CN107947178B - 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: it is directed to and includes wind-power electricity generation, photovoltaic power generation, battery, micro turbine, the alternating current-direct current mixing microgrid of fuel cell and diesel-driven generator, establish multiple target, multiple constraint, nonlinear optimization runs mathematical model, objective 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 time accumulator cell charging and discharging bound, storage battery charge state bound, battery surrounding time section power-balance and the constant constraint condition of storage battery charge state；Alternating current-direct current mixing microgrid mathematical model is solved using cultural gene algorithm；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 the nonlinear optimization objective function of multiple target, 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 methods.It is calculated more particularly to one kind based on cultural gene
The alternating current-direct current mixing microgrid optimizing operation method of method.
Background technique
As traditional energy is increasingly depleted, the new generation mode such as wind-power electricity generation, photovoltaic power generation is due to its good environment
Concern of the benefit by domestic and foreign scholars.Microgrid is the novel supply network comprising devices such as distributed energy, load, energy storage,
Since it has 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.Currently, load type is more and more abundant, the power supply reliability of DC load becomes a big academic project.
Alternating current-direct current mixing microgrid has merged AC load and DC load, can be improved power quality, suitably reduction electric power
The use of electronic device is to reduce harmonic pollution.In order to guarantee the safe and reliable operation of alternating current-direct current mixing microgrid, domestic foreign minister
Scholar is closed just to study the development and application of alternating current-direct current mixing microgrid operation control technology in Efforts To Develop.Alternating current-direct current mixing is micro-
Net economic optimization operation be one of research topic, with microgrid structure is complicated diversification, find optimization performance it is better
Good intelligent search algorithm is crucial.
Currently used intelligent algorithm optimizing works well, but slow there are still some convergence rates or convergence essence is not achieved
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, is similar to heredity and calculates
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 hereditary to 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 based on this thought, algorithm iteration process is divided into local search and global search, can respectively have a set of individual theory right
Individual is screened, to accelerate individual to approach optimal value from two levels, improves algorithmic statement precision.
Summary of the invention
Multidimensional, multiple target, multiple constraint and non-thread can be effectively solved the technical problem to be solved by the invention is to provide a kind of
The alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm of the objective function of property.
The technical scheme adopted by the invention is that: a kind of alternating current-direct current mixing microgrid optimization operation based on cultural gene algorithm
Method includes the following steps:
1) straight for the friendship comprising wind-power electricity generation, photovoltaic power generation, battery, micro turbine, fuel cell and diesel-driven generator
Stream mixing microgrid, establish multiple target, multiple constraint, nonlinear optimization operation mathematical model, objective function consideration power grid power purchase at
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 time accumulator cell charging and discharging bound, above and below storage battery charge state
It limits, the constraint condition that battery surrounding time section power-balance and storage battery charge state are constant；
2) alternating current-direct current mixing microgrid mathematical model is solved using cultural gene algorithm；
3) correctness and validity of the alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm are verified.
Objective 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 economic cost；FlossFor network loss；FomFor equipment operation maintenance cost；
FACGridFor exchanging area power grid purchases strategies；FACecFor exchanging area economic 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 economic 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 FDCGridIt is respectively as follows:
Wherein, △ TACIt is exchanging area to the period of 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；△TDCIt is DC area to the period of power grid power purchase；It is DC area to electricity
Online shopping power consumption；For power grid sale of electricity price in the corresponding period；
(2) exchanging area economic cost FACecWith DC area economic cost FDCec:
FACec=FACfuel+FACen
FDCec=FDCfuel+FDCen
Exchanging area includes wind-power electricity generation, micro turbine and diesel-driven generator, and DC area includes photovoltaic power generation, fuel cell and storage
Battery, exchanging area fuel cost FACfuelWith DC area fuel cost FDCfuelIt can be calculated by following formula:
FACfuel=FMTfuel+FDEGfuel
FDCfuel=FFCfuel
ηFC=-0.0023 × PFC+0.6735
Wherein, FMTfuelFor micro turbine fuel cost；CMTFor micro turbine cooler fuel price；LHV is the low heat value of fuel gas；
PMTFor micro turbine output power；ηMTFor the generating efficiency of micro turbine；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 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,jFor the unit discharge for the jth kind pollutant that i-th of micro- source generates, 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 is the operation expense coefficient in i-th of micro- source.
The method of weighting is taken, objective function is finally obtained are as follows:
MinF=(1- λ) × (FGrid+Fec+Fom)+λFloss
Wherein, λ is Web-based exercise coefficient.
The Web-based exercise coefficient lambda is 0.1.
Constraint condition described in step 1) includes:
(1) microgrid internal power equilibrium constraint
(2) points of common connection transmission capacity constraint condition
(3) controllable micro- source climbing rate constraint condition
(4) unit time accumulator cell charging and discharging bound constraint condition
(5) storage battery charge state (State of Charge, SOC) bound constraint condition
Socmin≤Soct≤Socmax
(6) 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；Indicate t-th i-th of the period micro- source power output；Indicate that t-th of period is purchased from power grid
Electrical power；For t-th of period battery power variable quantity, electric discharge is positive, and charging is negative；Respectively indicate t
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 time 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 battery in t-th of period；
Socmin、SocmaxThe respectively upper lower limit value of state-of-charge；Uu indicates charge and discharge electrostrictive coefficient, is 1 when 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) it initializes
Initial individuals are generated using random generating mode, the value range of each variable is indicated with varmin, varmax,
In any one variable xmInitial value obtained by following formula:
xm=varminm+rand(0,1)*(varmaxm-varminm)
If M is all individual numbers in group, then m=1,2 ..., M, MagentFor the number of intelligent body, MpublicIt is 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 is that fitness value comes preceding M in groupagentThe individual of position, remaining individual is average individual in group,
Group 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 are as follows:
M.Sa=round { Pa×Mpublic}
M.SaThe average individual number possessed by a-th of intelligent body region, thus the intelligent body after initialization
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 moving distance Move obedience is uniformly distributed,
It is expressed as follows:
Move~U (0, β × d)
Wherein, U is to be uniformly distributed symbol；β is polymerizing factor, and β takes 2；D be in the same area intelligent body and average individual it
Between distance；
(2.2) movement is changed
In order to which the mobile speed of the average individual in carrying out polymerization process 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,rpublicAre as follows:
Ma.rpublic=round (qa×Ma.public)
Wherein, round function is the function that rounds up, qaFor change rate, 0.3 is taken；
Inside each region after polymerizeing and changing movement, great changes have taken place for the strength value meeting of each individual, needs basis
Each region ideal adaptation angle value rearrangement, the ideal adaptation angle value to make number one is best, is new intelligent body；
(3) global search, comprising:
(3.1) it vies each other
Each iteration is by needing to calculate total strength value of each intelligent body after local search.Intelligent body in each region
Total strength value is codetermined by the strength value of itself and with the strength value of the average individual in region, is calculated by following formula:
T.SaFor total strength of a-th of intelligent body, ξ is a positive number less than 1, indicates 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.SaIndicate relatively total strength value of a-th of intelligent body, is 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 are as follows:
It introduces and vector p is with the random vector R of dimension, indicate are as follows:
Wherein, r~U (0,1) indicates 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 vector V；
(3.2) it cooperates
Increase after intelligent body is vied each other and cooperate operation, when the distance between the intelligent body in two regions is less than
When cooperation distance D, all average individuals that strength is worth in small intelligent body region in two intelligent bodies return strength value big
Intelligent body region it is all, i.e., two intelligent bodies merge to increase strength value, to increase itself competitiveness；Intelligent body xc
With xdBetween cooperation distance D is defined as:
D=norm (xc-xd)×u
Wherein, c=1,2 ... Magent, d=1,2 ... Magent, norm function is to seek Norm function, and u indicates 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 number of iterations, end of run.
A kind of alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm of the invention, can effectively solve more
Target, multiple constraint nonlinear optimization objective function, improve microgrid economic benefit and environmental benefit.Specifically have the advantages that
1, 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, objective function obey microgrid internal power balance, points of common connection transmission
Before capacity, controllable micro- source climbing rate, unit time accumulator cell charging and discharging bound, storage battery charge state bound, battery
Period power-balance and the constant constraint condition of storage battery charge state afterwards, are applied in Practical Project convenient for method；
2, in solving the objective function that cultural gene algorithm is applied to microgrid cost optimization, multidimensional, more can effectively be solved
Target, multiple constraint and nonlinear objective function provide a kind of new method and new approaches for the optimization operation of alternating current-direct current mixing microgrid；
3, using competition-collaboration mode global search strategy, it is contemplated that global diversity improves algorithm low optimization accuracy.
Detailed description of the invention
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 structural schematic 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 power 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 power 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 graph after present invention optimization；
Fig. 9 a is each power supply power output situation map in microgrid exchanging area one day after present invention optimization；
Fig. 9 b is each power supply power output situation map in microgrid DC area one day after present invention optimization；
Fig. 9 c is microgrid DC area battery discharge scenario figure after present invention optimization；
Fig. 9 d is microgrid DC area battery charging situation figure after present invention optimization；
Figure 10 is points of common connection flowing power diagram after present invention optimization.
Specific embodiment
It is excellent to a kind of alternating current-direct current mixing microgrid based on cultural gene algorithm of the invention below 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 invention, including, including such as
Lower step:
1) straight for the friendship comprising wind-power electricity generation, photovoltaic power generation, battery, micro turbine, fuel cell and diesel-driven generator
Stream mixing microgrid, establish multiple target, multiple constraint, nonlinear optimization operation mathematical model, objective function consideration power grid power purchase at
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 time accumulator cell charging and discharging bound, above and below storage battery charge state
It limits, the constraint condition that battery surrounding time section power-balance and storage battery charge state are constant；Wherein,
The objective 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 economic cost；FlossFor network loss；FomFor equipment operation maintenance cost；
FACGridFor exchanging area power grid purchases strategies；FACecFor exchanging area economic 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 economic 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 FDCGridIt is respectively as follows:
Wherein, △ TACIt is exchanging area to the period of 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；△TDCIt is DC area to the period of power grid power purchase；It is DC area to electricity
Online shopping power consumption；For power grid sale of electricity price in the corresponding period；
(2) exchanging area economic cost FACecWith DC area economic cost FDCec:
FACec=FACfuel+FACen
FDCec=FDCfuel+FDCen
Exchanging area includes wind-power electricity generation (WT), micro turbine (MT) and diesel-driven generator (DEG), and DC area includes photovoltaic power generation
(PV), fuel cell (FC) and battery (ES), exchanging area fuel cost FACfuelWith DC area fuel cost FDCfuelIt can be by
Following formula is calculated:
FACfuel=FMTfuel+FDEGfuel
FDCfuel=FFCfuel
ηFC=-0.0023 × PFC+0.6735
Wherein, FMTfuelFor micro turbine fuel cost；CMTFor micro turbine cooler fuel price；LHV is the low heat value of fuel gas；
PMTFor micro turbine output power；ηMTFor the generating efficiency of micro turbine；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 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,jFor the unit discharge for the jth kind pollutant that i-th of micro- source generates, 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 is the operation expense coefficient in i-th of micro- source.
The method of weighting is taken, objective function is finally obtained are as follows:
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 constraint condition includes:
(1) microgrid internal power equilibrium constraint
(2) points of common connection transmission capacity constraint condition
(3) controllable micro- source climbing rate constraint condition
(4) unit time accumulator cell charging and discharging bound constraint condition
(5) storage battery charge state (State of Charge, SOC) bound constraint condition
Socmin≤Soct≤Socmax
(6) 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；Indicate t-th i-th of the period micro- source power output；Indicate that t-th of period is purchased from power grid
Electrical power；For t-th of period battery power variable quantity, electric discharge is positive, and charging is negative；It respectively indicates t-th
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 biography
Defeated capacity limit value；riFor the unit time 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 battery in t-th of period；
Socmin、SocmaxThe respectively upper lower limit value of state-of-charge；Uu indicates charge and discharge electrostrictive coefficient, is 1 when 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) alternating current-direct current mixing microgrid mathematical model is solved using cultural gene algorithm；Include:
(1) it initializes
Initial individuals are generated using random generating mode, the value range of each variable is indicated with varmin, varmax,
In any one variable xmInitial value obtained by following formula:
xm=varminm+rand(0,1)*(varmaxm-varminm)
If M is all individual numbers in group, then m=1,2 ..., M, MagentFor the number of intelligent body, MpublicIt is 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 is that fitness value comes preceding M in groupagentThe individual of position, remaining individual is average individual in group,
Group 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 are as follows:
M.Sa=round { Pa×Mpublic}
M.SaThe average individual number possessed by a-th of intelligent body region, thus the intelligent body after initialization
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 based on competition-collaboration mode cultural gene algorithm, the average individual in each region is gradually to one's respective area
Intelligent body is close, and moving distance Move obedience is uniformly distributed, and is expressed 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
Cultural gene algorithm is after carrying out polymerization campaign, and average individual can be drawn close to intelligent body rapidly in each region, mentions
High convergence speed of the algorithm, but be likely to result in algorithm and fall into local convergence, finally obtained solution is not optimal solution.In order to
The mobile speed of average individual slows down in carrying out polymerization process, increases population diversity, carries out to each average individual random
It changes, the average individual number that a-th of intelligent body region is possessed is Ma.public, wherein common changed
Body number Ma,rpublicAre as follows:
Ma.rpublic=round (qa×Ma.public)
Wherein, round function is the function that rounds up, qaFor change rate, q is obtained after many experimentsaAlgorithm is sought when taking 0.3
Excellent better performances；
Inside each region after polymerizeing and changing movement, great changes have taken place for the strength value meeting of each individual, needs basis
Each region ideal adaptation angle value rearrangement, the ideal adaptation angle value to make number one is best, is new intelligent body；
(3) global search, comprising:
(3.1) it vies each other
Each iteration is by needing to calculate total strength value of each intelligent body after local search.Intelligent body in each region
Total strength value is codetermined by the strength value of itself and with the strength value of the average individual in region, is calculated by following formula:
T.SaFor total strength of a-th of intelligent body, ξ is a positive number less than 1, indicates 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；
It is embodied between intelligent body based on competition in competition-collaboration mode cultural gene algorithm and is obtained by strength value size
Corresponding probability is obtained to compete the average individual that strength is most weak in all individuals, to enhance 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.SaIndicate relatively total strength value of a-th of intelligent body, is 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 are as follows:
It introduces and vector p is with the random vector R of dimension, indicate are as follows:
Wherein, r~U (0,1) indicates 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 vector V；
(3.2) it cooperates
Iteration of the cultural gene algorithm Jing Guo former steps, can satisfy algorithm diversity, effectively prevent algorithmic statement to office
Portion's optimal solution.But experimental data shows that, in the algorithm later period, algorithm the convergence speed is slower, it is therefore desirable to increase acceleration mechanism, mention
High algorithm the convergence speed.Increase after intelligent body is vied each other and cooperate operation, when between the intelligent body in two regions
When distance is less than cooperation distance D, all average individuals that strength is worth in small intelligent body region in two intelligent bodies are returned
The big intelligent body region of strength value is all, i.e., two intelligent bodies merge to increase strength value, to increase itself competitiveness；
Intelligent body xcWith xdBetween cooperation distance D is defined as:
D=norm (xc-xd)×u
Wherein, c=1,2 ... Magent, d=1,2 ... Magent, norm function is to seek Norm function, and u indicates 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 number of iterations, 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, the flow controller between exchanging area and DC area is used
Bi-directional current controller realizes that the power circulation between exchanging area and DC area improves to reduce microgrid operating cost to micro-
The utilization rate in source.The micro- source in exchanging area includes micro turbine, diesel-driven generator and wind-power electricity generation, the micro- source in DC area include fuel cell,
Photovoltaic power generation and capacity are battery 1 of 250kWh, and each micro- source parameter is as shown in table 1.Storage battery charge state changes model
It encloses for 0.3~0.9, SocinitialAnd Socend0.3 is taken, in order to give full play to the effect of battery peak load shifting, according to Fig. 4 institute
The load prediction curve figure shown determines battery in peak times of power consumption 11:00-13:00 and 20:00-24:00, as long as charged shape
State meets constraint condition, and battery necessarily is in discharge condition, remaining period battery charges, when state-of-charge reaches
When 0.9, stop charging.Table 2 is that cost and emission factor are converted in micro- source, and table 3 is different moments power grid purchase electricity price.
The parameter in each micro- source of table 1
Convert cost and emission factor in the micro- source of table 2
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
24 hours each micro- source power outputs optimize in it, to consider power grid purchases strategies, micro- source fuel cost, environmental benefit cost, net
Damage and the minimum objective function of operation expense, and obey microgrid internal power balance, points of common connection transmission capacity, can
Control micro- source climbing rate, unit time accumulator cell charging and discharging bound, storage battery charge state bound, battery surrounding time section
Multiple constraints such as power-balance and storage battery charge state are constant；
(2) objective function is solved with cultural gene algorithm, it is micro- obtains alternating current-direct current mixing as shown in Figure 4-Figure 7
Each micro- source power output situation curve and cost curve of exchanging area and DC area are netted, wherein economic 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, battery and photovoltaic power 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 constraint condition；
(4) pass through cultural gene algorithm solving optimization objective function, finally obtain each micro- source in alternating current-direct current mixing microgrid
Situation of contributing and points of common connection flowing power diagram are as shown in Fig. 9 a- Figure 10.Wherein PLA and PLD respectively indicates exchanging area and straight
The load in area is flowed, GRID indicates that, from power grid power purchase electricity, ESC and ESD respectively indicate battery and be charged and discharged electricity.
Example of the invention is solved with cultural gene algorithm, 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 power output can
Meet various constraints, battery reaches full state when load is larger, since the micro- source cost of electricity-generating in exchanging area is more micro- than DC area
Source is higher, so there are the flow of power of DC area to exchanging area.
In conclusion test result through this embodiment, illustrates a kind of friendship based on cultural gene algorithm of the invention
Direct current mixing microgrid optimizing operation method can effectively solve the nonlinear optimization objective function of multiple target, multiple constraint, improve microgrid
Economic benefit and environmental benefit, it was confirmed that the validity and correctness of cultural gene algorithm.
Claims (4)
1. a kind of alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm, which is characterized in that including walking as follows
It is rapid:
1) mixed for the alternating current-direct current comprising wind-power electricity generation, photovoltaic power generation, battery, micro turbine, fuel cell and diesel-driven generator
Microgrid is closed, multiple target, multiple constraint, nonlinear optimization operation mathematical model, objective function consideration power grid purchases strategies, micro- are established
Source fuel cost, environmental benefit cost, network loss and operation expense, and obey microgrid internal power and balance, is commonly connected
Point transmission capacity, unit time accumulator cell charging and discharging bound, storage battery charge state bound, stores controllable micro- source climbing rate
Battery surrounding time section power-balance and the constant constraint condition of storage battery charge state；
2) alternating current-direct current mixing microgrid mathematical model is solved using cultural gene algorithm；Include:
(1) it initializes
Initial individuals are generated using random generating mode, use varminm、varmaxmIndicate the value range of m-th of variable, wherein
Any one variable xmInitial value obtained by following formula:
xm=varminm+rand(0,1)*(varmaxm-varminm)
If M is all individual numbers in group, 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 groupagentThe individual of position, remaining individual is average individual in group, 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:
The average individual number that each intelligent body region is assigned to are as follows:
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 initialization 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 moving distance Move obedience is uniformly distributed, and indicates
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 be the same area between intelligent body and average individual
Distance；
(2.2) movement is changed
In order to which the mobile speed of the average individual in carrying out polymerization process slows down, increase population diversity, to each average individual
It is changed at random, the average individual number changed in the average individual that a-th of intelligent body region is possessed
Ma,rpublicAre as follows:
Ma.rpublic=round (qa×M.Sa)
Wherein, round function is the function that rounds up, qaFor change rate, 0.3 is taken；
Inside each region after polymerizeing and changing movement, great changes have taken place for the strength value meeting of each individual, needs according to each
The rearrangement of region ideal adaptation angle value, the ideal adaptation angle value to make number one is best, is new intelligent body；
(3) global search, comprising:
(3.1) it vies each other
Each iteration is by needing to calculate total strength value of each intelligent body after local search, and intelligent body is always real in each region
Force value is codetermined by the strength value of itself and with the strength value of the average individual in region, is calculated by following formula:
T.SaFor total strength of a-th of intelligent body, ξ is a positive number less than 1, indicates that average individual strength accounts in the same area
The weight of the total strength of intelligent body, takes 0.1, wbFor the strength value of average individual；
A-th of intelligent body competition probability is expressed as:
Wherein, M.T.SaIndicate relatively total strength value of a-th of intelligent body, is 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 are as follows:
It introduces and vector p is with the random vector R of dimension, indicate are as follows:
Wherein, r~U (0,1) indicates 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 vector V；
(3.2) it cooperates
Increase after intelligent body is vied each other and cooperate operation, when the distance between the intelligent body in two regions is less than cooperation
When distance D, strength is worth the intelligence that all average individuals in small intelligent body region return strength value big in two intelligent bodies
Energy body region is all, i.e., two intelligent bodies merge to increase strength value, to increase itself competitiveness；Intelligent body xcWith xd
Between cooperation distance D is defined as:
D=norm (xc-xd)×u
Wherein, c=1,2 ... Magent, d=1,2 ... Magent, norm function is to seek Norm function, and u indicates cooperation coefficient, value
It is 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 number of iterations, 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.
2. a kind of alternating current-direct current mixing microgrid optimizing operation method based on cultural gene algorithm according to claim 1,
It is characterized in that, objective 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 economic cost；FlossFor network loss；FomFor equipment operation maintenance cost；
FACGridFor exchanging area power grid purchases strategies；FACecFor exchanging area economic 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 economic 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 FDCGridIt is respectively as follows:
Wherein, Δ TACIt is exchanging area to the period of 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；ΔTDCIt is DC area to the period of 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 economic cost FACecWith DC area economic cost FDCec:
FACec=FACfuel+FACen
FDCec=FDCfuel+FDCen
Exchanging area includes wind-power electricity generation, micro turbine and diesel-driven generator, and DC area includes photovoltaic power generation, fuel cell and electric power storage
Pond, exchanging area fuel cost FACfuelWith DC area fuel cost FDCfuelIt can be calculated by following formula:
FACfuel=FMTfuel+FDEGfuel
FDCfuel=FFCfuel
ηFC=-0.0023 × PFC+0.6735
Wherein, FMTfuelFor micro turbine fuel cost；CMTFor micro turbine cooler fuel price；LHV is the low heat value of fuel gas；PMTFor
Micro turbine output power；ηMTFor the generating efficiency of micro turbine；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 is given
Out；
Exchanging area environmental benefit cost FACenWith DC area environmental benefit cost FDCenIt is calculated by 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,jFor the unit discharge for the jth kind pollutant that i-th of micro- source generates, 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:
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 is the operation expense coefficient in i-th of micro- source；
The method of weighting is taken, objective function is finally obtained are as follows:
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,
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,
It is characterized in that, constraint condition described in step 1) includes:
(1) microgrid internal power equilibrium constraint
(2) points of common connection transmission capacity constraint condition
(3) controllable micro- source climbing rate constraint condition
Pi t-Pi t-1≤riΔt
(4) unit time accumulator cell charging and discharging bound constraint condition
(5) storage battery charge state (State of Charge, SOC) bound constraint condition
Socmin≤Soct≤Socmax
(6) 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；Pi tIndicate t-th i-th of the period micro- source power output；Indicate t-th of period from power grid power purchase function
Rate；For t-th of period battery power variable quantity, electric discharge is positive, and charging is negative；Respectively indicate t-th of period
AC and DC area payload；For the net flow power of t-th of period points of common connection；PG,maxIt transmits and holds for points of common connection
Measure limit value；riFor the unit time climbing rate in i-th of micro- source；For the absolute value of t-th of period battery power variable quantity；For the limit value of t-th of period battery power variable quantity；SoctFor the state-of-charge of battery in t-th of period；
Socmin、SocmaxThe respectively upper lower limit value of state-of-charge；Uu indicates charge and discharge electrostrictive coefficient, is 1 when 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.
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