CN102622530B - Improved genetic algorithm-based method for distributing and optimizing thermal and electrical load of steam extraction and heating unit - Google Patents

Abstract

The invention provides an improved genetic algorithm-based method for distributing and optimizing thermal and electrical load of a steam extraction and heating unit, and belongs to the technical field of energy saving and monitoring of a power station. The invention aims to distribute and optimize the thermal and electrical load of a plurality of heating units of the power station, optimally distribute the electrical load and the thermal load of the power station when the electrical load and the thermal load meet requirements of users, and reduce total energy consumption and save energy. An actual heat consumption and consumption difference curve of the unit is set, and a design heat consumption curve of the unit is obtained; the electrical load and steam extraction amount of each unit are acquired; and the electrical load and the steam extraction amount value of each unit are obtained based on the improved genetic algorithm when the total thermal consumption values of the units are minimum, and the electrical load, the steam extraction amount value optimal solution and the corresponding minimum total thermal consumption of each unit are output when the total thermal consumption values of the units are minimum by improving genetic coding and fitness function, and selecting, crossing and mutating the genetic algorithm under the condition that the optimization process meets constraint conditions. By the method, the speed of optimization process and accuracy of an optimization result are improved.

Description

Based on the steam extraction heat supply unit thermoelectricity schedule model method of improved adaptive GA-IAGA

Technical field

The present invention relates to a kind of power plant multiple stage thermal power plant unit thermoelectricity schedule model method, belong to generating plant energy-saving monitoring technical field.

Background technology

Along with the raising of expanding economy and people's living standard, urban district heating system is developed rapidly, and wherein cogeneration of heat and power energy conversion efficiency has clear superiority, and therefore, heat supply extraction steam unit obtains development energetically.

Steam extraction heat supply unit provides electric power and heating heat to user, the heating power that power plant provides and electric power number, be controlled by the demand of heat user and electric user, therefore, power plant must according to the thermoelectricity load of the demand adjustment heat supply extraction steam unit of heat user and electric user.

For the thermoelectricity load determined, power plant, how according to the type of unit and the difference of unit efficiency, carries out thermoelectricity burden apportionment between each unit, makes the heat consumption rate of whole power plant minimum, making the economic benefit of whole power plant best, is problems faced in power plant's production run.This just to need power plant's heat supply extraction steam unit between electric load and thermal load carry out allocation optimized, determine electric load and the thermal load of every platform unit.

The optimization distribution of load refers to, under the scheduling load that full factory is total, determines that each unit agrees the load of band according to the thermodynamic property of each unit, thus a kind of Optimized Operation that the coal consumption amount of Shi Quan factory is minimum.

For the load optimal distribution of power plant, comparatively early carrying out also comparatively ripe is the electric load allocation optimized research of pure condensate unit, equal increment method is widely used, because steam extraction heat supply unit thermal load also needs to participate in optimizing to distribute, therefore, no matter be obtain or optimize complicacy angle from heat consumption curve all compared with the electric load allocation optimized complexity of pure condensate unit.At present, for the optimized distributionl allocation optimized of steam extraction heat supply unit, carry out much research.

Document [1] (Wei Hao; Song Baofeng; Zhao Weidong; Wang Yi; " Jilin electric power ", 5th phase in 2002, " development & application of heat supply steam turbine group thermoelectricity Optimized Load Dispatch System ") the middle thermoelectricity schedule model system introduced: adopt the mathematical model that " point by point method " distributes, utilize equivalent heat drop theory to carry out variance analysis to the major parameter affecting Steam Turbine economy, the function that economic indexes calculation and Analysis of Energy Loss, parameter display, inquiry and warning and steam turbine analog quantity system figure show can be realized.The mathematical model poor stability that " point by point method " in the literature distributes, computing velocity is comparatively slow, cannot carry out Filled function.Simultaneously to be applied to heat supply extraction steam unit comparatively complicated for Equivalent Entropy Drop Method.

Document [2] (Ran Peng, Zhang Shufang, " steam turbine technology ", 48th volume the 1st phase in 2006: " Thermal Power Generating Unit Load Optimal Distribution based on genetic algorithm ") in application genetic algorithm set up the method for cogeneration plant's load optimal model, solve when problem scale expand, variable and constraint condition a lot of time, can be easy to be absorbed in local optimum, and numerical stability is reduced, finally cause the problem of convergence difficulties.Document [2] is although part solves the problem in document [1], but genetic algorithm exists when initial population is excessive, the problem that computing velocity is slower, this algorithm does not realize the online real-time optimization function of thermal power plant unit simultaneously, can not widespread use in actual production, do not consider that the actual motion condition of unit is on the impact of hear rate yet.

Can find out that the solution of current thermal power plant unit thermoelectricity load distribution on-line optimization exists some problems, therefore need to study further thermal power plant unit thermoelectricity load distribution on-line optimization problem for these problems, enable power plant's multiple stage thermal power plant unit realize thermoelectricity load shifting rate, reach energy-saving and cost-reducing object.

Summary of the invention

The present invention, in order to realize power plant's multiple stage thermal power plant unit thermoelectricity schedule model, enables the optimum allocation while meeting consumers' demand of the electric load of power plant, thermal load, and reduces the object that total energy consumption reaches energy-conservation; And then provide a kind of steam extraction heat supply unit thermoelectricity schedule model method based on improved adaptive GA-IAGA.

The present invention solves the problems of the technologies described above the technical scheme taked to be:

The detailed process of the steam extraction heat supply unit thermoelectricity schedule model method based on improved adaptive GA-IAGA of the present invention is:

Step one, the actual heat consumption curve of unit is set: the actual heat consumption curve obtaining every platform unit according to test; Described actual heat consumption curve refers to that hear rate value R is gang's curve of dependent variable (ordinate), namely with power P and the amount of drawing gas for Q is for independent variable (horizontal ordinate)

1st unit: R ₁=f (P ₁, Q ₁);

2nd unit: R ₂=f (P ₂, Q ₂);

……

N-th unit: R _n=f (P _n, Q _n);

Step 2, arrange unit consumption difference fair curve, determine unit consumption difference revise overall coefficient θ _ii is machine group #, i=1,2, n, n represent unit number: all can have an impact to hear rate based on when condenser back pressure, main steam pressure, main steam temperature, reheat pressure, reheat steam temperature, this six factor off-design values of feed temperature, then provide according to producer or the consumption difference fair curve of power plant checks in the hear rate correction factor Δ of each influence factor of every platform unit _1iΔ _2iΔ _3iΔ _6i, Δ _1iΔ _2iΔ _3iΔ _6ibe respectively the hear rate correction factor of the condenser back pressure of every platform unit, main steam pressure, main steam temperature, reheat pressure, reheat steam temperature, feed temperature; Make θ _i=Δ _1iΔ _2iΔ _3iΔ _6i;

Step 3, obtain unit design heat consumption curve: revise overall coefficient θ according to every platform unit consumption difference _icarry out correction to the actual hear rate of unit and obtain unit design heat consumption curve (i.e. the actual heat consumption curve of revised unit), the design heat consumption curve of each unit is:

1st unit: R ₁=θ ₁f (P ₁, Q ₁);

2nd unit: R ₂=θ ₂f (P ₂, Q ₂);

……

N-th unit: R _n=θ _nf (P _n, Q _n);

Step 4, (from system, obtaining data) obtain the amount of the drawing gas Q of each unit _i(characterizing thermal load with it) and electric load P _i: the amount of the drawing gas Q first recording each unit _iwith electric load P _i, then obtain corresponding hear rate R by the unit design heat consumption curve described in step 3 _i, i ∈ [1, n],

The electric load obtaining power plant n platform unit is respectively P ₁, P ₂..., P _n, the amount of drawing gas is respectively Q ₁, Q ₂..., Q _n(characterizing thermal load with it), hear rate value is R ₁, R ₂..., R _n, n is unit number;

The object optimized is the overall heat consumption value obtaining making all units p time minimum ₁, P ₂..., P _n, Q ₁, Q ₂..., Q _nallocative decision, wherein objective function is:

$\stackrel{\‾}{R}=\frac{P_1\×R_1+P_2\×R_2+\·\·\·P_n\×R_n}{P_1+P_2+\·\·\·+P_n}---\left(1\right)$

Setting constraint condition:

First constraint condition is: Q _z=Q ₁+ Q ₂+ ... + Q _n=const, P _z=P ₁+ P ₂+ ... + P _n=const (2)

The i.e. always amount of the drawing gas Q of all units _zwith total electric load P _zbe respectively constant;

Second constraint condition is: Q _i∈ (Q _imin, Q _imax), P _i∈ (P _imin, P _imin) (3)

Namely the minimax electric load of every platform unit is respectively: P _1min, P _1max; P _2min, P _2max; P _nmin, P _nmax; The minimax amount of drawing gas is respectively Q _1min, Q _1max; Q _2min, Q _2max; Q _nmin, Q _nnax);

Step 5, obtain the overall heat consumption value meeting all units based on improved adaptive GA-IAGA the electric load of each unit time minimum and the value that draws gas: detailed process is as follows,

1, initial population setting

Then can initial population be represented with the matrix of 2n × m:

$\left(\begin{array}{c}{P}_{11}{P}_{12}......P_{1n}{Q}_{11}{Q}_{12}......Q_{1n}\\ P_{21}{P}_{22}......P_{2n}{Q}_{21}{Q}_{22}......Q_{2n}\\ ............................\\ P_{m1}{P}_{m2}......P_{\mathrm{mn}}{Q}_{m1}{Q}_{m2}......Q_{\mathrm{mn}}\end{array}\right)$

M is the individual amount of setting, the amount of drawing gas Q _iwith electric load P _ibe the random number of satisfied second constraint condition; Above-mentioned initial population adopts the formal construction of the constraint coding meeting second constraint condition;

The electric load of (n-1) individual unit before in above-mentioned initial population and the amount of drawing gas are carried out to the constraint coding of satisfied second constraint condition, and last unit is calculated by following formula:

$\left\{\begin{array}{c}{P}_n=P_z-P_1-P_2-\·\·\·-P_{n-1}\\ Q_n=Q_z-Q_1-Q_2-\·\·\·-Q_{n-1}\end{array}\right.---\left(4\right)$

All units can be obtained like this and meet the initial population that first constraint condition and front (n-1) individual unit meet the second constraint condition:

$\left(\begin{array}{c}{P}_{11}{P}_{12}......P_{1(n-1)}{Q}_{11}{Q}_{12}......Q_{1(n-1)}\\ P_{21}{P}_{22}......P_{2(n-1)}{Q}_{21}{Q}_{22}......Q_{2(n-1)}\\ ............................\\ P_{m1}{P}_{m2}......P_{m(n-1)}{Q}_{m1}{Q}_{m2}......Q_{m(n-1)}\end{array}\right)$

Above formula n-th unit is meet the difference of its electric load maxima and minima maximum and maximum heating load and minimum thermal load unit not etc., that is:

P _nmax-P _nmin＞P _imax-P _imin

Q _nmax-Q _nmin≠0

P _nmax, P _nminrepresent the maximum electric load of selected n-th unit out and minimum electric load; P _imax, P _iminrepresent the maximum electric load of residue unit and minimum electric load; Q _nmax, Q _nminrepresent maximum heating load and the minimum thermal load of selected n-th unit out;

2, build fitness function: calculated by fitness, realize individual optimum choice, make n-th unit in optimum results also meet second constraint condition simultaneously;

Ineligible individuality is:

P _n＜P _minOR P _n＞P _max

Q _n＜Q _minOR Q _n＞Q _max

Thermoelectricity schedule model is the minimum value asking objective function, and the optimization aim of genetic algorithm finds the individuality with maximum adaptation degree, therefore definition fitness function ObjV is defined as follows:

1), for qualified individuality:

P _min＜P _n＜P _max&Q _min＜Q _n＜Q _max

$\mathrm{ObjV}=\frac{1}{\stackrel{\‾}{R}}---\left(5\right)$

2), for ineligible individuality: P _n< P _minoR P _n> P _max, Q _n< Q _minoR Q _n> Q _max. adopt index measure transform (1) objective function

$\mathrm{ObjV}=\frac{1}{\stackrel{\&OverBar;}{R_{\max }}\&times;\exp \left(\mathrm{\&alpha;\&beta;}\right)}---\left(6\right)$

Wherein: work as P _n< P _mintime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_p=\frac{P_{\min }-P_n}{P_{\min }}$

Work as P _n> P _maxtime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_p=\frac{P_n-P_{\max }}{P_{\max }}$

For Q _nin like manner can obtain:

Work as Q _n< Q _mintime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_Q=\frac{Q_{\min }-Q_n}{Q_{\min }}$

Work as Q _n> Q _maxtime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_Q=\frac{Q_n-Q_{\max }}{Q_{\max }}$

For β _p, β _qwhen existing simultaneously, β=max (β _p, β _q)

for the maximum hear rate of unit in operational process, obtained by power plant's production and test figure;

α is constant coefficient, and target makes the P when calculating _n, Q _nwhen exceeding setting threshold value 100%, its fitness value is greater than 100 times of the lower fitness value that satisfies condition, i.e. β=1, exp (α) > 100; α gets 5 in an experiment;

So in the selection process, the individuality that fitness is little is eliminated there being very large probability, the individuality simultaneously not meeting second constraint condition also will have very large probability to be eliminated, thus realizes individual optimum choice, ideally obtains maximum adaptation degree and overall heat consumption value minimum individuality;

3, after completing step, then carry out based on the selection of traditional genetic algorithm, intersection, mutation process; When genetic algebra reach end condition N for time, genetic process stops, and exports and meets the overall heat consumption value of all units the electric load of each unit time minimum and draw gas value optimum solution, the hear rate value of each unit and the minimum overall heat consumption of corresponding all units.

The invention has the beneficial effects as follows:

1, in whole optimizing process, make use of power consumption analysis.The factors such as condenser economy, main steam pressure, main steam temperature, reheat pressure, reheat steam temperature, feed temperature that taken into full account, on the impact of hear rate, substantially increase the accuracy of optimum results.

2, the improvement of optimized algorithm.In optimizing process, solve problem with equality constraint, fitness function is revised meanwhile, substantially increase optimal speed and the accuracy of system.

When utilizing this method to carry out specific implementation on hardware, there is following beneficial effect:

1, can realizing providing unit heat consumption curve and consumption difference curve interface are set, the actual heat consumption curve of unit revised through consuming difference can being obtained.User, by power plant's real data, arranges the actual heat consumption curve of unit.System is according to the deviation of current parameter and design parameter, and inquiry consumption difference curve, obtaining the influence value of parameters to unit hear rate, revise actual heat consumption curve, obtaining the actual heat consumption curve of unit through revising.

2, managerial personnel can limit every thermal load of platform unit and the scope of electric load.

3, online real-time optimization and offline optimization two kinds of patterns are achieved.Native system completes whole optimizing process alternately by computing machine and mis system, computing machine reads the current status data needing to optimize unit from mis system, again optimum results is write mis system after optimization, achieve the online real-time optimization of thermoelectricity load distribution.User also manually can input and carry out offline optimization to unit, to carry out com-parison and analysis to historical data.

Concrete quantification effect: when having considered each parameter influences such as condenser economy, main steam pressure, main steam temperature, reheat pressure, reheat steam temperature, feed temperature, for 200MW and two 600MW unit heat supply phase of two, certain generating plant and non-heat supply phase operating mode, the heat supply phase, unit hear rate reduces to the 7632.4441KJ/KWh after optimization by the 7654.2609KJ/KWh before optimizing, and hear rate reduces 21.817KJ/KWh; The non-heat supply phase, unit hear rate reduces to the 8373.638KJ/KWh after optimization by the 8429.5307KJ/KWh before optimizing, and hear rate reduces 55.893KJ/KWh, has huge energy-saving potential.

Accompanying drawing explanation

Fig. 1 is the logic diagram (empty wire frame representation improved adaptive GA-IAGA) based on power plant's thermoelectricity load distribution method for on-line optimization of improved adaptive GA-IAGA;

Fig. 2 utilizes the power plant's thermoelectricity schedule model system architecture schematic diagram based on improved adaptive GA-IAGA of the present invention;

Fig. 3 is that certain Power Plant heat consumption curve arranges sectional drawing;

Fig. 4 is thermoelectricity schedule model platform interface sectional drawing.

Embodiment

Embodiment one: as shown in Figure 1, the detailed process of the steam extraction heat supply unit thermoelectricity schedule model method based on improved adaptive GA-IAGA described in present embodiment is:

Step one, the actual heat consumption curve of unit is set: the actual heat consumption curve obtaining every platform unit according to test; Described actual heat consumption curve refers to that hear rate value R is gang's curve of dependent variable (ordinate), namely with power P and the amount of drawing gas for Q is for independent variable (horizontal ordinate)

1st unit: R ₁=f (P ₁, Q ₁);

2nd unit: R ₂=f (P ₂, Q ₂);

……

N-th unit: R _n=f (P _n, Q _n);

Step 2, arrange unit consumption difference fair curve, determine unit consumption difference revise overall coefficient θ _ii is machine group #, i=1,2, n, n represent unit number: all can have an impact to hear rate based on when condenser back pressure, main steam pressure, main steam temperature, reheat pressure, reheat steam temperature, this six factor off-design values of feed temperature, then provide according to producer or the consumption difference fair curve of power plant checks in the hear rate correction factor Δ of each influence factor of every platform unit _1iΔ _2iΔ _3iΔ _6i, Δ _1iΔ _2iΔ _3iΔ _6ibe respectively the hear rate correction factor of the condenser back pressure of every platform unit, main steam pressure, main steam temperature, reheat pressure, reheat steam temperature, feed temperature; Make θ _i=Δ _1iΔ _2iΔ _3iΔ _6i;

Step 3, obtain unit design heat consumption curve: revise overall coefficient θ according to every platform unit consumption difference _icarry out correction to the actual hear rate of unit and obtain unit design heat consumption curve (i.e. the actual heat consumption curve of revised unit), the design heat consumption curve of each unit is:

1st unit: R ₁=θ ₁f (P ₁, Q ₁);

2nd unit: R ₂=θ ₂f (P ₂, Q ₂);

……

N-th unit: R _n=θ _nf (P _n, Q _n);

Step 4, (from system, obtaining data) obtain the amount of the drawing gas Q of each unit _i(characterizing thermal load with it) and electric load P _i: the amount of the drawing gas Q first recording each unit _iwith electric load P _i, then obtain corresponding hear rate R by the unit design heat consumption curve described in step 3 _i, i ∈ [1, n],

The electric load obtaining power plant n platform unit is respectively P ₁, P ₂..., P _n, the amount of drawing gas is respectively Q ₁, Q ₂..., Q _n(characterizing thermal load with it), hear rate value is R ₁, R ₂..., R _n, n is unit number;

The object optimized is the overall heat consumption value obtaining making all units p time minimum ₁, P ₂..., P _n, Q ₁, Q ₂..., Q _nallocative decision, wherein objective function is:

$\stackrel{\&OverBar;}{R}=\frac{P_1\&times;R_1+P_2\&times;R_2+\&CenterDot;\&CenterDot;\&CenterDot;P_n\&times;R_n}{P_1+P_2+\&CenterDot;\&CenterDot;\&CenterDot;+P_n}---\left(1\right)$

Setting constraint condition:

First constraint condition is: Q _z=Q ₁+ Q ₂+ ... + Q _n=const, P _z=P ₁+ P ₂+ ... + P _n=const (2)

The i.e. always amount of the drawing gas Q of all units _zwith total electric load P _zbe respectively constant.

Second constraint condition is: Q _i∈ (Q _imin, Q _imax), P _i∈ (P _imin, P _imin) (3)

Namely the minimax electric load of every platform unit is respectively: P _1min, P _1max; P _2min, P _2max; P _nmin, P _nmax; The minimax amount of drawing gas is respectively Q _1min, Q _1max; Q _2min, Q _2max; Q _nmin, Q _nmax;

Step 5, obtain the overall heat consumption value meeting all units based on improved adaptive GA-IAGA the electric load of each unit time minimum and the value that draws gas: detailed process is as follows,

1, initial population setting

Then can initial population be represented with the matrix of 2n × m:

$\left(\begin{array}{c}{P}_{11}{P}_{12}......P_{1n}{Q}_{11}{Q}_{12}......Q_{1n}\\ P_{21}{P}_{22}......P_{2n}{Q}_{21}{Q}_{22}......Q_{2n}\\ ............................\\ P_{m1}{P}_{m2}......P_{\mathrm{mn}}{Q}_{m1}{Q}_{m2}......Q_{\mathrm{mn}}\end{array}\right)$

M is the individual amount of setting, the amount of drawing gas Q _iwith electric load P _ibe the random number of satisfied second constraint condition; Above-mentioned initial population adopts the formal construction of the constraint coding meeting second constraint condition;

The electric load of (n-1) individual unit before in above-mentioned initial population and the amount of drawing gas are carried out to the coding of satisfied second constraint condition, and last unit is calculated by following formula:

$\left\{\begin{array}{c}{P}_n=P_z-P_1-P_2-\&CenterDot;\&CenterDot;\&CenterDot;-P_{n-1}\\ Q_n=Q_z-Q_1-Q_2-\&CenterDot;\&CenterDot;\&CenterDot;-Q_{n-1}\end{array}\right.---\left(4\right)$

All units can be obtained like this and meet the initial population that first constraint condition and front (n-1) individual unit meet the second constraint condition:

$\left(\begin{array}{c}{P}_{11}{P}_{12}......P_{1(n-1)}{Q}_{11}{Q}_{12}......Q_{1(n-1)}\\ P_{21}{P}_{22}......P_{2(n-1)}{Q}_{21}{Q}_{22}......Q_{2(n-1)}\\ ............................\\ P_{m1}{P}_{m2}......P_{m(n-1)}{Q}_{m1}{Q}_{m2}......Q_{m(n-1)}\end{array}\right)$

Above formula n-th unit is meet the difference of its electric load maxima and minima maximum and maximum heating load and minimum thermal load unit not etc., that is:

P _nmax-P _nmin＞P _imax-P _imin

Q _nmax-Q _nmin≠0

P _nmax, P _nminrepresent the maximum electric load of selected n-th unit out and minimum electric load; P _imax, P _iminrepresent the maximum electric load of residue unit and minimum electric load; Q _nmax, Q _nminrepresent maximum heating load and the minimum thermal load of selected n-th unit out;

2, build fitness function: calculated by fitness, realize individual optimum choice, make n-th unit in optimum results also meet second constraint condition simultaneously;

Ineligible individuality is:

P _n＜P _minOR P _n＞P _max

Q _n＜Q _minOR Q _n＞Q _max

Thermoelectricity schedule model is the minimum value asking objective function, and the optimization aim of genetic algorithm finds the individuality with maximum adaptation degree, therefore definition fitness function ObjV is defined as follows:

1), for qualified individuality; P _min< P _n< P _maxaMP.AMp.Amp Q _min< Q _n< Q _max

$\mathrm{ObjV}=\frac{1}{\stackrel{\&OverBar;}{R}}---\left(5\right)$

2), for ineligible individuality: P _n< P _minoR P _n> P _max, Q _n< Q _minoR Q _n> Q _max, adopt index measure transform (1) objective function

$\mathrm{ObjV}=\frac{1}{\stackrel{\&OverBar;}{R_{\max }}\&times;\exp \left(\mathrm{\&alpha;\&beta;}\right)}---\left(6\right)$

Wherein: work as P _n< P _mintime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_p=\frac{P_{\min }-P_n}{P_{\min }}$

Work as P _n> P _maxtime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_p=\frac{P_n-P_{\max }}{P_{\max }}$

For Q _nin like manner can obtain:

Work as Q _n< Q _mintime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_Q=\frac{Q_{\min }-Q_n}{Q_{\min }}$

Work as Q _n> Q _maxtime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_Q=\frac{Q_n-Q_{\max }}{Q_{\max }}$

For β _p, β _qwhen existing simultaneously, β=max (β _p, β _q)

for the maximum hear rate of unit in operational process, obtained by power plant's production and test figure.

α is constant coefficient, and target makes the P when calculating _n, Q _nwhen exceeding setting threshold value 100%, its fitness value is greater than 100 times of the lower fitness value that satisfies condition, i.e. β=1, exp α) > 100; α gets 5 in an experiment;

So in the selection process, the individuality that fitness is little is eliminated there being very large probability, the individuality simultaneously not meeting second constraint condition also will have very large probability to be eliminated, thus realizes individual optimum choice, ideally obtains maximum adaptation degree and overall heat consumption value minimum individuality;

3, after completing step, then carry out based on the selection of traditional genetic algorithm, intersection, mutation process; When genetic algebra reach end condition N for time, genetic process stops, and exports and meets the overall heat consumption value of all units the electric load of each unit time minimum and draw gas value optimum solution, the hear rate value of each unit and the minimum overall heat consumption of corresponding all units.

Be described further again (step 5) for the inventive method:

Genetic algorithm is based on natural selection and theory of heredity, by the efficient global optimization approach that survival of the fittest rule in biological evolution process combines with the random information exchanging mechanism of colony intrinsic stain body.Genetic algorithm has abandoned traditional way of search, the evolutionary process of simulation organic sphere, adopts the mode of artificial evolution to carry out random optimization search to object space.It solution will may regard body one by one in colony as in problem, and each coding is weaved into the form of symbol string, simulate the evolutionary process of Darwinian heredity selection and natural selection, the operation (heredity, intersection, variation) based on heredity is carried out repeatedly to colony.Target fitness function according to intended target is evaluated each individuality, according to the evolutionary rule of the survival of the fittest, the survival of the fittest, constantly obtain optimum colony, search the optimum individual optimized in colony in overall parallel search mode, in the hope of the optimum solution satisfied condition simultaneously.

The general process of genetic algorithm is: arrange initial population, calculate fitness, select, intersect, variation, produce new population, recalculate fitness, successively loop iteration, until iterations reaches initial set value, heredity terminates, and last population obtained is optimum population in generation, and the individuality in population is optimum individual.In this example, the object that we optimize is the overall heat consumption value making all units minimum, wherein

$\stackrel{\&OverBar;}{R}=\frac{P_1\&times;R_1+P_2\&times;R_2+......P_n\&times;R_n}{P_1+P_2+......+P_n}---\left(1\right)$

In interface user's input or from system the amount of the drawing gas Q of each unit of Real-time Obtaining _i(characterizing thermal load with it) and electric load P _i, each electric load and the corresponding hear rate R of the amount of drawing gas can be obtained by checking and verify border heat consumption curve _i(i ∈ [1, n]), passes through R _ijust the overall heat consumption of all units can be calculated with above formula.Optimization method is as follows:

1, initial population setting

Because genetic algorithm directly can not process the parameter of problem space, therefore must by coding requiring that the feasible solution of problem is expressed as chromosome or the individuality in hereditary space.Conventional coding method has binary coding, gray encoding, multistage parameter coding, orderly string encoding etc.Due to the continuous function optimization problem that this optimization problem is multidimensional, high-precision requirement, the codings such as scale-of-two are used to represent that individuality has some disadvantages, some invaluable experiences that people sum up in the research of some classic optimisation algorithms also just cannot be used, and are also not easy to the constraint condition processing non-trivial.In order to overcome the shortcoming of binary coding method, adopt floating-point encoding herein.For the requirement that population is arranged, each individuality must be the feasible solution of this optimization problem, optimizes like this and is just of practical significance.Illustrate: if formula (1) is the equation of a pure mathematics, P _i, Q _iany value are all feasible solutions of formula (1), as long as the matrix that then cataloged procedure generates a 2n*m then can represent initial population:

$\left(\begin{array}{c}{P}_{11}{P}_{12}......P_{1n}{Q}_{11}{Q}_{12}......Q_{1n}\\ P_{21}{P}_{22}......P_{2n}{Q}_{21}{Q}_{22}......Q_{2n}\\ ............................\\ P_{m1}{P}_{m2}......P_{\mathrm{mn}}{Q}_{m1}{Q}_{m2}......Q_{\mathrm{mn}}\end{array}\right)$

M is the individual amount of setting, P _i, Q _ibe random number.Such coding is called for encoding without constraint.

But the electric load of each unit has bound, i.e. Q with the amount of drawing gas in real process _max, Q _minand P _max, P _min, the electric load that all units are overall and the amount of drawing gas are definite value, namely

Q _z＝Q ₁+Q ₂+…+Q _n＝const，P _z＝P ₁+P ₂+…+P _n＝const

Therefore electric load and the amount of drawing gas are equality constraint, and genetic algorithm is due to its randomness, be difficult to solve equality constraint, even if or adopt constrained coding, then initial population then produces under a qualifications, so just run counter to the principle of genetic algorithm simulation biological evolution, individual randomness and diversity are restricted, and the effect of optimization of algorithm can be had a greatly reduced quality.So our innovation is the problem being solved equality constraint by a series of conversion, and can ensure that the final individuality optimizing out is optimum solution.

Known constraints condition 1 is: Q _z=Q ₁+ Q ₂+ ... + Q _n=const, P _z=P ₁+ P ₂+ ... + P _n=const (2)

The i.e. always amount of the drawing gas Q of all units _zwith total electric load P _zbe respectively constant.

Constraint condition 2 is: Q _i∈ (Q _min, Q _max), P ∈ (P _min, P _max) (3)

Namely the minimax electric load of every platform unit is respectively: P _1min, P _1max; P _2min, P _2max; P _nmin, P _nmax; The minimax amount of drawing gas is respectively Q _1min, Q _1max; Q _2min, Q _2max; Q _nmin, Q _nmax;

In order to the actual conditions that coding will be made to meet unit operation, namely constraint condition 1 is met, coding is revised as the coding electric load of (n-1) individual unit before in above-mentioned initial population and the amount of drawing gas being carried out to satisfied second constraint condition by us, and last unit is calculated by following formula:

$\left\{\begin{array}{c}{P}_n=P_z-P_1-P_2-\&CenterDot;\&CenterDot;\&CenterDot;-P_{n-1}\\ Q_n=Q_z-Q_1-Q_2-\&CenterDot;\&CenterDot;\&CenterDot;-Q_{n-1}\end{array}\right.---\left(4\right)$

The initial population obtained is:

$\left(\begin{array}{c}{P}_{11}{P}_{12}......P_{1(n-1)}{Q}_{11}{Q}_{12}......Q_{1(n-1)}\\ P_{21}{P}_{22}......P_{2(n-1)}{Q}_{21}{Q}_{22}......Q_{2(n-1)}\\ ............................\\ P_{m1}{P}_{m2}......P_{m(n-1)}{Q}_{m1}{Q}_{m2}......Q_{m(n-1)}\end{array}\right)$

Above formula n-th unit is meet the difference of its electric load maxima and minima maximum and maximum heating load and minimum thermal load unit not etc., that is:

P _nmax-P _nmin＞P _imax-P _imin

Q _nmax-Q _nmin≠0

P _nmax, P _nminrepresent the maximum electric load of selected n-th unit out and minimum electric load; P _imax, P _iminrepresent the maximum electric load of residue unit and minimum electric load; Q _nmax, Q _nminrepresent maximum heating load and the minimum thermal load of selected n-th unit out.

So not only completely random during the arranging of initial population, first constraint condition (formula (2)) also well meets.But for formula (3), initial population carries out not being well positioned to meet when encoding, that is, some insignificant individualities can be produced in cataloged procedure and make:

P _n＜P _minOR P _n＞P _max

Q _n＜Q _minOR Q _n＞Q _max

We need to be cast out by these individualities in the process of the optimization below as far as possible, and left is exactly qualified individuality.

2, fitness function

Fitness function is used to the standard distinguishing individual in population quality, is unique foundation of carrying out natural selection.Thermoelectricity schedule model is the minimum value found a function, and the optimization aim of genetic algorithm finds the individuality with maximum adaptation degree, therefore definition fitness function ObjV is defined as follows:

1), for qualified individuality: P _min< P _n< P _maxaMP.AMp.Amp Q _min< Q _n< Q _max

$\mathrm{ObjV}=\frac{1}{\stackrel{\&OverBar;}{R}}---\left(5\right)$

2), for ineligible individuality: P _n< P _minoR P _n> P _max, Q _n< Q _minoR Q _n> Q _max. adopt index measure transform (1) objective function

$\mathrm{ObjV}=\frac{1}{\stackrel{\&OverBar;}{R_{\max }}\&times;\exp \left(\mathrm{\&alpha;\&beta;}\right)}---\left(6\right)$

Wherein: work as P _n< P _mintime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_p=\frac{P_{\min }-P_n}{P_{\min }}$

Work as P _n> P _maxtime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_p=\frac{P_n-P_{\max }}{P_{\max }}$

For Q _nin like manner can obtain:

Work as Q _n< Q _mintime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_Q=\frac{Q_{\min }-Q_n}{Q_{\min }}$

Work as Q _n> Q _maxtime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_Q=\frac{Q_n-Q_{\max }}{Q_{\max }}$

For β _p, β _qwhen existing simultaneously, β=max (β _p, β _q)

for the maximum hear rate of unit in operational process, obtained by power plant's production and test figure.

α is constant coefficient, and target makes the P when calculating _n, Q _nwhen exceeding setting threshold value 100%, its fitness value is greater than 100 times of the lower fitness value that satisfies condition, i.e. β=1, exp (α) > 100; α gets 5 in an experiment.

We just can draw such conclusion like this, as electric load and the amount of the drawing gas Pn of last unit calculated by population, when Qn does not satisfy condition, the degree that its fitness value can exceed setting threshold value according to it is amplified, exceed more, amplify more severe (exponential increase).(from formula (5) and formula (6), the degree exceeding setting threshold value is more amplified, fitness value is less) so in the selection process, the individuality that fitness is little is eliminated there being very large probability, thus realize individual optimum choice, ideally obtain maximum adaptation degree and overall heat consumption value minimum individuality.

3, select

Selection operation forms new population with certain probability selection defect individual from old colony, obtains individuality of future generation to breed.Individual selected probability is relevant with fitness value, and ideal adaptation degree is higher, and selected probability is larger.Adopt roulette method herein, namely based on the selection strategy of fitness ratio, individual selected probability is:

$p_i=\frac{F_i}{{\&Sum;}_j^N{F}_j}$

Fi is the fitness value of this individuality, for all ideal adaptation angle value sums.

4, interlace operation

Owing to adopting floating-point encoding herein, therefore corresponding Crossover Strategy chooses arithmetic crossover, is to be produced two new individualities by two individual linear combinations.Suppose at two individual X _a, X _bbetween carry out arithmetic crossover, then by two the new individualities produced after arithmetical operation be:

X′ _A＝aX _B+(1-a)X _A

X′ _B＝aX _A+(1-a)X _B

Wherein a is a parameter, and a can be a constant, also can be the variable determined by evolutionary generation.Adopting herein and arranging a is a constant 0.8.

5, make a variation

Variation can improve the local search ability of genetic algorithm and can maintain the diversity of population.Conventional Mutation Strategy has basic bit mutation, evenly makes a variation, boundary mutation etc.Thermoelectricity load distribution problem is complicated nonlinear problem, good effect can be had close to optimum solution, but be difficult to determinacy and must search optimum solution, in order to address this problem, there is employed herein Gaussian approximation variation, the local search ability of genetic algorithm to focussing search region can be improved, and have certain probability to make algorithm jump out local minimum point.Be original parameter value with meeting average during concrete operations, variance be a random number of the normal distribution of original parameter value square to replace original genic value, from the characteristic of normal distribution, Gaussian mutation is also the regional area near the original individuality of focussing search.Concrete formula is as follows:

$f\left(x\right)=\frac{1}{\sqrt{2\mathrm{\&pi;}}{q}^2}{e}^{-\frac{{(x-q)}^2}{2q^2}}$

Wherein q is original genic value.

6, end condition

When genetic algebra reached for 50 generation, genetic process stops automatically, exports optimum solution and corresponding minimum overall heat consumption.

Utilize the embodiment (with reference to figure 1 ~ 4) of the inventive method:

For certain power plant 4 units:

1 pair of every platform unit heat consumption curve is arranged.Carry out site test, draw the heat consumption curve under the different amount of drawing gas of every platform unit.According to the size of the factor off-design values such as every platform train condenser economy, main steam pressure, main steam temperature, reheat pressure, reheat steam temperature, feed temperature, inquiry consumption difference curve, revises heat consumption curve.Obtain the final heat consumption curve entering correction, as shown in Figure 3.

2 pairs of optimal conditions are arranged.Input every platform unit maximum/miniwatt, maximum/little amount of drawing gas, as shown in Figure 4.

3 select " Automatic Optimal ", are optimized.Optimum results as shown in Figure 4, can find out that, after this system optimization, power plant's hear rate decreases 45.1kJ/kWh.Result after optimization can write mis system, and operations staff controls steam turbine according to result.

4 supervisory computers read current set state data (condenser economy, main steam pressure, main steam temperature, reheat pressure, reheat steam temperature, feed temperature) from mis system, obtain electric load Q, the thermal load P of user's request up till now, obtain current each unit hear rate value R and power plant's overall heat consumption value by heat consumption curve C under each unit design operating mode in searching and managing computing machine and consumption difference fair curve

5 managerial personnel can be limited by the optimal conditions (scope as electric load, thermal load) of computing machine to every platform unit.

6 utilize improved adaptive GA-IAGA to be optimized.Export optimum results, namely every platform unit optimum thermoelectricity load distribution scheme and power plant's hear rate value, reduce hear rate value.

7 optimum results input mis systems, operations staff modifies to unit parameter according to optimum results, realizes the control to unit.

As shown in Figure 2, utilize the software of the method for the invention to write in supervisory computer in fig. 2, electric power supply plant managerial personnel carry out energy-saving monitoring and adjust.Supervisory computer reads the parameters of current unit from mis system: electric load, thermal load, condenser back pressure, main steam pressure, main steam temperature, reheat pressure, reheat steam temperature, feed temperature.By inquiry unit design heat consumption curve and consumption difference fair curve, obtain current each unit hear rate value R ₁, R ₂..., R _nwith power plant's overall heat consumption value .Managerial personnel can by optimal conditions (the minimax electric load P of supervisory computer to every platform unit _1min, P _1max, P _2min, P _2max..., P _nmin, P _nmaxwith the minimax amount of drawing gas Q _1min, Q _1max, Q _2min, Q _2max..., Q _nmin, Q _nmax) limit.

Claims (1)

1., based on a steam extraction heat supply unit thermoelectricity schedule model method for improved adaptive GA-IAGA, it is characterized in that: the detailed process of described method is:

Step one, the actual heat consumption curve of unit is set: the actual heat consumption curve obtaining every platform unit according to test; Described actual heat consumption curve refers to electric load P and the amount of drawing gas Q for independent variable, gang's curve that hear rate value R ' is dependent variable, namely

1st unit: R ₁'=f (P ₁, Q ₁);

2nd unit: R ₂'=f (P ₂, Q ₂);

……

N-th unit: R _n'=f (P _n, Q _n);

Step 2, arrange unit consumption difference fair curve, determine unit consumption difference revise overall coefficient θ _ii is machine group #, i=1,2, n, n represent unit number: all can have an impact to hear rate based on when condenser back pressure, main steam pressure, main steam temperature, reheat pressure, reheat steam temperature, this six factor off-design values of feed temperature, then provide according to producer or the consumption difference fair curve of power plant checks in the hear rate correction factor △ of each influence factor of every platform unit _1i, △ _2i, △ _3i..., △ _6i, △ _1i, △ _2i, △ _3i..., △ _6ibe respectively the hear rate correction factor of the condenser back pressure of every platform unit, main steam pressure, main steam temperature, reheat pressure, reheat steam temperature, feed temperature; Make θ _i=△ _1i△ _2i△ _3i△ _6i;

Step 3, obtain unit design heat consumption curve: revise overall coefficient θ according to every platform unit consumption difference _icarry out correction to the actual hear rate of unit and obtain unit design heat consumption curve, the design heat consumption curve of each unit is:

1st unit: R ₁=θ ₁f (P ₁, Q ₁);

2nd unit: R ₂=θ ₂f (P ₂, Q ₂);

……

N-th unit: R _n=θ _nf (P _n, Q _n);

Step 4, obtain the amount of the drawing gas Q of each unit _iwith electric load P _i: the amount of the drawing gas Q first recording each unit _iwith electric load P _i, then obtain corresponding hear rate R by the unit design heat consumption curve described in step 3 _i, i ∈ [1, n],

The electric load obtaining power plant n platform unit is respectively P ₁, P ₂..., P _n, the amount of drawing gas is respectively Q ₁, Q ₂..., Q _n, hear rate value is R ₁, R ₂..., R _n, n is unit number;

The object optimized is the overall heat consumption value obtaining making all units p time minimum ₁, P ₂..., P _n, Q ₁, Q ₂..., Q _nallocative decision, wherein objective function is: $\stackrel{\&OverBar;}{R}=\frac{P_1\&times;R_1+P_2\&times;R_2+...+P_n\&times;R_n}{P_1+P_2+...+P_n}---\left(1\right)$

Setting constraint condition:

First constraint condition is: Q _z=Q ₁+ Q ₂+ ... + Q _n=const, P _z=P ₁+ P ₂+ ... + P _n=const (2)

The i.e. always amount of the drawing gas Q of all units _zwith total electric load P _zbe respectively constant;

Second constraint condition is: Q _i∈ (Q _imin, Q _imax), P _i∈ (P _imin, P _imax) (3)

Namely the minimax electric load of every platform unit is respectively: P _1min, P _1max; P _2min, P _2max; P _nmin, P _nmax; The minimax amount of drawing gas is respectively Q _1min, Q _1max; Q _2min, Q _2max; Q _nmin, Q _nmax;

Step 5, to obtain based on improved adaptive GA-IAGA the overall heat consumption value R meeting all units minimum time the electric load of each unit and the value that draws gas: detailed process is as follows,

A, initial population set

Then can initial population be represented with the matrix of m × 2n:

$\left(\begin{array}{c}{P}_{11}{P}_{12}......P_{1n}{Q}_{11}{Q}_{12}......Q_{1n}\\ P_{21}{P}_{22}......P_{2n}{Q}_{21}{Q}_{22}......Q_{2n}\\ ............................\\ P_{m1}{P}_{m2}......P_{\mathrm{mn}}{Q}_{m1}{Q}_{m2}......Q_{\mathrm{mn}}\end{array}\right)$

M is the individual amount of setting, and n is unit quantity, the amount of drawing gas Q _iwith electric load P _ibe the random number of satisfied second constraint condition; Above-mentioned initial population adopts the formal construction of the constraint coding meeting second constraint condition;

The electric load of (n-1) individual unit before in above-mentioned initial population and the amount of drawing gas are carried out to the constraint coding of satisfied second constraint condition, and last unit is calculated by following formula:

$\left\{\begin{array}{c}{P}_n=P_z-P_1-P_2-...-P_{n-1}\\ Q_n=Q_z-Q_1-Q_2-...-Q_{n-1}\end{array}\right.---\left(4\right)$

All units can be obtained like this and meet the constraint coding initial population that first constraint condition and front (n-1) individual unit meet the second constraint condition:

$\left(\begin{array}{c}{P}_{11}{P}_{12}......P_{1(n-1)}{Q}_{11}{Q}_{12}......Q_{1(n-1)}\\ P_{21}{P}_{22}......P_{2(n-1)}{Q}_{21}{Q}_{22}......Q_{2(n-1)}\\ ............................\\ P_{m1}{P}_{m2}......P_{m(n-1)}{Q}_{m1}{Q}_{m2}......Q_{m(n-1)}\end{array}\right)$

Above formula n-th unit is meet the unit that the difference of its electric load maxima and minima maximum and the maximum amount of drawing gas and the minimum amount of drawing gas do not wait, that is:

P _nmax-P _nmin>P _imax-P _imin

Q _nmax-Q _nmin≠0

P _nmax, P _nminrepresent the maximum electric load of selected n-th unit out and minimum electric load; P _imax, P _iminrepresent the maximum electric load of remaining any unit i and minimum electric load; Q _nmax, Q _nminrepresent the maximum amount of drawing gas of selected n-th unit out and the minimum amount of drawing gas;

B, structure fitness function: calculated by fitness, realize individual optimum choice, make n-th unit in optimum results also meet second constraint condition simultaneously;

Ineligible individuality is:

P _n<P _minOR P _n>P _max

Q _n<Q _minOR Q _n>Q _max

Thermoelectricity schedule model is the minimum value asking objective function, the optimization aim of genetic algorithm be find have maximum

The individuality of fitness, therefore fitness function ObjV is defined as follows:

1), for qualified individuality: P _min<P _n<P _maxaMP.AMp.Amp Q _min<Q _n<Q _max

$\mathrm{ObjV}=\frac{1}{\stackrel{\&OverBar;}{R}}---\left(5\right)$

2), for ineligible individuality: P _n<P _minoR P _n>P _max, Q _n<Q _minoR Q _n>Q _max, adopt index measure transform (1) to represent objective function

$\mathrm{ObjV}=\frac{1}{\stackrel{\&OverBar;}{R_{\max }}\&times;\exp \left(\mathrm{\&alpha;\&beta;}\right)}---\left(6\right)$

Wherein: work as P _n<P _mintime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_p=\frac{P_{\min }-P_n}{P_{\min }}$

Work as P _n>P _maxtime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_p=\frac{P_n-P_{\max }}{P_{\max }}$

For Q _nin like manner can obtain:

Work as Q _n<Q _mintime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_Q=\frac{Q_{\min }-Q_n}{Q_{\min }}$

Work as Q _n>Q _maxtime, $\mathrm{\&beta;}={\mathrm{\&beta;}}_Q=\frac{Q_n-Q_{\max }}{Q_{\max }}$

For β _p, β _qwhen existing simultaneously, β=max (β _p, β _q)

for the maximum hear rate of unit in operational process, obtained by power plant's production and test figure;

α is constant coefficient, and target makes the P when calculating _n, Q _nwhen exceeding setting threshold value 100%, its fitness value is greater than 100 times of the lower fitness value that satisfies condition, i.e. β=1, exp (α) >100; α gets 5 in an experiment;

So in the selection process, the individuality that fitness is little is eliminated there being very large probability, the individuality simultaneously not meeting second constraint condition also will have very large probability to be eliminated, thus realizes individual optimum choice, ideally obtains maximum adaptation degree and overall heat consumption value minimum individuality;

C, complete step after, then carry out based on the selection of traditional genetic algorithm, intersection, mutation process; When genetic algebra reach end condition N for time, genetic process stops, and exports and meets the overall heat consumption value of all units the electric load of each unit time minimum and draw gas value optimum solution, the hear rate value of each unit and the minimum overall heat consumption of corresponding all units.