CN113836778B - Power plant deep peak shaver set load distribution optimization method based on chaos traversal correction iterative algorithm - Google Patents
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Abstract
The invention discloses a power plant deep peak shaver set load distribution optimization method based on a chaos traversal correction iteration algorithm. The optimization method adopts a chaos traversal iterative algorithm, a chaos mapping algorithm is introduced by adding chaos search pre-processing to a traditional random optimization algorithm, the chaos disorder is utilized, the searching of a global optimal value and the setting of an initial searching position are initially completed, and the problems that a classical random optimization algorithm is sensitive to an initial value and is easy to fall into a local minimum value are optimized. The dimension matching algorithm is designed, and the problem that the chaotic mapping kernel dimension is inconsistent with the objective function (parameter configuration) dimension is solved. The method can give the optimal parameter configuration scheme of the power plant in different operation modes, improves the energy efficiency of the power plant under different working conditions, and improves the flexibility and peak shaving capacity of the power plant.
Description
Technical Field
The invention relates to a thermal power plant model optimizing algorithm, in particular to a power plant deep peak shaver set load distribution optimizing method based on a chaos traversal correction iteration algorithm.
Background
The heat supply modification of the traditional thermal generator set is a great trend of the energy industry in China, and when a plurality of units with different heat supply modes in a power plant run simultaneously, unreasonable load distribution can cause the reduction of the overall energy utilization efficiency and economic benefit of the whole plant. The digital model of the power plant is a digital expression of an actual power plant by using a dynamic simulation technology, and can simulate the operation behaviors of the power plant under different parameters. If an optimization algorithm with strong global optimizing capability is developed on the basis of the digital model of the power plant, the optimization algorithm is used for giving an optimal load distribution scheme based on the digital model of the power plant, and has important significance for improving energy efficiency, deeply regulating peak and reducing carbon emission of the power plant.
Disclosure of Invention
Aiming at the defects in the background technology, the invention provides a power plant depth peak shaver set load distribution optimization method based on a chaos traversal correction iteration algorithm.
The invention is realized by adopting the following technical scheme:
a power plant deep peak shaver set load distribution optimization method based on a chaos traversal correction iterative algorithm comprises the following steps:
step1: and establishing a steady-state simulation model of the actual power plant. The thermodynamic system of the cogeneration unit of the power plant generally consists of equipment such as a boiler, a steam turbine, a condenser, a feed water heater and the like and connecting pipelines, and a mechanism or data model is built for each part, so that a steady-state simulation model of the whole plant is built. The steady-state simulation model is used for calculating the performance indexes of the whole plant under various working conditions, such as power supply coal consumption, heat supply quantity, power generation quantity and the like. The parameters of each part and equipment in the power plant form a working condition, and the parameter configuration of the working condition is recorded asx 1 ,x 2 ,......,x n Is a variable to be decided, such as distribution among electric load units, distribution among heat load units, output of an electric boiler, proportioning of coal feeding amount and the like.
Step2: according to the operation requirement, the corresponding performance index of the whole plant is selected as an optimization target, and the optimization target can be maximizing heat supply quantity, maximizing power generation efficiency and the like. And calculating the optimization indexes under different working conditions by using a steady-state simulation model.
Step3: m chaotic points are generated, and the chaotic point state is initialized. Each chaotic point represents a parameter configuration of the operation condition of the power plantEach chaotic point is assigned with an initial value, and the configuration values of the m chaotic points are respectively recorded as +.>
Step4: calculating the current optimization index value f (x) of each chaotic point through a steady-state simulation model of the power plant, comparing the optimization index values of the chaotic points, and recording the parameter configuration reaching the optimal optimization index value.
Step5: updating the parameter configuration of m chaotic points by using a chaotic mapping algorithm:
wherein:
and (3) parameter configuration of the ith chaotic point in the kth iteration.
And (3) parameter configuration of the ith chaotic point in the k+1 step iteration.
i: step3 generates index numbers of chaotic points, i=1, 2,3, … …, m; m is the number of chaotic points.
Alpha: jump amplitude.
chaos (): chaos mapping algorithm, the chaos () algorithm includes a chaos mapping kernel and a dimension matching algorithm.
Chaos mapping kernel:
the p-dimensional chaotic mapping core can be selected from a Logistic chaotic mapping core with p=1, a henon chaotic mapping core with p=2 or other suitable chaotic mapping cores.
Dimension matching algorithm:
when the dimension p of the chaotic mapping core is not equal to the dimension n of the chaotic point parameter configuration, a dimension matching algorithm is required to be used for matching the dimension of the chaotic mapping core with the dimension of the chaotic point parameter configuration.
A. Calculation of
Where d is the integer portion of the result, c is the remainder portion of the result, and there is c < p.
B. Complete grouping
For the first p x d components of x, the mapping can be done as one group per p components, for a total of d groups, i.e
[x 1 ,x 2 ,……,x p ],[x p+1 ,x p+2 ,……,x 2p ],……，[x (d-1)*p+1 ,x (d-1)*P+2 ,……,x d*p ]
C. Supplemental grouping
For the remaining c components, dimension is increased to p by supplementing the dimension, i.e
[x d*p+1 ,x d*p+2 ,……,x d*p+c ,……,r 1 ,r 2 ,……,r p-c ]
Wherein r is 1 ,r 2 ,……,r p-c To supplement the number of component dimensions, the value may be a fixed value, or may be generated by a random algorithm or other algorithm.
To this end, n dimensions are to be madeThe method is divided into d+1 groups, each group is p in dimension, and then the next iteration can be performed by using chaotic mapping of p dimensions.
Step6: and checking whether the chaotic optimizing termination condition is satisfied. If not, returning to step4; if so, step7 is performed. The termination condition may be a fixed number of iterations, or may be the current x best The function value of the position meets the requirement of preliminary optimization, and can also be other termination conditions.
Step7: from the pre-processed coarse optimum position x best Iteration is carried out to obtain a large number of chaotic points, and the chaotic point groups are formed by the chaotic pointsInformation provided, combined with other sophisticated random optimizationsAnd (5) an algorithm, namely finding out an optimal solution of the problem. X is x best Is the optimal point obtained by chaotic search, x best The global optimal value can be quickly found by the heuristic optimization algorithm, so that the convergence speed is increased. Iterative chaos point group->There is good dispersion throughout the feasible region for improving the initial conditions of other optimization algorithms that follow. Other well-established random optimization algorithms include, but are not limited to, particle swarm algorithms, genetic algorithms, and various modified algorithms based thereon.
The beneficial effects of the invention are as follows:
the invention develops a method for searching the optimal parameter configuration (load distribution) under different operation conditions for the deep peak shaving unit of the power plant, has the characteristic of strong global optimizing capability, and can guide the power plant unit to operate according to the parameter configuration with optimal performance in real time, thereby improving energy efficiency. The digital modeling of the power plant unit and the configuration of the operation parameters is completed by establishing a steady-state simulation model of an actual power plant and generating chaotic points, and a digital foundation is laid for the development of an optimizing method; the chaos traversal iterative algorithm realizes the preliminary search of the global optimal parameter configuration, and the chaos mapping algorithm has the characteristics of simple mathematical form and strong disorder, can improve the global optimizing capability and quicken the calculation speed; the problem of inconsistent chaotic mapping kernel dimensions and parameter configuration dimensions is solved through a dimension matching algorithm, so that various chaotic mapping kernels can be selected according to actual problems; the search result of the chaotic traversal iterative algorithm is combined with other mature random optimization algorithms, the searched globally optimal parameter configuration and a large number of chaotic points with good dispersibility are fully utilized as heuristic conditions, the convergence speed of the other mature random optimization algorithms is accelerated, and the problems that the classical random optimization algorithm is sensitive to initial values and is easy to fall into local minimum values are solved.
Drawings
FIG. 1 is a flow chart of a power plant deep peak shaver set load distribution optimization method based on a chaos traversal correction iteration algorithm.
Detailed Description
The algorithm will be specifically described with reference to the accompanying drawings.
In this example, the operation of the method in a thermal power plant in northeast is shown. A flow chart of the method of the invention is shown in figure 1.
Step1: as shown at 1 in FIG. 1, a steady-state simulation model of an actual power plant is built. A steady-state simulation model of the whole plant is established for the power plant by using mechanism modeling software Ebsilon. The steady-state simulation model is used for calculating the performance index of the whole plant under various working conditions, including power supply coal consumption, heat supply quantity, power generation quantity and the like. The parameters of each part and equipment in the power plant form a working condition which is recorded asx 1 ，x 2 ，......，x 7 Is the variable to be decided. In this example, x 1 Is the steam extraction quantity, x of No. 1 unit 2 Is the steam extraction quantity x of No. 2 machine set 3 Is the steam extraction quantity, x of No. 3 machine set 4 For the output of electric boilers, x 5 Is the electric load, x of the No. 1 unit 6 For No. 2 machine set electric load, x 7 Is the electric load of the No. 3 machine set.
Step2: as shown in fig. 1,2, according to the operation requirement, a corresponding performance index of the whole plant is selected as an optimization index, and the optimization targets may be maximizing the heat supply amount, maximizing the power generation efficiency, and the like. In this case the plant wishes to obtain a government subsidy with deep peak shaving, so the minimum power generation is chosen as the optimization objective with guaranteed heating. The generated energy under different working conditions is calculated by a steady-state simulation model.
Step3: as shown in fig. 1 at 3, m chaotic points are generated and the chaotic point state is initialized. Each chaotic point represents a parameter configuration of the operation condition of the power plantEach chaotic point is assigned with an initial value, and the m chaotic points are marked as +.>And assigning values to the initial vectors according to the design working condition values.
Step4: as shown in fig. 1 and 4, the generating capacity of each chaotic point under the current working condition is calculated through a steady-state simulation model of the power plant, the generating capacity of each chaotic point is compared, and the position of the chaotic point reaching the minimum generating capacity is recorded
Step5: as shown in fig. 1 at 5, the configuration values of m chaotic points are updated using a chaotic mapping algorithm:
wherein:
and (3) parameter configuration of the ith chaotic point in the kth iteration.
And (3) parameter configuration of the ith chaotic point in the k+1 step iteration.
i: index numbers of chaotic points generated by step2, i=1, 2,3, … …, m and m are the number of the chaotic points.
Alpha: jump amplitude, here α=1.
The chaos () is a chaotic mapping algorithm, and comprises a chaotic mapping kernel and a dimension matching algorithm.
Chaos mapping kernel:
the p-dimensional chaotic mapping core may be a henon chaotic mapping core with p=2 or other suitable chaotic mapping core.
Here, a henon chaotic mapping kernel, a transform kernel, is used:
wherein a, b are parameters, and have the following limitations:
0＜a＜0.4
0.2＜b≤0.314
here a=1.28 and b=0.3 are chosen.
k is the number of iteration steps.
p, q are iteration variables whose initial values have been set in step 3.
Dimension matching algorithm:
when the dimension p of the chaotic mapping core is not equal to the dimension n of the chaotic point parameter configuration, a dimension matching algorithm is required to be used for matching the dimension of the chaotic mapping core with the dimension of the chaotic point parameter configuration. Here p=2, n=7, and the combination of the groups is needed to match the dimensions of the chaotic algorithm and the chaotic point.
A. Calculation of
Calculated d=3, c=1
B. Complete grouping
For configuration vectorsCan iterate by grouping every 2 components into 3 groups, i.e
C. Supplemental grouping
For the remaining c=1 components, the dimension is increased to p=2 by supplementing the number of bits, i.e
Here, a fixed value of 0 is supplemented, which value can also be generated by a random algorithm or other algorithms.
To this end, the chaotic points of n=7 dimensions are divided into 4 groups, each group has dimensions of 2, and then the next iteration can be performed using the henon chaotic map of p=2 dimensions.
Step6: and checking whether the chaotic optimizing termination condition is satisfied. If not, returning to step4; if so, step7 is performed. The termination condition here is a fixed number of iterations of 1000.
Step7: as shown in fig. 1 at 6, a coarse optimal position x is obtained from the preprocessing best Iterative chaos point groupAnd the provided information is combined with other mature random optimization algorithms to find out the optimal solution of the problem. X is x best The method is an optimal point obtained by chaos search, is used for other heuristic optimization algorithms to quickly find out a global optimal value, and accelerates the convergence rate. Iterative chaos point group->There is good dispersion throughout the feasible region for improving the initial conditions of other optimization algorithms that follow.
The classical particle swarm algorithm is selected and specifically comprises the following steps.
Step7.1: generating a particle swarm according to the heuristic information of the chaos search. The initial value of the particle swarm comprises the initial position and the initial speed of particles, wherein the initial position is selected from the values of chaotic points at the end of the previous chaotic searchThe initial velocities of the particles in the population are randomly generated:
v i ＝rand()，i＝1，2，3，......，m
rand (): random number generation algorithm
Step7.2: calculating the function value of each particle as the local optimum value x of the current particle pbest And obtains the global optimum value x gbest 。
Step7.3: the velocity and position of each particle are updated.
The speed is updated according to the following formula:
v i ＝ω·v+c 1 ·rand()·(x pbest -x i )+c 2 ·rand()·(x gbest -x i )
wherein the method comprises the steps of
Omega: the inertia factor, the value of which is non-negative, the larger the value is, the stronger the global optimizing capability, and the smaller the local optimizing capability is.
rand (): a random number between 0 and 1;
c 1 and c 2 : learning factor, taking c 1 ＝c 2 ＝2。
Step7.4: calculating a function value of the new particle, evaluating and updating a local optimum value x of the particle pbest Updating the global optimum x gbest 。
Step7.5: and checking whether the ending condition is met, and if not, continuing to execute the step7.3. If the end condition is satisfied, the iteration is stopped.
Step7.6: and obtaining the optimal solution of the problem according to the search result.
Claims (4)
1. A power plant deep peak shaver set load distribution optimization method based on a chaos traversal correction iteration algorithm is characterized by establishing a steady-state simulation model of an actual power plant, and searching various working conditions by using the chaos traversal iteration algorithm based on the steady-state simulation model; after the search is finished, the search result is used as a heuristic condition, and the working condition configuration which enables the optimization index to be optimal is obtained by searching in combination with other optimization algorithms;
searching various working conditions by using a chaotic traversal iterative algorithm based on a steady-state simulation model, wherein the method specifically comprises the following steps: initializing a chaotic point state by generating a chaotic point; each chaotic point represents a parameter configuration of the operation condition of the power plant; calculating the current optimization index value of each chaotic point, comparing the optimization index values of the chaotic points, and recording the parameter configuration reaching the optimal optimization index value; updating the parameter configuration of each chaotic point by using a chaotic mapping algorithm;
the parameter configuration of all chaotic points is updated by using a chaotic mapping algorithm, and the method specifically comprises the following steps:
wherein:configuring parameters of the ith chaotic point in the kth iteration; />Parameter configuration of the ith chaotic point in the k+1 step iteration, wherein i=1, 2,3, … …, m and m are the number of the chaotic points; alpha is the jump amplitude; the chaos () is a chaos mapping algorithm, and the chaos mapping algorithm comprises a chaos mapping kernel and a dimension matching algorithm; the chaotic mapping core is used for carrying out iterative updating on the configuration value of the chaotic point; the dimension matching algorithm is used for matching the dimension of the chaotic mapping core with the dimension of the parameter configuration;
the dimension matching algorithm specifically comprises the following steps:
A. calculation of
Wherein n is the dimension of the chaotic point parameter configuration, p is the dimension of the chaotic mapping kernel, d is the integer part of the result, c is the remainder part of the result, and c < p exists;
B. complete grouping
For the first p x d components of x, the mapping can be done as one group per p components, for a total of d groups, i.e
[x 1 ,x 2 ,……,x p ],[x p+1 ,x p+2 ,……,x 2p ],……，[x (d-1)*p+1 ,x (d-1)*P+2 ,……,x d*p ]
C. Supplemental grouping
For the remaining c components, dimension is increased to p by supplementing the dimension, i.e
[x d*p+1 ,x d*p+2 ,……,x d*p+c ,……,r 1 ,r 2 ,……,r p-c ]
Wherein r is 1 ,r 2 ,……,r p-c To supplement the number of component dimensions.
2. The power plant deep peak shaver set load distribution optimization method based on the chaos traversal correction iteration algorithm according to claim 1, wherein the optimization index is to select a corresponding whole plant performance index as the optimization index according to the operation requirement; and calculating the optimization indexes under different working conditions by using a steady-state simulation model.
3. The power plant depth peak shaver set load distribution optimization method based on the chaos traversal correction iteration algorithm according to claim 1, wherein the search result of the chaos traversal iteration algorithm is a chaos point group obtained through rough parameter configuration and iteration.
4. The power plant depth peak shaver set load distribution optimization method based on the chaos traversal correction iteration algorithm according to claim 1, wherein the other optimization algorithm is a random particle swarm algorithm, a genetic algorithm, a simulated annealing algorithm, a cuckoo algorithm or a neural network algorithm.
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| CN109754108A (en) * | 2017-11-08 | 2019-05-14 | 上海明华电力技术工程有限公司 | Unit Economic load distribution method based on fluctuating acceleration coefficient Chaos-Particle Swarm Optimization |
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| CN109754108A (en) * | 2017-11-08 | 2019-05-14 | 上海明华电力技术工程有限公司 | Unit Economic load distribution method based on fluctuating acceleration coefficient Chaos-Particle Swarm Optimization |
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