CN108288112A - Region electric automobile charging station load forecasting method based on user's trip simulation - Google Patents
Region electric automobile charging station load forecasting method based on user's trip simulation Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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Abstract
The invention discloses a kind of region electric automobile charging station load forecasting methods based on user's trip simulation, according to user's trip purpose and the type of residing region division electric automobile charging station, using travel activity of the discrete markoff process analog subscriber between different zones, Trip chain is constructed；Extracting influences the space-time characteristic amount of electric automobile charging station charging load in Trip chain, and uses Probability Distribution Fitting；Charge condition is set according to the trip requirements of user, establishes electric automobile charging station load forecasting model in region；The load prediction curve of different types of electric automobile charging station is calculated using Monte Carlo simulation approach.The present invention can effectively reflect the randomness of automobile user trip, accurately predict different types of electric automobile charging station power load distributing, powerful guarantee is provided to study influence and orderly charging of the extensive electric vehicle access to power grid, there is good practicability.
Description
Technical field
The present invention relates to electric automobile charging station load prediction field more particularly to a kind of areas based on user's trip simulation
Domain electric automobile charging station load forecasting method.
Background technology
Today's society, environmental crisis are constantly aggravated, and conventional fuel oil automobile generates a large amount of pernicious gases so that global greenhouse is imitated
It should be more and more obvious.Electric vehicle is small with its noise, at low cost, and the advantages such as environmental protection become automobile in recent years and research and develop grinding for field
Study carefully hot spot.The auxiliary facilities such as ev industry and electric automobile charging station also come in a short time by rapid promote.
Critical facility of the electric automobile charging station as electric vehicle large-scale promotion, power load distributing is for the steady of power grid
Fixed operation and Optimized Operation are particularly significant.Currently, electric automobile charging station load forecasting method is imitative comprising probabilistic Modeling method, process
True method and the regression algorithm based on support vector machines.However, family trip requirements and other rings are benefited from electric vehicle charging behavior
, all there is randomness over time and space in the influence of border factor.Although algorithm above ensure that precision to a certain extent,
It still is limited to simple probability distribution rule and specific charging station historical data, electric automobile charging station type is considered
It is not comprehensive enough, it cannot effectively reflect the randomness of automobile user trip rule.
Invention content
Goal of the invention：In view of the above problems, the present invention proposes that a kind of region electric vehicle based on user's trip simulation fills
Plant load prediction technique.
Technical solution：To achieve the purpose of the present invention, the technical solution adopted in the present invention is：One kind is gone on a journey based on user
The region electric automobile charging station load forecasting method of simulation, including step：
(1) according to user's trip purpose and residing region division electric automobile charging station type；
(2) Trip chain is constructed in the travel activity of different zones using discrete markoff process analog subscriber；
(3) probability that user stops in different type charging station is calculated according to the statistical data of every section of stroke in Trip chain；
(4) charge condition is set according to the traveling demand of user, establishes electric automobile charging station load prediction mould in region
Type；
(5) Monte Carlo simulation approach is utilized to calculate the load prediction curve of different type electric automobile charging station, using side
The precision of poor coefficient assessment load prediction results.
In the step (1), the region includes residential quarter (H), workspace (W), public leisure area (P), other areas
(O), electric automobile charging station type includes that residential quarter charging station, workspace charging station, public leisure area charging station, other areas are filled
Power station.
In the step (3), the space-time characteristic amount that electric automobile charging station charging load is influenced in Trip chain is extracted, respectively
Using normal distyribution function and logarithm normal distribution Function Fitting temporal characteristics amount and space characteristics amount.The temporal characteristics amount is
It sets out moment, arrival time, stay time, the space characteristics amount is mileage travelled.
In the step (4), charge condition is：
Wherein, SOCtIndicate the state-of-charge in t moment electric vehicle；Indicate the warning value of batteries of electric automobile；L
(SOCt) indicate the mileage travelled that electric vehicle can also be completed under current state；LnextIt indicates to reach next area from current region
Domain needs the mileage travelled.
Load forecasting model is：
Wherein, PA,tIndicate total charge power of the electric automobile charging station in the A of t moment region；NAIt indicates to fill in the A of region
Power station service vehicle number, pcIndicate the charge power of single vehicle, xi,tIndicate the charged state of electric vehicle.
Advantageous effect：The electric automobile charging station load forecasting method of the present invention, depth excavate resident trip investigational data,
Analyze influence of the user behavior to electric vehicle charge requirement, it is contemplated that charging load randomness over time and space and
The otherness in geographical location residing for electric automobile charging station, the prediction result obtained are more in line with actual conditions.The present invention is carried
Method is highly practical, and reliable foundation is provided to study influence of the extensive electric vehicle access to power grid.
Description of the drawings
Fig. 1 is that the present invention is based on the region electric automobile charging station load prediction flow charts of user's trip simulation；
Fig. 2 is different type electric automobile charging station load prediction curve.
Specific implementation mode
Technical scheme of the present invention is further described with reference to the accompanying drawings and examples.
As shown in Figure 1, the region electric automobile charging station load prediction side of simulation of the present invention of being gone on a journey based on user
Method, including step：
(1) according to the trip purpose of user and the type of residing region division electric automobile charging station；
Region includes residential quarter (H), workspace (W), public leisure area (P), other areas (O), electric automobile charging station class
Type includes residential quarter charging station, workspace charging station, public leisure area charging station, other area's charging stations.
(2) travel activity using discrete markoff process analog subscriber between different zones constructs Trip chain；
Trip chain refers to resident in certain space-time unique, to complete one or time that several activities generate, sky
Between the process that changes, this process contains the largely characteristic quantity with temporal and spatial correlations.
Present invention assumes that electric vehicle has trip characteristics similar with conventional fuel oil automobile, according to data statistics, private savings
Vehicle average travel chain length is 3.02, and goes home, works and total accounting of amusement and recreation is close to 80% in resident trip purpose,
Therefore, it is considered that charging behavior is happened at electric automobile charging station in above-mentioned four class region.
Discrete markoff process refers to the discrete event random process with Markov property, process in mathematics
In, the transfer per next state is only related and unrelated with past state with the state of previous moment.
Transfer of the electric vehicle between different zones, can be indicated with conditional probability：
P(Ei→Ej)=P (Ei|Ej)=pij
Wherein, EiFor the state at current time, EjFor the state of subsequent time, pijFor from state EiSwitch to state EjTurn
Move probability.
Each trip region is considered as a state, then space migrating probability matrix of the electric vehicle between different zones
It is represented by：
Wherein, pijMeet following condition：
Wherein, M is region sum, and the present invention will go on a journey region division as four major class, i.e. M=4, pijIt is obtained according to statistical data
Go out, the probability of every Trip chain can be calculated by the combination of different trips.
(3) probability that user stops in different type charging station is calculated according to the statistical data of every section of stroke in Trip chain；
Extracting influences the space-time characteristic amount of electric automobile charging station charging load in Trip chain, normal distribution letter is respectively adopted
Number and logarithm normal distribution Function Fitting temporal characteristics amount and space characteristics amount, when temporal characteristics amount corresponds to the moment of setting out, reaches
It carves, stay time；Space characteristics amount corresponds to mileage travelled.
Present invention assumes that electric vehicle starts to charge up after reaching charging station, without being lined up, therefore arrival time is to open
The probability density expression formula difference of beginning charging moment, normal distribution and logarithm normal distribution is as follows：
Wherein, μSTo start to charge up the desired value at moment；σSFor standard deviation, h is obtained by statistical data.
Wherein, μDFor the desired value of mileage travelled；σDFor standard deviation, km is obtained by statistical data.
(4) charge condition is set according to the traveling demand of user, establishes electric automobile charging station load prediction mould in region
Type；
Charge condition is as follows：
Wherein, SOCtIndicate the state-of-charge in t moment electric vehicle；Indicate the warning value of batteries of electric automobile；L
(SOCt) indicate the mileage travelled that electric vehicle can also be completed under current state；LnextIt indicates to reach next area from current region
Domain needs the mileage travelled.
Wherein, LnextIt is generated at random by the probability distribution of fitting, L (SOCt) calculation formula it is as follows：
Wherein, E100For hundred km power consumption, fixed value 15kWh is taken.
Load forecasting model is：
Wherein, PA,tIndicate total charge power of the electric automobile charging station in the A of t moment region, A ∈ { H, W, P, O }；NA
Indicate charging station service vehicle number in the A of region, pcIndicate the charge power of single vehicle, xi,tIndicate the charged state of electric vehicle,
If it is not 0 in charging being charged as 1.
Wherein, NACalculation formula it is as follows：
NA=NEV*∑PA
Wherein, NEVIndicate electric vehicle total scale；PAIndicate that Trip chain related with electric automobile charging station in the A of region turns
Probability is moved, is obtained by resident trip investigational data statistics.
(5) load prediction curve of different type electric automobile charging station is calculated using Monte Carlo simulation approach, adopts
The precision of load prediction results is assessed with coefficient of variation.
It is as follows：
(1) initial data, including battery capacity Bc, electric vehicle automobile total scale N are inputtedEV；
(2) Monte Carlo simulation number K and convergence precision are set；
(3) charged area is selected, K=1, setting charge power Pc, charge efficiency η are enabled；
(4) n=1 is enabled；
(5) according to probability distribution, initial SOC, mileage travelled is randomly selected and starts to charge up the moment；
(6) judge whether to meet charge condition, if so, continuing, otherwise go to step 8；
(7) charging duration is calculated, add up charging load；
(8) judge n ＞ N, if so, continuing, otherwise enable n=n+1, go to step 5；
(9) judge whether to meet convergence precision, if so, going to step 11, otherwise continue；
(10) judge k ＞ K, if so, continuing, otherwise enable k=k+1, go to step 4；
(11) electric automobile charging station load curve in output area.
State-of-charge when starting SOC refers to electric vehicle arrival electric automobile charging station, Normal Distribution N (0.5,
0.1)；The calculation formula of charging duration and coefficient of variation difference is as follows：
Wherein, TCFor the duration that charges；SSOCFor initial SOC.
Wherein, βtFor the coefficient of variation of t moment charging load；For the variance of t moment charging load；For t moment
The desired value of charging load；For the standard deviation of t moment charging load.With the maximum value β of coefficient of variation in each time point
=max (βt) it is used as accuracy evaluation foundation.
It is illustrated below with a specific embodiment.
The American family trip survey number that resident trip investigational data is issued using US Department of Transportation in the present embodiment
According to NHTS2009 databases, estimation range includes the areas H, the areas W, the areas P, four major class of the areas O, and electric vehicle total scale is 10000, electricity
Electrical automobile charging station charging modes include two kinds of fast charge and trickle charge, due in most cases, electric vehicle the areas H and the areas W when
Between it is long, stay in that the time is shorter in the areas P and the areas O, it is therefore assumed that being fast charge in the charging modes in the areas H and the areas W, in the areas P
Charging modes with the areas O are trickle charge.The electric automobile charging station load in region is predicted using the method for the present invention, finally
Obtain the daily load distribution curve of different type electric automobile charging station, as shown in Figure 2.
As seen from Figure 2, the power load distributing of different type electric automobile charging station have apparent difference, mainly by
User is caused by the traveling rule, charging behavior difference of different zones.For most users, it is usually chosen in one day
Trip after go home to charge, therefore, the charging load that residential quarter charging station undertakes is most, the charging load in other regions
Relatively disperse.The load curve of electric automobile charging station is flat without other regions in public leisure area and other regions
It is sliding, it is primarily due to the mode using fast charge in the two regions, charge power is big, causes load fluctuation big.
The power load distributing of electric automobile charging station in different zones embodies the different charge requirements of user, accesses power grid
Influence afterwards to the generation of system total load is also variant.Therefore, the random spy of the trip requirements of user, meter and charging behavior is analyzed
Property, different type electric automobile charging station load prediction is carried out, is had for the economical operation of power grid, Optimized Operation particularly significant
Meaning.
Claims (6)
1. a kind of region electric automobile charging station load forecasting method for simulation of being gone on a journey based on user, it is characterised in that：Including step
Suddenly：
(1) according to user's trip purpose and residing region division electric automobile charging station type；
(2) Trip chain is constructed in the travel activity of different zones using discrete markoff process analog subscriber；
(3) probability that user stops in different type charging station is calculated according to the statistical data of every section of stroke in Trip chain；
(4) charge condition is set according to the traveling demand of user, establishes electric automobile charging station load forecasting model in region；
(5) Monte Carlo simulation approach is utilized to calculate the load prediction curve of different type electric automobile charging station, using variance system
The precision of number assessment load prediction results.
2. the region electric automobile charging station load forecasting method of simulation according to claim 1 of being gone on a journey based on user,
It is characterized in that：In the step (1), the region includes residential quarter (H), workspace (W), public leisure area (P), other areas
(O), electric automobile charging station type includes that residential quarter charging station, workspace charging station, public leisure area charging station, other areas are filled
Power station.
3. the region electric automobile charging station load forecasting method of simulation according to claim 1 of being gone on a journey based on user,
It is characterized in that：In the step (3), the space-time characteristic amount that electric automobile charging station charging load is influenced in Trip chain is extracted, point
It Cai Yong not normal distyribution function and logarithm normal distribution Function Fitting temporal characteristics amount and space characteristics amount.
4. the region electric automobile charging station load forecasting method of simulation according to claim 3 of being gone on a journey based on user,
It is characterized in that：The temporal characteristics amount is set out moment, arrival time, stay time, and the space characteristics amount is mileage travelled.
5. the region electric automobile charging station load forecasting method of simulation according to claim 1 of being gone on a journey based on user,
It is characterized in that：In the step (4), charge condition is：
Wherein, SOCtIndicate the state-of-charge in t moment electric vehicle；Indicate the warning value of batteries of electric automobile；L(SOCt) table
Show the mileage travelled that electric vehicle can also be completed under current state；LnextIndicate that reaching next region from current region needs to go
The mileage sailed.
6. the region electric automobile charging station load forecasting method of simulation according to claim 5 of being gone on a journey based on user,
It is characterized in that：In the step (4), load forecasting model is：
Wherein, PA,tIndicate total charge power of the electric automobile charging station in the A of t moment region；NAIndicate charging station clothes in the A of region
Business vehicle number, pcIndicate the charge power of single vehicle, xi,tIndicate the charged state of electric vehicle.
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Cited By (12)
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| CN109583654A (en) * | 2018-12-03 | 2019-04-05 | 北京科东电力控制***有限责任公司 | A kind of public charging network analysis method of city electric car and device |
| CN110363332A (en) * | 2019-06-21 | 2019-10-22 | 国网天津市电力公司电力科学研究院 | A kind of electric car charging load spatial and temporal distributions prediction technique based on individual behavior characteristic |
| CN110826813A (en) * | 2019-11-13 | 2020-02-21 | 上海恒能泰企业管理有限公司璞能电力科技工程分公司 | Power grid optimization method based on charging difference requirements of household electric vehicle users |
| CN110929921A (en) * | 2019-11-06 | 2020-03-27 | 中国南方电网有限责任公司 | Charging station load prediction method, charging station load prediction device, computer equipment and storage medium |
| CN110956329A (en) * | 2019-12-02 | 2020-04-03 | 国网浙江省电力有限公司绍兴供电公司 | Load prediction method based on distributed photovoltaic and electric vehicle space-time distribution |
| CN111242403A (en) * | 2019-11-08 | 2020-06-05 | 武汉旌胜科技有限公司 | Charging load prediction method and device for charging station and storage medium |
| CN111784027A (en) * | 2020-06-04 | 2020-10-16 | 国网上海市电力公司 | Urban range electric vehicle charging demand prediction method considering geographic information |
| CN112036624A (en) * | 2020-08-21 | 2020-12-04 | 上海电力大学 | Power grid dispatching method based on charging load prediction of electric vehicles in region |
| CN112348387A (en) * | 2020-11-16 | 2021-02-09 | 中原工学院 | Dynamic power dispatching method for assisting user in traveling through charging and discharging strategies |
| CN112380664A (en) * | 2020-08-27 | 2021-02-19 | 国电南瑞南京控制***有限公司 | Characteristic simulation method and system for electric vehicle virtual energy storage to participate in power grid regulation |
| CN113361587A (en) * | 2021-06-02 | 2021-09-07 | 东南大学 | Electric vehicle charging station load characteristic clustering modeling method based on POI information |
| CN113592152A (en) * | 2021-07-07 | 2021-11-02 | 国网上海市电力公司 | Residential community multi-type charging facility configuration method |
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| CN109583654A (en) * | 2018-12-03 | 2019-04-05 | 北京科东电力控制***有限责任公司 | A kind of public charging network analysis method of city electric car and device |
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| CN110956329B (en) * | 2019-12-02 | 2023-04-25 | 国网浙江省电力有限公司绍兴供电公司 | Load prediction method based on distributed photovoltaic and electric automobile space-time distribution |
| CN110956329A (en) * | 2019-12-02 | 2020-04-03 | 国网浙江省电力有限公司绍兴供电公司 | Load prediction method based on distributed photovoltaic and electric vehicle space-time distribution |
| CN111784027A (en) * | 2020-06-04 | 2020-10-16 | 国网上海市电力公司 | Urban range electric vehicle charging demand prediction method considering geographic information |
| CN112036624A (en) * | 2020-08-21 | 2020-12-04 | 上海电力大学 | Power grid dispatching method based on charging load prediction of electric vehicles in region |
| CN112380664A (en) * | 2020-08-27 | 2021-02-19 | 国电南瑞南京控制***有限公司 | Characteristic simulation method and system for electric vehicle virtual energy storage to participate in power grid regulation |
| CN112348387A (en) * | 2020-11-16 | 2021-02-09 | 中原工学院 | Dynamic power dispatching method for assisting user in traveling through charging and discharging strategies |
| CN113361587B (en) * | 2021-06-02 | 2022-11-01 | 东南大学 | Electric vehicle charging station load characteristic clustering modeling method based on POI information |
| CN113361587A (en) * | 2021-06-02 | 2021-09-07 | 东南大学 | Electric vehicle charging station load characteristic clustering modeling method based on POI information |
| CN113592152A (en) * | 2021-07-07 | 2021-11-02 | 国网上海市电力公司 | Residential community multi-type charging facility configuration method |
| CN113592152B (en) * | 2021-07-07 | 2023-08-22 | 国网上海市电力公司 | Configuration method for multi-type charging facilities of residential area |
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