CN109878369A - A kind of electric car charge and discharge Optimization Scheduling based on fuzzy Spot Price - Google Patents
A kind of electric car charge and discharge Optimization Scheduling based on fuzzy Spot Price Download PDFInfo
- Publication number
- CN109878369A CN109878369A CN201910181905.6A CN201910181905A CN109878369A CN 109878369 A CN109878369 A CN 109878369A CN 201910181905 A CN201910181905 A CN 201910181905A CN 109878369 A CN109878369 A CN 109878369A
- Authority
- CN
- China
- Prior art keywords
- electric car
- moment
- discharge
- electric
- charge
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
Landscapes
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
A kind of electric car charge and discharge Optimization Scheduling based on fuzzy Spot Price.This method fully considers that electric car trip is uncertain, using charging station Income Maximum as objective function, construct centralized economic load dispatching model, and it is based on distribution network load and electric car charge-discharge electric power, it is proposed the Spot Price strategy based on fuzzy PID algorithm, it is decentralized model that Lagrangian Relaxation, which is chosen, by centralized economic load dispatching model decoupling, solves the charge-discharge electric power at each electric car each moment.Simulation result shows that charging station income can be significantly increased in the present invention, effectively improves the computational efficiency of electric car Real-Time Scheduling and stabilizes load fluctuation.
Description
Technical field
The present invention relates to a kind of electric car charge and discharge Optimized Operation fields, specially a kind of electric in real time based on fuzzy
The electric car charge and discharge Optimization Scheduling of valence.
Background technique
At present about in the economic load dispatching of electric automobile charging station, time sharing segment Electricity Price Strategy is mostly used.The strategy is real-time
Property is poor, cannot accurately reflect the variation of real-time load, fail sufficiently to be combined with the market demand, also not fully demonstrate the lever of electricity price
Effect.And electric automobile charging station mostly uses centralized optimal control method for electric car electric discharge scheduling, in face of big
Scale electric car participate in scheduling when, this control mode will lead to center processor communication it is crowded, over-burden, give electronic vapour
The Real-Time Scheduling of vehicle brings limitation.
Summary of the invention
In order to solve the above technical problems, the present invention provides a kind of electric car charge and discharge based on fuzzy Spot Price
Charging station income can be significantly increased in Optimization Scheduling, effectively improve the computational efficiency of electric car Real-Time Scheduling and stabilize
Load fluctuation.
The technical scheme adopted by the invention is as follows:
A kind of electric car charge and discharge Optimization Scheduling based on fuzzy Spot Price, comprising the following steps:
Step 1: acquisition charging station trip information, sale of electricity electricity price, transformer capacity including the charging station, it is each when
The base load at quarter, the information such as rated power of charge and discharge, following one day electric car trip situation of prediction；
Step 2: building scene tree-model cuts down scene using positive algorithm；
Step 3: building always runs centralized economic load dispatching model of the Income Maximum as objective function using charging station；
Step 4: in conjunction with power distribution network basic load and electric car charge-discharge electric power, adjusting fuzzy parameter, solves real
When electricity price；
Step 5: Lagrangian Relaxation is used, centralized economic load dispatching model is decoupled, each electronic vapour is solved
The charge-discharge electric power at vehicle each moment；
Step 6: judging whether the state-of-charge of each electric car meets travel requirement, if so, terminate process, it is no
Then, return step five.
The step 1 includes: the time probability density function that electric car leaves power grid are as follows:
In formula, tl,nIt indicates that electric car leaves power grid time after n-th of time window normalizes, is defined as
tdep,n/△(t)；tdep,nFor the power grid time of leaving of n-th time window, △ (t) is scheduling time scale；μ is to utilize sequence
Quadratic programming minimizes the parameter that mean square error acquires, and Γ is the gamma function of standard.
The time probability density function of electric car access power grid can be expressed as leaving the conditional probability of power grid time:
In formula, tcTo access power grid time, μnThe mean value that power grid time is accessed in power grid time window, σ are left for n-thn
The standard deviation that power grid time is accessed in power grid time window, t are left for n-thdep,nIt is n-th time window when leaving power grid
Between.
The probability density function of electric automobile during traveling mileage are as follows:
F (d)=(db+d)-ηexp(-d/ε)
In formula, parameter η=1.37, ε=18, db=1.79.
In the step 2, scene tree-model are as follows:
Assuming that available charging station historical data has R group, every group has r physical quantity (including in unit time scale
Charge electricity price, electric discharge electricity price, electric car charging and discharging state, state-of-charge etc.), these data are arranged simultaneously sequentially in time
It is placed on matrix
In, wherein t row At=(At,1,At,2,…At,n) t=1,2 ..., N is r physical quantity t-th of period of history
Vector.If current state is AR, then the scene of subsequent time can be taken as in historical data with ARSimilar point it is next when
The data at quarter.Specific steps are as follows:
1) history data set { A is chosen1, A2... AN, and determine that the quantity for the scene to be generated is N；
2) current state A is calculatedRWith ' similarity ' of historical data, ' similarity ' expression formula are as follows:
Above formula denominator adds 1, be in order to avoid denominator value be 0 the case where, and ' similarity ' is sorted from large to small and is denoted as
{di1, di2... diR-1}；
3) it choosesScene as next stage；
4) it determines the probability of each scene, for the sake of simplicity, takes equiprobability situation, i.e. 1/N.
In the step 3, centralized economic load dispatching model are as follows:
Income Maximum is always run using charging station as objective function, expression formula are as follows:
In formula, F is charging station day maximum return, and N is the total number for generating scene, and T is day scheduling time scale number, and E is
Electric car number, PnFor the probability of n-th of scene, △ (t) is unit time scale, Pc、PdRespectively in unit time scale
Charging, electric discharge rated power,Respectively electric car charging per hour, electric discharge electricity price, Gr (t) are power grid sale of electricity electricity
Valence,For electric car e the n-th scene t moment state variable.
Its value expression formula are as follows:
Constraint condition has:
1) batteries of electric automobile state-of-charge updates constraint:
In formula:Electric car e is in t moment, the state-of-charge at t-1 moment under respectively n-th of scene,
Pc、PdThe charge and discharge rated power of electric car, η respectively in unit time scalec、ηdRespectively electric car is filled, is put
Electrical efficiency, △ (t) are unit time scale, EEVbFor the battery capacity of electric car, Tc,e、Tl,eRespectively indicate the e electronic vapour
Vehicle accesses the power grid moment and leaves the power grid moment.
2) electric car charge-discharge electric power constrains:
In formula,Expectation state-of-charge at the end of for electric car charge and discharge, EEVbHold for the battery of electric car
Amount, Pc、PdCharging, electric discharge rated power, η respectively in unit time scalec、ηdThe respectively effect of electric car charging, electric discharge
Rate,For electric car e the n-th scene t moment state variable.
3) power distribution network power constraint:
In formula,For the basic load of t moment power distribution network, Pc、PdCharging, electric discharge volume respectively in unit time scale
Determine power,State variable for electric car e in the n-th scene t moment, PDNFor the maximum power of interconnection transmission, E
For total number of electric car, n is scene label.
In the step 4, proposes a kind of fuzzy Spot Price strategy, include the following steps:
1) error of t moment load and per day load is calculated.T moment error are as follows:
In formula, e (t) is the load error of t moment,For the synthetic load of t moment power distribution network, i.e. power distribution network basic load
The sum of with electric car charge-discharge electric power,For power distribution network expected load.
2) fuzzy control rule is formulated, fuzzy coefficient is adjusted:
If t moment synthetic load is excessive, reduce integral coefficient；If the t moment synthetic load rise time is excessive, preferentially
Scaling up coefficient, then it is gradually increased integral coefficient；If the fluctuation of t moment synthetic load is larger, increase differential coefficient.According to upper
State fuzzy control rule, debugging adjusting PID coefficient.
3) t moment charge and discharge electricity price correction amount is calculated:
In formula,Respectively t moment charging, electric discharge electricity price correction amount, e (t) are t moment load error, Proportionality coefficient, integral coefficient and the differential coefficient of load error when respectively calculating charging electricity price, Proportionality coefficient, integral coefficient and the differential coefficient of load error when respectively calculating electric discharge electricity price.
4) the electricity price correction amount of t moment is normalized:
In formula, △ Pr_Normc(t)、△Pr_NormdIt (t) is respectively charging after t moment normalizes, electric discharge electricity price amendment
Amount, △ Prc(t)、△Prd(t) be respectively t moment charging, electric discharge electricity price correction amount,Respectively charging electricity
The minimum value and maximum value of valence correction amount,The minimum value and maximum value for electricity price correction amount of respectively discharging,The minimum value and maximum value of respectively practical charging electricity price,The respectively minimum of actual discharge electricity price
Value and maximum value.
5) the practical electricity price of t moment is calculated:
In formula, Prc(t)、PrcIt (t-1) is respectively t moment and the practical electricity price of the charging at t-1 moment, Prd(t)、Prd(t-1)
The practical electricity price of the electric discharge at respectively t moment and t-1 moment, △ Pr_Normc(t)、△Pr_NormdIt (t) is respectively t moment normalizing
It charges after change, electricity price correction amount of discharging.
In the step 5, chooses Lagrangian Relaxation and centralized economic load dispatching model is decoupled, it is former after decoupling
Model conversation is the decentralized model for each electric car, and expression is as follows:
Objective function:
In formula, L is charging station day minimum operating cost, and T is day scheduling time scale number, and E is electric car number, PnFor
The probability of n-th of scene, △ (t) are unit time scale, Pc、PdIt respectively charges in unit time scale, discharge specified function
Rate,Respectively electric car charging per hour, electric discharge electricity price, Gr (t) are power grid sale of electricity electricity price,For electricity
Electrical automobile e the n-th scene t moment state variable,For Lagrange multiplier,For the basic load of t moment power distribution network,
PDNFor the peak load that power distribution network is able to bear, E is total number of electric car.
Constraint condition are as follows:
In formula,Expectation state-of-charge at the end of for electric car charge and discharge, EEVbHold for the battery of electric car
Amount, Pc、PdCharging, electric discharge rated power, η respectively in unit time scalec、ηdThe respectively effect of electric car charging, electric discharge
Rate,State variable for electric car e in the n-th scene t moment, Tc,eThe power grid moment is accessed for the e electric car
Final time scale, Tl,eThe initial time scale at power grid moment is left for the e electric car.
A kind of electric car charge and discharge Optimization Scheduling based on fuzzy Spot Price of the present invention, technical effect is such as
Under: traditional centralized scheduling of 1. present invention relative to electric automobile charging station, computational efficiency is very high, and substantially not by electronic
The influence of Truck dispartching scale, the Real-Time Scheduling suitable for extensive electric car；
2. fuzzy Spot Price proposed by the present invention is able to reflect supply in electric automobile charging station operational process and needs
The variation relation asked controls to adjust the charge and discharge behavior of electric car, effectively increases the operation income of electric automobile charging station.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples:
Fig. 1 is the structure chart of electric automobile charging station；
Fig. 2 is flow chart of the invention；
Fig. 3 is the control block diagram of fuzzy Spot Price strategy；
Fig. 4 is decentralized model and centralized model computational efficiency comparison diagram；
Fig. 5 is the load chart after charge and discharge Optimized Operation；
Fig. 6 is traditional tou power price and fuzzy Spot Price effect contrast figure.
Specific embodiment
The present invention will be further described with reference to the accompanying drawing.But it should be recognized that embodiments of the present invention are unlimited
In this, any modification and improvement that this field practitioner makes technical solution provided by the invention should all fall into the present invention
In the protection scope that claims determine.
Electric automobile charging station structure chart of the invention as shown in Figure 1: power distribution network pass sequentially through transformer, bus with it is electronic
Vehicle charging station connection.Simultaneously to different electric automobile charging stations equipped with local controller, all local controller systems
One connect with central controller.Central controller will charge electricity price, electric discharge electricity price be sent to local controller, local controller exists
After receiving the information such as charging electricity price, electric discharge electricity price, charge and discharge plan is formulated according to the specific trip information of each electric car
And central controller is transferred back to, after being unifiedly calculated by central controller, again by the charging electricity price of update and electric discharge electricity price transmission
To local controller.Local controller combines the electricity price updated, formulates electric car charge and discharge plan, realizes to each electronic vapour
The Optimized Operation of vehicle charge and discharge.
Process of the invention is as shown in Figure 2, comprising the following steps:
Step 1: acquisition charging station trip information, sale of electricity electricity price, transformer capacity including the charging station, it is each when
The base load at quarter, the information such as rated power of charge and discharge), following one day electric car trip situation of prediction；
Step 2: building scene tree-model cuts down scene using positive algorithm；
Step 3: building always runs centralized economic load dispatching model of the Income Maximum as objective function using charging station；
Step 4: in conjunction with power distribution network basic load and electric car charge-discharge electric power, adjusting fuzzy parameter, solves real
When electricity price；
Step 5: Lagrangian Relaxation is used, centralized economic load dispatching model is decoupled, each electronic vapour is solved
The charge-discharge electric power at vehicle each moment；
Step 6: judging whether the state-of-charge of each electric car meets travel requirement, if so, terminate process, it is no
Then, return step five.
Wherein, in the trip information of electric car, electric car leaves the time probability density function of power grid are as follows:
In formula, tl,nIt indicates that electric car leaves power grid time after n-th of time window normalizes, is defined as
tdep,n/△(t)；tdep,nFor the power grid time of leaving of n-th time window, △ (t) is scheduling time scale；μ is to utilize sequence
Quadratic programming minimizes the parameter that mean square error acquires, and Γ is the gamma function of standard.
The time probability density function of electric car access power grid can be expressed as leaving the conditional probability of power grid time:
In formula, tcTo access power grid time, μnThe mean value that power grid time is accessed in power grid time window, σ are left for n-thn
The standard deviation that power grid time is accessed in power grid time window is left for n-th.
The probability density function of electric automobile during traveling mileage are as follows:
F (d)=(db+d)-ηexp(-d/ε)
In formula, parameter η=1.37, ε=18, db=1.79.
After counting the trip information of electric car and modeling sampling, charging station centralization economic load dispatching model is constructed, specifically
It is as follows: Income Maximum always to be run using charging station as objective function, expression formula are as follows:
In formula, F is charging station day maximum return, and T is day scheduling time scale number, and E is electric car number, PnIt is n-th
The probability of a scene, △ (t) are unit time scale, Pc、PdCharging, electric discharge rated power respectively in unit time scale,Respectively electric car charging per hour, electric discharge electricity price, Gr (t) are power grid sale of electricity electricity price,It is electronic
State variable of the automobile e in the n-th scene t moment, value expression formula are as follows:
Constraint condition has:
1) batteries of electric automobile state-of-charge updates constraint:
In formula:Electric car e is in t moment, the state-of-charge at t-1 moment under respectively n-th of scene,
Pc、PdThe respectively charge and discharge rated power of electric car, ηc、ηdRespectively the charge and discharge efficiency of electric car, △ (t) are
Unit time scale, EEVbFor the battery capacity of electric car.
2) electric car charge-discharge electric power constrains:
In formula,Expectation state-of-charge at the end of for electric car charge and discharge, EEVbHold for the battery of electric car
Amount, Pc、PdCharging, electric discharge rated power, η respectively in unit time scalec、ηdThe respectively effect of electric car charging, electric discharge
Rate,For electric car e the n-th scene t moment state variable.
3) power distribution network power constraint:
In formula,For the basic load of t moment power distribution network, Pc、PdCharging, electric discharge volume respectively in unit time scale
Determine power,State variable for electric car e in the n-th scene t moment, PDNFor the maximum power of interconnection transmission.
Fuzzy Spot Price structural block diagram is as shown in figure 3, the fuzzy Spot Price strategy of specific implementation is as follows:
1) error of t moment load and per day load is calculated.T moment error are as follows:
In formula, e (t) is the load error of t moment,For the synthetic load of t moment power distribution network, i.e. power distribution network basic load
The sum of with electric car charge-discharge electric power,For power distribution network expected load.
2) fuzzy control rule is formulated, fuzzy coefficient is adjusted:
If t moment synthetic load is excessive, reduce integral coefficient；If the t moment synthetic load rise time is excessive, preferentially
Scaling up coefficient, then it is gradually increased integral coefficient；If the fluctuation of t moment synthetic load is larger, increase differential coefficient.According to upper
State fuzzy control rule, debugging adjusting PID coefficient.
3) t moment charge and discharge electricity price correction amount is calculated:
In formula,Respectively t moment charging, electric discharge electricity price correction amount, e (t) are t moment load error, Proportionality coefficient, integral coefficient and the differential coefficient of load error when respectively calculating charging electricity price, Proportionality coefficient, integral coefficient and the differential coefficient of load error when respectively calculating electric discharge electricity price.
4) the electricity price correction amount of t moment is normalized:
In formula, △ Pr_Normc(t)、△Pr_NormdIt (t) is respectively charging after t moment normalizes, electric discharge electricity price amendment
Amount, △ Prc(t)、△Prd(t) be respectively t moment charging, electric discharge electricity price correction amount,Respectively charging electricity
The minimum value and maximum value of valence correction amount,The minimum value and maximum value for electricity price correction amount of respectively discharging,The minimum value and maximum value of respectively practical charging electricity price,The respectively minimum of actual discharge electricity price
Value and maximum value.
5) the practical electricity price of t moment is calculated
In formula, Prc(t)、PrcIt (t-1) is respectively t moment and the practical electricity price of the charging at t-1 moment, Prd(t)、Prd(t-1)
The practical electricity price of the electric discharge at respectively t moment and t-1 moment, △ Pr_Normc(t)、△Pr_NormdIt (t) is respectively t moment normalizing
It charges after change, electricity price correction amount of discharging.
It chooses Lagrangian Relaxation to decouple centralized economic load dispatching model, master mould, which is converted into, after decoupling is directed to
The decentralized model of each electric car, expression are as follows:
Objective function:
In formula, L is charging station day minimum operating cost, and T is day scheduling time scale number, and E is electric car number, PnFor
The probability of n-th of scene, △ (t) are unit time scale, Pc、PdIt respectively charges in unit time scale, discharge specified function
Rate,Respectively electric car charging per hour, electric discharge electricity price, Gr (t) are power grid sale of electricity electricity price,For electricity
Electrical automobile e the n-th scene t moment state variable,For Lagrange multiplier.
Constraint condition are as follows:
In formula,Expectation state-of-charge at the end of for electric car charge and discharge, EEVbHold for the battery of electric car
Amount, Pc、PdCharging, electric discharge rated power, η respectively in unit time scalec、ηdThe respectively effect of electric car charging, electric discharge
Rate,State variable for electric car e in the n-th scene t moment, Tc,eThe power grid moment is accessed for the e electric car
Final time scale, Tl,eThe initial time scale at power grid moment is left for the e electric car.
Simulation analysis is carried out using matlab software:
By establishing scene tree-model and cutting down scene using positive algorithm, the market out of electric car under more scenes are obtained
Condition emulates the above method using the function in the tool box Yalmip and Cplex in matlab.Decentralized model and collection
The computational efficiency comparison diagram of Chinese style model is as shown in Figure 4.With the increase of electric car quantity, the calculating time of centralized model
It is significantly increased, and the calculating time of decentralized model is basically unchanged.Load point after centralized model and decentralized model optimization
As shown in figure 5, compared with centralized model, decentralized model has more apparent cloth in terms of peak load shifting and load fluctuation
Superiority.Using electric automobile charging station after tou power price and fuzzy Spot Price operation income as shown in fig. 6, using
When centralized Optimized model, the operation income of fuzzy Spot Price strategy increases 9.02% than tou power price, and uses and divide
When dissipating formula Optimized model, the operation income of fuzzy Spot Price strategy increases 10.87% than tou power price.
Claims (7)
1. a kind of electric car charge and discharge Optimization Scheduling based on fuzzy Spot Price, it is characterised in that including following
Step:
Step 1: acquisition charging station trip information, following one day electric car trip situation of prediction；
Step 2: building scene tree-model cuts down scene using positive algorithm；
Step 3: building always runs centralized economic load dispatching model of the Income Maximum as objective function using charging station；
Step 4: in conjunction with power distribution network basic load and electric car charge-discharge electric power, adjusting fuzzy parameter, solves electricity in real time
Valence；
Step 5: Lagrangian Relaxation is used, centralized economic load dispatching model is decoupled, it is each to solve each electric car
The charge-discharge electric power at moment；
Step 6: judging whether the state-of-charge of each electric car meets travel requirement, otherwise returns if so, terminating process
Return step 5.
2. a kind of electric car charge and discharge Optimization Scheduling based on fuzzy Spot Price according to claim 1,
Be characterized in that: the step 1 includes:
Electric car leaves the time probability density function of power grid are as follows:
In formula, tl,nIt indicates that electric car leaves power grid time after n-th of time window normalizes, is defined as tdep,n/△
(t)；tdep,nFor the power grid time of leaving of n-th time window, △ (t) is scheduling time scale；μ is to utilize sequential quadratic programming
Method minimizes the parameter that mean square error acquires, and Γ is the gamma function of standard；
The time probability density function of electric car access power grid can be expressed as leaving the conditional probability of power grid time:
In formula, tcTo access power grid time, μnThe mean value that power grid time is accessed in power grid time window, σ are left for n-thnIt is
N are left the standard deviation that power grid time is accessed in power grid time window；
The probability density function of electric automobile during traveling mileage are as follows:
F (d)=(db+d)-ηexp(-d/ε)
In formula, parameter η=1.37, ε=18, db=1.79.
3. a kind of electric car charge and discharge Optimization Scheduling based on fuzzy Spot Price according to claim 1,
It is characterized in that: in the step 2, scene tree-model are as follows:
Assuming that available charging station historical data has R group, every group has r physical quantity, including the charging in unit time scale
These data are arranged and are placed on sequentially in time by electricity price, electric discharge electricity price, electric car charging and discharging state, state-of-charge
Matrix:
In, wherein t row At=(At,1,At,2,…At,n) t=1,2 ..., N is r physical quantity t-th period of history to
Amount；If current state is AR, then the scene of subsequent time can be taken as in historical data with ARNext moment of similar point
Data；Specific steps are as follows:
1) history data set { A is chosen1, A2... AN, and determine that the quantity for the scene to be generated is N；
2) current state A is calculatedRWith ' similarity ' of historical data, ' similarity ' expression formula are as follows:
Above formula denominator adds 1, be in order to avoid denominator value be 0 the case where, and ' similarity ' is sorted from large to small and is denoted as { di1,
di2... diR-1}；
3) it choosesScene as next stage；
4) it determines the probability of each scene, for the sake of simplicity, takes equiprobability situation, i.e. 1/N.
4. a kind of electric car charge and discharge Optimization Scheduling based on fuzzy Spot Price according to claim 1,
It is characterized in that: in the step 3, centralized economic load dispatching model are as follows:
Income Maximum is always run using charging station as objective function, expression formula are as follows:
In formula, F is charging station day maximum return, and T is day scheduling time scale number, and E is electric car number, PnFor n-th of scene
Probability, △ (t) be unit time scale, Pc、PdCharging, electric discharge rated power respectively in unit time scale,Respectively electric car charging per hour, electric discharge electricity price, Gr (t) are power grid sale of electricity electricity price,It is electronic
State variable of the automobile e in the n-th scene t moment, value expression formula are as follows:
5. a kind of electric car charge and discharge Optimization Scheduling based on fuzzy Spot Price according to claim 4,
Be characterized in that: the constraint condition of centralized economic load dispatching model has:
1) batteries of electric automobile state-of-charge updates constraint:
In formula:Electric car e is in t moment, the state-of-charge at t-1 moment, P under respectively n-th of scenec、Pd
The respectively charge and discharge rated power of electric car, ηc、ηdThe respectively charge and discharge efficiency of electric car, when △ (t) is unit
Between scale, EEVbFor the battery capacity of electric car；
2) electric car charge-discharge electric power constrains:
In formula,Expectation state-of-charge at the end of for electric car charge and discharge, EEVbFor the battery capacity of electric car,
Pc、PdCharging, electric discharge rated power, η respectively in unit time scalec、ηdThe respectively efficiency of electric car charging, electric discharge,For electric car e the n-th scene t moment state variable；
3) power distribution network power constraint:
In formula,For the basic load of t moment power distribution network, Pc、PdIt respectively charges in unit time scale, discharge specified function
Rate,State variable for electric car e in the n-th scene t moment, PDNFor the maximum power of interconnection transmission.
6. a kind of electric car charge and discharge Optimization Scheduling based on fuzzy Spot Price according to claim 1,
It is characterized in that: in the step 4, proposing a kind of fuzzy Spot Price strategy, include the following steps:
1) error of t moment load and per day load is calculated；T moment error are as follows:
In formula, e (t) is the load error of t moment,For the synthetic load of t moment power distribution network, i.e. power distribution network basic load and electricity
The sum of electrical automobile charge-discharge electric power,For power distribution network expected load；
2) fuzzy control rule is formulated, fuzzy coefficient is adjusted:
If t moment synthetic load is excessive, reduce integral coefficient；It is preferential to increase if the t moment synthetic load rise time is excessive
Proportionality coefficient, then it is gradually increased integral coefficient；If the fluctuation of t moment synthetic load is larger, increase differential coefficient；According to above-mentioned mould
Paste control rule, debugging adjusting PID coefficient；
3) t moment charge and discharge electricity price correction amount is calculated:
In formula,Respectively t moment charging, electric discharge electricity price correction amount, e (t) are t moment load error, Proportionality coefficient, integral coefficient and the differential coefficient of load error when respectively calculating charging electricity price, Proportionality coefficient, integral coefficient and the differential coefficient of load error when respectively calculating electric discharge electricity price；
4) the electricity price correction amount of t moment is normalized:
In formula, △ Pr_Normc(t)、△Pr_NormdIt (t) is respectively charging after t moment normalizes, electric discharge electricity price correction amount, △
Prc(t)、△Prd(t) be respectively t moment charging, electric discharge electricity price correction amount,Respectively charging electricity price amendment
The minimum value and maximum value of amount,The minimum value and maximum value for electricity price correction amount of respectively discharging,The minimum value and maximum value of respectively practical charging electricity price,The respectively minimum of actual discharge electricity price
Value and maximum value；
5) the practical electricity price of t moment is calculated:
In formula, Prc(t)、PrcIt (t-1) is respectively t moment and the practical electricity price of the charging at t-1 moment, Prd(t)、Prd(t-1) respectively
For the practical electricity price of electric discharge at t moment and t-1 moment, △ Pr_Normc(t)、△Pr_NormdIt (t) is respectively after t moment normalizes
Charging, electric discharge electricity price correction amount.
7. a kind of electric car charge and discharge Optimization Scheduling based on fuzzy Spot Price according to claim 1,
It is characterized in that: in the step 5, choosing Lagrangian Relaxation and centralized economic load dispatching model is decoupled, it is former after decoupling
Model conversation is the decentralized model for each electric car, and expression is as follows:
Objective function:
In formula, L is charging station day minimum operating cost, and T is day scheduling time scale number, and E is electric car number, PnIt is n-th
The probability of scene, △ (t) are unit time scale, Pc、PdCharging, electric discharge rated power respectively in unit time scale,Respectively electric car charging per hour, electric discharge electricity price, Gr (t) are power grid sale of electricity electricity price,It is electronic
Automobile e the n-th scene t moment state variable,For Lagrange multiplier；
Constraint condition are as follows:
In formula,Expectation state-of-charge at the end of for electric car charge and discharge, EEVbFor the battery capacity of electric car,
Pc、PdCharging, electric discharge rated power, η respectively in unit time scalec、ηdThe respectively efficiency of electric car charging, electric discharge,State variable for electric car e in the n-th scene t moment, Tc,eThe power grid moment is accessed most for the e electric car
Whole time scale, Tl,eThe initial time scale at power grid moment is left for the e electric car.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910181905.6A CN109878369B (en) | 2019-03-11 | 2019-03-11 | Electric vehicle charging and discharging optimal scheduling method based on fuzzy PID real-time electricity price |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910181905.6A CN109878369B (en) | 2019-03-11 | 2019-03-11 | Electric vehicle charging and discharging optimal scheduling method based on fuzzy PID real-time electricity price |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109878369A true CN109878369A (en) | 2019-06-14 |
| CN109878369B CN109878369B (en) | 2021-08-31 |
Family
ID=66931664
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910181905.6A Active CN109878369B (en) | 2019-03-11 | 2019-03-11 | Electric vehicle charging and discharging optimal scheduling method based on fuzzy PID real-time electricity price |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109878369B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110443415A (en) * | 2019-07-24 | 2019-11-12 | 三峡大学 | It is a kind of meter and dynamic electricity price strategy electric automobile charging station Multiobjective Optimal Operation method |
| CN110570007A (en) * | 2019-06-17 | 2019-12-13 | 上海交通大学 | Multi-time scale optimized scheduling method for electric vehicle |
| CN110733371A (en) * | 2019-10-30 | 2020-01-31 | 深圳供电局有限公司 | electric vehicle charging pile charging analysis method |
| CN111055719A (en) * | 2019-12-30 | 2020-04-24 | 云南电网有限责任公司 | Electric vehicle charging station profit maximization decision method |
| CN111628500A (en) * | 2020-06-11 | 2020-09-04 | 吉林省中暖新能源有限公司 | Electric power heat accumulation cold-storage energy management system |
| CN114742118A (en) * | 2020-12-23 | 2022-07-12 | 中国科学院广州能源研究所 | Electric vehicle cluster charging and discharging load combination prediction method |
| WO2023052678A1 (en) | 2021-09-29 | 2023-04-06 | Kempower Oyj | Charging arrangement and method for controlling charging of electric vehicles and computer program product |
| CN118003953A (en) * | 2024-04-09 | 2024-05-10 | 青岛城运数字科技有限公司 | Control method and device for charging behavior in charging station |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130311658A1 (en) * | 2012-05-18 | 2013-11-21 | James Solomon | Flexible administrative model in an electric vehicle charging service network |
| CN105024432A (en) * | 2015-07-30 | 2015-11-04 | 浙江工业大学 | Electric vehicle charge-discharge optimized dispatching method based on virtual electricity price |
| CN106096773A (en) * | 2016-06-07 | 2016-11-09 | 三峡大学 | A kind of electric automobile serves as the Multiobjective Optimal Operation method of energy storage |
| CN107491812A (en) * | 2016-06-13 | 2017-12-19 | 中国农业大学 | Short-term load forecasting method based on Spot Price |
| CN109050284A (en) * | 2018-07-09 | 2018-12-21 | 华中科技大学 | A kind of electric car charge and discharge electricity price optimization method considering V2G |
| CN109214095A (en) * | 2018-09-13 | 2019-01-15 | 云南民族大学 | Electric car charge and discharge Multiobjective Optimal Operation method |
-
2019
- 2019-03-11 CN CN201910181905.6A patent/CN109878369B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130311658A1 (en) * | 2012-05-18 | 2013-11-21 | James Solomon | Flexible administrative model in an electric vehicle charging service network |
| CN105024432A (en) * | 2015-07-30 | 2015-11-04 | 浙江工业大学 | Electric vehicle charge-discharge optimized dispatching method based on virtual electricity price |
| CN106096773A (en) * | 2016-06-07 | 2016-11-09 | 三峡大学 | A kind of electric automobile serves as the Multiobjective Optimal Operation method of energy storage |
| CN107491812A (en) * | 2016-06-13 | 2017-12-19 | 中国农业大学 | Short-term load forecasting method based on Spot Price |
| CN109050284A (en) * | 2018-07-09 | 2018-12-21 | 华中科技大学 | A kind of electric car charge and discharge electricity price optimization method considering V2G |
| CN109214095A (en) * | 2018-09-13 | 2019-01-15 | 云南民族大学 | Electric car charge and discharge Multiobjective Optimal Operation method |
Non-Patent Citations (1)
| Title |
|---|
| 程杉，王贤宁，冯毅煁: "电动汽车充电站有序充电调度的分散式优化", 《电力系统自动化》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110570007A (en) * | 2019-06-17 | 2019-12-13 | 上海交通大学 | Multi-time scale optimized scheduling method for electric vehicle |
| CN110570007B (en) * | 2019-06-17 | 2023-05-09 | 上海交通大学 | Multi-time-scale optimal scheduling method for electric automobile |
| CN110443415A (en) * | 2019-07-24 | 2019-11-12 | 三峡大学 | It is a kind of meter and dynamic electricity price strategy electric automobile charging station Multiobjective Optimal Operation method |
| CN110443415B (en) * | 2019-07-24 | 2022-07-15 | 三峡大学 | Electric vehicle charging station multi-objective optimization scheduling method considering dynamic electricity price strategy |
| CN110733371A (en) * | 2019-10-30 | 2020-01-31 | 深圳供电局有限公司 | electric vehicle charging pile charging analysis method |
| CN111055719A (en) * | 2019-12-30 | 2020-04-24 | 云南电网有限责任公司 | Electric vehicle charging station profit maximization decision method |
| CN111055719B (en) * | 2019-12-30 | 2023-09-22 | 云南电网有限责任公司 | Method for maximizing income of electric vehicle charging station |
| CN111628500A (en) * | 2020-06-11 | 2020-09-04 | 吉林省中暖新能源有限公司 | Electric power heat accumulation cold-storage energy management system |
| CN114742118A (en) * | 2020-12-23 | 2022-07-12 | 中国科学院广州能源研究所 | Electric vehicle cluster charging and discharging load combination prediction method |
| CN114742118B (en) * | 2020-12-23 | 2023-10-27 | 中国科学院广州能源研究所 | Electric automobile cluster charge-discharge load combination prediction method |
| WO2023052678A1 (en) | 2021-09-29 | 2023-04-06 | Kempower Oyj | Charging arrangement and method for controlling charging of electric vehicles and computer program product |
| CN118003953A (en) * | 2024-04-09 | 2024-05-10 | 青岛城运数字科技有限公司 | Control method and device for charging behavior in charging station |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109878369B (en) | 2021-08-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109878369A (en) | A kind of electric car charge and discharge Optimization Scheduling based on fuzzy Spot Price | |
| CN112467722B (en) | Active power distribution network source-network-load-storage coordination planning method considering electric vehicle charging station | |
| CN109816171B (en) | Double-layer distributed optimal scheduling method for electric vehicle regional micro-grid cluster | |
| Zheng et al. | A novel real-time scheduling strategy with near-linear complexity for integrating large-scale electric vehicles into smart grid | |
| CN109559035B (en) | Urban distribution network double-layer planning method considering flexibility | |
| CN106651059B (en) | Optimal configuration method for electric vehicle charging station | |
| CN109599856B (en) | Electric vehicle charging and discharging management optimization method and device in micro-grid multi-building | |
| CN108494034A (en) | A kind of power distribution network electric vehicle charging sharing of load computational methods | |
| CN110119886A (en) | Dynamic planning method for active distribution network | |
| CN109936128A (en) | A kind of dynamic need response method under scale electric car access conditions | |
| Yang et al. | Optimal dispatching strategy for shared battery station of electric vehicle by divisional battery control | |
| CN113326467B (en) | Multi-target optimization method, storage medium and optimization system for multi-station fusion comprehensive energy system based on multiple uncertainties | |
| CN107104454A (en) | Meter and the optimal load flow node electricity price computational methods in electric automobile power adjustable control domain | |
| CN103840521A (en) | Large-scale electric vehicle optimized charging and discharging system and method based on the optimal power flow | |
| Huang | Day-ahead optimal control of PEV battery storage devices taking into account the voltage regulation of the residential power grid | |
| CN108631344A (en) | A kind of meter and the orderly charge/discharge control method of electric vehicle for network operation constraint of transmitting electricity | |
| CN110065410A (en) | A kind of electric car charge and discharge rate control method based on fuzzy control | |
| CN112865190A (en) | Optimal scheduling method and system for photovoltaic and charging demand-based optical storage charging station | |
| CN110739690A (en) | Power distribution network optimal scheduling method and system considering electric vehicle quick charging station energy storage facility | |
| CN113437754A (en) | Electric automobile ordered charging method and system based on platform area intelligent fusion terminal | |
| CN112183882B (en) | Intelligent charging station charging optimization method based on electric vehicle quick charging requirement | |
| CN113222241B (en) | Taxi quick-charging station planning method considering charging service guide and customer requirements | |
| CN109950900B (en) | Micro-grid load reduction control method based on electric vehicle load minimum peak model | |
| CN112001598A (en) | Energy storage configuration evaluation and operation optimization method for different users based on energy storage type selection | |
| CN111738518A (en) | Electric vehicle charging and discharging scheduling method based on average discharge rate |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |