CN110365032A - A kind of lotus source optimization control method for considering electric car and participating in wind electricity digestion - Google Patents
A kind of lotus source optimization control method for considering electric car and participating in wind electricity digestion Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H02J3/386—
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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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
- 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
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
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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
- 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
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
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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
- 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
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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Abstract
A kind of lotus source optimization control method for considering electric car and participating in wind electricity digestion provided by the invention, passes through: operation of power networks control parameter in reading area；It the wind-powered electricity generation amount of being obstructed and is obstructed the period in zoning；Obtain electric automobile load operating parameter in region；It is up to the lotus source optimization Controlling model that target building electric car participates in wind electricity digestion with wind electricity digestion amount of being obstructed；Using differential evolution algorithm solving model, electric car after obtaining lotus source optimization scheduling scheme a few days ago.The present invention carries out fining analysis to the wind-powered electricity generation problem of being obstructed, and deeply excavates the adjusting potentiality of electric automobile load, obtains maximum Load Regulation electricity as far as possible under the premise of ensuring power grid security, further promotes the digestion capability of new energy.
Description
Technical field
The invention belongs to electric system lotus source optimization control technology fields more particularly to a kind of consideration electric car to participate in wind
The lotus source optimization control method of electricity consumption.
Background technique
As large-scale wind power accesses power grid, demodulating peak character possessed by wind power output keeps electric network night electric energy abundant,
System loading is generally in trough period at this time, cause source side can only abandonment with maintain power grid lotus source power balance.On the other hand,
Randomness of the electric automobile load with temporal discreteness and spatially, if not guiding electric car reasonably to be charged
Behavior, not only will increase dispatching of power netwoks pressure, also will cause the schedulable wasting of resources of Demand-side.Therefore, for extensive new energy
The power grid that source, new load access, needing to make overall plans considers the optimal control of wind-electricity integration and electric automobile load.
At present be directed to lotus source optimization control problem, domestic and foreign scholars to corresponding demand side load responding overall characteristic
Many researchs have been done, have been summarized as follows:
1) after large-scale wind power integration caused by system power grid influence carry out fining analysis, define system wind-powered electricity generation by
Hinder period and wind-powered electricity generation amount of being obstructed.
2) quantity to the electric car in region and charging habit are analyzed, and determine electric car regulating power.
3) it is directed to electric automobile load characteristic and wind-powered electricity generation operation characteristic, proposes lotus source coordinated control prioritization scheme, establishes electricity
The lotus source optimization Controlling model of electrical automobile participation wind electricity digestion.
Although in conclusion existing lotus source optimization control method comparative maturity, on engineer application still
In the presence of the improved direction of needs: 1) electric automobile load will not only consider to access grid charging, more consider to power grid electric discharge
Situation；2) control of lotus source optimization need to consider normal power supplies in electricity optimization control, give way for electric car to power grid electric discharge.
Therefore, on the basis of above method, propose that a kind of consideration electric car participates in the lotus source optimization control method of wind electricity digestion, energy
Enough consider that the effect of electric car charge and discharge and normal power supplies optimal control to wind electricity digestion, further lifting system consumption are obstructed
Wind-powered electricity generation ability reduces dispatching of power netwoks pressure, provides scientific and rational decision guidance for dispatcher.
Summary of the invention
The object of the present invention is to provide it is a kind of consideration electric car participate in wind electricity digestion lotus source optimization control method,
Wind electricity digestion is obstructed when for solving the problems, such as extensive new-energy grid-connected, provides reference for dispatching of power netwoks.
To achieve the above object, technical solution provided by the invention is, a kind of to consider that electric car participates in wind electricity digestion
Lotus source optimization control method method comprising following steps:
S1: operation of power networks control parameter in reading area；
S2: it the wind-powered electricity generation amount of being obstructed and is obstructed the period in zoning；
S3: electric automobile load operating parameter in region is obtained；
S4: the lotus source optimization control mould that target building electric car participates in wind electricity digestion is up to wind electricity digestion amount of being obstructed
Type；
S5: utilizing differential evolution algorithm solving model, electric car after obtaining lotus source optimization scheduling scheme a few days ago.
Preferably, the S1 the following steps are included:
S101: the network architecture parameters of regional power grid are obtained；
S102: obtain regional power grid in normal power supplies contribute a few days ago plan, wind-powered electricity generation contribute a few days ago predict and system loading day
Preceding predicted value.
Preferably, the S2 is the following steps are included: prediction and system loading predicted value meter a few days ago of being contributed according to wind-powered electricity generation a few days ago
Calculation system equivalent load, the difference between system equivalent load and normal power supplies minimum output are then the wind-powered electricity generation amount of being obstructed.
Preferably, the S3 the following steps are included:
S301: the operating parameters such as electric car quantity in region, charge-discharge electric power and trip rule are obtained；
S302: one day internal loading distribution situation of electric car is calculated.
Preferably, the S4 is the following steps are included: building is up to target building electric car with wind electricity digestion amount of being obstructed
Participate in the lotus source optimization Controlling model of wind electricity digestion.Standardized model are as follows:
In formula: f (X) is objective function；X is indicated by normal power supplies unit output, wind power output and electronic vapour in the region
The decision vector to be optimized of vehicle load structure.
Preferably, the S5 the following steps are included:
S501: setting population scale NP, crossover probability CR, zoom factor F and the number of iterations λ；
S502: random in solution space uniformly to generate M individual, each individual is made of n-dimensional vector, wherein conventional power unit
Active power output, wind-powered electricity generation scheduling power output be continuous variable, electric automobile load charging and discharging state is discrete variable, and is calculated each
The fitness f of individuali,j(0)。
Xi(0)=(xi,1(0),xi,2(0),…,xi,n(0)), i=1,2,3 ..., M (2)
S503: arbitrarily select two individual vector differences and third individual summation to generate new individual in population；
S504: population is intersected, mutation operation；
S505: by being made a variation to previous generation group, crossover operation, population of new generation is generated；
S506: judging whether to reach the number of iterations, evolves if so, terminating, and output optimized individual is exported as optimal solution；
If it is not, then going to S503 continues iteration；
S507: according to calculated result, electric car Optimized Operation scheme a few days ago is formulated.
A kind of lotus source optimization control method for considering electric car and participating in wind electricity digestion provided by the invention, passes through: reading
Operation of power networks control parameter in region；It the wind-powered electricity generation amount of being obstructed and is obstructed the period in zoning；Obtain electric automobile load in region
Operating parameter；It is up to the lotus source optimization control mould that target building electric car participates in wind electricity digestion with wind electricity digestion amount of being obstructed
Type；Using differential evolution algorithm solving model, electric car after obtaining lotus source optimization scheduling scheme a few days ago.The present invention is to wind-powered electricity generation
The problem of being obstructed carries out fining analysis, the adjusting potentiality of electric automobile load is deeply excavated, under the premise of ensuring power grid security
Maximum Load Regulation electricity is obtained as far as possible, further promotes the digestion capability of new energy.
Detailed description of the invention
Below by drawings and examples, technical scheme of the present invention will be described in further detail.
Fig. 1 is a kind of lotus source optimization control method process for considering electric car and participating in wind electricity digestion provided by the invention
Figure.
Fig. 2 is the system lotus source power curve figure after the lotus source optimization control of electric car participation wind electricity digestion.
Specific embodiment
In order to have a clear understanding of technical solution of the present invention, its detailed structure will be set forth in the description that follows.Obviously, originally
The specific execution of inventive embodiments is simultaneously insufficient to be limited to the specific details that those skilled in the art is familiar with.Preferred reality of the invention
It applies example to be described in detail as follows, in addition to these embodiments of detailed description, can also have other embodiments.
The present invention is described in further details with reference to the accompanying drawings and examples.
Embodiment 1
Fig. 1 is a kind of flow chart of the lotus source optimization control method of consideration electric car participation wind electricity digestion.Comprising:
S1: operation of power networks control parameter in reading area；
S2: it the wind-powered electricity generation amount of being obstructed and is obstructed the period in zoning；
S3: electric automobile load operating parameter in region is obtained；
S4: the lotus source optimization control mould that target building electric car participates in wind electricity digestion is up to wind electricity digestion amount of being obstructed
Type；
S5: utilizing differential evolution algorithm solving model, electric car after obtaining lotus source optimization scheduling scheme a few days ago.
The S1 the following steps are included:
S101: the network architecture parameters of regional power grid are obtained；
S102: obtain regional power grid in normal power supplies contribute a few days ago plan, wind-powered electricity generation contribute a few days ago predict and system loading day
Preceding predicted value.
The S2 the following steps are included:
S201: it calculates wind-powered electricity generation and predicts electricity.
With wind power output prediction curve Pwf(i) based on, integral seeks the wind-powered electricity generation prediction electricity of period t
In formula: t0For initial time period；Δ t is period step-length.
S202: wind-powered electricity generation block time section is defined.
Compare wind-powered electricity generation prediction electricityWith wind-powered electricity generation plan electricityNext day it will meet inequalityT0When a
Section entirety is denoted as wind-powered electricity generation and is obstructed period, remaining is denoted as unimpeded period.
S203: it calculates wind-powered electricity generation and is obstructed electricity.
In each period during wind-powered electricity generation is obstructed, establishes wind-powered electricity generation and be obstructed electricityQuantitative model it is as follows:
The S3 the following steps are included:
S301: electric car quantity N in region is obtainedev, charge-discharge electric power PG2V、PV2GWith the operating parameters such as rule of going on a journey；
S302: one day internal loading distribution situation of electric car is calculated.
The S4 includes:
Building is up to the lotus source optimization control that target building electric car participates in wind electricity digestion with wind electricity digestion amount of being obstructed
Model.Objective function are as follows:
In formula, Δ T is that each duration period, (scale was divided into 96 periods a few days ago, and temporal resolution is
15min, i.e. Δ T=15min)；TdownFor lower peak regulation period number；Increment is dissolved for the wind-powered electricity generation t period.
Wherein constraint condition includes system power Constraints of Equilibrium, wind power output constraint and electric car charge and discharge constraint etc.
1) system power Constraints of Equilibrium
In formula:For normal power supplies the t period original plan active power output；For normal power supplies the t period tune
Power is saved, being positive indicates that unit increases power output, and being negative indicates that unit reduces power output；For system loading the t period value；Charge-discharge electric power of respectively i-th electric car in the t period；Indicate i-th electric car within the t period
Charging and discharging state,Indicate that automobile is in charged state,Indicate that automobile is in discharge condition；NCIt is adjustable in region
With electric car quantity.
2) single electric car charge and discharge constraint
In formula:Respectively electric car charge-discharge electric power maximum value.
3) electric car cluster constrains
0≤NC≤NEV (11)
In formula: NEVFor electric car total amount in region.
4) normal power supplies operation constraint
In formula: (12) are conventional power unit power constraint；It (13) is conventional power unit Climing constant；It (14) is conventional power unit power
Adjust total amount constraint；(15) it is constrained for conventional power unit start-off time constraints.For Pgen,max、Pgen,minFor conventional power unit j active power output
Bound；It is conventional power unit j in the maximum allowed from the t-1 period to the t period, minimum climbing rate；
EG,maxElectricity limitation is adjusted for conventional power unit；Start and stop state variable for conventional power unit j in the t period,Indicate conventional
Unit is in shutdown status,Indicate that conventional power unit is in open state；WithRespectively conventional machine
Booting duration and shutdown duration of the group j in period t.
The S5 the following steps are included:
S501: setting population scale NP, crossover probability CR, zoom factor F and the number of iterations λ；
S502: random in solution space uniformly to generate M individual, each individual is made of n-dimensional vector, wherein conventional power unit
Active power output, wind-powered electricity generation scheduling power output be continuous variable, electric automobile load charging and discharging state is discrete variable, and is calculated each
The fitness f of individuali,j(0)；
S503: in g in iteration, 3 individual X are randomly choosed from populationp1(g)、Xp2(g)、Xp3And p1 (g), ≠
P2 ≠ p3 ≠ i generates variation vector Hi(g)=Xp1(g)+F·(Xp2(g)-Xp3(g))；
S504: being randomly generated the number between one [0,1], if being less than crossover probability CR, then test individual and take hi,j(g)；If
Greater than crossover probability CR, then test individual and take xi,j(g)；
S505: by being made a variation to previous generation group, crossover operation, population of new generation is generated.
S506: judging whether to reach the number of iterations, evolves if so, terminating, and output optimized individual is exported as optimal solution；
If it is not, then going to S503 continues iteration；
S507: according to calculated result, electric car Optimized Operation scheme a few days ago is formulated.
Embodiment 2:
According to the method described above, example 2 verifies a kind of consideration electric car proposed and participates in wind-powered electricity generation using certain power grid as example
The feasibility and validity of the lotus source optimization control method of consumption.
There is the power grid large-scale wind power and a large amount of electric cars to access, and conventional power unit contributes plan, wind-powered electricity generation a few days ago a few days ago
Power output prediction, predicted value is as shown in table 1 a few days ago for system loading, conventional power unit parameter as shown in table 2, electric car parameter such as table 3
It is shown.
1 regional power grid He Yuan of table predicted value a few days ago
| Moment | Conventional power unit is contributed plan/MW a few days ago | Wind-powered electricity generation is contributed prediction/MW a few days ago | System loading predicted value/MW a few days ago |
| 00:00 | 397.68 | 263.80 | 442.3 |
| 02:00 | 390.49 | 335.39 | 408.85 |
| 04:00 | 389.06 | 360.90 | 398.45 |
| 06:00 | 412.18 | 303.66 | 448.35 |
| 08:00 | 455.09 | 168.87 | 550.50 |
| 10:00 | 468.00 | 108.00 | 588.00 |
| 12:00 | 450.47 | 146.33 | 551.85 |
| 14:00 | 417.29 | 120.40 | 516.25 |
| 16:00 | 445.03 | 124.58 | 551.85 |
| 18:00 | 503.84 | 74.82 | 646.85 |
| 20:00 | 486.85 | 82.16 | 621.75 |
| 22:00 | 417.14 | 103.30 | 521.75 |
| 24:00 | 384.22 | 161.07 | 458.60 |
2 conventional power unit parameter (power reference value 100MW) of table
| Unit | Pgenj,min/pu | Pgenj,max/pu | Pgenj,up/pu | Pgenj,down/pu | Tgenj,on/h | Tgenj,off/h |
| 1 | 0.5 | 2.0 | 0.5 | 0.5 | 4 | 3 |
| 2 | 0.25 | 1.0 | 0.3 | 0.3 | 4 | 3 |
| 3 | 0.25 | 0.6 | 0.15 | 0.15 | 3 | 3 |
| 4 | 0.2 | 0.8 | 0.2 | 0.2 | 3 | 2 |
| 5 | 0.1 | 0.4 | 0.15 | 0.15 | 1 | 1 |
| 6 | 0.1 | 0.4 | 0.15 | 0.15 | 1 | 1 |
3 electric car parameter of table
The parameter setting of differential evolution algorithm is as follows: population scale 100, zoom factor 0.6, changes at crossover probability 0.5
Generation number is 500.The lotus source optimization Controlling model that above-mentioned consideration electric car participates in wind electricity digestion is solved, regional power grid wind is obtained
Electricity be obstructed electricity and the period of being obstructed it is as shown in table 4, electric car charge-discharge electric power is as shown in table 5.
4 wind-powered electricity generation of table, which is obstructed, wind-powered electricity generation amount and to be obstructed the period
5 electric car charge-discharge electric power of table
| Period | Discharge regime/MWh | Period | Charging stage/MWh |
| 17:00~18:00 | -42.593 | 00:00~1:00 | 57.95 |
| 18:00~19:00 | -168.15 | 1:00~2:00 | 220.45 |
| 19:00~20:00 | -193.41 | 2:00~3:00 | 286.78 |
| 20:00~21:00 | -118.39 | 3:00~4:00 | 275.81 |
| 4:00~5:00 | 206.46 | ||
| 5:00~6:00 | 97.598 |
The peak regulation stage, the wind-powered electricity generation amount such as table 4 that is obstructed showed under wind-powered electricity generation, and wind-powered electricity generation total amount of being obstructed is 2259.3MWh, was much larger than electronic vapour
Vehicle maximum charge 1260MWh, then electric car enters scheduling controlling mode and charges, to dissolve the wind-powered electricity generation that is obstructed.Solve electricity
Electrical automobile participates in wind electricity digestion lotus source optimization model, obtains maximum wind electricity digestion electricity consumption of being obstructed and is obstructed wind-powered electricity generation 1145.0MWh,
50.67% and the electric car charge and discharge control plan for accounting for wind-powered electricity generation amount of being obstructed are as shown in table 5.As shown in Fig. 2, electric car
The lotus source optimization control method for participating in wind electricity digestion can effectively dissolve the wind-powered electricity generation that is obstructed of lower peak regulation period, while also reduce equivalent
The peak value of load, plays the role of peak load shifting.
Finally it should be noted that: the above embodiments are merely illustrative of the technical scheme of the present invention and are not intended to be limiting thereof, to the greatest extent
Invention is explained in detail referring to above-described embodiment for pipe, and those of ordinary skill in the art still can be to this hair
Bright specific embodiment is modified or replaced equivalently, these without departing from spirit and scope of the invention any modification or
Equivalent replacement is being applied within pending claims.
Claims (6)
1. a kind of lotus source optimization control method for considering electric car and participating in wind electricity digestion, which is characterized in that the lotus source optimization
Control method the following steps are included:
S1: operation of power networks control parameter in reading area；
S2: it the wind-powered electricity generation amount of being obstructed and is obstructed the period in zoning；
S3: electric automobile load operating parameter in region is obtained；
S4: the lotus source optimization Controlling model that target building electric car participates in wind electricity digestion is up to wind electricity digestion amount of being obstructed；
S5: utilizing differential evolution algorithm solving model, electric car after obtaining lotus source optimization scheduling scheme a few days ago.
2. the lotus source optimization control method according to claim 1 for considering electric car and participating in wind electricity digestion, feature exist
In, the S1 the following steps are included:
S101: the network architecture parameters of regional power grid are obtained；
S102: obtain regional power grid in normal power supplies contribute a few days ago plan, wind-powered electricity generation contribute a few days ago predict and system loading it is a few days ago pre-
Measured value.
3. the lotus source optimization control method according to claim 1 for considering electric car and participating in wind electricity digestion, feature exist
In, the S2 the following steps are included:
S201: it calculates wind-powered electricity generation and predicts electricity；
With wind power output prediction curve Pwf(i) based on, integral seeks the wind-powered electricity generation prediction electricity of period t
In formula: t0For initial time period；Δ t is period step-length；
S202: wind-powered electricity generation block time section is defined；
Compare wind-powered electricity generation prediction electricityWith wind-powered electricity generation plan electricityNext day it will meet inequalityT0A period is whole
Body is denoted as wind-powered electricity generation and is obstructed period, remaining is denoted as unimpeded period；
S203: it calculates wind-powered electricity generation and is obstructed electricity；
In each period during wind-powered electricity generation is obstructed, establishes wind-powered electricity generation and be obstructed electricityQuantitative model it is as follows:
4. pair lotus source optimization control method described in claim 1 for considering electric car and participating in wind electricity digestion, which is characterized in that
The S3 the following steps are included:
S301: electric car quantity N in region is obtainedev, charge-discharge electric power PG2V、PV2GWith the operating parameters such as rule of going on a journey；
S302: one day internal loading distribution situation of electric car is calculated.
5. pair lotus source optimization control method described in claim 1 for considering electric car and participating in wind electricity digestion, which is characterized in that
The S4 the following steps are included:
Building is up to the lotus source optimization Controlling model that target building electric car participates in wind electricity digestion with wind electricity digestion amount of being obstructed；
Objective function are as follows:
In formula, Δ T is that each duration period, (scale was divided into 96 periods a few days ago, temporal resolution 15min, i.e.,
Δ T=15min)；TdownFor lower peak regulation period number；Increment is dissolved for the wind-powered electricity generation t period；
Wherein constraint condition includes system power Constraints of Equilibrium, wind power output constraint and electric car charge and discharge constraint etc.
1) system power Constraints of Equilibrium
In formula:For normal power supplies the t period original plan active power output；For normal power supplies the t period adjusting function
Rate, being positive indicates that unit increases power output, and being negative indicates that unit reduces power output；For system loading the t period value；Charge-discharge electric power of respectively i-th electric car in the t period；Indicate i-th electric car within the t period
Charging and discharging state,Indicate that automobile is in charged state,Indicate that automobile is in discharge condition；NCIt is adjustable in region
With electric car quantity；
2) single electric car charge and discharge constraint
In formula:Respectively electric car charge-discharge electric power maximum value；
3) electric car cluster constrains
0≤NC≤NEV (9)
In formula: NEVFor electric car total amount in region；
4) normal power supplies operation constraint
In formula: (12) are conventional power unit power constraint；It (13) is conventional power unit Climing constant；It (14) is conventional power unit power regulation
Total amount constraint；(15) it is constrained for conventional power unit start-off time constraints；For Pgen,max、Pgen,minAbove and below conventional power unit j active power output
Limit；It is conventional power unit j in the maximum allowed from the t-1 period to the t period, minimum climbing rate；EG,maxFor routine
Unit adjusts electricity limitation；S is start and stop state variable of the conventional power unit j in the t period,It indicates that conventional power unit is in shut down
State,Indicate that conventional power unit is in open state；WithRespectively conventional power unit j is held in the booting of period t
Continuous time and shutdown duration.
6. pair lotus source optimization control method described in claim 1 for considering electric car and participating in wind electricity digestion, which is characterized in that
The S5 the following steps are included:
S501: setting population scale NP, crossover probability CR, zoom factor F and the number of iterations λ；
S502: random in solution space uniformly to generate M individual, each individual is made of n-dimensional vector, and wherein conventional power unit has
Function power output, wind-powered electricity generation scheduling power output are continuous variable, and electric automobile load charging and discharging state is discrete variable, and calculates each individual
Fitness fi,j(0)；
S503: in g in iteration, 3 individual X are randomly choosed from populationp1(g)、Xp2(g)、Xp3And p1 ≠ p2 ≠ p3 (g),
≠ i generates variation vector Hi(g)=Xp1(g)+F·(Xp2(g)-Xp3(g))；
S504: being randomly generated the number between one [0,1], if being less than crossover probability CR, then test individual and take hi,j(g)；If more than
Crossover probability CR, then test individual and take xi,j(g)；
S505: by being made a variation to previous generation group, crossover operation, population of new generation is generated；
S506: judging whether to reach the number of iterations, evolves if so, terminating, and output optimized individual is exported as optimal solution；If
It is no, then it goes to S503 and continues iteration；
S507: according to calculated result, electric car Optimized Operation scheme a few days ago is formulated.
Priority Applications (1)
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| CN114725969A (en) * | 2022-04-19 | 2022-07-08 | 华北电力大学 | Electric automobile load aggregation method based on continuous tracking of wind power curve |
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