CN110033119A - A kind of family electric car charging optimization method and system - Google Patents
A kind of family electric car charging optimization method and system Download PDFInfo
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Abstract
The present invention provides a kind of family electric car charging optimization methods and system, it include: the day charging load curve model based on the electric car pre-established, family is optimized with the electric car charging time using integer programming method, obtains electric car in the charging prioritization scheme in each season；According to charging prioritization scheme, carries out family and optimized with electric car charging；Wherein, the day charging load curve model of electric car is formulated based on seasonal factor.The integer programming method that this method and system use is really with good performance in terms of reducing network peak valley gap, voltage fluctuation rate and transmission loss.In addition, it is contemplated that Seasonal, can further decrease network outages rate, and significant difference before best electric car charging time and optimization.
Description
Technical field
The invention belongs to energy Internet technical fields, and in particular to a kind of family electric car charging optimization method and be
System.
Background technique
The energy crisis and environmental crisis got worse in world wide accelerates the electrified paces of transportation industry.Due to
Family electric car has efficiency of energy utilization height, and directly pollution is few, and environmental-friendly equal many advantages receive the world in recent years
The most attention of national governments and mechanism.However, the access of a large amount of families electric car will bring large-scale load to power grid
Increase, meanwhile, network power transmission loss will be increased.And the unplanned charging behavior of user can be further exacerbated by distribution voltage wave
Dynamic, this will increase operation of power networks and plan difficulty, can also endanger the safe and stable operation of power grid.Therefore, it is necessary to propose effective
Electric car charging method copes with the adverse effect that electric car access power grid generates.
Have some researchs about the optimal charging method of electric car at present, for example is based on real-time intelligent load management
(RT-SLM) control strategy is reduced system loss and improves voltage curve and mitigated based on the method for sensitivity optimization plug-in
Voltage fluctuation in the presence of mixed power electric car.However, the calculating of the above method is multiple with the increase of family electric car
The significant raising of polygamy.Meanwhile electric car charging behavior reacts very sensitive to seasonal variations, electric car is filled in existing research
The Seasonal of electric behavior considers insufficient.
Summary of the invention
To overcome above-mentioned the deficiencies in the prior art, the present invention proposes a kind of family electric car charging optimization method and is
System.The purpose of this method and system is the charging optimization method of the electric car based on Zero-one integer programming, considers that seasonal factor is excellent
Change family and charge load with electric car come regulating networks load, reduces electrical power system transmission loss.
Realize solution used by above-mentioned purpose are as follows:
A kind of family electric car charging optimization method, it is improved in that including:
Charge day based on the electric car pre-established load curve model, using integer programming method to family with electronic
The automobile charging time optimizes, and obtains electric car in the charging prioritization scheme in each season；
According to the charging prioritization scheme, carries out family and optimized with electric car charging；
Wherein, the day charging load curve model of the electric car is formulated based on seasonal factor.
First optimal technical scheme provided by the invention, the load it is improved in that day of the electric car charges
The foundation of curve model, comprising:
Establish the daily time of return probability density function of user and the charging time probability density letter based on seasonal factor
Number；
Based on the daily time of return probability density function and charging time probability density function, it is single to calculate each moment
The probability of electric car charging；
According to the probability that each moment single motor automobile charges, whole electric cars are calculated in the charging load at each moment.
Second optimal technical scheme provided by the invention, it is improved in that the daily time of return probability density
Function is shown below:
Wherein, fend(tr) indicate the probability density function of daily time of return since last time stroke, trIt indicates
Daily time of return since last time stroke, σendFor standard deviation, uendFor the mathematical expectation of daily time of return,
Subscript end indicates last time stroke.
Third optimal technical scheme provided by the invention, it is improved in that when the charging based on seasonal factor
Between probability density function, be shown below:
Wherein, ftev(tev) indicate the charging time probability density function based on seasonal factor, tevIndicate the charging time,
σendFor standard deviation, uendFor the mathematical expectation of daily time of return, subscript end indicates last time stroke；tevSuch as following formula
It calculates:
Wherein, k indicates that seasonal factors, s indicate that daily operating range, c indicate each season unit distance electric car energy consumption,
PcIndicate that the charge power of single motor automobile, subscript ev indicate charging, subscript c indicates single.
4th optimal technical scheme provided by the invention, it is improved in that each moment single motor automobile fills
The probability of electricity such as following formula calculates:
Wherein, ptIndicate the probability of t moment single motor automobile charging, tevIndicate charging time, TmaxIndicate the charging time
tevThe upper limit, fendFor the probability density function of the daily time of return of user's last time stroke, ftevFor user's electric car
Charging time tevProbability density function, subscript ev indicate charging, subscript end indicate last time stroke.
5th optimal technical scheme provided by the invention, it is improved in that the whole electric car is at each moment
Charging load for example following formula calculate:
Pev(t)=NPcpt
Wherein, Pev(t) whole electric cars are indicated in the charging load of t moment, N indicates electric car quantity, PcIt indicates
The charge power of single motor automobile, ptIndicate that the probability of t moment electric car charging, subscript ev indicate charging, subscript c is indicated
Individually.
6th optimal technical scheme provided by the invention, it is improved in that described based on the electronic vapour pre-established
Charge the day of vehicle load curve model, is optimized to family with the electric car charging time using integer programming method, obtains electricity
Charging prioritization scheme of the electrical automobile in each season, comprising:
Charge day based on the electric car for being in advance based on seasonal factor foundation load curve model, using integer programming side
Method establishes the objective function of the daily charging load variance of minimum in each season；
Using electric car charge power, charging time, battery charging state and duration of charge as constraint condition, respectively
The objective function in each season is optimized, obtains electric car in the charging prioritization scheme in each season.
7th optimal technical scheme provided by the invention, it is improved in that the objective function is shown below:
Wherein, f1Indicate objective function, m indicate by the number of cycles of charging duration discretization, L (j) be do not consider it is discrete
Change the total electricity demand of the electric system of the electric car charging load at period j, Pev(j) time discretization section j is indicated
The electric car charging load at place, subscript ev indicate charging, SijIndicate that binary system of the electric car i in time discretization section j fills
Electricity condition, 1 indicates charging, and 0 indicates not charge, and N indicates electric car quantity.
8th optimal technical scheme provided by the invention, it is improved in that shown electric car charge power constrains
Are as follows: single motor automobile charge power is constant；
The charging time constraint is shown below:
tr≤t≤ts
Wherein, trIndicate the daily time of return since last time stroke, tsIndicate the daily beginning of first time stroke
Time, t indicate the charging time；
The battery charging state constraint is shown below:
SOCExp<SOCa<SOCFull
Wherein, SOCsIndicate the charged state of i-th of batteries of electric automobile before stroke starts, k indicates seasonal factors, s
Indicate that daily operating range, c indicate each season unit distance electric car energy consumption, C indicates the capacity of batteries of electric automobile, SOCb
Indicate the charged state of preceding i-th of the batteries of electric automobile of charging, SOCaIndicate the charging shape of i-th of batteries of electric automobile after charging
State, PcIndicate the charge power of single motor automobile, SijIndicate electric car i in the binary system charging shape of time discretization section j
State, 1 indicates charging, and 0 indicates not charge, and Δ t indicates discrete time length, SOCExpIndicate the expected charging of batteries of electric automobile
State, SOCFullIndicate the fully charged charged state of batteries of electric automobile；
The duration of charge constraint are as follows: battery charge time is at least Δ t.
9th optimal technical scheme provided by the invention, it is improved in that it is described according to the charging prioritization scheme,
It is charged before optimization with electric car at progress family, further includes:
Based on the charging prioritization scheme, the transmission loss and voltage fluctuation of distributed power system are emulated.
A kind of family electric car charging optimization system, it is improved in that including: that plan optimization module and charging are excellent
Change module；
The plan optimization module is used for the load curve model that charges the day based on the electric car pre-established
Integer programming method optimizes family with the electric car charging time, obtains electric car in the charging optimization side in each season
Case；
The charging optimization module, for carrying out family and being optimized with electric car charging according to the charging prioritization scheme；
Wherein, the day charging load curve model of the electric car is formulated based on seasonal factor.
Tenth optimal technical scheme provided by the invention, it is improved in that further including for establishing day charging load
The modeling module of curve model, the modeling module include: probability density function unit, charging probability unit and charging load list
Member；
The probability density function unit, for establishing the daily time of return probability density function of user and based on season
The charging time probability density function of factor；
The charging probability unit, for close based on the daily time of return probability density function and charging time probability
Function is spent, the probability of each moment single motor automobile charging is calculated；
The charging load cell, the probability for being charged according to each moment single motor automobile calculate all electronic vapour
Charging load of the vehicle at each moment.
Compared with the immediate prior art, the device have the advantages that as follows:
The present invention is based on the load curve models that charges the day of the electric car pre-established, using integer programming method to family
It is optimized with the electric car charging time, obtains electric car in the charging prioritization scheme in each season；According to charging optimization side
Case carries out family and is optimized with electric car charging；Wherein, the day charging load curve model of electric car is based on seasonal factor system
It is fixed.Integer programming method proposed by the invention is certain in terms of reducing network peak valley gap, voltage fluctuation rate and transmission loss
It is with good performance.In addition, it is contemplated that Seasonal, can further decrease network outages rate, and best electricity
Significant difference before electrical automobile charging time and optimization.
Zero-one integer programming method proposed by the invention is reducing network peak valley gap, voltage fluctuation rate and transmission loss
Aspect is with good performance.
Detailed description of the invention
Fig. 1 is a kind of family electric car charging optimization method flow diagram provided by the invention；
Fig. 2 is the average daily load curve schematic diagram of 2000 electric cars of Various Seasonal involved in the embodiment of the present invention；
Fig. 3 is 33 Node distribution formula electric network composition schematic diagram of IEEE involved in the embodiment of the present invention；
Fig. 4 (a) is the spring that network is given in the case of not considering any charging optimization method involved in the embodiment of the present invention
Season every daily load curve schematic diagram；
Fig. 4 (b) is the summer that network is given in the case of not considering any charging optimization method involved in the embodiment of the present invention
Season every daily load curve schematic diagram；
Fig. 4 (c) is the autumn that network is given in the case of not considering any charging optimization method involved in the embodiment of the present invention
Season every daily load curve schematic diagram；
Fig. 4 (d) is the winter that network is given in the case of not considering any charging optimization method involved in the embodiment of the present invention
Season every daily load curve schematic diagram；
Fig. 5 (a) is the spring that network is given in the case of not considering any charging optimization method involved in the embodiment of the present invention
Season daily node voltage curve synoptic diagram；
Fig. 5 (b) is the summer that network is given in the case of not considering any charging optimization method involved in the embodiment of the present invention
Season daily node voltage curve synoptic diagram；
Fig. 5 (c) is the autumn that network is given in the case of not considering any charging optimization method involved in the embodiment of the present invention
Season daily node voltage curve synoptic diagram；
Fig. 5 (d) is the winter that network is given in the case of not considering any charging optimization method involved in the embodiment of the present invention
Season daily node voltage curve synoptic diagram；
Fig. 6 (a) is to adopt user power utilization electrical automobile charging optimization method involved in the embodiment of the present invention to optimize given network
The every daily load curve of spring afterwards；
Fig. 6 (b) is to adopt user power utilization electrical automobile charging optimization method involved in the embodiment of the present invention to optimize given network
The every daily load curve of summer afterwards；
Fig. 6 (c) is to adopt user power utilization electrical automobile charging optimization method involved in the embodiment of the present invention to optimize given network
The every daily load curve of autumn afterwards；
Fig. 6 (d) is to adopt user power utilization electrical automobile charging optimization method involved in the embodiment of the present invention to optimize given network
The every daily load curve of winter afterwards；
Fig. 7 (a) is to adopt user power utilization electrical automobile charging optimization method involved in the embodiment of the present invention to optimize given network
The daily node voltage curve of spring afterwards；
Fig. 7 (b) is to adopt user power utilization electrical automobile charging optimization method involved in the embodiment of the present invention to optimize given network
The daily node voltage curve of summer afterwards；
Fig. 7 (c) is to adopt user power utilization electrical automobile charging optimization method involved in the embodiment of the present invention to optimize given network
The daily node voltage curve of autumn afterwards；
Fig. 7 (d) is to adopt user power utilization electrical automobile charging optimization method involved in the embodiment of the present invention to optimize given network
The daily node voltage curve of winter afterwards；
Fig. 8 (a) is the charging optimization method schematic diagram for selecting family electric car in spring involved in the embodiment of the present invention；
Fig. 8 (b) is the charging optimization method schematic diagram for summer involved in the embodiment of the present invention selecting family electric car；
Fig. 8 (c) is the charging optimization method schematic diagram for selecting family electric car in autumn involved in the embodiment of the present invention；
Fig. 8 (d) is the charging optimization method schematic diagram for selecting family electric car in winter involved in the embodiment of the present invention；
Fig. 9 is a kind of family electric car charging optimization system basic structure schematic diagram provided by the invention；
Figure 10 is a kind of family electric car charging optimization system detailed construction schematic diagram provided by the invention.
Specific embodiment
A specific embodiment of the invention is described in further detail with reference to the accompanying drawing.
Embodiment 1:
A kind of family provided by the invention is as shown in Figure 1 with electric car charging optimization method flow diagram, comprising:
Step 1: charge the day based on the electric car pre-established load curve model, using integer programming method to family
It is optimized with the electric car charging time, obtains electric car in the charging prioritization scheme in each season；
Step 2: according to charging prioritization scheme, carrying out family and optimized with electric car charging；
Wherein, the day charging load curve model of electric car is formulated based on seasonal factor.
Step 101: the key factor of analyzing influence user charging behavior considers Seasonal for the first time, special using covering
Monte Carlo Simulation of Ions Inside models the day charging load curve of Various Seasonal electric car；
In order to develop the electric car charging method of optimization, it is important that analysis may influence family and be charged with electric car
The key factor of behavior:
101.1 user driving habities and electric car charging preference
As key parameter, need to carefully analyze daily time of return of the user from last time stroke, because of them and user
Driving habit and to electric car charging preference it is directly related.
Daily return trip time's probability distribution through statistics display user's last time stroke, the probability of the daily time of return of user
It is distributed similar to normal distribution.Therefore, the probability density function of user from the daily return trip time of last time stroke is writeable such as
Under:
Wherein, fend(tr) indicate the probability density function of daily time of return since last time stroke, trIt indicates
Daily time of return since last time stroke, σendFor standard deviation, uendFor the mathematical expectation of daily time of return,
Subscript end indicates last time stroke.
101.2 environment temperature
Environment temperature is very big according to seasonal variations, has a significant impact to the charging behavior of user.This is because user likes
Air-conditioning is opened in summer, heater is opened in winter, to keep vehicle temperature in comfortable range.Therefore, electric car
Average daily load curve is different in Various Seasonal.Fig. 2 shows the average daily load of Various Seasonal electric car in 2000
Curve.
Fig. 2 shows that electric car charging load is minimum in spring, and summer is maximum.For convenience of comparative analysis, need to standardize not
With the electric car charging load in season.Minimum in spring in view of electric car charging load, the application is with spring electric car
On the basis of charging load, the charging load in other seasons of specification.The charging in spring and autumn electric car known to being standardized
Load is almost equal；But the electric car in summer and winter charging load increases separately about 30% and 19%.Therefore, environment
Temperature is very big to electric car charging loading effects, directly affects the charging behavior of user.According to above-mentioned analysis, it is respectively set each
The seasonal factors k in a season.
User's electric car charging load modeling:
If the electric car charging method not optimized, electric car usually after completing last time stroke shortly
It can charge, and stop charging when battery is fully charged.Therefore, electric car is writeable in the charging load of time t are as follows:
Pev(t)=NPcpt (2)
Wherein, Pev(t) electric car is indicated in the charging load of t moment, and N indicates electric car quantity, PcIndicate single
The charge power of electric car, ptIndicate that the probability of t moment electric car charging, subscript ev indicate charging, subscript c indicates single
It is a.ptAs following formula calculates:
Wherein, tevIndicate charging time, TmaxIndicate charging time tevThe upper limit, fendFor user's last time stroke
The probability density function of daily time of return, ftevFor user electric car charging time tevProbability density function, subscript ev table
Show charging, subscript end indicates last time stroke.ftevAs following formula calculates:
tevAs following formula calculates:
Wherein, k indicates that seasonal factors, s indicate that daily operating range, c indicate each season unit distance electric car energy consumption,
PcIndicate the charge power of single motor automobile.
Step 102: proposing the electric car charging optimization method based on Zero-one integer programming, electric car charging optimization
Scheme reduces the transmission loss of distributed power system to adjust the charging load of electric car；
Electric system efficiency can be reduced with the increase that feeder load fluctuates.Therefore, in order to reduce electric system loss,
It is necessary to the average charge load of daily electric car is adjusted in Various Seasonal.
102.1 0-1 adjust the integer programming of electric car charging load
Vehicle only has two states: whether charging or not.In this case, the binary condition of vehicle can be expressed as
1 indicates charging, and 0 indicates not charge.It in this case, can if exploitation 0-1 integer programming model adjusts charging load
To reduce the complexity of electric car charging load optimization.
To reduce system loss, the fluctuation of feeder line load should be reduced.By by charging duration it is discrete be m period, this is special
Benefit is intended to minimize the variance of daily charging load according to Zero-one integer programming.Therefore, the mathematic(al) representation asked a question can indicate
Are as follows:
Wherein:
In (6) and (7), f1It is objective function；L (j) is the electric car charging not considered at time discretization section j
The total electricity demand of the electric system of load, j=1,2,3 ... m；SijIt is binary system charging shape of the electric car i in period j
State；And PavIt is the average power demand of electric system.In view of the model proposed only mobile load, PavIt is given electric system
Constant.Therefore, objective function can simplify are as follows:
Above-mentioned Zero-one integer programming model is integer quadratic programming problem.For this problem, as sample (is connected to electricity
The electric car of net) increase, the complexity of the quantity of decision variable and calculating increases.In order to accelerate a large amount of electric cars
It calculates, proposes a kind of equivalent linear method for linearizing objective function, it significantly reduces computation complexity.Using same
Method, the aggregate demand of electric system may be expressed as: in period j
In equation (9), S is segment number；αn(j) be linearisation after jth section load slope；And δnIt (j) is linearisation
N-th section of the load value of period j afterwards.Therefore, the linearisation equivalent-simplification objective function of the model proposed can be write
At:
In order to develop above-mentioned model, it is emphasized that four it is assumed that be arranged four constraint conditions:
1. electric car charge power supply
There are two process, i.e. invariable power charged state and charge power linearly reduces state for electric car charging.Constant function
Rate charged state is the main process of electric car charging, and the process takes relatively long period of time and and charge power
The linear state that reduces is compared with relatively high efficiency.In addition, with the development of electric car charging technique, charge power is linear
Reduction state tends to disappear.Thus it is assumed herein that electric car is with constant power charge, i.e. electric car charge power PcFor
Constant.
2. the electric car charging time
It for most users, charges soon to electric car preferably after one day last time stroke, to the greatest extent
Manage in them some may be their Vehicular charging when they are in office.It is assumed herein that user completes last
Soon the Vehicular charging for them is begun to after secondary stroke, and must be stopped before working to Vehicular charging.Based on this it is assumed that
The electric car charging time may be expressed as:
tr≤t≤ts (11)
In formula: trFor the daily time of return since last time stroke, tsFor first time stroke daily beginning when
Between.
3. charging batteries of electric automobile state (SOC)
For the safe operation for guaranteeing electric vehicle battery system, meet the walking requirement of user, family batteries of electric automobile
SOC should be limited in a certain range:
SOCExp<SOCa<SOCFull (14)
Wherein, SOCsIndicate the charged state of i-th of batteries of electric automobile before stroke starts, C indicates electric car electricity
The capacity in pond, SOCbIndicate the charged state of preceding i-th of the batteries of electric automobile of charging, SOCaIndicate i-th of electric car after charging
The charged state of battery, SOCExpIndicate the expection charged state of batteries of electric automobile, SOCFullIndicate batteries of electric automobile
Fully charged charged state, Δ t indicate discrete time length, be inversely proportional with the quantity of discrete charging load period m.
4. the batteries of electric automobile service life
Since charge cycle can seriously affect the service life of battery, in order to extend the service life of battery, preferably to battery
Switching frequency is reduced when charging.At least one integer discrete time section (Δ it is assumed herein that battery of electric car needs to charge
T), to avoid the frequent charged state for changing battery.
102.2 transmission loss optimization analysis
User may increase electric system loss by irregularly consuming electric power.In this part, electric power is developed
System transmission loss model carrys out the key factor of analyzing influence transmission loss and computing system transmission loss.
Transmission loss is that one of most important part, the direct phase of resistance and electric current of it and transmission line is lost in electric system
It closes.Equation (15) is the mathematic(al) representation of electrical power system transmission loss:
In formula: ElossFor electrical power system transmission loss；R is the resistance of transmission line；TrFor one day length；I (t) is the time
The electric current of the transmission line of section t.In addition, in equation (15), the electric current of the transmission line at time period t and total daily demand, electric power
Difference between the voltage of system and the electric current of time period t and per day electric current is related, and therefore, i (t) can be indicated are as follows:
In formula: EtotalFor total daily demand of electric system；U is the voltage of electric system；Δ i (t) is the electricity of time period t
Difference between stream and per day electric current.
By combined type (15) and (16), the mathematic(al) representation of electrical power system transmission loss can be modified are as follows:
Assuming that power system voltage U is almost constant, then the first item on the right of equation (17) is constant, and equation (17) is right
The Section 2 on side is that second order is a small amount of, can approximation 0.Therefore, the fluctuation of electric current can greatly influence the transmission loss of electric system.
Step 103: by taking 33 distributed power supply system of IEEE as an example, using MATLAB simulation calculation distributed power system
Transmission loss and voltage fluctuation.Fig. 3 is the structure of 33 Node distribution formula power grid of IEEE.Select node 0 as reference mode, because
Main grid is directly connected to for it.For the system, 12.66kV is selected as the reference voltage, and the maximum of the network is effectively
Loading (ignoring electric car charging load) is 3.72MW.Fig. 5 is that given distributed power grid is bent in the daily load of Various Seasonal
Line.
In this network, about 950 families, in the demand peak period, the average demand of each family is 4,000
Watt.Assuming that the popularity rate of electric car is 30% in the system, therefore the sum of electric car is 285 in the network.In addition, false
If all electric cars are evenly distributed in all nodes to charge to its battery.It will then provide and situation given based on this
The simulation result of the control method proposed, to show the validity of proposed optimization method.
Step 104: according to charging prioritization scheme, carrying out family and optimized with electric car charging.
Embodiment 2:
With reference to the accompanying drawing, it elaborates to case of the invention.It is an object of the invention to be based on Zero-one integer programming
Optimal electric car charge optimization method, optimization family charges load with electric car come regulating networks load, reduces power train
System transmission loss.
By taking 33 distributed power supply system of IEEE as an example, using MATLAB simulation calculation distributed power system transmission loss
And voltage fluctuation.Fig. 3 is the structure of 33 Node distribution formula power grid of IEEE.Select node 0 as reference mode, because it is directly
It is connected to main grid.For the system, 12.66kV is selected as the reference voltage, and the Maximum Payload of the network (is ignored
Electric car charging load) it is 3.72MW.
In this network, about 950 families, in the demand peak period, the average demand of each family is 4,000
Watt.Assuming that the popularity rate of electric car is 30% in the system, therefore the sum of electric car is 285 in the network.In addition, false
If all electric cars are evenly distributed in all nodes to charge to its battery.It will then provide and situation given based on this
The simulation result of the optimization method that most charges proposed, to show the validity of proposed optimization method.
As a result with analysis:
(1) charge without the electric car of any optimization method
Fig. 4 (a)-Fig. 4 (d) shows the diurnal load curve in given network structure lower four seasons, and the network structure is not
Consider any optimal charge method of four Various Seasonals.Fig. 4 (a)-Fig. 4 (d) display, spring and autumn gave net in one day
Gap between the greatest requirements and minimum essential requirement of network is about 1.4 megawatts.However, this gap can increase in summer and winter
To 2.2 megawatts.This demonstrate that seasonal factor has an impact to electric system peak valley difference is calculated, electric system biography is further affected
The calculating of defeated loss.
In addition, simulation result shows if it is considered that seasonal factor, the practical aggregate demand of given network are answered in summer and winter
This is higher.On the contrary, the practical aggregate demand that spring and autumn give network should be lower.In addition, in summer, practical daily load curve
(considering Seasonal) lags behind average daily load curve (ignoring Seasonal).But winter shows opposite to become
Gesture.For spring and autumn, practical daily load curve and average daily load curve are in same stage.This is because user is to electricity
The preference of electrical automobile charging, early stage and latter stage summer complete last time stroke to user in winter.Therefore, summer practical daily load
Curve lags behind average daily load curve, and winter, practical daily load curve led over average daily load curve.In short, it is seasonal because
Element will affect the shape of network daily load curve.
By taking node 8 as an example, Fig. 5 (a)-Fig. 5 (d) shows the day of network node in given network structure lower four seasons
It buckles line, which does not consider any optimal charging method of four Various Seasonals.Fig. 5 (a)-Fig. 5 (d) display, in the summer
Season and winter, the voltage fluctuation rate for giving node is about 2%, and spring and this ratio of autumn will drop to 1% or so.It is worth noting
, with the connection of electric car and power grid, this rate can all increase about 50% in all seasons.In other words, voltage
Stability bandwidth increases to about 1.5% in spring and autumn, increases about 3% in summer.If winter electric car charging load connects
To power grid.In addition, being similar to load curve analog result, virtual voltage curve lags behind the average voltage curve of summer, and
Lead over the average voltage curve in winter.
Simulation result, which shows Seasonal not only, influences the aggregate demand of given network, but also influences node voltage.Cause
This, considers that these factors are vital when dispatching electric car charging load.In addition, simulation result is also shown that such as
Fruit does not have electric car charging optimization method appropriate, and the maximum peak valley difference for giving electric system is 2.2 megawatts, this is network
More than half of peak demand.The power and voltage of the network are adjusted therefore, it is necessary to application electric car charging optimization method
Fluctuation.
It (2) is electric car charging with the charging optimization method proposed
By the proposed electric car charging optimization method of application, Fig. 6 (a)-Fig. 6 (d) is shown in four differences
The optimization daily load curve of network is given in season.From Fig. 6 (a)-Fig. 6 (d) as can be seen that passing through the optimization method proposed, one
The gap given between the minimum and maximum demand of network in it is reduced to about 0.8 megawatt in spring and autumn, in summer and
Winter is reduced to 1.2 megawatts.Gap between greatest requirements and Minimum requirements reduces 45% four different seasons.Pass through
Using the charging optimization method proposed, electric car charging load has been successfully moved to valley required time.This demonstrate that
Validity of the method proposed in load transfer.
Fig. 7 (a)-Fig. 7 (d) shows the charging optimization method by considering four Various Seasonals, gives in network structure
The daily voltage curve of node 8.As shown in Fig. 7 (a)-Fig. 7 (d), as electric car charging load enters power grid, node 8
Voltage fluctuation rate is down to 1% in spring and autumn, and in summer and winter, than spring, higher about 50% autumn is this ratio
1.5%.Although the voltage fluctuation rate in summer and winter is higher by 50% than spring and autumn, with do not use any charging method to electricity
Electrical automobile load carries out charging and compares, and the voltage fluctuation rate of node 8 passes through in each season to be filled using proposed electric car
Method for electrically.Simulation result shows that the average voltage stability bandwidth of node 8 reduces about 45% in 1 year.Although summer and winter
Voltage fluctuation rate it is higher than spring and autumn by 50%, but with do not use any charging method to electric car load carry out charging phase
Than being significantly reduced by the voltage fluctuation rate of application proposed electric car charging method, node 8 in each season.Emulation
The result shows that the average voltage stability bandwidth of node 8 reduces about 45% in 1 year.
In short, Fig. 6 (a)-Fig. 6 (d) and 7 (a)-Fig. 7 (d) is shown, the optimization electric automobile load that the application is proposed is filled
Method for electrically not only has preferable advantage on conversion load, but also has the very strong ability for reducing voltage fluctuation rate.Emulation knot
Fruit shows that the peak valley gap of network and voltage fluctuation rate reduce about using the electric car load charging method proposed
45%.Therefore, according to the above results, it can predict that transmission loss can be reduced to a certain extent in given network.
(3) transmission loss optimum results
Based on the Zero-one integer programming model proposed, Fig. 8 (a)-Fig. 8 (d) shows family selected by four seasons with electronic
The charging optimization method of automobile.In addition, the transmission damage of given network can be calculated by the power swing for analyzing given network
Consumption summarizes the transmission loss for giving network at different conditions.
Fig. 8 (a)-Fig. 8 (d) display, for selected family electric car, if it is considered that Seasonal, optimal charge
Time has very big difference, this is mainly reflected in two aspects.Firstly, if it is considered that Seasonal, selected electronic vapour
Vehicle needs longer charge period in summer and winter, and shorter charge period is needed in spring and autumn.This is because
When considering Seasonal, the system of cooling and heating can consume more energy.Another importance is if it is considered that season
Sexual factor then can change best electric car charging time section.For example, in Fig. 8 c, it is selected when considering seasonal factor
Electric car charges between 23:00 to 24:00, and if ignoring Seasonal, which is postponed till into 2:00 and is arrived
3:00.The reason is that when considering Seasonal, load charging curve may be lagged behind or average electronic more than 1 year
Automobile load charging curve.
The transmission loss that network is given under different condition shows that 1 year averaging network transmission loss rate increases to
3.65%, if electric car charging load is connected to network, increase about 13.06%.Therefore, with family electric car number
The increase of amount, the significant increase of averaging network transmission loss.In order to reduce transmission loss, it should be charged using optimal electric car
Method.By the proposed electric car charging optimization method of application, 1 year averaging network loss late is down to 3.50%, if
Ignore Seasonal, then it is average to reduce about 4.11%.On the contrary, when considering seasonal factor, by applying proposed charging
Optimization method, network outages rate can be further decreased to 3.45%.Therefore, simulation results show the 0-1 that is proposed
The validity of integer programming model, and highlight the necessity that seasonal factor is considered when adjusting electric car charging load.
Embodiment 3:
Based on the same inventive concept, the present invention also provides a kind of family electric car charging optimization systems, due to these
The principle that equipment solves technical problem is charged to family with electric car, and optimization method is similar, and overlaps will not be repeated.
The system basic system is as shown in Figure 9, comprising:
Plan optimization module and charging optimization module；
Wherein, plan optimization module is used for the load curve model that charges the day based on the electric car pre-established
Integer programming method optimizes family with the electric car charging time, obtains electric car in the charging optimization side in each season
Case；
Charge optimization module, for carrying out family and being optimized with electric car charging according to charging prioritization scheme；
Wherein, the day charging load curve model of electric car is formulated based on seasonal factor.
Family is as shown in Figure 10 with electric car charging optimization system detailed construction.
Wherein, which further includes the modeling module for establishing day charging load curve model, and modeling module includes: general
Rate density function unit, charging probability unit and charging load cell；
Probability density function unit, for establishing the daily time of return probability density function of user and based on seasonal factor
Charging time probability density function；
Charge probability unit, for being based on daily time of return probability density function and charging time probability density function,
Calculate the probability of each moment single motor automobile charging；
Charge load cell, the probability for being charged according to each moment single motor automobile, calculates whole electric cars and exists
The charging load at each moment.
Wherein, plan optimization module includes: objective function unit and optimization calculation unit；
Objective function unit, for the load curve mould that charges the day based on the electric car for being in advance based on seasonal factor foundation
Type establishes the objective function of the daily charging load variance of minimum in each season using integer programming method；
Optimization calculation unit, when for being continued with electric car charge power, charging time, battery charging state and charging
Between be constraint condition, the objective function in each season is optimized respectively, it is excellent in the charging in each season to obtain electric car
Change scheme.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Finally it should be noted that: above embodiments are merely to illustrate the technical solution of the application rather than to its protection scopes
Limitation, although the application is described in detail referring to above-described embodiment, those of ordinary skill in the art should
Understand: those skilled in the art read the specific embodiment of application can still be carried out after the application various changes, modification or
Person's equivalent replacement, but these changes, modification or equivalent replacement, are applying within pending claims.
Claims (12)
1. a kind of family electric car charging optimization method characterized by comprising
Charge day based on the electric car pre-established load curve model, using integer programming method to family electric car
Charging time optimizes, and obtains electric car in the charging prioritization scheme in each season；
According to the charging prioritization scheme, carries out family and optimized with electric car charging；
Wherein, the day charging load curve model of the electric car is formulated based on seasonal factor.
2. the method as described in claim 1, which is characterized in that the day charging load curve model of the electric car is built
It is vertical, comprising:
Establish the daily time of return probability density function of user and the charging time probability density function based on seasonal factor；
Based on the daily time of return probability density function and charging time probability density function, each moment single motor is calculated
The probability of automobile charging；
According to the probability that each moment single motor automobile charges, whole electric cars are calculated in the charging load at each moment.
3. method according to claim 2, which is characterized in that the daily time of return probability density function, such as following formula institute
Show:
Wherein, fend(tr) indicate the probability density function of daily time of return since last time stroke, trIt indicates from most
The daily time of return that one stroke starts afterwards, σendFor standard deviation, uendFor the mathematical expectation of daily time of return, subscript
End indicates last time stroke.
4. method according to claim 2, which is characterized in that the charging time probability density letter based on seasonal factor
Number, is shown below:
Wherein, ftev(tev) indicate the charging time probability density function based on seasonal factor, tevIndicate charging time, σendFor mark
Quasi- deviation, uendFor the mathematical expectation of daily time of return, subscript end indicates last time stroke；tevAs following formula calculates:
Wherein, k indicates that seasonal factors, s indicate that daily operating range, c indicate each season unit distance electric car energy consumption, PcTable
Show that the charge power of single motor automobile, subscript ev indicate charging, subscript c indicates single.
5. method according to claim 2, which is characterized in that the probability such as following formula of each moment single motor automobile charging
It calculates:
Wherein, ptIndicate the probability of t moment single motor automobile charging, tevIndicate charging time, TmaxIndicate charging time tev's
The upper limit, fendFor the probability density function of the daily time of return of user's last time stroke, ftevFor the charging of user's electric car
Time tevProbability density function, subscript ev indicate charging, subscript end indicate last time stroke.
6. method according to claim 2, which is characterized in that the whole electric car is as follows in the charging load at each moment
Formula calculates:
Pev(t)=NPcpt
Wherein, Pev(t) whole electric cars are indicated in the charging load of t moment, N indicates electric car quantity, PcIndicate single
The charge power of electric car, ptIndicate that the probability of t moment electric car charging, subscript ev indicate charging, subscript c indicates single
It is a.
7. the method as described in claim 1, which is characterized in that charge the day based on the electric car pre-established load
Curve model optimizes family with the electric car charging time using integer programming method, obtains electric car in each season
Charging prioritization scheme, comprising:
Charge day based on the electric car for being in advance based on seasonal factor foundation load curve model, is built using integer programming method
Found the objective function of the daily charging load variance of minimum in each season；
Using electric car charge power, charging time, battery charging state and duration of charge as constraint condition, respectively to each
The objective function in season optimizes, and obtains electric car in the charging prioritization scheme in each season.
8. the method for claim 7, which is characterized in that the objective function is shown below:
Wherein, f1Indicate that objective function, m indicate that L (j) is not consider time discretization by the number of cycles of charging duration discretization
The total electricity demand of the electric system of electric car charging load at section j, Pev(j) electricity at time discretization section j is indicated
Electrical automobile charging load, subscript ev indicate charging, SijIndicate electric car i time discretization section j binary system charged state,
1 indicates charging, and 0 indicates not charge, and N indicates electric car quantity.
9. the method for claim 7, which is characterized in that shown electric car charge power constraint are as follows: single motor vapour
Vehicle charge power is constant；
The charging time constraint is shown below:
tr≤t≤ts
Wherein, trIndicate the daily time of return since last time stroke, tsWhen indicating the daily beginning of first time stroke
Between, t indicates the charging time；
The battery charging state constraint is shown below:
SOCExp<SOCa<SOCFull
Wherein, SOCsIndicate the charged state of i-th of batteries of electric automobile before stroke starts, k indicates that seasonal factors, s indicate every
Day operating range, c indicate each season unit distance electric car energy consumption, and C indicates the capacity of batteries of electric automobile, SOCbExpression is filled
The charged state of i-th of batteries of electric automobile, SOC before electricityaIndicate the charged state of i-th of batteries of electric automobile after charging, PcTable
Show the charge power of single motor automobile, SijIndicate that electric car i is indicated in the binary system charged state of time discretization section j, 1
Charging, 0 indicates not charge, and Δ t indicates discrete time length, SOCExpIndicate the expection charged state of batteries of electric automobile,
SOCFullIndicate the fully charged charged state of batteries of electric automobile；
The duration of charge constraint are as follows: battery charge time is at least Δ t.
10. the method as described in claim 1, which is characterized in that it is described according to the charging prioritization scheme, family is carried out with electronic
Before automobile charging optimization, further includes:
Based on the charging prioritization scheme, the transmission loss and voltage fluctuation of distributed power system are emulated.
11. a kind of family electric car charging optimization system characterized by comprising plan optimization module and charging optimization mould
Block；
The plan optimization module, for the load curve model that charges the day based on the electric car pre-established, using integer
Planing method optimizes family with the electric car charging time, obtains electric car in the charging prioritization scheme in each season；
The charging optimization module, for carrying out family and being optimized with electric car charging according to the charging prioritization scheme；
Wherein, the day charging load curve model of the electric car is formulated based on seasonal factor.
12. system as claimed in claim 11, which is characterized in that further include for establishing building for day charging load curve model
Mould module, the modeling module include: probability density function unit, charging probability unit and charging load cell；
The probability density function unit, for establishing the daily time of return probability density function of user and based on seasonal factor
Charging time probability density function；
The charging probability unit, for being based on the daily time of return probability density function and charging time probability density letter
Number calculates the probability of each moment single motor automobile charging；
The charging load cell, the probability for being charged according to each moment single motor automobile calculate whole electric cars and exist
The charging load at each moment.
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| CN113922422B (en) * | 2021-10-22 | 2024-03-22 | 国网经济技术研究院有限公司 | Constant-power flexible operation control method, system, equipment and storage medium |
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