CN109149564A - A kind of alternating current-direct current mixing power distribution network distributed generation resource Optimal Configuration Method - Google Patents
A kind of alternating current-direct current mixing power distribution network distributed generation resource Optimal Configuration Method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J1/00—Circuit arrangements for dc mains or dc distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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Abstract
The invention discloses a kind of alternating current-direct current mixing power distribution network distributed generation resource Optimal Configuration Methods.Traditional Optimal Configuration Method based on certainty scene not can guarantee security and stability of the system in fluctuation out, can not analyze flexible influence of the control mode to steady-state performance in alternating current-direct current mixing power distribution network yet.The present invention is using non-dominant genetic algorithm as Optimization Solution algorithm, during the analysis and assessment of configuration scheme, the injection of uncertain power is modeled using neat promise polyhedron, and pass through all changes range of linearisation load flow calculation system state variable, to judge level of security of the configuration scheme under condition of uncertainty, the robust allocation plan for meeting security constraint is obtained.The calculating speed of the method for the present invention is fast, and all probable values of state variable under condition of uncertainty can be obtained by a Load flow calculation and linear transformation, and can adapt to high dimensional variable space, improves the computational efficiency that distributed generation resource distributes solution rationally.
Description
Technical field
It is distributed in especially a kind of consideration alternating current-direct current mixing power distribution network the present invention relates to electric power system optimization field of configuration
The probabilistic Optimal Configuration Method of power supply power output.
Background technique
With the development of clean energy technology and alternating current-direct current distribution technique, a large amount of generation of electricity by new energy devices power supply in a distributed manner
Form access power grid.Distributed generation resource, which accesses alternating current-direct current mixing power distribution network, can use flexible grid-connected mode, and it is convenient to have
Efficiently, it reduces via net loss, improve the advantages that system reliability.But renewable energy power output is affected by environment larger, has wave
Dynamic property, intermittence and uncertainty, may threaten the safety and stability of system, bring challenges to the traffic control of electric system.
Therefore, it power source planning and is distributing rationally the stage, is just needing to fully consider the uncertainty of distributed generation resource power output, reducing distribution
Formula power supply goes out influence of the fluctuation to systematic steady state performance.
Traditional Optimal Configuration Method based on certainty scene not can guarantee safety of the system in fluctuation out
Stability can not also analyze flexible influence of the control mode to steady-state performance in alternating current-direct current mixing power distribution network.Probability analysis side
Method needs to obtain the probability distribution of distributed generation resource power output according to a large amount of historical data first when handling uncertain factor,
Calculation amount is very big, while it is possible to ignoring certain extreme cases that may cause unsafe condition.Using uncertain set
The uncertain factor of distributed generation resource can be effectively treated in analysis method, and considers the most harsh conditions under uncertain condition, obtain
To the robust allocation plan for meeting safety and stability constraint.
Summary of the invention
The technical problem to be solved by the present invention is to overcome the problems of the above-mentioned prior art, it is straight to provide a kind of consideration friendship
The probabilistic Optimal Configuration Method of stream mixing Distributed Generation in Distribution System power output, this method are made using non-dominant genetic algorithm
Uncertain power is injected using neat promise polyhedron during the analysis and assessment of configuration scheme for Optimization Solution algorithm
It is modeled, and by all changes range of linearisation load flow calculation system state variable, to judge configuration scheme
Level of security under condition of uncertainty obtains the robust allocation plan for meeting security constraint.
For this purpose, The technical solution adopted by the invention is as follows: a kind of alternating current-direct current mixing power distribution network distributed generation resource is distributed rationally
Method comprising step:
1) network parameter for initializing distribution network system, determines optimized variable:
1.1) it initializes, the network data of typing distribution network system；
1.2) variable of optimization allocation is determined；
2) mathematical model of optimization allocation is established:
2.1) with network day active loss minimum and the minimum optimization object function of power distribution network operation and maintenance cost；
2.2) the conventional constraint condition of optimization object function is established；
2.3) consider that power is uncertain, using security constraint item of the neat promise polyhedron computing system under condition of uncertainty
Part；
3) optimization allocation of distributed generation resource is solved using the genetic algorithm of non-dominated ranking.
Neat promise polyhedron can be a kind of with effective expression high dimensional variable space as a kind of special set expression-form
The effective ways of uncertainty analysis.The present invention is during assessing allocation plan security constraint, using neat promise polyhedron to can
Renewable sources of energy injecting power establishes uncertain set, and passes through the uncertain collection of linearisation tide model solving system state variable
It closes, to analyze the variation range of state variable under uncertain condition, as the constraint condition of distributed generation resource configuration, can obtain
To the robust allocation plan for meeting security constraint.
Supplement as above-mentioned technical proposal, in step 1.1), the network data of typing distribution network system is that each node is compiled
Number and node voltage amplitude allowed band, input each route electric parameter and inverter parameter；The electric parameter
Including line impedance and maximum allowable trend.
In step 1.2), the type of distributed generation resource, on-position and access are held for supplement as above-mentioned technical proposal
Amount is used as optimized variable:
X=[T1,L1,N1,…,Tn,Ln,Nn]
In formula: T is type coding, characterize at this access be photovoltaic, wind-powered electricity generation or gas turbine distributed generation resource；L is
On-position, i.e. node serial number of the plant-grid connection in AC or DC system；N is the number of modules of distributed generation resource access
Amount；N is the summation for accessing distributed generation resource number；Each optimized variable is integer coding.
Supplement as above-mentioned technical proposal, in step 2.1), with the smallest objective function of network day active loss are as follows:
In formula, N is system traffic coverage number, and Δ T is to calculate time interval, Ploss,tFor the system-wide power damage of t moment
Consumption；
With the smallest objective function of power distribution network operation and maintenance cost are as follows:
In formula:For the cost of system power purchase from bulk power grid；For the investment and operating cost of distributed generation resource；It is purchased from bulk power grid for system
Into electricity；CSFor conventional energy resource power generation unit cost；CGiBy the distributed generation resource that is configured at node i unit power generation at
This；PLossFor the active power loss of system；PLiFor the load power of system；PGiFor total generated output of system；PDGiFor distributed electrical
The generated output in source.
Supplement as above-mentioned technical proposal, in step 2.2), conventional constraint condition includes: 1) power balance equation；2)
The constraint of distributed generation resource installed capacity；3) the transmission capacity constraint of alternating current-direct current inverter.
Supplement as above-mentioned technical proposal in step 2.3), utilizes the polyhedral expression-form of Cino Da Pistoia and linear becomes
The property changed constructs the uncertain set of system input, output variable；Using neat promise polyhedron analysis distribution formula power wave
System state change under dynamic increases security constraint, including the voltage constraint and trend constraint under condition of uncertainty.
Supplement as above-mentioned technical proposal, building system inputs, the method for the uncertain set of output variable is as follows:
1. building inputs uncertain set W according to the uncertainty of renewable energy injecting power；Utilize renewable energy
The rated value of generated output and corresponding fluctuation range obtain the input polyhedral central point of Cino Da Pistoia and generate vector；
2. being linearized to system load flow equation in specified operating point, i.e.,
Δ w=J Δ x,
In formula: Δ w, Δ x are the departure of distributed generation resource injecting power and system state variables respectively, and J is that system exists
The Jacobian matrix of specified operating point；
3. doing linear transformation using power flow equation, by Δ x=H Δ w, the uncertain set X of state variable is constructed；In formula,
H=J-1。
Supplement as above-mentioned technical proposal, the voltage constraint representation are as follows:
In formula: FvioIndicate the overall performane of voltage out-of-limit；Uvio,iIndicate that the voltage of node i is possible under condition of uncertainty
Out-of-limit range determines that simplest method is by Qi Nuoduo by calculating volume of the neat promise polyhedron beyond safety zone
Face body is superimposed in each dimension Upper Threshold-crossing Value, calculation formula are as follows:
In formula:For k-th of generation vector g of neat promise polyhedronkI-th of element, correspond to node i voltage；Umax
And UminIt is the upper limit value and lower limit value of node voltage respectively；P is the number for generating vector.
Supplement as above-mentioned technical proposal, by constructing the neat promise polyhedron of Line Flow, to establish uncertain item
Trend constraint under part.
Supplement as above-mentioned technical proposal, step 3) the following steps are included:
1. determining each parameter value of algorithm, including Population Size Np, maximum evolutionary generation Gn, gene Fg, mutagenic factor
CrAnd penalty coefficient ω, form initial population；Relative to next-generation population, also referred to as parent population；
2. constructing the neat promise polyhedron of distributed generation resource injecting power；
3. carrying out the calculating of alternating current-direct current Unified Power Flow, and linearized in specified operating point, obtains system Jacobian matrix；
4. constructing the uncertain set of state variable by linear transformation, calculates voltage and trend constraint gets over limit value；
5. calculating the fitness value of each individual of population according to objective function and constraint condition, non-dominated ranking is carried out, and count
Calculate each layer of individual crowding distance；
6. selecting N from current population by selection operationp/ 2 excellent individuals are used to generate offspring, then by intersecting, becoming
ETTHER-OR operation generates progeny population；
7. progeny population and parent population are merged into a new population, carries out non-dominated ranking and crowding calculates, choose
Select NpA excellent individual is as population of new generation；
8. carrying out condition judgement: if reaching maximum evolutionary generation, entering step 9., otherwise 2. return step recycles；
9. weighted value is arranged to each objective function, allocation optimum scheme is chosen.
The device have the advantages that as follows: the calculating speed of the method for the present invention is fast, passes through a Load flow calculation and line
Property transformation can obtain all probable values of state variable under condition of uncertainty, and can adapt to high dimensional variable space, improve
Distributed generation resource distributes the computational efficiency of solution rationally.
Specific embodiment
It describes in detail below to technical solution of the present invention.
A kind of alternating current-direct current mixing power distribution network distributed generation resource Optimal Configuration Method, its step are as follows:
1) grid parameter is initialized, determines optimized variable
1.1) it initializes, the network data of input system:
The initial primary topology of power grid is inputted, is each node serial number (and limiting the permitted range of voltage magnitude), input
The information such as the electric parameter (including line impedance, maximum allowable trend) of each route, inverter parameter；
1.2) variable of optimization problem is determined
Using the type of distributed generation resource, on-position, access capacity as optimized variable
X=[T1,L1,N1,…,Tn,Ln,Nn],
In formula: T is type coding, characterize at this access be photovoltaic, wind-powered electricity generation or other kinds of distributed generation resource；L is
On-position, i.e. node serial number of the plant-grid connection in AC or DC system；N is the number of modules of distributed generation resource access
Amount；N is the summation for accessing distributed generation resource number.Here each optimized variable is integer coding.
2) mathematical model of optimization allocation is established
2.1) with network day active loss minimum and the minimum optimization object function of power distribution network operation and maintenance cost.
With the smallest objective function of network day active loss are as follows:
In formula, N is system traffic coverage number, and Δ T is to calculate time interval, Ploss,tIt is damaged for the system-wide power of t moment
Consumption.
With the smallest objective function of power distribution network operation and maintenance cost are as follows:
In formula:For the cost of system power purchase from bulk power grid；For the investment and operating cost of distributed generation resource；It is purchased from bulk power grid for system
Into electricity；CSFor conventional energy resource power generation unit cost；CGiBy the distributed generation resource that is configured at node i unit power generation at
This；PLossFor the active power loss of system；PLiFor the load power of system；PGiFor total generated output of system；PDGiFor distributed electrical
The generated output in source.
2.2) the conventional constraint condition of Optimized model is established
Conventional constraint condition includes:
(1) power balance equation
The power flow equation of alternating current-direct current mixing power distribution network can be uniformly written as:
In formula: FACAnd FDCRespectively indicate the trend equation of alternating current and direct current system, FVSCPower including alternating current-direct current inverter
Equilibrium equation and governing equation.XAC、XDC、XVSCFor quantity of states such as node voltage, phase angles in ac and dc systems.
Specifically, the power equation of AC system are as follows:
In formula: PDGiAnd QDGiThe active and reactive power of distributed generation resource respectively at node i；PCGiAnd QCGiRespectively save
The active and reactive power that normal power supplies issue at point i；PciAnd QciFor AC system inject inverter power, for not in
The connected node of inverter, value zero；PLiAnd QLiFor the active and load or burden without work at node i；UiFor the voltage amplitude of node i
Value；UjFor the voltage magnitude of node j, node i is connected with node j by route；δijIndicate the voltage phase difference of node i and j；Gij
And BijIt is node i and the transconductance and mutual susceptance of j respectively.
The power equation of direct current system are as follows:
In formula: PdgiAnd PcgiFor the active power of direct current system distributed generation resource and normal power supplies；Pc,dciFor inverter note
The active power for entering direct current system, for the node not being connected with inverter, value zero；PdiFor DC load power；UdciWith
UdcjFor the voltage of DC node i and j；YdcijIndicate the transadmittance between node i and j.
The power equation of VSC inverter are as follows:
Pc,dci=Pci-Pc,lossi,
In formula: Pc,lossiFor the power loss of VSC inverter.The governing equation of inverter has according to the difference of control mode
Different expression-forms.
(2) distributed generation resource installed capacity constrains
0≤PDGi≤Pmax,i,
In formula: Pmax,iFor the maximum allowable installed capacity of distributed generation resource at node i.
(3) the transmission capacity constraint of alternating current-direct current inverter
-Pci,max≤Pci≤Pci,max,
In formula: Pci,maxFor the maximum allowable capacity of inverter i.
2.3) consider that power is uncertain, using security constraint item of the neat promise polyhedron computing system under condition of uncertainty
Part
Neat promise polyhedron is a kind of special convex polyhedron, can be defined as limited line segment Minkowski and,
Definition is
In formula: w0The center of referred to as neat promise polyhedron W, g are known as generating vector；N is the dimension of variable,Indicate that n dimension becomes
Quantity space；P is the number for generating vector.W indicates that the either element for including in neat promise polyhedron W, α can take up an official post in section [- 1,1]
Meaning value.
The polyhedral linear transformation of Qi Nuo is closed, it may be assumed that the product of neat promise polyhedron and the matrix of a linear transformation is still one
A neat promise polyhedron, is shown below:
In formula: H is the matrix of a linear transformation.
Using the polyhedral expression-form of Cino Da Pistoia and the property of linear transformation, system input, output variable are constructed not
The method for determining set is as follows:
1. building inputs uncertain set W according to the uncertainty of renewable energy injecting power.Utilize renewable energy
The rated value of generated output and corresponding fluctuation range, the polyhedral central point of available input Cino Da Pistoia and generate to
Amount；
2. being linearized to system load flow equation in specified operating point, i.e.,
Δ w=J Δ x,
In formula: Δ w, Δ x are the deviation of distributed generation resource injecting power and system state variables (voltage, phase angle) respectively
Amount, J are Jacobian matrix of the system in specified operating point.
3. doing linear transformation using power flow equation, by Δ x=H Δ w, the uncertain set X of state variable is constructed.In formula,
H=J-1。
Using the system state change under the fluctuation of neat promise polyhedron analysis distribution formula power, increased security constraint packet
Include voltage constraint and the trend constraint under condition of uncertainty.
Voltage constraint can be expressed as
In formula: FvioIndicate the overall performane of voltage out-of-limit；Uvio,iIndicate that the voltage of node i is possible under condition of uncertainty
Out-of-limit range can determine that simplest method is will be neat by calculating volume of the neat promise polyhedron beyond safety zone
Promise polyhedron is superimposed in each dimension Upper Threshold-crossing Value, calculation formula are as follows:
In formula:For k-th of generation vector g of neat promise polyhedronkI-th of element, correspond to node i voltage；
UmaxAnd UminIt is the upper lower limit value of node voltage respectively；P is the number for generating vector.
Likewise it is possible to construct the neat promise polyhedron of Line Flow, the trend constraint under condition of uncertainty is established.
3) optimization allocation of distributed generation resource is solved using the genetic algorithm of non-dominated ranking: the following steps are included:
1. determining each parameter value of algorithm, including Population Size Np, maximum evolutionary generation Gn, gene Fg, mutagenic factor
CrAnd penalty coefficient ω, it is formed initial population (relative to next-generation population, also referred to as parent population)；
2. constructing the neat promise polyhedron of distributed generation resource injecting power；
3. carrying out the calculating of alternating current-direct current Unified Power Flow, and linearized in specified operating point, obtains system Jacobian matrix；
4. constructing the uncertain set of state variable by linear transformation, calculates voltage and trend constraint gets over limit value；
5. calculating the fitness value of each individual of population according to objective function and constraint condition, non-dominated ranking is carried out, and count
Calculate each layer of individual crowding distance；
6. selecting N from current population by selection operationp/ 2 excellent individuals are used to generate offspring, then by intersecting, becoming
ETTHER-OR operation generates progeny population；
7. progeny population and parent population are merged into a new population, carries out non-dominated ranking and crowding calculates, choose
Select NpA excellent individual is as population of new generation；
8. carrying out condition judgement: if reaching maximum evolutionary generation, entering step 9., otherwise 2. return step recycles；
9. weighted value is arranged to each objective function, allocation optimum scheme is chosen.
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
Claims (10)
1. a kind of alternating current-direct current mixing power distribution network distributed generation resource Optimal Configuration Method, which is characterized in that comprising steps of
1) network parameter for initializing distribution network system, determines optimized variable:
1.1) it initializes, the network data of typing distribution network system；
1.2) variable of optimization allocation is determined；
2) mathematical model of optimization allocation is established:
2.1) with network day active loss minimum and the minimum optimization object function of power distribution network operation and maintenance cost；
2.2) the conventional constraint condition of optimization object function is established；
2.3) consider that power is uncertain, using security constraints of the neat promise polyhedron computing system under condition of uncertainty；
3) optimization allocation of distributed generation resource is solved using the genetic algorithm of non-dominated ranking.
2. alternating current-direct current mixing power distribution network distributed generation resource Optimal Configuration Method according to claim 1, which is characterized in that step
It is rapid 1.1) in, the network data of typing distribution network system is that the allowed band of each node serial number and node voltage amplitude, input are each
The electric parameter and inverter parameter of route；The electric parameter includes line impedance and maximum allowable trend.
3. alternating current-direct current mixing power distribution network distributed generation resource Optimal Configuration Method according to claim 2, which is characterized in that step
It is rapid 1.2) in, using the type of distributed generation resource, on-position and access capacity as optimized variable:
X=[T1,L1,N1,···,Tn,Ln,Nn]
In formula: T is type coding, characterize at this access be photovoltaic, wind-powered electricity generation or gas turbine distributed generation resource；L is access
Position, i.e. node serial number of the plant-grid connection in AC or DC system；N is the module number of distributed generation resource access；N is
Access the summation of distributed generation resource number；Each optimized variable is integer coding.
4. alternating current-direct current mixing power distribution network distributed generation resource Optimal Configuration Method according to claim 1-3, special
Sign is, in step 2.1), with the smallest objective function of network day active loss are as follows:
In formula, N is system traffic coverage number, and Δ T is to calculate time interval, Ploss,tFor the system-wide power loss of t moment；
With the smallest objective function of power distribution network operation and maintenance cost are as follows:
In formula:For the cost of system power purchase from bulk power grid；For
The investment and operating cost of distributed generation resource；The electricity bought from bulk power grid for system；CSIt is normal
Advise energy power generation unit cost；CGiUnit cost of electricity-generating by the distributed generation resource configured at node i；PLossFor having for system
Function network loss；PLiFor the load power of system；PGiFor total generated output of system；PDGiFor the generated output of distributed generation resource.
5. alternating current-direct current mixing power distribution network distributed generation resource Optimal Configuration Method according to claim 1-3, special
Sign is, in step 2.2), conventional constraint condition includes: 1) power balance equation；2) distributed generation resource installed capacity constrains；3)
The transmission capacity of alternating current-direct current inverter constrains.
6. alternating current-direct current mixing power distribution network distributed generation resource Optimal Configuration Method according to claim 1-3, special
Sign is, in step 2.3), using the polyhedral expression-form of Cino Da Pistoia and the property of linear transformation, constructs system and inputs, is defeated
The uncertain set of variable out；Using the system state change under the fluctuation of neat promise polyhedron analysis distribution formula power, increase
Security constraint, including the voltage constraint and trend constraint under condition of uncertainty.
7. alternating current-direct current mixing power distribution network distributed generation resource Optimal Configuration Method according to claim 6, which is characterized in that structure
Build system input, output variable uncertain set method it is as follows:
1. building inputs uncertain set W according to the uncertainty of renewable energy injecting power；Utilize renewable energy power generation
The rated value of power and corresponding fluctuation range obtain the input polyhedral central point of Cino Da Pistoia and generate vector；
2. being linearized to system load flow equation in specified operating point, i.e.,
Δ w=J Δ x,
In formula: Δ w, Δ x are the departure of distributed generation resource injecting power and system state variables respectively, and J is system specified
The Jacobian matrix of operating point；
3. doing linear transformation using power flow equation, by Δ x=H Δ w, the uncertain set X of state variable is constructed；In formula, H=J-1。
8. alternating current-direct current mixing power distribution network distributed generation resource Optimal Configuration Method according to claim 6, which is characterized in that institute
The voltage constraint representation stated are as follows:
In formula: FvioIndicate the overall performane of voltage out-of-limit；Uvio,iIndicate that the voltage of node i is possible out-of-limit under condition of uncertainty
Range determines that simplest method is by neat promise polyhedron by calculating volume of the neat promise polyhedron beyond safety zone
It is superimposed in each dimension Upper Threshold-crossing Value, calculation formula are as follows:
In formula:For k-th of generation vector g of neat promise polyhedronkI-th of element, correspond to node i voltage；UmaxWith
UminIt is the upper limit value and lower limit value of node voltage respectively；P is the number for generating vector.
9. alternating current-direct current mixing power distribution network distributed generation resource Optimal Configuration Method according to claim 6, which is characterized in that logical
The neat promise polyhedron for crossing building Line Flow, thus the trend constraint under establishing condition of uncertainty.
10. alternating current-direct current mixing power distribution network distributed generation resource Optimal Configuration Method according to claim 1-3, special
Sign is, step 3) the following steps are included:
1. determining each parameter value of algorithm, including Population Size Np, maximum evolutionary generation Gn, gene Fg, mutagenic factor Cr, with
And penalty coefficient ω, form initial population；Relative to next-generation population, also referred to as parent population；
2. constructing the neat promise polyhedron of distributed generation resource injecting power；
3. carrying out the calculating of alternating current-direct current Unified Power Flow, and linearized in specified operating point, obtains system Jacobian matrix；
4. constructing the uncertain set of state variable by linear transformation, calculates voltage and trend constraint gets over limit value；
5. calculating the fitness value of each individual of population according to objective function and constraint condition, non-dominated ranking is carried out, and is calculated every
One layer of individual crowding distance；
6. selecting N from current population by selection operationp/ 2 excellent individuals are grasped for generating offspring, then by intersecting, making a variation
Make, generates progeny population；
7. progeny population and parent population are merged into a new population, carries out non-dominated ranking and crowding calculates, pick out Np
A excellent individual is as population of new generation；
8. carrying out condition judgement: if reaching maximum evolutionary generation, entering step 9., otherwise 2. return step recycles；
9. weighted value is arranged to each objective function, allocation optimum scheme is chosen.
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