CN105552892A - Distribution network reconfiguration method - Google Patents

Distribution network reconfiguration method Download PDF

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Publication number
CN105552892A
CN105552892A CN201511005653.XA CN201511005653A CN105552892A CN 105552892 A CN105552892 A CN 105552892A CN 201511005653 A CN201511005653 A CN 201511005653A CN 105552892 A CN105552892 A CN 105552892A
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China
Prior art keywords
distribution network
chromosome
fitness
power distribution
node
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CN201511005653.XA
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Chinese (zh)
Inventor
冯煜尧
崔勇
苏运
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State Grid Shanghai Electric Power Co Ltd
East China Power Test and Research Institute Co Ltd
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State Grid Shanghai Electric Power Co Ltd
East China Power Test and Research Institute Co Ltd
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Priority to CN201511005653.XA priority Critical patent/CN105552892A/en
Publication of CN105552892A publication Critical patent/CN105552892A/en
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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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • 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]
    • 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
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a distribution network reconfiguration method, which comprises the following steps: building a distribution network load optimization model; and optimizing the distribution network load optimization model on the basis of a genetic algorithm, and obtaining a distribution network reconfiguration scheme. The reconfiguration is carried out on a distribution network on the basis of a genetic method; and load reconfiguration is carried out by changing the state of a switch, so that line loss of the distribution network is reduced; the benefits are improved; and the power supply reliability and safety are improved.

Description

A kind of reconstruction method of power distribution network
Technical field
The present invention relates to field of distribution network, be specifically related to a kind of reconstruction method of power distribution network.
Background technology
The loss of Line Loss of Distribution Network System especially 10kV low-pressure side circuit, being that power distribution network loses one of more link, is one of emphasis of Controlling line loss work.For ensureing the reliability that electric power networks is powered and fail safe, the mode of connection of mesolow distribution network and operational mode become and become increasingly complex, and tend to diversification gradually, and many circuits are got in touch with mutually, carry out the powering mode of looped network.At present, For Distribution Networks Reconfiguration is except the simple heuritic approach that minority adopts physically based deformation model, and great majority adopt intelligent algorithm to solve this problem.Distribution network has the mandatory of radial open loop operation and will be subordinate to, Existing methods ubiquity in code Design can not ensure the disadvantage that network is feasible, in computational process, inevitably producing a large amount of infeasible network containing ring or isolated island, making to need in computational process to verify network continually and for obtaining active block and repeatable operation repairs invalid network.The existing operator of coding method structure and the change of actual distribution network structure simultaneously contacts not tight.Above-mentioned all deficiencies cause existing intelligent algorithm to there is the problem of many, the consuming time length of iterations, are difficult to the reconstruct applying to solve large-scale distribution network network.
Summary of the invention
The invention provides a kind of reconstruction method of power distribution network, based on genetic method, distribution is reconstructed, balance the load factor of heavily loaded transformer, reduce network loss, improve power supply reliability.
For achieving the above object, the present invention puies forward a kind of reconstruction method of power distribution network, is characterized in, the method comprises:
Set up distribution network load Optimized model;
Based on genetic algorithm, distribution network load Optimized model is optimized, obtains power distribution network reconfiguration scheme.
Above-mentioned distribution network load Optimized model of setting up comprises:
Set up distribution network load topological structure;
Set up target function;
Determine constraints.
The above-mentioned method setting up target function comprises:
Take loss minimization as target function;
The computing formula PLoss of distribution network loss is such as formula shown in (1);
P L o s s = Σ i = 1 n b k i r i | I · i | 2 - - - ( 1 )
In formula (1): nb is the circuitry number of power distribution network; Ri is the resistance of the i-th branch road; for flowing through the electric current of i-th branch road; Ki is the state of switch (node) i, and ki=0 represents disjunction, and ki=1 represents closed;
Distribution network loss PLoss is obtained by Load flow calculation.
Above-mentioned constraints comprises: tributary capacity constraint and node voltage constraint;
Above-mentioned tributary capacity is constrained to the maximum power that the power flowing through branch road can not be greater than this branch road, shown in (2):
P ij≤P ijmax
Q ij≤Q ijmax(2)
In formula (2), P ij, P ijmaxbe respectively the active power of branch road i-j and the meritorious maximum of branch road i-j; Q ij, Q ijmaxbe respectively the reactive power of branch road i-j and the idle maximum of branch road i-j;
The voltage that above-mentioned node voltage is constrained to node can not exceed the voltage bound that this node allows to pass through, shown in (3):
U imin≤U i≤U imax(3)
In formula (3), U i, U imin, U imaxbe respectively the voltage max of the voltage of node i, the voltage minimum of node i and node i.
Above-mentionedly based on genetic algorithm, the method that distribution network load Optimized model is optimized to be comprised:
The initial network coding of power distribution network generates chromosome;
Decoding chromosome obtains chromosomal fitness;
Select the male parent for breeding filial generation individual according to fitness;
Male parent is individual successively carries out interlace operation and mutation operation;
Iteration exports the highest chromosome of fitness and corresponding reconfiguration scheme.
The initial network coding of above-mentioned power distribution network generates chromosome and comprises:
Distribution network adopts breadth First spanning tree algorithm to produce an initial network;
This initial network carries out coding generation chromosome;
This chromosome generates N number of chromosome by mutation operator, and N number of chromosome forms the initial population of genetic algorithm, and N is even number;
Wherein, each chromosome correspondence one can realize the radial distribution networks of operation.
Above-mentioned decoding chromosome obtains chromosomal fitness and comprises:
All chromosome is decoded according to the inverse process of coding;
All chromosome obtains the network loss of initial network through Load flow calculation; Each chromosome obtain network loss inverse be taken as this chromosomal fitness;
Wherein, if the node voltage of initial network and line power out-of-limit, then chromosomal fitness is taken as zero.
Above-mentioned according to fitness select comprise for the male parent individuality of breeding filial generation:
The mode that fitness retains with elite higher than the chromosome of predetermined threshold value directly copies to filial generation, and the number that elite retains is M, M is even number;
Fitness is individual for the male parent of breeding filial generation with half random half optimum method choice (N-M)/2 lower than the chromosome of predetermined threshold value.
The individual priority of above-mentioned male parent carries out interlace operation and mutation operation comprises:
Even number male parent individuality carries out interlace operation by by crossover operator;
After interlace operation, the individuality that intersection obtains is made a variation by mutation operator, completes breeding and obtain child chromosome group;
Wherein crossing-over rate is set to 0.5; Aberration rate is set to 1.
Above-mentioned iteration exports the highest chromosome of fitness and corresponding reconfiguration scheme comprises:
Repeat decoding chromosome obtaining chromosomal fitness, selecting the male parent for breeding filial generation individual according to fitness, and the individual priority of male parent carries out interlace operation and mutation operation, carries out iteration;
Judge whether the chromosome that fitness is the highest continues to keep filial generation constant, if then stop iteration exporting the reconfiguration scheme of the highest chromosome of fitness and correspondence thereof; Then proceed iteration if not.
Compared to the prior art, its advantage is a kind of reconstruction method of power distribution network of the present invention, the present invention is based on genetic method and is reconstructed distribution, load reconstruct is carried out by the state changing switch, reduce the line loss of power distribution network, Improve Efficiency, improve power supply reliability and fail safe.
Accompanying drawing explanation
Fig. 1 is the method flow diagram of reconstruction method of power distribution network of the present invention;
Fig. 2 is the topological diagram of ieee standard 3 feeder line 16 Node power distribution system;
Fig. 3 is the flow chart be optimized distribution network load Optimized model based on genetic algorithm.
Embodiment
Below in conjunction with accompanying drawing, further illustrate specific embodiments of the invention.
As shown in Figure 1, the invention discloses a kind of reconstruction method of power distribution network based on genetic method, the method specifically comprises following steps:
S1, set up distribution network load Optimized model.
S1.1, set up distribution network load topological structure.
First to power distribution network Data acquisition: determine the object analyzed, nodes, circuit number, switch number, and circuit and node parameter, comprise line resistance, node burden with power and load or burden without work.
Then analyze and draw the topology diagram of system, determining the position of switch, each node and on off state, and circuit and node parameter.
As shown in Figure 2, be the topological diagram of ieee standard 3 feeder line 16 Node power distribution system.Wherein, sequence number (1)-(16) represent load, and sequence number 1-16 represents switch, and the on off state before reconstruct is: switch 4,11 and 13 disconnects, and rest switch is all closed.
S1.2, set up target function, it comprises:
S1.2.1, be target function with loss minimization.
The computing formula PLoss of S1.2.2, distribution network loss is such as formula shown in (1);
P L o s s = Σ i = 1 n b k i r i | I · i | 2 - - - ( 1 )
In formula (1): nb is the circuitry number of power distribution network; Ri is the resistance of the i-th branch road; for flowing through the electric current of i-th branch road; Ki is the state of switch (node) i, and ki=0 represents disjunction, and ki=1 represents closed;
S1.2.3, obtain distribution network loss PLoss by Load flow calculation.
S1.3, determine constraints.Constraints comprises: tributary capacity constraint and node voltage constraint.
Tributary capacity is constrained to the maximum power that the power flowing through branch road can not be greater than this branch road, shown in (2):
P ij≤P ijmax
Q ij≤Q ijmax(2)
In formula (2), P ij, P ijmaxbe respectively the active power of branch road i-j and the meritorious maximum of branch road i-j; Q ij, Q ijmaxbe respectively the reactive power of branch road i-j and the idle maximum of branch road i-j;
The voltage that node voltage is constrained to node can not exceed the voltage bound that this node allows to pass through, shown in (3):
U imin≤U i≤U imax(3)
In formula (3), U i, U imin, U imaxbe respectively the voltage max of the voltage of node i, the voltage minimum of node i and node i.
S2, as shown in Figure 3, based on genetic algorithm, distribution network load Optimized model to be optimized, to obtain power distribution network reconfiguration scheme.
The initial network coding of S2.1, power distribution network generates chromosome.
S2.1.1, will treat that reconstruct distribution network all switches close, distribution network adopts breadth First spanning tree algorithm to produce an initial network.
S2.1.2, this initial network carry out coding generation chromosome.
S2.1.3, this chromosome are that source generates N number of chromosome by mutation operator, and N number of chromosome forms the initial population of genetic algorithm, and N is even number; Wherein, each chromosome correspondence one can realize the radial distribution networks of operation.
S2.2, decoding chromosome obtain chromosomal fitness.
S2.2.1, all chromosome are decoded according to the inverse process of coding;
S2.2.2, all chromosome obtain the network loss of initial network through Load flow calculation; Each chromosome obtain network loss inverse be taken as this chromosomal fitness.Here simultaneously check-node voltage and line power, if the node voltage of initial network and line power out-of-limit, then chromosomal fitness is taken as zero.
S2.3, select the male parent for breeding filial generation individual according to fitness.
S2.3.1, first to the chromosome of fitness higher than predetermined threshold value, the mode retained with elite without cross and variation directly copies to filial generation, the elite's retention strategy namely extensively adopted, and the number that elite retains is M, and M is even number.
S2.3.2, fitness, lower than the chromosome of predetermined threshold value, adopt half random half optimum method choice (N-M)/2 individual for the male parent of breeding filial generation.
S2.4, male parent are individual successively carries out interlace operation and mutation operation.
S2.4.1, to selecting (N-M)/2 that obtain a male parent individuality to carry out interlace operation by certain probability by crossover operator in S2.3.2.
After S2.4.2, interlace operation, by certain probability, the individuality that intersection obtains is made a variation by mutation operator, complete breeding and obtain child chromosome group.Wherein intersect that meeting with the probability of variation makes a variation and be lead, to intersect be auxiliary principle: crossing-over rate is set as 0.5; Aberration rate is set as l.
S2.5, iteration export the highest chromosome of fitness and corresponding reconfiguration scheme.
Repeat decoding chromosome obtaining chromosomal fitness, selecting the male parent for breeding filial generation individual according to fitness, and the individual priority of male parent carries out interlace operation and mutation operation, carries out iteration.Some generations are kept not become the condition of convergence continuously with the chromosome that fitness in colony is the highest.
In an iterative process, judge whether the chromosome that fitness is the highest continues to keep the filial generation in some generations constant, if so, meets the condition of convergence, then stop iteration exporting reconfiguration scheme and the network loss of the highest chromosome of fitness and correspondence thereof, this result is optimum; If not, then jump to S2.3, proceed iteration.
S2.6, complete above-mentioned calculating after, according to result of calculation, select optimal case according to table 1, the state of its each switch is as shown in table 2.
Network loss value under table 1, different on off state
Switch number On off state Switch number On off state Switch number On off state
1 C 7 C 13 C
2 C 8 C 14 C
3 C 9 O 15 C
4 C 10 C 16 C
5 O 11 C
6 O 12 C
The on off state of optimal network after table 2, reconstruct
In table 2, C represents that switch closes, and O represents that switch disconnects.
Although content of the present invention has done detailed introduction by above preferred embodiment, will be appreciated that above-mentioned description should not be considered to limitation of the present invention.After those skilled in the art have read foregoing, for multiple amendment of the present invention and substitute will be all apparent.Therefore, protection scope of the present invention should be limited to the appended claims.

Claims (10)

1. a reconstruction method of power distribution network, is characterized in that, the method comprises:
Set up distribution network load Optimized model;
Based on genetic algorithm, distribution network load Optimized model is optimized, obtains power distribution network reconfiguration scheme.
2. reconstruction method of power distribution network as claimed in claim 1, it is characterized in that, described distribution network load Optimized model of setting up comprises:
Set up distribution network load topological structure;
Set up target function;
Determine constraints.
3. reconstruction method of power distribution network as claimed in claim 2, it is characterized in that, the described method setting up target function comprises:
Take loss minimization as target function;
The computing formula P of distribution network loss lossshown in (1);
P L o s s = Σ i = 1 n b k i r i | I · i | 2 - - - ( 1 )
In formula (1): n bfor the circuitry number of power distribution network; r ibe the resistance of the i-th branch road; for flowing through the electric current of i-th branch road; k ifor the state of switch (node) i, ki=0 represents disjunction, and ki=1 represents closed;
Distribution network loss P is obtained by Load flow calculation loss.
4. reconstruction method of power distribution network as claimed in claim 2, it is characterized in that, described constraints comprises: tributary capacity constraint and node voltage constraint;
Described tributary capacity is constrained to the maximum power that the power flowing through branch road can not be greater than this branch road, shown in (2):
P ij≤P ijmax
Q ij≤Q ijmax(2)
In formula (2), P ij, P ijmaxbe respectively the active power of branch road i-j and the meritorious maximum of branch road i-j; Q ij, Q ijmaxbe respectively the reactive power of branch road i-j and the idle maximum of branch road i-j;
The voltage that described node voltage is constrained to node can not exceed the voltage bound that this node allows to pass through, shown in (3):
U imin≤U i≤U imax(3)
In formula (3), U i, U imin, U imaxbe respectively the voltage max of the voltage of node i, the voltage minimum of node i and node i.
5. reconstruction method of power distribution network as claimed in claim 1, is characterized in that, describedly comprises the method that distribution network load Optimized model is optimized based on genetic algorithm:
The initial network coding of power distribution network generates chromosome;
Decoding chromosome obtains chromosomal fitness;
Select the male parent for breeding filial generation individual according to fitness;
Male parent is individual successively carries out interlace operation and mutation operation;
Iteration exports the highest chromosome of fitness and corresponding reconfiguration scheme.
6. reconstruction method of power distribution network as claimed in claim 5, is characterized in that, the initial network coding of described power distribution network generates chromosome and comprises:
Distribution network adopts breadth First spanning tree algorithm to produce an initial network;
This initial network carries out coding generation chromosome;
This chromosome generates N number of chromosome by mutation operator, and N number of chromosome forms the initial population of genetic algorithm, and N is even number;
Wherein, each chromosome correspondence one can realize the radial distribution networks of operation.
7. reconstruction method of power distribution network as claimed in claim 5, it is characterized in that, described decoding chromosome obtains chromosomal fitness and comprises:
All chromosome is decoded according to the inverse process of coding;
All chromosome obtains the network loss of initial network through Load flow calculation; Each chromosome obtain network loss inverse be taken as this chromosomal fitness;
Wherein, if the node voltage of initial network and line power out-of-limit, then chromosomal fitness is taken as zero.
8. reconstruction method of power distribution network as claimed in claim 6, is characterized in that, describedly selects to comprise for the male parent individuality of breeding filial generation according to fitness:
The mode that fitness retains with elite higher than the chromosome of predetermined threshold value directly copies to filial generation, and the number that elite retains is M, M is even number;
Fitness is individual for the male parent of breeding filial generation with half random half optimum method choice (N-M)/2 lower than the chromosome of predetermined threshold value.
9. the reconstruction method of power distribution network as described in claim 5 or 8, is characterized in that, the individual priority of described male parent carries out interlace operation and mutation operation comprises:
Even number male parent individuality carries out interlace operation by by crossover operator;
After interlace operation, the individuality that intersection obtains is made a variation by mutation operator, completes breeding and obtain child chromosome group;
Wherein crossing-over rate is set to 0.5; Aberration rate is set to 1.
10. reconstruction method of power distribution network as claimed in claim 5, is characterized in that, described iteration exports the highest chromosome of fitness and corresponding reconfiguration scheme comprises:
Repeat decoding chromosome obtaining chromosomal fitness, selecting the male parent for breeding filial generation individual according to fitness, and the individual priority of male parent carries out interlace operation and mutation operation, carries out iteration;
Judge whether the chromosome that fitness is the highest continues to keep filial generation constant, if then stop iteration exporting the reconfiguration scheme of the highest chromosome of fitness and correspondence thereof; Then proceed iteration if not.
CN201511005653.XA 2015-12-28 2015-12-28 Distribution network reconfiguration method Pending CN105552892A (en)

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WO2019030246A1 (en) * 2017-08-11 2019-02-14 Commissariat A L'energie Atomique Et Aux Energies Alternatives Computer-implemented method for reconstructing the topology of a network of cables, using a genetic algorithm
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CN107979092A (en) * 2017-12-18 2018-05-01 国网宁夏电力有限公司经济技术研究院 It is a kind of to consider distributed generation resource and the power distribution network dynamic reconfiguration method of Sofe Switch access
CN107994577A (en) * 2017-12-29 2018-05-04 天津大学 Consider the power distribution network dynamic reconfiguration method of the switch constraint of continuous action in short-term
CN107994577B (en) * 2017-12-29 2020-09-01 天津大学 Power distribution network dynamic reconstruction method considering switch short-time continuous action constraint
CN108062604B (en) * 2018-01-05 2022-09-23 国网河南省电力公司 Power distribution network planning method considering network reconfiguration
CN108062604A (en) * 2018-01-05 2018-05-22 国网河南省电力公司 A kind of distribution network planning method of meter and network reconfiguration
CN108390370A (en) * 2018-01-18 2018-08-10 南京邮电大学 A kind of dynamic reconfiguration method for considering state of weather and power distribution network operation risk being influenced
CN108491922A (en) * 2018-03-21 2018-09-04 华南理工大学 Active distribution network Intelligent Hybrid reconstructing method based on teaching and particle cluster algorithm
CN108537369A (en) * 2018-03-21 2018-09-14 华南理工大学 Improvement population algorithm for distribution network reconfiguration based on local search
CN110391660B (en) * 2018-04-17 2023-01-24 中国电力科学研究院有限公司 Network reconstruction method and device for improving power supply capacity of power distribution network
CN110391660A (en) * 2018-04-17 2019-10-29 中国电力科学研究院有限公司 A kind of network reconstruction method and device promoting power distribution network power supply capacity
CN109412145A (en) * 2018-10-16 2019-03-01 国网上海市电力公司 A kind of active distribution network dynamic characteristic appraisal procedure based on synchro measure data
CN109412145B (en) * 2018-10-16 2022-03-29 国网上海市电力公司 Active power distribution network dynamic characteristic evaluation method based on synchronous measurement data
CN110348048B (en) * 2019-05-31 2022-09-30 国网河南省电力公司郑州供电公司 Power distribution network optimization reconstruction method based on consideration of heat island effect load prediction
CN110348048A (en) * 2019-05-31 2019-10-18 国网河南省电力公司郑州供电公司 Based on the power distribution network optimal reconfiguration method for considering tropical island effect load prediction
CN111832836B (en) * 2020-07-23 2021-05-28 广东电网有限责任公司 Power distribution network reconstruction method and system considering load power utilization characteristics
CN111832836A (en) * 2020-07-23 2020-10-27 广东电网有限责任公司 Power distribution network reconstruction method and system considering load power utilization characteristics

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Application publication date: 20160504