CN107681669A - Using the power network distribution idle work optimization method of shuffled frog leaping algorithm - Google Patents

Using the power network distribution idle work optimization method of shuffled frog leaping algorithm Download PDF

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Publication number
CN107681669A
CN107681669A CN201710483143.6A CN201710483143A CN107681669A CN 107681669 A CN107681669 A CN 107681669A CN 201710483143 A CN201710483143 A CN 201710483143A CN 107681669 A CN107681669 A CN 107681669A
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Prior art keywords
frog
msub
branch road
group
mrow
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潘艳红
杨继革
王翔
郑波
胡斌
王晓东
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State Grid Corp of China SGCC
Quzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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State Grid Corp of China SGCC
Quzhou Power Supply Co of State Grid Zhejiang 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
    • H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in 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]
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/30—Reactive power compensation

Abstract

The present invention relates to a kind of power network distribution idle work optimization method using shuffled frog leaping algorithm, solve the deficiencies in the prior art, technical scheme is：Step 1：Initiation parameter, including：The quantity F of frog group；The quantity m of group；The quantity n of frog in group；Maximum allowable bounce step-length Smax；Globally optimal solution Pz；Locally optimal solution Pb；Local worst solution Pw；Global iterative evolution times Ng, local iteration's evolution times N1, each compensation point reactive-load compensation upper limit QI, max；Step 2：Initial frog group is generated at random, calculates the evaluation of estimate of each frog；Step 3：Ascending sort is carried out according to evaluation of estimate size, records optimal solution Pz, step 4：Evolutional operation S=ceil (Rand () × (P are carried out to each group according to the following formulaw‑Pb))；NewPw=Pw+ S, Smin≤S≤Smax；Corresponding actions are performed according to optimal selection.

Description

Using the power network distribution idle work optimization method of shuffled frog leaping algorithm
Technical field
This patent is related to network optimization method, and in particular to a kind of power network distribution idle work optimization using shuffled frog leaping algorithm Method.
Background technology
Distribution idle work optimization is typically by pinpointing switching reactive-load compensation equipment Distribution system and distribution idle work optimization is used as and matched somebody with somebody 2 important technicals of network optimizationization operation, it is ensureing the quality of power supply, is reducing via net loss etc. important role. Distribution system obtains the network topology knot under optimum optimization desired value to realize active damage by changing the closure of network switching Consumption minimizes and ensures higher voltage level.Substantially, Distribution system is nonlinear combinatorial optimization problem, distribution idle work optimization It is nature of nonlinear integral programming problem, the complex optimum of the two make it that the solution of problem is more complicated, for this problem, prior art Using the method for first reconstructing post-compensation alternating iteration.Also have and optimized respectively based on reconstruct and idle work optimization, all employed Intelligent algorithm solves, and compared to alternative iteration method, improves computational accuracy, but these prior arts are not truly synchronous It is reconstructed and idle work optimization, the potential value of complex optimum is not well studied and excavated.Meanwhile above distribution integrates Optimizing research does not consider the economy of reactive-load compensation using network loss as optimization aim.
The content of the invention
It is an object of the invention to solve above-mentioned prior art distribution complex optimum research using network loss as optimization aim, Not a kind of the problem of not considering the economy of reactive-load compensation, there is provided power network distribution idle work optimization method.The present invention solves its skill Technical scheme is used by art problem：A kind of power network distribution idle work optimization method using shuffled frog leaping algorithm, its feature exist In following steps：
Step 1：Initiation parameter, including：The quantity F of frog group；The quantity m of group；The quantity n of frog in group；It is maximum Allow the step-length S that beatsmax；Globally optimal solution Pz；Locally optimal solution Pb；Local worst solution Pw；Global iterative evolution times Ng, it is local Iterative evolution times N1, each compensation point reactive-load compensation upper limit QI, max；
Step 2：Initial frog group is generated at random, calculates the evaluation of estimate of each frog；
Step 3：Ascending sort is carried out according to evaluation of estimate size, records optimal solution Pz, and by frog group in the following manner It is divided into group：1st frog is put into the 1st group, and the 2nd frog is put into the 2nd group, and the m frog is put into the m group, m+ 1 frog is put into the 1st group, by that analogy, until all frogs are placed into specified location；Step 4：According to the following formula to each race Group carries out evolutional operation
S=ceil (Rand () × (Pw-Pb))；
NewPw=Pw+ S ,-Smin≤S≤Smax；
Wherein, ceil represents to round, and rand () represents to produce 0~1 random number, and S represents the step-length to leapfrog, Smax, Smin For the step-length limitation to leapfrog, NewPwRepresent the P after renewalw；
Step 5：After all group's renewals, the evaluation of estimate of all frogs in frog group is calculated；
Step 6：Judge whether to meet stop condition；Stopped search if meeting, otherwise go to step 3；
Step 7：Corresponding actions are performed according to optimal selection.
The comprehensive idle work optimization of the present invention and two technologies of Distribution system, are established with the distribution of the minimum target of year comprehensive cost Integrated Optimization Model, solved using shuffled frog leaping algorithm, can not only further reduce network loss, lifting node voltage, it is also abundant Consider the economy of reactive-load compensation so that distribution complex optimum more conforms to economy principle, i.e., it is excellent fully to have excavated synthesis The economic value of change.
Preferably, the specific steps of step 4 include following sub-step：
Step 4-1：If IM=IN=0, IMRepresent the counter that group evolves, INRepresent Local Evolution counter；
Step 4-2：Select the P of current groupbAnd Pw, IMAdd 1；
Step 4-3：INAdd 1；
Step 4-4：According to
S=ceil (Rand () × (Pw-Pb))；
NewPw=Pw+ S ,-Smin≤S≤SmaxImprove the worst frog in group；
Step 4-5：If upper step improves the worst frog, substitute the worst frog with the new frog, otherwise use PzSubstituted (8) In Pb, evolve again；
Step 4-6：If upper step randomly generates a feasible solution to replace the worst frog still without the worst frog is improved；
Step 4-7：If INLess than Local Evolution number LN, then it is transferred to step 4-3；
Step 4-8：If IMLess than group number m, then step 4-2 is transferred to, otherwise into the step 5 of global search.
Preferably, the optimization object function of target grid is：
λ is electricity price；TmaxHourage is lost for annual peak load；k1To compensate the year maintenance cost rate of equipment；k2For investment Recovery coefficient；QiFor the reactive-load compensation amount of i-th of node, C1For the price of reactive-load compensation；C2For the installation fee of single compensation point With n is reactive-load compensation point number；
PlossFor the active loss of network, for the summation of every circuit active loss, expression formula is：
NbRepresent circuitry number；RkRepresent branch road k resistance；SkBranch road closure state is represented, 1 represents closure, and 0 represents to open； PkRepresent branch road k active power；QkRepresent branch road k reactive power；VkRepresent branch road k terminal voltage；
Constraints is：
VminRepresent the upper limit of node voltage during distribution normal operation；
VmaxRepresent the lower limit of node voltage during distribution normal operation；
SmaxRepresent circuit k maximum carrying capacity；
QI, maxRepresent i-th of compensation point compensation capacity upper limit.
Preferably, in the active loss P of calculating networklossWhen set some branch roads as change adjust branch road, if Shuffled frog leaping algorithm carries out that during the optimal selection of Distribution system branch road constraints can not be met, will in change adjusts branch road S in the branch road of change regulation beforekThe optimal selection that shuffled frog leaping algorithm carries out Distribution system branch road is re-started after value change； It is repeated in performing, meets constraints when shuffled frog leaping algorithm carries out the optimal selection of Distribution system branch road.
Preferably, the change regulation branch road is carried out according to the active total load of Distribution system branch road or idle total load Sequence, clooating sequence is higher for the active total load or the smaller then Sort Priority of idle total load of Distribution system branch road, if mixed The algorithm that leapfrogs is closed to carry out not meeting that constraints then first changes priority high distribution during the optimal selection of Distribution system branch road Reconstruct branch road.
Preferably, the change regulation branch road is carried out according to the active total load of Distribution system branch road or idle total load Sequence, clooating sequence SkThe active total load or the smaller then Sort Priority of idle total load of the Distribution system branch road of value=1 It is higher, clooating sequence SkThe active total load or the smaller then Sort Priority of idle total load of the Distribution system branch road of value=0 It is lower,
First changed if can not meeting constraints when shuffled frog leaping algorithm carries out the optimal selection of Distribution system branch road excellent The high Distribution system branch road of first level.
The present invention substantial effect be：The comprehensive idle work optimization of the present invention and two technologies of Distribution system, establish with year it is comprehensive The distribution Integrated Optimization Model of the minimum target of conjunction expense, is solved using shuffled frog leaping algorithm, can not only further reduce net Damage, lifting node voltage, have also taken into full account the economy of reactive-load compensation so that distribution complex optimum more conforms to economy original Then, moreover it is possible to fully excavate the value of complex optimum economically.
Embodiment
Below by specific embodiment, technical scheme is described in further detail.
Embodiment 1：
A kind of power network distribution idle work optimization method using shuffled frog leaping algorithm, following steps：
Step 1：Initiation parameter, including：The quantity F of frog group；The quantity m of group；The quantity n of frog in group；It is maximum Allow the step-length S that beatsmax；Globally optimal solution Pz；Locally optimal solution Pb；Local worst solution Pw；Global iterative evolution times Ng, it is local Iterative evolution times N1, each compensation point reactive-load compensation upper limit QI, max；
Step 2：Initial frog group is generated at random, calculates the evaluation of estimate of each frog；
Step 3：Ascending sort is carried out according to evaluation of estimate size, records optimal solution Pz, and by frog group in the following manner It is divided into group：1st frog is put into the 1st group, and the 2nd frog is put into the 2nd group, and the m frog is put into the m group, m+ 1 frog is put into the 1st group, by that analogy, until all frogs are placed into specified location；
Step 4：Evolutional operation is carried out to each group according to the following formula
S=ceil (Rand () × (Pw-Pb))；
NewPw=Pw+ S ,-Smin≤S≤Smax；
Wherein, ceil represents to round, and rand () represents to produce 0~1 random number, and S represents the step-length to leapfrog, Smax, Smin For the step-length limitation to leapfrog, NewPwRepresent the P after renewalw；
Step 5：After all group's renewals, the evaluation of estimate of all frogs in frog group is calculated；
Step 6：Judge whether to meet stop condition；Stopped search if meeting, otherwise go to step 3；
Step 7：Corresponding actions are performed according to optimal selection.
The specific steps of step 4 include following sub-step：
Step 4-1：If IM=IN=0, IMRepresent the counter that group evolves, INRepresent Local Evolution counter；
Step 4-2：Select the P of current groupbAnd Pw, IMAdd 1；
Step 4-3：INAdd 1；
Step 4-4：According to
S=ceil (Rand () × (Pw-Pb))；
NewPw=Pw+ S ,-Smin≤S≤SmaxImprove the worst frog in group；
Step 4-5：If upper step improves the worst frog, substitute the worst frog with the new frog, otherwise use PzSubstituted (8) In Pb, evolve again；
Step 4-6：If upper step randomly generates a feasible solution to replace the worst frog still without the worst frog is improved；
Step 4-7：If INLess than Local Evolution number LN, then it is transferred to step 4-3；
Step 4-8：If IMLess than group number m, then step 4-2 is transferred to, otherwise into the step 5 of global search.
The optimization object function of target grid is：
λ is electricity price；TmaxHourage is lost for annual peak load；k1To compensate the year maintenance cost rate of equipment；k2For investment Recovery coefficient；QiFor the reactive-load compensation amount of i-th of node, C1For the price of reactive-load compensation；C2For the installation fee of single compensation point With n is reactive-load compensation point number；
PlossFor the active loss of network, for the summation of every circuit active loss, expression formula is：
NbRepresent circuitry number；RkRepresent branch road k resistance；SkBranch road closure state is represented, 1 represents closure, and 0 represents to open； PkRepresent branch road k active power；QkRepresent branch road k reactive power；VkRepresent branch road k terminal voltage；
Constraints is：
VminRepresent the upper limit of node voltage during distribution normal operation；
VmaxRepresent the lower limit of node voltage during distribution normal operation；
SmaxRepresent circuit k maximum carrying capacity；
QI, maxRepresent i-th of compensation point compensation capacity upper limit.
The algorithm parameter that leapfrogs sets as follows：To active total load scope in 3000kW to 4000kW, idle total load scope In 2000kvar to 2500kvar power network, frog group's size is set as 80, and group's number is 20, and global evolution number is 50, local Evolution number is 3；Reactive-load compensation amount is using 1.2 times of upper limits as reactive-load compensation of the total load or burden without work of system, minimum step-size in search Set manually, λ=0.5 yuan/kWh, Tmax=5000h, k1=0.13, k2=0.1, C1=60 yuan/kvar, C2=5000 Member/node.
Minimum step-size in search is 10kvar.The node that all load or burden without work are most heavy in power network is selected as reactive-load compensation point. In the active loss P of calculating networklossWhen set some branch roads as change adjust branch road, if shuffled frog leaping algorithm is matched somebody with somebody Constraints can not be met during the optimal selection of net reconstruct branch road, in change adjusts branch road, by before in change regulation branch road SkThe optimal selection that shuffled frog leaping algorithm carries out Distribution system branch road is re-started after value change；It is repeated in performing, until Shuffled frog leaping algorithm carries out meeting constraints during the optimal selection of Distribution system branch road.In the active loss P of calculating networkloss When set some branch roads as change adjust branch road, if shuffled frog leaping algorithm carry out Distribution system branch road optimal selection when without Method meets constraints, in change adjusts branch road, by the S in the branch road of change regulation beforekMixing is re-started after value change The algorithm that leapfrogs carries out the optimal selection of Distribution system branch road；It is repeated in performing, until shuffled frog leaping algorithm carries out Distribution system Meet constraints during the optimal selection of branch road.
Embodiment 2：
The present embodiment is substantially the same manner as Example 1, and difference is, the change regulation branch road is according to Distribution system branch The active total load or idle total load on road are ranked up, clooating sequence SkThe active of the Distribution system branch road of value=1 is always born Lotus or the smaller then Sort Priority of idle total load are higher, clooating sequence SkThe active of the Distribution system branch road of value=0 is always born Lotus or the smaller then Sort Priority of idle total load are lower, if shuffled frog leaping algorithm carries out the optimal selection of Distribution system branch road It can not meet that constraints then first changes the high Distribution system branch road of priority.
The comprehensive idle work optimization of the present invention and two technologies of Distribution system, are established with the distribution of the minimum target of year comprehensive cost Integrated Optimization Model, solved using shuffled frog leaping algorithm, can not only further reduce network loss, lifting node voltage, it is also abundant Consider the economy of reactive-load compensation so that distribution complex optimum more conforms to economy principle, moreover it is possible to it is excellent fully to excavate synthesis Change value economically.
The present embodiment integrates idle work optimization and two technologies of Distribution system, establishes matching somebody with somebody with the minimum target of year comprehensive cost Net Integrated Optimization Model, is solved using shuffled frog leaping algorithm, can not only further be reduced network loss, lifting node voltage, also be filled Divide the economy for considering reactive-load compensation so that distribution complex optimum more conforms to economy principle, moreover it is possible to fully excavate synthesis The value of optimization economically.
Embodiment described above is a kind of preferable scheme of the present invention, not the present invention is made any formal Limitation, there are other variants and remodeling on the premise of without departing from the technical scheme described in claim.

Claims (6)

  1. A kind of 1. power network distribution idle work optimization method using shuffled frog leaping algorithm, it is characterised in that following steps：
    Step 1：Initiation parameter, including：The quantity F of frog group；The quantity m of group；The quantity n of frog in group；It is maximum allowable Step-length of beating Smax；Globally optimal solution Pz；Locally optimal solution Pb；Local worst solution Pw；Global iterative evolution times Ng, local iteration Evolution times N1, each compensation point reactive-load compensation upper limit QI, max；
    Step 2：Initial frog group is generated at random, calculates the evaluation of estimate of each frog；
    Step 3：Ascending sort is carried out according to evaluation of estimate size, records optimal solution Pz, and frog group is divided into race in the following manner Group：1st frog is put into the 1st group, and the 2nd frog is put into the 2nd group, and the m frog is put into m-th of group, and the m+1 frog is put Enter the 1st group, by that analogy, until all frogs are placed into specified location；
    Step 4：Evolutional operation is carried out to each group according to the following formula
    S=ceil (Rand () × (Pw-Pb))；
    NewPw=Pw+ S ,-Smin≤S≤Smax；
    Wherein, ceil represents to round, and rand () represents to produce 0~1 random number, and S represents the step-length to leapfrog, Smax, SminFor the frog The step-length limitation of jump, NewPwRepresent the P after renewalw；
    Step 5：After all group's renewals, the evaluation of estimate of all frogs in frog group is calculated；
    Step 6：Judge whether to meet stop condition；Stopped search if meeting, otherwise go to step 3；
    Step 7：Corresponding actions are performed according to optimal selection.
  2. 2. the power network distribution idle work optimization method according to claim 1 using shuffled frog leaping algorithm, it is characterised in that
    The specific steps of step 4 include following sub-step：
    Step 4-1：If IM=IN=0, IMRepresent the counter that group evolves, INRepresent Local Evolution counter；
    Step 4-2：Select the P of current groupbAnd Pw, IMAdd 1；
    Step 4-3：INAdd 1；
    Step 4-4：According to
    S=ceil (Rand0 × (Pw-Pb))；
    NewPw=Pw+ S ,-Smin≤S≤SmaxImprove the worst frog in group；
    Step 4-5：If upper step improves the worst frog, substitute the worst frog with the new frog, otherwise use PzP in substituted (8)b, Again evolve；
    Step 4-6：If upper step randomly generates a feasible solution to replace the worst frog still without the worst frog is improved；
    Step 4-7：If INLess than Local Evolution number LN, then it is transferred to step 4-3；
    Step 4-8：If IMLess than group number m, then step 4-2 is transferred to, otherwise into the step 5 of global search.
  3. 3. the power network distribution idle work optimization method according to claim 1 using shuffled frog leaping algorithm, it is characterised in that mesh Mark power network optimization object function be：
    <mrow> <mi>min</mi> <mi> </mi> <mi>F</mi> <mo>=</mo> <msub> <mi>&amp;lambda;T</mi> <mi>max</mi> </msub> <msub> <mi>P</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> </msub> <mo>+</mo> <mrow> <mo>(</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mn>1</mn> </msub> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>Q</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>nC</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
    λ is electricity price；TmaxHourage is lost for annual peak load；k1To compensate the year maintenance cost rate of equipment；k2Reclaimed for investment Coefficient；QiFor the reactive-load compensation amount of i-th of node, C1For the price of reactive-load compensation；C2For the mounting cost of single compensation point, n is Reactive-load compensation point number；
    PlossFor the active loss of network, for the summation of every circuit active loss, expression formula is：
    <mrow> <msub> <mi>P</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>b</mi> </msub> </munderover> <msub> <mi>S</mi> <mi>k</mi> </msub> <msub> <mi>R</mi> <mi>k</mi> </msub> <mfrac> <mrow> <msup> <msub> <mi>P</mi> <mi>k</mi> </msub> <mn>2</mn> </msup> <mo>+</mo> <msup> <msub> <mi>Q</mi> <mi>k</mi> </msub> <mn>2</mn> </msup> </mrow> <mrow> <msup> <msub> <mi>V</mi> <mi>k</mi> </msub> <mn>2</mn> </msup> </mrow> </mfrac> </mrow>
    NbRepresent circuitry number；RkRepresent branch road k resistance；SkBranch road closure state is represented, 1 represents closure, and 0 represents to open；PkTable Show branch road k active power；QkRepresent branch road k reactive power；VkRepresent branch road k terminal voltage；
    Constraints is：
    Vmin≤Vj≤Vmax；0≤Qi≤QI, max；
    VminRepresent the upper limit of node voltage during distribution normal operation；
    VmaxRepresent the lower limit of node voltage during distribution normal operation；
    SmaxRepresent circuit k maximum carrying capacity；
    QI, maxRepresent i-th of compensation point compensation capacity upper limit.
  4. 4. the power network distribution idle work optimization method according to claim 3 using shuffled frog leaping algorithm, it is characterised in that The active loss P of calculating networklossWhen set some branch roads as change adjust branch road, if shuffled frog leaping algorithm carry out distribution Constraints can not be met during the optimal selection for reconstructing branch road, in change adjusts branch road, by the branch road of change regulation before SkThe optimal selection that shuffled frog leaping algorithm carries out Distribution system branch road is re-started after value change；It is repeated in performing, until mixed The algorithm that leapfrogs is closed to carry out meeting constraints during the optimal selection of Distribution system branch road.
  5. 5. power network distribution idle work optimization method according to claim 4, it is characterised in that it is described change regulation branch road according to The active total load or idle total load of Distribution system branch road are ranked up, and clooating sequence is the active total negative of Distribution system branch road Lotus or the smaller then Sort Priority of idle total load are higher, if shuffled frog leaping algorithm carries out the optimal selection of Distribution system branch road It can not meet that constraints then first changes the high Distribution system branch road of priority.
  6. 6. power network distribution idle work optimization method according to claim 4, it is characterised in that it is described change regulation branch road according to The active total load or idle total load of Distribution system branch road are ranked up, clooating sequence SkThe Distribution system branch road of value=1 Active total load or the smaller then Sort Priority of idle total load it is higher, clooating sequence SkThe Distribution system branch road of value=0 Active total load or idle total load smaller then Sort Priority it is lower, if shuffled frog leaping algorithm carries out Distribution system branch road It can not meet that constraints then first changes priority high Distribution system branch road during optimal selection.
CN201710483143.6A 2017-06-22 2017-06-22 Using the power network distribution idle work optimization method of shuffled frog leaping algorithm Pending CN107681669A (en)

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CN110401193A (en) * 2019-08-15 2019-11-01 国网冀北电力有限公司秦皇岛供电公司 A kind of reconstruction method of power distribution network and power distribution network reconfiguration system
CN113591378A (en) * 2021-07-27 2021-11-02 上海电机学院 Hybrid energy storage capacity configuration method and device based on improved hybrid frog-leaping algorithm

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Application publication date: 20180209