CN108921331A - It is a kind of meter and V2G function electric car and new energy optimizing scheduling modeling and algorithm - Google Patents
It is a kind of meter and V2G function electric car and new energy optimizing scheduling modeling and algorithm Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
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- 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
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Abstract
The invention discloses the optimizing scheduling modeling of the electric car and new energy of a kind of meter and V2G function and algorithms, electric system containing electric car, wind-powered electricity generation and the solar energy that can network is research object, construct the dispatching method of meter and electric car V2G function, this method to reduce system loading fluctuation and automobile user charging cost as target simultaneously, pass through the Fuzzy processing to multiple targets, single-object problem is converted by the problem of demand solution, and the newest optimization algorithm of intersecting in length and breadth of application is solved, and optimal scheduling scheme is obtained.
Description
Technical field
The present invention relates to power system optimal dispatch fields, more particularly, to a kind of meter and the electric car of V2G function
Optimizing scheduling modeling and algorithm with new energy.
Background technique
As energy shortages and problem of environmental pollution become increasingly conspicuous, clean reproducible energy such as wind energy, solar energy etc. by
Extensive concern.Electric car (Eletric Vehicle, EV) is reducing exhaust emissions, is reducing society to fossil energy dependency degree
Aspect has the irreplaceable advantage of orthodox car.The development of electric car V2G (Vehicle to Grid, V2G) technology makes electricity
Electrical automobile acts not only as load and charges, and can also be used as energy storage device and feeds to power grid, therefore meter and electric car
V2G function, to containing wind, light power generation electric system take effectively scheduling and control strategy, this can not only effectively be stabilized
Area power grid load peak-valley difference can also be brought with reducing the impact that new energy goes out fluctuation to power grid for automobile user
Economic benefit.
The shortcomings that prior art：Under traditional mode, the Optimized Operation containing new energy and electric car does not account for electronic
The V2G function of automobile only accesses power grid using electric car as pure load.However under without the guidance of any incentive measure, vehicle
Chief commander carries out random, disorderly charge and discharge to electric car according to the driving habits of itself.It, should compared with orderly charge and discharge mode
Mode has stronger uncertainty, or even will increase the risk of electric power netting safe running.
Summary of the invention
Present invention aim to address said one or multiple defects, propose the electric car of meter and V2G function a kind of with
The optimizing scheduling of new energy models and algorithm.
To realize the above goal of the invention, the technical solution adopted is that：
It is a kind of meter and V2G function electric car and new energy optimizing scheduling modeling and algorithm, include the following steps：
S1：Receive the following 24 hours workload demand data of system that power grid machine unit scheduling center obtains；Receive wind power plant pair
The prediction data of wind power output size；Receive photovoltaic plant to the prediction data of photovoltaic power output size；Receive the phase of electric car
Close performance data；
S2：Consider the electric car of V2G function and new energy Optimized Operation models and algorithm, building automobile user is filled
Electric cost is minimum and the minimum Optimized Operation multiple objective function of grid side equivalent load variance；
S3：Fuzzy processing is carried out to objective function and is converted into single object optimization function, and Optimized model is dropped
Dimension processing；While considering that charging demand for electric vehicles constraint, charging time constraint, battery constraint, charge continuity constraint
On the basis of, objective function is optimized using crossover algorithm in length and breadth.
Preferably, the minimum objective function of grid side equivalent load variance such as following formula described in step S2：
In formula：T is the when number of segment in dispatching cycle；Nw、NS、NEVRespectively wind-powered electricity generation, photovoltaic plant and electric car
Quantity；For the active power output of j-th of Wind turbines of t period；For the active power output of t period each photovoltaic plant；When for t
The conventional load power of section；For the charge power of t the r electric car of period；For the r electric car of t period
Discharge power；PavFor the equivalent load average value in dispatching cycle.
Preferably, with the minimum objective function of electric car charging cost such as following formula described in step S2：
In formula：For the electricity that vehicle r is obtained in the t period, positive value be that charge, bear be electric discharge；CeIt (t) is the t period point
When electricity price.
Preferably, objective function is carried out Fuzzy processing to be converted into single object optimization function being to avoid described in step S3
Solution to above-mentioned Complex multi-target problem Pareto disaggregation, introduce the concept of blurring, reflects decision with degree of membership size
Then person obscures satisfaction degree method for system optimization target and minimum turn of user's charging cost with maximum to the satisfaction of target value
Turn to single-object problem；Select drop half line type as degree of membership, such as following formula：
In formula：μ(fa) it is equivalent load variance objective function subordinating degree function；μ(fb) it is that electric car charging cost is subordinate to
Spend function；fa2And fb2For maximum acceptable equivalent load variance and charging cost, can be determined according to the result before optimization；fa1With
fb1For the desired value of corresponding objective function, can be determined by simple target extreme value of a function；
According to the minimax rule of fuzzy set theory, if λ is satisfaction, for the minimum value of all subordinating degree functions.It can
It is converted into the maximization problems for meeting institute's Prescribed Properties, i.e. max λ problem, mathematical description is as follows：
By above formula, objective function, which is converted into, seeks max λ, converts the extreme value solution of certainty objective function to
The Satisfaction index of ambiguity function solves.
Preferably, Optimized model dimension-reduction treatment described in step S3 includes the following steps：
S3.1：The electric car quantity that is reached using each time interval of Monte Carlo simulation (totally 24), each is electronic
The charge requirement of automobile, estimated stay time and respective charging urgency index value；
S3.2：Calculate the total charge requirement electricity of electric car that day part reaches；If the electric car of same period fills
A length of integer time interval when electric, it is therefore an objective to keep the stability of charge power as far as possible；According to total charge requirement and in advance
Meter stay time calculates this period electric car average charge power bound；
S3.3：By two above step, particle is set as 24 dimensions；The definition of dimension is：Assuming that d dimension represents d-th
The parameter for the electric car that period reaches, specific value represent the average charge-discharge electric power of the electric car of the period, expire
The corresponding bound constraint of foot；
S3.4：Charge power value is established according to the every dimension of particle.
Preferably, the constraint of charging demand for electric vehicles described in step S3 be electric car should meet user use vehicle substantially
State-of-charge (state of charge, SOC) when demand, i.e. electric car stop charging will reach user and require, and n-th
The constraint expression formula of vehicle is as follows：
In formula：At the time of accessing charging station for n-th electric car；Charging station is left for n-th electric car
Moment；For the charge efficiency of n-th electric car, value is between 0~1；Enter for n-th electric car and fills
The state of battery when power station；Battery status when charging station is left for n-th electric car；For n-th electric car
The total electricity of battery.
Preferably, the constraint of charging time described in step S3 refers to that the charge and discharge time of electric car should reach charging station
It is completed among the hair period with drawing up next time, i.e.,：
In formula：tactThe moment occurs for charge and discharge behavior；TarriveThe charging station moment is reached for electric car；TstayIt is electronic
The estimated stay time of automobile；Tch_dchThe charge and discharge time needed for electric car.
Preferably, batteries of electric automobile described in step S3 is constrained to the damage reduced to battery, need to be to electric car
Charge-discharge electric power is constrained；Wherein following formula respectively indicates the practical charge-discharge electric power of electric car and electric car charged shape in real time
Modal constraint；
SOCmin≤SOC≤SOCmax
In formula：For n-th maximum allowable charge-discharge electric power of electric car；SOCminThe minimum allowed for electric car
SOC value；SOCmaxThe maximum SOC value allowed for electric car pond.
Preferably, the charging of electric car described in step S3 continuity constraint is the service life for guaranteeing batteries of electric automobile,
Need to guarantee that the charge and discharge behavior of battery is disposably completed；It is located at period tiThe charged state of jth electric car, ti=0 indicates
It is uncharged, ti=1 indicates charging：
In formula：Indicate the initiation of charge moment of jth electric car.
Preferably, objective function is optimized including following step using crossover algorithm in length and breadth described in step S3
Suddenly：
S4.1：Input system initial data, including each period load, wind-power electricity generation, photovoltaic power generation predicted mean vote,
The degree of membership parameter of honourable load and the subordinating degree function of objective function；
S4.2：The aggregate demand electricity for determining the electric car that each period reaches, according to the stop of the period electric car
Duration determines the electric car minimax average charge power of the period；
S4.3：Initialization population：
ElementThe automotive average charge-discharge electric power of charging station is reached for t-th of period；ElementFor t-th period
Load initial value；ElementFor NsOutput of a photovoltaic plant in period t is active；ElementFor NwA wind power plant when
The active power of section t；The active output power and payload of wind light generation should be in corresponding [Pw1,Pw4] the interior initialization of range；
S4.4：Set the number of iterations k=1；
S4.5：Calculate the degree of membership and each wind power plant, photovoltaic plant of the corresponding equivalent load variance of each parent particle
And load is in the degree of membership of each period, and finds out Satisfaction index λ；
S4.6：Calculate pbest, gbest；
S4.7：If k > kmax, calculating terminates；Otherwise, S4.8 is entered step；
S4.8：The lateral cross for executing CSO algorithm generates filial generation and is stored in golden mean of the Confucian school dematrix MShcIn；Using competition operator
It obtains lateral cross and is dominant and solution and save to DShcIn；If paying attention to, filial generation is more than that upper lower limit value is replaced with boundary value；
S4.9：To DShcOperation is normalized, and carries out crossed longitudinally generation filial generation, then saves to golden mean of the Confucian school dematrix
MSvcIn；Crossed longitudinally be dominant is obtained using competition operator solution and to save to DSvcIn；
S4.10：K=k+1 is enabled, S4.5 is gone to step.
Compared with prior art, the beneficial effects of the invention are as follows：
1) present invention constructs the electric car of meter and V2G function, wind-light power generation electric system Multiobjective Optimal Operation
Model is obtained with grid side equivalent load variance minimum and the minimum target of automobile user electric cost using CSO algorithm
Optimal scheduling scheme；
2) wind light generation curve and load curve are obtained according to grid dispatching center first, then with maximum fuzzy satisfaction
Multi-objective problem is converted single-objective problem by degree method, and the optimal charge power of electric car is determined finally by CSO algorithm, into
And obtain optimal scheduling strategy；
3) Optimized model control variable is come using the dimension of average charge power as the particle of day part electric car, avoided
Directly each electric car is modeled, which significantly reduces the dimension of Optimized model, reduces the solution time
And increase the convergence rate solved, " dimension calamity " or local optimum problem are avoided, with good practicability and efficiently
Property.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is the average value of day part scene power output and load prediction；
Fig. 3 is drop half line type subordinating degree function.
Specific embodiment
The attached figures are only used for illustrative purposes and cannot be understood as limitating the patent；
Below in conjunction with drawings and examples, the present invention is further elaborated.
Embodiment 1
It is a kind of meter and V2G function electric car and new energy optimizing scheduling modeling and algorithm, referring to FIG. 1, including
Following steps：
S1：Receive the following 24 hours workload demand data of system that power grid machine unit scheduling center obtains；Receive wind power plant pair
The prediction data of wind power output size and receive photovoltaic plant to photovoltaic power output size prediction data, as shown in Figure 2 and Table 1；
Receive the correlation properties data of electric car, as shown in table 2；
1 tou power price parameter of table
The battery parameter of 2 electric car of table
S2：Consider the electric car of V2G function and new energy Optimized Operation models and algorithm, building automobile user is filled
Electric cost is minimum and the minimum Optimized Operation multiple objective function of grid side equivalent load variance；
S3：Fuzzy processing is carried out to objective function and is converted into single object optimization function, and Optimized model is dropped
Dimension processing；While considering that charging demand for electric vehicles constraint, charging time constraint, battery constraint, charge continuity constraint
On the basis of, objective function is optimized using crossover algorithm in length and breadth.
Preferably, the minimum objective function of grid side equivalent load variance such as following formula described in step S2：
In formula：T is the when number of segment in dispatching cycle；Nw、NS、NEVRespectively wind-powered electricity generation, photovoltaic plant and electric car
Quantity；For the active power output of j-th of Wind turbines of t period；For the active power output of t period each photovoltaic plant；When for t
The conventional load power of section；For the charge power of t the r electric car of period；For the r electric car of t period
Discharge power；PavFor the equivalent load average value in dispatching cycle.
Preferably, with the minimum objective function of electric car charging cost such as following formula described in step S2：
In formula：For the electricity that vehicle r is obtained in the t period, positive value be that charge, bear be electric discharge；CeIt (t) is the t period point
When electricity price.
Preferably, the processing of objective function described in step S3 turns as shown in figure 3, carrying out Fuzzy processing to objective function
Turning to single object optimization function is to avoid to the solution of above-mentioned Complex multi-target problem Pareto disaggregation, introduces the general of blurring
Read, with degree of membership size come reflect policymaker to the satisfaction of target value, it is then with maximum fuzzy satisfaction degree method that system is excellent
Change target and user's charging cost is minimum is converted into single-object problem；Select drop half line type as degree of membership, such as following formula：
In formula：μ(fa) it is equivalent load variance objective function subordinating degree function；μ(fb) it is that electric car charging cost is subordinate to
Spend function；fa2And fb2For maximum acceptable equivalent load variance and charging cost, can be determined according to the result before optimization；fa1With
fb1For the desired value of corresponding objective function, can be determined by simple target extreme value of a function；
According to the minimax rule of fuzzy set theory, if λ is satisfaction, for the minimum value of all subordinating degree functions.It can
It is converted into the maximization problems for meeting institute's Prescribed Properties, i.e. max λ problem, mathematical description is as follows：
By above formula, objective function, which is converted into, seeks max λ, converts the extreme value solution of certainty objective function to
The Satisfaction index of ambiguity function solves.
Preferably, Optimized model dimension-reduction treatment described in step S3 includes the following steps：
S3.1：The electric car quantity that is reached using each time interval of Monte Carlo simulation (totally 24), each is electronic
The charge requirement of automobile, estimated stay time and respective charging urgency index value；
S3.2：Calculate the total charge requirement electricity of electric car that day part reaches；If the electric car of same period fills
A length of integer time interval when electric, it is therefore an objective to keep the stability of charge power as far as possible；According to total charge requirement and in advance
Meter stay time calculates this period electric car average charge power bound；
S3.3：By two above step, particle is set as 24 dimensions；The definition of dimension is：Assuming that d dimension represents d-th
The parameter for the electric car that period reaches, specific value represent the average charge-discharge electric power of the electric car of the period, expire
The corresponding bound constraint of foot；
S3.4：Charge power value is established according to the every dimension of particle.
Preferably, the constraint of charging demand for electric vehicles described in step S3 be electric car should meet user use vehicle substantially
State-of-charge (state of charge, SOC) when demand, i.e. electric car stop charging will reach user and require, and n-th
The constraint expression formula of vehicle is as follows：
In formula：At the time of accessing charging station for n-th electric car；Charging station is left for n-th electric car
Moment；For the charge efficiency of n-th electric car (value is between 0~1)；Enter for n-th electric car and fills
The state of battery when power station；Battery status when charging station is left for n-th electric car；For n-th electric car
The total electricity of battery.
Preferably, the constraint of charging time described in step S3 refers to that the charge and discharge time of electric car should reach charging station
It is completed among the hair period with drawing up next time, i.e.,：
In formula：tactThe moment occurs for charge and discharge behavior；TarriveThe charging station moment is reached for electric car；TstayIt is electronic
The estimated stay time of automobile；Tch_dchThe charge and discharge time needed for electric car.
Preferably, batteries of electric automobile described in step S3 is constrained to the damage reduced to battery, need to be to electric car
Charge-discharge electric power is constrained；Wherein following formula respectively indicates the practical charge-discharge electric power of electric car and electric car charged shape in real time
Modal constraint；
SOCmin≤SOC≤SOCmax
In formula：For n-th maximum allowable charge-discharge electric power of electric car；SOCminThe minimum allowed for electric car
SOC value；SOCmaxThe maximum SOC value allowed for electric car pond.
Preferably, the charging of electric car described in step S3 continuity constraint is the service life for guaranteeing batteries of electric automobile,
Need to guarantee that the charge and discharge behavior of battery is disposably completed；It is located at period tiThe charged state of jth electric car, ti=0 indicates
It is uncharged, ti=1 indicates charging：
In formula：Indicate the initiation of charge moment of jth electric car.
Preferably, objective function is optimized using crossover algorithm in length and breadth described in step S3, design parameter is shown in
Table 3：
Table 3 crossover algorithm parameter and its meaning in length and breadth
Include the following steps：
S4.1：Input system initial data, including each period load, wind-power electricity generation, photovoltaic power generation predicted mean vote,
The degree of membership parameter of honourable load and the subordinating degree function of objective function；
S4.2：The aggregate demand electricity for determining the electric car that each period reaches, according to the stop of the period electric car
Duration determines the electric car minimax average charge power of the period；
S4.3：Initialization population：
ElementThe automotive average charge-discharge electric power of charging station is reached for t-th of period；ElementFor t-th period
Load initial value；ElementFor NsOutput of a photovoltaic plant in period t is active；ElementFor NwA wind power plant when
The active power of section t；The active output power and payload of wind light generation should be in corresponding [Pw1,Pw4] the interior initialization of range；
S4.4：Set the number of iterations k=1；
S4.5：Calculate the degree of membership and each wind power plant, photovoltaic plant of the corresponding equivalent load variance of each parent particle
And load is in the degree of membership of each period, and finds out Satisfaction index λ；
S4.6：Calculate pbest, gbest；
S4.7：If k > kmax, calculating terminates；Otherwise, S4.8 is entered step；
S4.8：The lateral cross for executing CSO algorithm generates filial generation and is stored in golden mean of the Confucian school dematrix MShcIn；Using competition operator
It obtains lateral cross and is dominant and solution and save to DShcIn；If paying attention to, filial generation is more than that upper lower limit value is replaced with boundary value；
S4.9：To DShcOperation is normalized, and carries out crossed longitudinally generation filial generation, then saves to golden mean of the Confucian school dematrix
MSvcIn；Crossed longitudinally be dominant is obtained using competition operator solution and to save to DSvcIn；
S4.10：K=k+1 is enabled, S4.5 is gone to step.
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
Claims (10)
1. it is a kind of meter and V2G function electric car and new energy optimizing scheduling modeling and algorithm, which is characterized in that including with
Lower step：
S1：Receive the following 24 hours workload demand data of system that power grid machine unit scheduling center obtains；Receive wind power plant to wind-powered electricity generation
The prediction data for size of contributing；Receive photovoltaic plant to the prediction data of photovoltaic power output size；The correlation for receiving electric car is special
Property data；
S2：Consider the electric car of V2G function and new energy Optimized Operation models and algorithm, building automobile user is charged to
This minimum Optimized Operation multiple objective function of minimum and grid side equivalent load variance；
S3：Fuzzy processing is carried out to objective function and is converted into single object optimization function, and Optimized model is carried out at dimensionality reduction
Reason；Considering that charging demand for electric vehicles constraint, charging time constrain, battery constrains, the basis of charging continuity constraint simultaneously
On, objective function is optimized using crossover algorithm in length and breadth.
2. the optimizing scheduling modeling and calculation of a kind of meter according to claim 1 and the electric car and new energy of V2G function
Method, which is characterized in that the minimum objective function of grid side equivalent load variance such as following formula described in step S2：
In formula：T is the when number of segment in dispatching cycle；Nw、NS、NEVThe respectively quantity of wind-powered electricity generation, photovoltaic plant and electric car；For the active power output of j-th of Wind turbines of t period；For the active power output of t period each photovoltaic plant；Pt LIt is normal for the t period
Advise load power；For the charge power of t the r electric car of period；For the electric discharge of t the r electric car of period
Power；PavFor the equivalent load average value in dispatching cycle.
3. the optimizing scheduling modeling and calculation of a kind of meter according to claim 1 and the electric car and new energy of V2G function
Method, which is characterized in that with the minimum objective function of electric car charging cost such as following formula described in step S2：
In formula：For the electricity that vehicle r is obtained in the t period, positive value be that charge, bear be electric discharge；CeIt (t) is t period timesharing electricity
Valence.
4. the optimizing scheduling modeling and calculation of a kind of meter according to claim 1 and the electric car and new energy of V2G function
Method, which is characterized in that objective function is carried out Fuzzy processing to be converted into single object optimization function being to avoid pair described in step S3
The solution of above-mentioned Complex multi-target problem Pareto disaggregation, introduce the concept of blurring, reflects policymaker with degree of membership size
To the satisfaction of target value, then with the maximum satisfaction degree method that obscures by system optimization target and the minimum conversion of user's charging cost
For single-object problem；Select drop half line type as degree of membership, such as following formula：
In formula：μ(fa) it is equivalent load variance objective function subordinating degree function；μ(fb) it is electric car charging cost degree of membership letter
Number；fa2And fb2For maximum acceptable equivalent load variance and charging cost, can be determined according to the result before optimization；fa1And fb1For
The desired value of corresponding objective function, can be determined by simple target extreme value of a function；
According to the minimax rule of fuzzy set theory, if λ is satisfaction, for the minimum value of all subordinating degree functions.It can convert
The maximization problems of Prescribed Properties to meet, i.e. max λ problem, mathematical description are as follows：
μ (Load)=min { μ (Load1),μ(Load2),…,μ(LoadT)}
λj=min { μ (fa),μ(fb),μ(WT1),μ(WT2),μ(PV),μ(Load)}
By above formula, objective function, which is converted into, seeks max λ, converts the extreme value solution of certainty objective function to fuzzy
The Satisfaction index of function solves.
5. the optimizing scheduling modeling and calculation of a kind of meter according to claim 1 and the electric car and new energy of V2G function
Method, which is characterized in that Optimized model dimension-reduction treatment described in step S3 includes the following steps：
S3.1：The charging of the electric car quantity, each electric car that are reached using each time interval of Monte Carlo simulation is needed
It asks, expect stay time and respective charging urgency index value；
S3.2：Calculate the total charge requirement electricity of electric car that day part reaches；If when the charging of the electric car of same period
A length of integer time interval, it is therefore an objective to keep the stability of charge power as far as possible；According to total charge requirement and it is expected that stop
Duration is stayed to calculate this period electric car average charge power bound；
S3.3：By two above step, particle is set as 24 dimensions；The definition of dimension is：Assuming that d dimension represents d-th of period
The parameter of the electric car of arrival, specific value represent the average charge-discharge electric power of the electric car of the period, meet phase
The bound constraint answered；
S3.4：Charge power value is established according to the every dimension of particle.
6. the optimizing scheduling modeling and calculation of a kind of meter according to claim 1 and the electric car and new energy of V2G function
Method, which is characterized in that the constraint of charging demand for electric vehicles described in step S3 is that electric car should meet the basic with vehicle need of user
It asks, i.e., state-of-charge when electric car stops charging, state of charge, SOC will reach user's requirement, n-th vehicle
Constraint expression formula is as follows：
In formula：At the time of accessing charging station for n-th electric car；At the time of leaving charging station for n-th electric car；For the charge efficiency of n-th electric car, value is between 0~1；When entering charging station for n-th electric car
The state of battery；Battery status when charging station is left for n-th electric car；For n-th batteries of electric automobile
Total electricity.
7. the optimizing scheduling modeling and calculation of a kind of meter according to claim 1 and the electric car and new energy of V2G function
Method, which is characterized in that the charging time described in step S3 constraint refer to electric car the charge and discharge time should reach charging station with
It draws up next time and is completed among the hair period, i.e.,：
tact∈[Tarrive+1,Tarrive+Tstay-Tch_dch]
In formula：tactThe moment occurs for charge and discharge behavior；TarriveThe charging station moment is reached for electric car；TstayFor electric car
It is expected that stay time；Tch_dchThe charge and discharge time needed for electric car.
8. the optimizing scheduling modeling and calculation of a kind of meter according to claim 1 and the electric car and new energy of V2G function
Method, which is characterized in that batteries of electric automobile described in step S3 is constrained to the damage reduced to battery, need to be to the charge and discharge of electric car
Electrical power is constrained；Wherein following formula respectively indicates the practical charge-discharge electric power of electric car and the real-time state-of-charge of electric car about
Beam；
SOCmin≤SOC≤SOCmax
In formula：For n-th maximum allowable charge-discharge electric power of electric car；SOCminThe minimum SOC value allowed for electric car；
SOCmaxThe maximum SOC value allowed for electric car pond.
9. the optimizing scheduling modeling and calculation of a kind of meter according to claim 1 and the electric car and new energy of V2G function
Method, which is characterized in that the charging continuity constraint of electric car described in step S3 is the service life for guaranteeing batteries of electric automobile, is needed
Guarantee that the charge and discharge behavior of battery is disposably completed；It is located at period tiThe charged state of jth electric car, ti=0 indicates not fill
Electricity, ti=1 indicates charging：
In formula：Indicate the initiation of charge moment of jth electric car.
10. the optimizing scheduling modeling and calculation of a kind of meter according to claim 1 and the electric car and new energy of V2G function
Method, which is characterized in that objective function is optimized using crossover algorithm in length and breadth described in step S3 and is included the following steps：
S4.1：Input system initial data, including each period load, wind-power electricity generation, photovoltaic power generation predicted mean vote, scene
The degree of membership parameter of load and the subordinating degree function of objective function；
S4.2：The aggregate demand electricity for determining the electric car that each period reaches, according to the stay time of the period electric car
Determine the electric car minimax average charge power of the period；
S4.3：Initialization population：
ElementThe automotive average charge-discharge electric power of charging station is reached for t-th of period；ElementFor the load of t-th of period
Initial value；ElementFor NsOutput of a photovoltaic plant in period t is active；ElementFor NwA wind power plant is in period t
Active power；The active output power and payload of wind light generation should be in corresponding [Pw1,Pw4] the interior initialization of range；
S4.4：Set the number of iterations k=1；
S4.5：Calculate the degree of membership and each wind power plant, photovoltaic plant and negative of the corresponding equivalent load variance of each parent particle
Degree of membership of the lotus in each period, and find out Satisfaction index λ；
S4.6：Calculate pbest, gbest；
S4.7：If k > kmax, calculating terminates；Otherwise, S4.8 is entered step；
S4.8：The lateral cross for executing CSO algorithm generates filial generation and is stored in golden mean of the Confucian school dematrix MShcIn；It is obtained using competition operator
Lateral cross, which is dominant, solution and to be saved to DShcIn；If paying attention to, filial generation is more than that upper lower limit value is replaced with boundary value；
S4.9：To DShcOperation is normalized, and carries out crossed longitudinally generation filial generation, then saves to golden mean of the Confucian school dematrix MSvc
In；Crossed longitudinally be dominant is obtained using competition operator solution and to save to DSvcIn；
S4.10：K=k+1 is enabled, S4.5 is gone to step.
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Cited By (5)
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| CN110943475A (en) * | 2019-11-08 | 2020-03-31 | 广东电网有限责任公司 | Distribution network collaborative optimization scheduling method considering distributed photovoltaic and electric automobile |
| CN111311089A (en) * | 2020-02-12 | 2020-06-19 | 深圳供电局有限公司 | Big data statistical method and system of power Internet of things |
| CN113794215A (en) * | 2021-08-20 | 2021-12-14 | 国网电力科学研究院有限公司 | Electric automobile and renewable energy source coordinated optimization method and system |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110943475A (en) * | 2019-11-08 | 2020-03-31 | 广东电网有限责任公司 | Distribution network collaborative optimization scheduling method considering distributed photovoltaic and electric automobile |
| CN111311089A (en) * | 2020-02-12 | 2020-06-19 | 深圳供电局有限公司 | Big data statistical method and system of power Internet of things |
| CN111311089B (en) * | 2020-02-12 | 2023-06-09 | 深圳供电局有限公司 | Big data statistics method and system for electric power Internet of things |
| CN115081777A (en) * | 2021-03-16 | 2022-09-20 | 中国科学院广州能源研究所 | V2G scheduling two-phase stochastic programming method for maximizing operator revenue |
| CN113794215A (en) * | 2021-08-20 | 2021-12-14 | 国网电力科学研究院有限公司 | Electric automobile and renewable energy source coordinated optimization method and system |
| CN116579475A (en) * | 2023-05-08 | 2023-08-11 | 浙江大学 | Electric vehicle charging scheduling and charging station configuration joint optimization method considering charging randomness |
| CN116579475B (en) * | 2023-05-08 | 2024-02-13 | 浙江大学 | Electric vehicle charging scheduling and charging station configuration joint optimization method considering charging randomness |
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