CN103440535A - Multi-target plant level load optimization method based on immune optimization and fuzzy decision - Google Patents

Abstract

The invention discloses a multi-target plant level load optimization method based on immune optimization and fuzzy decision in the technical field of energy-saving power generation. The multi-target plant level load optimization method comprises the steps that a thermal power plant coal consumption model, a model of time needed for completing distributed loads, and a nitric oxide emission model are established respectively; coal consumption characteristic parameters, current bearing loads, permitted load change rates and nitric oxide emission characteristic coefficients of all units in a thermal power plant are determined; constraint conditions of the models are determined, including upper limit and lower limit constraint conditions and balance constraint conditions of active power; a solution set including the active power of all the units in the thermal power plant, thermal power plant coal consumption, time needed for completing the distributed loads of the thermal power plant, and the nitric oxide emission of the thermal power plant is obtained through an immune multi-target algorithm; an optimal solution is selected from the solution set to serve as output power of all the units in the thermal power plant. According to the multi-target plant level load optimization method based on immune optimization and fuzzy decision, the immune multi-target algorithm is used for realizing multi-target plant level load distribution, an optimal power distribution scheme for the power plant units is decided according to a fuzzy decision theory, and therefore the computational process is faster and more accurate.

Description

Multiple goal level of factory load optimization method based on immune optimization and fuzzy decision

Technical field

The invention belongs to the energy-saving power generation technical field, relate in particular to a kind of multiple goal level of factory load optimization method based on immune optimization and fuzzy decision.

Background technology

Along with the progressively enforcement of " separating the factory and network; surf the Net at a competitive price " and the development of intelligent grid, new forms of energy have obtained greatly developing, but the core pillar status of thermal power generation in electrical network not only do not change, on the contrary it has been proposed to requirements at the higher level, be with the multiobject Optimized Operation such as security, rapidity, economy, the feature of environmental protection but not single goal or Bi-objective Optimized Operation in the urgent need to the level of factory Optimization of Load Dispatching in the past.

It is a high dimension, non-protruding, discrete, nonlinear optimal problem that the level of factory load is optimized distribution, and common power plant load optimized algorithm is mainly following several:

1, traditional optimized algorithm

Between unit the classic method of load distribution mainly contain average distribution system, merit-order, etc. micro-gaining rate method.Wherein, waiting micro-gaining rate method simple and clear, easy to use, is the main method that current various countries electric power is carried out economic load dispatching, and effect is remarkable.But be based upon on classic variational calculus basis etc. micro-gaining rate method, requiring the total consumption of coal objective function is protruding strict funciton, and the micro-gaining rate curve to steam turbine, boiler and the various combination thereof of power plant has strict accuracy requirement, generally is difficult to meet, thereby limited the range of application of this algorithm.

2, the optimized algorithm based on mathematical programming

Mainly comprise linear programming technique, dynamic programming, Nonlinear Programming Method, Lagrangian Relaxation etc.Wherein, dynamic programming both can be avoided micro-gaining rate, also without any requirement to the thermodynamic property of unit, thereby was widely applied.But dynamic programming needs global optimizing, can obtain optimum solution with dynamic model, it adopts enumerative technique to compare and calculate each stage and each decision-making, therefore there is " dimension calamity " phenomenon when the dimension of state variable becomes large, and there are a plurality of local minimums during global optimizing, once it is improper that step-length is selected, and can only find suboptimal solution.

3, modern intelligence optimization algorithm

Be not need accurate mathematical model with the remarkable difference of classic method, allow non-linear and uncontinuity, objective function is not had to special requirement.Based on these advantages, various intelligence genetic algorithms, Tabu search algorithm, simulated annealing, neural network and immune algorithm are used in power plant load optimization aspect in recent years.In general, the advantage of these intelligent algorithms is mainly that cost function is unrestricted, and structure is applicable to being achieved with parallel processing technique, can promote its operation efficiency.Shortcoming is that the parameter decision is difficult for, and the direction of search determines to be difficult for, the overlong time of search optimum solution, and frequent Premature Convergence is to suboptimal solution.

At present, domestic level of factory load is optimized the research distributed, and still mainly round the algorithm based on the characteristic economic optimum of coal consumption, improves.Yet, development along with electricity market, need for electricity constantly changes, the electrical network peak-valley difference continues aggravation, becoming increasingly conspicuous of problem of environmental pollution, the adding and improved the requirement that fired power generating unit is participated in to the rapidities such as peak regulation of new forms of energy, economy is no longer to weigh the sole criterion of thermal power plant's performance, and the multiple goal level of factory load of reflection economy, the feature of environmental protection, rapidity is optimized to distribute and is subject to extensive concern.

Summary of the invention

The object of the invention is to, provide a kind of multiple goal level of factory load optimization method based on immune optimization and fuzzy decision, the problem existed for solving existing Power Plant stage load optimizing distribution method.

To achieve these goals, the technical scheme that the present invention proposes is that a kind of multiple goal level of factory load optimization method based on immune optimization and fuzzy decision is characterized in that described method comprises:

Step 1: set up respectively thermal power plant coal consumption model, complete the required model consuming time of load and the nitrogen oxide emission model of distribution;

Described thermal power plant coal consumption model is $\min F=\min \left\{\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}(a_{\mathrm{<figure-callout\; id="2"\; label="i\; P\; i"\; filenames="HDA0000376029480000021.png"\; state="\{\{state\}\}">i</figure-callout>}}{P}_i^{}{}_2^{}+b_i{P}_i+c_i)\right\};$ Wherein, F is the thermal power plant coal consumption, P _ifor the active power of the i of thermal power plant platform unit, a _i, b _iand c _ibe respectively the coal consumption characterisitic parameter of the i of thermal power plant platform unit;

The required model consuming time of load that described thermal power plant completes distribution be minT=min{max (| P _i-P _inow/ v _i|); Wherein, T is that thermal power plant's distribution load is consuming time, P _inowfor the current load of bearing of the i of thermal power plant platform unit, v _ipermissible load rate of change for the i of thermal power plant platform unit;

Described thermal power plant nitrogen oxide emission model is $\min E=\min \left\{\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{\&alpha;}}_{\mathrm{<figure-callout\; id="3"\; label="i\; P\; i"\; filenames="HDA0000376029480000021.png"\; state="\{\{state\}\}">i</figure-callout>}}{P}_i^{}{}_3^{}+{\mathrm{\&beta;}}_i{\mathrm{<figure-callout\; id="2"\; label="P\; i"\; filenames="HDA0000376029480000021.png"\; state="\{\{state\}\}">P</figure-callout>}}_i^{}+{\mathrm{\&gamma;}}_i{P}_i+{\mathrm{\&lambda;}}_i)\right\};$ Wherein, the nitrogen oxide emission that E is thermal power plant, α _i, β _i, γ _iand λ _ibe respectively the discharged nitrous oxides characteristic coefficient of the i of thermal power plant platform unit;

In above-mentioned three models, i=1,2 ..., N, the unit sum that N is thermal power plant;

Step 2: determine the coal consumption characterisitic parameter of each unit of thermal power plant, current load, discharged nitrous oxides characteristic coefficient and the unit permissible load rate of change born;

Step 3: determine the constraint condition of above-mentioned model, described constraint condition comprises active power bound constraint condition and active power balance constraint condition;

Wherein, described active power bound constraint condition is P _{i, min}≤ P _i≤ P _{i, max}, P _ifor the active power of the i of thermal power plant platform unit, P _{i, min}for the lower limit of the active power of the i of thermal power plant platform unit, P _{i, max}the upper limit for the active power of the i of thermal power plant platform unit;

Described active power balance constraint condition is

total active power that P is thermal power plant;

Step 4: active power, thermal power plant coal consumption, the thermal power plant that adopts the immune multi-object algorithm to ask for each unit of thermal power plant completes the disaggregation of the required consuming time and thermal power plant's nitrogen oxide emission of the load of distribution;

Step 5: the required consuming time and concentrated output power of optimum solution as each unit of thermal power plant of selecting of solution thermal power plant's nitrogen oxide emission of load that completes distribution from active power, thermal power plant coal consumption, the thermal power plant of each unit of thermal power plant.

The described active power from each unit of thermal power plant, thermal power plant coal consumption, thermal power plant complete required consuming time and solution thermal power plant's nitrogen oxide emission of the load of distribution and concentrate and select optimum solution and specifically comprise following sub-step:

Sub-step 101: respectively thermal power plant coal consumption, thermal power plant are completed to the required consuming time and thermal power plant's nitrogen oxide emission of the load of distribution as influence factor, calculate the desired value of each influence factor and form index value matrix;

Wherein, the desired value computing formula of thermal power plant coal consumption is

The required desired value computing formula consuming time of load that thermal power plant completes distribution is $r_{2j}=0.1+\frac{E_j-\underset{1\&le;j\&le;M}{\min }\left(E_j\right)}{\underset{1\&le;j\&le;M}{\max }\left(E_j\right)-\underset{1\&le;j\&le;M}{\min }\left(E_j\right)};$

The desired value computing formula of thermal power plant's nitrogen oxide emission is

Described index value matrix $R=\left[\begin{array}{c}{r}_{1j}\\ r_{2j}\\ r_{3j}\end{array}\right];$

F _jconcentrate the j group for described solution and separate corresponding thermal power plant coal consumption;

E _jthe load that completes distribution for thermal power plant corresponding to described solution concentrated j group solution is required consuming time;

T _jconcentrate the j group for described solution and separate corresponding thermal power plant's nitrogen oxide emission;

J=1,2 ..., M, M is that described solution is concentrated the group number of separating;

Sub-step 102: the weight matrix A=[w that determines each influence factor ₁, w ₂, w ₃]; Wherein, w ₁for the weights of thermal power plant coal consumption, w ₂for thermal power plant completes the required weights consuming time of load of distribution, w ₃for the weights of thermal power plant's nitrogen oxide emission, and

Sub-step 103: according to formula

calculate optimum solution and estimate matrix, calculate the component that optimum solution is estimated matrix B intermediate value maximum

solution corresponding to component that described optimum solution is estimated matrix intermediate value maximum is optimum solution.

The weight w of described thermal power plant coal consumption ₁=0.698.

Described thermal power plant completes the required weight w consuming time of load of distribution ₂=0.261.

The weight w of described thermal power plant nitrogen oxide emission ₃=0.041.

The present invention is by the immune multi-object algorithm application in the multiple goal level of factory load distribution problem that considers economy, rapidity and the feature of environmental protection, and according to Fuzzy Decision Theory, decision-making goes out Power Plant optimal power allocation scheme, and its computation process more quick and precisely.

The accompanying drawing explanation

Fig. 1 is the process flow diagram of immune multi-object algorithm;

Fig. 2 is based on the multiple goal level of factory load optimization method process flow diagram of immune optimization and fuzzy decision;

Fig. 3 is the Pareto forward position disaggregation distribution plan of immune multi-object algorithm.

Embodiment

Below in conjunction with accompanying drawing, preferred embodiment is elaborated.Should be emphasized that, following explanation is only exemplary, rather than in order to limit the scope of the invention and to apply.

The immune multi-object algorithm has stronger adaptivity and diversity, also has the functions such as study, identification and memory, and it can solve a large amount of nonlinear problems, in science and technology field, is used widely.The immune multi-object algorithm has been ripe algorithm, and a lot of engineering calculation softwares all provide the immune multi-object algorithm.

As shown in Figure 1, the flow process of immune multi-object algorithm is as follows:

(1) produce at random initial population;

(2) calculate the target function value of each antibody;

(3) according to the noninferior solution level of antibody, colony is carried out to non-bad layer sorting, and antibody copies to immunological memory cell by the highest grade when the first generation;

(4) antibody in same layer is calculated to its concentration;

(5) select operation according to noninferior solution sequence grade and concentration;

(6) intersected, mutation operation, produce new population;

(7) carry out the renewal of population and memory cell;

(8) end condition is judged, if satisfy condition, and the out of service and Output rusults of algorithm; Otherwise rotate back into (2).

Fig. 2 is based on the multiple goal level of factory load optimization method process flow diagram of immune optimization and fuzzy decision.As shown in Figure 2, the load of the multiple goal level of factory based on immune optimization and fuzzy decision optimization method comprises:

Step 1: set up respectively thermal power plant coal consumption model, complete the required model consuming time of load and the nitrogen oxide emission model of distribution.

Thermal power plant coal consumption model is $\min F=\min \left\{\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}(a_i{P}_i^2+b_i{P}_i+c_i)\right\}.$ Wherein, F is the thermal power plant coal consumption, P _ifor the active power of the i of thermal power plant platform unit, a _i, b _iand c _ibe respectively the coal consumption characterisitic parameter of the i of thermal power plant platform unit.The thermal power plant coal consumption is by each unit active power curve reflection of thermal power plant, and each unit active power curve of thermal power plant adopts quafric curve commonly used in engineering usually, its coal consumption characterisitic parameter a _i, b _iand c _ithe curve that the historical data matching gathered in each unit running process of thermal power plant generates obtains.

The required model consuming time of load that thermal power plant completes distribution be minT=min{max (| P _i-P _inow/ v _i|).Wherein, it is required consuming time that T is that thermal power plant completes the load of distribution, P _inowfor the current load of bearing of the i of thermal power plant platform unit, v _ipermissible load rate of change for the i of thermal power plant platform unit.These data can obtain by the real-time monitor data of power plant units.

Thermal power plant's nitrogen oxide emission model is $\min E=\min \left\{\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{\&alpha;}}_i{\mathrm{<figure-callout\; id="3"\; label="P\; i"\; filenames="HDA0000376029480000021.png"\; state="\{\{state\}\}">P</figure-callout>}}_i^{}+{\mathrm{\&beta;}}_i{\mathrm{<figure-callout\; id="2"\; label="P\; i"\; filenames="HDA0000376029480000021.png"\; state="\{\{state\}\}">P</figure-callout>}}_i^{}+{\mathrm{\&gamma;}}_i{P}_i+{\mathrm{\&lambda;}}_i)\right\}.$ Wherein, the nitrogen oxide emission that E is thermal power plant, α _i, β _i, γ _iand λ _ibe respectively the discharged nitrous oxides characteristic coefficient of the i of thermal power plant platform unit.Thermal power plant's nitrogen oxide emission is relevant to the active power of each unit of thermal power plant, and thermal power plant's nitrogen oxide emission is expressed as the cubic curve form of active power, its characteristic coefficient α usually _i, β _i, γ _iand λ _ithe curve that the historical data matching gathered in each unit running process of thermal power plant generates obtains.

In above-mentioned three models, i=1,2 ..., N, the unit sum that N is thermal power plant.

Step 2: determine the coal consumption characterisitic parameter of each unit of thermal power plant, current load, permissible load rate of change and the discharged nitrous oxides characteristic coefficient born.

Step 3: determine the constraint condition of above-mentioned model, described constraint condition comprises active power bound constraint condition and active power balance constraint condition.

Above-mentioned three models all relate to the active power of power plant units, so constraint condition mainly considers the constraint to active power, comprise active power bound constraint condition and active power balance constraint condition.

Active power bound constraint condition is P _{i, min}≤ P _i≤ P _{i, max}, P _ifor the active power of the i of thermal power plant platform unit, P _{i, min}for the lower limit of the active power of the i of thermal power plant platform unit, P _{i, max}the upper limit for the active power of the i of thermal power plant platform unit.Active power balance constraint condition is

total active power that P is thermal power plant.

Step 4: active power, thermal power plant coal consumption, the thermal power plant that adopts the immune multi-object algorithm to ask for each unit of thermal power plant completes the disaggregation of the required consuming time and thermal power plant's nitrogen oxide emission of the load of distribution.

In the present embodiment, using 4 units of certain thermal power plant as research object, wherein the power of 2 units is 300MW, and the power of 2 units is 600MW, and its net coal consumption rate characterisitic parameter and discharged nitrous oxides characteristic coefficient are as shown in table 1.

Table 1: unit net coal consumption rate characterisitic parameter and discharged nitrous oxides characteristic coefficient table

The population number of setting the immune multi-object algorithm is 200, and evolutionary generation was 100 generations, and crossover probability is 0.9, and the variation probability is 0.1.Immune multi-object algorithm routine (running environment is Matlab7.0) by the establishment of the data substitution Matlab language in table 1, by model in this paper and Multipurpose Optimal Method, it is carried out to load distribution, the Pareto forward position disaggregation drawn as shown in Table 2 and Figure 3.

Table 2: calculate the Pareto forward position disaggregation table obtained through the immune multi-object algorithm

The disaggregation provided from table 2 can find out, every group of solution of Pareto disaggregation comprises that active power, thermal power plant coal consumption, the thermal power plant of each unit of thermal power plant complete the required consuming time and thermal power plant's nitrogen oxide emission of load of distribution.

By table 2 and Fig. 3, be it can also be seen that, the Pareto disaggregation of the immune multi-object algorithm optimization gained that adopts the present invention to propose has good diversity, and distribution uniform in three dimensions can solve multiple goal load optimization problem effectively.Because the optimization of multiple goal load is a more complicated problem, desired each objective function restricts mutually, and the optimum of a target often be take other a certain target, and to become bad be cost.If only consider the single goal load distribution, when full factory net coal consumption rate minimum is 311.82g/ (kWh), its NOx discharge capacity is larger, comparatively unfavorable from environment protection significance.Consider the factor of each side, in numerous Pareto optimum solutions, select most suitable solution only to complete artificially comparatively complicated, getting sth into one's head property is larger, and rationality can't guarantee.The present invention takes full advantage of the information of Pareto disaggregation itself, adopts Multicriteria fuzzy decision-making method to select optimum solution.

Carrying out Multicriteria fuzzy decision-making method while selecting optimum solution, generally to be converted into relative degree of membership (or utility function) to each target signature amount, then give each target respective weights, remake comprehensive evaluation, thereby determine satisfactory solution.At first the present invention is set to the weight of three indexs respectively [w1=1, w2=0, w3=0], and [w1=0, w2=1, w3=0], [w1=0, w2=0, w3=1], consider respectively economy, rapidity and the feature of environmental protection, obtain optimum single index.Wherein, economy is by the reflection of thermal power plant coal consumption, and rapidity completes the required reflection consuming time of load of distribution by thermal power plant, and the feature of environmental protection reflects by thermal power plant's nitrogen oxide emission.The optimum single index that immune multi-object algorithm and self-adaptation immune vaccine algorithm and NSGA-II method are obtained is contrasted, as shown in table 3.

Table 3: immune multi-object algorithm and AIVA method and NAGA-II method optimum results contrast table

By table 3, can find out, the individual event desired value that the immune multi-object algorithm optimization obtains all is better than projects scale value that NSGA-II draws, therefore the immune multi-object convergence is better than NSGA-II.And the individual event desired value that the immune multi-object algorithm optimization obtains is close with self-adaptation immune vaccine algorithm optimization value, verified the validity of algorithm.

After the target load assignment problem to three methods is carried out single index optimization contrast, the present invention is to considering economy, and the level of factory of rapidity and three targets of feature of environmental protection load is optimized the distribution mathematical model and furtherd investigate.

Step 5: the required consuming time and concentrated output power of optimum solution as each unit of thermal power plant of selecting of solution thermal power plant's nitrogen oxide emission of load that completes distribution from active power, thermal power plant coal consumption, the thermal power plant of each unit of thermal power plant.

Utilize the immune multi-object algorithm to be optimized, after drawing the Pareto disaggregation, the present invention is based on Multicriteria fuzzy decision-making method and carry out decision-making, select optimum fired power generating unit output power.Consider economy, rapidity and the feature of environmental protection, the economic factors that will consider the thermal power plant coal consumption as thermal power plant, thermal power plant completes the required rapidity factor that will consider as thermal power plant consuming time of load of distribution, the feature of environmental protection factor that thermal power plant's nitrogen oxide emission will be considered as thermal power plant.

Multicriteria fuzzy decision-making method is to seek to make a plurality of targets all to reach the scheme of satisfactory value under certain constraint condition, is to concentrate after each target of comprehensive balance from limited optional program, and the scheme collection is sorted and selects the most satisfied scheme.Due to the conflict between each target, generally to be converted into relative degree of membership (or utility function) to each target signature amount, then give each target respective weights, then do comprehensive evaluation, thereby determine the most satisfied scheme.

In the present invention, based on Multicriteria fuzzy decision-making method, from separating, concentrate the process of selecting optimum solution to be:

Sub-step 101: respectively thermal power plant coal consumption, thermal power plant are completed to the required consuming time and thermal power plant's nitrogen oxide emission of the load of distribution as influence factor, calculate the desired value of each influence factor and form index value matrix.

The desired value computing formula of thermal power plant coal consumption is

The required desired value computing formula consuming time of load that thermal power plant completes distribution is $r_{2j}=0.1+\frac{E_j-\underset{1\&le;j\&le;M}{\min }\left(E_j\right)}{\underset{1\&le;j\&le;M}{\max }\left(E_j\right)-\underset{1\&le;j\&le;M}{\min }\left(E_j\right)}.$

The desired value computing formula of thermal power plant's nitrogen oxide emission is

And then obtain index value matrix $\left[\begin{array}{c}{r}_{1j}\\ r_{2j}\\ r_{3j}\end{array}\right]=\left[\begin{array}{cccc}{r}_{11}& r_{12}& ...& r_{1M}\\ r_{21}& r_{22}& ...& r_{2M}\\ r_{31}& r_{32}& ...& r_{3M}\end{array}\right].$ Wherein, F _jfor the solution that the immune multi-object algorithm calculates concentrates the j group to separate corresponding thermal power plant coal consumption, E _jit is required consuming time that the solution calculated for the immune multi-object algorithm concentrates the j group to separate the load that corresponding thermal power plant completes distribution, T _jfor the solution that the immune multi-object algorithm calculates concentrates the j group to separate corresponding thermal power plant's nitrogen oxide emission, j=1,2 ..., M, M is that the solution that the immune multi-object algorithm calculates is concentrated the group number of separating.

Sub-step 102: the weight matrix A=[w that determines each influence factor ₁, w ₂, w ₃].W ₁for the weights of thermal power plant coal consumption, w ₂for thermal power plant completes the required weights consuming time of load of distribution, w ₃for the weights of thermal power plant's nitrogen oxide emission, and

the get fire weight w of power plant's coal consumption amount of the present embodiment ₁=0.698, thermal power plant completes the required weight w consuming time of load of distribution ₂=0.261, the weight w of thermal power plant's nitrogen oxide emission ₃=0.041.These weights are set according to experience, and the technician of thermal power plant can also suitably regulate according to the actual conditions of this thermal power plant certainly.

Sub-step 103: according to formula

calculate optimum solution and estimate matrix.

Make B=[b ₁, b ₂..., b _m],

j=1,2 ..., M.Calculate optimum solution and estimate the component of matrix B intermediate value maximum

? solution corresponding to component of optimum solution being estimated to matrix intermediate value maximum is optimum solution.In the present embodiment, through the optimum solution calculated, it is solution 99, be that optimum load distribution scheme should be { 244.5148,162.8790,519.9771,476.9591}, each desired value is respectively coal consumption amount: 312.3910g/ (kWh), the load distributed is consuming time: 3.2000min, nitrogen oxide emission: 1.4400t/h.

In the present embodiment, this thermal power plant load distribution originally is { 288.606,181.511,507.595,426.617}, each desired value is respectively coal consumption amount: 316.4558g/ (kWh), the load distributed is consuming time: 6.4896min, nitrogen oxide emission: 2.1151t/h.

Utilize NSGA-II to be optimized, after drawing the Pareto disaggregation, the multiple attributive decision making method based on basic point and entropy carries out decision-making, tries to achieve economy, and the weights of rapidity and each attribute of the feature of environmental protection are respectively w ₁=0.698, w ₂=0.261, w ₃=0.041.After decision-making is calculated, the optimum solution of selection is for separating 9, and optimum load distribution scheme should be { 253.982,183.661,495.863,470.823}, each desired value is respectively coa consumption rate: 315.6058g/ (kWh), adjustment time: 6.0296min, amount of nitrogen oxides: 1.3251t/h.Each index contrast of three is in Table 4.

Table 4: thermal power plant's initial value, NSGA-II and immune multi-object method desired value contrast table table

As can be seen from Table 4, the immune multi-object algorithm is utilizing different decision-making techniques from the NSGA-II method, when identical index weights is optimized to decision-making, except nitrogen oxide emission a little more than the NSGA-II method, coal consumption amount and the distribution load NSGA-II method that all is less than consuming time, trace it to its cause, aspect economy, coal consumption coefficient by known No. 1 and No. 4 unit of unit coal consumption characterisitic parameter is larger, be that coal consumption amount is larger, and the load of No. 1 and No. 4 unit that the immune multi-object algorithm optimization obtains and be less than load and the 724.8050MW that NSGA-II optimizes No. 1 and No. 4 unit obtaining for 721.4739MW, therefore obtaining economic index, the immune multi-object algorithm optimization is better than NSGA-II, aspect rapidity, No. 3 the fastest unit load values of the elevation rate that the immune multi-object algorithm optimization obtains are greater than NSGA-II and optimize No. 3 unit load values that obtain, and the load value of the slowest No. 2 units of elevation rate is less than No. 2 unit load values that NSGA-II obtains, therefore the load adjustment time that the immune multi-object algorithm optimization obtains is shorter, demonstrated the superiority of immune multi-object algorithm.And compare with the load distribution of this factory's script, optimize and make coal consumption amount reduce 4.0648g/ (kWh), distribution load shortening consuming time 3.2896min, the NOx discharge capacity reduces 0.6751t/h, has obtained significant effect of optimization.

While for three targets, carrying out complex optimum, can, by the flexible processing to weight, for power plant has solved the load distribution problem dexterously, meet power plant load optimization and be distributed in not in the same time to economic index, the different demands of rapidity index and the feature of environmental protection.

The above; be only the present invention's embodiment preferably, but protection scope of the present invention is not limited to this, anyly is familiar with in technical scope that those skilled in the art disclose in the present invention; the variation that can expect easily or replacement, within all should being encompassed in protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claim.

Claims (5)

1. multiple goal level of factory based on an immune optimization and fuzzy decision load optimization method is characterized in that described method comprises:

Step 1: set up respectively thermal power plant coal consumption model, complete the required model consuming time of load and the nitrogen oxide emission model of distribution;

Described thermal power plant coal consumption model is $\min F=\min \left\{\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}(a_i{P}_i^2+b_i{P}_i+c_i)\right\};$ Wherein, F is the thermal power plant coal consumption, P _ifor the active power of the i of thermal power plant platform unit, a _i, b _iand c _ibe respectively the coal consumption characterisitic parameter of the i of thermal power plant platform unit;

The required model consuming time of load that described thermal power plant completes distribution be minT=min{max (| P _i-P _inow/ v _i|); Wherein, it is required consuming time that T is that thermal power plant completes the load of distribution, P _inowfor the current load of bearing of the i of thermal power plant platform unit, v _ipermissible load rate of change for the i of thermal power plant platform unit;

Described thermal power plant nitrogen oxide emission model is $\min E=\min \left\{\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{\&alpha;}}_i{P}_i^3+{\mathrm{\&beta;}}_i{P}_i^2+{\mathrm{\&gamma;}}_i{P}_i+{\mathrm{\&lambda;}}_i)\right\};$ Wherein, the nitrogen oxide emission that E is thermal power plant, α _i, β _i, γ _iand λ _ibe respectively the discharged nitrous oxides characteristic coefficient of the i of thermal power plant platform unit;

In above-mentioned three models, i=1,2 ..., N, the unit sum that N is thermal power plant;

Step 2: determine the coal consumption characterisitic parameter of each unit of thermal power plant, current load, discharged nitrous oxides characteristic coefficient and the permissible load rate of change born;

Step 3: determine the constraint condition of above-mentioned model, described constraint condition comprises active power bound constraint condition and active power balance constraint condition;

Wherein, described active power bound constraint condition is P _{i, min}≤ P _i≤ P _{i, max}, P _ifor the active power of the i of thermal power plant platform unit, P _{i, min}for the lower limit of the active power of the i of thermal power plant platform unit, P _{i, max}the upper limit for the active power of the i of thermal power plant platform unit;

Described active power balance constraint condition is

total active power that P is thermal power plant;

Step 4: active power, thermal power plant coal consumption, the thermal power plant that adopts the immune multi-object algorithm to ask for each unit of thermal power plant completes the disaggregation of the required consuming time and thermal power plant's nitrogen oxide emission of the load of distribution;

Step 5: the required consuming time and concentrated output power of optimum solution as each unit of thermal power plant of selecting of solution thermal power plant's nitrogen oxide emission of load that completes distribution from active power, thermal power plant coal consumption, the thermal power plant of each unit of thermal power plant.

2. method according to claim 1 is characterized in that the described active power from each unit of thermal power plant, thermal power plant coal consumption, thermal power plant complete required consuming time and solution thermal power plant's nitrogen oxide emission of the load of distribution and concentrate and select optimum solution and specifically comprise following sub-step:

Sub-step 101: respectively thermal power plant coal consumption, thermal power plant are completed to the required consuming time and thermal power plant's nitrogen oxide emission of the load of distribution as influence factor, calculate the desired value of each influence factor and form index value matrix;

Wherein, the desired value computing formula of thermal power plant coal consumption is

The required desired value computing formula consuming time of load that thermal power plant completes distribution is $r_{2j}=0.1+\frac{E_j-\underset{1\&le;j\&le;M}{\min }\left(E_j\right)}{\underset{1\&le;j\&le;M}{\max }\left(E_j\right)-\underset{1\&le;j\&le;M}{\min }\left(E_j\right)};$

The desired value computing formula of thermal power plant's nitrogen oxide emission is

Described index value matrix $R=\left[\begin{array}{c}{r}_{1j}\\ r_{2j}\\ r_{3j}\end{array}\right];$

F _jconcentrate the j group for described solution and separate corresponding thermal power plant coal consumption;

E _jthe load that completes distribution for thermal power plant corresponding to described solution concentrated j group solution is required consuming time;

T _jconcentrate the j group for described solution and separate corresponding thermal power plant's nitrogen oxide emission;

J=1,2 ..., M, M is that described solution is concentrated the group number of separating;

Sub-step 102: the weight matrix A=[w that determines each influence factor ₁, w ₂, w ₃]; Wherein, w ₁for the weights of thermal power plant coal consumption, w ₂for thermal power plant completes the required weights consuming time of load of distribution, w ₃for the weights of thermal power plant's nitrogen oxide emission, and

Sub-step 103: according to formula

calculate optimum solution and estimate matrix, calculate the component that optimum solution is estimated matrix B intermediate value maximum solution corresponding to component that described optimum solution is estimated matrix intermediate value maximum is optimum solution.

3. method according to claim 2, is characterized in that the weight w of described thermal power plant coal consumption ₁=0.698.

4. method according to claim 2, is characterized in that described thermal power plant completes the required weight w consuming time of load of distribution ₂=0.261.

5. method according to claim 2, is characterized in that the weight w of described thermal power plant nitrogen oxide emission ₃=0.041.

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