CN109510189A - Distribution network planning method based on Credibility Theory - Google Patents

Distribution network planning method based on Credibility Theory Download PDF

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Publication number
CN109510189A
CN109510189A CN201811289598.5A CN201811289598A CN109510189A CN 109510189 A CN109510189 A CN 109510189A CN 201811289598 A CN201811289598 A CN 201811289598A CN 109510189 A CN109510189 A CN 109510189A
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fuzzy
distribution network
power
constraint
model
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CN109510189B (en
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徐斌
马骏
丛宝松
陈青
刘红新
丁倩
徐璐
李葆
汤远红
汪君
段丽
张跃
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State Grid Corp of China SGCC
Liuan Power Supply Co of State Grid Anhui Electric Power Co Ltd
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State Grid Corp of China SGCC
Liuan Power Supply Co of State Grid Anhui Electric Power Co Ltd
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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
    • 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • 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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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The distribution network planning method based on Credibility Theory that the invention discloses a kind of, comprising the following steps: S1: the power distribution network Fuzzy Programming Model based on Credibility Theory is established；S2: the equivalence model of given confidence level target is established；S3: it is calculated using Fuzzy Power Flow and genetic algorithm solves model.The present invention is based on fuzzy mathematics theories to establish the distribution network planning model based on Credibility Theory, consider the fuzzy uncertainty of load, with power distribution network fixed investment in project period and the minimum objective function of fuzzy expectation cost of losses, confidence level target is introduced, branch power is handled using Fuzzy Chance Constraint and node voltage constrains；Secondly, using confidence level target equivalence theorem, by the distribution network planning model that the distribution network planning model equivalency under confidence level target is under load in section, so that Fuzzy Loads is distributed corresponding cut set is indicated with intuitive section, and method for solving reduces model solution difficulty, reduces the model solution time.

Description

Distribution network planning method based on Credibility Theory
Technical field
The present invention relates to Power System Planning fields, more particularly to a kind of distribution network planning side based on Credibility Theory Method.
Background technique
Traditional distribution network planning optimization method be based on determining projecting parameter, acquire meet the environmental constraints and pass through The optimal certainty programme of index of helping.Traditional planning method, which does not account for the uncertain factor in planning, to be influenced, and causes to advise Flexibility and the economy for drawing rack are poor.
For the processing of uncertain factor in distribution network planning, mainly there are stochastic programming, fuzzy programming, section rule at present It draws, scene analysis method and risk assessment method.The influence of the less fuzzy uncertainty for considering load of distribution network planning at present, distribution The content that network planning stroke is related to is very extensive, and mathematical model contains numerous variables and constraint again, therefore traditional planning algorithm is difficult Obtain optimal case.Existing uncertain programming model is based on uncertain programming theory, and model is complicated and solving speed is slower, Obtained scheme is also relatively conservative, and lacks the mathematical criterion of effective evaluation system risk level.
Credibility measure index is one of the important indicator in Credibility Theory, because having self-duality and subadditivity, It can judge whether event centainly occurs, risk can be quantified, along with the confidence level of Fuzzy Chance Constraint can control wind Danger.Therefore it is urgent to provide a kind of novel distribution network planning methods based on Credibility Theory to solve the above problems.
Summary of the invention
The distribution network planning method based on Credibility Theory that technical problem to be solved by the invention is to provide a kind of, can Consider the fuzzy uncertainty of load, the solution difficulty of built power distribution network Fuzzy Programming Model is low, it is short to solve the time.
In order to solve the above technical problems, one technical scheme adopted by the invention is that: it provides a kind of based on Credibility Theory Distribution network planning method, comprising the following steps:
S1: establish the power distribution network Fuzzy Programming Model based on Credibility Theory: with power distribution network fixed investment in project period and The minimum objective function of fuzzy expectation cost of losses introduces confidence level target, about using Fuzzy Chance Constraint processing branch power Beam and node voltage constraint；
S2: the equivalence model of given confidence level target is established: will be under confidence level target using confidence level target equivalence theorem Distribution network planning model equivalency be distribution network planning model under load in section, so that Fuzzy Loads is distributed corresponding cut set and use Intuitive section indicates；
S3: it is calculated using Fuzzy Power Flow and genetic algorithm solves model.
In a preferred embodiment of the present invention, in step sl, the objective function are as follows:
minZcost=Z1+β·Z2 (1)
Wherein, Zcost is power distribution network fixed investment and network loss total cost in project period；Z1For rack construction cost；Z2For year net Damage expense；β is cost of losses conversion factor, i.e. the planning time limit；Cj、LjRespectively unit length investment cost and line length；xj For decision variable；D is alternative line set；C0For unit electricity price；τjHourage is lost for each route peak load；For Each route obscures network loss；
The branch power constraint and node voltage constraint are respectively as follows:
Wherein,PjmaxEach route obscures active power, allows the line power upper limit；Vimax、ViminRespectively each section Point fuzziness voltage value allows voltage upper lower limit value；α is the model risk evaluation index introduced, i.e. confidence level target；Respectively route obscures reactive power, node obscures active power and reactive power；L is in certain programme Power distribution network circuitry number；N is the power distribution network number of nodes in certain programme.
In a preferred embodiment of the present invention, in step s 2, under given confidence level target α (>=0.5), formula (1)-formula (6) can be converted to formula (7)-formula (13) and be solved:
minZcost=Z1+β·Z2 (7)
Wherein, μξIt (y) is the subordinating degree function of Fuzzy Loads；Y is Fuzzy LoadsAll load values that can be obtained； The section representation of fuzzy variable is respectively corresponded in (formula (1)-formula (6)).
In a preferred embodiment of the present invention, the specific steps of step S3 include:
S3.1: input rack initial data and genetic algorithm basic parameter, the chromosome number including initial population；
S3.2: a binary-coded chromosome is randomly generated and carries out connection radiativity inspection, judges whether to meet company Logical emission requirements, if not satisfied, being then modified to rack；
S3.3: the distribution of the grid structure and Fuzzy Loads that are determined according to the chromosome, using fuzzy simulation to fuzzy negative Lotus is sampled, and to load data the being determined property Load flow calculation sampled each time, judges whether to meet node voltage Fuzzy Chance Constraint and Branch Power Flow Fuzzy Chance Constraint, if meet constraint, as in initial population one by one Body；
S3.4: repeating step S3.2-S3.3, the chromosome until generating initial population specified quantity；
S3.5: the target function value of all chromosomes, the as phase of the fixed investment of space truss project year and cost of losses are calculated Prestige value, for being unsatisfactory for the scheme of node voltage Fuzzy Chance Constraint and Branch Power Flow Fuzzy Chance Constraint, using penalty Method calculate chromosome fitness value；
S3.6: the smallest chromosome of fitness value in population is selected using wheel disc bet method；
S3.7: to the chromosome in population carry out intersect and mutation operation, obtain new generation chromosome, equally to its into The inspection of row node voltage Fuzzy Chance Constraint and Branch Power Flow Fuzzy Chance Constraint；
S3.8: repeating step S3.5-S3.7, arrives until chromosome reaches maximum allowable the number of iterations optimal Scheme.
Further, the chromosome number of the initial population is determined according to the scale of grid plan modification.
Further, described to meet node voltage Fuzzy Chance Constraint and the scheme of Branch Power Flow Fuzzy Chance Constraint is Using the scheme of Fuzzy Chance Constraint method of calibration assessment risk level.
Further, the desired value of cost of losses is calculated using Algorithm of Fuzzy Load Flow.
The beneficial effects of the present invention are:
(1) the present invention is based on fuzzy mathematics theories to establish the distribution network planning model based on Credibility Theory, considers first The fuzzy uncertainty of load, with power distribution network fixed investment in project period and the minimum objective function of fuzzy expectation cost of losses, Confidence level target is introduced, branch power is handled using Fuzzy Chance Constraint and node voltage constrains, had both reached control system risk Effect prevent again gained optimal case it is overly conservative；Secondly, using confidence level target equivalence theorem, it will be under confidence level target Distribution network planning model equivalency is the distribution network planning model under load in section, and Fuzzy Loads is enable to be distributed corresponding cut set with directly The section of sight indicates, and is calculated using Fuzzy Power Flow and solved with genetic algorithm, reduces model solution difficulty, reduces mould Type solves the time；
(2) the mentioned grid plan modification method of the present invention, suitable for the similar power distribution network for considering fuzzy uncertain factor power grid Frame planning has preferable normative and replicability.
Detailed description of the invention
Fig. 1 is the flow chart of the distribution network planning method the present invention is based on Credibility Theory；
Fig. 2 is the flow chart of the genetic algorithm.
Specific embodiment
The preferred embodiments of the present invention will be described in detail with reference to the accompanying drawing, so that advantages and features of the invention energy It is easier to be readily appreciated by one skilled in the art, so as to make a clearer definition of the protection scope of the present invention.
Referring to Fig. 1, the embodiment of the present invention includes:
A kind of distribution network planning method based on Credibility Theory, comprising the following steps:
S1: the power distribution network Fuzzy Programming Model based on Credibility Theory is established:
The confidence level concept that fuzzy theory axiomatic foundations based on measure theory introduce, so that all operations of fuzzy number can Based on confidence level, so that fuzzy evaluation be made to be quantified.The present invention is with power distribution network fixed investment in project period and fuzzy expectation net The minimum objective function of damage expense, it is contemplated that power-balance constraint, voltage constraint, trend constraint and be connected to radiativity constrain, draw Enter confidence level target, is constrained using Fuzzy Chance Constraint processing branch power constraint and node voltage；The objective function are as follows:
minZcost=Z1+β·Z2 (1)
Wherein, Zcost is power distribution network fixed investment and network loss total cost in project period；Z1For rack construction cost；Z2For year net Damage expense；β is cost of losses conversion factor, i.e. the planning time limit；Cj、LjRespectively unit length investment cost and line length；xj For decision variable；D is alternative line set；C0For unit electricity price；τjHourage is lost for each route peak load；For Each route obscures network loss；
The branch power constraint and node voltage constraint are respectively as follows:
Wherein,PjmaxEach route obscures active power, allows the line power upper limit；Vimax、ViminRespectively each section Point fuzziness voltage value allows voltage upper lower limit value；α is the model risk evaluation index introduced, i.e. confidence level target；Respectively route obscures reactive power, node obscures active power and reactive power；L is in certain programme Power distribution network circuitry number；N is the power distribution network number of nodes in certain programme.
Formula (4) and formula (5) are the form of Fuzzy Chance Constrained Programming.This constraint requirements power distribution network branch power and node electricity The confidence level that not out-of-limit credibility is previously given not less than policymaker is pressed, programme obtained by conventional constraint condition is avoided Overly conservative problem, while also functioning to the effect of control overrun risk.
S2: the equivalence model of given confidence level target is established:
Based on confidence level target equivalence theorem, the Fuzzy Programming Model under given confidence level target can be equivalent to Interval Programming Model.Therefore, under given confidence level target α (>=0.5), formula (1)-formula (6) can be converted to formula (7)-formula (13) and carry out It solves:
minZcost=Z1+β·Z2 (7)
Wherein, μξIt (y) is the subordinating degree function of Fuzzy Loads；Y is Fuzzy LoadsAll load values that can be obtained； The section representation of fuzzy variable is respectively corresponded in (formula (1)-formula (6)).Due to obscuring network lossIt is calculated using fuzzy expected value method, therefore without the form of equal value at interval number；Wherein, the confidence level known to formula (12) Index α need to be greater than 0.5, to avoid unsuitable out-of-limit risk.
Use confidence level target equivalence theorem by the distribution network planning model equivalency under confidence level target under load in section Distribution network planning model, so that Fuzzy Loads is distributed corresponding cut set is indicated with intuitive section；
S3: it is calculated using Fuzzy Power Flow and genetic algorithm solves model.
Specifically, Fuzzy Chance Constraint distribution network planning model is solved using the genetic algorithm based on Monte Carlo simulation, It is out-of-limit with penalty processing constraint function.Main solution procedure is as follows:
S3.1: input rack initial data and genetic algorithm basic parameter, chromosome number, intersection including initial population With mutation probability etc., wherein the chromosome number of the initial population is determined according to the scale of grid plan modification；
S3.2: a binary-coded chromosome is randomly generated and carries out connection radiativity inspection, judges whether to meet company Logical emission requirements, if not satisfied, being then modified to rack.Makeover process is divided into three kinds of situations: if there are rings for rack, at random Route in ring is disconnected, then carries out connection radiativity inspection；If rack is there are isolated island, unselected and connect the standby of the isolated island In route selection road, randomly selects a route and rack is added, then carry out connection radiation monitoring；If there is lonely chain in rack, not by It selects and rack is added with a route is selected in orphan's chain in the related alternative route of any node at random, then be connected to Radiativity inspection, until meeting connection emission requirements；
S3.3: the distribution of the grid structure and Fuzzy Loads that are determined according to the chromosome, using fuzzy simulation to fuzzy negative Lotus is sampled, and to load data the being determined property Load flow calculation sampled each time, judges whether to meet node voltage Fuzzy Chance Constraint and Branch Power Flow Fuzzy Chance Constraint, if meet constraint, as in initial population one by one Body；
The node voltage Fuzzy Chance Constraint and the scheme of Branch Power Flow Fuzzy Chance Constraint of meeting is using fuzzy The scheme of chance constraint method of calibration assessment risk level.Specific method step are as follows:
In conjunction with being examined in confidence level equivalence theorem and fuzzy simulationMethod, can It must examineMethod.
Known confidence level target α, for any given decision variable x, it is assumed thatFor n dimension obscure to The interval number of amount:
(1) k=0 is set；
(2) respectively from2 cut set sections (1- α) in one group of sample value is randomly generated
(3) ifMeetIt returns (2)；Conversely, then Fuzzy Chance Constraint is invalid, decision variable x is Infeasible solution.
(4) when k reaches largest sample number M,It can meetThen Fuzzy Chance Constraint condition is set up, should Decision variable x is feasible solution.
S3.4: repeating step S3.2-S3.3, the chromosome until generating initial population specified quantity；
S3.5: the target function value of all chromosomes, the as phase of the fixed investment of space truss project year and cost of losses are calculated Prestige value, for being unsatisfactory for the scheme of node voltage Fuzzy Chance Constraint and Branch Power Flow Fuzzy Chance Constraint, using penalty Method calculate chromosome fitness value；
Specifically, the desired value for calculating cost of losses uses Algorithm of Fuzzy Load Flow,To be defined on possibility space Fuzzy Loads vector on (Θ, p (Θ), Pos), network lossForFunction, which can be pushed away by Load flow calculation equation group ?Then the desired value of network loss is
Specific calculating process is as follows:
(1) e=0 is set；
(2) θ is uniformly generated from Θ respectivelyk, make Pos (θk) >=ε, enables vk=Pos (θk), k=1,2 ..., N, wherein ε be A sufficiently small number.
(3) it sets
(4) r is uniformly generated from [a, b]；
(5) if r >=0,
(6) if r < 0,
(7) step (4) to step (6) n times altogether are repeated；
Wherein, credible when N is sufficiently big to arbitrary r >=0It is approximately equal to
And to any r < 0, it is credible when N is sufficiently bigIt is approximately equal to
S3.6: the smallest chromosome of fitness value in population is selected using wheel disc bet method；
S3.7: to the chromosome in population carry out intersect and mutation operation, obtain new generation chromosome, equally to its into The inspection of row node voltage Fuzzy Chance Constraint and Branch Power Flow Fuzzy Chance Constraint；
S3.8: repeating step S3.5-S3.7, arrives until chromosome reaches maximum allowable the number of iterations optimal Scheme.
The present invention is based on fuzzy mathematics theories to establish the distribution network planning model based on Credibility Theory, considers the mould of load Paste is uncertain, with power distribution network fixed investment in project period and the minimum objective function of fuzzy expectation cost of losses, introduces credible Spend index, branch power and node voltage handled using Fuzzy Chance Constraint and constrained, not only achieved the effect that control system risk but also Prevent gained optimal case overly conservative；Secondly, using confidence level target equivalence theorem, by the distribution network planning under confidence level target Drawing model equivalency is the distribution network planning model under load in section, and Fuzzy Loads is enable to be distributed the intuitive section of corresponding cut set It indicates, and is calculated using Fuzzy Power Flow and solved with genetic algorithm, model solution difficulty is reduced, when reducing model solution Between；The mentioned grid plan modification method of the present invention, suitable for the similar Distribution Network Frame planning for considering fuzzy uncertain factor power grid, tool There is preferable normative and replicability.
The above description is only an embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills Art field, is included within the scope of the present invention.

Claims (7)

1. a kind of distribution network planning method based on Credibility Theory, comprising the following steps:
S1: it establishes the power distribution network Fuzzy Programming Model based on Credibility Theory: with power distribution network fixed investment in project period and obscuring It is expected that the minimum objective function of cost of losses, introduce confidence level target, using Fuzzy Chance Constraint processing branch power constraint and Node voltage constraint；
S2: the equivalence model of given confidence level target is established: using confidence level target equivalence theorem by matching under confidence level target Electric Power Network Planning model equivalency is the distribution network planning model under load in section, and Fuzzy Loads is enable to be distributed corresponding cut set with intuitively Section indicate；
S3: it is calculated using Fuzzy Power Flow and genetic algorithm solves model.
2. the distribution network planning method according to claim 1 based on Credibility Theory, which is characterized in that in step S1 In, the objective function are as follows:
minZcost=Z1+β·Z2 (1)
Wherein, Zcost is power distribution network fixed investment and network loss total cost in project period；Z1For rack construction cost；Z2For
Year cost of losses；β is cost of losses conversion factor, i.e. the planning time limit；Cj、LjRespectively unit length investment cost and line Road length；xjFor decision variable；D is alternative line set；C0For unit electricity price；τjHourage is lost for each route peak load；Network loss is obscured for each route；
The branch power constraint and node voltage constraint are respectively as follows:
Wherein,PjmaxEach route obscures active power, allows the line power upper limit；Vimax、ViminRespectively each node mould It pastes voltage value, allow voltage upper lower limit value；α is the model risk evaluation index introduced, i.e. confidence level target；Point It Wei not the fuzzy reactive power of route, the fuzzy active power of node and reactive power；L is the power distribution network branch in certain programme Number；N is the power distribution network number of nodes in certain programme.
3. the distribution network planning method according to claim 2 based on Credibility Theory, which is characterized in that in step S2 In, under given confidence level target α (>=0.5), formula (1)-formula (6) can be converted to formula (7)-formula (13) and be solved:
minZcost=Z1+β·Z2 (7)
Wherein, μξIt (y) is the subordinating degree function of Fuzzy Loads；Y is Fuzzy LoadsAll load values that can be obtained； The section representation of fuzzy variable is respectively corresponded in (formula (1)-formula (6)).
4. the distribution network planning method according to claim 1 based on Credibility Theory, which is characterized in that the tool of step S3 Body step includes:
S3.1: input rack initial data and genetic algorithm basic parameter, the chromosome number including initial population；
S3.2: being randomly generated a binary-coded chromosome and carry out connection radiativity inspection, judges whether to meet connection spoke Requirement is penetrated, if not satisfied, being then modified to rack；
S3.3: according to the chromosome determine grid structure and Fuzzy Loads distribution, using fuzzy simulation to Fuzzy Loads into Line sampling judges whether that meeting node voltage obscures to load data the being determined property Load flow calculation sampled each time Chance constraint and Branch Power Flow Fuzzy Chance Constraint, if meeting constraint, as the individual in initial population；
S3.4: repeating step S3.2-S3.3, the chromosome until generating initial population specified quantity；
S3.5: calculating the target function value of all chromosomes, the as desired value of the fixed investment of space truss project year and cost of losses, For being unsatisfactory for the scheme of node voltage Fuzzy Chance Constraint and Branch Power Flow Fuzzy Chance Constraint, using the method for penalty Calculate the fitness value of chromosome；
S3.6: the smallest chromosome of fitness value in population is selected using wheel disc bet method；
S3.7: intersection and mutation operation are carried out to the chromosome in population, new generation chromosome is obtained, equally it is saved The inspection of point voltage Fuzzy Chance Constraint and Branch Power Flow Fuzzy Chance Constraint；
S3.8: repeating step S3.5-S3.7, arrives optimal side until chromosome reaches maximum allowable the number of iterations Case.
5. the distribution network planning method according to claim 4 based on Credibility Theory, which is characterized in that the initial population The chromosome number of body is determined according to the scale of grid plan modification.
6. the distribution network planning method according to claim 4 based on Credibility Theory, which is characterized in that the satisfaction section The scheme of point voltage Fuzzy Chance Constraint and Branch Power Flow Fuzzy Chance Constraint is that Fuzzy Chance Constraint method of calibration is used to comment Estimate the scheme of risk level.
7. the distribution network planning method according to claim 4 based on Credibility Theory, which is characterized in that calculate network loss and take Desired value uses Algorithm of Fuzzy Load Flow.
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