CN108764509A - A method of carrying out mutually coordinated optimization between power generating facilities and power grids load three - Google Patents
A method of carrying out mutually coordinated optimization between power generating facilities and power grids load three Download PDFInfo
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Abstract
The invention discloses a kind of methods that mutually coordinated optimization is carried out between load three to power generating facilities and power grids.It is under the background of urban energy internet, other than considering traditional unit, it generates electricity to distributed new, the systematic consideration of energy-storage system, the demand response policy for carrying out zone user direct load control plans above-mentioned system using the method for Unit Combination and builds economic environment Optimized model in conjunction with user satisfaction, and model is optimized using Niche Genetic particle swarm optimization and optimal solution set decision-making technique, obtain optimal decision data.While this method considers economy with environmental factor, user power utilization experience satisfaction is taken into account, the power interaction problems at feed end and demand end has been fully considered, discharge capacity and the cost of system can be efficiently reduced using this method, is conducive to environmental protection.
Description
Technical field
The invention belongs to network optimization technical fields, and in particular to be carried out between a kind of load three to power generating facilities and power grids mutual
The method of coordination optimization.
Background technology
With industrial production and resident living to energy demand increasingly increase and the day of environmental problem and energy development
Beneficial contradiction, energy problem become the hot spot inquired into recent years.And with the popularization of the energy and the reform of power domain supply side, the energy
This concept of internet occurs gradually in face of people, becomes the Main Trends of The Development of urban power distribution network.
Under the system of energy internet, urban distribution network be done step-by-step using Internet technology power supply in region,
The coordination of energy storage device and load.Thermal power generation proportion shared in traditional power grid is gradually reduced, is formed laterally from source
With longitudinal complementation, in addition to this, load is again nor the unrelated person of network optimization, but as the participant of optimization, with electricity
Source, power grid form various complementations, accomplish that economic environment is grabbed on the other hand, the operation of urban distribution network can be made more safe and reliable.
But still lack effective method at present.
Invention content
To solve the above-mentioned problems, the purpose of the present invention is to provide carry out phase between a kind of load three to power generating facilities and power grids
The method mutually coordinated and optimized.
In order to achieve the above object, mutually coordinated optimization is carried out between the load three provided by the invention to power generating facilities and power grids
Method includes the following steps carried out in order：
Step 1：Economic cost model and Environmental costs model are established, wherein economic cost includes fuel cost, distribution
Power supply cost, Demand Side Response cost, energy storage cost and unit starting cost；
Step 2：Using microhabitat multi-objective Genetic particle swarm optimization to above-mentioned economic cost FcWith Environmental costs FeIt carries out excellent
Change, obtains Pareto optimal solution sets；
Step 3：Above-mentioned Pareto optimal solution sets are combined using optimal solution set decision-making technique, obtain optimizing decision amount, later
The power of each unit is measured according to optimizing decision.
In step 1, described to establish economic cost model and Environmental costs model, wherein economic cost include fuel at
The method of sheet, distributed generation resource cost, Demand Side Response cost, energy storage cost and unit starting cost is：
Fuel cost：
Fuel cost is the fuel cost of traditional fired power generating unit, with generated output at the relationship of quadratic function：
PitIndicate tradition fired power generating unit i in the generated output of t periods, ai, bi, ciIndicate the cost system of traditional fired power generating unit i
Number；
Distributed generation resource cost：
Distributed generation resource only considers to generate power and convey to the part of power grid, user's transmission grid parts of distributed generation resource is seen
For a special unit, then its cost of electricity-generating just as traditional fired power generating unit it is the same with generated output at the pass of quadratic function
System；
DGtIndicate distributed generation resource in the output power of t periods, aDG, bDG, cDGIndicate the cost coefficient of distributed generation resource；
Demand Side Response cost：
The characteristics of being controlled by direct load finds out the square formation of control coefrficient matrix N x N, and value range is in [- 1,1]；0
Indicate uncontrolled, -1 indicates maximum abatement load, and 1 indicates maximum payback load；Control coefrficient matrix is an inferior triangular flap,
It is the slave mode of the period on diagonal line, other is rebound influence；
The relationship of controlled-load and payback load is：
ΔPt=αfPt-1+βfPt-2+γfPt-3 (3)
Wherein, αf, βf, γfIndicate rebound coefficient, and αf+βf+γf=1；
ΔPtIndicate the payback load of t periods, PtIndicate the controllable burden of t periods,Indicate that t periods practical change is born
Lotus；
Then Demand Side Response costFor：
Lk, ρk-1Indicate that kth stage load is cut down and abatement cost, L indicate the step-length of the abatement per stage load；
Energy storage cost：
Energy storage costFor：
Lck, ρc(k-1)Indicate that kth grade energy storage calling and energy storage call cost, LcIndicate the step-length that every grade of energy storage is called,Table
Show the energy storage calling amount of t periods；
Unit starting cost：
Unit starting cost Sci,tFor：
hcostiIndicate the thermal starting cost of unit i, ccostiIndicate the cold start-up cost of unit i, CDTiIndicate unit i
The continuous downtime of minimum of permission, cshiIndicate that the cold start-up time of unit i, DT indicate the downtime of unit i；
Therefore, economic cost FcFor：
UitOn off states of the expression unit i in the t periods；
Environmental costs：
Environmental costs FeFor：
δi, εi, ∈iIndicate the emission factor of unit i.
In step 2, the utilization microhabitat multi-objective Genetic particle swarm optimization is to above-mentioned economic cost FcWith environment at
This FeIt optimizes, the method for obtaining Pareto optimal solution sets is：
The location update formula of particle cluster algorithm,
Wherein, c1,c2For two constant values, rand1, rand2It is the random number between [- 1,1], PbestIndicate individual
Optimal location, GbestIndicate global optimum position；XiIndicate individual；Indicate speed when individual i+1 iteration of kth；
Weight coefficient ω is：
ω=ωmax-(ωmax-ωmin)iter/maxgen (16)
ωmaxWith ωminIt is the maximum and MINIMUM WEIGHT weight values of setting, iter is current iterations, and maxgen is maximum
Iterations；
To economic cost FcWith Environmental costs FeIt optimizes, the method for obtaining Pareto optimal solution sets includes the following steps：
A：Generate initial population P0With speed V0If initial population P0In a certain individual it is non-be inferior to it is more than half in population
Other individuals, then initial individuals optimal location Pbest=X0, otherwise personal best particle P is selected with randomizedbest；
One optimal population H is set, if population can accommodate a certain number of individuals, by initial population P0In individual
All it is put into optimal population H；
B：Calculate the economic cost F of each individual in optimal population HcWith Environmental costs Fe；
C：Individual X in optimal population H is calculated using microhabitat shared mechanismiFitness Fi, calculation formula is as follows：
dijIt is individual XiWith XjEuclidean distance, σshareIt is the shared distance being previously set；
According to fitness ratio global optimum position G is selected with roulette methodbest；
D：Population is updated by formula (14), (15) and (16), while updating personal best particle Pbest；According to Pareto
The definition more solved is completed to update optimal population H, if individual amount is beyond most to the noninferior solution in population by microhabitat mechanism
The maximum quantity of excellent population H then deletes the small individual of fitness according to fitness, thus completes the update to optimal population H；
E：The some individuals selected in optimal population H carry out cross and variation, if result is non-bad and fitness is better than previous
Individual then retains this cross and variation；Otherwise it abandons；
Cross and variation process is：
(1) crossover process
1) random number is generated, it is no with regard to carrying out in next step if this random number is more than numerical value defined in formula (25)
Then terminate to intersect；
In formula：CPmaxWith CPminMaximum and minimum crossover probability is indicated respectively；
2) two freely individual X are selectedi=(xi1,xi2,…xin) and Xj=(xj1,xj2,…xjn), and randomly choose individual
The dimension of position vector；
3) v is enabled1=xik, v2=xjk, the random number R of one (0,1) is generated, is completed to x using formula (26) (27)ikAnd xjk
Update；
xik=Rv2+(1-R)·v1 (26)
xjk=Rv1+(1-R)·v2 (27)
(2) mutation process
1) random number is generated, it is no with regard to carrying out in next step if this random number is more than numerical value defined in formula (28)
Then terminate to make a variation；
In formula：MPmaxAnd MPminMinimum and maximum mutation probability is indicated respectively；
2) a freely individual X is selectedj=(xj1,xj2,…xjn), and randomly choose the dimension x of individual position vectorjk；
3) it enablesThe random number R for generating two (0,1), followed by formula (29) and
(30) x is updatedjk；
F：If reaching maximum iteration, Pareto optimal solution sets, otherwise, return to step C are just exported.
In step 3, the utilization optimal solution set decision-making technique combines above-mentioned Pareto optimal solution sets, obtains optimal determine
Plan amount, the method for measuring the power of each unit according to optimizing decision later are：
A：Environmental costs size is not considered, is only considered Optimum Economic cost, is obtained least cost Fm, then calculate at this moment
Discharge capacity is denoted as EM；
B：Economic cost size is not considered, is only considered suitable environment cost, is obtained minimum discharge capacity Em, then calculate at this moment
Economic cost, be denoted as FM；
C：Establish economic extent function, environment extent function and abatement load extent function；
Economic extent function is：
Environment extent function is：
Consider user satisfaction, establishing abatement load extent function is：
Wherein f1For the value of economic cost in Pareto optimal solution sets, f2For the value of Environmental costs in Pareto optimal solution sets,Cut down the value of load for whole day；
D：It is learnt according to economic extent function, environment extent function and the abatement load extent function in step C,
Global optimum's decision position of satisfaction is (1,1,1), therefore obtains final decision formula and be：
Y is decision content, and α, β, γ are economic factor, environmental factor and the weight coefficient for cutting down load factor；
E：Finally, it obtains one group and takes into account economy, environment and the decision data of user tripartite, obtained by this group of data each
The power of unit.
The method that mutually coordinated optimization is carried out between the load three provided by the invention to power generating facilities and power grids has following beneficial
Effect：
Under the background of urban energy internet, other than considering traditional unit, generate electricity to distributed new,
The demand response policy of direct load control is carried out zone user in the systematic consideration of energy-storage system, in conjunction with user satisfaction,
Above-mentioned system is planned using the method for Unit Combination and builds economic environment Optimized model, and utilizes Niche Genetic grain
Subgroup method and optimal solution set decision-making technique optimize model, obtain optimal decision data.This method considers economy and ring
While the factor of border, user power utilization experience satisfaction is taken into account, has fully considered the power interaction problems at feed end and demand end, profit
Discharge capacity and the cost that system can be efficiently reduced with this method, are conducive to environmental protection.
Description of the drawings
The characteristics of Fig. 1 is Demand Side Response cost is schemed.
The square formation figure of Fig. 2 coefficient matrix N x N in order to control.
Fig. 3 is energy storage cost characteristics figure.
Fig. 4 carries out the method flow diagram of mutually coordinated optimization between the load three provided by the invention to power generating facilities and power grids.
Fig. 5 is the load optimal curve graph of Demand Side Response in embodiment 1.
Specific implementation mode
The specific implementation mode of the present invention is described further in the following with reference to the drawings and specific embodiments：
As shown in figure 4, carrying out the method packet of mutually coordinated optimization between the load three provided by the invention to power generating facilities and power grids
Include the following steps carried out in order：
Step 1：Economic cost model and Environmental costs model are established, wherein economic cost includes fuel cost, distribution
Power supply cost, Demand Side Response cost, energy storage cost and unit starting cost；
Fuel cost：
Fuel cost is the fuel cost of traditional fired power generating unit, with generated output at the relationship of quadratic function：
PitIndicate tradition fired power generating unit i in the generated output of t periods, ai, bi, ciIndicate the cost system of traditional fired power generating unit i
(scholars have found that the cost of electricity-generating of conventional rack and generated output can approximatively be fitted to unitary two to number according to data statistics
Secondary function curve, the determination of coefficient here is by paper《A Novel Approach for Unit Commitment
Problem via an Effective Hybrid Particle Swarm Optimization》Middle offer, concrete numerical value
Refer to table one hereinafter).
Distributed generation resource cost：
Distributed generation resource only considers to generate power and convey to the part of power grid, user's transmission grid parts of distributed generation resource is seen
For a special unit, then its cost of electricity-generating just as traditional fired power generating unit it is the same with generated output at the pass of quadratic function
System；
DGtIndicate distributed generation resource in the output power of t periods, aDG, bDG, cDGIndicate the cost coefficient of distributed generation resource
(determination of coefficient is by paper here《A Fuzzy Bi-objective Unit Commitment Model
Considering Source-grid-load Interactions》Middle offer, concrete numerical value refers to table hereinafter
Three).
Demand Side Response cost：
Consider the demand response in urban distribution network region, control the region using direct load control methods rings into row energization
It answers.Its cost characteristics is as shown in Figure 1.
Cut down customer charge, provide compensation in the grade of different load, and these compensation also can be regarded as be always run at
This part.
The characteristics of being controlled by direct load finds out the square formation of control coefrficient matrix N x N.Value range is in [- 1,1].0
Indicate uncontrolled, -1 indicates maximum abatement load, and 1 indicates maximum payback load.Control coefrficient matrix is an inferior triangular flap,
It is the slave mode of the period on diagonal line, other is rebound influence, as shown in Figure 2.
The relationship of controlled-load and payback load is：
ΔPt=αfPt-1+βfPt-2+γfPt-3 (3)
Wherein, αf, βf, γfIndicate rebound coefficient, and αf+βf+γf=1；
ΔPtIndicate the payback load of t periods, PtIndicate the controllable burden of t periods,Indicate that t periods practical change is born
Lotus；
Then Demand Side Response costFor：
Lk, ρk-1Indicate that kth stage load is cut down and abatement cost, L indicate the step-length of the abatement per stage load.
Energy storage cost：
The calculation of energy storage cost is similar with Demand Side Response cost, and uses segment processing method, such as Fig. 3 institutes
Show,
Energy storage costFor：
Lck, ρc(k-1)Indicate that kth grade energy storage calling and energy storage call cost, LcIndicate the step-length that every grade of energy storage is called,Table
Show the energy storage calling amount of t periods；
Unit starting cost：
Unit starting cost Sci,tFor：
hcostiIndicate the thermal starting cost of unit i, ccostiIndicate the cold start-up cost of unit i, CDTiIndicate unit i
The continuous downtime of minimum of permission, cshiIndicate that the cold start-up time of unit i, DT indicate the downtime of unit i；
Therefore, economic cost FcFor：
UitOn off states of the expression unit i in the t periods；
Environmental costs：
It is considered herein that Environmental costs only are from traditional fired power generating unit, other parts are considered as zero-emission, and are polluted
Discharge is with generated output at the relationship of quadratic function；
Then Environmental costs FeFor：
δi, εi, ∈i(determination of coefficient is by paper to the emission factor of expression unit i here
《Intelligentunitcommitmentwithvehicle-to-grid》Middle offer, concrete numerical value refers to hereinafter
Table two)；
Step 2：Using microhabitat multi-objective Genetic particle swarm optimization to above-mentioned economic cost FcWith Environmental costs FeIt carries out excellent
Change, obtains Pareto optimal solution sets；
The location update formula of particle cluster algorithm,
Wherein, c1,c2For two constant values, rand1, rand2It is the random number between [- 1,1], PbestIndicate individual
Optimal location, GbestIndicate global optimum position；XiIndicate individual；Indicate speed when individual i+1 iteration of kth；
Weight coefficient ω is：
ω=ωmax-(ωmax-ωmin)iter/maxgen (16)
ωmaxWith ωminIt is the maximum and MINIMUM WEIGHT weight values of setting, iter is current iterations, and maxgen is maximum
Iterations.
To economic cost FcWith Environmental costs FeIt optimizes, the method for obtaining Pareto optimal solution sets includes the following steps：
A：Generate initial population P0With speed V0If initial population P0In a certain individual it is non-be inferior to it is more than half in population
Other individuals, then initial individuals optimal location Pbest=X0, otherwise personal best particle P is selected with randomizedbest；
One optimal population H is set, if population can accommodate a certain number of individuals, by initial population P0In individual
All it is put into optimal population H；
B：Calculate the economic cost F of each individual in optimal population HcWith Environmental costs Fe；
C：Individual X in optimal population H is calculated using microhabitat shared mechanismiFitness Fi, calculation formula is as follows：
dijIt is individual XiWith XjEuclidean distance, σshareIt is the shared distance being previously set；
According to fitness ratio global optimum position G is selected with roulette methodbest；
D：Population is updated by formula (14), (15) and (16), while updating personal best particle Pbest.According to Pareto
The definition more solved is completed to update optimal population H, if individual amount is beyond most to the noninferior solution in population by microhabitat mechanism
The maximum quantity of excellent population H then deletes the small individual of fitness according to fitness, thus completes the update to optimal population H；
E：The some individuals selected in optimal population H carry out cross and variation, if result is non-bad and fitness is better than previous
Individual then retains this cross and variation；Otherwise it abandons.
Cross and variation process is：
(1) crossover process
1) random number is generated, it is no with regard to carrying out in next step if this random number is more than numerical value defined in formula (25)
Then terminate to intersect.
In formula：CPmaxWith CPminMaximum and minimum crossover probability is indicated respectively；
2) two freely individual X are selectedi=(xi1,xi2,…xin) and Xj=(xj1,xj2,…xjn), and randomly choose individual
The dimension of position vector；
3) v is enabled1=xik, v2=xjk, the random number R of one (0,1) is generated, is completed to x using formula (26) (27)ikAnd xjk
Update；
xik=Rv2+(1-R)·v1 (26)
xjk=Rv1+(1-R)·v2 (27)
(2) mutation process
1) random number is generated, it is no with regard to carrying out in next step if this random number is more than numerical value defined in formula (28)
Then terminate to make a variation；
In formula：MPmaxAnd MPminMinimum and maximum mutation probability is indicated respectively；
2) a freely individual X is selectedj=(xj1,xj2,…xjn), and randomly choose the dimension x of individual position vectorjk；
3) it enablesThe random number R for generating two (0,1), followed by formula (29) and
(30) x is updatedjk。
F：If reaching maximum iteration, Pareto optimal solution sets, otherwise, return to step C are just exported.
Step 3：Above-mentioned Pareto optimal solution sets are combined using optimal solution set decision-making technique, obtain optimizing decision amount, later
The power of each unit is measured according to optimizing decision.
It is as follows：
A：Environmental costs size is not considered, is only considered Optimum Economic cost, is obtained least cost Fm, then calculate at this moment
Discharge capacity is denoted as EM；
B：Economic cost size is not considered, is only considered suitable environment cost, is obtained minimum discharge capacity Em, then calculate at this moment
Economic cost, be denoted as FM；
C：Establish economic extent function, environment extent function and abatement load extent function；
Economic extent function is：
Environment extent function is：
Consider user satisfaction, establishing abatement load extent function is：
Wherein f1For the value of economic cost in Pareto optimal solution sets, f2For the value of Environmental costs in Pareto optimal solution sets,Cut down the value of load for whole day；
D：It is learnt according to economic extent function, environment extent function and the abatement load extent function in step C,
Global optimum's decision position of satisfaction is (1,1,1), therefore obtains final decision formula and be：
Y is decision content, and α, β, γ are economic factor, environmental factor and the weight coefficient for cutting down load factor；
E：Finally, it obtains one group and takes into account economy, environment and the decision data of user tripartite, obtained by this group of data each
The power of unit.
Embodiment 1
The present invention illustrates by taking ten units as an example：
The systematic economy coefficient of ten units is as shown in table 1, and the system emission factor of ten units is as shown in table 2, and table 3 is
Distributed generation resource regards the parameter of special unit as, according to the coefficient value of 1-table of table 3, by microhabitat multi-objective Genetic population
Method and optimal solution set decision-making technique obtain power, distributed electrical source power, Demand Side Response power and the energy storage system of ten units
System power, as shown in table 4.The economic cost and discharge capacity of final optimization pass are shown in Table 5.
Table 1：The systematic economy coefficient of ten units
Table 2：The system emission factor of ten units
Table 3：Distributed generation resource regards the parameter of special unit as
Table 4：The power meter of each unit after optimization
Table 5：The economic cost and discharge capacity of final optimization pass
Certainly, above description is not limitation of the present invention, and the present invention is also not limited to the example above, this technology neck
The variations, modifications, additions or substitutions that the technical staff in domain is made in the essential scope of the present invention should also belong to the present invention's
Protection domain.
Claims (4)
1. a kind of method for carrying out mutually coordinated optimization between load three to power generating facilities and power grids, it is characterised in that：The method
Including the following steps carried out in order：
Step 1：Economic cost model and Environmental costs model are established, wherein economic cost includes fuel cost, distributed generation resource
Cost, Demand Side Response cost, energy storage cost and unit starting cost；
Step 2：Using microhabitat multi-objective Genetic particle swarm optimization to above-mentioned economic cost FcWith Environmental costs FeIt optimizes, obtains
To Pareto optimal solution sets；
Step 3：Above-mentioned Pareto optimal solution sets are combined using optimal solution set decision-making technique, obtain optimizing decision amount, later basis
Optimizing decision measures the power of each unit.
2. the method for carrying out mutually coordinated optimization between the load three according to claim 1 to power generating facilities and power grids, feature
It is：In step 1, described to establish economic cost model and Environmental costs model, wherein economic cost include fuel cost,
Distributed generation resource cost, Demand Side Response cost, the method for energy storage cost and unit starting cost are：
Fuel cost：
Fuel cost is the fuel cost of traditional fired power generating unit, with generated output at the relationship of quadratic function：
PitIndicate tradition fired power generating unit i in the generated output of t periods, ai, bi, ciIndicate the cost coefficient of traditional fired power generating unit i；
Distributed generation resource cost：
Distributed generation resource only considers to generate power and convey to the part of power grid, and the user of distributed generation resource, which is transmitted grid parts, is seen as one
A special unit, then its cost of electricity-generating just as traditional fired power generating unit it is the same with generated output at the relationship of quadratic function；
DGtIndicate distributed generation resource in the output power of t periods, aDG, bDG, cDGIndicate the cost coefficient of distributed generation resource；
Demand Side Response cost：
The characteristics of being controlled by direct load finds out the square formation of control coefrficient matrix N x N, and value range is in [- 1,1]；0 indicates
Uncontrolled, -1 indicates maximum abatement load, and 1 indicates maximum payback load；Control coefrficient matrix is an inferior triangular flap, diagonally
It is the slave mode of the period on line, other is rebound influence；
The relationship of controlled-load and payback load is：
ΔPt=αfPt-1+βfPt-2+γfPt-3 (3)
Wherein, αf, βf, γfIndicate rebound coefficient, and αf+βf+γf=1；
ΔPtIndicate the payback load of t periods, PtIndicate the controllable burden of t periods,Indicate t periods practical change load；
Then Demand Side Response costFor：
Lk, ρk-1Indicate that kth stage load is cut down and abatement cost, L indicate the step-length of the abatement per stage load；
Energy storage cost：
Energy storage costFor：
Lck, ρc(k-1)Indicate that kth grade energy storage calling and energy storage call cost, LcIndicate the step-length that every grade of energy storage is called,Indicate t
The energy storage calling amount of period；
Unit starting cost：
Unit starting cost Sci,tFor：
hcostiIndicate the thermal starting cost of unit i, ccostiIndicate the cold start-up cost of unit i, CDTiIndicate that unit i allows
The continuous downtime of minimum, cshiIndicate that the cold start-up time of unit i, DT indicate the downtime of unit i；
Therefore, economic cost FcFor：
UitOn off states of the expression unit i in the t periods；
Environmental costs：
Environmental costs FeFor：
δi, εi, ∈iIndicate the emission factor of unit i.
3. the method for carrying out mutually coordinated optimization between the load three according to claim 1 to power generating facilities and power grids, feature
It is：In step 2, the utilization microhabitat multi-objective Genetic particle swarm optimization is to above-mentioned economic cost FcWith Environmental costs Fe
It optimizes, the method for obtaining Pareto optimal solution sets is：
The location update formula of particle cluster algorithm,
Wherein, c1,c2For two constant values, rand1, rand2It is the random number between [- 1,1], PbestIndicate that individual is optimal
Position, GbestIndicate global optimum position；XiIndicate individual；Indicate speed when individual i+1 iteration of kth；
Weight coefficient ω is：
ω=ωmax-(ωmax-ωmin)iter/maxgen (16)
ωmaxWith ωminIt is the maximum and MINIMUM WEIGHT weight values of setting, iter is current iterations, and maxgen is greatest iteration
Number；
To economic cost FcWith Environmental costs FeIt optimizes, the method for obtaining Pareto optimal solution sets includes the following steps：
A：Generate initial population P0With speed V0If initial population P0In a certain individual is non-is inferior to more than half its in population
It is individual, then initial individuals optimal location Pbest=X0, otherwise personal best particle P is selected with randomizedbest；
One optimal population H is set, if population can accommodate a certain number of individuals, by initial population P0In individual all
It is put into optimal population H；
B：Calculate the economic cost F of each individual in optimal population HcWith Environmental costs Fe；
C：Individual X in optimal population H is calculated using microhabitat shared mechanismiFitness Fi, calculation formula is as follows：
dijIt is individual XiWith XjEuclidean distance, σshareIt is the shared distance being previously set；
According to fitness ratio global optimum position G is selected with roulette methodbest；
D：Population is updated by formula (14), (15) and (16), while updating personal best particle Pbest；It is solved according to Pareto more
Definition, to the noninferior solution in population by microhabitat mechanism complete to optimal population H update, if individual amount exceed optimal kind
The maximum quantity of group H then deletes the small individual of fitness according to fitness, thus completes the update to optimal population H；
E：The some individuals selected in optimal population H carry out cross and variation, if result is non-bad and fitness is individual better than previously,
Then retain this cross and variation；Otherwise it abandons；
Cross and variation process is：
(1) crossover process
1) random number is generated, if this random number is more than numerical value defined in formula (25), with regard in next step, otherwise tie
Beam intersects；
In formula：CPmaxWith CPminMaximum and minimum crossover probability is indicated respectively；
2) two freely individual X are selectedi=(xi1,xi2,…xin) and Xj=(xj1,xj2,…xjn), and randomly choose a body position
The dimension of vector；
3) v is enabled1=xik, v2=xjk, the random number R of one (0,1) is generated, is completed to x using formula (26) (27)ikAnd xjkMore
Newly；
xik=Rv2+(1-R)·v1 (26)
xjk=Rv1+(1-R)·v2 (27)
(2) mutation process
1) random number is generated, if this random number is more than numerical value defined in formula (28), with regard in next step, otherwise tie
Shu Bianyi；
In formula：MPmaxAnd MPminMinimum and maximum mutation probability is indicated respectively；
2) a freely individual X is selectedj=(xj1,xj2,…xjn), and randomly choose the dimension x of individual position vectorjk；
3) it enablesThe random number R for generating two (0,1), followed by formula (29) and (30)
Update xjk；
F：If reaching maximum iteration, Pareto optimal solution sets, otherwise, return to step C are just exported.
4. the method for carrying out mutually coordinated optimization between the load three according to claim 1 to power generating facilities and power grids, feature
It is：In step 3, the utilization optimal solution set decision-making technique combines above-mentioned Pareto optimal solution sets, obtains optimizing decision
Amount, the method for measuring the power of each unit according to optimizing decision later are：
A：Environmental costs size is not considered, is only considered Optimum Economic cost, is obtained least cost Fm, then calculate discharge at this moment
Amount, is denoted as EM；
B：Economic cost size is not considered, is only considered suitable environment cost, is obtained minimum discharge capacity Em, then calculate warp at this moment
Ji cost, is denoted as FM；
C：Establish economic extent function, environment extent function and abatement load extent function；
Economic extent function is：
Environment extent function is：
Consider user satisfaction, establishing abatement load extent function is：
Wherein f1For the value of economic cost in Pareto optimal solution sets, f2For the value of Environmental costs in Pareto optimal solution sets,Cut down the value of load for whole day；
D：It is learnt according to economic extent function, environment extent function and the abatement load extent function in step C, it is satisfied
Global optimum's decision position of degree is (1,1,1), therefore obtains final decision formula and be：
Y is decision content, and α, β, γ are economic factor, environmental factor and the weight coefficient for cutting down load factor；
E：Finally, it obtains one group and takes into account economy, environment and the decision data of user tripartite, each unit is obtained by this group of data
Power.
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