CN110232481A - Gas distributing system Multiobjective Optimal Operation method based on MQPSO - Google Patents
Gas distributing system Multiobjective Optimal Operation method based on MQPSO Download PDFInfo
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Abstract
The invention discloses a kind of the gas distributing system Multiobjective Optimal Operation method based on MQPSO, specific steps are as follows: according to the distribution situation of node, pipeline section and compressor in gas distributing system, establish natural gas tube pessimistic concurrency control；Using the user traffic data, the operation data of compressor as variable, and initial value is assigned to user traffic data, the operation data of compressor, generates initial population；It is randomly provided an external archival, then designs MQPSO algorithm, solves pipe network multi-objective optimization scheduling, obtains the optimal value of each node flow distribution and compressor operating parameter.The experimental results showed that equally distributed optimal solution set can be quickly obtained by this method, dispatched using the decision parameters Instructing manufacture of acquisition.Compared with other algorithms, user maximum satisfaction improves 75%, while compressor station energy consumption reduces by 26%, it was demonstrated that the validity of this algorithm.
Description
Technical field
The present invention relates to natural gas dispatching technique field, the more mesh of specifically a kind of gas distributing system based on MQPSO
Mark Optimization Scheduling.
Background technique
Gas distributing system traffic control is an extremely complex industrial process, non-linear, the changeable measure feature having
Many problems are brought to the scheduling of practical pipe network.Currently, what scheduling process relied on mostly is artificial experience, pass through dispatcher's experience
The mode of regulation is allocated natural gas, however which usually will cause that compressor station energy consumption is huge, assignment of traffic efficiency
The problems such as low.Therefore, in order to effectively solve the above problems, people begin one's study intelligent algorithm answering on pipe network operation
With.Wherein, a kind of gas distributing system running optimizatin, Xiang Gui based on improved mode searching algorithm that Li Ligang et al. is proposed
The selection that treasured et al. proposes that the simulation based on the long defeated pipe network of Aspen and Isight natural gas is proposed with optimization, Yu Jin et al. changes
Calculating research is optimized to gas distributing system operation energy consumption into Linear Approximation Algorithm and Sequential Quadratic Programming method.
However, most of for the research of gas distributing system scheduling process at present is all single-object problem, with system
The research that optimization method carries out multi-objective problem analysis is less.The gas distributing system operation optimization discussion that Li Bo et al. is proposed mentions
Go out using the check of pipeline hydraulic thermodynamic computing, compressor station sharing of load as the method for solving of optimization object, but can not establish precisely
Optimized model.The small towns gas distributing system optimizing research based on genetic algorithm that document Ao Jing et al. is proposed fully considers multiple excellent
Change target, but algorithm local search ability itself is poor, causes later stage of evolution efficiency lower.And natural gas is produced and is looked forward to
Industry should consider whole user satisfaction, also consider the energy consumption level of compressor.Therefore how gas distributing system was dispatched
Cheng Jinhang multiple-objection optimization analysis, is current natural gas manufacturing enterprise important topic urgently to be resolved.
Summary of the invention
In view of the above-mentioned problems, the present invention provides a kind of gas distributing system Multiobjective Optimal Operation method based on MQPSO,
External archive is updated by the relationship between balancing user satisfaction and energy consumption of compressor, to safeguard the diversity of optimal solution,
It is adaptively adjusted the position of population simultaneously, algorithm is avoided to occur restraining too early problem.Using this method to natural gas tube
Multi-objective optimization question is solved during net production scheduling, obtains optimized operation parameter.
In order to achieve the above objectives, the specific technical solution that the present invention uses is as follows:
A kind of gas distributing system Multiobjective Optimal Operation method based on MQPSO, key technology are specific steps are as follows:
S1: according to the distribution situation of node, pipeline section and compressor in gas distributing system, establishing natural gas tube pessimistic concurrency control,
And obtain all user traffic datas in gas distributing system, the operation data of compressor；
S2: using the user traffic data, the operation data of compressor as variable, and to user traffic data, compression
The operation data of machine assigns initial value, generates initial population, which is N；
It is randomly provided an external archival, and defining the external archival capacity is N；
Set maximum number of iterations G, current iteration number t=1；
S3: processing of crossing the border is done to the particle of initial population；
S4: external archival is updated；
The fitness of step S3 treated initial population is calculated, and all fitness are ranked up, selects fitness
High particle is put into external archival or substitutes the particle in external archival；
S5: MQPSO algorithm is used, speed and the position of population are adjusted；And current iteration number t=t+1 is enabled, judge t
Whether t is met more than or equal to G；If so, entering step S6；Otherwise return step S3；
S6: using particle all in the external archival as objective optimization solution, and the flat of all objective optimization solutions is calculated
Mean value is as optimal solution；Obtain the operation data of corresponding user traffic data and compressor.
Using the above method, from practical mini gas pipe network operation scheduling process experimental result it is found that utilizing multiple target
Quantum particle swarm optimization can reduce energy consumption of compressor, realize energy-efficient purpose while improving satisfaction.
Further, cross the border the formula of processing in step S3 are as follows:
xi,d=lbd+(hbd-lbd)·rand,xi,d< lbd(16)；
xi,d=ubd-(hbd-lbd)·rand,xi,d> hbd(17)；
It will extend over the particle x on boundaryi,dIt is withdrawn into any random site in feasible zone, hb in formuladAnd lbdFor Particle confinement side
Boundary；I represents i-th of particle, i=1,2 ..., N；
If particle violates performance constraints after location updating, allow the particle to cross the border in previous each generation individual by formula (18)
Particle position is randomly selected in optimal location:
T is current iteration number in formula, and k is current iteration number pervious any generation, and PLast is that i-th of particle is previous
Each generation individual desired positions array, Pop is when former generation population number group.
Initialization population size N, maximum number of iterations G, external archive size N, by the decision variable value of optimization problem
Range initializes particle location information.
Further, the calculating content of step S4 fitness are as follows:
During pipe network operation, compressor station provides pressure loss of the energy to overcome natural gas to flow in the duct.
Energy consumption of compressor optimization refers to the operating parameter for adjusting each compressor station and booting number of units, is ensuring pipe network completion transfer gas times
While business, the total energy consumption of compressor station is reduced.
The calculation expression of the operation data fitness of compressor are as follows:
W is compressor total energy consumption in formula；Γ be pipe network system in compression mechanism at set, QcinFor compressor inlet shape
Volume flow under state；ρcinFor the natural gas density under compressor inlet state；H is compressor lift, and n turns for compressor
Speed,；η is compressor efficiency；
The target that responsible consumer preferentially distributes is: specific pipe network structure, user's gas ability, gas source feed ability and
In the case where pipe network gas transmission ability, according to the importance of user, pipe network user structure is adjusted, realizes that natural gas supply and user need
The best configuration asked.
The calculation expression of user traffic data fitness are as follows:
F is objective function in formula, specifically:
X indicates that decision vector, y indicate object vector in formula (1)；X indicates the decision space that decision vector x is formed, Y table
Show the object space that object vector y is formed；F is the majorized function that x is mapped to object vector space；gi(x)≤0, (i=1,
2 ..., h) it is that x is permitted the h constraint condition met；
Vd is the user node number in pipe network system, MiFor the different degree that meets of user i, riFor the load system of user i
Number is defined by formula (4):
ri=qi/q(i,max) (4)
Q in formulaiFor the air demand of user i；q(i,max)For a maximum demand of user i.
Since every class independent variable has different property, so needing to carry out independent variable certain limit in design
System, is known as constraint condition for the restrictive mathematical function of design variable.Gas distributing system optimized operation scheme should meet use
The constraint conditions such as family demand, pipeline pressure and the reasonable operating parameter range of compressor station.
Further, the volume flow Q under the compressor inlet statecinThe constraint relationship between compressor lift H
Are as follows:
Volume flow Q under the compressor inlet statecinWith the constraint relationship between compressor efficiency η are as follows:
A in formula1, b1, c1, d1, a2, b2, c2, d2It is the nominal parameter coefficient of compressor, it can be according to compressor nominal parameter number
It is returned to obtain according to using least square method；
The constraint relationship of compressor rotary speed are as follows:
Compressor rotary speed n is in minimum speed nminWith maximum (top) speed nmaxBetween:
nmin≤n≤nmax(7)；
The constraint relationship of volume flow under compressor inlet state are as follows:
Q(i,min)≤Qi≤Q(i,max)(i=1,2 ..., N) (8)
Formula QiFor the air demand of i-th of admission, Q(i,min)For the minimum air demand of i-th of admission, Q(i,max)It is i-th
The maximum air demand of a admission, N are total air inlet number of nodes；
Natural gas from source of supply through pipeline to each point of gas point, into the constraint relationship for the pressure for dividing gas point are as follows:
P(i,min)≤Pi≤P(i,max)(i=1,2 ..., N) (9)
P in formulaiIt isiThe pressure of a admission, P(i,min)For the minimum pressure of i-th of admission, P(i,max)It isiIt is a into
The maximum pressure of gas point.
Further, in step S3, if external archival is not filled with, directly the high particle of fitness is put into described
In external archival；
If external archival is filled with, by fitness value minimum in the fitness value of the particle of selection and the external archival
It is compared, if the fitness value of the particle of selection is greater than fitness value, the particle of selection is replaced in the external archival
The corresponding particle of minimum fitness value；Otherwise it is not processed.
External archival is exported at the end of algorithm and is calculated for saving particle non-domination solution obtained in search process
As a result, the iterative process of bootstrap algorithm is simultaneously to guarantee the global convergence of algorithm.Control the maximum-norm of external archive, Ke Yibao
It demonstrate,proves the diversity of non-domination solution and saves optimum individual, be of great significance to algorithm.
Further, in step S5, the speed of population and the content of position are adjusted are as follows:
Pij(t)=rij(t)×bij(t)+(1-rij(t))×bgj(t)(14)
I (i=1,2 ..., N) represents i-th of particle in formula, and N is initial population size, and j (j=1,2 ..., D) is represented
The jth of particle is tieed up, and D is search space dimension, and t is evolutionary generation, rij(t) and sijIt (t) is to be uniformly distributed in [0,1] section
Random number, xij(t) current location of particle i when evolutionary generation is t is indicated；bij(t) particle i when evolutionary generation is t is indicated
Personal best particle；pij(t) the attractor position of particle i when evolutionary generation is t, b are indicatedgj(t) when indicating that evolutionary generation is t
Global optimum position；C (t) indicates average optimal position when evolutionary generation is t, and α is known as expansion-contraction factor, and α is convergence system
Number, the convergence of single particle are influenced by α value.
Beneficial effects of the present invention: using multi-target quantum particle swarm optimization algorithm can while improving satisfaction,
Energy consumption of compressor is reduced, realizes energy-efficient purpose.It can be quickly obtained equally distributed optimal solution set through the invention, utilize
The decision parameters Instructing manufacture of acquisition is dispatched.Compared with other algorithms, user's maximum satisfaction improves 75%, while compressor
Energy consumption of standing reduces by 26%, it was demonstrated that the validity of this algorithm.
Detailed description of the invention
Fig. 1 is mini gas pipe net leakage rate schematic diagram；
Fig. 2 is flow chart of the method for the present invention；
It is respectively 25m that Fig. 3, which is in total flow,3When/d, the user satisfaction and pressure of MQPSO algorithm, DE algorithm, MPSO algorithm
Contracting function consumes contrast schematic diagram；
It is respectively 40m that Fig. 4, which is in total flow,3When/d, the user satisfaction and pressure of MQPSO algorithm, DE algorithm, MPSO algorithm
Contracting function consumes contrast schematic diagram；
It is respectively 55m that Fig. 5, which is in total flow,3When/d, the user satisfaction and pressure of MQPSO algorithm, DE algorithm, MPSO algorithm
Contracting function consumes contrast schematic diagram；
Fig. 6 is energy consumption of compressor contrast schematic diagram under the conditions of more algorithm various flows.
Specific embodiment
Specific embodiment and working principle of the present invention will be described in further detail with reference to the accompanying drawing.
In conjunction with Fig. 2 as can be seen that a kind of gas distributing system Multiobjective Optimal Operation method based on MQPSO, specific steps
Are as follows:
S1: according to the distribution situation of node, pipeline section and compressor in gas distributing system, establishing natural gas tube pessimistic concurrency control,
And obtain all user traffic datas in gas distributing system, the operation data of compressor；
In the present embodiment, by taking Sichuan Province's Luzhou City mini gas pipe net leakage rate as an example, concrete model such as Fig. 1 institute
Show, including an admission, three compressors, five segment pipes and two points of gas points.
The variable of each node, pipeline section and compressor has been labeled in Fig. 1 in model.Assuming that the temperature T of entire pipe network
The basic parameter of=300K, each pipeline section and compressor is shown in Table 3 and table 4, and each node control parameters of natural gas are shown in Table 5.
Each compressor basic parameter of table 3
Each pipeline section basic parameter of table 4
Each node control parameters of 5 natural gas of table
Following sequence: X=[X is taken herein1,X2,X3], wherein X1Indicate flow item, X2Indicate pressure term, X3Compressor turns
Fast item.Flow X1=[Q1,Q2,Q3,Q4,Q5,Q6,Q7,Q8]；Pressure X2=[P1,P2,P3,P4,P5,P6,P7,P8]；Revolving speed X3=
[n1,n2,n3]。
S2: using the user traffic data, the operation data of compressor as variable, and to user traffic data, compression
The operation data of machine assigns initial value, generates initial population, which is N；
It is randomly provided an external archival, and defining the external archival capacity is N；
Set maximum number of iterations G, current iteration number t=1；
S3: processing of crossing the border is done to the particle of initial population；
Cross the border the formula of processing in step S3 are as follows:
xi,d=lbd+(hbd-lbd)·rand,xi,d< lbd(16)；
xi,d=ubd-(hbd-lbd)·rand,xi,d> hbd(17)；
It will extend over the particle x on boundaryi,dIt is withdrawn into any random site in feasible zone, hb in formuladAnd lbdFor Particle confinement side
Boundary；I represents i-th of particle, i=1,2 ..., N；
If particle violates performance constraints after location updating, allow the particle to cross the border in previous each generation individual by formula (18)
Particle position is randomly selected in optimal location:
T is current iteration number in formula, and k is current iteration number pervious any generation, and PLast is that i-th of particle is previous
Each generation individual desired positions array, Pop is when former generation population number group.
S4: external archival is updated；
The fitness of step S3 treated initial population is calculated, and all fitness are ranked up, selects fitness
High particle is put into external archival or substitutes the particle in external archival；
The calculating content of step S4 fitness are as follows:
The calculation expression of the operation data fitness of compressor are as follows:
W is compressor total energy consumption in formula；Γ be pipe network system in compression mechanism at set, QcinFor compressor inlet shape
Volume flow under state；ρcinFor the natural gas density under compressor inlet state；H is compressor lift, and n turns for compressor
Speed,；η is compressor efficiency；
The calculation expression of user traffic data fitness are as follows:
F is objective function in formula, specifically:
X indicates that decision vector, y indicate object vector in formula (1)；X indicates the decision space that decision vector x is formed, Y table
Show the object space that object vector y is formed；F is the majorized function that x is mapped to object vector space；gi(x)≤0, (i=1,
2 ..., h) it is that x is permitted the h constraint condition met；
Vd is the user node number in pipe network system, MiFor the different degree that meets of user i, riFor the load system of user i
Number is defined by formula (4):
ri=qi/q(i,max) (4)
Q in formulaiFor the air demand of user i；q(i,max)For a maximum demand of user i.
Since every class independent variable has different property, so needing to carry out independent variable certain limit in design
System, is known as constraint condition for the restrictive mathematical function of design variable.Gas distributing system optimized operation scheme should meet use
The constraint conditions such as family demand, pipeline pressure and the reasonable operating parameter range of compressor station.
Volume flow Q under the compressor inlet statecinThe constraint relationship between compressor lift H are as follows:
Volume flow Q under the compressor inlet statecinWith the constraint relationship between compressor efficiency η are as follows:
A in formula1, b1, c1, d1, a2, b2, c2, d2It is the nominal parameter coefficient of compressor, it can be according to compressor nominal parameter number
It is returned to obtain according to using least square method；
The constraint relationship of compressor rotary speed are as follows:
Compressor rotary speed n is in minimum speed nminWith maximum (top) speed nmaxBetween:
nmin≤n≤nmax(7)；
The constraint relationship of volume flow under compressor inlet state are as follows:
Q(i,min)≤Qi≤Q(i,max)(i=1,2 ..., N) (8)
Formula QiFor the air demand of i-th of admission, Q(i,min)For the minimum air demand of i-th of admission, Q(i,max)It is i-th
The maximum air demand of a admission, N are total air inlet number of nodes；
Natural gas from source of supply through pipeline to each point of gas point, into the constraint relationship for the pressure for dividing gas point are as follows:
P(i,min)≤Pi≤P(i,max)(i=1,2 ..., N) (9)
P in formulaiFor the pressure of i-th of admission, P(i,min)For the minimum pressure of i-th of admission, P(i,max)It is i-th
The maximum pressure of admission.
In step S3, if external archival is not filled with, directly the high particle of fitness is put into the external archival；
If external archival is filled with, by fitness value minimum in the fitness value of the particle of selection and the external archival
It is compared, if the fitness value of the particle of selection is greater than fitness value, the particle of selection is replaced in the external archival
The corresponding particle of minimum fitness value；Otherwise it is not processed.
The it is proposed of gas distributing system multiple-objection optimization based on MQPSO:
User's different degree determines: user's different degree plays decisive role to optimization object function, it will directly affect excellent
Change as a result, the optimal gas discharge that as each user provides.Determination for user's different degree, at present using level point
Analysis method is studied.
The determination of comparator matrix: comparator matrix is one of most important step in analytic hierarchy process (AHP), is beaten by comparing matrix
Divide principle, comparator matrix can be found out, Tables 1 and 2 is respectively that comparator matrix fills in schematic diagram and each scale meaning of comparator matrix:
1 comparator matrix of table fills out A and writes schematic diagram
| A | B1 | B2 | B3 | B4 | B5 | B6 |
| B1 | 1 | 1/8 | 1/3 | 1/4 | 1/7 | 1/5 |
| B2 | 8 | 1 | 2 | 2 | 1 | 1 |
| B3 | 3 | 1/2 | 1 | 2 | 2 | 2 |
| B4 | 4 | 1/2 | 1 | 1 | 1 | 1 |
| B5 | 7 | 1 | 1/2 | 1/3 | 1 | 1 |
| B6 | 5 | 1 | 1/2 | 1 | 1 | 1 |
Each scale meaning of table 2
| Scale | Meaning |
| 1 | It indicates that two factors are compared, there is equal importance |
| 3 | Indicate that two factors are compared, the former is slightly more important than the latter |
| 5 | Indicate that two factors are compared, the former is more obvious than the latter important |
| 7 | Indicate that two factors are compared, the former is stronger than the latter important |
| 9 | Indicate that two factors are compared, the former is more extremely important than the latter |
| 2,4,6,8 | Indicate the median of above-mentioned adjacent judgement |
Consistency check:
Corresponding to judgment matrix Maximum characteristic root λmaxFeature vector, being normalized (is equal to the sum of each element in vector
1) postscript is W.The element of W is sequencing weight of the same level factor for upper level factor factor relative importance, this
One process is known as Mode of Level Simple Sequence.It can confirm Mode of Level Simple Sequence, then need to carry out consistency check, so-called consistency check is
Refer to and determines inconsistent allowed band to comparing matrix A.Wherein, unique non-zero characteristics root of the consistent battle array of n rank is n, and n rank is just reciprocal
Maximum characteristic root λ >=n of battle array A, when λ=n, A is Consistent Matrix, is detailed in document Lu Fuqiang, and Xue Yansong is based on random
Risk Appraisal of Virtual Enterprise [J] the information and control of analytic hierarchy process (AHP), 2012,41 (01): 110-116.
Since λ continuously depends on aij, then big more of λ ratio n, the inconsistency of A is more serious, and coincident indicator is counted with CI
It calculates, CI is smaller, illustrates that consistency is bigger.Use the corresponding feature vector of maximum eigenvalue as being compared factor to upper layer factor
The weight vector of influence degree, inconsistent degree is bigger, and caused error in judgement is bigger.The size of λ-n numerical value can thus be used
To measure the inconsistent degree of A.Define coincident indicator are as follows:
If CI=0, there is complete consistency；CI has satisfied consistency close to 0；CI is bigger, inconsistent more serious.
For the size for measuring CI, random index RI is introduced:
In view of whether the deviation of consistency may be to have as caused by random cause in test and judge matrix
When satisfied consistency, also needs for CI and random index RI to be compared, show that test coefficient CR, formula are as follows:
Generally, if CR < 0.1, then it is assumed that otherwise the judgment matrix does not just have satisfied consistent by consistency check
Property.
In step S5, quantum behavior particle swarm optimization algorithm is proposed based on quantum well model, the present invention proposes MQPSO
Algorithm realizes the speed of adjustment population and the content of position are as follows:
Pij(t)=rij(t)×bij(t)+(1-rij(t))×bgj(t) (14)
I (i=1,2 ..., N) represents i-th of particle in formula, and N is initial population size, and j (j=1,2 ..., D) is represented
The jth of particle is tieed up, and D is search space dimension, and t is evolutionary generation, rij(t) and sijIt (t) is to be uniformly distributed in [0,1] section
Random number, xij(t) particle when evolutionary generation is t is indicatediCurrent location；bij(t) particle i when evolutionary generation is t is indicated
Personal best particle；pij(t) the attractor position of particle i when evolutionary generation is t, b are indicatedgj(t) when indicating that evolutionary generation is t
Global optimum position；C (t) indicates average optimal position when evolutionary generation is t, and α is known as expansion-contraction factor, and α is convergence system
Number, the convergence of single particle are influenced by α value.
S5: speed and the position of population are adjusted；And enable current iteration number t=t+1, judge t whether meet t be greater than etc.
In G；If so, entering step S6；Otherwise return step S3；
S6: using particle all in the external archival as objective optimization solution, and the flat of all objective optimization solutions is calculated
Mean value is as optimal solution；Obtain the operation data of corresponding user traffic data and compressor.
Three groups of representative datas on flows, respectively total flow 25m are had chosen in traffic constraints condition and range3/
d、40m3/ d and 55m3/ d (wherein 25m3/ d and 55m3/ d is close to traffic constraints boundary, 40m3/ d is traffic constraints median), and
With differential evolution algorithm (Differential Evolution Algorithm, DE), multi-objective particle swarm algorithm Multi-
Objective Particle Swarm Optimization, MPSO) and tri- kinds of optimization algorithms of MQPSO compare experiment, run
Parameter such as table 6: by contrast table 6 it is found that being respectively 25m in total flow3/d、40m3/ d and 55m3When/d, either convergent
Or MQPSO will be better than DE and MPSO in terms of runing time.
6 algorithm parameter of table
It can be seen that from Fig. 2, Fig. 3 and Fig. 4 that MQPSO algorithm ratio DE and MPSO algorithm optimization result are more preferable: when user is satisfied
When degree is between 0.75-0.80, when being optimized using MQPSO algorithm, energy consumption of compressor is lower；When user satisfaction is minimum
When, the energy consumption of compressor optimized using MQPSO algorithm is well below DE and MPSO algorithm.Therefore, MQPSO algorithm pair is utilized
Gas distributing system traffic control effect of optimization is more preferably.
Table 7 is in total flow 25m3/d、40m3/ d and 55m3Energy consumption of compressor optimization and use are carried out to gas distributing system when/d
Family satisfaction calculates；
7 25,40 and 55m of flow of table3Energy consumption of compressor and user satisfaction when/d
As can be seen from Table 7 after three kinds of algorithm optimizations of gas distributing system, MQPSO algorithm ratio MPSO algorithm pressure is utilized
The energy consumption of contracting machine reduces 560kW, 401kW and 2214kW；The energy consumption of MQPSO algorithm ratio DE compression algorithm machine reduces 3822kW,
1459kW and 2411kW.
As shown in figure 5, the effect of optimization for this mathematical model MQPSO is better than MPSO and DE, while each data exist
In zone of reasonableness, the validity of three kinds of algorithms is illustrated.
It should be pointed out that the above description is not a limitation of the present invention, the present invention is also not limited to the example above,
Variation, modification, addition or the replacement that those skilled in the art are made within the essential scope of the present invention, are also answered
It belongs to the scope of protection of the present invention.
Claims (6)
1. a kind of gas distributing system Multiobjective Optimal Operation method based on MQPSO, it is characterised in that specific steps are as follows:
S1: according to the distribution situation of node, pipeline section and compressor in gas distributing system, natural gas tube pessimistic concurrency control is established, and obtain
Take all user traffic datas in gas distributing system, the operation data of compressor；
S2: using the user traffic data, the operation data of compressor as variable, and to user traffic data, compressor
Operation data assigns initial value, generates initial population, which is N；
It is randomly provided an external archival, and defining the external archival capacity is N；
Set maximum number of iterations G, current iteration number t=1；
S3: processing of crossing the border is done to the particle of initial population；
S4: external archival is updated；
The fitness of step S3 treated initial population is calculated, and all fitness are ranked up, selects fitness high
Particle is put into external archival or substitutes the particle in external archival；
S5: MQPSO algorithm is used, speed and the position of population are adjusted；And current iteration number t=t+1 is enabled, whether judge t
Meet t more than or equal to G；If so, entering step S6；Otherwise return step S3；
S6: using particle all in the external archival as objective optimization solution, and the average value of all objective optimization solutions is calculated
As optimal solution；Obtain the operation data of corresponding user traffic data and compressor.
2. the gas distributing system Multiobjective Optimal Operation method according to claim 1 based on MQPSO, it is characterised in that:
Cross the border the formula of processing in step S3 are as follows:
xi,d=lbd+(hbd-lbd)·rand,xi,d< lbd(16)；
xi,d=ubd-(hbd-lbd)·rand,xi,d> hbd(17)；
It will extend over the particle x on boundaryi,dIt is withdrawn into any random site in feasible zone, hb in formuladAnd lbdFor Particle confinement boundary；i
Represent i-th of particle, i=1,2 ..., N；
If particle violates performance constraints after location updating, allow the particle to cross the border optimal in previous each generation individual by formula (18)
Particle position is randomly selected in position:
T is current iteration number in formula, and k is current iteration number pervious any generation, and PLast is i-th of particle each generation in the past
Individual desired positions array, Pop are when former generation population number group.
3. the gas distributing system Multiobjective Optimal Operation method according to claim 1 based on MQPSO, it is characterised in that:
The calculating content of step S4 fitness are as follows:
The calculation expression of the operation data fitness of compressor are as follows:
W is compressor total energy consumption in formula；Γ be pipe network system in compression mechanism at set, QcinFor under compressor inlet state
Volume flow；ρcinFor the natural gas density under compressor inlet state；H is compressor lift, and n is compressor rotary speed,；η is
Compressor efficiency；
The calculation expression of user traffic data fitness are as follows:
F is objective function in formula, specifically:
X indicates that decision vector, y indicate object vector in formula (1)；X indicates that the decision space that decision vector x is formed, Y indicate mesh
Mark the object space that vector y is formed；F is the majorized function that x is mapped to object vector space；gi(x)≤0, (i=1,
2 ..., h) it is that x is permitted the h constraint condition met；
Vd is the user node number in pipe network system, MiFor the different degree that meets of user i, riFor the load coefficient of user i, by
Formula (4) definition:
ri=qi/q(i,max) (4)
Q in formulaiFor the air demand of user i；q(i,max)For a maximum demand of user i.
4. the gas distributing system Multiobjective Optimal Operation method according to claim 3 based on MQPSO, it is characterised in that:
Volume flow Q under the compressor inlet statecinThe constraint relationship between compressor lift H are as follows:
Volume flow Q under the compressor inlet statecinWith the constraint relationship between compressor efficiency η are as follows:
A in formula1, b1, c1, d1, a2, b2, c2, d2It is the nominal parameter coefficient of compressor, can be adopted according to compressor nominal parameter data
It is returned to obtain with least square method；
The constraint relationship of compressor rotary speed are as follows:
Compressor rotary speed n is in minimum speed nminWith maximum (top) speed nmaxBetween:
nmin≤n≤nmax(7)；
The constraint relationship of volume flow under compressor inlet state are as follows:
Q(i,min)≤Qi≤Q(i,max)(i=1,2 ..., N) (8)
Formula QiFor the air demand of i-th of admission, Q(i,min)For the minimum air demand of i-th of admission, Q(i,max)For i-th into
The maximum air demand of gas point, N are total air inlet number of nodes；
Natural gas from source of supply through pipeline to each point of gas point, into the constraint relationship for the pressure for dividing gas point are as follows:
P(i,min)≤Pi≤P(i,max)(i=1,2 ..., N) (9)
P in formulaiFor the pressure of i-th of admission, P(i,min)For the minimum pressure of i-th of admission, P(i,max)For i-th of air inlet
The maximum pressure of point.
5. the gas distributing system Multiobjective Optimal Operation method according to claim 1 based on MQPSO, it is characterised in that:
In step S3, if external archival is not filled with, directly the high particle of fitness is put into the external archival；
If external archival is filled with, fitness value minimum in the fitness value of the particle of selection and the external archival is carried out
Compare, if the fitness value of the particle of selection is greater than fitness value, the particle of selection is replaced minimum in the external archival
The corresponding particle of fitness value；Otherwise it is not processed.
6. the gas distributing system Multiobjective Optimal Operation method according to claim 1 based on MQPSO, it is characterised in that:
In step S5, the speed of population and the content of position are adjusted are as follows:
Pij(t)=rij(t)×bij(t)+(1-rij(t))×bgj(t) (14)
I (i=1,2 ..., N) represents i-th of particle in formula, and N is initial population size, and j (j=1,2 ..., D) represents particle
Jth dimension, D be search space dimension, t is evolutionary generation, rij(t) and sij(t) be in [0,1] section it is equally distributed with
Machine number, xij(t) current location of particle i when evolutionary generation is t is indicated；bij(t) individual of particle i when evolutionary generation is t is indicated
Optimal location；pij(t) the attractor position of particle i when evolutionary generation is t, b are indicatedgj(t) indicate global when evolutionary generation is t
Optimal location；C (t) indicates average optimal position when evolutionary generation is t, and α is known as expansion-contraction factor, and α is convergence coefficient, single
The convergence of a particle is influenced by α value.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110704935A (en) * | 2019-10-12 | 2020-01-17 | 中国石油天然气集团有限公司 | Automatic debugging and outputting method of SPS software |
| CN110750846A (en) * | 2019-10-15 | 2020-02-04 | 中国海洋石油集团有限公司 | Fluid parameter optimal configuration method for fluid pipe network system and pipe network system |
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| CN113129164A (en) * | 2021-04-13 | 2021-07-16 | 中海石油气电集团有限责任公司 | Calculation method for natural gas flow pressure scheduling decision index of natural gas pipeline network |
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070107809A1 (en) * | 2005-11-14 | 2007-05-17 | The Regents Of The Univerisity Of California | Process for making corrosion-resistant amorphous-metal coatings from gas-atomized amorphous-metal powders having relatively high critical cooling rates through particle-size optimization (PSO) and variations thereof |
| CN102081692A (en) * | 2011-01-06 | 2011-06-01 | 西北工业大学 | Method for keeping design dependence load equivalent in topological optimization |
| CN103136410A (en) * | 2011-11-29 | 2013-06-05 | 张智灵 | Solution method of stem and branch pipe network layout optimization model |
| CN103136411A (en) * | 2011-11-29 | 2013-06-05 | 张智灵 | Optimization method of mixed particle swarm |
| CN105447567A (en) * | 2015-11-06 | 2016-03-30 | 重庆科技学院 | BP neural network and MPSO algorithm-based aluminium electrolysis energy-saving and emission-reduction control method |
| CN106444377A (en) * | 2016-10-09 | 2017-02-22 | 江苏大学 | Soft measuring method and system for key variables of lysine fermentation process based on PSO-FSVM |
| CN107464008A (en) * | 2016-06-02 | 2017-12-12 | 南京理工大学 | A kind of Optimization Scheduling of large buildings cooling heating and power generation system |
| CN108445756A (en) * | 2018-03-09 | 2018-08-24 | 重庆科技学院 | Aluminium electroloysis energy-saving and emission-reduction intelligent control method based on AR dominance relations |
| CN108846507A (en) * | 2018-05-29 | 2018-11-20 | 西安交通大学 | Electric-gas coupled system based on MIXED INTEGER Second-order cone programming economic load dispatching method a few days ago |
-
2019
- 2019-06-17 CN CN201910521394.8A patent/CN110232481B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070107809A1 (en) * | 2005-11-14 | 2007-05-17 | The Regents Of The Univerisity Of California | Process for making corrosion-resistant amorphous-metal coatings from gas-atomized amorphous-metal powders having relatively high critical cooling rates through particle-size optimization (PSO) and variations thereof |
| CN102081692A (en) * | 2011-01-06 | 2011-06-01 | 西北工业大学 | Method for keeping design dependence load equivalent in topological optimization |
| CN103136410A (en) * | 2011-11-29 | 2013-06-05 | 张智灵 | Solution method of stem and branch pipe network layout optimization model |
| CN103136411A (en) * | 2011-11-29 | 2013-06-05 | 张智灵 | Optimization method of mixed particle swarm |
| CN105447567A (en) * | 2015-11-06 | 2016-03-30 | 重庆科技学院 | BP neural network and MPSO algorithm-based aluminium electrolysis energy-saving and emission-reduction control method |
| CN107464008A (en) * | 2016-06-02 | 2017-12-12 | 南京理工大学 | A kind of Optimization Scheduling of large buildings cooling heating and power generation system |
| CN106444377A (en) * | 2016-10-09 | 2017-02-22 | 江苏大学 | Soft measuring method and system for key variables of lysine fermentation process based on PSO-FSVM |
| CN108445756A (en) * | 2018-03-09 | 2018-08-24 | 重庆科技学院 | Aluminium electroloysis energy-saving and emission-reduction intelligent control method based on AR dominance relations |
| CN108846507A (en) * | 2018-05-29 | 2018-11-20 | 西安交通大学 | Electric-gas coupled system based on MIXED INTEGER Second-order cone programming economic load dispatching method a few days ago |
Non-Patent Citations (3)
| Title |
|---|
| 张春燕，等: "MQPSO：一种具有多群体与多阶段的QPSO算法", 《计算机应用研究》 * |
| 易军，等: "基于拥挤距离排序的铝电解多目标优化", 《仪器仪表学报》 * |
| 范彬，等: "基亏改进蝙蝠算法的配电网分布式电源规划", 《电力建设》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110704935A (en) * | 2019-10-12 | 2020-01-17 | 中国石油天然气集团有限公司 | Automatic debugging and outputting method of SPS software |
| CN110704935B (en) * | 2019-10-12 | 2023-10-17 | 中国石油天然气集团有限公司 | Automatic debugging and outputting method of SPS software |
| CN110750846A (en) * | 2019-10-15 | 2020-02-04 | 中国海洋石油集团有限公司 | Fluid parameter optimal configuration method for fluid pipe network system and pipe network system |
| CN110750846B (en) * | 2019-10-15 | 2024-05-24 | 中国海洋石油集团有限公司 | Fluid parameter optimization configuration method of fluid pipe network system and pipe network system |
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| CN113326984A (en) * | 2021-05-28 | 2021-08-31 | 重庆能源大数据中心有限公司 | Global scheduling method based on natural gas pipe network system |
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