CN107919675A - Consider the charging station load scheduling model of car owner and operator's interests - Google Patents
Consider the charging station load scheduling model of car owner and operator's interests Download PDFInfo
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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
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Abstract
Consider the charging station load scheduling model of car owner and operator's interests, including pattern analysis, establish four object function, structure constraints, model solution and optimizing steps.If tstartTo dispatch start time, tendFor the finishing scheduling moment, within the period：(1) pattern analysis；(2) object function is established；(3) constraints is built；(4) model solution and optimizing.The model belongs to double-goal optimal model, is solved to obtain one group of non-domination solution using NSGA II algorithms, then filter out the solution of compromise by fuzzy expert theory.The present invention is using car owner and operator's interests as optimization aim, consider power-balance, charging coverage rate and the constraint of electricity price situation, so as to establish electric automobile charging schedule model, trip requirements, the charge requirement of collection charging station regenerative resource output situation, echelon battery energy storage situation and car owner in real time, then scheduling result is obtained by NSGA II algorithms and fuzzy expert theory solving model.
Description
Technical field
The present invention relates to electric automobile charging station scheduling field, more particularly to consider car owner and operator's interests
Charging station load scheduling model.
Background technology
Under the background that environmental protection is becoming tight day with traditional energy supply, China greatly develops regenerative resource and electronic vapour
Car, supporting electrically-charging equipment also begin to rise.But the whether effective rate of utilization of regenerative resource or electric automobile charging
The operation and development stood are all not fully up to expectations.Trace it to its cause in addition to Facilities Construction and relevant policies factor, scheduling model
And vital one side.Since huge bear can be caused when electric automobile accesses grid charging to power transmission network, power distribution network
Face is rung, so the main target at scheduling model initial stage is the harmful effect weakened when electric automobile networks；With the depth of research
Enter, people start electric automobile being regarded as energy storage device even energy source, and scheduling model starts to enter hand adjustment from market etc.
Car owner's charging behavior is spent, reaches and power grid peak load shifting etc. is acted on.Related data shows that current most of scheduling models base oneself upon
Point is the stabilization of power grids and optimization, and department pattern considers car owner's interests, but exists and pay little attention to and consider incomplete problem, these
Problem causes the real response of car owner not reach anticipation target.Meanwhile with the development of electric automobile industry, echelon battery problems
Start the concern for causing masses.Referred to as echelon battery when battery capacity decays to the 80% of initial capacity.According to Chinese energy storage
The data of press center are netted, China's batteries of electric automobile generally became echelon battery in 3~5 years.And expect 2019, the latest
Not over the year two thousand twenty, China can have more than the echelon battery scale of 10GWh.And China since in recent years just carry out related skill
The theoretical research of art and demonstration project, paces are more relatively slow, and commercial operations on a large scale do not start really also.Existing scheduling
Model infirmities：
The foothold of current most of scheduling models is the stabilization of power grids and optimization, and department pattern considers car owner's interests, but deposits
Paying little attention to and considering incomplete problem, these problems cause the real response of car owner not reach the state of anticipation.In addition,
Current scheduling model ignores effect of the increasingly huge echelon battery of capacity in electric automobile charging schedule.Therefore, have
Necessity proposes a kind of electric automobile charging station scheduling model to solve the above problems.
The content of the invention
The object of the present invention is to provide a kind of new charging station scheduling model.For current scheduling model there are the problem of,
This model synthetically considers the interests of car owner and operator, at the same by the stand-by period of car owner, Trip Costs, battery loss and
Charging expense is taken into account, establishes based on regenerative resource, and echelon battery uses load scheduling mode for energy-storage units
Charging station scheduling model.
The present invention is that technical solution is used by solving its technical problem：
Consider the charging station load scheduling model of car owner and operator's interests, including pattern analysis, establish target letter
Four number, structure constraints, model solution and optimizing steps；
If tstartTo dispatch start time, tendFor the finishing scheduling moment, within the period：
(1) pattern analysis：
1. car owner waits：
Car owner's expense：
In formula, A represents the stand-by period cost of car owner, and B represents the battery depreciable cost of car owner, and E represents the charging of car owner
Expense, W are the stand-by period cost in the unit time, and Ts is the stand-by period of the s electric automobile, and N is mutually should situation
Automobile quantity, depreciable costs of the Z for battery per charge and discharge kilowatt-hour, m1(t)For the price that charges, p1.s(t)For in this case the s electricity
The electric energy that electrical automobile needs；
Operator loses：
Assuming that the vehicle waited may finally all be charged, that is, the vehicle number waited is equal to the vehicle number of charging, fortune
The electricity of battalion's business's production is all sold, loss zero；
F1.station(t)=0；
In formula：M is unit cost of electricity-generating；
2. car owner leaves, charging station is separately sought：
Car owner's expense：
In formula：C represents the Trip Costs that car owner returns from charging station, SsFor the traveling distance of the s electric automobile, V is
The travel speed of the s electric automobile, m2(t)For the price that charges, P2.s(t)For the electricity that in this case the s electric automobile needs
Energy；
Operator loses：
3. car owner leaves, using engine when driving：
Car owner's expense：
In formula：D represents the fuel cost of car owner, P3.s(t)The electric energy needed for electric automobile, η convert the fuel into for automobile
For and other effects electric energy when transformation efficiency, qgasFor the fuel value of fuel, mgasFor fuel price, m3(t)For the price that charges；；
Operator loses：
(2) object function is established：
1. operator's loss is minimum：
2. car owner's expense is minimum：
(3) constraints is built：
1. power constraint：
Pg=P1+Pb；
In formula, PgFor the output of regenerative resource, P1For the electric automobile power demand after scheduling, PbFor echelon battery
The electricity of storage, positive number represent charging, negative number representation electric discharge；
2. charge coverage rate：
In formula, DafterFor the electric automobile of response scheduling, D is total electric automobile, and m is constant, which represents scheduling
Charging station afterwards must is fulfilled for a certain number of electric automobile charge requirements；
3. electricity tariff constraint：
mmin≤m(t)≤mmax；
In formula, mminTo ensure the minimum electricity price of the loss of charging station operator, mmaxThe highest electricity that can be received for car owner
Valency.
(4) model solution and optimizing：The model belongs to double-goal optimal model, using NSGA II algorithms solve
To one group of non-domination solution, then the solution of compromise is filtered out by fuzzy expert theory, its detailed step is as follows：
1. gene code：
Characteristic quantity according to that can obtain model above is charging station output N, including renewable energy power generation and echelon electricity
Pond energy storage, electric automobile charge requirement L and scheduling beginning and ending time T, so characteristic quantity is encoded using floating-point encoding method
Obtain coding expression；
X=[N, L, T]；
2. fitness function：
F=α Fstation+βFowner；
In formula, α, β are respectively loss and the weight coefficient of car owner's cost function of operator, and characterization designer considers to stress
The difference of point.Assuming that α=0.4, β=0.6, the fitness f of individual in population i is tried to achieve using the formulai；
3. select function：
Selection roulette rule is compared parent individuality, it is assumed that colony's number is n, then the probability that individual i is selected
For：
4. solve and preferred：
Fuzzy expert theory is by the way that expertise and fuzzy mathematics are combined, and the concept of membership function is incorporated into specially
In the knowledge fuzzy representation of family's system, the corresponding scheme of optimal membership function value is selected by the quick fuzzy reasoning of expert, its
Theoretical key is the framework of membership function and expert knowledge library, and it is as follows to define membership function herein：
Appropriate u is selected to choose maximum herein for most compromise solution as most compromise solution according to expertise；
The specific algorithm flow of model solution is as follows in step (4)：
1. the parameter value used in initialization program, including population number N, maximum iteration G, gene g, variation because
Sub- c；
2. according to initialization data, by using degree function, fitness f is tried to achievei；
3. sorted using non-dominant property ordering strategy to population at individual memory；
4. calculate the crowding distance of each level in non-dominant property ranking results；
5. picking out N/2 advantages individual from current population by roulette wheel selection is used as parent population；
6. the parent population picked out carries out heredity, mutation operation, N/2 progeny population is produced；
7. calculate the fitness of each progeny population；
8. N/2 progeny population and current population are merged, carry out non-dominant property sequence and calculate crowding distance；
9. picking out N number of advantage individual by the use of hierarchical clustering algorithm is used as population of future generation；
10. judge end condition：If iterations reaches maximum evolutionary generation, enter stepOtherwise step is entered
Suddenly 5. circulate；
Most compromise solution is picked out according to fuzzy expert theory.
Beneficial effect：
The present invention considers the interests of car owner and operator, by influence car owner's interests each side factor (stand-by period,
Trip Costs, battery loss and charging expense) take into account, so the present invention can more effectively dispatch car owner's charging row is,
Realize the purpose that electric automobile tracking regenerative resource is contributed, improve renewable energy utilization rate, realize car owner and operator
Two-win.
Brief description of the drawings
Fig. 1 is that the flow of the charging station load scheduling model proposed by the present invention for considering car owner and operator's interests is shown
It is intended to；
Fig. 2 is fuzzy expert theoretical diagram；
Fig. 3 is the algorithm flow chart of the model.
Embodiment
In order to make the technical means, the creative features, the aims and the efficiencies achieved by the present invention easy to understand, tie below
Diagram and specific embodiment are closed, the present invention is further explained.
As shown in Figure 1, Figure 2, Figure 3 shows, the charging station load tune proposed by the present invention for considering car owner and operator's interests
Degree model includes pattern analysis, establishes four object function, structure constraints, model solution and optimizing steps；
If tstartTo dispatch start time, tendFor the finishing scheduling moment, within the period：
(1) pattern analysis：
1. car owner waits：
Car owner's expense：
In formula, A represents the stand-by period cost of car owner, and B represents the battery depreciable cost of car owner, and E represents the charging of car owner
Expense, W are the stand-by period cost in the unit time, and Ts is the stand-by period of the s electric automobile, and N is mutually should situation
Automobile quantity, depreciable costs of the Z for battery per charge and discharge kilowatt-hour, m1(t)For the price that charges, p1.s(t)For in this case the s electricity
The electric energy that electrical automobile needs；
Operator loses：
Assuming that the vehicle waited may finally all be charged, that is, the vehicle number waited is equal to the vehicle number of charging, fortune
The electricity of battalion's business's production is all sold, loss zero；
F1.station(t)=0；
In formula：M is unit cost of electricity-generating；
2. car owner leaves, charging station is separately sought：
Car owner's expense：
In formula：C represents the Trip Costs that car owner returns from charging station, SsFor the traveling distance of the s electric automobile, V is
The travel speed of the s electric automobile, m2(t)For the price that charges, P2.s(t)For the electricity that in this case the s electric automobile needs
Energy；
Operator loses：
3. car owner leaves, using engine when driving：
Car owner's expense：
In formula：D represents the fuel cost of car owner, P3.s(t)The electric energy needed for electric automobile, η convert the fuel into for automobile
For and other effects electric energy when transformation efficiency, qgasFor the fuel value of fuel, mgasFor fuel price, m3(t)For the price that charges；；
Operator loses：
(2) object function is established：
1. operator's loss is minimum：
2. car owner's expense is minimum：
(3) constraints is built：
1. power constraint：
Pg=P1+Pb；
In formula, PgFor the output of regenerative resource, P1For the electric automobile power demand after scheduling, PbFor echelon battery
The electricity of storage, positive number represent charging, negative number representation electric discharge；
2. charge coverage rate：
In formula, DafterFor the electric automobile of response scheduling, D is total electric automobile, and m is constant, which represents scheduling
Charging station afterwards must is fulfilled for a certain number of electric automobile charge requirements；
3. electricity tariff constraint：
mmin≤m(t)≤mmax；
In formula, mminTo ensure the minimum electricity price of the loss of charging station operator, mmaxThe highest electricity that can be received for car owner
Valency.
(4) model solution and optimizing：The model belongs to double-goal optimal model, using NSGA II algorithms solve
To one group of non-domination solution, then the solution of compromise is filtered out by fuzzy expert theory, its detailed step is as follows：
1. gene code：
Characteristic quantity according to that can obtain model above is charging station output N, including renewable energy power generation and echelon electricity
Pond energy storage, electric automobile charge requirement L and scheduling beginning and ending time T, so characteristic quantity is encoded using floating-point encoding method
Obtain coding expression；
X=[N, L, T]；
2. fitness function：
F=α Fstation+βFowner；
In formula, α, β are respectively loss and the weight coefficient of car owner's cost function of operator, and characterization designer considers to stress
The difference of point.Assuming that α=0.4, β=0.6, the fitness f of individual in population i is tried to achieve using the formulai；
3. select function：
Selection roulette rule is compared parent individuality, it is assumed that colony's number is n, then the probability that individual i is selected
For：
4. solve and preferred：
Fuzzy expert theory is by the way that expertise and fuzzy mathematics are combined, and the concept of membership function is incorporated into specially
In the knowledge fuzzy representation of family's system, the corresponding scheme of optimal membership function value is selected by the quick fuzzy reasoning of expert, its
Theoretical key is the framework of membership function and expert knowledge library, and it is as follows to define membership function herein：
Appropriate u is selected to choose maximum herein for most compromise solution as most compromise solution according to expertise.
The specific algorithm flow of model solution is as follows in step (4)：
1. the parameter value used in initialization program, including population number N, maximum iteration G, gene g, variation because
Sub- c；
2. according to initialization data, by using degree function, fitness f is tried to achievei；
3. sorted using non-dominant property ordering strategy to population at individual memory；
4. calculate the crowding distance of each level in non-dominant property ranking results；
5. picking out N/2 advantages individual from current population by roulette wheel selection is used as parent population；
6. the parent population picked out carries out heredity, mutation operation, N/2 progeny population is produced；
7. calculate the fitness of each progeny population；
8. N/2 progeny population and current population are merged, carry out non-dominant property sequence and calculate crowding distance；
9. picking out N number of advantage individual by the use of hierarchical clustering algorithm is used as population of future generation；
10. judge end condition：If iterations reaches maximum evolutionary generation, enter stepOtherwise step is entered
Suddenly 5. circulate；
Most compromise solution is picked out according to fuzzy expert theory.
Embodiment of above only technical concepts and features to illustrate the invention, its object is to allow those skilled in the art
Member understands present disclosure and is carried out, and it is not intended to limit the scope of the present invention, all spiritual according to the present invention
The equivalent change or modification that essence is done, should all cover within the scope of the present invention.
Claims (2)
1. consider the charging station load scheduling model of car owner and operator's interests, including pattern analysis, establish object function,
Build four constraints, model solution and optimizing steps, it is characterised in that：
If tstartTo dispatch start time, tendFor the finishing scheduling moment, within the period：
(1) pattern analysis：
1. car owner waits：
Car owner's expense：
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In formula, A represents the stand-by period cost of car owner, and B represents the battery depreciable cost of car owner, and E represents the charging expense of car owner,
W be the unit time in stand-by period cost, TsFor the stand-by period of the s electric automobile, N be mutually should situation automobile number
Amount, depreciable costs of the Z for battery per charge and discharge kilowatt-hour, m1(t)For the price that charges, p1.s(t)For in this case the s electric automobile
The electric energy needed；
Operator loses：
Assuming that the vehicle waited may finally all be charged, that is, the vehicle number waited is equal to the vehicle number of charging, operator
The electricity of production is all sold, loss zero；
F1.station(t)=0；
In formula：M is unit cost of electricity-generating；
2. car owner leaves, charging station is separately sought：
Car owner's expense：
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</msub>
<msub>
<mi>p</mi>
<mrow>
<mn>2.</mn>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
In formula：C represents the Trip Costs that car owner returns from charging station, SsFor the traveling distance of the s electric automobile, V is the s
The travel speed of electric automobile, m2(t)For the price that charges, P2.s(t)For the electric energy that in this case the s electric automobile needs；
Operator loses：
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mn>2.</mn>
<mi>s</mi>
<mi>t</mi>
<mi>a</mi>
<mi>h</mi>
<mi>o</mi>
<mi>n</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>s</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<msub>
<mi>t</mi>
<mrow>
<mi>s</mi>
<mi>t</mi>
<mi>a</mi>
<mi>r</mi>
<mi>t</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>e</mi>
<mi>n</mi>
<mi>d</mi>
</mrow>
</msub>
</munderover>
<mrow>
<mo>(</mo>
<msub>
<mi>m</mi>
<mrow>
<mn>2</mn>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</msub>
<msub>
<mi>p</mi>
<mrow>
<mn>2.</mn>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
3. car owner leaves, using engine when driving：
Car owner's expense：
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mn>3.</mn>
<mi>o</mi>
<mi>w</mi>
<mi>n</mi>
<mi>e</mi>
<mi>r</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</msub>
<mo>=</mo>
<mi>C</mi>
<mo>+</mo>
<mi>B</mi>
<mo>+</mo>
<mi>D</mi>
<mo>+</mo>
<mi>E</mi>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>s</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<msub>
<mi>t</mi>
<mrow>
<mi>s</mi>
<mi>t</mi>
<mi>a</mi>
<mi>r</mi>
<mi>t</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>e</mi>
<mi>n</mi>
<mi>d</mi>
</mrow>
</msub>
</munderover>
<mrow>
<mo>(</mo>
<mi>w</mi>
<mfrac>
<msub>
<mi>S</mi>
<mi>s</mi>
</msub>
<msub>
<mi>V</mi>
<mi>s</mi>
</msub>
</mfrac>
<mo>+</mo>
<msub>
<mi>zP</mi>
<mrow>
<mn>3.</mn>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</msub>
<mo>+</mo>
<mfrac>
<msub>
<mi>P</mi>
<mrow>
<mn>3.</mn>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</msub>
<mrow>
<msub>
<mi>&eta;q</mi>
<mrow>
<mi>g</mi>
<mi>a</mi>
<mi>s</mi>
</mrow>
</msub>
</mrow>
</mfrac>
<msub>
<mi>m</mi>
<mrow>
<mi>g</mi>
<mi>a</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>m</mi>
<mrow>
<mn>3</mn>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</msub>
<msub>
<mi>p</mi>
<mrow>
<mn>3.</mn>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
In formula：D represents the fuel cost of car owner, P3.s(t)For electric automobile need electric energy, η be automobile convert the fuel into for etc.
Transformation efficiency during effect electric energy, qgasFor the fuel value of fuel, mgasFor fuel price, m3(t)For the price that charges；；
Operator loses：
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mn>3.</mn>
<mi>s</mi>
<mi>t</mi>
<mi>a</mi>
<mi>t</mi>
<mi>i</mi>
<mi>o</mi>
<mi>n</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>s</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<msub>
<mi>t</mi>
<mrow>
<mi>s</mi>
<mi>t</mi>
<mi>a</mi>
<mi>r</mi>
<mi>t</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>e</mi>
<mi>n</mi>
<mi>d</mi>
</mrow>
</msub>
</munderover>
<mrow>
<mo>(</mo>
<msub>
<mi>m</mi>
<mrow>
<mn>3</mn>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</msub>
<msub>
<mi>p</mi>
<mrow>
<mn>3.</mn>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
(2) object function is established：
1. operator's loss is minimum：
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mi>s</mi>
<mi>t</mi>
<mi>a</mi>
<mi>t</mi>
<mi>i</mi>
<mi>o</mi>
<mi>n</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>3</mn>
</munderover>
<msub>
<mi>F</mi>
<mrow>
<mi>j</mi>
<mo>&CenterDot;</mo>
<mi>s</mi>
<mi>t</mi>
<mi>a</mi>
<mi>t</mi>
<mi>i</mi>
<mi>o</mi>
<mi>n</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</msub>
<mo>;</mo>
</mrow>
2. car owner's expense is minimum：
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mi>o</mi>
<mi>w</mi>
<mi>n</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>3</mn>
</munderover>
<msub>
<mi>F</mi>
<mrow>
<mi>j</mi>
<mo>&CenterDot;</mo>
<mi>o</mi>
<mi>w</mi>
<mi>n</mi>
<mi>e</mi>
<mi>r</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</msub>
<mo>;</mo>
</mrow>
(3) constraints is built：
1. power constraint：
Pg=P1+Pb；
In formula, PgFor the output of regenerative resource, P1For the electric automobile power demand after scheduling, PbFor echelon battery storage
Electricity, positive number represent charging, negative number representation electric discharge；
2. charge coverage rate：
<mrow>
<mi>m</mi>
<mo>&le;</mo>
<mfrac>
<msub>
<mi>D</mi>
<mrow>
<mi>a</mi>
<mi>f</mi>
<mi>t</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mi>D</mi>
</mfrac>
<mo>;</mo>
</mrow>
In formula, DafterFor the electric automobile of response scheduling, D is total electric automobile, and m is constant, which represents after scheduling
Charging station must is fulfilled for a certain number of electric automobile charge requirements；
3. electricity tariff constraint：
mmin≤m(t)≤mmax；
In formula, mminTo ensure the minimum electricity price of the loss of charging station operator, mmaxThe highest electricity price that can be received for car owner.
(4) model solution and optimizing：The model belongs to double-goal optimal model, is solved to obtain one group using NSGAII algorithms
Non-domination solution, then the solution of compromise is filtered out by fuzzy expert theory, its detailed step is as follows：
1. gene code：
Characteristic quantity according to that can obtain model above is charging station output N, including renewable energy power generation and the storage of echelon battery
Can, electric automobile charge requirement L and scheduling beginning and ending time T, so characteristic quantity is encoded to obtain using floating-point encoding method
Coding expression；
X=[N, L, T]；
2. fitness function：
F=α Fstation+βFowner；
In formula, α, β are respectively loss and the weight coefficient of car owner's cost function of operator, and characterization designer considers emphasis
It is different.Assuming that α=0.4, β=0.6, the fitness f of individual in population i is tried to achieve using the formulai；
3. select function：
Selection roulette rule is compared parent individuality, it is assumed that colony's number is n, then the probability that individual i is selected is：
<mrow>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>f</mi>
<mi>i</mi>
</msub>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<msub>
<mi>f</mi>
<mi>i</mi>
</msub>
</mrow>
</mfrac>
<mo>;</mo>
</mrow>
4. solve and preferred：
Fuzzy expert theory is by the way that expertise and fuzzy mathematics are combined, and the concept of membership function is incorporated into expert system
In the knowledge fuzzy representation of system, the corresponding scheme of optimal membership function value is selected by the quick fuzzy reasoning of expert, it is theoretical
Key be the framework of membership function and expert knowledge library, it is as follows to define membership function herein：
<mrow>
<msub>
<mi>u</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mn>1</mn>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>f</mi>
<mi>i</mi>
</msub>
<mo><</mo>
<msubsup>
<mi>f</mi>
<mi>i</mi>
<mi>min</mi>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mfrac>
<mrow>
<msubsup>
<mi>f</mi>
<mi>i</mi>
<mi>max</mi>
</msubsup>
<mo>-</mo>
<mi>f</mi>
</mrow>
<mrow>
<msubsup>
<mi>f</mi>
<mi>i</mi>
<mi>max</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>f</mi>
<mi>i</mi>
<mi>min</mi>
</msubsup>
</mrow>
</mfrac>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<msubsup>
<mi>f</mi>
<mi>i</mi>
<mi>min</mi>
</msubsup>
<mo>&le;</mo>
<msub>
<mi>f</mi>
<mi>i</mi>
</msub>
<mo>&le;</mo>
<msubsup>
<mi>f</mi>
<mi>i</mi>
<mi>max</mi>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mn>0</mn>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>f</mi>
<mo>></mo>
<msubsup>
<mi>f</mi>
<mi>i</mi>
<mi>max</mi>
</msubsup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>;</mo>
</mrow>
Appropriate u is selected to choose maximum herein for most compromise solution as most compromise solution according to expertise.
2. the charging station load scheduling model according to claim 1 for considering car owner and operator's interests, its feature
It is：
The specific algorithm flow of model solution is as follows in step (4)：
1. the parameter value used in initialization program, including population number N, maximum iteration G, gene g, mutagenic factor c；
2. according to initialization data, by using degree function, fitness f is tried to achievei；
3. sorted using non-dominant property ordering strategy to population at individual memory；
4. calculate the crowding distance of each level in non-dominant property ranking results；
5. picking out N/2 advantages individual from current population by roulette wheel selection is used as parent population；
6. the parent population picked out carries out heredity, mutation operation, N/2 progeny population is produced；
7. calculate the fitness of each progeny population；
8. N/2 progeny population and current population are merged, carry out non-dominant property sequence and calculate crowding distance；
9. picking out N number of advantage individual by the use of hierarchical clustering algorithm is used as population of future generation；
10. judge end condition：If iterations reaches maximum evolutionary generation, enter stepOtherwise enter step and 5. follow
Ring；
Most compromise solution is picked out according to fuzzy expert theory.
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| CN111682567A (en) * | 2020-04-30 | 2020-09-18 | 浙江工业大学 | Ordered charging and discharging control method considering user evaluation based on fuzzy control technology |
| CN111697603A (en) * | 2020-05-15 | 2020-09-22 | 西安联创分布式可再生能源研究院有限公司 | Configuration and strategy making method of electrochemical energy storage system based on battery attenuation |
| CN112238782A (en) * | 2020-10-23 | 2021-01-19 | 中国科学技术大学 | Method and device for determining charging strategy of electric vehicle |
| CN113011652A (en) * | 2021-03-18 | 2021-06-22 | 山东交通学院 | Site selection optimization method and system for electric vehicle charging station |
| CN114742284A (en) * | 2022-03-25 | 2022-07-12 | 福建师范大学 | Electric vehicle charging scheduling method oriented to multi-objective optimization |
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| CN114742284A (en) * | 2022-03-25 | 2022-07-12 | 福建师范大学 | Electric vehicle charging scheduling method oriented to multi-objective optimization |
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