CN104037757B - A kind of thermal power plant's economic environment dispatching method based on brainstorming - Google Patents

Abstract

The invention discloses a kind of thermal power plant's economic environment dispatching method based on brainstorming, step comprises: step 1, determine the Mathematical Modeling of thermal power plant's environmental economy scheduling problem; Step 2, all kinds of parameters obtained in model; The initial feasible schedule set of step 3, acquisition environmental economy scheduling problem; Step 4, feasible schedule set to be evaluated; Step 5, feasible schedule to be upgraded; Step 6, judge whether whole feasible schedule has upgraded; Step 7, to outside filing concentrate non-bad scheduling disaggregation upgrade; Step 8, iteration of carrying out export final feasible schedule scheme.Method of the present invention; using thermal power plant as electric power system; utilize the collection analysis to electric power system and thermal power plant's data, in conjunction with the brainstorming optimized algorithm based on cluster and variation thought, realize protecting solving of thermal power plant's Economic Dispatch Problem of demand to integrated environment.

Description

Thermal power plant economic environment scheduling method based on brainstorming

Technical Field

The invention belongs to the technical field of intelligent control, and relates to a thermal power plant economic environment scheduling method based on a brainstorming.

Background

Thermal power generation is the main force of current power supply, and because thermal power plants discharge a large amount of harmful gases such as oxysulfide, nitric oxide and carbon dioxide, atmospheric environment is directly polluted, and greenhouse effect is caused, therefore, the research of the environmental economic scheduling problem of the power system comprehensively considering environmental protection and economic benefits not only has important theoretical significance, but also is the most realistic choice of the strategy of sustainable development of energy and power. If the pollution discharge amount is considered, the original single-target optimization problem is changed into a multi-target optimization problem, so that the complexity of the problem is increased, and difficulty and challenge are brought to the implementation of scheduling. Because the objectives in the power system environmental economic dispatching problem are conflicting, making a reasonable dispatching plan is the focus of current research.

The brainstorm method, which was first proposed by the american creators A.F austin 1939 and is also called intellectual motivation method, is a method for creating new ideas or inspiring innovative ideas, i.e., creating new ideas around a specific field of interest, and this situation is called brainstorm. By utilizing the characteristics of freedom, unrestraint and positive optimism in the brainstorm meeting, 2011 scholar provides a new group intelligent optimization algorithm, namely brainstorming optimization algorithm (BSO), in the second group intelligent international meeting (ICSI 11 for short).

Disclosure of Invention

The invention aims to provide a thermal power plant economic environment scheduling method based on a brainstorming, and solves the problems that the existing scheduling method is difficult to adjust between the economic benefit and the environmental benefit of a power grid and is difficult to obtain the best benefit.

The technical scheme adopted by the invention is as follows: a thermal power plant economic environment scheduling method based on a brainstorming is implemented according to the following steps:

step1, determining mathematical model of environmental economic dispatching problem of thermal power plant

The mathematical model for setting the environmental economic scheduling problem is as follows:

$\min [\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{F}_i\left(P_i\right),\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{E}_i\left(P_i\right)],---\left(1\right)$

wherein N is_GI represents the ith generator, i is 1,2_G；P_iRepresenting the active power of the ith generator; f_i(P_i) The total fuel consumption of the power generation fuel of the thermal power plant at a certain moment is represented, and the calculation expression is as follows:

$F_i\left(P_i\right)=a_i+b_i{P}_i+c_i{p}_i^2,---\left(2\right)$

a_i、b_i、c_ithe constant term, the primary term coefficient and the secondary term coefficient respectively represent consumption characteristics of the ith generator set and are known parameters in the system;

E_i(P_i) The pollution emission amount of the ith generator is represented by the calculation expression:

$E_i\left(P_i\right)={\mathrm{\α}}_i+{\mathrm{\β}}_i{P}_i+{\mathrm{\γ}}_i{P}_i^2+{\mathrm{\ξ}}_i\exp \left({\mathrm{\λ}}_i{P}_i\right),---\left(3\right)$

α therein_i、β_i、γ_i、ξ_i、λ_iAre all system parameters, α_i、β_i、γ_iConstant term, primary term coefficient and secondary term coefficient respectively representing the pollution emission characteristics of the ith generating set, ξ_i、λ_iThe related parameters representing the exponential terms are subject to the following constraint conditions in the environment economic dispatching optimization process:

constraint 1: the inequality constraint of the generating capacity of the unit:

$P_i^{\min }<P_i<P_i^{\max },---\left(4\right)$

wherein,respectively outputting the minimum active power and the maximum active power of the ith thermal generator;

constraint 2: balancing equality constraints, namely the sum of the generated power of each generator set of the system is equal to the sum of the total required power of the load and the network loss:

$P_D+P_{\mathrm{loss}}-\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{P}_i=0,---\left(5\right)$

in the formula (5), the reaction mixture is,the total power of the thermal power at a certain moment; p_DThe system load requirement at the moment; p_LossThe method is expressed as active network loss in a power grid at the moment t, the active network loss is obtained by adopting a B coefficient method, and the formula is as follows:

$P_{\mathrm{loss}}=\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{P}_i{B}_{\mathrm{ij}}{P}_j+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{B}_{0i}{P}_i+B_{00},---\left(6\right)$

wherein B is_ij、B_0i、B₀₀Is the coefficient of B;

step2, obtaining various parameters in the model;

step3, obtaining an initial feasible scheduling set of the environmental economic scheduling problem

In the above model, it is necessary to determine that the decision variable is N_GActive power of individual unitGenerating a feasible scheduling set according to the optimal value of the scheduling parameter;

step4, evaluating the feasible scheduling set

Respectively substituting the N feasible scheduling sets generated in the step3 into an objective function formula (2) and a formula (3), respectively evaluating the environment and economic benefits of each feasible scheduling, and storing the feasible scheduling sequences which are not dominant in each other in an external set, namely an external filing set, because each feasible scheduling corresponds to two objective functions, the N feasible scheduling sequences need to be sorted in a non-inferior solution manner;

step5, updating the feasible scheduling

Before updating, an initial value T of the iteration number is required to be set to 0, and the maximum iteration number T of the iteration number is required to be set_max；

Firstly, randomly selecting m different feasible schedules as class centers of m classes, clustering N feasible schedules according to Euclidean distances from active power of all other feasible schedules to each class center, and using the feasible schedules to simulate a formation process of an idea in a brainstorming process; defining a class containing non-inferior solutions as an elite class and a class without the non-inferior solutions as a common class, and iteratively updating the active power of each feasible scheduling generator set through selection operation and variation operation on the basis of new information obtained by clustering;

step 6, judging whether the whole feasible scheduling is updated or not

If i is equal to N, the N feasible schedules are updated, and then the step 7 is carried out; otherwise, returning to the step 5;

step 7, updating the non-bad scheduling solution set in the external filing set

Storing the obtained non-inferior feasible scheduling of each comparison of economic benefit and pollution emission in an external filing set;

step 8, iteration output is carried out to finally obtain a feasible scheduling scheme

Judging whether the iteration number T reaches the maximum value T_maxIf not, setting the iteration time t as t +1, and turning to the step 4; and if so, outputting the feasible scheduling in the current non-inferior solution set, and thus obtaining the scheduling.

The method has the advantages that the thermal power plant is used as a power supply system, the collection and analysis of the data of the power system and the thermal power plant are utilized, and the solution of the economic dispatching problem of the thermal power plant for the comprehensive environmental protection requirement is realized by combining the mental storm optimization algorithm based on the clustering and variation ideas. By adjusting the output of each generator set of the thermal power plant, the generated energy of each thermal power unit in each time period is reasonably arranged under the conditions of satisfying production balance, output constraint of each generator set and the like, a plurality of feasible schemes are provided for operation and control of a power system, and a decision maker can determine a final scheme according to different requirements, so that the pollution emission and the economic benefit in the control period are comprehensively optimal.

The method has the advantages that:

firstly, in the aspect of real-time performance, the method has good overall convergence performance and high convergence speed, is easy to realize, is used for solving the problem of environmental economic load distribution of the power system, and can effectively realize real-time and rapid scheduling of the power grid.

Secondly, in terms of practicability, under the condition of the power market, the power dispatching center considers each power plant combination which is mutually coordinated according to indexes such as coal consumption characteristics, pollutant gas emission characteristics and quotation of each thermal power plant, and uniformly considers the overall planning generated energy under the interests of energy, environmental protection and market parties. For the power generation side of a power plant, the cost can be saved and the benefit can be improved; for the public users, the method not only can obtain satisfactory electric quantity, but also can protect the environment, save energy and achieve the effect of comprehensively considering various factors. Compared with the method which simply carries out bidding for each power plant, the method is more in line with the sustainable development strategy, and more in line with the marketization requirement of the power system than the method which simply carries out overall arrangement by saving energy.

Detailed Description

The present invention will be described in detail with reference to the following embodiments.

The invention discloses a thermal power plant economic environment scheduling method based on a brainstorming, which is implemented according to the following steps:

step1, determining mathematical model of environmental economic dispatching problem of thermal power plant

The environmental economic dispatching problem of the thermal power plant is an obvious two-target optimization problem, and a mathematical model for setting the environmental economic dispatching problem is as follows:

$\min [\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{F}_i\left(P_i\right),\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{E}_i\left(P_i\right)],---\left(1\right)$

wherein N is_GI represents the ith generator, i is 1,2_G；P_iRepresenting the active power of the ith generator; f_i(P_i) The total fuel consumption of the power generation fuel of the thermal power plant at a certain moment is represented, and the calculation expression is as follows:

$F_i\left(P_i\right)=a_i+b_i{p}_i+c_i{p}_i^2,---\left(2\right)$

F_i(P_i) And simultaneously, the fuel consumption characteristic of the ith generator is also embodied, namely the cost generated by generating consumed fuel is obtained, and based on the unit price of the market generated fuel, a_i、b_i、c_iThe constant term, the primary term coefficient and the secondary term coefficient respectively represent consumption characteristics of the ith generator set and are known parameters in the system;

E_i(P_i) The pollution emission amount of the ith generator is represented by the calculation expression:

$E_i\left(P_i\right)={\mathrm{\&alpha;}}_i+{\mathrm{\&beta;}}_i{P}_i+{\mathrm{\&gamma;}}_i{P}_i^2+{\mathrm{\&xi;}}_i\exp \left({\mathrm{\&lambda;}}_i{P}_i\right),---\left(3\right)$

α therein_i、β_i、γ_i、ξ_i、λ_iAre all system parameters, α_i、β_i、γ_iConstant term, primary term coefficient and secondary term coefficient respectively representing the pollution emission characteristics of the ith generating set, ξ_i、λ_iThe relevant parameters, which represent the exponential terms, are constant for a particular system.

The environmental economic scheduling optimization process is subject to the following constraint conditions:

constraint 1: the inequality constraint of the generating capacity of the unit:

$P_i^{\min }<P_i<P_i^{\max },---\left(4\right)$

wherein,respectively outputting the minimum active power and the maximum active power of the ith thermal generator;

constraint 2: balancing equality constraints, namely the sum of the generated power of each generator set of the system is equal to the sum of the total required power of the load and the network loss:

$P_D+P_{\mathrm{loss}}-\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{P}_i=0,---\left(5\right)$

in the formula (5), the reaction mixture is,the total power of the thermal power at a certain moment; p_DThe system load requirement at the moment; p_LossThe method is expressed as active network loss in a power grid at the moment t, the active network loss is obtained by adopting a B coefficient method, and the formula is as follows:

$P_{\mathrm{loss}}=\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{P}_i{B}_{\mathrm{ij}}{P}_j+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{B}_{0i}{P}_i+B_{00},---\left(6\right)$

wherein B is_ij、B_0i、B₀₀All the coefficients are B coefficients which are obtained according to the properties of the unit and are known parameters.

Step2, obtaining various parameters in the model

Determining load valley and load peak time periods of the power grid according to the load curve of the power grid, and determining the power demand of load distribution in each time period

Obtaining data of a system at the current moment from statistical data of a power grid dispatching center, wherein the data mainly comprises a total load P of the system_DTotal network loss P_LossParameter value B of_ij、B_0i、B₀₀；

The operating performance parameters of the unit mainly comprise coal consumption, smoke dust and dioxide during operationThe discharge amount of carbon and other harmful gases is obtained as the parameter data α of pollution discharge amount according to the smoke discharge pricing, emission reduction pricing and carbon dioxide discharge allowable price_i、β_i、γ_i、ξ_i、λ_i；

Obtaining a parameter a of total fuel cost according to auxiliary service pricing of a power system, coal consumption pricing in unit electric energy production, raw coal price and diesel oil price during operation of a thermal power generating unit_i、b_i、c_i。

Step3, obtaining an initial feasible scheduling set of the environmental economic scheduling problem

In the above model, it is necessary to determine that the decision variable is N_GActive power of individual unit $P_1,P_2,...,P_{N_G}$ The optimum value of (c).

The invention is based on the existing swarm intelligence optimization algorithm and the brainstorming optimization algorithm, creates a new swarm intelligence-brainstorming optimization algorithm, realizes the continuous updating of feasible scheduling through clustering and variation operations, has simple operation and easy realization, and has obvious effect in solving the multi-target problems of the power system and the like.

In this step, a feasible scheduling set is mainly generated, specifically in the following manner:

step 1: to front N_G-1The generator sets randomly generate the front N of each generator set within the range of meeting the maximum active power and the minimum active power_G-1Active power P of generator set_i(i＝1,2,...,N_G-1)；

step 2: constraint according to equalityCalculate the Nth_GThe active power of each unit;

step 3: calculating network loss P_Loss；

step 4: taking into account network losses P_LossAccording to formula (I)Calculating the active power P of the last dimension of each unit according with the equality constraint condition_D；

step 5: judging the Nth_GWhether the individual unit meets the condition of capacity constraint or not, if not, executing the step1 to step4 again, otherwise, reserving the generated feasible scheduling;

the N feasible scheduling sets are generated according to the same procedure.

Step4, evaluating the feasible scheduling set

Respectively substituting the N feasible scheduling sets generated in the step3 into an objective function formula (2) and a formula (3), respectively evaluating the environment and economic benefits of each feasible scheduling, and storing the feasible scheduling sequences which are not dominant in each other in an external set, namely an external filing set, because each feasible scheduling corresponds to two objective functions, the N feasible scheduling sequences need to be sorted in a non-inferior solution manner;

step5, updating the feasible scheduling

And 5, updating the feasible scheduling by adopting an optimization idea of the brainstorming. Before updating, an initial value T of the iteration number is required to be set to 0, and the maximum iteration number T of the iteration number is required to be set_max；

Firstly, randomly selecting m different feasible schedules as class centers of m classes, clustering N feasible schedules according to Euclidean distances from active power of all other feasible schedules to each class center, and using the feasible schedules to simulate a formation process of an idea in a brainstorming process; the method comprises the steps of defining a class containing non-inferior solutions as an elite class and a class without the non-inferior solutions as a common class, and iteratively updating the active power of each feasible scheduling generator set through selection operation and variation operation on the basis of new information obtained by clustering, wherein the updating mode is the most important innovation point of the method. The specific implementation process is as follows:

5.1) selection operation

In the t-th iteration, for any current feasible scheduling, the feasible scheduling to be updated is selected according to a selection mechanism specific to the following brainstorm algorithm.

Specifically, for the ith parent feasible schedule, a random number rand1 between random values 0-1 is generated,

if rand1 is less than probability P1, selecting feasible scheduling in the current class for updating; specifically, a random number rand2 is generated, if rand2 is less than probability P2, a class center or feasible schedule in the class is selected as the updating object,

when the generated random number is less than P3, selecting a feasible schedule in the elite class center or class; otherwise, selecting a common class center or feasible scheduling in the class;

if the rand2 is greater than or equal to the probability P2, randomly selecting two classes to generate active power for new feasible scheduling; specifically, a random value is generated, and if the random number is less than the probability P4, the cluster centers in the two selected classes are linearly combined; otherwise, carrying out linear combination on active power randomly selected from two feasible schedules in the two selected classes;

otherwise, the feasible scheduled active power to be mutated is selected from the archive set with the probability of 1-P1.

The P1, P2, P3 and P4 are self-carried probability parameters in a brainstorming algorithm, and a definite number between 0 and1 is selected;

5.2) mutation operation

5.2.1) from 1 to N in the t iteration history schedules before the jth feasible schedule obtained by the selection operation_G-1 randomly selecting the active power of the ith unit asThe active power of the ith unit in the jth feasible scheduling at the t +1 th timeThe iterative formula of (a) is as follows:

$P_{\mathrm{idx}\_{\mathrm{popu}}_{\mathrm{ji}}}(t+1)=\left\{\begin{array}{cc}\mathrm{random}(P_i^{\min },P_i^{\max })& \mathrm{rand}\left(\mathrm{0,1}\right)<0.05\\ P_{\mathrm{idx}\_{\mathrm{popu}}_{\mathrm{ji}}}\left(t\right)+\mathrm{rand}\left(\mathrm{0,1}\right)\&times;(P_{{\mathrm{best}1}_{\mathrm{ji}}}\left(t\right)-P_{{\mathrm{best}2}_{\mathrm{ji}}}\left(t\right))& \mathrm{otherwise}\end{array}\right.,---\left(7\right)$

in the formula (7), the reaction mixture is,andthe active power of the jth unit of the ith feasible scheduling in the tth iteration filing set is obtained, and if the feasible scheduling number in the filing set is less than 2, two feasible scheduling active powers are randomly selected in the current generation; generating a top N of a new feasible schedule according to equation (7)_G-1Maintenance and inspection ofChecking whether the boundary is out of range, if so, determining the boundary as an upper boundary and a lower boundary; otherwise, determining the last dimension of each feasible scheduling active power according to power balance constraint, and ensuring that each dimension of each feasible scheduling active power is within the defined range;

5.2.2) calculating an objective function value of feasible scheduling newly generated by the ith sub-generation, comparing the descendants with the parent generation, and reserving better feasible scheduling according to the domination relationship;

through the iterative formula, the change rate of each generator set and the generated active power of the generator sets are continuously updated, so that N feasible scheduling sets of the generated power of the generator sets which are more in line with the target function under the comprehensive environmental factors are conveniently found out.

The innovation points of the steps mainly lie in that: clustering operation is carried out on all feasible schedules, the feasible schedules are divided into fine English classes and non-fine English classes according to existence or nonexistence solutions, and active power of the generator set corresponding to a better target in a filing set is introduced in the updating process of the feasible schedulesAndthe introduction of the innovation point enables the flight of feasible scheduling to be more directional, so that the scheduling method has stronger exploration capability and the optimization capability of the scheduling method is enhanced.

Step 6, judging whether the whole feasible scheduling is updated or not

If i is equal to N, the N feasible schedules are updated, and then the step 7 is carried out; otherwise, returning to the step 5;

step 7, updating the non-bad scheduling solution set in the external filing set

The optimization of the scheduling sequence is realized by a method of extracting and analyzing related information in the power system and combining a clustering idea with a comprehensive head storm algorithm; because the aim is that the economic benefit and the pollution discharge are optimal simultaneously, the non-inferior feasible scheduling obtained by comparing the economic benefit and the pollution discharge each time is stored in an external filing set,

the external archive set is maintained by adopting a congestion distance method besides updating the non-support scheduling in the population, and the specific method is as follows: putting the non-dominated active power which can be scheduled in the population into an external filing set one by one, if the feasible active power which can be scheduled is dominated by the feasible active power which can be scheduled in the external filing set, deleting the feasible active power which can be scheduled from the filing set, otherwise, adding the feasible active power which can be scheduled into the filing set; and if the number of the active power of the feasible scheduling in the archive set is smaller than the maximum capacity, deleting the active power of the feasible scheduling in the archive set, otherwise, calculating the congestion distance of the active power of all the feasible scheduling in the current archive set, and deleting the active power of the feasible scheduling with the minimum congestion distance to ensure that the feasible scheduling in the archive set is always kept at the number smaller than or equal to the maximum capacity.

The innovation points of the steps mainly lie in that: the method is different from the method that NSGA-II and the like sort all non-dominated feasible scheduling solutions generated in the population and all non-inferior scheduling solutions in an external archive set from large to small according to the congestion distance, and then select the feasible scheduling with the large congestion distance to enter the next generation, so that the distribution of the feasible scheduling solutions is more uniform.

Step 8, iteration output is carried out to finally obtain a feasible scheduling scheme

Judging whether the iteration number T reaches the maximum value T_maxIf not, setting the iteration time t as t +1, and turning to the step 4; and if so, outputting the feasible scheduling in the current non-inferior solution set, and thus obtaining the scheduling.

Examples

The parameter setting related in the implementation process of the method is explained by taking the environmental economic scheduling problem of the power system of a certain heat-engine plant 6 unit as an example.

Setting various parameters in the step1 and the step 2: the mathematical model of the environmental economic dispatching problem is shown in formulas (1) to (7), wherein constant terms, primary terms and secondary term coefficient matrixes of the fuel consumption characteristics of the ith generator set in formula (2) are fixed values of the system and are respectively as follows:

a_i＝[101020102010]

b_i＝[200150180100180150]

c_i＝[100120406040100]

the correlation coefficients of the constant term, the first term and the second term coefficient matrix index term of the pollution emission characteristic of the ith generating set in the formula (3) are all fixed values of a system, and are respectively:

α_i＝[4.0912.5434.2585.3264.2586.131]

β_i＝[-5.554-6.047-5.094-3.55-5.094-5.555]

γ_i＝[6.495.6384.5863.384.5865.151]

ξ_i＝[0.00020.0050.0000010.0020.0000010.00001]

λ_i＝[2.8573.3338286.667]

the total load P borne by the generator in equation (5)_D＝2.834MW，

The B coefficient for calculating the network loss in equation (6) is an existing fixed value, and specifically is as follows:

$B_{\mathrm{ij}}=\left[\begin{array}{cccccc}0.0218& 0.0107& -0.00036& -0.0011& 0.00055& 0.0033\\ 0.0107& 0.01704& -0.0001& -0.00179& 0.00026& 0.0028\\ -0.0004& -0.0002& 0.02459& -0.01328& -0.0118& -0.0079\\ -0.0011& -0.00179& -0.01328& 0.0265& 0.0098& 0.0045\\ 0.00055& 0.00026& -0.0118& 0.0098& 0.0216& -0.0001\\ 0.0033& 0.0028& -0.00792& 0.0045& -0.00012& 0.02978\end{array}\right];$

B_0i＝[0.0107311.7704-4.06453.84531.38325.5503]×e-03；

B₀₀＝0.0014；

in step three, the unit N_GThe initial feasible scheduling number N is empirically selected to be 100, and the maximum iteration number is empirically selected to be T_max＝1000；

And the values of the four probability parameters in the step five are respectively as follows according to experience: p1-0.8, P2-0.4P 3-0.2P 4-0.2.

Through the optimizing and iteration process based on the brain storm thought, the method can obtain the load distribution scheme of each unit in a short time, the optimizing process has no any limitation on the objective function and the constraint condition of the system, does not depend on a mathematical model, and has good universality. Compared with other algorithms, the optimization process is simple to realize, easy to operate, and has good overall convergence performance and higher convergence speed.

In a word, the scheduling method of the invention provides a clustering multi-target brainstorming optimization algorithm based on knowledge extraction aiming at double factors of comprehensive environmental protection and economic benefit, and the operation requirement of the system can be reflected more practically by the scheduling constraint existing in the peak and the valley of power utilization, so that the environmental economic scheduling of the thermal power plant is realized.

Claims (2)

1. A thermal power plant economic environment scheduling method based on brainstorming is characterized by being implemented according to the following steps:

step1, determining mathematical model of environmental economic dispatching problem of thermal power plant

The mathematical model for setting the environmental economic scheduling problem is as follows:

$min\&lsqb;\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{F}_i\left(P_i\right),\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{E}_i\left(P_i\right)\&rsqb;,---\left(1\right)$

wherein N is_GI represents the ith generator, i is 1,2_G；P_iRepresenting the active power of the ith generator; f_i(P_i) The total fuel consumption of the power generation fuel of the thermal power plant at a certain moment is represented, and the calculation expression is as follows:

$F_i\left(P_i\right)=a_i+b_i{P}_i+c_i{p}_i^2,---\left(2\right)$

a_i、b_i、c_ithe constant term, the primary term coefficient and the secondary term coefficient respectively represent consumption characteristics of the ith generator set and are known parameters in the system;

E_i(P_i) The pollution emission amount of the ith generator is represented by the calculation expression:

$E_i\left(P_i\right)={\mathrm{\&alpha;}}_i+{\mathrm{\&beta;}}_i{P}_i+{\mathrm{\&gamma;}}_i{P}_i^2+{\mathrm{\&xi;}}_i\exp \left({\mathrm{\&lambda;}}_i{P}_i\right),---\left(3\right)$

α therein_i、β_i、γ_i、ξ_i、λ_iAre all system parameters, α_i、β_i、γ_iConstant term, primary term coefficient and secondary term coefficient respectively representing the pollution emission characteristics of the ith generating set, ξ_i、λ_iThe related parameters representing the exponential terms are subject to the following constraint conditions in the environment economic dispatching optimization process:

constraint 1: the inequality constraint of the generating capacity of the unit:

$P_i^{\min }<P_i<P_i^{\max },---\left(4\right)$

wherein,respectively outputting the minimum active power and the maximum active power of the ith thermal generator;

constraint 2: balancing equality constraints, namely the sum of the generated power of each generator set of the system is equal to the sum of the total required power of the load and the network loss:

$P_D+P_{loss}-\underset{i=1}{\overset{N_G}{\&Sigma;}}{P}_i=0,---\left(5\right)$

in the formula (5), the reaction mixture is,the total power of the thermal power at a certain moment; p_DThe system load requirement at the moment; p_LossThe method is expressed as active network loss in a power grid at the moment t, the active network loss is obtained by adopting a B coefficient method, and the formula is as follows:

$P_{loss}=\underset{i=1}{\overset{N_G}{\&Sigma;}}\underset{j=1}{\overset{N_G}{\&Sigma;}}{P}_i{B}_{ij}{P}_j+\underset{i=1}{\overset{N_G}{\&Sigma;}}{B}_{0i}{P}_i+B_{00},---\left(6\right)$

wherein B is_ij、B_0i、B₀₀Is the coefficient of B;

step2, obtaining various parameters in the model, specifically comprising the following steps,

determining load valley and load peak time periods of the power grid according to the load curve of the power grid, and determining the power demand of load distribution in each time period

Obtaining data of the system at the current moment from statistical data of a power grid dispatching center, wherein the data comprises total load P of the system_DTotal network loss P_LossParameter value B of_ij、B_0i、B₀₀；

Obtaining the parameter data α of pollution emission according to the price of smoke emission, emission reduction and carbon dioxide emission_i、β_i、γ_i、ξ_i、λ_i；

Obtaining parameters of total fuel cost according to auxiliary service pricing of a power system, coal consumption pricing in unit electric energy production, raw coal price and diesel price of power generation cost of a thermal power generating unit during operation;

step3, obtaining an initial feasible scheduling set of the environmental economic scheduling problem

In the above model, it is necessary to determine that the decision variable is N_GActive power of individual unitGenerating a feasible scheduling set, wherein the concrete generation mode of the feasible scheduling set is as follows:

step 1: to front N_G-1The generator sets randomly generate the front N of each generator set within the range of meeting the maximum active power and the minimum active power_G-1Active power Pi, i-1, 2, …, N of a generator set_G-1；

step 2: constraint according to equality $P_D=P_M-(P_{G_1}+P_{G_2}+......+P_{G_{n-1}})$ Calculate the Nth_GThe active power of each unit;

step 3: calculating network loss P_Loss；

step 4: taking into account network losses P_LossAccording to formula (I) $P_D=P_M+P_{loss}-(P_{G_1}+P_{G_2}+......+P_{G_{n-1}})$ Calculating the active power P of the last dimension of each unit according with the equality constraint condition_D；

step 5: judging the Nth_GWhether the individual unit meets the condition of capacity constraint or not, if not, executing the step1 to step4 again, otherwise, reserving the generated feasible scheduling;

generating N feasible scheduling sets according to the same process;

step4, evaluating the feasible scheduling set

Respectively substituting the N feasible scheduling sets generated in the step3 into an objective function formula (2) and a formula (3), respectively evaluating the environment and economic benefits of each feasible scheduling, and storing the feasible scheduling sequences which are not dominant in each other in an external set, namely an external filing set, because each feasible scheduling corresponds to two objective functions, the N feasible scheduling sequences need to be sorted in a non-inferior solution manner;

step5, updating the feasible scheduling

Before updating, an initial value T of the iteration number is required to be set to 0, and the maximum iteration number T of the iteration number is required to be set_max；

Firstly, randomly selecting m different feasible schedules as class centers of m classes, clustering N feasible schedules according to Euclidean distances from active power of all other feasible schedules to each class center, and using the feasible schedules to simulate a formation process of an idea in a brainstorming process; defining the class containing non-inferior solutions as elite class and the class without non-inferior solutions as common class, and iteratively updating the active power of each feasible scheduling generator set by selecting operation and variation operation on the basis of new information obtained by clustering,

the specific implementation process is as follows:

5.1) selection operation

In the t-th iteration, for any current feasible scheduling, the feasible scheduling to be updated is selected according to the following brainstorming algorithm,

for the ith parent feasible schedule, a random number rand1 between random values 0-1 is generated,

if rand1 is less than probability P1; selecting feasible scheduling in the current class for updating; generating a random number rand2, if rand2 is less than probability P2, selecting a class center or feasible scheduling in the class as an updating object,

when the generated random number is less than P3, selecting a feasible schedule in the elite class center or class; otherwise, selecting a common class center or feasible scheduling in the class;

if the rand2 is greater than or equal to the probability P2, randomly selecting two classes to generate active power for new feasible scheduling; generating a random value, and if the random number is less than the probability P4, linearly combining the cluster centers in the two selected classes; otherwise, carrying out linear combination on active power randomly selected from two feasible schedules in the two selected classes;

otherwise, the feasible scheduled active power to be mutated is selected from the archive set with the probability of 1-P1,

the P1, P2, P3 and P4 are self-carried probability parameters in a brainstorming algorithm, and a definite number between 0 and1 is selected;

5.2) mutation operation

5.2.1) taking the active power of the ith unit in the t iteration historical scheduling before the jth feasible scheduling obtained by the selection operation as the active powerThe active power of the ith unit in the jth feasible scheduling at the t +1 th timeThe iterative formula of (a) is as follows:

$P_{idx\_{\mathrm{popu}}_{ji}}(t+1)=\left\{\begin{array}{cc}random(P_i^{\min },P_i^{\max })& rand(0,1)<0.05\\ P_{idx\_{\mathrm{popu}}_{ji}}\left(t\right)+rand(0,1)\&times;\left(P_{best{1}_{ji}}\right(t)-P_{best{2}_{ji}}\left(t\right))& otherwise\end{array}\right.,---\left(7\right)$

in the formula (7), the reaction mixture is,andthe active power of the jth unit of the ith feasible scheduling in the tth iteration filing set is obtained, and if the feasible scheduling number in the filing set is less than 2, two feasible scheduling active powers are randomly selected in the current generation; generating a top N of a new feasible schedule according to equation (7)_G1-dimension and checking whether the boundary is out of range, and if so, determining upper and lower boundaries; otherwise, determining the last dimension of each feasible scheduling active power according to power balance constraint, and ensuring that each dimension of each feasible scheduling active power is within the defined range;

5.2.2) calculating an objective function value of feasible scheduling newly generated by the ith sub-generation, comparing the descendants with the parent generation, and reserving better feasible scheduling according to the domination relationship;

step 6, judging whether the whole feasible scheduling is updated or not

If i is equal to N, the N feasible schedules are updated, and then the step 7 is carried out; otherwise, returning to the step 5;

step 7, updating the non-bad scheduling solution set in the external filing set

Storing the obtained non-inferior feasible scheduling of each comparison of economic benefit and pollution emission in an external filing set;

step 8, iteration output is carried out to finally obtain a feasible scheduling scheme

Judging whether the iteration number T reaches the maximum value T_maxIf not, setting the iteration time t as t +1, and turning to the step 4; and if so, outputting the feasible scheduling in the current non-inferior solution set, and thus obtaining the scheduling.

2. The brainstorm-based thermal power plant economic environment scheduling method according to claim 1, wherein: in step 7, the external archive set performs maintenance by using a congestion distance method in addition to updating the non-allocation scheduling in the population, and the specific method is as follows: putting the non-dominated active power which can be scheduled in the population into an external filing set one by one, if the feasible active power which can be scheduled is dominated by the feasible active power which can be scheduled in the external filing set, deleting the feasible active power which can be scheduled from the filing set, otherwise, adding the feasible active power which can be scheduled into the filing set; and if the number of the active power of the feasible scheduling in the archive set is smaller than the maximum capacity, deleting the active power of the feasible scheduling in the archive set, otherwise, calculating the congestion distance of the active power of all the feasible scheduling in the current archive set, and deleting the active power of the feasible scheduling with the minimum congestion distance to ensure that the feasible scheduling in the archive set is always kept at the number smaller than or equal to the maximum capacity.