CN107392418A - A kind of urban power distribution network network reconstruction method and system - Google Patents

A kind of urban power distribution network network reconstruction method and system Download PDF

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CN107392418A
CN107392418A CN201710428595.4A CN201710428595A CN107392418A CN 107392418 A CN107392418 A CN 107392418A CN 201710428595 A CN201710428595 A CN 201710428595A CN 107392418 A CN107392418 A CN 107392418A
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distribution network
node
power distribution
particle
network
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CN201710428595.4A
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罗海荣
焦龙
田蓓
杨雪红
梁剑
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国网宁夏电力公司电力科学研究院
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0631Resource planning, allocation or scheduling for a business operation
    • G06Q10/06313Resource planning in a project environment
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/004Artificial life, i.e. computers simulating life
    • G06N3/006Artificial life, i.e. computers simulating life based on simulated virtual individual or collective life forms, e.g. single "avatar", social simulations, virtual worlds or particle swarm optimisation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06Electricity, gas or water supply

Abstract

The present invention discloses a kind of urban power distribution network network reconstruction method and system.This method includes:Establish the multi-goal optimizing function for lacking the loss minimization that delivery, system averagely interruption duration, system averagely power off frequency and urban power distribution network system year;Determine the constraints of multi-goal optimizing function and the weight of each objective optimization function;Set each load bus in urban power distribution network switches on-off initial value;Initial value, multi-goal optimizing function and constraints are switched on-off according to each load bus, multi-goal optimizing function is solved using the multi-objective particle based on decomposition and differential evolution;According to switching on-off for each load bus in urban power distribution network corresponding to the solution of the minimum multi-goal optimizing function of weighted sum, urban power distribution network network reconfiguration is carried out.The present invention can optimize the via net loss of the urban power distribution network containing distributed power source and vehicle charging station, reduce operating cost, while urban power distribution network supply voltage quality is improved.

Description

A kind of urban power distribution network network reconstruction method and system
Technical field
The present invention relates to distribution network planning technical field, more particularly to a kind of urban power distribution network network reconstruction method and is System.
Background technology
In recent years, with the fast development of cities and towns economy, the demand impetus of electric energy to be shown in and risen, electric load increases year by year, City power distribution web frame is increasingly sophisticated, causes urban power distribution network network loss increasing year by year.Meanwhile China various energy in recent years Generated energy becomes clear day by day far from the power demand for meeting people, power cuts to limit consumption phenomenon, and imbalance between supply and demand increasingly sharpens, and city is passed through Ji development and the influence of people's living standard aggravate year by year.In order to solve problem above, country is honest to be pushed into urban power distribution network Intelligent construction and retrofit work, as the large-scale distributed energy and electric automobile charging station progressively access urban power distribution network, The features such as urban power distribution network scale is big, node is more, equipment is miscellaneous, the method for operation is more is more obvious.Need to carry by some way Economy, reliability and the security of high city power distribution network operation.
Network reconfiguration is the important means for improving the economy of city power distribution network operation, power supply reliability and security.It is logical Cross network reconfiguration to plan urban power distribution network, adjust system architecture, can make to make net while urban power distribution network is more economical Network has more excellent electric power quality.But the method for urban power distribution network network reconfiguration connects to novel energy and novel load at present The consideration entered is relatively fewer.Meanwhile current urban power distribution network Network Reconfiguration Algorithm has some problems, such as compiled based on binary system Code particle cluster algorithm is easily trapped into local convergence when carrying out population parsing, and produces substantial amounts of infeasible solutions, and amount of calculation is huge Greatly, time-consuming, and solution has very big limitation, it is difficult to tackles increasingly sophisticated urban power distribution network.
The content of the invention
The embodiment of the present invention provides a kind of urban power distribution network network reconstruction method and system, for distributed energy and electronic The urban power distribution network of the novel loads such as automobile access, is advantageous to urban power distribution network economy, safe and reliable operation.
First aspect, there is provided a kind of urban power distribution network network reconstruction method, including:Establish and lack delivery, system in system year Average interruption duration, system averagely power off the multi-goal optimizing function of the loss minimization of frequency and urban power distribution network;It is determined that The weight of the constraints of the multi-goal optimizing function and each objective optimization function;Set every in the urban power distribution network One load bus switches on-off initial value;According to each load bus to switch on-off initial value, the multiple target excellent Change function and the constraints, more mesh are solved using the multi-objective particle based on decomposition and differential evolution Mark majorized function;According in the urban power distribution network corresponding to the solution of the minimum multi-goal optimizing function of the weighted sum Each load bus switches on-off, and carries out urban power distribution network network reconfiguration.
Second aspect, there is provided a kind of urban power distribution network network reconfiguration system, including:Module is established, for establishing system year Lack the multiple target that delivery, system averagely interruption duration, system averagely power off the loss minimization of frequency and urban power distribution network Majorized function;Determining module, for determining the constraints of the multi-goal optimizing function and the power of each objective optimization function Weight;Setting module, initial value is switched on-off for set each load bus in the urban power distribution network;Solve module, For switching on-off initial value, the multi-goal optimizing function and the constraints according to each load bus, adopt The multi-goal optimizing function is solved with the multi-objective particle based on decomposition and differential evolution;Reconstructed module, use Each load according to the urban power distribution network corresponding to the solution of the minimum multi-goal optimizing function of the weighted sum Node switches on-off, and carries out urban power distribution network network reconfiguration.
So, the embodiment of the present invention can optimize the network of the urban power distribution network containing distributed power source and vehicle charging station Loss, operating cost is reduced, while urban power distribution network supply voltage quality is improved.
Brief description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below by institute in the description to the embodiment of the present invention The accompanying drawing needed to use is briefly described, it should be apparent that, drawings in the following description are only some implementations of the present invention Example, for those of ordinary skill in the art, without having to pay creative labor, can also be according to these accompanying drawings Obtain other accompanying drawings.
Fig. 1 is the flow chart of the urban power distribution network network reconstruction method of the embodiment of the present invention;
Fig. 2 is that the use of the embodiment of the present invention is more with the solution of the multi-objective particle of differential evolution based on decomposing The flow chart of the step of objective optimization function;
The flow chart for the step of Fig. 3 is the more new individual optimal particle of the embodiment of the present invention;
The flow chart for the step of Fig. 4 is renewal global optimum's particle of the embodiment of the present invention;
Fig. 5 is the structured flowchart of the urban power distribution network network reconfiguration system of the embodiment of the present invention;
Fig. 6 is the structure chart of the IEEE69 node power distribution networks of the embodiment of the present invention;
Fig. 7 is the design sketch of each node voltage before and after the IEEE69 node power distribution Webwebs network of the embodiment of the present invention reconstructs.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete Site preparation describes, it is clear that described embodiment is part of the embodiment of the present invention, rather than whole embodiments.Based on this hair Embodiment in bright, the every other implementation that those of ordinary skill in the art are obtained under the premise of creative work is not made Example, belongs to the scope of protection of the invention.
The embodiment of the present invention discloses a kind of urban power distribution network network reconstruction method, be directed in the large-scale distributed energy and A kind of meter proposed under charging station access urban power distribution network new scene and operating cost, power supply reliability, the city of the quality of power supply Power distribution network multiple-objection optimization network reconstruction method.As shown in figure 1, this method includes the steps:
Particle in the embodiment of the present invention is each on off state set (0 or 1) in power distribution network, and particle position is power distribution network pair Inductive switch state, particle rapidity are that power distribution network corresponds on off state correction.
Step S10:Establish and lack delivery, system average interruption duration system year, system averagely powers off frequency and city The multi-goal optimizing function of the loss minimization of city's power distribution network.
Wherein, the objective optimization function of system year scarce delivery is:
The objective optimization function of the average interruption duration of system is:
The objective optimization function that system averagely powers off frequency is:
Wherein, i be system in load bus, m be system in load bus summation, La(i)For the flat of load bus i Equal load, ΨiFor load bus i annual idle time, λiFor load bus i annual stoppage in transit frequency, SiFor load section Point i apparent energy.
The objective optimization function of the loss minimization of urban power distribution network is:
Wherein, f1For system losses, L is system branch number, kiRepresent to open on off state variable, 0,1 represents closure, riFor branch road i resistance, PiAnd QiThe active and reactive power that respectively branch road i flows through end, ViFor branch road endpoint node voltage.
Step S20:Determine the constraints of multi-goal optimizing function and the weight of each objective optimization function.
Preferably, constraints includes:The constraint of distribution network topological structure, node power Constraints of Equilibrium, line power are about Beam, distributed power source power constraint, node voltage constraint and node current constraint.
Specifically, the constraint of distribution network topological structure includes:G ∈ G, wherein, g is the net that the target of power distribution network reconfiguration is found Network topological structure, G are all topological structures for meeting network constraint condition.
Node power Constraints of Equilibrium includes:With Wherein, PisAnd QisRespectively node i active injection and idle injection;UiFor node i voltage magnitude;J ∈ i represent node j and section Point i is connected;GijAnd BijThe respectively real and imaginary parts of bus admittance matrix;θijPhase difference of voltage between node i and j.
Line power constraint includes:Si< Simax, i=1,2 ... ..., n, wherein, SiFor circuit actual transmission power, Simax For the maximum capacity of circuit i conveying, n is the number of node.
Distributed power source power constraint includes:Wherein, SDGi,maxFor i-th of distributed electrical The maximum apparent energy in source, PDGiFor the active power of i-th of distributed power source, QDGiFor the idle work(of i-th of distributed power source Rate.
Node voltage constraint includes:Uimin≤Ui≤Uimax, i=1,2 ... ..., m, wherein, UiminFor node i minimum allowable Voltage, UimaxFor node i maximum permissible voltage, m is the number of node;
Power conserving current constraint includes:Iimin≤Ii≤Iimax, i=1,2 ... ..., m, wherein, IiminFor node i minimum allowable Electric current, IimaxFor node i maximum allowed current, m is the number of node.
Weight method to set up:Each object function is on influenceing accounting, example after the combination of micro-judgment object function expected from optimization Weight as lacked the objective optimization function of delivery system year is 0.1, the objective optimization function of the average interruption duration of system Weight be 0.3, system averagely power off the objective optimization function of frequency weight be 0.4, the loss minimization of urban power distribution network The weight of objective optimization function be 0.2, then in step S40 general objective functional value for each target function value solution be multiplied by corresponding to After weight plus and.
Step S30:Set each load bus in urban power distribution network switches on-off initial value.
The initial value that switches on-off of each load bus in urban power distribution network is preset by the step.According to every One default result, follow-up step can be used to solve multi-goal optimizing function.
Step S40:Initial value, multi-goal optimizing function and constraints are switched on-off according to each load bus, adopted The multi-goal optimizing function is solved with the multi-objective particle based on decomposition and differential evolution.
Specifically, step S40 can solve objective optimization based on decomposing with the multi-objective particle of differential evolution Parameter.As shown in Fig. 2 the algorithm comprises the following steps that:
Step S401:Urban power distribution network initial information is inputted, sets population scale.
The initial information of the urban power distribution network includes solving the parameter needed for multi-goal optimizing function.
Step S402:N number of inertia weight vector is produced according to homogenization direction vector, and sets iterations.
The step is equivalent to the weight for setting each objective optimization function, i.e., by the multiple-objection optimization of N number of objective optimization function Problem is converted into the weighted sum problem of N number of single-goal function optimization problem.
Step S403:More new individual optimal particle.
Specifically, as shown in figure 3, step S403 includes following process:
Step S4031:Input population information.
Step S4032:Carry out Load flow calculation.
The objective optimization functional value of current state can be obtained by the step.
After vehicle charging station and distributed power source are incorporated to power distribution network, according to its access running situation in a network, distribution Formula power supply DG can be represented with three class nodes:The constant type nodes of PQ, the constant type nodes of PV and the constant type nodes of PI.Carrying out power distribution network During Load flow calculation, different node types need to be directed to and construct corresponding mathematical modeling.
(1) the constant DG models of PQ
Current newly-built grid-connected Wind turbines use synchronizing direct-drive and double fed induction generators, such DG and watt level more Equal load is compared, and simply therefore power flow direction is on the contrary, can be considered the constant type nodes of PQ.When distributed power source is that PQ is constant During type node, the model of Load flow calculation is:
Wherein, P1sAnd Q1sThe constant type DG of respectively PQ active power and reactive power.
(2) the constant DG models of PV
Vehicle charging station and energy-storage battery it is grid-connected output voltage it is constant, active power of output is controllable, for energy storage device Load reservoir electric energy is can not only be used in power network, distributed power source can be used as to be powered to power network again.When being operated in rectification state When, energy storage device is in charged state, and energy flows to DC side from grid side;When being operated in inverter mode, at energy storage device In discharge condition, by the energy feedback of DC side to power network.Therefore the constant type nodes of PV are considered as.When distributed power source is PV During constant type node, the model of the Load flow calculation in discharge condition energy-storage battery is:
Wherein, P2sAnd V2sThe constant type DG of respectively PV active power and voltage.
The model of charging electric vehicle and Load flow calculation in charged state energy-storage battery is:
Wherein, P3sAnd V3sRespectively charging electric vehicle and active power and voltage in charged state energy-storage battery.
(3) the constant DG models of PI
It is grid-connected to use voltage-source type current control inverter more.Therefore, carry out regarding photovoltaic during Load flow calculation as PI perseverances Sizing node.When distributed power source type node constant for PI, the model of the Load flow calculation of photovoltaic is:
Wherein, P4sAnd I4sThe constant type DG of respectively PI active power and electric current.
Step S4033:Update particle rapidity and position.
Update particle rapidity and position respectively according to following formula:
Wherein:Speed and position for i-th of particle in t generations,For i-th of t generations Particle individual optimal particle and global optimum's particle, w are that inertia constant takes 0.5, c1、c2For two Studying factors, r1、r2For two Random number between individual (0,1).
Because particle is on off state set in the embodiment of the present invention, only comprising 0,1 two states, therefore particle position is updated Only negated.
Step S4034:According to the particle position after renewal, Load flow calculation is carried out again.
Step S4035:Judge the particle position after renewal whether better than the particle position not updated.
If so, then carry out step S4036;Otherwise, step S4037 is carried out.
Step S4036:Update individual particles optimal location.
Step S4037:Keep individual particles optimal location.
Step S4038:By the new set correspondence position of information write-in of individual particles.
Step S4039:Judge whether to have traveled through all particles.
If so, then carry out step S40310;Otherwise, step S4033 is carried out.
Step S40310:Export individual optimal particle group.
Step S404:Update global optimum's particle.
Specifically, as shown in figure 4, step S404 includes following process:
Step S4041:New population is merged with old population, forms the population that scale is 2N.
Step S4042:Obtain the aggregate weight ω that each particle corresponds to objective optimization function.
ω=ω0+ρ(1-ω0)。
Wherein:ω0Take 0.5;ρ obeys [0,1] equally distributed random number.
Step S4043:Make the minimum scale of objective optimization function summation after weighting is asked in the population that scale is 2N For N global optimum's population.
Step S4044:Export global optimum's population.
Step S405:Judge whether to reach maximum iteration.
If so, then carry out step S406;Otherwise, return to step S403, until reaching maximum iteration.
Step S406:Export population.
By step S40, the algorithm is applied to distributed energy and vehicle charging station accesses urban power distribution network scene, is asked Solution considers to reduce network loss and improves the multiple target urban power distribution network reconstruction of power supply reliability simultaneously.Pass through constructed base In decompose and the multi-objective particle of differential evolution can reduce the via net loss of urban power distribution network with optimize operation into This, while the power supply reliability of overall lifting urban power distribution network is horizontal.Therefore, this method has notable social and economic benefit.
Step S50:According to each negative in urban power distribution network corresponding to the solution of the minimum multi-goal optimizing function of weighted sum Lotus node switches on-off, and carries out urban power distribution network network reconfiguration.
Wherein, the weighted sum is according to the solution of multi-goal optimizing function and the Weight Acquisition of each objective optimization function.Specifically , the solution of each objective optimization function is multiplied by the product that its corresponding weight obtains, then sums.
To sum up, the urban power distribution network network reconstruction method of the embodiment of the present invention, delivery, system are lacked by selecting system year The average power off time of System average interruption frequency, system as reliability optimization target, respectively from the power failure electricity of system, frequency, when Between three angles make more comprehensive evaluation to reliability, while active loss is chosen as network loss optimization aim, based on decomposition With Multi-variables optimum design mechanism in urban power distribution network network reconfiguration of the multi-objective particle of differential evolution and changeable Encoding mechanism is measured, optional node in urban power distribution network is reconstructed, while to distributed energy and electric automobile charging station Multinomial variable is optimized, and ensures distribution network radially, meets feeder line thermal capacitance, voltage landing requirement and transformer capacity Deng under the premise of, consider that the distinctive distributed energy of urban power distribution network generates electricity and the problem of charging electric vehicle, by changing line The on off state of way switch, change the power supply approach of user, to ensure that urban power distribution network network loss and supply voltage quality are in most The good distribution method of operation, the via net loss of the urban power distribution network containing distributed power source and vehicle charging station can be optimized, dropped Low operating cost, while urban power distribution network reliability is improved;This method automatically generates multigroup suggested design, operating personnel Suitable scheme can be flexibly selected voluntarily according to balance economy and reliability requirement is actually needed.
The embodiment of the present invention also provides a kind of urban power distribution network network reconfiguration system.As shown in figure 5, the system includes:
Module 501 is established, is averagely powered off for establishing system year scarce delivery, the average interruption duration of system, system The multi-goal optimizing function of the loss minimization of frequency and urban power distribution network;
Determining module 502, for determining the constraints of multi-goal optimizing function;
Setting module 503, for setting switching on-off for each load bus in urban power distribution network.
Module 504 is solved, for according to the switching on-off of each load bus, multi-goal optimizing function and constraint bar Part, multi-goal optimizing function is solved using the multi-objective particle based on decomposition and differential evolution.
Reconstructed module 505, in urban power distribution network corresponding to the solution according to the minimum multi-goal optimizing function of weighted sum Each load bus switch on-off, carry out urban power distribution network network reconfiguration.
To sum up, the urban power distribution network network reconfiguration system of the embodiment of the present invention, can optimize containing distributed power source and vapour The via net loss of the urban power distribution network of car charging station, operating cost is reduced, while urban power distribution network reliability is improved;This Method automatically generates multigroup suggested design, and operating personnel voluntarily basis can be actually needed balance economy and reliability requirement, Flexibly selection suitable scheme.
Specifically, using IEEE69 node power distributions network as example, the system has 69 nodes, 74 circuits, 5 connection Network switchs, total load 3802.2kW+j2694.6kvar, and the network structure is as shown in Figure 6.Pass through above-mentioned specific example The method for verifying the embodiment of the present invention.
In IEEE69 node test examples, node 27,30,39 is given a dinner for a visitor from afar power station, rated capacity 200kW;Node 41, 48th, 56 photovoltaic plant, rated capacity 100kW are met;Node 4,50,68 meets energy-accumulating power station, rated capacity 125kW;Node 3, 19th, 49 vehicle charging station, maximum capacity 100kW are met.The switch of disconnection is before reconstruct:11-66、13-20、15-69、27-54、 39-48.After being reconstructed by optimized algorithm, it is determined that the switch disconnected is:14-15,44-45,50-51,11-66,13-20, more mesh The weighted sum for marking majorized function is minimum.
Using the method for the embodiment of the present invention, the power distribution network after optimal reconfiguration scheme is selected, network loss reduces 42.92%, variation index improves 53.21%.This patent only shows the variation index of one of object function, such as schemes It is each node voltage before and after the reconstruct of 69 node power distribution Webweb networks shown in 7.Lowest section of the reconstructing method to 69 node distributions Point voltage is lifted, and improves whole network voltage distribution, effectively increases the economy and power supply reliability of urban power distribution network.
The foregoing is only a specific embodiment of the invention, but protection scope of the present invention is not limited thereto, any Those familiar with the art the invention discloses technical scope in, change or replacement can be readily occurred in, should all be contained Cover within protection scope of the present invention.Therefore, protection scope of the present invention should be defined by scope of the claims.

Claims (10)

  1. A kind of 1. urban power distribution network network reconstruction method, it is characterised in that including:
    Establish and lack the net that delivery, system averagely interruption duration, system averagely power off frequency and urban power distribution network system year Damage minimum multi-goal optimizing function;
    Determine the constraints of the multi-goal optimizing function and the weight of each objective optimization function;
    Set each load bus in the urban power distribution network switches on-off initial value;
    Initial value, the multi-goal optimizing function and the constraints are switched on-off according to each load bus, adopted The multi-goal optimizing function is solved with the multi-objective particle based on decomposition and differential evolution;
    According to each negative in the urban power distribution network corresponding to the solution of the minimum multi-goal optimizing function of the weighted sum Lotus node switches on-off, and carries out urban power distribution network network reconfiguration.
  2. 2. according to the method for claim 1, it is characterised in that
    The system year lack delivery objective optimization function be:
    The objective optimization function of the average interruption duration of the system is:
    The objective optimization function that the system averagely powers off frequency is:
    Wherein, i be system in load bus, m be system in load bus summation, La(i)For the average negative of load bus i Lotus, ΨiFor load bus i annual idle time, λiFor load bus i annual stoppage in transit frequency, SiFor load bus i Apparent energy.
  3. 3. according to the method for claim 1, it is characterised in that the objective optimization letter of the loss minimization of the urban power distribution network Number is:
    <mrow> <msub> <mi>f</mi> <mn>1</mn> </msub> <mo>=</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>L</mi> </munderover> <msub> <mi>k</mi> <mi>i</mi> </msub> <msub> <mi>r</mi> <mi>i</mi> </msub> <mfrac> <mrow> <msubsup> <mi>P</mi> <mi>i</mi> <mn>2</mn> </msubsup> <mo>+</mo> <msubsup> <mi>Q</mi> <mi>i</mi> <mn>2</mn> </msubsup> </mrow> <msubsup> <mi>V</mi> <mi>i</mi> <mn>2</mn> </msubsup> </mfrac> <mo>;</mo> </mrow>
    Wherein, f1For system losses, L is system branch number, kiRepresent to open on off state variable, 0,1 represents closure, riFor Branch road i resistance, PiAnd QiThe active and reactive power that respectively branch road i flows through end, ViFor branch road endpoint node voltage.
  4. 4. according to the method for claim 1, it is characterised in that the constraints includes:Distribution network topological structure is about Beam, node power Constraints of Equilibrium, line power constraint, the constraint of distributed power source power constraint, node voltage and node current are about Beam.
  5. 5. according to the method for claim 4, it is characterised in that
    The distribution network topological structure constraint includes:G ∈ G, wherein, g is that G is all by reconstructing the network structure found Meet the set of the topological structure of network constraint condition;
    The node power Constraints of Equilibrium includes:With Wherein, PisAnd QisRespectively node i active injection and idle injection;UiFor node i voltage magnitude;J ∈ i represent node j and section Point i is connected;GijAnd BijThe respectively real and imaginary parts of bus admittance matrix;θijPhase difference of voltage between node i and j;
    The line power constraint includes:Si< Si max, i=1,2 ... ..., n, wherein, SiFor circuit actual transmission power, Simax For the maximum capacity of circuit i conveying, n is the number of node;
    The distributed power source power constraint includes:Wherein, SDGi,maxFor i-th of distributed electrical The maximum apparent energy in source, PDGiFor the active power of i-th of distributed power source, QDGiFor the idle work(of i-th of distributed power source Rate;
    The node voltage constraint includes:Uimin≤Ui≤Ui max, i=1,2 ... ..., m, wherein, UiminFor node i minimum allowable Voltage, UimaxFor node i maximum permissible voltage, m is the number of node;
    The node current constraint includes:Iimin≤Ii≤Ii max, i=1,2 ... ..., m, wherein, IiminFor node i minimum allowable Electric current, IimaxFor node i maximum allowed current, m is the number of node.
  6. 6. according to the method for claim 1, it is characterised in that the switching on-off according to each load bus Initial value, the multi-goal optimizing function and the constraints, using excellent with the multi-objective particle swarm of differential evolution based on decomposing The step of changing multi-goal optimizing function described in Algorithm for Solving, including:
    Urban power distribution network initial information is inputted, sets population scale;
    N number of inertia weight vector is produced according to homogenization direction vector, and sets iterations;
    More new individual optimal particle;
    Update global optimum's particle;
    Judge whether to reach maximum iteration;
    If so, then export population;Otherwise, the step of returning to more new individual optimal particle, the step of renewal global optimum particle Suddenly the step of and judging whether to reach maximum iteration, until reaching maximum iteration.
  7. 7. according to the method for claim 6, it is characterised in that:
    When distributed power source type node constant for PQ, the model of the Load flow calculation is:Wherein, P1s And Q1sThe constant type DG of respectively PQ active power and reactive power;
    When distributed power source type node constant for PV, the model of the Load flow calculation in discharge condition energy-storage battery For:Wherein, P2sAnd V2sThe constant type DG of respectively PV active power and voltage;Charging electric vehicle and place It is in the model of the Load flow calculation of charged state energy-storage battery:Wherein, P3sAnd V3sRespectively electric automobile Charging and active power and voltage in charged state energy-storage battery;
    When distributed power source type node constant for PI, the model of the Load flow calculation of photovoltaic is: Wherein, P4sAnd I4sThe constant type DG of respectively PI active power and electric current.
  8. 8. according to the method for claim 6, it is characterised in that the step of the more new individual optimal particle group, including:
    Input population information;
    Carry out Load flow calculation;
    Update particle rapidity and position;
    According to the particle position after renewal, Load flow calculation is carried out again;
    Judge the particle position after renewal whether better than the particle position not updated;
    If so, then update individual particles optimal location;Otherwise, individual particles optimal location is kept;
    By the new set correspondence position of information write-in of the individual particles;
    Judge whether to have traveled through all particles;
    If so, then export individual optimal particle group;Otherwise, renewal particle rapidity and position step are returned.
  9. 9. according to the method for claim 6, it is characterised in that described the step of updating global optimum's particle, including:
    New population is merged with old population, forms the population that scale is 2N;
    Obtain the aggregate weight of objective optimization function corresponding to each particle;
    Make global optimum's grain that the minimum scale of objective optimization function summation is N after weighting is asked in the population that scale is 2N Subgroup;
    Export global optimum's population.
  10. A kind of 10. urban power distribution network network reconfiguration system, it is characterised in that including:
    Module is established, frequency and city are averagely powered off for establishing system year scarce delivery, the average interruption duration of system, system The multi-goal optimizing function of the loss minimization of city's power distribution network;
    Determining module, for determining the constraints of the multi-goal optimizing function and the weight of each objective optimization function;
    Setting module, initial value is switched on-off for set each load bus in the urban power distribution network;
    Module is solved, for switching on-off initial value, the multi-goal optimizing function and institute according to each load bus Constraints is stated, the multiple-objection optimization letter is solved using the multi-objective particle based on decomposition and differential evolution Number;
    Reconstructed module, for the city power distribution corresponding to the solution according to the minimum multi-goal optimizing function of the weighted sum Each load bus in net switches on-off, and carries out urban power distribution network network reconfiguration.
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