CN103440535B - Based on the multiple goal level of factory load optimal method of immune optimization and fuzzy decision - Google Patents

Abstract

The invention discloses a kind of multiple goal level of factory load optimal method based on immune optimization and fuzzy decision in energy-saving power generation technical field.Comprise: set up thermal power plant coal consumption model respectively, complete model consuming time and nitrogen oxide emission model needed for the load of distribution; Determine the coal consumption characterisitic parameter of each unit of thermal power plant, currently bear load, permissible load rate of change and discharged nitrous oxides characteristic coefficient; The constraint condition of Confirming model, comprises bound constraint condition and the equilibrium constraint of active power; Adopt disaggregation that is consuming time needed for immune multi-object algorithm asks for the active power of each unit of thermal power plant, thermal power plant coal consumption, thermal power plant complete distribution load and thermal power plant's nitrogen oxide emission; Concentrate from solution and select the output power of optimum solution as each unit of thermal power plant.The present invention is by immune multi-object algorithm application in multiple goal level of factory load distribution problem, and go out Power Plant optimal power allocation scheme according to Fuzzy Decision Theory decision-making, computation process more quick and precisely.

Description

Based on the multiple goal level of factory load optimal method of immune optimization and fuzzy decision

Technical field

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

Background technology

Along with the development of progressively enforcement and the intelligent grid of " separating the factory and network; surf the Net at a competitive price ", new forms of energy obtain to be greatly developed, but the core pillar status of thermal power generation in electrical network does not only change, on the contrary to which proposing requirements at the higher level, be namely single goal with multiobject Optimized Operations such as security, rapidity, economy, the feature of environmental protection but not in the past or biobjective scheduling scheduling in the urgent need to level of factory Optimization of Load Dispatching.

Load distribution in power plants is a high dimension, non-convex, discrete, nonlinear optimal problem, and common power plant load optimized algorithm is mainly following several:

1, traditional optimized algorithm

Between unit, the classic method of load distribution mainly contains average distribution system, merit-order, equal increment method.Wherein, equal increment method is simple and clear, easy to use, is the main method that current various countries electric power carries out economic load dispatching, Be very effective.But equal increment method is based upon on classic variational calculus basis, require that total consumption of coal objective function is convex strict funciton, and have strict accuracy requirement to the tiny increment curve of the steam turbine of power plant, boiler and various combination thereof, be generally difficult to meet, thus limit the range of application of this algorithm.

2, based on the optimized algorithm of mathematical programming

Mainly comprise linear programming technique, dynamic programming, Nonlinear Programming Method, Lagrangian Relaxation etc.Wherein, dynamic programming both can avoid tiny increment, also without any requirement to the thermodynamic property of unit, was thus widely applied.But dynamic programming needs global optimizing, optimum solution can be obtained with dynamic model, it adopts enumerative technique to compare each stage and each decision-making and calculate, therefore there is " dimension calamity " phenomenon when the dimension of state variable becomes large, and there is during global optimizing multiple local minimum, once step-length selection is improper, suboptimal solution can only be found.

3, modern intelligence optimization algorithm

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

At present, the research of domestic load distribution in power plants, still main round on the algorithm improvement of the Economic optimization based on coal consuming character.But, along with the development of electricity market, need for electricity constantly changes, electrical network peak-valley difference continues aggravation, becoming increasingly conspicuous of problem of environmental pollution, turn improve the adding of new forms of energy requirement fired power generating unit being participated in the rapidities such as peak regulation, economy is no longer weigh the sole criterion of thermal power plant's performance, and the multiple goal load distribution in power plants of reflection economy, the feature of environmental protection, rapidity is subject to extensive concern.

Summary of the invention

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

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

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

Described thermal power plant coal consumption model is wherein, F is thermal power plant coal consumption, P _ifor the active power of thermal power plant's i-th unit, a _i, b _iand c _ibe respectively the coal consumption characterisitic parameter of thermal power plant's i-th unit;

Model consuming time needed for the 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 _inowload is born, v for thermal power plant's i-th unit is current _ifor the permissible load rate of change of thermal power plant's i-th unit;

Described thermal power plant nitrogen oxide emission model is $\min E=\min \left\{\underset{i=1}{\overset{N}{\mathrm{\Σ}}}({\mathrm{\α}}_i{P}_i^3+{\mathrm{\β}}_i{P}_i^2+{\mathrm{\γ}}_i{P}_i+{\mathrm{\λ}}_i)\right\};$ Wherein, E is the nitrogen oxide emission of thermal power plant, α _i, β _i, γ _iand λ _ibe respectively the discharged nitrous oxides characteristic coefficient of thermal power plant's i-th unit;

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

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

Step 3: the constraint condition determining 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 thermal power plant's i-th unit, P _{i, min}for the lower limit of the active power of thermal power plant's i-th unit, P _{i, max}for the upper limit of the active power of thermal power plant's i-th unit;

Described active power balance constraint condition is p is total active power of thermal power plant;

Step 4: adopt disaggregation that is consuming time needed for immune multi-object algorithm asks for the active power of each unit of thermal power plant, thermal power plant coal consumption, thermal power plant complete distribution load and thermal power plant's nitrogen oxide emission;

Step 5: needed for the load completing distribution from the active power of each unit of thermal power plant, thermal power plant coal consumption, thermal power plant, solution consuming time and thermal power plant's nitrogen oxide emission is concentrated and selected the output power of optimum solution as each unit of thermal power plant.

Solution consuming time and thermal power plant's nitrogen oxide emission needed for the load that the described active power from each unit of thermal power plant, thermal power plant coal consumption, thermal power plant complete distribution is concentrated and is selected optimum solution and specifically comprise following sub-step:

Sub-step 101: consuming time and thermal power plant's nitrogen oxide emission needed for the load respectively thermal power plant coal consumption, thermal power plant being completed distribution, as influence factor, calculates the desired value of each influence factor and forms index value matrix;

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

Thermal power plant completes desired value computing formula consuming time needed for the load of distribution $r_{2j}=0.1+\frac{E_j-\underset{1\≤j\≤M}{\min }\left(E_j\right)}{\underset{1\≤j\≤M}{\max }\left(E_j\right)-\underset{1\≤j\≤M}{\min }\left(E_j\right)};$

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 _jfor the thermal power plant coal consumption that described solution concentrates jth group solution corresponding;

E _jconsuming time needed for the load that described solution concentrates thermal power plant corresponding to jth group solution to complete distribution;

T _jfor thermal power plant's nitrogen oxide emission that described solution concentrates jth group solution corresponding;

J=1,2 ..., M, M are the group number that described solution concentrates solution;

Sub-step 102: the weight matrix A=[w determining 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 weights consuming time needed for the 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 Evaluations matrix, calculate the component that optimum solution Evaluations matrix B intermediate value is maximum the solution that the maximum component of described optimum solution Evaluations matrix intermediate value is corresponding is optimum solution.

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

Described thermal power plant completes weight w consuming time needed for the 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 immune multi-object algorithm application in the multiple goal level of factory load distribution problem considering economy, rapidity and the feature of environmental protection, and go out Power Plant optimal power allocation scheme according to Fuzzy Decision Theory decision-making, its computation process more quick and precisely.

Accompanying drawing explanation

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

Fig. 2 is the multiple goal level of factory load optimal method flow diagram based on 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.It is emphasized that following explanation is only exemplary, instead of in order to limit the scope of the invention and apply.

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

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

(1) initial population is produced at random;

(2) target function value of each antibody is calculated;

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

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

(5) selection operation is carried out according to noninferior solution rank and concentration;

(6) carry out intersecting, mutation operation, produce new population;

(7) renewal of population and memory cell is carried out;

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

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

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

Thermal power plant coal consumption model is wherein, F is thermal power plant coal consumption, P _ifor the active power of thermal power plant's i-th unit, a _i, b _iand c _ibe respectively the coal consumption characterisitic parameter of thermal power plant's i-th unit.Thermal power plant coal consumption is by thermal power plant's each unit active power curves reflection, and thermal power plant's each unit active power curves adopts quafric curve conventional in engineering, its coal consumption characterisitic parameter a usually _i, b _iand c _ithe curve generated by the historical data matching gathered in thermal power plant's each unit running process obtains.

Model consuming time needed for the load that thermal power plant completes distribution be minT=min{max (| P _i-P _inow/ v _i|).Wherein, T completes needed for the load of distribution consuming time for thermal power plant, P _inowload is born, v for thermal power plant's i-th unit is current _ifor the permissible load rate of change of thermal power plant's i-th unit.These data can be obtained by power plant units real-time monitoring data.

Thermal power plant's nitrogen oxide emission model is $\min E=\min \left\{\underset{i=1}{\overset{N}{\mathrm{\Σ}}}({\mathrm{\α}}_i{P}_i^3+{\mathrm{\β}}_i{P}_i^2+{\mathrm{\γ}}_i{P}_i+{\mathrm{\λ}}_i)\right\}.$ Wherein, E is the nitrogen oxide emission of thermal power plant, α _i, β _i, γ _iand λ _ibe respectively the discharged nitrous oxides characteristic coefficient of thermal power plant's i-th unit.Thermal power plant's nitrogen oxide emission is relevant to the active power of thermal power plant's each unit, and usual thermal power plant nitrogen oxide emission is expressed as the cubic curve form of active power, its characteristic coefficient α _i, β _i, γ _iand λ _ithe curve generated by the historical data matching gathered in thermal power plant's each unit running process obtains.

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

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

Step 3: the constraint condition determining 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, and therefore 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 thermal power plant's i-th unit, P _{i, min}for the lower limit of the active power of thermal power plant's i-th unit, P _{i, max}for the upper limit of the active power of thermal power plant's i-th unit.Active power balance constraint condition is p is total active power of thermal power plant.

Step 4: adopt disaggregation that is consuming time needed for immune multi-object algorithm asks for the active power of each unit of thermal power plant, thermal power plant coal consumption, thermal power plant complete distribution load and thermal power plant's nitrogen oxide emission.

In the present embodiment, using 4 of certain thermal power plant units as research object, wherein the power of 2 units is 300MW, and the power of 2 units is 600MW, its net coal consumption rate characterisitic parameter and discharged nitrous oxides characteristic coefficient 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 immune multi-object algorithm is 200, and evolutionary generation was 100 generations, and crossover probability is 0.9, and mutation probability is 0.1.Data in table 1 are substituted into the immune multi-object algorithm routine (running environment is Matlab7.0) of Matlab language establishment, carry out load distribution by model in this paper and Multipurpose Optimal Method to it, 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 immune multi-object algorithm

The disaggregation provided as can be seen from table 2, Pareto disaggregation often organize consuming time and thermal power plant's nitrogen oxide emission needed for solution comprises the active power of each unit of thermal power plant, thermal power plant coal consumption, thermal power plant complete distribution load.

Be it can also be seen that by table 2 and Fig. 3, the Pareto disaggregation of the immune multi-object algorithm optimization gained adopting the present invention to propose has excellent diversity, distribution uniform in three dimensions, effectively can solve multiple goal load optimal problem.Because multiple goal load optimal is a more complicated problem, and required each objective function restricts mutually, the optimum of a target often becomes bad to cost with other a certain target.If only consider single goal load distribution, when full factory net coal consumption rate is minimum be 311.82g/ (kWh) time, its NOx discharge capacity is comparatively greatly, 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 comparatively large, and rationality cannot ensure.The present invention makes full use of the information of Pareto disaggregation itself, adopts Multicriteria fuzzy decision-making method to select optimum solution.

When carrying out Multicriteria fuzzy decision-making method and selecting optimum solution, generally each target signature amount to be converted into relative defects (or utility function), then give each target respective weights, remake comprehensive evaluation, thus determine most satisfactory solution.First the weight of three indexs is set to [w1=1, w2=0, w3=0] by the present invention respectively, [w1=0, w2=1, w3=0], and [w1=0, w2=0, w3=1], namely considers economy respectively, and rapidity and the feature of environmental protection obtain optimum single index.Wherein, economy is reflected by thermal power plant coal consumption, and rapidity completes reflection consuming time needed for the load of distribution by thermal power plant, and the feature of environmental protection is reflected 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 obtain is contrasted, as shown in table 3.

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

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

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

Step 5: needed for the load completing distribution from the active power of each unit of thermal power plant, thermal power plant coal consumption, thermal power plant, solution consuming time and thermal power plant's nitrogen oxide emission is concentrated and selected the output power of optimum solution as each unit of thermal power plant.

Utilize immune multi-object algorithm to be optimized, after drawing 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, using the economic factors that thermal power plant coal consumption will be considered as thermal power plant, the rapidity factor will considered as thermal power plant consuming time needed for the load that thermal power plant completes 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 will seek under certain constraint condition, make multiple target all reach the scheme of satisfactory value, is to concentrate after each target of choosing comprehensively from limited optional program, sorts and select the most satisfied scheme to scheme collection.Due to the conflicting between each target, generally each target signature amount to be converted into relative defects (or utility function), then give each target respective weights, then do comprehensive evaluation, thus determine the scheme that is satisfied with most.

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

Sub-step 101: consuming time and thermal power plant's nitrogen oxide emission needed for the load respectively thermal power plant coal consumption, thermal power plant being completed distribution, as influence factor, calculates the desired value of each influence factor and forms index value matrix.

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

Thermal power plant completes desired value computing formula consuming time needed for the load of distribution $r_{2j}=0.1+\frac{E_j-\underset{1\≤j\≤M}{\min }\left(E_j\right)}{\underset{1\≤j\≤M}{\max }\left(E_j\right)-\underset{1\≤j\≤M}{\min }\left(E_j\right)}.$

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

And then obtain index value matrix $R=\left[\begin{array}{c}{r}_{1j}\\ r_{2j}\\ r_{3j}\end{array}\right]=\left[\begin{array}{cccc}{r}_{11}& r_{12}& ...& r_{1M}\\ r_{21}& r_{22}& ...& r_{2M}\\ r_{31}& r_{32}& ...& r_{3M}\end{array}\right].$ Wherein, F _jthe thermal power plant coal consumption that the solution calculated for immune multi-object algorithm concentrates jth group solution corresponding, E _jconsuming time needed for the load that the solution calculated for immune multi-object algorithm concentrates thermal power plant corresponding to jth group solution to complete distribution, T _jthermal power plant's nitrogen oxide emission that the solution calculated for immune multi-object algorithm concentrates jth group solution corresponding, j=1,2 ..., M, M are the group number that solution that immune multi-object algorithm calculates concentrates solution.

Sub-step 102: the weight matrix A=[w determining each influence factor ₁, w ₂, w ₃].W ₁for the weights of thermal power plant coal consumption, w ₂for thermal power plant completes weights consuming time needed for the load of distribution, w ₃for the weights of thermal power plant's nitrogen oxide emission, and the present embodiment is got fire the weight w of power plant's coal consumption amount ₁=0.698, thermal power plant completes weight w consuming time needed for the load of distribution ₂=0.261, the weight w of thermal power plant's nitrogen oxide emission ₃=0.041.These weights empirically set, and certain thermal power plant technician suitably can also regulate according to the actual conditions of this thermal power plant.

Sub-step 103: according to formula calculate optimum solution Evaluations matrix.

Make B=[b ₁, b ₂..., b _m], then j=1,2 ..., M.Calculate the component that optimum solution Evaluations matrix B intermediate value is maximum namely solution corresponding for component maximum for optimum solution Evaluations matrix intermediate value is optimum solution.In the present embodiment, optimum solution through calculating is solution 99, namely 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 is originally { 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 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 calculates, the optimum solution of selection is solution 9, and namely 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), regulation time: 6.0296min, amount of nitrogen oxides: 1.3251t/h.The 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, immune multi-object algorithm is utilizing different decision-making technique from NSGA-II method, when decision-making is optimized to identical index weights, except nitrogen oxide emission is a little more than except NSGA-II method, coal consumption amount and distribution load is consuming time is all less than NSGA-II method, trace it to its cause, in economy, larger by the coal consumption coefficient of known No. 1 and No. 4 unit of unit coal consumption characterisitic parameter, namely coal consumption amount is larger, and the load of No. 1 and No. 4 unit that 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 obtained for 721.4739MW, therefore immune multi-object algorithm optimization obtains economic index is better than NSGA-II, in rapidity, the fastest No. 3 unit load values of the elevation rate that immune multi-object algorithm optimization obtains are greater than NSGA-II and optimize No. 3 unit load values obtained, 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 immune multi-object algorithm optimization obtains is shorter, shows 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, NOx discharge capacity reduces 0.6751t/h, achieves significant effect of optimization.

When complex optimum is carried out for three targets, by the sweetly disposition to weight, for power plant solves load distribution problem dexterously, power plant load optimization can be met and is 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 preferably embodiment, but protection scope of the present invention is not limited thereto, is anyly familiar with those skilled in the art in the technical scope that the present invention discloses; the change that can expect easily or replacement, all should be encompassed within 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., based on a multiple goal level of factory load optimal method for immune optimization and fuzzy decision, it is characterized in that described method comprises:

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

Described thermal power plant coal consumption model is wherein, F is thermal power plant coal consumption, P _ifor the active power of thermal power plant's i-th unit, a _i, b _iand c _ibe respectively the coal consumption characterisitic parameter of thermal power plant's i-th unit;

Model consuming time needed for the load that described thermal power plant completes distribution be minT=min{max (| P _i-P _inow/ v _i|); Wherein, T completes needed for the load of distribution consuming time for thermal power plant, P _inowload is born, v for thermal power plant's i-th unit is current _ifor the permissible load rate of change of thermal power plant's i-th unit;

Described thermal power plant nitrogen oxide emission model is $\min E=min\left\{\underset{i=1}{\overset{N}{\Σ}}({\mathrm{\α}}_i{P}_i^3+{\mathrm{\β}}_i{P}_i^2+{\mathrm{\γ}}_i{P}_i+{\mathrm{\λ}}_i)\right\};$ Wherein, E is the nitrogen oxide emission of thermal power plant, α _i, β _i, γ _iand λ _ibe respectively the discharged nitrous oxides characteristic coefficient of thermal power plant's i-th unit;

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

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

Step 3: the constraint condition determining 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 thermal power plant's i-th unit, P _{i, min}for the lower limit of the active power of thermal power plant's i-th unit, P _{i, max}for the upper limit of the active power of thermal power plant's i-th unit;

Described active power balance constraint condition is p is total active power of thermal power plant;

Step 4: adopt disaggregation that is consuming time needed for immune multi-object algorithm asks for the active power of each unit of thermal power plant, thermal power plant coal consumption, thermal power plant complete distribution load and thermal power plant's nitrogen oxide emission;

Step 5: needed for the load completing distribution from the active power of each unit of thermal power plant, thermal power plant coal consumption, thermal power plant, solution consuming time and thermal power plant's nitrogen oxide emission is concentrated and selected the output power of optimum solution as 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, solution consuming time and thermal power plant's nitrogen oxide emission needed for load that thermal power plant completes distribution concentrates and select optimum solution and specifically comprise following sub-step:

Sub-step 101: consuming time and thermal power plant's nitrogen oxide emission needed for the load respectively thermal power plant coal consumption, thermal power plant being completed distribution, as influence factor, calculates the desired value of each influence factor and forms index value matrix;

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

Thermal power plant completes desired value computing formula consuming time needed for the load of distribution $r_{2j}=0.1+\frac{E_j-\underset{1\≤j\≤M}{min}\left(E_j\right)}{\underset{1\≤j\≤M}{\max }\left(E_j\right)-\underset{1\≤j\≤M}{min}\left(E_j\right)};$

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 _jfor the thermal power plant coal consumption that described solution concentrates jth group solution corresponding;

E _jconsuming time needed for the load that described solution concentrates thermal power plant corresponding to jth group solution to complete distribution;

T _jfor thermal power plant's nitrogen oxide emission that described solution concentrates jth group solution corresponding;

J=1,2 ..., M, M are the group number that described solution concentrates solution;

Sub-step 102: the weight matrix A=[w determining 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 weights consuming time needed for the 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 Evaluations matrix, calculate the component that optimum solution Evaluations matrix B intermediate value is maximum the solution that the maximum component of described optimum solution Evaluations matrix intermediate value is corresponding 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 weight w consuming time needed for the 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.