forecasting based on multi-vehicle-type
Method.
Technical solution：In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is as follows：
The stochastic problems of electricity demanding with the time and spatially are changed for electric vehicle, the present invention proposes a kind of based on more
Vehicle is shared electrical changing station Dynamic Load Forecasting method and is based on the basis of electrical changing station is changed electric load state interval discretization
Markov time series models establish the Probabilistic Prediction Model of electrical changing station workload demand, to electrical changing station change electrical load requirement into
Row probabilistic forecasting.The interval probability and distribution function for obtaining changing electricity demanding according to Prediction of Markov simultaneously, using setting confidence
The uncertainty that the mode of degree exchanges electricity demanding is judged, data basis is provided for electrical changing station battery charging and discharging optimal control,
It is specific as follows：
The present invention's shares electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type, includes the following steps：
(1) state probability for changing electricity demanding of electric charging station is predicted using markoff process；
(2) inbound battery history data are counted, the discretization of inbound battery charge state is completed；
(3) initial battery status and charging pile parameter for reading in electric charging station, obtain electric charging station battery charge grade collection
It closes；
(4) battery charge and discharge number of units is optimized by two-stage charge and discharge system optimizing control, obtains battery charge and discharge control
Result processed.
Further, in step (1), predict that the flow for changing electricity demanding of electric charging station is by markoff process：System
System mode that meter sample data, system mode divide ... changes point, statistical sample data.
Further, in step (1), electric load is changed to electrical changing station first and is divided, if Lmax、LminFor sample data
Maxima and minima, if required state sum be n, then the siding-to-siding block length l of each status representative beWork as sample
Data fall into section [Lmin+(i-1)·l,Lmin+ il] in when, it is believed that system is in i-th of state.
Further, in step (1), change electricity demanding constraint requirements electric charging station t period starting points full charge pond number
Amount changes electricity demanding not less than the t periods, on changing electricity demanding constraint processing, is selected state by the way of confidence level is arranged
It takes.
Further, using the mode of setting confidence level come method that state is chosen for：
It adds up since changing the minimum state of electricity demanding, until state probability summation reaches confidence level, the last one state
It is denoted as q, to obtain changing the state set of electricity demanding；After obtaining state set, in order to which the uncertain band of electricity demanding should be exchanged
The risk come takes change electric constraint of the maximum section of load as electric charging station, constraint formula to be：
Wherein, η indicates that the confidence level of setting, p (s) are the probability for changing electricity demanding state S, and Ψ is the period of prediction to change electricity
The most section of demand,Electricity demanding is changed for the sections Ψ.
Further, in step (2), battery charge state is discretized into different grades, and battery charge etc.
Number of stages need to control in the acceptable range of scheduling；
If battery is divided into k different charged level status, j (j=1,2,3...k) is a certain state-of-charge etc. of battery
Grade mark, jc(jc=1,2,3...k jc≠j≠jd) indicate that the battery that charged class letter is j carries out single period charging operations
Charged class letter afterwards, charged class letter are cj(cj=1,2,3...k cj≠j≠dj) battery carry out single period charging
Charged class letter becomes j after operation.
Further, in step (3), the constraint formula of electrical changing station charging pile quantity is：
Wherein, NpumFor the number of electrical changing station charging pile,For the sum of t period charging operations mark.
Further, in step (4), two-stage charge and discharge system optimizing control：
First stage optimizes：After battery charge state is discrete, optimization object function is as follows：
Wherein,Indicate the quantity to the battery progress charging operations that battery charge class letter is j, xjBecome for Boolean type
Amount, E0For full electric battery capacity；
Second stage optimizes：Target is set as maximizing the sum of the full charge pond quantity of day part starting point fx：
Wherein, NpumFor the number of electrical changing station charging pile,For the sum of t period charging operations mark.
Advantageous effect：Compared with prior art, the present invention has the following advantages：
The stochastic problems of electricity demanding with the time and spatially are changed for electric vehicle, the present invention uses Markov mistake
Journey trend prediction method carries out probabilistic forecasting to the electricity demanding of changing of electrical changing station, policymaker is enable to be best understood from electrical changing station
The risk for changing electricity demanding range and its faced, to make more rational charging and conversion electric decision provide necessary data base
Plinth.
Description of the drawings
Fig. 1 is the flow chart for changing electricity demanding of markoff process prediction electric charging station in the present invention.
Specific implementation mode
With reference to specific embodiment, the present invention is furture elucidated.
Electrical changing station change electric load by electric automobile during traveling time and spatial distribution it is probabilistic influence to show it is stronger
Stochastic uncertainty, and electrical changing station changes electric load and is solely dependent upon present status in the state of future time instance, before system
State it is unrelated, meet random Markov property process.Dimension, the present invention uses markoff process trend prediction method, right
Electrical changing station change electricity demanding carry out probabilistic forecasting, enable policymaker be best understood from electrical changing station change electricity demanding range and its
The risk faced, to provide necessary data basis to make more rational charging and conversion electric decision.
Markoff process is the random process with " markov property ", in the case of given current knowledge or information,
Past (i.e. current pervious historic state), (i.e. current later future state) was unrelated in the future for prediction.It can be applied to
The research fields such as the hydrology, meteorology, earthquake, economic forecasting, administrative decision.
Markov chain, refer in mathematics with Markov property discrete event random process, should during, to
In the case of determining current knowledge or information, the past (i.e. current pervious historic state) is (i.e. current later in the future for prediction
Future state) it is unrelated.In each step of Markov chain, system can change to separately according to probability distribution from a state
One state, can also keep current state.The change of state is called transfer, and changing relevant probability from different states is called
Transition probability.
The markoff process that the present invention uses is that time and state are all finite discrete, i.e. Markov Chain.It is counted
Be expressed as follows：
If there are random process { Xt, t ∈ T }, wherein XtTime set be denoted as T, T={ 1,2,3... }, XtState
Set is denoted as M, M={ m1,m2,m3..., for it is arbitrary at the time of and state, there is following equation to set up：
P={ Xt+1=mi|X1=m1,X2=m2,X3=m3...Xt=mj}
Then this time is discrete, random process of state discrete is referred to as Markov Chain.
Claim conditional probability pji(t)=p { Xt+1=mi|Xt=mjBe t moment a step transition probability.If pji(t) with
It the variation of t and changes, then this Markov Chain is referred to as nonhomogeneous, conversely, being then referred to as homogeneous.
If Markov Chain state sum is limited, note state sum is n, then by a step transition probability pji(t) it is member
One step transition probability matrix P of element compositiontIt is represented by：
Markov Chain carves each shape probability of state at the beginningIt is represented by：
By each state probability of initial timeFor the initial time state probability vector S of element composition1It is represented by：
After defining initial state probability vector and Markov state transition probability matrix, the state probability of day part
Vector recurrence calculation mode can obtain as shown by：
S2=S1·P1,S3=S2·P2,...,St+1=St·Pt
The present invention calculates state transition probability matrix using Statistical Estimation Method, and concrete scheme is as follows：
If the sample number that t moment is in j states isThe t+1 moment is transferred to i state sample quantity by j states
A then step transition probability pji(t) it can be usedApproximate evaluation, and similarly estimate PtIn other elements.
Finally, by comparing the p under different periodsji(t) and PtValue, observe pji(t) and PtWith the presence or absence of notable difference
Property, you can distinguish markovian homogeneity and nonhomogeneous property.
It is of the invention that electrical changing station Dynamic Load Forecasting side is shared based on multi-vehicle-type based on the theory of above-mentioned markoff process
Method includes the following steps：
(1) state probability for changing electricity demanding of electric charging station is predicted using markoff process；
(2) inbound battery history data are counted, the discretization of inbound battery charge state is completed；
(3) initial battery status and charging pile parameter for reading in electric charging station, obtain electric charging station battery charge grade collection
It closes；
(4) battery charge and discharge number of units is optimized by two-stage charge and discharge system optimizing control, obtains battery charge and discharge control
Result processed.
Step (1) predicts the state probability for changing electricity demanding of electric charging station using markoff process；
Predict that the flow for changing electricity demanding of electric charging station is by markoff process：Statistical sample data, system mode
Divide ... system mode change point, statistical sample data, flow chart is as shown in Figure 1.
It changes electric load to electrical changing station first to divide, if Lmax、LminFor the maxima and minima of sample data, if institute
It is n to need state sum, then the siding-to-siding block length l of each status representative isWhen sample data falls into section [Lmin+(i-
1)·l,Lmin+ il] in when, it is believed that system is in i-th of state.
For example, it is assumed that it is 1000kWh, lower limit 0kWh that the electrical changing station list period, which changes the electric load upper limit, in sample data, use
Electrical changing station is changed electric load and is divided into 10 sections by the state demarcation method in equal sections, and it is negative then to change electricity by siding-to-siding block length 100kWh
Lotus change procedure can be considered the Markov random process of 10 states.Changing 10 states that electric load becomes is respectively：
1st state：[0,100kWh),
2nd state：[100kWh, 200kWh),
3rd state：[200kWh, 300kWh),
4th state：[300kWh, 400kWh),
5th state：[400kWh, 500kWh),
6th state：[500kWh, 600kWh),
7th state：[600kWh, 700kWh),
8th state：[700kWh, 800kWh),
9th state：[800kWh, 900kWh),
10th state：[900kWh,1000kWh).
Change electricity demanding constraint：
The full charge pond quantity that electricity demanding constraint requirements electric charging station is changed in t period starting points changes electricity demanding not less than the t periods,
On changing electricity demanding constraint processing, state is chosen in such a way that confidence level is set, method is：
η indicates the confidence level of setting, and p (s) is the probability for changing electricity demanding state S, since changing the minimum state of electricity demanding
Cumulative, until state probability summation reaches confidence level, the last one state is denoted as q, to obtain changing the state set of electricity demanding；
After obtaining state set, the risk that the uncertainty in order to exchange electricity demanding is brought takes the maximum section of load as charging and conversion electric
That stands changes electric constraint, and constraint formula is：
Wherein, Ψ is the period of prediction to change the most section of electricity demanding,Electricity demanding is changed for the sections Ψ.
Step (2)：Inbound battery history data are counted, the discretization of inbound battery charge state is completed；
State-of-charge (State of charge, English abbreviation SOC) is also remaining capacity, and representative is that battery uses one
The section time or it is long-term lie idle after residual capacity and the capacity of its fully charged state ratio, commonly use percentage and indicate.
Its value range is 0~1, indicates that battery discharge is complete as SOC=0, indicates that battery is completely filled with as SOC=1.
Formed with the Optimized model of battery charge and discharge number of units variable in order to control, first, by battery charge state (SOC) from
Dispersion is at different grades, and battery charge grade quantity needs to control in the acceptable range of scheduling.
If battery is divided into k different charged level status, j (j=1,2,3...k) is a certain state-of-charge etc. of battery
Grade mark, jc(jc=1,2,3...k jc≠j≠jd) indicate that the battery that charged class letter is j carries out single period charging operations
Charged class letter afterwards, charged class letter are cj(cj=1,2,3...k cj≠j≠dj) battery carry out single period charging
Charged class letter becomes j after operation.
In step (3), the initial battery status and charging pile parameter of electric charging station are read in, electric charging station battery charge is obtained
Class set；
The constraint formula of electrical changing station charging pile quantity is：
Wherein, NpumFor the number of electrical changing station charging pile,For the sum of t period charging operations mark.
Step (4) optimizes battery charge and discharge number of units by two-stage charge and discharge system optimizing control, obtains battery and fill
Put control result：
It in the case where changing power mode, charges since vehicle does not access power grid, the active part throttle characteristics of battery is mainly logical
What the reserve cell crossed in electrical changing station was realized.If battery is more than battery from electrical changing station is entered to the time span for needing to be paged out
Time span needed for full power state is charged to by empty electricity condition, then under the premise of not influencing electric vehicle and changing electricity demanding, changes electricity
Stand can be to possessing certain initiative in the charging plan of battery.
Dimension, the present invention disclose a kind of electrically optimized control of electrical changing station two-stage charge and discharge with battery charge and discharge number of units variable in order to control
Algorithm processed.
First stage optimizes：Economy objectives are pursued, after battery charge state is discrete, optimization object function is as follows
Shown in formula：
Wherein,Indicate the quantity to the battery progress charging operations that battery charge class letter is j, xjBecome for Boolean type
Amount, E0For full electric battery capacity.
Second stage optimizes：Target is set as maximizing the sum of the full charge pond quantity of day part starting point fx, it is contemplated that when each
The full electric number of batteries of Duan Qidian is more, and the ability that electrical changing station reply user changes electricity demanding is stronger, thus by second stage target
It is set as maximizing the sum of the full charge pond quantity of day part starting point fx：
Wherein, NpumFor the number of electrical changing station charging pile,For the sum of t period charging operations mark.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (8)
1. one kind sharing electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type；, which is characterized in that include the following steps：
(1) state probability for changing electricity demanding of electric charging station is predicted using markoff process；
(2) inbound battery history data are counted, the discretization of inbound battery charge state is completed；
(3) initial battery status and charging pile parameter for reading in electric charging station, obtain electric charging station battery charge class set；
(4) battery charge and discharge number of units is optimized by two-stage charge and discharge system optimizing control, obtains battery charge and discharge control knot
Fruit.
2. according to claim 1 share electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type, it is characterised in that：Step
(1) in, predict that the flow for changing electricity demanding of electric charging station is by markoff process：Statistical sample data, system mode are drawn
Point ... system mode changes point, statistical sample data.
3. according to claim 2 share electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type, it is characterised in that：In step
Suddenly in (1), electric load is changed to electrical changing station first and is divided, if Lmax、LminFor the maxima and minima of sample data, if institute
It is n to need state sum, then the siding-to-siding block length l of each status representative isWhen sample data falls into section [Lmin+(i-
1)·l,Lmin+ il] in when, it is believed that system is in i-th of state.
4. according to claim 1 share electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type, it is characterised in that：In step
Suddenly in (1), the full charge pond quantity for changing electricity demanding constraint requirements electric charging station in t period starting points changes electricity demanding not less than the t periods,
On changing electricity demanding constraint processing, state is chosen in such a way that confidence level is set.
5. according to claim 4 share electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type, it is characterised in that：Using
The mode of confidence level is arranged is come the method chosen to state：
It adds up since changing the minimum state of electricity demanding, until state probability summation reaches confidence level, the last one state is denoted as
Q, to obtain changing the state set of electricity demanding；After obtaining state set, what the uncertainty in order to exchange electricity demanding was brought
Risk takes change electric constraint of the maximum section of load as electric charging station, constraint formula to be：
Wherein, η indicates that the confidence level of setting, p (s) are the probability for changing electricity demanding state S, and Ψ is the period of prediction to change electricity demanding
Most sections,Electricity demanding is changed for the sections Ψ.
6. according to claim 1 share electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type, it is characterised in that：In step
Suddenly in (2), battery charge state is discretized into different grades, and battery charge grade quantity need to control and dispatch and can connect
By in the range of；
If battery is divided into k different charged level status, j (j=1,2,3...k) is a certain state-of-charge grade mark of battery
Know, jc(jc=1,2,3...k jc≠j≠jd) indicate that battery that charged class letter is j carries out single period charging operations after
Charged class letter, charged class letter are cj(cj=1,2,3...k cj≠j≠dj) battery carry out single period charging operations
Charged class letter becomes j afterwards.
7. according to claim 1 share electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type, it is characterised in that：Step
(3) in, the constraint formula of electrical changing station charging pile quantity is：
Wherein, NpumFor the number of electrical changing station charging pile,For the sum of t period charging operations mark.
8. according to claim 1 share electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type, it is characterised in that：In step
Suddenly in (4), two-stage charge and discharge system optimizing control：
First stage optimizes：After battery charge state is discrete, optimization object function is：
Wherein,Indicate the quantity to the battery progress charging operations that battery charge class letter is j, xjFor Boolean type variable, E0
For full electric battery capacity；
Second stage optimizes：Target is set as maximizing the sum of the full charge pond quantity of day part starting point fx：
Wherein, NpumFor the number of electrical changing station charging pile,For the sum of t period charging operations mark.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810205208.5A CN108573322A (en) | 2018-03-13 | 2018-03-13 | One kind sharing electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810205208.5A CN108573322A (en) | 2018-03-13 | 2018-03-13 | One kind sharing electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108573322A true CN108573322A (en) | 2018-09-25 |
Family
ID=63574053
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810205208.5A Pending CN108573322A (en) | 2018-03-13 | 2018-03-13 | One kind sharing electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108573322A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109635854A (en) * | 2018-11-26 | 2019-04-16 | 国网冀北电力有限公司 | Based on markovian charging pile failure prediction method and device |
| CN111260406A (en) * | 2020-01-18 | 2020-06-09 | 李琦 | Real estate sales target automatic setting method, device, server and storage medium |
| CN111983480A (en) * | 2020-08-19 | 2020-11-24 | 华晟（青岛）智能装备科技有限公司 | AGV electric quantity state prediction method and system based on Mahalanobis process |
| CN113459871A (en) * | 2021-05-21 | 2021-10-01 | 蓝谷智慧(北京)能源科技有限公司 | Battery charging method and device for battery replacement station, storage medium and electronic equipment |
| CN113887811A (en) * | 2021-10-13 | 2022-01-04 | 江苏明茂新能源科技有限公司 | Charging pile data management method and system |
| CN117035176A (en) * | 2023-08-09 | 2023-11-10 | 上海智租物联科技有限公司 | Markov chain-based user power-change behavior prediction and directional recall method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015153339A (en) * | 2014-02-19 | 2015-08-24 | Kddi株式会社 | Program, mobile terminal and method for estimating user-boarded moving vehicle |
| CN102509173B (en) * | 2011-10-28 | 2016-03-02 | 山东电力集团公司青岛供电公司 | A kind of based on markovian power system load Accurate Prediction method |
| CN107464007A (en) * | 2016-06-02 | 2017-12-12 | 南京理工大学 | Continuous time Probabilistic Load Flow Forecasting Methodology based on Markov theory and pro rate principle |
-
2018
- 2018-03-13 CN CN201810205208.5A patent/CN108573322A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102509173B (en) * | 2011-10-28 | 2016-03-02 | 山东电力集团公司青岛供电公司 | A kind of based on markovian power system load Accurate Prediction method |
| JP2015153339A (en) * | 2014-02-19 | 2015-08-24 | Kddi株式会社 | Program, mobile terminal and method for estimating user-boarded moving vehicle |
| CN107464007A (en) * | 2016-06-02 | 2017-12-12 | 南京理工大学 | Continuous time Probabilistic Load Flow Forecasting Methodology based on Markov theory and pro rate principle |
Non-Patent Citations (1)
| Title |
|---|
| 卢鹤: "电动汽车充换电站的优化控制策略研究", 《中国优秀硕士学位论文全文数据库 (工程科技Ⅱ辑)》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109635854A (en) * | 2018-11-26 | 2019-04-16 | 国网冀北电力有限公司 | Based on markovian charging pile failure prediction method and device |
| CN109635854B (en) * | 2018-11-26 | 2023-11-24 | 国网冀北电力有限公司 | Charging pile fault prediction method and device based on Markov chain |
| CN111260406A (en) * | 2020-01-18 | 2020-06-09 | 李琦 | Real estate sales target automatic setting method, device, server and storage medium |
| CN111983480A (en) * | 2020-08-19 | 2020-11-24 | 华晟（青岛）智能装备科技有限公司 | AGV electric quantity state prediction method and system based on Mahalanobis process |
| CN113459871A (en) * | 2021-05-21 | 2021-10-01 | 蓝谷智慧(北京)能源科技有限公司 | Battery charging method and device for battery replacement station, storage medium and electronic equipment |
| CN113887811A (en) * | 2021-10-13 | 2022-01-04 | 江苏明茂新能源科技有限公司 | Charging pile data management method and system |
| CN117035176A (en) * | 2023-08-09 | 2023-11-10 | 上海智租物联科技有限公司 | Markov chain-based user power-change behavior prediction and directional recall method |
| CN117035176B (en) * | 2023-08-09 | 2024-03-15 | 上海智租物联科技有限公司 | Markov chain-based user power-change behavior prediction and directional recall method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108573322A (en) | One kind sharing electrical changing station Dynamic Load Forecasting method based on multi-vehicle-type | |
| CN110774929A (en) | Real-time control strategy and optimization method for orderly charging of electric automobile | |
| CN108573317B (en) | Method for optimally controlling charging and discharging strategies of power change station | |
| CN105024432A (en) | Electric vehicle charge-discharge optimized dispatching method based on virtual electricity price | |
| CN103810539B (en) | Consider to change the electric automobile charging station capacity configuration optimizing method of electricity service availability | |
| CN103745110B (en) | Method of estimating operational driving range of all-electric buses | |
| CN106229565A (en) | Accumulator transfer auxiliary device and accumulator transfer householder method | |
| CN106355290A (en) | Electric vehicle charge load prediction method and system based on Markov chain | |
| CN113859018B (en) | Hierarchical charge-discharge optimization control method for large-scale electric automobile group | |
| CN110796286B (en) | Flexible planning method of power distribution system suitable for electric automobile large-scale application | |
| CN103997052A (en) | A method for controlling the active power of multiple energy-storage power stations | |
| CN109572478A (en) | Charging station electric car steady charging system and method online | |
| CN103078152B (en) | Intelligent charging method for centralized charging station | |
| CN109190812A (en) | A kind of virtual energy storage active volume prediction technique and system based on electric car | |
| CN111798121A (en) | Distributed collaborative optimization method for energy management scheduling of electric vehicle | |
| CN109921420A (en) | Elastic distribution network restoration power method for improving, device and terminal device | |
| CN113725861B (en) | Electric vehicle charging station distribution planning scheme based on wolf algorithm | |
| CN112550050B (en) | Electric vehicle charging method and system | |
| CN206542219U (en) | A kind of MW class energy-accumulating power station battery management system | |
| CN107016504A (en) | It is a kind of to count and the optimizing scheduling modeling of electric automobile Random-fuzzy demand response and algorithm | |
| CN110472841B (en) | Energy storage configuration method of electric vehicle rapid charging station | |
| CN112736941A (en) | Schedulable capacity prediction method and device based on mobile energy storage electric automobile | |
| CN111313449A (en) | Cluster electric vehicle power optimization management method based on machine learning | |
| Zhou et al. | Electric vehicle capacity forecasting model with application to load levelling | |
| CN114530872A (en) | Multilateral shared energy storage optimization configuration and cost sharing method thereof |
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 | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 212009 15 storeys, Block B, Twin Towers, 468 Ding Mao Zhi Hui Avenue, Zhenjiang New District, Jiangsu Province Applicant after: Zhongda Tongguang (Zhenjiang) New Energy Technology Co., Ltd. Address before: 212132 Gemini Tower A, 468 Ding Mao Zhi Hui Avenue, Zhenjiang New District, Jiangsu Province Applicant before: Zhong Lian Da Tong Guang (Zhenjiang) Amperex Technology Limited |
|
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180925 |