CN107947178A - A kind of alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm - Google Patents
A kind of alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm Download PDFInfo
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Abstract
A kind of alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm：For including wind-power electricity generation, photovoltaic generation, storage battery, miniature combustion engine, the alternating current-direct current mixing microgrid of fuel cell and diesel-driven generator, establish multiple target, multiple constraint, nonlinear optimization operation mathematical model, object function considers power grid purchases strategies, micro- source fuel cost, environmental benefit cost, network loss and operation expense, and obey microgrid internal power balance, points of common connection transmission capacity, controllable micro- source climbing rate, unit interval accumulator cell charging and discharging bound, storage battery charge state bound, storage battery surrounding time section power-balance and the constant constraints of storage battery charge state；Using cultural gene Algorithm for Solving alternating current-direct current mixing microgrid mathematical model；Verify the correctness and validity of the alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm.The present invention can effectively solve multiple target, the nonlinear optimization objective function of multiple constraint, improve microgrid economic benefit and environmental benefit.
Description
Technical field
The present invention relates to a kind of alternating current-direct current mixing microgrid optimizing operation method.Calculated more particularly to one kind based on cultural gene
The alternating current-direct current mixing microgrid optimizing operation method of method.
Background technology
With traditional energy increasingly depleted, the new generation mode such as wind-power electricity generation, photovoltaic generation is due to its good environment
Benefit is paid close attention to be subject to domestic and foreign scholars.Microgrid is the new supply network for including the devices such as distributed energy, load, energy storage,
Since it possesses complementary the advantages that utilizing for dissolving in time, realizing various forms of micro- sources to new energy, it has also become both at home and abroad
The hot spot of concern.At present, load species is increasingly abundanter, and the power supply reliability of DC load becomes a big academic problem.
Alternating current-direct current mixing microgrid has merged AC load and DC load, it is possible to increase power quality, suitably reduces electric power
The use of electronic device is so as to reduce harmonic pollution.In order to ensure the safe and reliable operation of alternating current-direct current mixing microgrid, domestic foreign minister
Scholar is closed just to study in development and application of the Efforts To Develop for alternating current-direct current mixing microgrid operation control technology.Alternating current-direct current mixing is micro-
The operation of net economic optimization is one of research topic, and with the complicated variation of microgrid, it is better to find optimization performance
Good intelligent search algorithm is crucial.
Currently used intelligent algorithm optimizing works well, but it is slow or do not reach convergence essence to still suffer from some convergence rates
The problems such as degree requires, it is therefore desirable to attempt new intelligent algorithm.There is a kind of evolutional algorithm in intelligent algorithm, calculated similar to heredity
Method, immune algorithm etc..This kind of algorithm is based on the rule of " survival of the fittest " in evolutionism, and the excellent genes in parent are entailed down
A generation.This theory is generalized in the development of culture in relation to scholar, foreign countries are referred to as " cultural volution ".Cultural gene algorithm is just
It is to be based on this thought, algorithm iteration process is divided into local search and global search, and it is a set of individually theoretical right each to have
Individual is screened, so as to accelerate individual to approach optimal value from two aspects, improves algorithmic statement precision.
The content of the invention
The technical problem to be solved by the invention is to provide one kind can effectively solve multidimensional, multiple target, multiple constraint and non-thread
The alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm of the object function of property.
The technical solution adopted in the present invention is：A kind of alternating current-direct current mixing microgrid optimization operation based on cultural gene algorithm
Method, includes the following steps：
1) it is straight for the friendship comprising wind-power electricity generation, photovoltaic generation, storage battery, miniature combustion engine, fuel cell and diesel-driven generator
Stream mixing microgrid, establish multiple target, multiple constraint, it is nonlinear optimization operation mathematical model, object function consider power grid power purchase into
Sheet, micro- source fuel cost, environmental benefit cost, network loss and operation expense, and obey microgrid internal power and balance, is public
Tie point transmission capacity, controllable micro- source climbing rate, unit interval accumulator cell charging and discharging bound, above and below storage battery charge state
Limit, the constraints that storage battery surrounding time section power-balance and storage battery charge state are constant；
2) cultural gene Algorithm for Solving alternating current-direct current mixing microgrid mathematical model is used；
3) correctness and validity of the alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm are verified.
Object function F described in step 1) is as follows：
F=FGrid+Fec+Floss+Fom
FGrid=FACGrid+FDCGrid
Fec=FACec+FDCec
Floss=FACloss+FDCloss
Fom=FACom+FDCom
Wherein, FGridFor power grid purchases strategies；FecFor financial cost；FlossFor network loss；FomFor equipment operation maintenance cost；
FACGridFor exchanging area power grid purchases strategies；FACecFor exchanging area financial cost, by the micro- source fuel cost F in exchanging areaACfuelAnd ring
Border benefit-cost FACenAddition obtains；FAClossFor exchanging area network loss；FAComFor exchanging area equipment operation maintenance cost；FDCGridFor
Exchanging area power grid purchases strategies；FDCecFor the micro- source financial cost in exchanging area, by the micro- source fuel cost F in DC areaDCfuelAnd Environmental Effect
Beneficial cost FDCenAddition obtains；FDClossFor exchanging area network loss；FDComFor exchanging area equipment operation maintenance cost；Wherein,
(1) exchanging area power grid purchases strategies FACGridWith DC area power grid purchases strategies FDCGridRespectively：
Wherein, △ TACPeriod for from exchanging area to power grid power purchase；T is total period in one day；T is day part in one day；It is exchanging area to power grid power purchase electricity；△TDCPeriod for from DC area to power grid power purchase；It is DC area to electricity
Net purchase power consumption；For power grid sale of electricity price in the corresponding period；
(2) exchanging area financial cost FACecWith DC area financial cost FDCec：
FACec=FACfuel+FACen
FDCec=FDCfuel+FDCen
Exchanging area includes wind-power electricity generation, miniature combustion engine and diesel-driven generator, and DC area includes photovoltaic generation, fuel cell and storage
Battery, exchanging area fuel cost FACfuelWith DC area fuel cost FDCfuelIt can be calculated by the following formula：
FACfuel=FMTfuel+FDEGfuel
FDCfuel=FFCfuel
ηFC=-0.0023 × PFC+0.6735
Wherein, FMTfuelFor miniature combustion engine fuel cost；CMTFor miniature combustion engine cooler fuel price；LHV is the low heat value of fuel gas；
PMTFor miniature combustion engine output power；ηMTFor the generating efficiency of miniature combustion engine；FFCfuelFor the fuel cost of fuel cell；CFCFor fuel electricity
The cooler fuel price in pond；PFCFor the output power of fuel cell；ηFCFor the generating efficiency of fuel cell；FDEGfuelFor diesel-driven generator
Fuel cost；PDEGFor the output power of diesel-driven generator；A, b, c are diesel-driven generator power generation coefficient, by diesel engine factory
Family provides；
Exchanging area environmental benefit cost FACenWith DC area environmental benefit cost FDCenIt is calculated by the following formula：
Wherein, n1 is the micro- source number in exchanging area；M is the type of pollutant；αjFor the conversion cost of corresponding pollutant, member/
kg；EFi,jThe unit discharge of the jth kind pollutant produced for i-th of micro- source, kg/kW；PiFor the output work in i-th of micro- source
Rate；n2For the micro- source number in DC area.
(3) network loss F is exchangedAClossWith direct current network loss FDCloss：
Wherein, L1 is exchanging area branch sum；Pk、QkActive power, reactive power for branch k transmission；L2 is DC area
Branch sum；RkFor the resistance of branch k；UkFor the voltage effective value of branch k；
(4) ac operation maintenance cost FAComWith DC operation maintenance cost FDCom：
Wherein, β i are the operation expense coefficient in i-th of micro- source.
The method of weighting is taken, finally obtaining object function is：
MinF=(1- λ) × (FGrid+Fec+Fom)+λFloss
Wherein, λ is Web-based exercise coefficient.
The Web-based exercise coefficient lambda is 0.1.
Constraints described in step 1) includes：
(1) microgrid internal power equilibrium constraint
(2) points of common connection transmission capacity constraints
(3) controllable micro- source climbing rate constraints
(4) unit interval accumulator cell charging and discharging bound constraints
(5) storage battery charge state (State of Charge, SOC) bound constraints
Socmin≤Soct≤Socmax
(6) storage battery surrounding time section power-balance constraint condition
(7) storage battery charge state constraint independent of time condition
Socinitial=Socend
Wherein, N is micro- source number；Represent that t-th of period, i-th of micro- source is contributed；Represent that t-th of period is purchased from power grid
Electrical power；For t-th of period battery power variable quantity, discharge just, to be charged as bearing；T is represented respectively
A period AC and DC area payload；For the net flow power of t-th of period points of common connection；PG,maxFor points of common connection
Transmission capacity limit value；riFor the unit interval climbing rate in i-th of micro- source；For the exhausted of t-th period battery power variable quantity
To value；For the limit value of t-th of period battery power variable quantity；SoctFor the state-of-charge of storage battery in t-th of period；
Socmin、SocmaxThe respectively upper lower limit value of state-of-charge；Uu represents charge and discharge electrostrictive coefficient, is 1 during charging, and when electric discharge is -1；η is
Accumulator cell charging and discharging efficiency, takes 95% here；QESFor battery rating；Socinitial、SocendFor the initial of state-of-charge
Value and end value.
Step 2) includes：
(1) initialize
Initial individuals are produced using random generating mode, the value range of each variable is represented with varmin, varmax, its
In any one variable xmInitial value drawn by following formula：
xm=varminm+rand(0,1)*(varmaxm-varminm)
If M is all individual numbers in colony, then m=1,2 ..., M, MagentFor the number of intelligent body, MpublicTo be common
The number of individual, that is, have：
M=Magent+Mpublic
Ascending order arrangement, preceding M are carried out according to fitness value size to initial individualsagentAs intelligent body, remaining is common
Individual, wherein intelligent body are that fitness value comes preceding M in colonyagentThe individual of position, remaining individual is average individual in colony,
Colony is divided into MagentRegion, average individual belong to the region where each intelligent body, and each intelligent body initial time is possessed
The number of average individual be to be determined by each intelligent body with respect to strength, a-th intelligent body (a=1,2 ..., Magent) it is relatively real
Power is specifically calculated by following formula：
Sa=max { sb}-sa, b=1,2 ..., Magent
saFor the fitness value of a-th of intelligent body；
The strength size of a-th of intelligent body is defined as：
The average individual number that each intelligent body region is assigned to is：
M.Sa=round { Pa×Mpublic}
M.SaThe average individual number possessed by a-th of intelligent body region, so that the intelligent body after initializing
Strength is stronger, and the number for the average individual that region is assigned to is more；
(2) local search, is specifically realized in two steps：
(2.1) polymerization movement
Intelligent body of the average individual gradually to one's respective area in each region is close, and displacement distance Move is obeyed and is uniformly distributed,
Represent as follows：
Move~U (0, β × d)
Wherein, U is to be uniformly distributed symbol；β is polymerizing factor, and β takes 2；D for intelligent body in the same area and average individual it
Between distance；
(2.2) movement is changed
In order to which the speed of the average individual movement in polymerization process is carried out slows down, increase population diversity, to each common
Individual is changed at random, and the average individual number that a-th of intelligent body region is possessed is Ma.public, wherein needing to carry out
The average individual number M of changea,rpublicFor：
Ma.rpublic=round (qa×Ma.public)
Wherein, round functions is round up function, qaFor change rate, 0.3 is taken；
Inside each region after polymerization and changing movement, each individual strength value meeting great changes have taken place, it is necessary to according to
The ideal adaptation angle value rearrangement of each region, the ideal adaptation angle value to make number one is best, is new intelligent body；
(3) global search, including：
(3.1) vie each other
Each iteration is by local search afterwards, it is necessary to calculate total strength value of each intelligent body.Intelligent body in each region
Total strength value by itself strength value and together decide on the strength value of the average individual in region, be calculated by following formula：
T.SaFor total strength of a-th of intelligent body, ξ is a positive number less than 1, represents that average individual is real in the same area
Power accounts for the weight of the total strength of intelligent body, takes 0.1, wnFor the strength value of average individual；
A-th of intelligent body competition probability is expressed as：
Wherein, N.T.SaRepresent relatively total strength value of a-th of intelligent body, be defined as：
M.T.Sa=max { T.Sb}-T.Sa, b=1,2 ... Magent
Thus the competition probability of each intelligent body is calculated, if vector p is：
Introduce and vector p is with the random vector R of dimension, be expressed as：
Wherein, r~U (0,1) represents being uniformly distributed for the element obedience 0 to 1 in R；
Definition vector V is the difference of vector p and vector R：
V=p-R
The corresponding intelligent body of maximum element finally obtains the average individual competed in vectorial V；
(3.2) cooperate
Increase cooperates operation after intelligent body is vied each other, when the distance between the intelligent body in two regions is less than
During cooperation distance D, all average individuals in two intelligent bodies in the small intelligent body region of strength value return strength value big
Intelligent body region own, i.e. two intelligent bodies merge to increase strength value, so as to increase itself competitiveness；Intelligent body xc
With xdBetween cooperation distance D be defined as：
D=norm (xc-xd)×u
Wherein, c=1,2 ... Magent, d=1,2 ... Magent, for norm functions to seek Norm function, u represents cooperation coefficient,
Value is 0~1；
(4) algorithm terminates
When running to there are not having average individual in some intelligent body region, which is eliminated, this
Sample intelligent body number gradually decreases, when iterating to maximum iteration, end of run.
A kind of alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm of the present invention, can effectively solve more
Target, the nonlinear optimization objective function of multiple constraint, improve microgrid economic benefit and environmental benefit.Specifically have the following advantages that：
1st, the alternating current-direct current mixing microgrid mathematical model established of the present invention, it is contemplated that power grid purchases strategies, micro- source fuel cost,
Environmental benefit cost, network loss and operation expense, object function obey microgrid internal power balance, points of common connection transmission
Before capacity, controllable micro- source climbing rate, unit interval accumulator cell charging and discharging bound, storage battery charge state bound, storage battery
Period power-balance and the constant constraints of storage battery charge state afterwards, are applied in Practical Project easy to method；
The 2nd, object function that cultural gene algorithm is applied to microgrid cost optimization solves, and can effectively solve multidimensional, more
Target, multiple constraint and nonlinear object function, a kind of new method and new approaches are provided for the optimization operation of alternating current-direct current mixing microgrid；
3rd, using the global search strategy of competition-collaboration mode, it is contemplated that global diversity, improves algorithm low optimization accuracy.
Brief description of the drawings
Fig. 1 is a kind of flow chart of the alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm of the present invention；
Fig. 2 is the structure diagram of alternating current-direct current mixing microgrid typical case's rack in the present invention；
Fig. 3 is example load prediction curve figure of the present invention；
Fig. 4 is each micro- source output situation map in exchanging area after present invention optimization；
Fig. 5 is exchanging area operating cost result figure after present invention optimization；
Fig. 6 is each micro- source output situation map in DC area after present invention optimization；
Fig. 7 is DC area operating cost result figure after present invention optimization；
Fig. 8 is storage battery charge state curve map after present invention optimization；
Fig. 9 a are each power supply output situation maps in microgrid exchanging area one day after present invention optimization；
Fig. 9 b are each power supply output situation maps in microgrid DC area one day after present invention optimization；
Fig. 9 c are battery discharging situation maps in microgrid DC area after present invention optimization；
Fig. 9 d are microgrid DC area storage battery charge condition figures after present invention optimization；
Figure 10 is points of common connection flowing power diagram after present invention optimization.
Embodiment
It is excellent to a kind of alternating current-direct current mixing microgrid based on cultural gene algorithm of the present invention with reference to embodiment and attached drawing
Change operation method to be described in detail.
A kind of alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm of the present invention, including, including such as
Lower step：
1) it is straight for the friendship comprising wind-power electricity generation, photovoltaic generation, storage battery, miniature combustion engine, fuel cell and diesel-driven generator
Stream mixing microgrid, establish multiple target, multiple constraint, it is nonlinear optimization operation mathematical model, object function consider power grid power purchase into
Sheet, micro- source fuel cost, environmental benefit cost, network loss and operation expense, and obey microgrid internal power and balance, is public
Tie point transmission capacity, controllable micro- source climbing rate, unit interval accumulator cell charging and discharging bound, above and below storage battery charge state
Limit, the constraints that storage battery surrounding time section power-balance and storage battery charge state are constant；Wherein,
The object function F is as follows：
F=FGrid+Fec+Floss+Fom
FGrid=FACGrid+FDCGrid
Fec=FACec+FDCec
Floss=FACloss+FDCloss
Fom=FACom+FDCom
Wherein, FGridFor power grid purchases strategies；FecFor financial cost；FlossFor network loss；FomFor equipment operation maintenance cost；
FACGridFor exchanging area power grid purchases strategies；FACecFor exchanging area financial cost, by the micro- source fuel cost F in exchanging areaACfuelAnd ring
Border benefit-cost FACenAddition obtains；FAClossFor exchanging area network loss；FAComFor exchanging area equipment operation maintenance cost；FDCGridFor
Exchanging area power grid purchases strategies；FDCecFor the micro- source financial cost in exchanging area, by the micro- source fuel cost F in DC areaDCfuelAnd Environmental Effect
Beneficial cost FDCenAddition obtains；FDClossFor exchanging area network loss；FDComFor exchanging area equipment operation maintenance cost；Wherein,
(1) exchanging area power grid purchases strategies FACGridWith DC area power grid purchases strategies FDCGridRespectively：
Wherein, △ TACPeriod for from exchanging area to power grid power purchase；T is total period in one day；T is day part in one day；It is exchanging area to power grid power purchase electricity；△TDCPeriod for from DC area to power grid power purchase；It is DC area to electricity
Net purchase power consumption；For power grid sale of electricity price in the corresponding period；
(2) exchanging area financial cost FACecWith DC area financial cost FDCec：
FACec=FACfuel+FACen
FDCec=FDCfuel+FDCen
Exchanging area includes wind-power electricity generation (WT), miniature combustion engine (MT) and diesel-driven generator (DEG), and DC area includes photovoltaic generation
(PV), fuel cell (FC) and storage battery (ES), exchanging area fuel cost FACfuelWith DC area fuel cost FDCfuelCan be by
The following formula is calculated：
FACfuel=FMTfuel+FDEGfuel
FDCfuel=FFCfuel
ηFC=-0.0023 × PFC+0.6735
Wherein, FMTfuelFor miniature combustion engine fuel cost；CMTFor miniature combustion engine cooler fuel price；LHV is the low heat value of fuel gas；
PMTFor miniature combustion engine output power；ηMTFor the generating efficiency of miniature combustion engine；FFCfuelFor the fuel cost of fuel cell；CFCFor fuel electricity
The cooler fuel price in pond；PFCFor the output power of fuel cell；ηFCFor the generating efficiency of fuel cell；FDEGfuelFor diesel-driven generator
Fuel cost；PDEGFor the output power of diesel-driven generator；A, b, c are diesel-driven generator power generation coefficient, by diesel engine factory
Family provides；
Exchanging area environmental benefit cost FACenWith DC area environmental benefit cost FDCenIt is calculated by the following formula：
Wherein, n1 is the micro- source number in exchanging area；M is the type of pollutant；αjFor the conversion cost of corresponding pollutant, member/
kg；EFi,jThe unit discharge of the jth kind pollutant produced for i-th of micro- source, kg/kW；PiFor the output work in i-th of micro- source
Rate；n2For the micro- source number in DC area.
(3) network loss F is exchangedAClossWith direct current network loss FDCloss：
Wherein, L1 is exchanging area branch sum；Pk、QkActive power, reactive power for branch k transmission；L2 is DC area
Branch sum；RkFor the resistance of branch k；UkFor the voltage effective value of branch k；
(4) ac operation maintenance cost FAComWith DC operation maintenance cost FDCom：
Wherein, β i are the operation expense coefficient in i-th of micro- source.
The method of weighting is taken, finally obtaining object function is：
MinF=(1- λ) × (FGrid+Fec+Fom)+λFloss
Wherein, λ is Web-based exercise coefficient, and the Web-based exercise coefficient lambda is 0.1.
The constraints includes：
(1) microgrid internal power equilibrium constraint
(2) points of common connection transmission capacity constraints
(3) controllable micro- source climbing rate constraints
(4) unit interval accumulator cell charging and discharging bound constraints
(5) storage battery charge state (State of Charge, SOC) bound constraints
Socmin≤Soct≤Socmax
(6) storage battery surrounding time section power-balance constraint condition
(7) storage battery charge state constraint independent of time condition
Socinitial=Socend
Wherein, N is micro- source number；Represent that t-th of period, i-th of micro- source is contributed；Represent that t-th of period is purchased from power grid
Electrical power；For t-th of period battery power variable quantity, discharge just, to be charged as bearing；Represent respectively t-th
Period AC and DC area payload；For the net flow power of t-th of period points of common connection；PG,maxPassed for points of common connection
Defeated capacity limit value；riFor the unit interval climbing rate in i-th of micro- source；For the absolute of t-th period battery power variable quantity
Value；For the limit value of t-th of period battery power variable quantity；SoctFor the state-of-charge of storage battery in t-th of period；
Socmin、SocmaxThe respectively upper lower limit value of state-of-charge；Uu represents charge and discharge electrostrictive coefficient, is 1 during charging, and when electric discharge is -1；η is
Accumulator cell charging and discharging efficiency, takes 95% here；QESFor battery rating；Socinitial、SocendFor the initial of state-of-charge
Value and end value.
2) cultural gene Algorithm for Solving alternating current-direct current mixing microgrid mathematical model is used；Including：
(1) initialize
Initial individuals are produced using random generating mode, the value range of each variable is represented with varmin, varmax, its
In any one variable xmInitial value drawn by following formula：
xm=varminm+rand(0,1)*(varmaxm-varminm)
If M is all individual numbers in colony, then m=1,2 ..., M, MagentFor the number of intelligent body, MpublicTo be common
The number of individual, that is, have：
M=Magent+Mpublic
Ascending order arrangement, preceding M are carried out according to fitness value size to initial individualsagentAs intelligent body, remaining is common
Individual, wherein intelligent body are that fitness value comes preceding M in colonyagentThe individual of position, remaining individual is average individual in colony,
Colony is divided into MagentRegion, average individual belong to the region where each intelligent body, and each intelligent body initial time is possessed
The number of average individual be to be determined by each intelligent body with respect to strength, a-th intelligent body (a=1,2 ..., Magent) it is relatively real
Power is specifically calculated by following formula：
Sa=max { sb}-sa, b=1,2 ..., Magent
saFor the fitness value of a-th of intelligent body；
The strength size of a-th of intelligent body is defined as：
The average individual number that each intelligent body region is assigned to is：
M.Sa=round { Pa×Mpublic}
M.SaThe average individual number possessed by a-th of intelligent body region, so that the intelligent body after initializing
Strength is stronger, and the number for the average individual that region is assigned to is more；
(2) local search, the local searching strategy used are for optimal Selection Strategy, i.e., real in each region in each iteration
Force value is best for intelligent body.Local search is specifically realized in two steps：
(2.1) polymerization movement
In the cultural gene algorithm based on competition-collaboration mode, the average individual in each region is gradually to one's respective area
Intelligent body is close, and displacement distance Move is obeyed and is uniformly distributed, and represents as follows：
Move~U (0, β × d)
Wherein, U is to be uniformly distributed symbol；β is polymerizing factor, by many experiments when β takes 2 algorithm optimizing performance compared with
It is good；D is the distance between intelligent body and average individual in the same area；
(2.2) movement is changed
After polymerization campaign is carried out, average individual can be drawn close rapidly cultural gene algorithm to intelligent body in each region, be carried
High convergence speed of the algorithm, but be likely to result in algorithm and be absorbed in local convergence, the solution finally obtained is not optimal solution.In order to
The speed that average individual moves in polymerization process is carried out slows down, and increases population diversity, each average individual is carried out random
Change, the average individual number that a-th of intelligent body region is possessed is Ma.public, wherein needing common changed
Body number Ma,rpublicFor：
Ma.rpublic=round (qa×Ma.public)
Wherein, round functions is round up function, qaFor change rate, q is drawn after many experimentsaAlgorithm is sought when taking 0.3
Excellent better performances；
Inside each region after polymerization and changing movement, each individual strength value meeting great changes have taken place, it is necessary to according to
The ideal adaptation angle value rearrangement of each region, the ideal adaptation angle value to make number one is best, is new intelligent body；
(3) global search, including：
(3.1) vie each other
Each iteration is by local search afterwards, it is necessary to calculate total strength value of each intelligent body.Intelligent body in each region
Total strength value by itself strength value and together decide on the strength value of the average individual in region, be calculated by following formula：
T.SaFor total strength of a-th of intelligent body, ξ is a positive number less than 1, represents that average individual is real in the same area
Power accounts for the weight of the total strength of intelligent body, takes 0.1, wnFor the strength value of average individual；
Competition is obtained between being embodied in intelligent body by strength value size in cultural gene algorithm based on competition-collaboration mode
Corresponding probability is obtained to compete the average individual that strength is most weak in all individuals, so as to strengthen the strength of itself.Strength is stronger
The probability that intelligent body competes to obtain the most weak average individual of global strength is bigger, and a-th of intelligent body competition probability is expressed as：
Wherein, N.T.SaRepresent relatively total strength value of a-th of intelligent body, be defined as：
M.T.Sa=max { T.Sb}-T.Sa, b=1,2 ... Magent
Thus the competition probability of each intelligent body is calculated, if vector p is：
Introduce and vector p is with the random vector R of dimension, be expressed as：
Wherein, r~U (0,1) represents being uniformly distributed for the element obedience 0 to 1 in R；
Definition vector V is the difference of vector p and vector R：
V=p-R
The corresponding intelligent body of maximum element finally obtains the average individual competed in vectorial V；
(3.2) cooperate
Iteration of the cultural gene algorithm Jing Guo former steps, disclosure satisfy that algorithm diversity, effectively prevent algorithmic statement to office
Portion's optimal solution.But experimental data shows, in the algorithm later stage, algorithm the convergence speed is slower, it is therefore desirable to increases acceleration mechanism, carries
High algorithm the convergence speed.Increase cooperates operation after intelligent body is vied each other, when between the intelligent body in two regions
When distance is less than cooperation distance D, all average individuals in two intelligent bodies in the small intelligent body region of strength value are returned
The big intelligent body region of strength value owns, i.e., two intelligent bodies merge to increase strength value, so as to increase itself competitiveness；
Intelligent body xcWith xdBetween cooperation distance D be defined as：
D=norm (xc-xd)×u
Wherein, c=1,2 ... Magent, d=1,2 ... Magent, for norm functions to seek Norm function, u represents cooperation coefficient,
Value is 0~1, and by many experiments, algorithmic statement performance is good when u takes 0.2；
(4) algorithm terminates
When running to there are not having average individual in some intelligent body region, which is eliminated, this
Sample intelligent body number gradually decreases, when iterating to maximum iteration, end of run.
3) correctness and validity of the alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm are verified.
Example is given below：
Consider alternating current-direct current mixing microgrid as shown in Figure 2, used for the flow controller between exchanging area and DC area
Bi-directional current controller, realizes the power circulation between exchanging area and DC area, so as to reduce microgrid operating cost, improves to micro-
The utilization rate in source.The micro- source in exchanging area includes miniature combustion engine, diesel-driven generator and wind-power electricity generation, the micro- source in DC area include fuel cell,
Photovoltaic generation and the storage battery 1 that capacity is 250kWh, each micro- source parameter are as shown in table 1.Storage battery charge state changes model
Enclose for 0.3~0.9, SocinitialAnd Socend0.3 is taken, in order to give full play to the effect of storage battery peak load shifting, according to Fig. 4 institutes
The load prediction curve figure shown, determines storage battery in peak times of power consumption 11:00-13:00 and 20:00-24:00, as long as charged shape
State meets constraints, and storage battery necessarily be in discharge condition, remaining period storage battery charges, when state-of-charge reaches
When 0.9, stop charging.Table 2 converts cost and emission factor for micro- source, and table 3 is power grid purchase electricity price at different moments.
The parameter in each micro- source of table 1
Convert cost and emission factor in 2 micro- source of table
3 tou power price of table
(1) load prediction data in alternating current-direct current mixing microgrid is collected, as shown in figure 3, to alternating current-direct current mixing microgrid future one
Each micro- source, which is contributed, when 24 is small in it optimizes, to consider power grid purchases strategies, micro- source fuel cost, environmental benefit cost, net
Damage and the minimum object function of operation expense, and obey microgrid internal power balance, points of common connection transmission capacity, can
Control micro- source climbing rate, unit interval accumulator cell charging and discharging bound, storage battery charge state bound, storage battery surrounding time section
Multiple constraints such as power-balance and storage battery charge state are constant；
(2) object function is solved with cultural gene algorithm, the alternating current-direct current mixing obtained as Figure 4-Figure 7 is micro-
Each micro- source output situation curve and cost curve of exchanging area and DC area are netted, wherein financial cost is micro- source operation expense
The sum of with fuel cost.As can be seen that since the micro- source fuel cell in DC area, storage battery and photovoltaic generation environmental benefit are preferable,
So environmental protection converts cost close to zero；
(3) implementation requirements storage battery charge state of the invention meets 0.3~0.9, and whole story state is 0.3 pact
Beam.Fig. 8 is storage battery charge state change curve after optimization, meets constraints；
(4) pass through cultural gene Algorithm for Solving optimization object function, finally obtain each micro- source in alternating current-direct current mixing microgrid
Output situation and points of common connection flow power diagram as shown in Fig. 9 a- Figure 10.Wherein PLA and PLD represents exchanging area and straight respectively
The load in area is flowed, GRID represents to represent that storage battery is charged and discharged electricity respectively from power grid power purchase electricity, ESC and ESD.
The example of the present invention uses cultural gene Algorithm for Solving, show that alternating current-direct current mixing microgrid total operating cost is in example
2608.0 yuan, wherein exchanging area operating cost is 1964.1 yuan, and DC area operating cost is 643.9 yuan, and each micro- source is contributed can
Meet various constraints, storage battery reaches full hair-like state when load is larger, since the micro- source cost of electricity-generating in exchanging area is more micro- than DC area
Source higher, so the flow of power there are DC area to exchanging area.
In conclusion test result through this embodiment, illustrates a kind of friendship based on cultural gene algorithm of the present invention
Direct current mixing microgrid optimizing operation method can effectively solve multiple target, the nonlinear optimization objective function of multiple constraint, improve microgrid
Economic benefit and environmental benefit, it was confirmed that the validity and correctness of cultural gene algorithm.
Claims (5)
1. a kind of alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm, it is characterised in that including following step
Suddenly：
1) mixed for the alternating current-direct current comprising wind-power electricity generation, photovoltaic generation, storage battery, miniature combustion engine, fuel cell and diesel-driven generator
Microgrid is closed, establishes multiple target, multiple constraint, nonlinear optimization operation mathematical model, object function considers power grid purchases strategies, micro-
Source fuel cost, environmental benefit cost, network loss and operation expense, and obey microgrid internal power and balance, is commonly connected
Point transmission capacity, controllable micro- source climbing rate, unit interval accumulator cell charging and discharging bound, storage battery charge state bound, storage
Battery surrounding time section power-balance and the constant constraints of storage battery charge state；
2) cultural gene Algorithm for Solving alternating current-direct current mixing microgrid mathematical model is used；
3) correctness and validity of the alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm are verified.
2. a kind of alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm according to claim 1, its
It is characterized in that, the object function F described in step 1) is as follows：
F=FGrid+Fec+Floss+Fom
FGrid=FACGrid+FDCGrid
Fec=FACec+FDCec
Floss=FACloss+FDCloss
Fom=FACom+FDCom
Wherein, FGridFor power grid purchases strategies；FecFor financial cost；FlossFor network loss；FomFor equipment operation maintenance cost；
FACGridFor exchanging area power grid purchases strategies；FACecFor exchanging area financial cost, by the micro- source fuel cost F in exchanging areaACfuelAnd ring
Border benefit-cost FACenAddition obtains；FAClossFor exchanging area network loss；FAComFor exchanging area equipment operation maintenance cost；FDCGridFor
Exchanging area power grid purchases strategies；FDCecFor the micro- source financial cost in exchanging area, by the micro- source fuel cost F in DC areaDCfuelAnd Environmental Effect
Beneficial cost FDCenAddition obtains；FDClossFor exchanging area network loss；FDComFor exchanging area equipment operation maintenance cost；Wherein,
(1) exchanging area power grid purchases strategies FACGridWith DC area power grid purchases strategies FDCGridRespectively：
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Wherein, △ TACPeriod for from exchanging area to power grid power purchase；T is total period in one day；T is day part in one day；
It is exchanging area to power grid power purchase electricity；△TDCPeriod for from DC area to power grid power purchase；It is DC area to power grid power purchase
Electricity；For power grid sale of electricity price in the corresponding period；
(2) exchanging area financial cost FACecWith DC area financial cost FDCec：
FACec=FACfuel+FACen
FDCec=FDCfuel+FDCen
Exchanging area includes wind-power electricity generation, miniature combustion engine and diesel-driven generator, and DC area includes photovoltaic generation, fuel cell and electric power storage
Pond, exchanging area fuel cost FACfuelWith DC area fuel cost FDCfuelIt can be calculated by the following formula：
FACfuel=FMTfuel+FDEGfuel
FDCfuel=FFCfuel
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ηFC=-0.0023 × PFC+0.6735
Wherein, FMTfuelFor miniature combustion engine fuel cost；CMTFor miniature combustion engine cooler fuel price；LHV is the low heat value of fuel gas；PMTFor
Miniature combustion engine output power；ηMTFor the generating efficiency of miniature combustion engine；FFCfuelFor the fuel cost of fuel cell；CFCFor fuel cell
Cooler fuel price；PFCFor the output power of fuel cell；ηFCFor the generating efficiency of fuel cell；FDEGfuelFor the combustion of diesel-driven generator
Expect cost；PDEGFor the output power of diesel-driven generator；A, b, c are diesel-driven generator power generation coefficient, and by diesel engine, manufacturer gives
Go out；
Exchanging area environmental benefit cost FACenWith DC area environmental benefit cost FDCenIt is calculated by the following formula：
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Wherein, n1 is the micro- source number in exchanging area；M is the type of pollutant；αjFor the conversion cost of corresponding pollutant, member/kg；
EFi,jThe unit discharge of the jth kind pollutant produced for i-th of micro- source, kg/kW；PiFor the output power in i-th of micro- source；n2
For the micro- source number in DC area.
(3) network loss F is exchangedAClossWith direct current network loss FDCloss：
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Wherein, L1 is exchanging area branch sum；Pk、QkActive power, reactive power for branch k transmission；L2 is DC area branch
Sum；RkFor the resistance of branch k；UkFor the voltage effective value of branch k；
(4) ac operation maintenance cost FAComWith DC operation maintenance cost FDCom：
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<mi>F</mi>
<mrow>
<mi>A</mi>
<mi>C</mi>
<mi>o</mi>
<mi>m</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>n</mi>
<mn>1</mn>
</mrow>
</munderover>
<msub>
<mi>&beta;</mi>
<mi>i</mi>
</msub>
<mo>&times;</mo>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
</mrow>
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mi>D</mi>
<mi>C</mi>
<mi>o</mi>
<mi>m</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>n</mi>
<mn>2</mn>
</mrow>
</munderover>
<msub>
<mi>&beta;</mi>
<mi>i</mi>
</msub>
<mo>&times;</mo>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
</mrow>
Wherein, β i are the operation expense coefficient in i-th of micro- source.
The method of weighting is taken, finally obtaining object function is：
MinF=(1- λ) × (FGrid+Fec+Fom)+λFloss
Wherein, λ is Web-based exercise coefficient.
3. a kind of alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm according to claim 2, its
It is characterized in that, the Web-based exercise coefficient lambda is 0.1.
4. a kind of alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm according to claim 1, its
It is characterized in that, the constraints described in step 1) includes：
(1) microgrid internal power equilibrium constraint
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mi>t</mi>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>P</mi>
<mi>G</mi>
<mi>t</mi>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>E</mi>
<mi>S</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>A</mi>
<mi>C</mi>
<mi>L</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>D</mi>
<mi>C</mi>
<mi>L</mi>
</mrow>
<mi>t</mi>
</msubsup>
</mrow>
(2) points of common connection transmission capacity constraints
<mrow>
<mo>|</mo>
<msubsup>
<mi>P</mi>
<mi>G</mi>
<mi>t</mi>
</msubsup>
<mo>|</mo>
<mo>&le;</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>G</mi>
<mo>,</mo>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</mrow>
(3) controllable micro- source climbing rate constraints
<mrow>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mi>t</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mrow>
<mi>t</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>&le;</mo>
<msub>
<mi>r</mi>
<mi>i</mi>
</msub>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
(4) unit interval accumulator cell charging and discharging bound constraints
<mrow>
<mo>|</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>E</mi>
<mi>S</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>|</mo>
<mo>&le;</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>E</mi>
<mi>S</mi>
<mo>,</mo>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
<mi>t</mi>
</msubsup>
</mrow>
(5) storage battery charge state (State of Charge, SOC) bound constraints
Socmin≤Soct≤Socmax
(6) storage battery surrounding time section power-balance constraint condition
<mrow>
<msup>
<mi>Soc</mi>
<mrow>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>=</mo>
<msup>
<mi>Soc</mi>
<mi>t</mi>
</msup>
<mo>+</mo>
<mi>u</mi>
<mi>u</mi>
<mo>&times;</mo>
<mi>&eta;</mi>
<mo>&times;</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>E</mi>
<mi>S</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mi>&Delta;</mi>
<mi>T</mi>
<mo>/</mo>
<msub>
<mi>Q</mi>
<mrow>
<mi>E</mi>
<mi>S</mi>
</mrow>
</msub>
</mrow>
(7) storage battery charge state constraint independent of time condition
Socinitial=Socend
Wherein, N is micro- source number；Represent that t-th of period, i-th of micro- source is contributed；Represent t-th of period from power grid power purchase work(
Rate；For t-th of period battery power variable quantity, discharge just, to be charged as bearing；When representing respectively t-th
Section AC and DC area payload；For the net flow power of t-th of period points of common connection；PG,maxTransmitted for points of common connection
Capacity limit value；riFor the unit interval climbing rate in i-th of micro- source；For the absolute of t-th period battery power variable quantity
Value；For the limit value of t-th of period battery power variable quantity；SoctFor the state-of-charge of storage battery in t-th of period；
Socmin、SocmaxThe respectively upper lower limit value of state-of-charge；Uu represents charge and discharge electrostrictive coefficient, is 1 during charging, and when electric discharge is -1；η is
Accumulator cell charging and discharging efficiency, takes 95% here；QESFor battery rating；Socinitial、SocendFor the initial of state-of-charge
Value and end value.
5. a kind of alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm according to claim 1, its
It is characterized in that, step 2) includes：
(1) initialize
Initial individuals are produced using random generating mode, the value range of each variable are represented with varmin, varmax, wherein appointing
Anticipate a variable xmInitial value drawn by following formula：
xm=varminm+rand(0,1)*(varmaxm-varminm)
If M is all individual numbers in colony, then m=1,2 ..., M, MagentFor the number of intelligent body, MpublicFor average individual
Number, that is, have：
M=Magent+Mpublic
Ascending order arrangement, preceding M are carried out according to fitness value size to initial individualsagentAs intelligent body, remaining is average individual,
Wherein intelligent body is that fitness value comes preceding M in colonyagentThe individual of position, remaining individual is average individual in colony, by group
Body is divided into MagentRegion, average individual belong to the region where each intelligent body, and each intelligent body initial time is possessed general
The number for leading to individual is to be determined by each intelligent body with respect to strength, a-th intelligent body (a=1,2 ..., Magent) opposite strength tool
Body is calculated by following formula：
Sa=max { sb}-sa, b=1,2 ..., Magent
saFor the fitness value of a-th of intelligent body；
The strength size of a-th of intelligent body is defined as：
<mrow>
<msub>
<mi>P</mi>
<mi>a</mi>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>S</mi>
<mi>a</mi>
</msub>
<mrow>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>a</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>M</mi>
<mrow>
<mi>a</mi>
<mi>g</mi>
<mi>e</mi>
<mi>n</mi>
<mi>t</mi>
</mrow>
</msub>
</msubsup>
<msub>
<mi>S</mi>
<mi>a</mi>
</msub>
</mrow>
</mfrac>
</mrow>
The average individual number that each intelligent body region is assigned to is：
M.Sa=round { Pa×Mpublic}
M.SaThe average individual number possessed by a-th of intelligent body region, so that the intelligent body strength after initializing is got over
By force, the number for the average individual that region is assigned to is more；
(2) local search, is specifically realized in two steps：
(2.1) polymerization movement
Intelligent body of the average individual gradually to one's respective area in each region is close, and displacement distance Move is obeyed and is uniformly distributed, and represents
It is as follows：
Move~U (0, β × d)
Wherein, U is to be uniformly distributed symbol；β is polymerizing factor, and β takes 2；D is in the same area between intelligent body and average individual
Distance；
(2.2) movement is changed
In order to which the speed of the average individual movement in polymerization process is carried out slows down, increase population diversity, to each average individual
Changed at random, the average individual number that a-th of intelligent body region is possessed is Ma.public, wherein needing to be changed
Average individual number Ma,rpublicFor：
Ma.rpublic=round (qa×Ma.public)
Wherein, round functions is round up function, qaFor change rate, 0.3 is taken；
Inside each region after polymerizeing and changing movement, great changes have taken place, it is necessary to according to each for each individual strength value meeting
Region ideal adaptation angle value rearrangement, the ideal adaptation angle value to make number one is best, is new intelligent body；
(3) global search, including：
(3.1) vie each other
Each iteration is by local search afterwards, it is necessary to calculate total strength value of each intelligent body.Intelligent body is always real in each region
Force value by itself strength value and together decide on the strength value of the average individual in region, be calculated by following formula：
<mrow>
<mi>T</mi>
<mo>.</mo>
<msub>
<mi>S</mi>
<mi>a</mi>
</msub>
<mo>=</mo>
<msub>
<mi>s</mi>
<mi>a</mi>
</msub>
<mo>+</mo>
<mi>&xi;</mi>
<mo>&times;</mo>
<mfrac>
<mrow>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>b</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>M</mi>
<mo>.</mo>
<msub>
<mi>S</mi>
<mi>a</mi>
</msub>
</mrow>
</msubsup>
<msub>
<mi>w</mi>
<mi>b</mi>
</msub>
</mrow>
<mrow>
<mi>M</mi>
<mo>.</mo>
<msub>
<mi>S</mi>
<mi>a</mi>
</msub>
</mrow>
</mfrac>
</mrow>
T.SaFor total strength of a-th of intelligent body, ξ is a positive number less than 1, represents that average individual strength accounts in the same area
The weight of the total strength of intelligent body, takes 0.1, wnFor the strength value of average individual；
A-th of intelligent body competition probability is expressed as：
<mrow>
<msub>
<mi>p</mi>
<mrow>
<mi>P</mi>
<mi>a</mi>
</mrow>
</msub>
<mo>=</mo>
<mo>|</mo>
<mfrac>
<mrow>
<mi>M</mi>
<mo>.</mo>
<mi>T</mi>
<mo>.</mo>
<msub>
<mi>S</mi>
<mi>a</mi>
</msub>
</mrow>
<mrow>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>b</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>M</mi>
<mrow>
<mi>a</mi>
<mi>g</mi>
<mi>e</mi>
<mi>n</mi>
<mi>t</mi>
</mrow>
</msub>
</msubsup>
<mi>M</mi>
<mo>.</mo>
<mi>T</mi>
<mo>.</mo>
<msub>
<mi>S</mi>
<mi>b</mi>
</msub>
</mrow>
</mfrac>
<mo>|</mo>
</mrow>
Wherein, N.T.SaRepresent relatively total strength value of a-th of intelligent body, be defined as：
M.T.Sa=max { T.Sb}-T.Sa, b=1,2 ... Magent
Thus the competition probability of each intelligent body is calculated, if vector p is：
<mrow>
<mi>p</mi>
<mo>=</mo>
<mo>&lsqb;</mo>
<mtable>
<mtr>
<mtd>
<msub>
<mi>p</mi>
<msub>
<mi>P</mi>
<mn>1</mn>
</msub>
</msub>
</mtd>
<mtd>
<msub>
<mi>p</mi>
<msub>
<mi>P</mi>
<mn>2</mn>
</msub>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>p</mi>
<msub>
<mi>P</mi>
<msub>
<mi>M</mi>
<mrow>
<mi>a</mi>
<mi>g</mi>
<mi>e</mi>
<mi>n</mi>
<mi>t</mi>
</mrow>
</msub>
</msub>
</msub>
</mtd>
</mtr>
</mtable>
<mo>&rsqb;</mo>
</mrow>
Introduce and vector p is with the random vector R of dimension, be expressed as：
<mrow>
<mi>R</mi>
<mo>=</mo>
<mo>&lsqb;</mo>
<mtable>
<mtr>
<mtd>
<msub>
<mi>r</mi>
<mn>1</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>r</mi>
<mn>2</mn>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>r</mi>
<msub>
<mi>M</mi>
<mrow>
<mi>a</mi>
<mi>g</mi>
<mi>e</mi>
<mi>n</mi>
<mi>t</mi>
</mrow>
</msub>
</msub>
</mtd>
</mtr>
</mtable>
<mo>&rsqb;</mo>
<mo>,</mo>
<mi>r</mi>
<mo>~</mo>
<mi>U</mi>
<mrow>
<mo>(</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, r~U (0,1) represents being uniformly distributed for the element obedience 0 to 1 in R；
Definition vector V is the difference of vector p and vector R：
V=p-R
The corresponding intelligent body of maximum element finally obtains the average individual competed in vectorial V；
(3.2) cooperate
Increase cooperates operation after intelligent body is vied each other, when the distance between the intelligent body in two regions is less than cooperation
During distance D, all average individuals in two intelligent bodies in the small intelligent body region of strength value return the big intelligence of strength value
Energy body region owns, i.e., two intelligent bodies merge to increase strength value, so as to increase itself competitiveness；Intelligent body xcWith xd
Between cooperation distance D be defined as：
D=norm (xc-xd)×u
Wherein, c=1,2 ... Magent, d=1,2 ... Magent, for norm functions to seek Norm function, u represents cooperation coefficient, value
For 0~1；
(4) algorithm terminates
When running to there are not having average individual in some intelligent body region, which is eliminated, such intelligence
Energy body number gradually decreases, when iterating to maximum iteration, end of run.
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| CN111697635A (en) * | 2020-06-03 | 2020-09-22 | 华北电力大学（保定） | Alternating current-direct current hybrid micro-grid optimized operation method considering random fuzzy double uncertainty |
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