CN110414810A - Meter and the multiterminal intelligence Sofe Switch Optimal Configuration Method and system for losing load risk - Google Patents
Meter and the multiterminal intelligence Sofe Switch Optimal Configuration Method and system for losing load risk Download PDFInfo
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Abstract
The invention discloses a kind of meter and the multiterminal intelligence Sofe Switch Optimal Configuration Methods and system of mistake load risk, multiterminal intelligence Sofe Switch Optimal Configuration Method includes: step S1: constructing responsible consumer loss of outage model by DEA and IDEA method based on the production factors data, obtains the loss of outage of responsible consumer；Step S2: the internal structure based on SOP constructs multi state reliability model, and the mistake load risk of user under SOP different conditions is obtained by quickly losing load Risk Calculation method；Step S3: establishing meter and responsible consumer loses the SOP Bi-level Programming Models of load risk, and SOP Bi-level Programming Models are solved using the hybrid optimization algorithm that the multi-objective Evolutionary Algorithm based on non-dominated sorted genetic algorithm is combined with cone planning, obtain the best decision scheme for considering mistake load risk and performance driving economy.
Description
Technical field
The invention belongs to power electronic equipments to distribute field more particularly to a kind of full-control type electricity rationally in power distribution network
Power electronic device --- intelligent Sofe Switch (SOP) provides multiterminal intelligence in the case where considering that responsible consumer loses load risk
Sofe Switch Optimal Configuration Method, realization promote customer power supply reliability while meeting distribution network planning and performance driving economy.
Background technique
Intelligent Sofe Switch (SOP) is used as all-controlling power electronics device, is installed at traditional interconnection switch, is to adapt to divide
Cloth power grid, it is comprehensive to solve trend unevenness, the effective way of quality of voltage and power supply reliability.But the throwing of SOP at this stage
Money and operation expense are higher, reasonably select configuration its comprehensive value of Scene realization and are of great significance.
Currently, existing experts and scholars study SOP optimization access problem in power distribution network, the prior art 1 is shown in
What " Proceedings of the CSEE " the 7th phase of volume 37 published " considers that the active power distribution network of distributed generation resource operation characteristic is intelligently soft
Switch SOP planing method ", more scene generation techniques of the technology based on Wasserstein distance propose a kind of consideration point
The active power distribution network SOP Bi-level Programming Models of cloth power supply operation characteristic, upper layer planning with the minimum target of year overall cost,
Lower layer's operation can fully consider the randomness and fluctuation of DG with the minimum target of operating cost under each scene, improve different
Power distribution network synthesis on-road efficiency under scene；The prior art 2 is shown in " having for " Automation of Electric Systems " o. 11th publication of volume 42
Three layers of coordinated planning model of source power distribution network distributed generation resource and intelligent Sofe Switch ", it is excellent which proposes that a kind of DG combines with SOP
The three-level programming model of change, upper layer planning turn to target with DG operator unit capacity Income Maximum, and middle layer planning is with distribution public affairs
Department's year overall cost is minimised as target, and lower layer's operation is minimised as target with operating cost in scene, while having planned SOP
And DG, be conducive to the interests demand for meeting different subjects, promote DG grid-connected extensively, effectively improve DG consumption rate；Prior art
[1] and [2] consider the factors such as the fluctuation of DG, the diversity of load, it is therefore an objective to system load flow is improved by SOP as far as possible,
Power distribution network performance driving economy and DG consumption rate are improved, but it is to realize non-faulting section that SOP, which is applied to a significant contribution of power distribution network,
Uninterrupted power supply reduces faulty section power off time, promotes failover capability, and then improving power supply reliability, existing related
In the technology that SOP is distributed rationally without reference to.
It is several that the reliability assessment of the power distribution network containing SOP is related to SOP state model, fail-over policy and appraisal procedure
A critical issue.The prior art 3, see that " Proceedings of the CSEE " the 15th phase of volume 38 publishes " based on intelligent Sofe Switch
Active power distribution network service restoration method ", which is directed to the passive power distribution network containing SOP, according to abort situation, using range
First traversal searches for effective service restoration region to determine SOP, and has carried out reliability assessment based on simulation.Existing skill
Art 4 is shown in " the active power distribution network fault recovery plan containing flexible Sofe Switch of " Automation of Electric Systems " the 1st phase of volume 42 publication
Slightly ", which establishes SOP and distributed generation resource association always to lose power load, lose DG generated energy and loss minimization as target
With participate in failure optimize Restoration model, and on the basis of be simulated method reliability assessment.The prior art 3 and 4, equal base
Optimize operation and fault recovery in SOP, further reduced power distribution network simulation reliability assessment computational efficiency, directly applies
It is distributed rationally in SOP, will lead to Optimized model excessively complexity and convergence difficulties.
Therefore it is badly in need of developing a kind of meter for overcoming drawbacks described above and responsible consumer loses the multiterminal intelligence Sofe Switch of load risk
Optimal Configuration Method and system.
Summary of the invention
Technical problem to be solved by the present invention lies in the comprehensive effectivenesses for raising SOP access to power distribution network, in meter and again
A kind of multiterminal intelligence Sofe Switch (SOP) Optimal Configuration Method is proposed in the case where wanting user to lose load risk, to invest operation
Totle drilling cost and mistake load least risk are that target plans the position SOP and capacity, are answered for reality of the SOP in power distribution network
It is referred to theory and technology is provided.
The present invention provides a kind of multiterminal intelligence Sofe Switch Optimal Configuration Method counted and lose load risk, wherein includes:
Step S1: constructing responsible consumer loss of outage model by DEA and IDEA method based on the production factors data,
Obtain the loss of outage of responsible consumer；
Step S2: the internal structure based on SOP constructs multi state reliability model, by quickly losing load Risk Calculation
Method obtains the mistake load risk of user under SOP different conditions；
Step S3: establishing meter and responsible consumer loses the SOP Bi-level Programming Models of load risk, and using based on non-dominant
The hybrid optimization algorithm that the multi-objective Evolutionary Algorithm of Sorting Genetic Algorithm and cone planning combine to SOP Bi-level Programming Models into
Row solves, and obtains the best decision scheme for considering to lose load risk and performance driving economy.
Above-mentioned multiterminal intelligence Sofe Switch Optimal Configuration Method, wherein in Yu Suoshu step S1 further include:
Step S11: the production factors data by obtaining investment using indirect surveys method to responsible consumer；
Step S12: output efficiency is assessed by DEA process, obtains the relationship between production factors and output；
Step S13: extrapolating the variable quantity of output by IDEA method, obtains the loss of outage of responsible consumer.
Above-mentioned multiterminal intelligence Sofe Switch Optimal Configuration Method, wherein include: in Yu Suoshu step S2
Step S21: it is established by the MMC reliability model and CTM Approach of bridge arm valve group reliability dynamic change
Multi state reliability model；
Step S22: considering that the port the MMC maximum of SOP turns to turn for ability and responsible consumer for priority, tired using probability
The mode added obtains the mistake load risk of user under SOP different conditions.
Above-mentioned multiterminal intelligence Sofe Switch Optimal Configuration Method, wherein include: in Yu Suoshu step S3
Step S31: establishing meter and responsible consumer loses the SOP Bi-level Programming Models of load risk；
Step S32: using the multi-objective Evolutionary Algorithm based on non-dominated sorted genetic algorithm (NSGA- II) and planning is bored
The hybrid optimization algorithm combined solves SOP Bi-level Programming Models, obtains and considers to lose load risk and performance driving economy
Best decision scheme.
Above-mentioned multiterminal intelligence Sofe Switch Optimal Configuration Method, wherein in step S31, using upper layer optimization planning,
The mode of lower layer's running optimizatin establishes SOP Bi-level Programming Models.
The present invention also provides a kind of meter and the multiterminal intelligence Sofe Switch Optimizing Configuration Systems of mistake load risk, wherein packet
It includes:
Loss of outage acquiring unit constructs responsible consumer by DEA and IDEA method based on the production factors data and has a power failure
Loss model obtains the loss of outage of responsible consumer；
Load risk acquiring unit is lost, the internal structure based on SOP constructs multi state reliability model, by quickly losing
Load Risk Calculation method obtains the mistake load risk of user under SOP different conditions；
Decision scheme acquiring unit, establishes meter and responsible consumer loses the SOP Bi-level Programming Models of load risk, and uses
The hybrid optimization algorithm that multi-objective Evolutionary Algorithm and cone planning based on non-dominated sorted genetic algorithm combine is to SOP bilayer
Plan model is solved, and the best decision scheme for considering to lose load risk and performance driving economy is obtained.
Above-mentioned multiterminal intelligence Sofe Switch Optimizing Configuration System, wherein loss of outage acquiring unit includes:
Production factors data acquisition module, by the production factors for obtaining investment using indirect surveys method to responsible consumer
Data；
Output efficiency obtains module, assesses output efficiency by DEA process, obtains the relationship between production factors and output；
Loss of outage obtains module, and the variable quantity of output is extrapolated by IDEA method, obtains the power failure damage of responsible consumer
It loses.
Above-mentioned multiterminal intelligence Sofe Switch Optimizing Configuration System, wherein losing load risk acquiring unit includes:
Multi state reliability model constructs module, by the MMC reliability model of bridge arm valve group reliability dynamic change and
CTM Approach establishes multi state reliability model；
It loses load risk and obtains module, consider that the port the MMC maximum of SOP turns to turn for ability and responsible consumer for priority,
The mistake load risk of user under SOP different conditions is obtained in such a way that probability is cumulative.
Above-mentioned multiterminal intelligence Sofe Switch Optimizing Configuration System, wherein decision scheme acquiring unit includes:
SOP Bi-level Programming Models construct module, and building meter and responsible consumer lose the SOP Bi-level Programming Models of load risk；
Decision scheme obtains module: using the multi-objective Evolutionary Algorithm for being based on non-dominated sorted genetic algorithm (NSGA- II)
The hybrid optimization algorithm combined with cone planning solves SOP Bi-level Programming Models, obtains and considers to lose load risk and fortune
The best decision scheme of row economy.
Above-mentioned multiterminal intelligence Sofe Switch Optimizing Configuration System, wherein SOP Bi-level Programming Models construct module using upper
Layer optimization planning, lower layer's running optimizatin mode establish SOP Bi-level Programming Models.
The present invention is directed to the prior art its effect and is: being to improve SOP access to the comprehensive effectiveness of power distribution network, is counting
And a kind of multiterminal intelligence Sofe Switch (SOP) Optimal Configuration Method is proposed in the case where responsible consumer mistake load risk, with investment
Operation totle drilling cost and mistake load least risk are that target plans the position SOP and capacity, are reality of the SOP in power distribution network
Border application provides theory and technology reference.
Compared with prior art, the invention has the following advantages:
1) relationship between production factors, power-off fault and output is analyzed, backup power source is to production after considering power-off fault
The influence of element proposes the responsible consumer loss of outage model based on DEA and IDEA method；
2) turn to propose for ability and consider that responsible consumer turns for excellent based on SOP multi state reliability model and port maximum
The quick mistake load Risk Calculation method of first grade, it is more efficient compared to traditional minimal path method and hybrid analog-digital simulation method；
3) it establishes to invest operation totle drilling cost and lose load least risk as the SOP Bi-level Programming Models of target, full
The power supply reliability of user is improved while sufficient distribution network planning and performance driving economy again, can get and comprehensively consider mistake load
The practical application that the best decision scheme of risk and performance driving economy is SOP in power distribution network provides theory and technology reference.
Detailed description of the invention
Fig. 1 is the flow chart of the multiterminal intelligence Sofe Switch Optimal Configuration Method of present invention meter and mistake load risk；
Fig. 2-Fig. 4 is the flow chart step by step of Fig. 1；
Fig. 5 is the relation schematic diagram of production factors and output of the invention；
Fig. 6 is MMC of the invention and its submodule physical structure；
Fig. 7 is flexible multimode switch access power distribution network schematic diagram of the invention；
Fig. 8 is hybrid optimization calculation flow chart of the invention；
Fig. 9 is example distribution net work structure figure of the invention；
Figure 10 is two kinds of addressing constant volume schemes of example of the invention；
Figure 11 is two kinds of programme results of example of the invention；
Figure 12 is that responsible consumer of the invention it is expected short of electricity amount schematic diagram；
Figure 13 is the structural schematic diagram that the present invention counted and lost that the multiterminal intelligence Sofe Switch of load risk is distributed rationally.
Specific embodiment
Hereby detailed content and technology for the present invention explanation is now described further with a preferred embodiment, but not
It should be interpreted the limitation implemented to the present invention.
Fig. 1-Fig. 4 is please referred to, Fig. 1 is the multiterminal intelligence Sofe Switch optimization that present invention meter and responsible consumer lose load risk
The flow chart of configuration method；Fig. 2-Fig. 4 is the flow chart step by step of Fig. 1.As shown in Fig. 1-Fig. 4, meter of the invention and mistake load
The multiterminal intelligence Sofe Switch Optimal Configuration Method of risk the following steps are included:
Step S1: constructing responsible consumer loss of outage model by DEA and IDEA method based on the production factors data,
Obtain the loss of outage of responsible consumer；
Step S2: the internal structure based on SOP constructs multi state reliability model, by quickly losing load Risk Calculation
Method obtains the mistake load risk of user under SOP different conditions；
Step S3: establishing meter and responsible consumer loses the SOP Bi-level Programming Models of load risk, and using based on non-dominant
The hybrid optimization algorithm that the multi-objective Evolutionary Algorithm of Sorting Genetic Algorithm and cone planning combine to SOP Bi-level Programming Models into
Row solves, and obtains the best decision scheme for considering to lose load risk and performance driving economy.
Further, in step S1 further include:
Step S11: the production factors data by obtaining investment using indirect surveys method to responsible consumer；
Step S12: output efficiency is assessed by DEA process, obtains the relationship between production factors and output；
Step S13: extrapolating the variable quantity of output by IDEA method, obtains the loss of outage of responsible consumer.
Still further, including: in step S2
Step S21: it is established by the MMC reliability model and CTM Approach of bridge arm valve group reliability dynamic change
Multi state reliability model；
Step S22: considering that the port the MMC maximum of SOP turns to turn for ability and responsible consumer for priority, tired using probability
The mode added obtains the mistake load risk of user under SOP different conditions.
Further, include: in step S3
Step S31: establishing meter and responsible consumer loses the SOP Bi-level Programming Models of load risk；
Step S32: using the multi-objective Evolutionary Algorithm based on non-dominated sorted genetic algorithm (NSGA- II) and planning is bored
The hybrid optimization algorithm combined solves SOP Bi-level Programming Models, obtains and considers to lose load risk and performance driving economy
Best decision scheme.
Further, in step S31, it is bis- by the way of upper layer optimization planning, lower layer's running optimizatin to establish SOP
Layer plan model.
It is referring to figure 5. the relation schematic diagram of production factors and output of the invention；Fig. 6 is MMC and its son of the invention
Module physical structure chart；Fig. 7 is flexible multimode switch access power distribution network schematic diagram of the invention；Fig. 8 is mixing of the invention
Optimize calculation flow chart；Fig. 9 is example distribution net work structure figure of the invention；Figure 10 is two kinds of addressing constant volumes of example of the invention
Scheme；Figure 11 is two kinds of programme results of example of the invention；Figure 12 is that responsible consumer of the invention it is expected the signal of short of electricity amount
Figure.Present invention meter is illustrated below in conjunction with Fig. 1-Figure 12 and responsible consumer loses the multiterminal intelligence Sofe Switch optimization of load risk
The course of work of configuration method.
The calculating that power distribution network containing SOP loses load risk is related to assessment of both Custom interruption cost and power failure probability.This
Invention obtains the production factors data of investment for responsible consumer using indirect surveys method, first assesses its output efficiency, must be born
The relationship between element and output is produced, then analyzes influence of the power-off fault to production factors, and then extrapolate power-off fault pair
The influence of output obtains unit loss of outage by comparing investment and variable quantity of the output before and after considering power-off fault.
It is primarily based on DEA and IDEA method and proposes responsible consumer loss of outage model.The wherein pass of production factors and output
It is the variable quantity as shown in figure 5, by extrapolating output after DEA process assessment output efficiency with IDEA method, finally obtains unit
The Custom interruption cost CIC of short of electricity amountQ。
Secondly based on the internal structure of SOP, multi state reliability model is established.SOP passes through DC side by three MMC
It is connected in parallel, exchange side is connected respectively to three different feeder lines, whole to use hybrid modularization topological structure, packet
Containing 2 ports semibridge system MMC and 1 port full-bridge type MMC, full bridge structure has better adaptability in case of a fault, directly
The utilization rate of galvanic electricity pressure is higher；The driving circuit of half-bridge structure is simple, and anti-unbalance ability is strong, and submodule quantity is less, cost
It is lower, it is lost smaller.The hybrid modularization topological structure is suitable for different operating conditions, can make the Power Exchange between feeder line
More flexible, deployment cost is less, stronger to the adaptability of line fault.Wherein, MMC convertor unit is as shown in fig. 6, packet
Containing 6 bridge arms, each bridge arm is connected in series by n identical submodules and 1 reactor.Wherein, each SM submodule packet
Containing 2 IGBT modules, 1 storage capacitor, bypass thyristor and by-pass switch, there are also submodule block controllers for auxiliary element.Root
According to the functional characteristics of SOP, 4 subsystems: 3 MMC subsystems and 1 Unit Level Control protection system can be divided into.Its
With following four operational mode: (1) device operates normally；(2) when there is the stoppage in transit of one end MMC failure, in addition both ends are normally transported
Row, can still carry out power transmission；(3) MMC failure in both ends is stopped transport, and is worked in STATCOM mode, only by one end for working with electricity
Hold compensation way and carries out idle control；(4) when the MMC at three ends stops transport or Unit Level Control protection system failure, entirely
Flexible multimode switching device is stopped transport.The present invention has used for reference the MMC reliability model based on bridge arm valve group reliability dynamic change
The SOP state reliability model eight-shaped established with CTM Approach.
The port the MMC maximum for considering SOP on this basis turns to turn to propose for priority for ability and responsible consumer
Quickly lose load Risk Calculation method.Power distribution network Connection Mode containing SOP is as shown in fig. 7, TSCsop qIt, should for port transmission power
The maximum of port turns for ability TSCsop q,max, PSOP q(t), QSOP q(t) be respectively t period SOP q-th of port output have
Function power and reactive power.To meet feeder line trend constraint, node voltage constraint, branch power constraint and SOP capacity-constrained
It is lower allow by the maximum value of transimission power can be calculate by the following formula:
Responsible consumer turns for priority indicator fQAre as follows:
Wherein, responsible consumer Q turn for index computer capacity be ΩQ, turn for the load on path to be PL, responsible consumer
Unit short of electricity amount loss of outage be CICQ, the loss of outage of the unit short of electricity amount of ordinary user is this area's synthetic user
Loss of outage CCDF.
Feedback built-in unit j breaks down, XijFor turning for state for load point i, it is a 0-1 variable, and 1 indicate can be with
Turn to supply, 0 indicates to turn to supply or do not need to turn to supply.Fail-over policy are as follows:
(1) judge SOP port status: if the SOP port status K of guilty culprit feeder linesop qIt is 0, then SOP can not turn to supply；
If Ksop qIt is 1, judgment principle (2)；
(2) judge faulty equipment position: if faulty equipment j not on the minimal path of SOP access point to power supply point, SOP
It can not turn to supply；Conversely, judgment principle (3)；
(3) judgement is maximum turns for ability: if not the total load of faulty section is greater than the port MMC maximum and turns then to judge for ability
Principle (4)；Conversely, non-faulting section user can all be turned to supply away by SOP；
(4) judgement turns for priority: since multiple feeder terminals of non-faulting section, non-faulting section being turned for priority
Ordinary user in minimum responsible consumer and its segmentation removes, if the total load of remaining non-faulting section is still greater than the port MMC
Maximum turns for ability, then repeatedly principle (4)；Conversely, remaining non-faulting section user can all be turned to supply away by SOP.
The present invention is based on minimal path methods to calculate quick load risk of losing, and concretism is the failure of each user
Rate is added up by the sum of the equipment fault probability of happening of the user that has an impact on minimal path with non-minimum road using probability
Mode calculates the mistake load risk of user under SOP different conditions:
Load mean failure rate power off time caused by equipment fault on minimal path are as follows:
Load mean failure rate power off time, that is, grid switching operation time caused by the equipment fault of non-minimum road are as follows:
μij=λjrtts (4)
Comprehensively consider the dependability parameter of each equipment of power distribution network, the working condition of fail-over policy and SOP, obtains negative
Lotus i mean failure rate power off time μiAre as follows:
In formula, load point i's turns for state Xij, the minimal path of load point i is Ωi, the non-minimum road of load point i is
ΩOi, state probability when SOP state is Sk is Psop Sk, the state for the port MMC that corresponding the q articles feeder line is connect at this time is
Ksop q, the failure rate of equipment is λj, the fault outage time is rj, the grid switching operation time comprising breaker and block switch is
rtts。
Meter and responsible consumer lose load risk, present invention employs the mode of upper layer optimization planning, lower layer's running optimizatin,
Establish SOP Bi-level Programming Models:
Upper layer using invest operation totle drilling cost and lose load least risk as target, investment cost, Nian Yunhang including SOP
Maintenance cost, year running wastage expense and year Custom interruption cost, wherein investment cost considers time value on assets, planning
Cost can be calculated with following formula:
MinC=Cinvest+Ccons+Closs+CL (6)
In formula, d is discount rate；M is the life cycle of SOP；csopAnd clossThe unit capacity cost and list of respectively SOP
Position active loss coefficient, ω are year operation and maintenance cost coefficient；ΩsopFor the alternative point set for installing SOP；Ssop qIt is q-th
SOP capacity at candidate point, kqFor nonnegative integer, Ssop perFor SOP unit installed capacity；xqFor 0-1 variable, 0 and 1 difference is taken
Indicate that position q does not install and install equipment, Pnet loss(t) and Psop lossIt (t) is respectively t hours active damages of power distribution network and SOP
Consumption, can be solved by power flow equation.M is the number of distribution network users, and N is the equipment number that probability of malfunction is considered in power distribution network,
Ei fCause the amount of user i mistake load, s for equipment fault after configuration SOPijCause the failure rate of user's i failure for equipment j,
λjFor the failure rate of equipment j, CICQFor the Custom interruption cost of unit power failure amount, resident is calculated by average electricity price, weight
User is wanted by the calculated result of responsible consumer loss of outage evaluation model to calculate.
Lower layer runs network loss and the minimum target of SOP running wastage, operating cost with power distribution network and can be calculated with following formula:
Constraint condition contains SOP operation constraint and distribution power flow constraint:
The capacity-constrained of (1) three end SOP
In formula, PSOP 1(t), PSOP 2(t), PSOP 3(t), QSOP 1(t), QSOP 2(t), QSOP 3It (t) is respectively that t period SOP is respectively held
The active power and reactive power of mouth output；Psop i(t) active power loss for being port i；miIt is damaged for the active power of port i
Consume coefficient；Ssop 1、Ssop 2And Ssop 3The access capacity of each port current transformer of respectively SOP.
(2) power-balance constraint of system
In formula,Respectively indicate the burden with power and load or burden without work of node i；UiAnd UjRespectively indicate node i and
The voltage magnitude of node j；Gij、BijRespectively indicate the conductance and susceptance of branch ij；δijIndicate node i and node j voltage phase angle
Difference.
(3) node voltage constrains
(4) tributary capacity constrains
In formula, IijFor the electric current for flowing through branch ij.
Above-mentioned model is that the extensive MIXED INTEGER comprising Custom interruption cost calculating and fast reliability calculating is non-
Linear bilevel program problem, the present invention use based on non-dominated sorted genetic algorithm (NSGA- II) multi-objective Evolutionary Algorithm and
The hybrid optimization algorithm that cone planning combines is solved.
Cone planning algorithm is used to solve the optimal operation mode of power distribution network, will by way of variable replacement or convex relaxation
All nonlinear restrictions are converted into linear restriction, the constraint of standard second order cone or rotation second order cone constraint, the following institute of linearization procedure
Show:
(1) linearisation of nonlinear restriction is realized by variable replacement, introduces Xi、Yij、ZijBy the U in modeli、Uj、
δijVariable replacement is carried out, formula is as follows:
(2) the replacement variable introduced is a rotation second order cone constraint, so that Optimized model still exists after excess convexity relaxation
In the restriction range of sharp convex cone.The constraint is that nature is set up, therefore not will cause the variation of former solution in a model.
Following linear restriction forms can be obtained:
Multi-objective Evolutionary Algorithm based on non-dominated sorted genetic algorithm (NSGA- II) is entire hybrid optimization algorithm
Frame stores the information of discrete optimization variable using chromosome coding mode, for determining the installation site and capacity of SOP.
In each iterative process, the fitness of individual is calculated, searching process is finally completed.Optimum results show as optimal solution set
Form, Optimizing Flow are as shown in Figure 8.
In the case where meter and responsible consumer lose load risk, in order to verify the feasibility of the method for the present invention, with reference to IEEE
4 structure of main feeder of RBTS BUS 6 devises example, and system wiring figure is as shown in Figure 9.
Wherein, route unit length impedance value is 0.45+j0.368 Ω.The feeder line includes 23 load points, 1183 use
Family has 33.63MW altogether comprising 8 responsible consumers, discount rate 0.08, and resident's average electricity price is 0.67 yuan/
KWh, industrial user's average electricity price are 0.85 yuan/kWh.Due to the presence of fault self-recovery process, the operating time of block switch is
1min；The back brake time of disconnecting switch is 2min.In view of the limitation in geographical location, the position to be selected of SOP is due to tradition contact
Junction, including node 2/12/16/21/26 are switched, SOP installation site and capacity are in optimized selection.
Multi-objective Evolutionary Algorithm is realized using Matlab shell script, and calls cone planning algorithm kit Mosek6.0,
The hardware environment of test macro is Intel Core i5-8400, dominant frequency 2.80GHz, inside saves as 8GB, operating system is
Win10 64bit, exploitation environment are Matlab R2016a.
Based on the information investigation table of responsible consumer in example, DEA and IDEA model evaluation is first used, responsible consumer is obtained
Output efficiency and unit short of electricity amount loss of outage, according to the results show that the unit loss of outage of eight responsible consumers in section
In [14.43 182.9] member/kWh.It is bis- that SOP proposed by the present invention is updated to according to the responsible consumer loss of outage being calculated
In layer plan model, and solved using the hybrid optimization algorithm that the multi-objective Evolutionary Algorithm and cone planning that propose combine.
Wherein given 2 kinds of programmes: 1) only planning the port single group SOP；2) port multiple groups SOP is planned.Respectively such as Figure 10, Figure 11
It is shown.The results show that two schemes year overall cost, Web-based exercise, Custom interruption cost have reduction, the year of scheme two is comprehensive
Synthesis is a further reduction line loss and responsible consumer loss of outage originally with slightly above scheme 1.It can be seen that in systems
Configuration SOP can bring obvious income for power distribution network, not only meet power distribution network performance driving economy demand, but also improve the power supply of user
Reliability requirement.
Influence for research configuration SOP to power supply reliability, calculates system power supply reliability under different schemes herein and refers to
Mark contains the average power supply availability of the system, it is expected that lacking power supply volume, system System average interruption frequency, system averagely has a power failure and hold
Continuous time and user's System average interruption duration.The results show that in system power supply reliability index, ASAI does not have before and after planning
Significant change, SAIDI and CAIDI and SOP access capacity do not have a correlativity, the access of SOP can be significantly reduced EENS and
SAIFI, compared to scheme 1, the year overall cost of scheme 2 increases, but system lacks power supply volume and reduces, system System average interruption frequency
It reduces, considers from economy, then preference scheme 1, consider from power supply reliability, then preference scheme 2.
For the scarce power supply figureofmerit of responsible consumer each under different schemes, histogram is obtained as shown in 12.As a result it shows
Show, when using scheme 1, the port SOP access node 21, close to LP15And LP18, short of electricity amount is preceding compared to planning to be significantly reduced,
When using scheme 2, the port SOP access node 20 and node 25, close to LP18、LP20、LP21And LP23, short of electricity amount is compared to rule
Draw before significantly reduce, why LP15Short of electricity amount do not significantly reduce, be because access SOP capacity be only 2.7MVA, no
It is enough LP15Turn to supply away completely.
Comparison takes no account of responsible consumer mistake load risk and plans that SOP, discovery is only by promoting power distribution network economical operation
Property plan SOP, only played the effect of SOP Optimal Power Flow, it is uninterrupted to turn not show for ability and failover capability
Out, it is unfavorable for the popularization and application of SOP.In view of the development of the following converter topology, production cost is further decreased, SOP's
Comprehensive benefit just can be promoted further, but at this stage, and meter and responsible consumer mistake load risk are one effective and feasible
Thinking.
Compare quick mistake load Risk Calculation method proposed by the invention and traditional minimal path method and hybrid analog-digital simulation
Method, calculated result difference are little, it was demonstrated that the correctness of mentioned algorithm of the invention.In addition, computational efficiency is than traditional minimal path method
Improve 34%, it is seen that the mentioned algorithm of the present invention is more efficient.
Figure 13 is please referred to, Figure 13 is the structure that the present invention counted and lost that the multiterminal intelligence Sofe Switch of load risk is distributed rationally
Schematic diagram.As shown in figure 13, the multiterminal intelligence Sofe Switch that meter of the invention and responsible consumer lose load risk, which is distributed rationally, is
System, comprising: loss of outage acquiring unit 11 loses load risk acquiring unit 12 and decision scheme acquiring unit 13；Loss of outage
Acquiring unit 11 is based on the production factors data and constructs responsible consumer loss of outage model by DEA and IDEA method, is weighed
Want the loss of outage of user；12 internal structure based on SOP of load risk acquiring unit is lost, multi state reliability model is constructed,
The mistake load risk of user under SOP different conditions is obtained by quickly losing load Risk Calculation method；Decision scheme obtains single
Member 13 establishes meter and responsible consumer loses the SOP Bi-level Programming Models of load risk, and uses and be based on non-dominated sorted genetic algorithm
Multi-objective Evolutionary Algorithm and the hybrid optimization algorithm that combines of cone planning SOP Bi-level Programming Models are solved, examined
Consider the best decision scheme for losing load risk and performance driving economy.
Further, loss of outage acquiring unit 11 includes: production factors data acquisition module 111, output efficiency acquisition
Module 112 and loss of outage obtain module 113；Production factors data acquisition module 111 is by adjusting responsible consumer using indirect
Look into the production factors data that method obtains investment；Output efficiency obtains module 112 and assesses output efficiency by DEA process, obtains production
Relationship between element and output；Loss of outage obtains the variable quantity that module 113 extrapolates output by IDEA method, is weighed
Want the loss of outage of user.
Include: multi state reliability model building module 121 and lose still further, losing load risk acquiring unit 12
Load risk obtains module 122；Multi state reliability model constructs module 121 and passes through bridge arm valve group reliability dynamic change
MMC reliability model and CTM Approach establish multi state reliability model；It loses load risk and obtains the consideration of module 122 SOP
The port MMC maximum turn to turn for ability and responsible consumer to obtain SOP different conditions in such a way that probability is cumulative for priority
The mistake load risk of lower user.
Further, decision scheme acquiring unit 13 includes: SOP Bi-level Programming Models building module 131 and decision-making party
Case obtains module 132；SOP Bi-level Programming Models construct the building of module 131 meter and responsible consumer loses the SOP bilayer of load risk
Plan model；Decision scheme obtains module 132 using the multi-target evolution for being based on non-dominated sorted genetic algorithm (NSGA- II)
The hybrid optimization algorithm that algorithm and cone planning combine solves SOP Bi-level Programming Models, obtains and considers to lose load risk
With the best decision scheme of performance driving economy.
Further, SOP Bi-level Programming Models structure 131 models block using upper layer optimization planning, lower layer running optimizatin
Mode establishes SOP Bi-level Programming Models.
The invention has the following advantages: 1) analyze the relationship between production factors, power-off fault and output, consider
Influence of the backup power source to production factors after power-off fault proposes the responsible consumer loss of outage mould based on DEA and IDEA method
Type；2) turn to propose for ability and consider that responsible consumer turns for priority based on SOP multi state reliability model and port maximum
Quick mistake load Risk Calculation method, it is more efficient compared to traditional minimal path method and hybrid analog-digital simulation method；3) it establishes to invest
It runs totle drilling cost and loses the SOP Bi-level Programming Models that load least risk is target, meeting distribution network planning and economical operation
Property while improve the power supply reliability of user again, can get to comprehensively consider and lose the best of load risk and performance driving economy
The practical application that decision scheme is SOP in power distribution network provides theory and technology reference.
Above are only presently preferred embodiments of the present invention, not be used to limit the scope of implementation of the present invention, without departing substantially from
In the case where spirit of that invention and its essence, those skilled in the art make various corresponding in accordance with the present invention
Change and modification, but these corresponding changes and modifications all should fall within the scope of protection of the appended claims of the present invention.
Claims (10)
1. the multiterminal intelligence Sofe Switch Optimal Configuration Method of a kind of meter and mistake load risk characterized by comprising
Step S1: responsible consumer loss of outage model is constructed by DEA and IDEA method based on production factors data, obtains important use
The loss of outage at family；
Step S2: the internal structure based on SOP constructs multi state reliability model, by quickly losing load Risk Calculation method
Obtain the mistake load risk of user under SOP different conditions；
Step S3: establishing meter and responsible consumer loses the SOP Bi-level Programming Models of load risk, and loses using based on non-dominated ranking
The hybrid optimization algorithm that the multi-objective Evolutionary Algorithm of propagation algorithm and cone planning combine solves SOP Bi-level Programming Models,
Obtain the best decision scheme for considering to lose load risk and performance driving economy.
2. multiterminal intelligence Sofe Switch Optimal Configuration Method as described in claim 1, which is characterized in that in Yu Suoshu step S1 also
Include:
Step S11: the production factors data by obtaining investment using indirect surveys method to responsible consumer；
Step S12: output efficiency is assessed by DEA process, obtains the relationship between production factors and output；
Step S13: extrapolating the variable quantity of output by IDEA method, obtains the loss of outage of responsible consumer.
3. multiterminal intelligence Sofe Switch Optimal Configuration Method as described in claim 1, which is characterized in that wrapped in Yu Suoshu step S2
It includes:
Step S21: multimode is established by the MMC reliability model and CTM Approach of bridge arm valve group reliability dynamic change
Reliability model；
Step S22: consider that the port the MMC maximum of SOP turns to turn the side for priority, to add up using probability for ability and responsible consumer
Formula obtains the mistake load risk of user under SOP different conditions.
4. multiterminal intelligence Sofe Switch Optimal Configuration Method as claimed in any one of claims 1-3, which is characterized in that Yu Suoshu
Include: in step S3
Step S31: establishing meter and responsible consumer loses the SOP Bi-level Programming Models of load risk；
Step S32: it is combined using the multi-objective Evolutionary Algorithm based on non-dominated sorted genetic algorithm (NSGA- II) with cone planning
Hybrid optimization algorithm SOP Bi-level Programming Models are solved, obtain and consider to lose the best of load risk and performance driving economy
Decision scheme.
5. multiterminal intelligence Sofe Switch Optimal Configuration Method as claimed in claim 4, which is characterized in that in step S31, use
Upper layer optimization planning, lower layer's running optimizatin mode establish SOP Bi-level Programming Models.
6. the multiterminal intelligence Sofe Switch Optimizing Configuration System of a kind of meter and mistake load risk characterized by comprising
Loss of outage acquiring unit constructs responsible consumer loss of outage by DEA and IDEA method based on the production factors data
Model obtains the loss of outage of responsible consumer；
Load risk acquiring unit is lost, the internal structure based on SOP constructs multi state reliability model, by quickly losing load
Risk Calculation method obtains the mistake load risk of user under SOP different conditions；
Decision scheme acquiring unit, establishes meter and responsible consumer loses the SOP Bi-level Programming Models of load risk, and using based on non-
The hybrid optimization algorithm that the multi-objective Evolutionary Algorithm of dominated Sorting Genetic Algorithm and cone planning combine is to SOP Bi-level Programming Models
It is solved, obtains the best decision scheme for considering to lose load risk and performance driving economy.
7. multiterminal intelligence Sofe Switch Optimizing Configuration System as claimed in claim 6, which is characterized in that loss of outage acquiring unit
Include:
Production factors data acquisition module, by the production factors data for obtaining investment using indirect surveys method to responsible consumer；
Output efficiency obtains module, assesses output efficiency by DEA process, obtains the relationship between production factors and output；
Loss of outage obtains module, and the variable quantity of output is extrapolated by IDEA method, obtains the loss of outage of responsible consumer.
8. multiterminal intelligence Sofe Switch Optimizing Configuration System as claimed in claim 6, which is characterized in that lose load risk and obtain list
Member includes:
Multi state reliability model constructs module, passes through the MMC reliability model and Ma Er of bridge arm valve group reliability dynamic change
Can husband's method establish multi state reliability model；
It loses load risk and obtains module, consider that the port the MMC maximum of SOP turns to turn to use for priority for ability and responsible consumer
The cumulative mode of probability obtains the mistake load risk of user under SOP different conditions.
9. the multiterminal intelligence Sofe Switch Optimizing Configuration System as described in any one of claim 6-8, which is characterized in that decision-making party
Case acquiring unit includes:
SOP Bi-level Programming Models construct module, and building meter and responsible consumer lose the SOP Bi-level Programming Models of load risk；
Decision scheme obtains module: being combined using the multi-objective Evolutionary Algorithm based on non-dominated sorted genetic algorithm with cone planning
Hybrid optimization algorithm SOP Bi-level Programming Models are solved, obtain and consider to lose the best of load risk and performance driving economy
Decision scheme.
10. the multiterminal intelligence Sofe Switch Optimizing Configuration System as described in right wants 9, which is characterized in that SOP Bi-level Programming Models structure
Modeling block establishes SOP Bi-level Programming Models by the way of upper layer optimization planning, lower layer's running optimizatin.
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