CN108899918A - A kind of Multipurpose Optimal Method of power distribution network containing blower based on operation level correlation scene - Google Patents
A kind of Multipurpose Optimal Method of power distribution network containing blower based on operation level correlation scene Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in 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
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Abstract
The invention belongs to Economical Operation of Power Systems technical fields, and in particular to a kind of Multipurpose Optimal Method of power distribution network containing blower based on operation level correlation scene.Include the following steps：Step 1, random variance model is established from operation level, the correlation scene of operation level is constructed with this；Step 2, best with static system voltage stability, network loss expectation minimum and variation expectation are at least objective function, establish the multi-objective reactive optimization model of power distribution network；Step 3, multiobjective decision-making is carried out according to running state dynamic adjustment Target Preference, with the optimal non-domination solution of determination.The optimization method combines operating status to carry out multiobjective decision-making by accurate simulation power distribution network actual motion state, can effectively improve the idle work optimization performance of power distribution network.
Description
Technical field
The invention belongs to Economical Operation of Power Systems technical fields, and in particular to one kind is based on operation level correlation scene
The Multipurpose Optimal Method of power distribution network containing blower.
Background technique
The grid-connected renewable energy such as wind-powered electricity generation are to solve traditional energy exhaustion and the instantly effective way of serious environmental problems.With
Wind-powered electricity generation, photovoltaic distributed power supply (Distributed Generation, DG) access, distribution power flow and operating status are equal
Great change occurs, certain influence is generated on network loss, voltage and static electric voltage stability.Power distribution network containing DG is carried out idle excellent
Change, the distribution of network trend can be improved, reduce network loss, improve operation stability and power quality.
Multi-objective reactive optimization for the power distribution network containing DG includes Run-time scenario building, idle work optimization model foundation, model
Solution and multiobjective decision-making.Wherein, existing Run-time scenario construction method is mostly based on the random variance model of long time scale, or
Correlation between stochastic variable is not considered, deviates the Run-time scenario of construction practical.Power distribution network operation, which also requires to realize, stablizes, passes through
It helps, multiple targets such as high-quality of powering, certain is mutually restricted due to existing between target, it is difficult to while realizing all targets most
It is excellent, can only decision go out one group of non-domination solution.For the decision problem of multiple target, can be used max-min and with idealization target Europe
Formula determines optimal non-domination solution apart from the methods of minimum, but does not consider running state, can limit in practical applications
Prepare the runnability of power grid.
Therefore, a kind of Multipurpose Optimal Method of power distribution network containing blower based on operation level correlation scene of the invention,
The correlation scene of operation level is constructed, carries out the modeling of the multi-objective reactive optimization of power distribution network containing blower based on this, and
It proposes that Multiobjective Decision Making Method determines optimal solution priority-based, so as to effectively promote power distribution network runnability, there is weight
Want application value.
Summary of the invention
In view of the deficiencies of the prior art, the present invention proposes a kind of distribution containing blower based on operation level correlation scene
Net Multipurpose Optimal Method.
This optimization method establishes random variance model from operation level, and the correlation scene of operation level is constructed with this；?
On the basis of this, network loss expectation minimum best with static system voltage stability and variation expectation are at least objective function, are built
The multi-objective reactive optimization model of vertical power distribution network, and multiple target is carried out according to running state dynamic adjustment Target Preference
Decision, with the optimal non-domination solution of determination.The optimization method combines operation shape by accurate simulation power distribution network actual motion state
State carries out multiobjective decision-making, can effectively improve the idle work optimization performance of power distribution network.
A kind of Multipurpose Optimal Method of power distribution network containing blower based on operation level correlation scene of the invention, feature
It is, includes the following steps：
Include the following steps：
Step 1, wind speed and load modeling based on operation level, and comprehensively consider correlation between stochastic variable, building fortune
The correlation scene of row level.
Step 2, best with static electric voltage stability, active power loss expectation minimum and variation expectation are at least optimization mesh
Mark is established the multi-objective reactive optimization model of the power distribution network containing blower, and is solved based on NSGA-II algorithm.
Step 3, one group of non-domination solution is obtained by step 2, is gone out most using Multiobjective Decision Making Method decision priority-based
Excellent solution obtains the OPTIMAL REACTIVE POWER prioritization scheme of power distribution network.
In a kind of above-mentioned Multipurpose Optimal Method of power distribution network containing blower based on operation level correlation scene, the step
Rapid 1 is realized based on following below scheme：
The wind speed of the above-mentioned operation level of step 1.1 models, that is, runs the actual wind speed probability density function p (v of levelw)
It can be described as：
Wherein, vw、Respectively actual wind speed, upper wind velocity limit, wind speed lower limit；p′(vw) it is without wind speed upper and lower limit
The wind speed probability density function of truncation；For wind speed value；σwFor the standard deviation of forecasting wind speed.
The load modeling of the above-mentioned operation level of step 1.2 runs the load active power probability density function p of level
(PL) can be described as：
Wherein, PL、Respectively load active power actual value, the active upper limit, active lower limit；p′(PL) be without
The load active power probability density function of upper and lower limit truncation；For load active power predicted value；σLIt is poor for prediction standard.
The correlation scenario building method of step 1.3 operation level is：Stochastic variable based on step 1.1 and step 1.2
The modeling of operation level, using LHS sampling and Cholesky decomposition method sequence building wind speed-load sample matrix, and according to wind
Wind speed in sample matrix is converted to wind power, forms correlation scene matrix by relationship between machine power output and wind speed.
In a kind of above-mentioned Multipurpose Optimal Method of power distribution network containing blower based on operation level correlation scene, the step
The multi-objective reactive optimization model of the power distribution network containing blower in rapid 2, active power loss best with static electric voltage stability expectation it is minimum and
Variation expectation is at least optimization aim, i.e. model objective function is：
minf1,f2,f3
Wherein, f1For static voltage stability index expectation；,f2For network loss expectation, f3For variation expectation.
Expectation function one, static voltage stability index expectation：
Wherein, N is scene number (namely LHS hits), aVSI,sFor the static system voltage stability index of s-th of scene.
Static system voltage stability index is sought based on following formula：
Wherein, i, j be respectively branch give, receiving end；aVSI,j、aVSIRespectively node j static voltage stability index and system are quiet
State voltage stability index；Ri、XiRespectively branch resistance, reactance；QjIt is sent out for node j idle； UiFor node i voltage；NSFor
All node sets of system.
Expectation function two, network loss expectation
Wherein, Ploss,sFor the active power loss of s-th of scene.
Expectation function three, variation expectation
Wherein, au,sFor the weighted voltage offset of s-th of scene.
Weighted voltage offset formula of seeking be：
Wherein, PL,i、PL,allThe respectively active power of node i and the total active power of system；UNFor the specified electricity of node i
Pressure.
In a kind of above-mentioned Multipurpose Optimal Method of power distribution network containing blower based on operation level correlation scene, the step
In rapid 3, Multiobjective Decision Making Method decision process is as follows priority-based：
Step 3.1 determines that static voltage stability index it is expected weight.Static voltage stability index expectation weighted associations are quiet
State voltage stability index desired value, works as f1When smaller, weight very little, if adjusting its weight to 1 close to critical value.Power
Function is again：
Wherein, f1、f1 TFor static voltage stability index expectation and its threshold values；A is form parameter.
Step 3.2 determines that variation it is expected weight.Premised on system is stablized, by variation expectation weighted associations electricity
Pressure offset desired value, weighting function are：
Wherein, f3、f3 TFor variation expectation and its threshold values；B is form parameter.
Step 3.3 determines that network loss it is expected weight.Determine wVSIAnd wuAfterwards, network loss expectation weight can be obtained：
wloss=1-wVSI-wu
Step 3.4 multiobjective decision-making.Assuming that the non-dominant disaggregation that multiple-objection optimization obtains is [x1x2x3…xm], it corresponds to
Objective function collection be：
Objective function is normalized, and weighted superposition, determines optimal solution：
Wherein, fij、wijCorresponding j-th of the objective function of respectively i-th of non-domination solution and its weight；Point
It Wei not the corresponding objective function of the disaggregation minimum value and maximum value of j-th of objective function concentrated.
Therefore, the invention has the advantages that：1. Run-time scenario construction method of the invention from firing floor in face of wind speed and
Load carries out uncertainties model, and considers correlation between stochastic variable, constructs the correlation scene based on operation level, institute
Scene is constructed closer to power distribution network practical operation situation；2. the present invention is best with static electric voltage stability, active power loss it is expected most
The minimum idle work optimization target as power distribution network of small and variation expectation, has comprehensively considered power distribution network to stable, economical
And the multiple target of quality supply is pursued, and using the variation index of load weighting, can more objectively reflect that system is true
Quality of voltage；3. Multiobjective Decision Making Method priority-based proposed by the invention pays the utmost attention to static system voltage stabilization
Property, secondly consider variation and network loss, journey can be stressed to each target according to the state dynamic adjustment of power distribution network actual motion
Degree helps to promote GA for reactive power optimization performance.
Detailed description of the invention
Fig. 1 is the main-process stream of multi-objective reactive optimization method of the present invention.
Fig. 2 is operation level correlation scene product process.
Fig. 3 is multiobjective decision-making process priority-based.
Fig. 4 is that static voltage stability index it is expected weighting function.
Fig. 5 is that variation it is expected weighting function.
Fig. 6 is improved 33 node power distribution net of IEEE.
Fig. 7 a is wind power sample distribution (method 1).
Fig. 7 b is wind power sample distribution (method 2).
Fig. 7 c is wind power sample distribution (method 3).
Fig. 7 d is wind power sample distribution (method 4).
Fig. 8 is non-domination solution spatial distribution.
Specific embodiment
Here is the preferred embodiment of the present invention, and in conjunction with attached drawing, is described further to concrete application of the invention.
Embodiment：
One, firstly, introducing Method And Principle of the present invention：
A kind of Multipurpose Optimal Method of power distribution network containing blower based on operation level correlation scene of the invention, it is total to flow
Journey is as shown in Figure 1, include the following steps：
(1) step 1：Based on the wind speed and load modeling of operation level, and comprehensively consider correlation between stochastic variable, constructs
The correlation scene of operation level.It is as follows in detail：
Step 1.1, the wind speed modeling of level is run.
The actual wind speed probability density function of operation level is：
Wherein, vw、Respectively actual wind speed, upper wind velocity limit, wind speed lower limit；p′(vw) it is without wind speed bound
The wind speed probability density function of truncation.
Wind speed probability density function p ' (v without the truncation of wind speed boundw) can be described as：
Wherein,For wind speed value；σwFor the standard deviation of forecasting wind speed.
Step 1.2, the load modeling of level is run.
The load active power probability density function of operation level is：
Wherein, PL、Respectively load active power actual value, the active upper limit, active lower limit；p′(PL) be without
The load active power probability density function of upper and lower limit truncation.
Load active power probability density function p ' (P without upper and lower limit truncationL) can be described as：
Wherein,For load active power predicted value, σLIt is poor for prediction standard.
Reactive load power equally exists fluctuation, it is contemplated that load power factor variation it is smaller, and it is idle with it is active just
It is related, it may be assumed that load power factor is constant, then reactive load can be described as：
Wherein,For load power-factor angle.
Step 1.3, the correlation scenario building of level is run.
Using LHS-Cholesky decomposition method sort building operation level correlation scene, process as shown in Fig. 2,
Detailed step is as follows：
1) LHS is sampled：Based on wind speed, load modeling, stochastic variable is sampled using LHS method.Assuming that there is M random changes
Amount, each variable sample n times, then obtain the initial sample matrix S of M × N rank0, each to be classified as a sample.
2) Cholesky decomposition method sorts：Firstly, generating the sequential matrix L of M × N rank at random, wherein the every row of L is by integer 1
~N random alignment forms.Then, the rank correlation coefficient matrix ρ of L is calculatedL, and according to formula (6) to ρLCholesky decomposition is carried out,
Obtain inferior triangular flap Q.
ρL=QQT (6)
G=Q-1L (7)
By formula (7) to sequential matrix L processing, the matrix G that rank correlation coefficient matrix is unit battle array is obtained, to eliminate
The correlation that L is presented due to generating at random.
Again to stochastic variable (each wind farm wind velocity and each node load) correlation matrix ρobjMake Cholesky decomposition,
Inferior triangular flap P is obtained, the G for eliminating correlation is handled according to formula (8), so that GuRank correlation coefficient matrix and ρobjApproximate phase
Deng.
Gu=PG (8)
Solve GuSequential matrix Lu, by initial sample matrix S0In every row element size order according to LuCorresponding row arrangement,
To obtain with the sample matrix S with practical stochastic variable approximate correlationu。
3) the correlation scene for running level generates：According to relationship between blower power output and wind speed, by sample matrix SuIn
Wind speed is converted to wind power, forms correlation scene matrix Vu, each to be classified as a scene.It is closed between blower power output and wind speed
System can approximate description be：
Wherein, vci、vr、vcoRespectively blower incision wind speed, rated wind speed and cut-out wind speed, PrFor blower rated power.
(2) step 2：, active power loss expectation minimum best with static electric voltage stability and variation expectation are at least excellent
Change target, establishes the multi-objective reactive optimization model of the power distribution network containing blower, and solve based on NSGA-II algorithm.It is as follows in detail：
The objective function of the multi-objective reactive optimization model of the power distribution network containing blower is：
min f1,f2,f3 (10)
Wherein, f1、f2、f3Respectively static voltage stability index expectation, network loss expectation and variation expectation.
1) static voltage stability index expectation is characterized as below：
Wherein, N is scene number (namely LHS hits), aVSI,sFor the static system voltage stability index of s-th of scene.
Static system voltage stability index is sought based on following formula：
Wherein, i, j be respectively branch give, receiving end；aVSI,j、aVSIRespectively node j static voltage stability index, system are quiet
State voltage stability index；Ri、XiRespectively branch resistance, reactance, QjIdle, the U sent out for node jiFor node i voltage；NSFor
All node sets of system.
2) network loss expectation is characterized as below：
Wherein, Ploss,sFor the active power loss of s-th of scene.
3) variation expectation is characterized as below：
Wherein, au,sFor the weighted voltage offset of s-th of scene.Weighted voltage offset is sought based on following formula：
Wherein, PL,i、PL,allFor active always active, the U with system of node iNFor node i voltage rating.
The constraint condition of the multi-objective reactive optimization model of the power distribution network containing blower includes power-balance constraint, state variable altogether
Constraint and control variables constraint.It is as follows：
1) power-balance constraint
Wherein, Pin,i、Qin,iRespectively node i injection is active and idle；J ∈ i indicates the institute being connected directly with node i
There is node；Gij、Bij、θijConductance, susceptance and phase difference of voltage respectively between two node of i, j.
2) state variable constrains
Node voltage uses chance constraint：
Wherein, Pr { } is the probability that inequality is set up,Respectively node voltage upper and lower limit, β are confidence
It is horizontal.
3) control variables constraint
Controlling variable includes the idle power output of DG and capacitor switching group number, is constrained to：
Wherein, QDG,i、Power output that respectively DG is idle and Reactive-power control upper and lower limit；nc,i、Nc,iFor capacitor
Switching group number and maximum switching group number.
Multi-objective reactive optimization model based on the power distribution network containing blower built is solved using NSGA-II algorithm, can be obtained
One group of non-domination solution.
(3) step 3：Optimal solution is gone out using Multiobjective Decision Making Method decision priority-based, obtains the optimal of power distribution network
Idle work optimization scheme.Multiobjective decision-making process is as shown in figure 3, as follows in detail priority-based：
Step 3.1, static voltage stability index expectation weight determines.
Static voltage stability index it is expected into weighted associations static voltage stability index desired value, relationship such as Fig. 4 institute
Show, works as f1When smaller, weight very little, if adjusting its weight to 1 close to critical value.Weighting function is：
Wherein, f1、f1 TIt is form parameter for static voltage stability index expectation and its threshold values, a.
Step 3.2, variation expectation weight determines.
Premised on system is stablized, variation it is expected into weighted associations variation desired value.Variation expectation power
Variation is as shown in figure 5, weighting function is again：
Wherein, f3、f3 TIt is form parameter for variation expectation and its threshold values, b.
Step 3.3, network loss expectation weight determines.
Determine wVSIAnd wuAfterwards, network loss expectation weight can be obtained：
wloss=1-wVSI-wu (21)
Step 3.4, multiobjective decision-making.
Assuming that the non-dominant disaggregation that multiple-objection optimization obtains is [x1x2x3…xm], corresponding objective function collection is：
Objective function is normalized, and weighted superposition, determines optimal solution：
Wherein, fij、wijCorresponding j-th of the objective function of respectively i-th of non-domination solution and its weight, For solution
Collect minimum value and maximum value that corresponding objective function concentrates j-th of objective function.
It two, is the specific case for using the above method below：
Use improved 33 node power distribution net of IEEE for example, structure is as shown in Figure 6.Two wind power plant of DWG1, DWG2
It is respectively connected to node 15,28,10 Fans of configuration installation, separate unit blower rated capacity 100kW cut wind speed 3.5m/s, volume
Determine wind speed 12m/s, cut-out wind speed 25m/s；Node 11 is connected to micro- gas turbine MGT；Node 24,32 and 13 configures capacitor group
C1, C2, C3, each node install 20 groups × 50kVar reactive capability altogether.
Run-time scenario parameter：DWG1, DWG2 and node load predicted value, distribution parameter are listed in table 1, and wind power plant is using permanent function
The control of rate factor, andMGT active power dispatch power output 300kW, idle -100~200kVar of variable capacity；Two wind power plants
Related coefficient is 0.9 between wind speed, and it is -0.3 between wind speed and load that related coefficient, which is 0.4, between node load.Node voltage constraint
Range is 0.97~1.03, confidence level 0.95.
1 stochastic variable Run-time scenario parameter of table
Model solution and algorithm parameter are set：LHS samples scale N=1000；NSGA-II population scale is 100, iteration time
Number is 100, is intersected, mutation operation distribution parameter is 20；The expectation of multiobjective decision-making static voltage stability index and variation
It is expected that threshold values is respectively f1 T=0.7, f3 T=0.03, form parameter is respectively a=2, b=2.
Firstly, whether comparison considers correlation and influence of the different level random variance model to idle work optimization, setting with
Lower 4 kinds of Run-time scenario construction methods：
Method 1：Correlation between stochastic variable is not considered, the Wind speed model building non-correlation operation based on long time scale
Scene.
Method 2：Correlation between stochastic variable is not considered, and the Wind speed model building non-correlation based on operation level runs field
Scape.
Method 3：Correlation between meter and stochastic variable, the Wind speed model building correlation based on long time scale run field
Scape.
Method 4：Correlation between meter and stochastic variable, the Wind speed model based on operation level construct correlation Run-time scenario,
Namely mentioned scenario building method in optimization method of the present invention.
Wherein, the wind speed of long time scale is using Weibull distribution description：Form parameter k=2.21, scale parameter c=
8.51.To be comparable, with identical upper and lower limit truncation long time scale Weibull distribution wind speed and level wind speed is run, with
Run-time scenario is constructed based on this.
Fig. 7 is two wind power plant wind power sample distributions under 4 kinds of scenario building methods.As it can be seen that correlation and different layers
Face random variance model will lead to sample distribution difference.System load flow can be caused to change as a result, influence node voltage, network loss and
Static electric voltage stability.
2 different scenes construction method optimum results of table
Table 2 is the idle work optimization result under 4 kinds of scenario building methods.It can be seen that, correlation and random variance model can
Optimum results are largely influenced, make idle power output scheme that difference be presented.Thus, it is constructed based on operation level random variance model
Correlation scene may consequently contribute to the idle work optimization performance for promoting power distribution network to simulate practical operation situation as far as possible.
Secondly, comparing influence of the different Multiobjective Decision Making Methods to idle work optimization.
After NSGA-II algorithm solves power distribution network multi-objective reactive optimization model, output is one group of non-domination solution,
Fig. 8 is its spatial distribution.As can be seen from Figure 7,3 objective functions mutually restrict, and can not obtain optimal solution simultaneously.
For the idle work optimization of power distribution network, one of solution need to be chosen, it is optimal to the synthesis of 3 objective functions to realize.To compare not
Following 6 kinds of decision schemes are chosen in influence with Multiobjective Decision Making Method to idle work optimization：
Scheme 1：The non-domination solution for selecting power distribution network static electric voltage stability preferably corresponding.
Scheme 2：Select non-domination solution corresponding to power distribution network active power loss expectation minimum.
Scheme 3：Select non-domination solution corresponding to distribution network voltage offset expectation minimum.
Scheme 4：Optimal non-domination solution is determined based on max-min method.
Scheme 5：Optimal non-domination solution is determined based on dreamboat function Euclidean distance minimum method.
Scheme 6：Optimal non-domination solution namely present invention optimization side are determined using Multiobjective Decision Making Method priority-based
Mentioned decision-making technique in method.
The different decision scheme optimum results of table 3
Table 3 is the optimum results under 6 kinds of multiobjective decision-making schemes.Wherein, scheme 1, scheme 2 and scheme 3 are non-domination solutions
Concentrate the optimal corresponding non-domination solution of single goal.Consider each target function value of scheme 1, scheme 2 and scheme 3, it is known that
Under current operating conditions, power distribution network air extract is big, and variation is smaller, thus 6 decision-making technique of scheme is with net
Damage is desired for main target of optimization, and the weight for distributing to each objective function is：wVSI=0.001, wu=0.175, wloss=
0.824.So network loss it is expected the optimal network loss expectation that very close non-domination solution is concentrated under 6 decision of scheme, only differ
0.43kW。
Comparison scheme 4, scheme 5 and scheme 6 are it is found that scheme 4 and scheme 5 are based only on pure mathematical method to carry out multiple target
Decision is not associated with power distribution network actual motion state, thus deviates from the Targets under power distribution network difference operating status, obtains
It is one more " equilibrium " non-domination solution.Compared to scheme 4 and scheme 5, the performance driving economy of scheme 6 is respectively increased
17.03% and 10.49%.
As it can be seen that scheme 6 use priority-based Multiobjective Decision Making Method can be adjusted according to running state
Target Preference substantially increases distribution performance driving economy in the case where having enough air extracts and small voltage offset.
Specific embodiment described herein is only an example for the spirit of the invention.The neck of technology belonging to the present invention
The technical staff in domain can make various modifications or additions to the described embodiments or replace by a similar method
In generation, however, it does not deviate from the spirit of the invention or beyond the scope of the appended claims.
Claims (4)
1. a kind of Multipurpose Optimal Method of power distribution network containing blower based on operation level correlation scene, which is characterized in that including
Following steps：
Step 1, wind speed and load modeling based on operation level, and comprehensively consider correlation between stochastic variable, construct firing floor
The correlation scene in face；
Step 2, best with static electric voltage stability, active power loss expectation minimum and variation expectation are at least optimization aim,
The multi-objective reactive optimization model of the power distribution network containing blower is established, and is solved based on NSGA-II algorithm；
Step 3, one group of non-domination solution is obtained by step 2, optimal solution is gone out using Multiobjective Decision Making Method decision priority-based,
Obtain the OPTIMAL REACTIVE POWER prioritization scheme of power distribution network.
2. a kind of multiple-objection optimization side of power distribution network containing blower based on operation level correlation scene according to claim 1
Method, which is characterized in that the step 1 is realized based on following below scheme：
The wind speed of the above-mentioned operation level of step 1.1 models, that is, runs the actual wind speed probability density function p (v of levelw) can describe
For：
Wherein, vw、Respectively actual wind speed, upper wind velocity limit, wind speed lower limit；p′(vw) it is to be truncated without wind speed upper and lower limit
Wind speed probability density function；For wind speed value；σwFor the standard deviation of forecasting wind speed；
The load modeling of the above-mentioned operation level of step 1.2 runs the load active power probability density function p (P of levelL) can
It is described as：
Wherein, PL、Respectively load active power actual value, the active upper limit, active lower limit；p′(PL) it is without upper and lower
Limit the load active power probability density function of truncation；For load active power predicted value；σLIt is poor for prediction standard；
The correlation scenario building method of step 1.3 operation level is：It is run based on the stochastic variable of step 1.1 and step 1.2
The modeling of level using LHS sampling and Cholesky decomposition method sequence building wind speed-load sample matrix, and goes out according to blower
Wind speed in sample matrix is converted to wind power, forms correlation scene matrix by relationship between power and wind speed.
3. a kind of multiple-objection optimization side of power distribution network containing blower based on operation level correlation scene according to claim 1
Method, which is characterized in that the multi-objective reactive optimization model of the power distribution network containing blower in the step 2, most with static electric voltage stability
Good, active power loss expectation minimum and variation expectation are at least optimization aim, i.e. model objective function is：
min f1,f2,f3
Wherein, f1For static voltage stability index expectation；,f2For network loss expectation, f3For variation expectation；
Expectation function one, static voltage stability index expectation：
Wherein, N is scene number (namely LHS hits), aVSI,sFor the static system voltage stability index of s-th of scene；
Static system voltage stability index is sought based on following formula：
Wherein, i, j be respectively branch give, receiving end；aVSI,j、aVSIRespectively node j static voltage stability index and static system electricity
Press stability index；Ri、XiRespectively branch resistance, reactance；QjIt is sent out for node j idle；UiFor node i voltage；NSFor system
All node sets；
Expectation function two, network loss expectation
Wherein, Ploss,sFor the active power loss of s-th of scene；
Expectation function three, variation expectation
Wherein, au,sFor the weighted voltage offset of s-th of scene；
Weighted voltage offset formula of seeking be：
Wherein, PL,i、PL,allThe respectively active power of node i and the total active power of system；UNFor the voltage rating of node i.
4. a kind of multiple-objection optimization side of power distribution network containing blower based on operation level correlation scene according to claim 1
Method, which is characterized in that in the step 3, Multiobjective Decision Making Method decision process is as follows priority-based：
Step 3.1 determines that static voltage stability index it is expected weight；Static voltage stability index it is expected into weighted associations Static Electro
Stability index desired value is pressed, f is worked as1When smaller, weight very little, if adjusting its weight to 1 close to critical value；Weight letter
Number is：
Wherein, f1、f1 TFor static voltage stability index expectation and its threshold values；A is form parameter；
Step 3.2 determines that variation it is expected weight；It is premised on system is stablized, variation expectation weighted associations voltage is inclined
Desired value is moved, weighting function is：
Wherein, f3、f3 TFor variation expectation and its threshold values；B is form parameter；
Step 3.3 determines that network loss it is expected weight；Determine wVSIAnd wuAfterwards, network loss expectation weight can be obtained：
wloss=1-wVSI-wu
Step 3.4 multiobjective decision-making；Assuming that the non-dominant disaggregation that multiple-objection optimization obtains is [x1x2x3…xm], corresponding target
Collection of functions is：
Objective function is normalized, and weighted superposition, determines optimal solution：
Wherein, fij、wijCorresponding j-th of the objective function of respectively i-th of non-domination solution and its weight；Respectively
The minimum value and maximum value for j-th of objective function that the corresponding objective function of disaggregation is concentrated.
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