CN110232481B - Multi-objective optimization scheduling method for natural gas pipe network based on MQPSO - Google Patents
Multi-objective optimization scheduling method for natural gas pipe network based on MQPSO Download PDFInfo
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Abstract
The invention discloses a multi-objective optimization scheduling method for a natural gas pipe network based on MQPSO, which comprises the following specific steps of: establishing a natural gas pipe network model according to the distribution conditions of nodes, pipe sections and compressors in the natural gas pipe network; taking the user flow data and the operation data of the compressor as variables, assigning initial values to the user flow data and the operation data of the compressor, and generating an initial group; and randomly setting an external file, designing an MQPSO algorithm, solving the multi-objective optimization scheduling problem of the pipe network, and obtaining the flow distribution of each node and the optimized value of the working parameter of the compressor. Experimental results show that the method can quickly obtain the uniformly distributed optimal solution set, and the obtained decision parameters are used for guiding production scheduling. Compared with other algorithms, the maximum satisfaction degree of the user is improved by 75%, and meanwhile, the energy consumption of the compressor station is reduced by 26%, so that the effectiveness of the algorithm is proved.
Description
Technical Field
The invention relates to the technical field of natural gas scheduling, in particular to a multi-objective optimization scheduling method for a natural gas pipe network based on MQPSO.
Background
The natural gas pipe network operation scheduling is a very complex industrial process, and the nonlinear and multivariable characteristics of the natural gas pipe network operation scheduling bring many problems to the actual pipe network scheduling. At present, the dispatching process mostly depends on human experience, natural gas is distributed in a mode of regulating and controlling through experience of a dispatcher, and the mode usually causes the problems of huge energy consumption, low flow distribution efficiency and the like of a compressor station. Therefore, in order to effectively solve the above problems, people begin to research the application of artificial intelligence algorithm in pipe network operation. The method comprises the following steps of optimizing the operation of a natural gas pipe network based on an improved pattern search algorithm, provided by Li Ligang et al, simulating and optimizing a natural gas long-distance pipe network based on Aspen and Isight, provided by Gui Jin et al, and performing optimization calculation research on the operation energy consumption of the natural gas pipe network by selecting an improved linear approximation algorithm and a sequence quadratic programming method.
However, most of the existing researches on the scheduling process of the natural gas pipeline network are single-target optimization problems, and the research on multi-target problem analysis by using a system optimization method is less. 5363 the optimization of natural gas pipe network operation, which is proposed by Li Bo, etc., proposes a solution method using pipeline hydraulic thermodynamic calculation check and gas station load distribution as optimization objects, but cannot establish an accurate optimization model. A plurality of optimization targets are fully considered in the genetic algorithm-based rural natural gas pipeline network optimization research proposed by the document Aojing et al, but the local search capability of the algorithm is poor, so that the efficiency in the later stage of evolution is low. For natural gas production enterprises, both the overall user satisfaction and the energy consumption level of the compressor are considered. Therefore, how to perform multi-objective optimization analysis on the scheduling process of the natural gas pipeline network is an important subject to be solved urgently by natural gas production enterprises at present.
Disclosure of Invention
Aiming at the problems, the invention provides a multi-objective optimization scheduling method for a natural gas pipe network based on MQPSO, which updates an external archive by balancing the relation between user satisfaction and compressor energy consumption so as to maintain the diversity of an optimal solution, and adaptively adjusts the position of a particle swarm at the same time, thereby avoiding the problem of premature convergence of an algorithm. The method is used for solving the multi-objective optimization problem in the production scheduling process of the natural gas pipe network to obtain the optimal operation parameters.
In order to achieve the purpose, the invention adopts the following specific technical scheme:
a multi-objective optimization scheduling method for a natural gas pipe network based on MQPSO is characterized by comprising the following specific steps:
s1: establishing a natural gas pipe network model according to the distribution conditions of nodes, pipe sections and compressors in the natural gas pipe network, and acquiring all user flow data and operation data of the compressors in the natural gas pipe network;
s2: taking the user flow data and the operation data of the compressor as variables, assigning initial values to the user flow data and the operation data of the compressor, and generating an initial group, wherein the size of the initial group is N;
randomly setting an external archive, and defining the external archive capacity as N;
setting a maximum iteration time G, wherein the current iteration time t =1;
s3: carrying out border crossing treatment on the particles of the initial population;
s4: updating an external archive;
calculating the fitness of the initial population processed in the step S3, sequencing all the fitness, and selecting the particles with high fitness to be put into an external archive or replace the particles in the external archive;
s5: adjusting the speed and the position of the particle swarm by adopting an MQPSO algorithm; enabling the current iteration time t = t +1, and judging whether t meets the condition that t is greater than or equal to G; if yes, entering step S6; otherwise, returning to the step S3;
s6: taking all particles in the external archive as target optimal solutions, and calculating an average value of all the target optimal solutions as an optimal solution; and obtaining corresponding user flow data and operation data of the compressor.
By adopting the method, the practical experiment result of the operation scheduling process of the small natural gas pipe network can be known, the satisfaction degree can be improved, the energy consumption of the compressor can be reduced, and the purposes of energy conservation and high efficiency can be realized by utilizing the multi-target quantum particle swarm optimization algorithm.
Further, the formula of the boundary crossing processing in step S3 is:
x i,d ＝lb d +(hb d -lb d )·rand,x i,d ＜lb d (16)；
x i,d ＝ub d -(hb d -lb d )·rand,x i,d ＞hb d (17)；
particle x to cross the boundary i,d Is pulled back toAt any random position in the line, where hb d And lb d A particle confinement boundary; i represents the ith particle, i =1,2, · N;
if the particle violates the performance constraint after the position update, the position of the particle is randomly extracted from the optimal positions of the past generations of individual according to the formula (18):
wherein t is the current iteration number, k is any generation before the current iteration number, the PLAst is the best position array of each past generation of individuals of the ith particle, and the Pop is the current generation population array.
Initializing the population size N, the maximum iteration times G and the external archive size N, and initializing the particle position information according to the decision variable value range of the optimization problem.
Still further, the calculation content of the fitness in step S4 is:
during operation of the pipeline network, the compressor stations provide energy to overcome pressure losses of the natural gas flowing in the pipeline. The optimization of the energy consumption of the compressor refers to the adjustment of the operating parameters and the number of the starting-up units of each compressor station, and the total energy consumption of the compressor stations is reduced while the pipeline network is ensured to complete the gas transportation task.
The calculation expression of the running data fitness of the compressor is as follows:
wherein W is the total energy consumption of the compressor; the gamma is the set formed by the compressor in the pipe network system, Q cin Is the volume flow at the inlet state of the compressor; ρ is a unit of a gradient cin Natural gas density at the inlet state of the compressor; h is the compressor lift, and n is the compressor rotation speed; eta is the compressor efficiency;
the targets of the priority allocation of the important users are: under the conditions of a specific pipe network structure, a user gas using capacity, a gas source gas supplying capacity and a pipe network gas transmission capacity, the pipe network user structure is adjusted according to the importance of the user, and the optimal configuration of natural gas supply and user requirements is realized.
The calculation expression of the user flow data fitness is as follows:
in the formula, F is an objective function, and specifically:
in formula (1), x represents a decision vector, and y represents a target vector; x represents a decision space formed by a decision vector X, and Y represents a target space formed by a target vector Y; f is an optimization function that maps x to a target vector space; g i (x) 0 or less, (i =1,2,.. H) is x allowed to satisfy h constraints;
vd is the number of user nodes in the pipe network system, M i For the coincidence importance of user i, r i The load factor for user i is defined by equation (4):
r i ＝q i /q (i,max) (4)
in the formula q i The amount of air supplied to user i; q. q.s (i,max) The maximum demand of user i.
Since each type of independent variable has different properties, a certain limitation needs to be imposed on the independent variable during design, and a constraint mathematical function for the design variable is called a constraint condition. The optimal operation scheme of the natural gas pipe network should meet the constraint conditions such as user requirements, pipeline pressure, reasonable operation parameter ranges of compressor stations and the like.
Still further, the volume flow Q at the inlet state of the compressor cin The constraint relation with the compressor lift H is as follows:
volume flow Q at the inlet of the compressor cin The constraint relation with the compressor efficiency eta is as follows:
in the formula a 1 ，b 1 ，c 1 ，d 1 ，a 2 ，b 2 ，c 2 ，d 2 The coefficient is the rated parameter coefficient of the compressor, and can be obtained by regression by adopting a least square method according to the rated parameter data of the compressor;
the constraint relation of the rotating speed of the compressor is as follows:
the compressor speed n is at the minimum speed n min And a maximum rotational speed n max The following steps:
n min ≤n≤n max (7)；
the volume flow constraint relationship in the inlet state of the compressor is as follows:
Q (i,min) ≤Q i ≤Q (i,max) (i＝1,2,...,N) (8)
formula Q i Amount of air supplied as the i-th air intake point, Q (i,min) Minimum amount of air supply, Q, for the i-th air intake point (i,max) The maximum air supply quantity of the ith air inlet point is obtained, and N is the total number of air inlet nodes;
the natural gas is conveyed to each gas distribution point from a supply source through a pipeline, and the constraint relation of the pressure of the gas inlet distribution point is as follows:
P (i,min) ≤P i ≤P (i,max) (i＝1,2,...,N) (9)
in the formula P i Pressure at the i-th intake point, P (i,min) Is the minimum pressure of the i-th intake point, P (i,max) The maximum pressure at the ith intake point.
Further, in step S4, if the external archive is not full, directly placing the particles with high fitness into the external archive;
if the external archive is full, comparing the fitness value of the selected particle with the minimum fitness value in the external archive, and if the fitness value of the selected particle is greater than the fitness value, replacing the particle corresponding to the minimum fitness value in the external archive by the selected particle; otherwise, no processing is performed.
And the external archive is used for storing the non-dominant solution obtained by the particles in the searching process, outputting a calculation result when the algorithm is ended, and guiding the iterative process of the algorithm to ensure the global convergence of the algorithm. The maximum scale of the external archive is controlled, so that the diversity of non-dominant solutions can be ensured, and the optimal individual can be stored, thereby having important significance on the algorithm.
Still further, in step S5, the content of adjusting the speed and position of the particle swarm is:
P ij (t)＝r ij (t)×b ij (t)+(1-r ij (t))×b gj (t) (14)
where i (i =1,2., N) represents the ith particle, N is the initial population size, j (j =1,2., D) represents the jth dimension of the particle, D is the search space dimension, t is the evolution algebra, r is the number of the evolutionary algebras ij (t) and s ij (t) are all [0,1]Random numbers, x, uniformly distributed within the interval ij (t) represents the current position of the particle i when the evolution algebra is t; b ij (t) representing the individual optimal position of the particle i when the evolution algebra is t; p is a radical of ij (t) denotes the attractor position of the particle i when the evolution algebra is t, b gj (t) representing the global optimal position when the evolution algebra is t; c (t) represents the average optimal position when the evolution algebra is t, alpha is called an expansion-contraction factor, alpha is a convergence coefficient, and the convergence of a single particle is influenced by the value of alpha.
The invention has the beneficial effects that: by utilizing the multi-target quantum particle swarm optimization algorithm, the energy consumption of the compressor can be reduced while the satisfaction degree is improved, and the aims of energy conservation and high efficiency are fulfilled. By the method, the uniformly distributed optimal solution set can be quickly obtained, and the obtained decision parameters are used for guiding production scheduling. Compared with other algorithms, the maximum satisfaction degree of the user is improved by 75%, and meanwhile, the energy consumption of the compressor station is reduced by 26%, so that the effectiveness of the algorithm is proved.
Drawings
FIG. 1 is a schematic view of a small natural gas pipeline network model;
FIG. 2 is a flow chart of a method of the present invention;
FIG. 3 shows the total flow rate at 25m 3 At/d, comparing user satisfaction and compressor energy consumption of the MQPSO algorithm, the DE algorithm and the MPSO algorithm;
FIG. 4 shows the total flow rate at 40m 3 At/d, comparing user satisfaction and compressor energy consumption of the MQPSO algorithm, the DE algorithm and the MPSO algorithm;
FIG. 5 shows the total flow rate at 55m 3 When the flow rate is/d, comparing user satisfaction degrees of an MQPSO algorithm, a DE algorithm and an MPSO algorithm with compressor energy consumption schematic diagrams;
FIG. 6 is a schematic diagram comparing compressor power consumption under multiple algorithm and multiple flow conditions.
Detailed Description
The following provides a more detailed description of the embodiments and the operation of the present invention with reference to the accompanying drawings.
As can be seen from fig. 2, a multi-objective optimization scheduling method for a natural gas pipe network based on MQPSO includes the following specific steps:
s1: establishing a natural gas pipe network model according to the distribution conditions of nodes, pipe sections and compressors in the natural gas pipe network, and acquiring all user flow data and operation data of the compressors in the natural gas pipe network;
in this embodiment, a model of a small natural gas pipe network in luzhou city, sichuan province is taken as an example, and the specific model is shown in fig. 1 and includes an air inlet point, three compressors, five sections of pipelines and two air distribution points.
The variables of the various nodes, pipe sections and compressors in the model have been labeled in fig. 1. Assuming that the temperature T =300K of the whole pipe network, the basic parameters of each pipe section and the compressor are shown in tables 3 and 4, and the control parameters of each natural gas node are shown in table 5.
TABLE 3 basic parameters of the compressors
TABLE 4 basic parameters of the respective pipe sections
TABLE 5 Natural gas respective node control parameters
The following sequence is taken herein: x = [ X = 1 ,X 2 ,X 3 ]Wherein X is 1 Representing flow terms, X 2 Denotes the pressure term, X 3 The term of the rotating speed of the compressor. Flow rate X 1 ＝[Q 1 ,Q 2 ,Q 3 ,Q 4 ,Q 5 ,Q 6 ,Q 7 ,Q 8 ](ii) a Pressure X 2 ＝[P 1 ,P 2 ,P 3 ,P 4 ,P 5 ,P 6 ,P 7 ,P 8 ](ii) a Rotational speed X 3 ＝[n 1 ,n 2 ,n 3 ]。
S2: taking the user flow data and the operation data of the compressor as variables, assigning initial values to the user flow data and the operation data of the compressor, and generating an initial group, wherein the size of the initial group is N;
randomly setting an external archive, and defining the external archive capacity as N;
setting a maximum iteration time G, wherein the current iteration time t =1;
s3: carrying out border crossing treatment on the particles of the initial population;
the formula of the border crossing processing in the step S3 is as follows:
x i,d ＝lb d +(hb d -lb d )·rand,x i,d ＜lb d (16)；
x i,d ＝ub d -(hb d -lb d )·rand,x i,d ＞hb d (17)；
particle x to cross the boundary i,d Pull back to any random position within the feasible region, where hb d And lb d A particle confinement boundary; i represents the ith particle, i =1,2.
If the particle violates the performance constraint after the position update, the position of the particle is randomly extracted from the optimal positions of the past generations of individual according to the formula (18):
wherein t is the current iteration number, k is any generation before the current iteration number, the PLAst is the best position array of each past generation of individuals of the ith particle, and the Pop is the current generation population array.
S4: updating an external archive;
calculating the fitness of the initial population processed in the step S3, sequencing all the fitness, and selecting the particles with high fitness to be put into an external archive or replace the particles in the external archive;
step S4, the calculation content of the fitness is as follows:
the calculation expression of the running data fitness of the compressor is as follows:
wherein W is the total energy consumption of the compressor; gamma is the set formed by compressors in the pipe network system, Q cin Is the volume flow at the inlet state of the compressor; rho cin Natural gas density at the inlet state of the compressor; h is the compressor lift, and n is the compressor rotation speed; eta is the compressor efficiency;
the calculation expression of the user flow data fitness is as follows:
in the formula, F is an objective function, and specifically:
in formula (1), x represents a decision vector, and y represents a target vector; x represents a decision space formed by a decision vector X, and Y represents a target space formed by a target vector Y; f is an optimization function that maps x to a target vector space; g i (x) No more than 0, (i =1,2,. Multidot., h) is x number of h constraints to be satisfied;
vd is the number of user nodes in the pipe network system, M i For the coincidence importance of user i, r i The load factor for user i is defined by equation (4):
r i ＝q i /q (i,max) (4)
in the formula q i The amount of supplied air for user i; q. q of (i,max) The maximum demand of user i.
Since each type of independent variable has different properties, a certain limitation needs to be imposed on the independent variable during design, and a constraint mathematical function for the design variable is called a constraint condition. The optimal operation scheme of the natural gas pipe network should meet the constraint conditions such as user requirements, pipeline pressure, reasonable operation parameter ranges of compressor stations and the like.
Volume flow rate Q at inlet state of the compressor cin The constraint relation with the compressor lift H is as follows:
volume flow rate Q at inlet state of the compressor cin The constraint relation with the compressor efficiency eta is as follows:
in the formula a 1 ，b 1 ，c 1 ，d 1 ，a 2 ，b 2 ，c 2 ，d 2 The coefficient is the rated parameter coefficient of the compressor, and can be obtained by regression by adopting a least square method according to the rated parameter data of the compressor;
the constraint relation of the rotating speed of the compressor is as follows:
the compressor speed n is at the minimum speed n min And a maximum rotational speed n max The following steps:
n min ≤n≤n max (7)；
the volume flow constraint relationship in the inlet state of the compressor is as follows:
Q (i,min) ≤Q i ≤Q (i,max) (i＝1,2,...,N) (8)
formula Q i Amount of air supplied as the i-th air intake point, Q (i,min) Minimum air supply quantity, Q, for the ith intake point (i,max) The maximum air supply quantity of the ith air inlet point is N, and the total number of air inlet nodes is N;
the natural gas is conveyed to each gas distribution point from a supply source through a pipeline, and the constraint relation of the pressure of the gas inlet distribution point is as follows:
P (i,min) ≤P i ≤P (i,max) (i＝1,2,...,N) (9)
in the formula P i Is the pressure of the ith intake point, P (i,min) Is the minimum pressure of the i-th intake point, P (i,max) The maximum pressure at the ith intake point.
In step S4, if the external archive is not full, directly putting the particles with high fitness into the external archive;
if the external archive is full, comparing the fitness value of the selected particle with the minimum fitness value in the external archive, and if the fitness value of the selected particle is greater than the fitness value, replacing the particle corresponding to the minimum fitness value in the external archive with the selected particle; otherwise, no processing is performed.
The method comprises the following steps of (1) providing multi-objective optimization of a natural gas pipe network based on MQPSO:
determining the importance of the user: the user importance plays a decisive role in the optimization objective function, which will directly influence the optimization result, i.e. the optimal natural gas flow rate provided for each user. For determining the importance of users, an analytic hierarchy process is currently used for research.
Determination of the comparison matrix: the comparison matrix is one of the most important steps in the analytic hierarchy process, the comparison matrix can be obtained through a comparison matrix scoring principle, and the table 1 and the table 2 respectively fill a schematic diagram for the comparison matrix and the scale meanings of the comparison matrix:
TABLE 1 comparison matrix fill A write schematic
| A | B1 | B2 | B3 | B4 | | B6 |
| B1 | ||||||
| 1 | 1/8 | 1/3 | 1/4 | 1/7 | 1/5 | |
| B2 | 8 | 1 | 2 | 2 | 1 | 1 |
| |
3 | 1/2 | 1 | 2 | 2 | 2 |
| |
4 | 1/2 | 1 | 1 | 1 | 1 |
| B5 | 7 | 1 | 1/2 | 1/3 | 1 | 1 |
| |
5 | 1 | 1/2 | 1 | 1 | 1 |
TABLE 2 meanings of the respective scales
| Scale | Means of |
| 1 | Indicates that the two factors have the |
| 3 | Indicating that the former is slightly more important than the latter |
| 5 | Indicating that the former is significantly more important than the latter in comparison with two factors |
| 7 | Indicating that the former is more important than the latter |
| 9 | Indicating that the former is extremely important than the latter in |
| 2，4，6，8 | Intermediate value representing the above-mentioned adjacent judgment |
And (3) checking consistency:
corresponding to the maximum characteristic root lambda of the decision matrix max The feature vector of (2) is normalized (the sum of the elements in the vector is equal to 1) and then is denoted as W. The elements of W are the sorting weights of relative importance of the same-level factor to a certain factor of the previous-level factor, and the process is called hierarchical list sorting. If the hierarchical single ordering can be confirmed, consistency check is needed, and the consistency check refers to determining an inconsistent allowable range for the comparison matrix A. The only non-zero characteristic root of the n-order uniform array is n, the maximum characteristic root lambda of the n-order positive reciprocal array A is more than or equal to n, if and only if lambda = n, A is a uniform matrix, as detailed in documents Lu Fujiang and Xue Yansong, and virtual enterprise risk evaluation based on a random analytic hierarchy process [ J]Information and control, 2012,41 (01): 110-116.
Due to continuous dependence of λ on a ij If λ is larger than n, the inconsistency of a is more serious, the consistency index is calculated by CI, and if CI is smaller, the consistency is higher. And using the feature vector corresponding to the maximum feature value as a weight vector of the influence degree of the compared factor on a certain factor of an upper layer, wherein the larger the inconsistency degree is, the larger the judgment error is caused. Thus can use λ -n The magnitude of the value is used to measure the degree of inconsistency of A. Defining the consistency index as:
if CI =0, there is complete consistency; CI is close to 0, and the consistency is satisfactory; the larger the CI, the more severe the inconsistency. To measure the magnitude of CI, a random consistency index RI is introduced:
considering that the deviation of consistency may be caused by random reasons, when checking whether the judgment matrix has satisfactory consistency, CI needs to be compared with the random consistency index RI to obtain a checking coefficient CR, where the following formula:
generally, the decision matrix is considered to pass the consistency check if CR <0.1, otherwise it does not have satisfactory consistency.
In step S5, a quantum behavior particle swarm optimization algorithm is provided based on a quantum well model, the invention provides an MQPSO algorithm, and the contents for realizing the adjustment of the speed and the position of the particle swarm are as follows:
P ij (t)＝r ij (t)×b ij (t)+(1-r ij (t))×b gj (t) (14)
where i (i =1,2., N) represents the ith particle, N is the initial population size, j (j =1,2., D) represents the jth dimension of the particle, D is the search space dimension, t is the evolution algebra, r is the number of the evolutionary algebras ij (t) and s ij (t) are all [0,1]Random numbers, x, uniformly distributed within the interval ij (t) represents the current position of the particle i when the evolution algebra is t; b ij (t) representing the individual optimal position of the particle i when the evolution algebra is t; p is a radical of formula ij (t) denotes the attractor position of the particle i when the evolution algebra is t, b gj (t) representing the global optimal position when the evolution algebra is t; c (t) represents the average optimal position when the evolution algebra is t, alpha is called an expansion-contraction factor, alpha is a convergence coefficient, and the convergence of a single particle is influenced by the value of alpha.
S5: adjusting the speed and position of the particle swarm; enabling the current iteration time t = t +1, and judging whether t meets the condition that t is greater than or equal to G; if yes, entering step S6; otherwise, returning to the step S3;
s6: taking all particles in the external archive as target optimal solutions, and calculating an average value of all the target optimal solutions as an optimal solution; and obtaining corresponding user flow data and operation data of the compressor.
Three representative groups of flow data are selected within the flow constraint condition range, and the flow data are respectively total flow 25m 3 /d、40m 3 D and 55m 3 D (wherein 25 m) 3 D and 55m 3 D is close to the flow constraint boundary, 40m 3 D is a flow constraint median), and compared with a Differential Evolution Algorithm (DE), a Multi-objective Particle Swarm Optimization (MPSO) and an MQPSO, the operation parameters are as shown in table 6: as can be seen from a comparison of Table 6, the total flow rates were 25m each 3 /d、40m 3 D and 55m 3 MQPSO is superior to DE and MPSO in both convergence and runtime at/d.
TABLE 6 Algorithm parameters
From fig. 2, 3 and 4, it can be seen that the MQPSO algorithm optimizes the results better than the DE and MPSO algorithms: when the user satisfaction is between 0.75 and 0.80, the energy consumption of the compressor is lower when the optimization is carried out by using the MQPSO algorithm; when the user satisfaction is the lowest, the compressor energy consumption optimized by the MQPSO algorithm is far lower than that of the DE and MPSO algorithms. Therefore, the optimization effect of the operation scheduling of the natural gas pipeline network by using the MQPSO algorithm is better.
Table 7 shows the total flow rate at 25m 3 /d、40m 3 D and 55m 3 Performing compressor energy consumption optimization and user satisfaction calculation on the natural gas pipeline network in the time of/d;
TABLE 7 flow rates 25, 40 and 55m 3 Compressor energy consumption per day and customer satisfaction
From table 7, it can be seen that after the natural gas pipe network is optimized by the three algorithms, the energy consumption of the compressor is reduced by 560kw,401kw and 2214kW by using the MQPSO algorithm compared with the MPSO algorithm; the MQPSO algorithm reduces the energy consumption of the compressor by 3822kW,1459kW and 2411kW compared with the DE algorithm.
As shown in FIG. 5, the optimization effect of the mathematical model MQPSO is better than that of MPSO and DE, and the data are in reasonable range, which illustrates the effectiveness of the three algorithms.
It should be noted that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make variations, modifications, additions or substitutions within the spirit and scope of the present invention.
Claims (4)
1. A multi-objective optimization scheduling method for a natural gas pipe network based on MQPSO is characterized by comprising the following specific steps:
s1: establishing a natural gas pipe network model according to the distribution conditions of nodes, pipe sections and compressors in the natural gas pipe network, and acquiring all user flow data and operation data of the compressors in the natural gas pipe network;
s2: taking the user flow data and the operation data of the compressor as variables, assigning initial values to the user flow data and the operation data of the compressor, and generating an initial group, wherein the size of the initial group is N;
randomly setting an external archive, and defining the external archive capacity as N;
setting a maximum iteration time G, wherein the current iteration time t =1;
s3: carrying out border crossing processing on the particles of the initial population;
s4: updating an external archive;
calculating the fitness of the initial population processed in the step S3, sequencing all the fitness, and selecting the particles with high fitness to be put into an external archive or replace the particles in the external archive;
the calculation content of the fitness is as follows:
the calculation expression of the running data fitness of the compressor is as follows:
wherein W is the total energy consumption of the compressor; gamma is the set formed by compressors in the pipe network system, Q cin Is the volume flow at the inlet state of the compressor; rho cin Natural gas density at the inlet state of the compressor; h is the compressor lift, and n is the compressor rotation speed; eta is the compressor efficiency;
the targets of the priority allocation of the important users are: under the conditions of a specific pipe network structure, user gas utilization capacity, gas source gas supply capacity and pipe network gas transmission capacity, the pipe network user structure is adjusted according to the importance of users, and the optimal configuration of natural gas supply and user requirements is realized;
the calculation expression of the user flow data fitness is as follows:
wherein F is an objective function, specifically:
in formula (1), x represents a decision vector, and y represents a target vector; x represents a decision space formed by a decision vector X, and Y represents a target space formed by a target vector Y; f is an optimization function mapping x to a target vector space; g i (x) No more than 0, (i =1,2,. Multidot., h) is x number of h constraints to be satisfied;
vd is the number of user nodes in the pipe network system, M i For the coincidence importance of user i, r i The load factor for user i is defined by equation (4):
r i ＝q i /q (i,max) (4)
in the formula q i The amount of air supplied to user i; q. q.s (i,max) Maximum demand for user i;
s5: adjusting the speed and the position of the particle swarm by adopting an MQPSO algorithm; enabling the current iteration time t = t +1, and judging whether t meets the condition that t is greater than or equal to G; if yes, entering step S6; otherwise, returning to the step S3;
the contents of adjusting the speed and the position of the particle swarm are as follows:
P ij (t)＝r ij (t)×b ij (t)+(1-r ij (t))×b gj (t) (14)
where i (i =1,2., N) represents the ith particle, N is the initial population size, j (j =1,2., D) represents the jth dimension of the particle, D is the search space dimension, t is the evolution algebra, r is the number of the evolutionary algebras ij (t) and s ij (t) are all [0,1]Random numbers, x, uniformly distributed within the interval ij (t) represents the current position of the particle i when the evolution algebra is t; b is a mixture of ij (t) representing the individual optimal position of the particle i when the evolution algebra is t; p is a radical of ij (t) denotes the attractor position of the particle i when the evolution algebra is t, b gj (t) representing the global optimal position when the evolution algebra is t; c (t) represents the average optimal position when the evolutionary algebra is t, alpha is called an expansion-contraction factor, alpha is a convergence coefficient, and the convergence of a single particle is influenced by the value of alpha;
s6: taking all particles in the external archive as target optimal solutions, and calculating an average value of all the target optimal solutions as an optimal solution; and obtaining corresponding user flow data and operation data of the compressor.
2. The MQPSO-based natural gas pipe network multi-objective optimization scheduling method according to claim 1, wherein the method comprises the following steps: the formula of the border crossing processing in the step S3 is as follows:
x i,d ＝lb d +(hb d -lb d )·rand,x i,d ＜lb d (16)；
x i,d ＝ub d -(hb d -lb d )·rand,x i,d ＞hb d (17)；
particle x to cross the boundary i,d Pull back to any random position in the feasible region, where hb d And lb d Constraining the boundary for the particle; i represents the ith particle, i =1,2.
If the particle violates the performance constraint after the position update, the position of the particle is randomly extracted from the optimal positions of the past generations of individual according to the formula (18):
wherein t is the current iteration number, k is any generation before the current iteration number, the PLAst is the best position array of each past generation of individuals of the ith particle, and the Pop is the current generation population array.
3. The MQPSO-based natural gas pipe network multi-objective optimization scheduling method according to claim 1, wherein the method comprises the following steps: volume flow Q at the inlet of the compressor cin The constraint relation with the compressor lift H is as follows:
volume flow Q at the inlet of the compressor cin The constraint relation with the compressor efficiency eta is as follows:
in the formula a 1 ，b 1 ，c 1 ，d 1 ，a 2 ，b 2 ，c 2 ，d 2 The coefficient is the rated parameter coefficient of the compressor, and can be obtained by regression by adopting a least square method according to the rated parameter data of the compressor;
the constraint relation of the rotating speed of the compressor is as follows:
the compressor speed n is at the minimum speed n min And a maximum rotation speed n max The method comprises the following steps:
n min ≤n≤n max (7)；
the volume flow constraint relationship in the inlet state of the compressor is as follows:
Q (i,min) ≤Q i ≤Q (i,max) (i＝1,2,...,N) (8)
formula Q i Amount of air supplied as the i-th air intake point, Q (i,min) Minimum amount of air supply, Q, for the i-th air intake point (i,max) The maximum air supply quantity of the ith air inlet point is N, and the total number of air inlet nodes is N;
the natural gas is conveyed to each gas distribution point from a supply source through a pipeline, and the constraint relation of the pressure of the gas inlet distribution point is as follows:
P (i,min) ≤P i ≤P (i,max) (i＝1,2,...,N) (9)
in the formula P i Pressure at the i-th intake point, P (i,min) Is the minimum pressure of the i-th intake point, P (i,max) The maximum pressure at the ith intake point.
4. The MQPSO-based natural gas pipe network multi-objective optimization scheduling method according to claim 1, wherein the method comprises the following steps: in step S4, if the external archive is not full, directly putting the particles with high fitness into the external archive;
if the external archive is full, comparing the fitness value of the selected particle with the minimum fitness value in the external archive, and if the fitness value of the selected particle is greater than the fitness value, replacing the particle corresponding to the minimum fitness value in the external archive by the selected particle; otherwise, no processing is performed.
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