CN110020505A - Extensive charging pile plan model modeling method based on small data - Google Patents
Extensive charging pile plan model modeling method based on small data Download PDFInfo
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- CN110020505A CN110020505A CN201910350294.3A CN201910350294A CN110020505A CN 110020505 A CN110020505 A CN 110020505A CN 201910350294 A CN201910350294 A CN 201910350294A CN 110020505 A CN110020505 A CN 110020505A
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Abstract
Extensive charging pile plan model modeling method based on small data, including according to small-scale electric car charging behavioural characteristic data, carry out charge rule modeling, obtain the data distribution model of each characteristic；Using Monte Carlo method, obtained data distribution model predicts extensive charge data, obtains charging behavior when electric car accesses on a large scale.Based on the extensive charge data and model hypothesis of Monte Carlo method prediction, derivation acquires equivalent charging load curve.Charging equipment investment and the unordered the smallest region charging pile plan model of charging load peak-valley difference are realized in building；In conjunction with relevant constraint, and consider the other influences factor in the charging pile plan model establishment process of region, optimizes.Using the modeling method of the invention, existing small-scale data can be based on, it is minimum come equipment investment and unordered charging load peak-valley difference when planning the setting of large-scale charging pile, and realizing electric car charging.
Description
Technical field
The present invention relates to electric automobile charging pile planning fields, and in particular to a kind of extensive charging pile based on small data
Plan model modeling method.
Background technique
In order to reduce the dependence to petroleum resources, world energy sources safety problem is cracked, realizes that human economic society is sustainable
Development, national governments are just greatly developing environmentally protective electric car industry under the background that new energy development utilizes, and correlation is asked
Topic also gets more and more people's extensive concerning.Also just constantly reinforce the industrialization of electric car as world energy consumption big country in China
Layout.Therefore, carrying out the research of charging infrastructure configuring has important theory and realistic meaning.
The charging behavior of electric car temporally and spatially has very big uncertainty and randomness, when scale is huge
When big electric car disorderly accesses power grid, charge and discharge behavior may influence the scheduling of electric system in power grid, planning, match
Peak-valley difference, power quality of power grid etc. may run distribution network when situation is serious and cause enormous impact.Therefore, it is necessary to tie
Family charging behavioural characteristic quantitative change law is shared, charging equipment of electric automobile is reasonably distributed rationally
Current research is configured around charging equipment, has achieved certain achievement, but existing charge data is often
Small-scale data, hardly result in large-scale electric car charge data to plan the setting of a wide range of charging pile,
Peak-valley difference influences when not yet accessing on a large scale on electric car and charging equipment investment carries out comprehensive analysis.
Summary of the invention
It is directed to extensive electric car charge data at present and is difficult to the problem of obtaining, the present invention provides a kind of based on decimal
According to extensive charging pile plan model modeling method existing small-scale data can be based on, to big using the modeling method
The charging pile setting of range is planned, and realizes that equipment investment and unordered charging load peak-valley difference are most when electric car charges
It is small.
The technical scheme adopted by the invention is as follows:
Extensive charging pile plan model modeling method based on small data, comprising the following steps:
Step 1: according to small-scale electric car charging behavioural characteristic data, including when charging start time, charging connection
Long, charge capacity data carry out charge rule modeling, obtain the data distribution model of each characteristic；
Step 2: using Monte Carlo method, the data distribution model obtained according to step 1 predicts extensive charging number
According to obtaining charging start time when electric car accesses on a large scale, charging connection duration, the data of charge capacity.
Step 3: using the electric car quantity of day part connection as equivalent load, based on the big of Monte Carlo method prediction
Scale charge data and model hypothesis, derivation acquires equivalent charging load curve, as shown in Fig. 2, i.e. each the equivalent of period is filled
Electric load curve, in which: M is charging peak, and N is charging low ebb.
Step 4: building realizes that mould is planned in charging equipment investment and the smallest region charging pile of unordered charging load peak-valley difference
Type；Since different charging equipment power grades are different, N is enabled1、N2、N3Respectively different grades of charging equipment quantity, K1、K2、K3
Their power is corresponded respectively to, W is to consider coefficient set by charging cost and efficiency, then charging equipment investment function f1Table
It is shown as:
f1=W (K1N1+K2N2+K3N3)
To make to charge, peak-valley difference is as small as possible, then all minimum power is used to charge when peak, when low ebb all uses most
High-power charging；Enable F1、F2、F3The charging equipment of each grade accesses quantity when the load peak that respectively charges；G1、G2、G3Respectively
The charging equipment of each grade accesses quantity when for charging load valley, then peak valley difference function f2It indicates are as follows:
f2=K1′F1+K2′F2+K3′F3-K1″G1-K2″G2-K3″G3
K ' is the minimum power of each equipment, and K " is the maximum power of each equipment, and definition region charging pile plan model is as follows:
Min f=f1+f2
Step 5: in conjunction with relevant constraint, the region charging pile plan model established in step 4 being optimized.
Relevant constraint is as follows:
(1), consider that the equality constraint of total number of units, charging equipment quantity are equal with Rechargeable vehicle number of units.
(2), consider the connection automobile quantity at peak valley, have equality constraint as follows:
Wherein, NFmax、NGmaxRespectively charge load peak, electric car quantity is accessed in low-valley interval.
(3), the minimum charge power of each electric car can be obtained, by charging behavioural characteristic data to make all electric cars
It can get expected charge capacity in charging connection duration, different charge power grade equipment quantity equally exist one and differ
Formula constraint should be greater than being equal to expected number of devices.
(4), for charging peak and low-valley interval, have inequality constraints as follows:
After solution, the access quantity of all kinds of charging equipments, as optimum programming configuration method can be obtained.
In the step 3, model hypothesis includes:
1., each electric car only fill primary electricity daily；
2., electric vehicle charge interface connection when as charged state；
3., in one day finish time, if still there is electric car charging, be regarded as the previous day to the shadow of start time
It rings；
4., temporally section carry out electric car connection judgment；
5., regard the electric car quantity of each moment connection as equivalent load.
In the step 5, since solution procedure is established on the basis of model hypothesis, thought based on Monte Carlo simulation
Think, the Optimization Solution process of model is repeated 100 times, combines sum to carry out modified result after being averaged, can be obtained final
Planning setting result.
A kind of extensive charging pile plan model modeling method based on small data of the present invention, advantage are:
It can be asked by means of the present invention by existing small-scale charge data, the planning for solving a wide range of electrically-charging equipment
Topic, the configuring of charging pile provides certain theoretical foundation when accessing on a large scale for electric car.
Detailed description of the invention
Fig. 1 is the modeling method of the invention flow chart.
Fig. 2 is equivalent charging load chart.
Fig. 3 is the characteristic pattern of charging start time in embodiment；
Fig. 4 is the characteristic pattern of charging connection duration in embodiment；
Fig. 5 is the characteristic pattern of charge capacity in embodiment.
Specific embodiment
Below with reference to flow chart shown in Fig. 1, the invention will be further described.
The planning of electric car region charging pile is a multiple target, multi-variable decision optimization problem, is directed to small-scale
Charge data, analyze every charging feature index respectively, can obtain the data distribution model of indices, including normal distribution,
Γ distribution, Rayleigh distribution etc..
Using Monte Carlo method, the extensive charge data of every charging feature index is predicted, including when charging beginning
Quarter, charging connection duration, charge capacity data.
Specific step is as follows for Monte Carlo method:
(1), random process models, i.e., carries out Probability Distribution Analysis to given sequence of random variables；
(2), it is generated based on gained probability Distribution Model and obeys the sequence of random variables of the distribution it is, in general, that constructing general
Rate model and after capable of therefrom sampling, i.e., after realization simulated experiment, determine a stochastic variable, as the solution of required problem,
It is referred to as unbiased esti-mator.Various estimators are established, is equivalent to and the result of simulated experiment is investigated and registered, therefrom asked
The solution of topic.
Based on extensive charge data and model hypothesis that Monte Carlo method predicts, derivation acquires equivalent charging load
Curve, concrete model are assumed as follows:
1., each electric car only fill primary electricity daily；
2., electric vehicle charge interface connection when as charged state；
3., in one day finish time, if still there is electric car charging, be regarded as the previous day to the shadow of start time
It rings；
4., temporally section carry out electric car connection judgment.Can be using 15min as interval, one day total duration is area for 24 hours
Between serial number be followed successively by 1,2 ..., 96.Any time connects within certain section period, is considered as the period as connection；At certain section
Any time disconnects in section, regards the period subsequent period to disconnect.
5., regard the electric car quantity of each moment connection as equivalent load.
Further building realizes that equipment investment minimum and the smallest region charging pile of unordered charging load peak-valley difference plan mould
Type defines the region charging pile plan model are as follows:
Min f=W (K1N1+K2N2+K3N3)+(K1′F1+K2′F2+K3′F3-K1″G1-K2″G2-K3″G3)
It is optimized in conjunction with related constraint, constraint is analyzed as follows:
(1), consider total number of units, there are an equality constraint, charging equipment quantity is equal with Rechargeable vehicle number of units.
(2), consider the connection automobile quantity at peak valley, have equality constraint as follows:
Wherein, NFmax、NGmaxRespectively charge load peak, electric car quantity is accessed in low-valley interval.
(3), the minimum charge power of each electric car can be obtained, by charging behavioural characteristic data to make all electric cars
It can get expected charge capacity in charging connection duration, different charge power grade equipment quantity equally exist one and differ
Formula constraint should be greater than being equal to expected number of devices.
(4), for charging peak and low-valley interval, have inequality constraints as follows:
The multi-objective optimization question can be converted to solution linear programming problem, since solution procedure is established in certain mould
On the basis of type is assumed, and electric car charging behavioral data, random fashion combination producing is used after Monte-carlo Simulation,
Thus result has certain randomness.Based on Monte Carlo simulation thought, 100 are repeated to above-mentioned Optimization Solution process
It is secondary, it combines sum to carry out modified result after being averaged, final configuration result can be obtained, accessed on a large scale for electric car
When charging pile planning certain theoretical foundation is provided.
Concrete case:
The charging feature data of known 100 electric cars in certain city day, according to these data to 10000 charging vapour
Charging pile when vehicle charges carries out configuring.Part of data are as shown in table 1 below:
1 10000 Rechargeable vehicle charge arrangement tables of table
Electric car charge power grade is as shown in table 2:
2 electric car charge power table of grading of table
1: according to foregoing invention specific embodiment, construct charging start time, charging connection duration, charge capacity number
According to distributed model, it is known that charging start time data meet normal distribution and see Fig. 3, and charging connection duration and charge capacity data are full
Sufficient Γ distribution, is shown in Fig. 4, Fig. 5.
2: data being extended using Monte Carlo method, and obtain equivalent charging load curve, see Fig. 2.
3: inquiring into equivalent load curve after predicting the extensive charge data of every charging feature index, building region is realized
Equipment investment minimum and the unordered the smallest region charging pile plan model of charging load peak-valley difference, and it is excellent to combine constraint condition to carry out
Change and solve:
1) N is enabled1、N2、N31 grade, exchange 2 grades, DC charging number of devices are respectively exchanged, then equipment investment function f1It can
It indicates are as follows:
f1=0.5N1+1.8N2+40N3
2) as small as possible for the peak-valley difference that makes to charge, then all minimum power is used to charge when peak, when low ebb all uses
Maximum power charging.Enable F1、F2、F31 grade of exchange, 2 grades of exchange, DC charging equipment access number when the load peak that respectively charges
Amount；G1、G2、G3Respectively charging load valley when 1 grade of exchange, 2 grades of exchange, DC charging equipment access quantity, then peak-valley difference letter
Number f2It may be expressed as:
f2=1.5F1+10F2+40F3-3G1-25G2-100G3
3) it is as follows to define integrated objective function, i.e. region charging pile plan model:
Min f=10f1+f2
4) it is analyzed according to foregoing invention, constraint condition is as follows:
The method that the configuration of charging pile optimum programming can be obtained by solution: 1 grade of exchange, 2 grades of exchange, the access of DC charging equipment
Quantity be respectively 6403,3538,59.
Claims (3)
1. the extensive charging pile plan model modeling method based on small data, it is characterised in that the following steps are included:
Step 1: being charged behavioural characteristic data, including charging start time, charging connection duration, filled according to small-scale electric car
Power consumption data carry out charge rule modeling, obtain the data distribution model of each characteristic；
Step 2: using Monte Carlo method, the data distribution model obtained according to step 1 is predicted extensive charge data, obtained
Charging start time, charging connection duration, the data of charge capacity when being accessed on a large scale to electric car；
Step 3: using the electric car quantity of day part connection as equivalent load, based on the extensive of Monte Carlo method prediction
Charge data and model hypothesis, derivation acquire equivalent charging load curve, i.e., the quantity of Rechargeable vehicle in each period, so as to
Obtain equivalent charging peak and the corresponding section of low ebb and corresponding Rechargeable vehicle quantity；
Step 4: charging equipment investment and the unordered the smallest region charging pile plan model of charging load peak-valley difference are realized in building；By
It is different in different charging equipment power grades, enable N1、N2、N3Respectively different grades of charging equipment quantity, K1、K2、K3Respectively
Corresponding to their power, W is to consider coefficient set by charging cost and efficiency, then charging equipment investment function f1It indicates
Are as follows:
f1=W (K1N1+K2N2+K3N3)
To make to charge, peak-valley difference is as small as possible, then all minimum power is used to charge when peak, when low ebb all uses maximum work
Rate charging；Enable F1、F2、F3The charging equipment of each grade accesses quantity when the load peak that respectively charges；G1、G2、G3Respectively fill
The charging equipment of each grade accesses quantity when electric load low ebb, then peak valley difference function f2It indicates are as follows:
f2=K1′F1+K2′F2+K3′F3-K1″G1-K2″G2-K3″G3
K ' is the minimum power of each equipment, and K " is the maximum power of each equipment, and definition region charging pile plan model is as follows:
Minf=f1+f2
Step 5: in conjunction with relevant constraint, the region charging pile plan model established in step 4 being optimized；
Relevant constraint is as follows:
(1), consider that the equality constraint of total number of units, charging equipment quantity are equal with Rechargeable vehicle number of units；
(2), consider the connection automobile quantity at peak valley, have equality constraint as follows:
Wherein, NFmax、NGmaxRespectively charge load peak, electric car quantity is accessed in low-valley interval；
(3), the minimum charge power of each electric car can be obtained, by charging behavioural characteristic data to fill all electric cars
It is electrically connected in duration and can get expected charge capacity, different charge power grade equipment quantity equally exist an inequality about
Beam should be greater than being equal to expected number of devices；
(4), for charging peak and low-valley interval, have inequality constraints as follows:
After solution, the access quantity of all kinds of charging equipments, as optimum programming configuration method can be obtained.
2. the extensive charging pile plan model modeling method based on small data according to claim 1, it is characterised in that:
In the step 3, model hypothesis includes:
1., each electric car only fill primary electricity daily；
2., electric vehicle charge interface connection when as charged state；
3., in one day finish time, if still there is electric car charging, be regarded as influence of the previous day to start time；
4., temporally section carry out electric car connection judgment；
5., regard the electric car quantity of each moment connection as equivalent load.
3. the extensive charging pile plan model modeling method based on small data according to claim 1, it is characterised in that:
In the step 5, since solution procedure is established on the basis of model hypothesis, it is based on Monte Carlo simulation thought, it is right
The Optimization Solution process of model repeats 100 times, combines sum to carry out modified result after being averaged, final planning can be obtained and set
Set result.
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| CN111597690A (en) * | 2020-04-26 | 2020-08-28 | 湖南省建筑设计院有限公司 | Method for establishing electric vehicle charging equipment demand coefficient calculation model |
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| CN111597690A (en) * | 2020-04-26 | 2020-08-28 | 湖南省建筑设计院有限公司 | Method for establishing electric vehicle charging equipment demand coefficient calculation model |
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