CN105069540A - Method for designing fuel procurement decision support system for thermal power enterprises - Google Patents

Abstract

The invention discloses a method for designing a fuel procurement decision support system for thermal power enterprises, comprising the following steps: selecting coal supply unit evaluation indexes according to the practical operation of thermal power enterprises and the condition of fuel for sale to evaluate potential coal supply units; calculating the weight of each evaluation index by an AHP algorithm; calculating the value of each evaluation index of each coal supply unit based on historical data, and working out the score of each evaluation index of each coal supply unit based on the values of the evaluation indexes and the boiler parameters of thermal power enterprises; calculating the product of the weight and score of each evaluation index of each coal supply unit as the weight score of the evaluation index, and working out the score of each potential coal supply unit by a technique for order preference by similarity to an ideal solution; and building a fuel order distribution model according to the purchase limit and score of each potential coal supply unit and the actual purchase demand of thermal power enterprises. By adopting the method, solution is accurate. By making full use of historical data, the method can better conform to the actual situation.

Description

The method for designing of Thermal Power Enterprises fuel purchases decision support system (DSS)

Technical field

The invention belongs to boiler fuel buying technical field, more specifically, relate to a kind of method for designing of Thermal Power Enterprises fuel purchases decision support system (DSS).

Background technology

Along with the foundation of supply chain management, the procurement strategy of past only centered by price and experience has not existed, and the substitute is the procurement strategy for supporting with procurement optimization model and science data.Thermal Power Enterprises is formulated rational procurement strategy and is minimizing cost of electricity-generating, guarantees Thermal Power Enterprises safety in production, ensures that fuel meets the key of power generation needs and timely supply and arrival; so will ensure the sustainable development of Thermal Power Enterprises, it is very important for working out rational fuel purchases strategy.

The people such as Zhou Yun, Zhao Lei of Tsing-Hua University to replenish the stock from uncertain supplier for retailer and meet random customer demand, obtain the Optimized model reducing retailer's overall operation cost, this model comprises the stochastic dynamic programming exterior layer problem that is determined optimum magnitude of recruitment, and the integer programming interior layer problem of a selection supplier under given magnitude of recruitment and Order splitting.The people such as the Xiao Lei of Southwest Jiaotong University think in purchasing management, and the Supplier Selection be familiar with based on procurement risk and Order splitting can improve the science of decision-making effectively.They utilize analytical hierarchy process (AnalyticHierarchyProcess, AHP) risk factor of product and the risk factor of supplier is calculated, the Bi-level Programming Models solving Supplier Selection and Order splitting is constructed based on these coefficients, this model considers multi-product, monocycle, the diversity of price and random demand, and utilizes genetic algorithm to solve this model.The Huo Hong of Harbin University of Commerce points out due in practice, the inaccuracy of target, constraint and parameter result in the complicacy of decision-making, thus propose one and represent the preference of decision maker and the multi-objective integer programming model of fuzzy object, with the Supplier Selection solved in supply chain system and Order Allocation.But all there is method complexity in existing technology, the shortcoming such as the not high and solving speed of solving precision is slow.

Summary of the invention

For above defect or the Improvement requirement of prior art, the invention provides a kind of method for designing of Thermal Power Enterprises fuel purchases decision support system (DSS), be intended to solve existing method and solve inaccurate, and can not the problems such as historical data be made full use of.

For achieving the above object, the invention provides a kind of method for designing of Thermal Power Enterprises fuel purchases decision support system (DSS), comprise the steps:

(1) evaluation index of coal supply unit is chosen according to the practical operation situation of Thermal Power Enterprises and the situation of fuel on sale, for evaluating potential coal supply unit; Wherein, evaluation index comprises one-level evaluation index and two-level appraisement index, and each one-level evaluation index comprises multiple two-level appraisement index;

(2) using minimum for the purchase cost of Thermal Power Enterprises as general objective, one-level evaluation index is as rule layer element, and two-level appraisement index, as secondary rule layer element, utilizes AHP algorithm to calculate the weight of each evaluation index;

(3) utilize historical data to calculate each evaluation index value of each potential coal supply unit, in conjunction with the boiler parameter of Thermal Power Enterprises, obtain the score of each evaluation index of each potential coal supply unit;

Wherein, to arbitrary evaluation index value x, the score x of this evaluation index is obtained in the following way ^*:

If x is the bigger the better, then $x^{*}=\left\{\begin{array}{cc}10,& x\&GreaterEqual;L\\ 10+(x-L)/a,& x<L\end{array}\right.,$ Wherein, L represents the lower limit being satisfied with evaluation index value most, and a represents the length of evaluation index interval;

If x is the smaller the better, then $x^{*}=\left\{\begin{array}{cc}10-(x-U)/a,& x>U\\ 10,& x\&le;U\end{array}\right.,$ Wherein, U represents the upper limit being satisfied with evaluation index value most;

If best during a certain scope of x convergence, then $x^{*}=\left\{\begin{array}{cc}10+(x-U)/a,& x>U\\ 10,& L\&le;x\&le;U\\ 10-(L-x)/a& x<L\end{array}\right.;$

(4) to each evaluation index of each potential coal supply unit, calculate the weighted score of product as this evaluation index of its weight and score, utilize good and bad solution Furthest Neighbor to calculate the scoring of each potential coal supply unit;

(5) according to the buying restriction of each potential coal supply unit and the actual purchase demand of scoring and Thermal Power Enterprises, fuel Order Allocation Model is built.

Preferably, in step (4), the scoring of the individual potential coal supply unit of t wherein, be the weighted score vector Y of the evaluation index of the individual potential coal supply unit of t _t=(y _t1, y _t2..., y _ts) to Negative ideal point y _min=(y _1min, y _2min..., y _smin) Euclidean distance, be the weighted score vector Y of the evaluation index of the individual potential coal supply unit of t _t=(y _t1, y _t2..., y _ts) to Positive ideal point y _max=(y _1max, y _2max..., y _smax) Euclidean distance, s is the number of evaluation index, t=1,2 ..., N, N are potential coal supply unit sum, y _t1, y _t2..., y _tsbe respectively the weighted score of 1st ~ s evaluation index of the individual potential coal supply unit of t, y _1min, y _2min..., y _sminbe respectively the minimum value of the weighted score of 1st ~ s evaluation index of all N number of potential coal supply units, y _1max, y _2max..., y _smaxfor the maximal value of the weighted score of 1st ~ s evaluation index of all N number of potential coal supply units.

Preferably, in step (5), fuel Order Allocation Model is as follows:

Constraint condition:

Objective function $P=min\left\{\underset{t=1}{\overset{N}{\&Sigma;}}(1-C_t)P_t{X}_t\right\},$

Wherein, N is potential coal supply unit sum, X _tfor the coal amount from the buying of t individual potential coal supply unit, Q _tbe the calorific value of the individual potential coal supply unit coal of t, Q _minand Q _maxbe respectively lower limit and the upper limit of the target calorific value that coal blending produces, V _tbe the volatile matter of the individual potential coal supply unit coal of t, V _minand V _maxbe respectively lower limit and the upper limit of the volatile matter that coal blending produces, M _tbe the moisture of the individual potential coal supply unit coal of t, M _maxfor the upper limit of the moisture that coal blending produces, S _tbe the sulfur value of the individual potential coal supply unit of t, S _maxfor the upper limit of the sulfur that coal blending produces, A _tbe the ash value of the individual potential coal supply unit of t, A _maxfor the upper limit of the ash content that coal blending produces, D is the target buying sum of this month fuel, XMIN _tand XMAX _tbe respectively the upper and lower bound of the coal amount t potential coal supply unit buying, P _tsingle coal cost of the individual potential coal supply unit of t, C _tit is the scoring of the individual potential coal supply unit of t; During potential coal supply unit buying coal individual from t, XMIN _t≤ X _t≤ XMAX _t, during not individual from t potential coal supply unit buying coal, X _t=0.

In general, the above technical scheme conceived by the present invention compared with prior art, there is following beneficial effect: owing to have employed step (1), choose the evaluation index of coal supply unit according to the practical operation situation of Thermal Power Enterprises and the situation of fuel on sale, therefore evaluation index choose realistic application; The piecewise function built according to the boiler parameter of Thermal Power Enterprises is utilized to obtain the evaluation index score of coal supply unit in step (3), make when taking Thermal Power Enterprises as evaluation criterion, the quality of each coal supply unit evaluation index data can better be reacted, therefore can tally with the actual situation better.

Accompanying drawing explanation

Fig. 1 is the method for designing process flow diagram of the Thermal Power Enterprises fuel purchases decision support system (DSS) of the embodiment of the present invention.

Embodiment

In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.In addition, if below in described each embodiment of the present invention involved technical characteristic do not form conflict each other and just can mutually combine.

As shown in Figure 1, the method for designing of the Thermal Power Enterprises fuel purchases decision support system (DSS) of the embodiment of the present invention comprises the steps:

(1) evaluation index of coal supply unit is chosen according to the practical operation situation of Thermal Power Enterprises and the situation of fuel on sale, for evaluating potential coal supply unit.

Have chosen comprehensive, scientifical, quantize simple evaluation index after, can evaluate potential coal supply unit better.Evaluation index comprises one-level evaluation index and two-level appraisement index.Wherein, one-level evaluation index comprises ature of coal, cost, prestige and the capability of sustainable development, and two-level appraisement index comprises calorific value, volatile matter, moisture, sulfur, ash content, mark coal unit price, transportation cost, coal amount coincidence rate, ature of coal coincidence rate, clearing profit and loss, ature of coal evening ratio, time coincidence rate, economic benefit and economic scale.

Respectively brief description is carried out to one-level evaluation index and two-level appraisement index below.

Ature of coal: come according to specific coal kind during design boiler, so boiler requires that the quality of coal can reach specific criteria.The coal of off-design can cause the reduction of the equipment general level of the health, increases equipment maintenance cost.If coal varitation is comparatively large and ature of coal is unstable, very large impact will be caused on the stability of boiler combustion and economy, also can affect the safety and reliability of boiler plant simultaneously significantly.So the calorific value of fuel, volatile matter, ash content, the parameter such as moisture and sulfur must meet boiler requirement in design.

Calorific value: calorific value is as one of important indicator analyzing ature of coal, and also known as net calorific value, the heat discharged after referring to unit coal Thorough combustion, does not comprise the heat energy that steam gasification discharges.The calorific value of coal is larger, and the electric energy of generation is larger, just can improve the economic benefit of power plant.If the net calorific value of institute's burning coal is less than the thermal value of the coal set by boiler, analysis meeting causes the reduction of temperature of combustion theoretically, and fire box temperature reduces, and the thermal efficiency of boiler will decline, and increases cost of electricity-generating.If but the net calorific value of coal is greater than the design coal of boiler, coal ash can be made to soften, form slagging scorification.Coal calorific value is not the bigger the better thus, must select with reference to the designing requirement of boiler.

Volatile matter: volatile matter is very important composition in coal burning is the first index determining coal fire behaviour.When volatile matter height, the combustion front of coal can be increased, reduce the insufficient loss brought of coal burning.If coal its volatile matter when entering stove burning is greater than boiler design coal, the damage of burner and final blowing out will be caused; If coal volatile matter is less than the volatile content that boiler is preset, insufficient operation that can damage burner equally of burning.So the volatile content of boiler design must as important reference when purchasing.

Ash content: ash content is the objectionable impurities in coal, can take away a large amount of heats when burning, cause the reduction of thermal value.And the increase of ash content likely can aggravate the wearing and tearing of boiler, affects the operation of boiler, stopping working in combustion process may be caused, equipment is impacted, even causes security incident.In coal, ash content is higher, and the weight of coal is then larger, can increase unnecessary transportation by railroad.

Moisture: moisture refers to the water overflowed in coal burning process is one of objectionable impurities in combustion process.Evaporation can be absorbed heat, and evaporation of water heat is very large, and can absorb a large amount of heat in the process of burning, this can cause thermal value relatively to reduce.And if water cut crosses conference affect burning of coal, so moisture is the very important factor affecting ature of coal quality.In Coal Transport process, weather and climate factor can have an impact to moisture in coal content, and the Coal ' moisture caused as rainfall increases, and transport and the storage work of therefore carrying out coal are very important.

Sulfur: sulphur is the objectionable impurities in coal.Although also can discharge a small amount of heat energy in element sulphur combustion process, element sulphur is totally more harm than good for coal burning.Sulfur content too high in ature of coal can cause equipment to be corroded and then affect the quality of boiler, the sulfur monoxide that sulfur burning simultaneously produces and sulphuric dioxide can produce air and pollute, destroy ecologic environment, so must control in certain level on the content of element sulphur in order to the coal of generating.

Cost: comprise mark coal unit price and transportation cost.

Mark coal unit price: standard coal equivalent refer to net calorific value be 7000 kilocalories or 29.271 million burnt/kilogram coal, be the leading indicator weighing cost of electricity-generating, the directly economic benefit of decision Thermal Power Enterprises.The raw coal price of each coal supply unit is generally changed into mark coal unit price according to certain rule by Thermal Power Enterprises, goes by mark coal unit price the quality weighing each coal supply unit coal price.

Transportation cost: the colliery major part of China concentrates on the north, and the Thermal Power Enterprises being therefore in the middle and lower reach of Yangtze River must consider the factor of transportation cost.Transporting the mode that electric coal mainly takes land highway, railway and water transportation to combine, when selecting transporter except minimizing cost, also will ensure the efficiency that electric coal is carried, ensure the timely arrival of coal.

Prestige: Thermal Power Enterprises fully must be understood it whether have certain credit worthiness when selecting coal supply unit, mainly comprises in the situation of coal supply unit performance of the contract and agreement list in cooperation once and its reputation two in industry.In conjunction with the actual traffic-operating period of Thermal Power Enterprises, select ature of coal coincidence rate, coal amount coincidence rate (the ore deposit amount of sending out and contracted quantity), settle accounts profit and loss (the ore deposit amount of sending out and examination amount), ature of coal evening ratio, arrival time coincidence rate be as the evaluation criterion of prestige.Ature of coal coincidence rate mainly refers to that the actual ature of coal chemically examined of checking and accepting of the ature of coal that ore deposit is sent out and power plant meets situation; Coal amount coincidence rate mainly refers to the situation that conforms to of the coal amount sent out in actual ore deposit and the coal amount that contract specifies; Clearing profit and loss refer to that the coal amount when Office Of Contract Settlements is the coal weighing sent out coal weighing with ore deposit or check and accept with reality, and this index can send out calculating by road consumption divided by actual ore deposit; Ature of coal evening ratio refers to the difference condition of the indices of ature of coal in all batches that coal supply unit ore deposit sends out, and mainly represents with the variance of ature of coal; Arrival time coincidence rate refers to the number of times of punctual arrival and the ratio of total arrival number of times, and punctual arrival is the important guarantee of Thermal Power Enterprises safety in production, so arrival time coincidence rate is an important indicator of supplier evaluation.

The capability of sustainable development: the economic benefit of paper examines coal supply unit and economic scale two aspects, if generally coal supply unit-economy is larger, better economic benefit, its ature of coal situation, throughput rate, can to exploit the time limit be all preferably comparatively speaking, and Thermal Power Enterprises just should set up stable cooperative relationship with this coal supply unit.

(2) using minimum for the purchase cost of Thermal Power Enterprises as general objective, one-level evaluation index is as rule layer element, and two-level appraisement index, as secondary rule layer element, utilizes AHP algorithm to calculate the weight of each evaluation index.

Concrete grammar is as follows:

If the catalogue of AHP structure is designated as P, rule layer is A, and secondary rule layer is B, then the number m=14 of the number n=4 of A layer element, B layer element.Take P as criterion, Mode of Level Simple Sequence is carried out to A layer element: elements A in contrast A _iand elements A _j(i, j=1,2 ..., n) to the disturbance degree size of P, way of contrast adopts 1 ~ 9 scoring criterion (from 1 to 9, the difference of importance of two elements increases gradually) shown in table 1, obtains comparator matrix W.

$W=\left[\begin{array}{cccc}{w}_{11}& w_{12}& ...& w_{1n}\\ w_{21}& w_{22}& ...& w_{2n}\\ ...& ...& ...& ...\\ w_{n1}& w_{n2}& ...& w_{nn}\end{array}\right]$

Table 1

Scale	Definition and explanation
		1	Fiducial value is of equal importance
3	One more important a little than another
		5	One more obvious than another important
7	One much more important than another
		9	One extremely more important than another
2,4,6,8	Use according to importance rate compromise

Wherein, w _ijrepresentative is criterion with P, A _iand A _jto the fiducial value of the disturbance degree size of P.Ask the eigenvalue of maximum λ of W _max, in order to draw the degree of reliability of W, needing to carry out consistency check to it, introducing coincident indicator CI:

$CI=\frac{{\mathrm{\&lambda;}}_{max}-n}{n-1}---\left(1\right)$

Definition CR=CI/RI is Consistency Ratio, and RI is the randomness index of the same order matrix of comparator matrix W, and RI value is as shown in table 2.If CR >=0.1, illustrate that the consistance of comparator matrix W is too poor, do not meet real logic, adjust the value of element in W according to table 1, regenerate comparator matrix; If CR < 0.1, illustrate that comparator matrix W has gratifying consistance, then to the row normalization of comparing matrix W, obtain matrix W ', obtain the vectorial w=[a that the eigenvalue of maximum of W ' is corresponding ₁, a ₂..., a _n], wherein, a ₁, a ₂..., a _nfor the weighted value of A layer element.

Table 2

n

1,2

3

4

5

6

7

8

9

10

RI

0.00

0.58

0.90

1.12

1.24

1.32

1.41

1.45

1.49

Carry out total hierarchial sorting, with A _i(i=1,2 ..., n) be criterion, to B layer element B _k(k=1,2 ..., m) carry out Mode of Level Simple Sequence, obtaining its weighted value is wherein, if B _k(k=1,2 ..., m) and A _iduring without contact, total ranking results of B layer is as shown in table 3.

Table 3

Carry out consistency check to the weighted value of B layer element, method is utilize CR=CI/RI equally, wherein cI _iand RI _iwith A respectively _ifor criterion, utilize coincident indicator and the randomness index of the comparator matrix that element obtains in B level.When the consistance of the weighted value of B layer element is undesirable, recalculate with A _i(i=1,2 ..., n) be weighted value that criterion obtains and then recalculate the weighted value of B layer element.

(3) utilize historical data to calculate each evaluation index value of each potential coal supply unit, in conjunction with the boiler parameter of Thermal Power Enterprises, obtain the score of each evaluation index of each potential coal supply unit.

Specifically comprise following sub-step:

(3-1) historical data is utilized to calculate each evaluation index value of each potential coal supply unit.

(3-2) staging treating function is built according to the boiler parameter of Thermal Power Enterprises.

Because whether the quality evaluating an index will see whether conform to the design coal of boiler, likely the calorific value of Liang Ge coal supply unit is different, but the class residing for its calorific value is the same.Therefore in order to distinguish the quality of ature of coal, need to carry out pre-service to evaluation index value.

Concrete grammar is as follows, to arbitrary evaluation index value x:

If x is the bigger the better, then the score of this evaluation index:

$x^{*}=\left\{\begin{array}{cc}10,& x\&GreaterEqual;L\\ 10+(x-L)/a,& x<L\end{array}\right.---\left(2\right)$

Wherein, L represents the lower limit being satisfied with evaluation index value most, and a represents the length of evaluation index interval.

If x is the smaller the better, then the score of this evaluation index:

$x^{*}=\left\{\begin{array}{cc}10-(x-U)/a,& x>U\\ 10,& x\&le;U\end{array}\right.---\left(3\right)$

Wherein, U represents the upper limit being satisfied with evaluation index value most.

If best during a certain scope of x convergence, then the score of this evaluation index:

$x^{*}=\left\{\begin{array}{cc}10+(x-U)/a,& x>U\\ 10,& L\&le;x\&le;U\\ 10-(L-x)/a& x<L\end{array}\right.---\left(4\right)$

(3-3) utilize the desired value of piecewise function to each potential coal supply unit to carry out pre-service, obtain the score of the evaluation index of each potential coal supply unit, make it make a distinction relative to the quality of Thermal Power Enterprises.

(4) to each evaluation index of each potential coal supply unit, calculate the weighted score of product as this evaluation index of its weight and score, good and bad solution Furthest Neighbor (TechniqueforOrderPreferencebySimilaritytoanIdealSolution, TOPSIS) is utilized to calculate the scoring of each potential coal supply unit.

The method main thought is, is namely optimal case closest to distance Negative ideal point method farthest while of Positive ideal point.Utilize queuing desired value C to weigh, shown in (5), wherein, y ⁺and y ^-the Euclidean distance of Positive ideal point and Negative ideal point is arrived in representative respectively, and queuing desired value is larger, and the program is better.

$C=\frac{y^{-}}{y^{+}+y^{-}}---\left(5\right)$

The advantage of this step is, the data inputting of coal supply unit and there is subjective factor when carrying out pre-service to the evaluation index value of coal supply unit, and can eliminate part subjective factor when utilizing TOPSIS algorithm to calculate the scoring of coal supply unit.

Particularly, the scoring of the individual potential coal supply unit of t wherein, be the weighted score vector Y of the evaluation index of the individual potential coal supply unit of t _t=(y _t1, y _t2..., y _ts) to Negative ideal point y _min=(y _1min, y _2min..., y _smin) Euclidean distance, be the weighted score vector Y of the evaluation index of the individual potential coal supply unit of t _t=(y _t1, y _t2..., y _ts) to Positive ideal point y _max=(y _1max, y _2max..., y _smax) Euclidean distance, s is the number of evaluation index, t=1,2 ..., N, N are potential coal supply unit sum, y _t1, y _t2..., y _tsbe respectively the weighted score of 1st ~ s evaluation index of the individual potential coal supply unit of t, y _1min, y _2min..., y _sminbe respectively the minimum value of the weighted score of 1st ~ s evaluation index of all N number of potential coal supply units, y _1max, y _2max..., y _smaxfor the maximal value of the weighted score of 1st ~ s evaluation index of all N number of potential coal supply units.

(5) according to the buying restriction of each potential coal supply unit and the actual purchase demand of scoring and Thermal Power Enterprises, fuel Order Allocation Model is built.

The coal blending principle of Thermal Power Enterprises is joined by two classes or the different coal of multiclass ature of coal are mixed with certain ratio, is allowed to condition at the combustion requirements meeting boiler in comprehensive calorific value, comprehensive volatilization in grading.And buying is the supply of burning, even can the buying of coal than the macro manifestations being considered as coal coal mixing combustion, so need when purchasing these five kinds of coal index to be met the requirements of the constraint condition of degree as buying, the fuel quantity on order apportion model as constraint condition therefore can be set up.

(5-1) constraint condition of fuel Order Allocation Model is determined.

Using minimum for the purchase cost of the Thermal Power Enterprises final goal as fuel Order Allocation Model, mass parameter, Coal Procurement restricted number and the buying condition etc. that the ature of coal parameter of each potential coal supply unit, buying will be reached are as constraint condition, set up coal quantity on order apportion model, just can obtain preferably coal supply unit coal quantity on order to model solution and distribute.

Suppose to be respectively X from the coal amount of N number of potential coal supply unit buying ₁, X ₂..., X _n, the buying that model is considered is the monthly buying for Thermal Power Enterprises.

1. calorific value

It is not more high better for having analyzed thermal value above, but must meet the designing requirement of boiler, and the weighting calorific value of the fuel of buying also must meet the desired value that coal blending will reach:

$\underset{t=1}{\overset{N}{\&Sigma;}}{X}_t{Q}_{min}\&le;\underset{t=1}{\overset{N}{\&Sigma;}}{X}_t{Q}_t\&le;\underset{t=1}{\overset{N}{\&Sigma;}}{X}_t{Q}_{max}---\left(6\right)$

Wherein, Q _tbe the calorific value of the individual potential coal supply unit coal of t, Q _minand Q _maxbe respectively lower limit and the upper limit of the target calorific value that coal blending produces.

2. volatile matter

In order to maintain burning and Accident prevention, volatile matter also will meet the desired value of coal blending:

$\underset{t=1}{\overset{N}{\&Sigma;}}{X}_t{V}_{\min }\&le;\underset{t=1}{\overset{N}{\&Sigma;}}{X}_t{V}_t\&le;\underset{t=1}{\overset{N}{\&Sigma;}}{X}_t{V}_{max}---\left(7\right)$

Wherein, V _tbe the volatile matter of the individual potential coal supply unit coal of t, V _minand V _maxbe respectively lower limit and the upper limit of the volatile matter that coal blending produces.

3. moisture

The weighting moisture of the coal of each coal supply unit will reach Target moisture lower than coal blending:

$\underset{t=1}{\overset{N}{\&Sigma;}}{X}_t{M}_t\&le;\underset{t=1}{\overset{N}{\&Sigma;}}{X}_t{M}_{max}---\left(8\right)$

Wherein, M _tbe the moisture of the individual potential coal supply unit coal of t, M _maxfor the upper limit of the moisture that coal blending produces.

4. sulfur

Produce polluter after combustion of sulfur and can cause corrosion to boiler, the weighting sulfur number of the coal supply unit purchased also must meet the requirement of coal blending:

$\underset{t=1}{\overset{N}{\&Sigma;}}{X}_t{S}_t\&le;\underset{t=1}{\overset{N}{\&Sigma;}}{X}_t{S}_{max}---\left(9\right)$

Wherein, S _tbe the sulfur value of the individual potential coal supply unit of t, S _maxfor the upper limit of the sulfur that coal blending produces.

5. ash content

The lime-ash produced after ash divided combustion can cause boiler abrasion, and therefore the weighting ash content of coal supply unit also must meet the requirement of coal blending:

$\underset{t=1}{\overset{N}{\&Sigma;}}{X}_t{A}_t\&le;\underset{t=1}{\overset{N}{\&Sigma;}}{X}_t{A}_{max}---\left(10\right)$

Wherein, A _tbe the ash value of the individual potential coal supply unit of t, A _maxfor the upper limit of the ash content that coal blending produces.

6. the requirement of amount of purchase

Thermal Power Enterprises must store a certain amount of coal to ensure the continuity of generating and the accident of reply coal shortage, and therefore, of that month amount of purchase must ensure of that month power generation needs and safety inventory:

$\underset{t=1}{\overset{N}{\&Sigma;}}{X}_t\&GreaterEqual;D---\left(11\right)$

Wherein, D is the target buying sum of this month fuel.

7. the restriction of coal supply unit self

Coal amount according to traffic condition and coal supply unit limits and national regulation, and when coal supply unit purchases coal, coal amount must ensure within certain scope:

XMIN _t≤ X _t≤ XMAX _tor X _t=0 (12)

Wherein, XMIN _tand XMAX _tbe respectively the upper and lower bound of the coal amount t potential coal supply unit buying, certainly, Thermal Power Enterprises also can be selected not at this coal supply unit buying coal, therefore has X _t=0 this supplement.

Fuel Order Allocation Model is mainly concerned with above constraint condition, as long as meet requirement when these constraint conditions just can make the ature of coal of purchased coal can meet coal blending, usually the scheme meeting these constraint conditions likely has, also likely do not have, it is whether strict that this depends primarily on the comprehensive coal quality requirements that coal blending reaches.Mainly choosing one when there being feasible solution concerning relatively preferably scheme the purchase cost of Thermal Power Enterprises, having drawn the objective function of coal quantity on order apportion model for this reason.

(5-2) select to determine the coal supply unit as buying object from potential coal supply unit, and then determine the objective function of fuel Order Allocation Model.

Thermal Power Enterprises is mainly from some comprehensive assessment following:

1. preferential providing the coal supply unit of coal to Thermal Power Enterprises from last month is selected, and ensures the stability of ature of coal.

2., when the coal supply unit of last month does not have coal or the coal amount that provides not to reach the requirement of amount of purchase, pay the utmost attention to the coal supply unit that scoring is higher.

3. the main purchase cost considering coal after comprehensive evaluation coal supply unit, mainly comprises coal price lattice and trucking costs.

Therefore, comprehensive above consideration, can draw the objective function of fuel Order Allocation Model:

$P=min\left\{\underset{t=1}{\overset{N}{\&Sigma;}}(1-C_t)P_t{X}_t\right\}---\left(13\right)$

Wherein, P _tbe single coal cost of the individual potential coal supply unit of t, mainly comprise single coal price lattice and unit cost, C _tbe the scoring of the individual potential coal supply unit of t, objective function made minimum, therefore need to use 1-C _trepresent the scoring that it is contrary, during potential coal supply unit buying coal individual from t, XMIN _t≤ X _t≤ XMAX _t, during not individual from t potential coal supply unit buying coal, X _t=0.Due to the coal supply unit of last month will be chosen, its score value can be improved to reach requirement.

(5-3) constraint condition of based on fuel Order Allocation Model and objective function, set up fuel Order Allocation Model:

Constraint condition:

Objective function:

$P=min\left\{\underset{t=1}{\overset{N}{\&Sigma;}}(1-C_t)P_t{X}_t\right\}$

So far, the mathematical linear model of fuel Order splitting has been set up complete, solves this fuel Order Allocation Model, can obtain preferably fuel Order splitting scheme, makes the purchase cost of Thermal Power Enterprises minimum under this scenario.

Those skilled in the art will readily understand; the foregoing is only preferred embodiment of the present invention; not in order to limit the present invention, all any amendments done within the spirit and principles in the present invention, equivalent replacement and improvement etc., all should be included within protection scope of the present invention.

Claims (3)

1. a method for designing for Thermal Power Enterprises fuel purchases decision support system (DSS), is characterized in that, comprises the steps:

(1) evaluation index of coal supply unit is chosen according to the practical operation situation of Thermal Power Enterprises and the situation of fuel on sale, for evaluating potential coal supply unit; Wherein, evaluation index comprises one-level evaluation index and two-level appraisement index, and each one-level evaluation index comprises multiple two-level appraisement index;

(2) using minimum for the purchase cost of Thermal Power Enterprises as general objective, one-level evaluation index is as rule layer element, and two-level appraisement index, as secondary rule layer element, utilizes AHP algorithm to calculate the weight of each evaluation index;

(3) utilize historical data to calculate each evaluation index value of each potential coal supply unit, in conjunction with the boiler parameter of Thermal Power Enterprises, obtain the score of each evaluation index of each potential coal supply unit;

Wherein, to arbitrary evaluation index value x, the score x of this evaluation index is obtained in the following way ^*:

If x is the bigger the better, then $x^{*}=\left\{\begin{array}{cc}10,& x\&GreaterEqual;L\\ 10+(x-L)/a,& x<L\end{array}\right.,$ Wherein, L represents the lower limit being satisfied with evaluation index value most, and a represents the length of evaluation index interval;

If x is the smaller the better, then $x^{*}=\left\{\begin{array}{cc}10-(x-U)/a,& x>U\\ 10,& x\&le;U\end{array}\right.,$ Wherein, U represents the upper limit being satisfied with evaluation index value most;

If best during a certain scope of x convergence, then $x^{*}=\left\{\begin{array}{cc}10+(x-U)/a,& x>U\\ 10,& L\&le;x\&le;U\\ 10-(L-x)/a& x<L\end{array}\right.;$

(4) to each evaluation index of each potential coal supply unit, calculate the weighted score of product as this evaluation index of its weight and score, utilize good and bad solution Furthest Neighbor to calculate the scoring of each potential coal supply unit;

(5) according to the buying restriction of each potential coal supply unit and the actual purchase demand of scoring and Thermal Power Enterprises, fuel Order Allocation Model is built.

2. the method for designing of Thermal Power Enterprises fuel purchases decision support system (DSS) as claimed in claim 1, is characterized in that, in described step (4), and the scoring of the individual potential coal supply unit of t wherein, be the weighted score vector Y of the evaluation index of the individual potential coal supply unit of t _t=(y _t1, y _t2..., y _ts) to Negative ideal point y _min=(y _1min, y _2min..., y _smin) Euclidean distance, be the weighted score vector Y of the evaluation index of the individual potential coal supply unit of t _t=(y _t1, y _t2..., y _ts) to Positive ideal point y _max=(y _1max, y _2max..., y _smax) Euclidean distance, s is the number of evaluation index, t=1,2 ..., N, N are potential coal supply unit sum, y _t1, y _t2..., y _tsbe respectively the weighted score of 1st ~ s evaluation index of the individual potential coal supply unit of t, y _1min, y _2min..., y _sminbe respectively the minimum value of the weighted score of 1st ~ s evaluation index of all N number of potential coal supply units, y _1max, y _2max..., y _smaxfor the maximal value of the weighted score of 1st ~ s evaluation index of all N number of potential coal supply units.

3. the method for designing of Thermal Power Enterprises fuel purchases decision support system (DSS) as claimed in claim 1 or 2, it is characterized in that, in described step (5), fuel Order Allocation Model is as follows:

Constraint condition:

Objective function $P=min\left\{\underset{t=1}{\overset{N}{\&Sigma;}}(1-C_t)P_t{X}_t\right\},$

Wherein, N is potential coal supply unit sum, X _tfor the coal amount from the buying of t individual potential coal supply unit, Q _tbe the calorific value of the individual potential coal supply unit coal of t, Q _minand Q _maxbe respectively lower limit and the upper limit of the target calorific value that coal blending produces, V _tbe the volatile matter of the individual potential coal supply unit coal of t, V _minand V _maxbe respectively lower limit and the upper limit of the volatile matter that coal blending produces, M _tbe the moisture of the individual potential coal supply unit coal of t, M _maxfor the upper limit of the moisture that coal blending produces, S _tbe the sulfur value of the individual potential coal supply unit of t, S _maxfor the upper limit of the sulfur that coal blending produces, A _tbe the ash value of the individual potential coal supply unit of t, A _maxfor the upper limit of the ash content that coal blending produces, D is the target buying sum of this month fuel, XMIN _tand XMAX _tbe respectively the upper and lower bound of the coal amount t potential coal supply unit buying, P _tsingle coal cost of the individual potential coal supply unit of t, C _tit is the scoring of the individual potential coal supply unit of t; During potential coal supply unit buying coal individual from t, XMIN _t≤ X _t≤ XMAX _t, during not individual from t potential coal supply unit buying coal, X _t=0.