CN109309392A - Distributed power source output power Optimal Configuration Method based on particle swarm algorithm - Google Patents

Distributed power source output power Optimal Configuration Method based on particle swarm algorithm Download PDF

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CN109309392A
CN109309392A CN201710627709.8A CN201710627709A CN109309392A CN 109309392 A CN109309392 A CN 109309392A CN 201710627709 A CN201710627709 A CN 201710627709A CN 109309392 A CN109309392 A CN 109309392A
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distribution network
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潘曙冰
陆政
高莹
汪立俊
毛涛
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Changzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Changzhou Power Supply Co of State Grid Jiangsu 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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Abstract

The present invention provides a kind of distributed power source output power Optimal Configuration Method based on particle swarm algorithm, and key step includes: topological structure, the impedance parameter of each branch and the load data of part of nodes for 1. obtaining power distribution network；2. calculating the load power of remaining node；3. the output power of power supply is decision variable in a distributed manner, using each node voltage and branch current as constraint condition, determine that minimum objective function is lost in distribution network line；4. establishing penalty function according to constraint condition, combined objective function determines fitness function；5. being solved using particle swarm algorithm, the power configuration of each distributed generation resource is obtained.The present invention provides a kind of distributed power source output power Optimal Configuration Method to reduce line loss as target for the power distribution network that distributed generation resource accesses；It can be effectively reduced the line loss of power distribution network.

Description

Distributed power source output power Optimal Configuration Method based on particle swarm algorithm
Technical field
The present invention relates to low and medium voltage distribution network saving energy and decreasing loss fields, and in particular to it is a kind of reduce Line Loss of Distribution Network System based on grain The distributed power source output power Optimal Configuration Method of swarm optimization.
Background technique
The electric energy loss of power grid is one of the main path of electric system energy consumption, is counted according to historical data, China's electricity Force system is in the electric energy transmission process middle age mean consumption electric energy of 2.818 trillion kWh, and this numerical value is with load capacity Promotion and increase year by year.Related data show that in the past 10 years, domestic grid line loss rate is higher by 1~2% than developed country, Economic loss is huge.For this problem, people from optimization distribution net work structure, improve the multi-angles such as dispatching of power netwoks, the method for operation Start with, take serial of methods, certain effect can be played to power network line loss is reduced.However, matching at present about reduction The method of grid line loss, it is mostly effective for conventional electrical distribution net, and distributed generation resource such as solar power generation, wind-power electricity generation are filled For setting the power distribution network of access, effect is often had a greatly reduced quality.After distributed generation resource accesses power distribution network, the power load distributing of power distribution network It will change with trend, distributed generation resource accesses the network topology of the position of power distribution network, output power size and power distribution network The factors such as structure can all influence the line loss of power distribution network.Therefore, for the power distribution network of distributed generation resource access, research can have Effect reduces the new method of its line loss, it appears very necessary.
Summary of the invention
The object of the present invention is to provide a kind of distributed power source output power side of distributing rationally based on particle swarm algorithm Method, topological structure of this method according to distribution and daily load data known to part of node, calculate remaining node in difference The payload of period；Power output limit value in conjunction with each distributed generation resource in different periods is used using loss minimization as optimization aim Particle swarm algorithm optimizes configuration to the output power of each distributed generation resource, and the line loss of power distribution network is effectively reduced.
The technical scheme is that the distributed power source output power of the invention based on particle swarm algorithm is distributed rationally Method, comprising the following steps:
Step 1 obtains the load active power of the topological structure of power distribution network, the impedance parameter of each branch and part of nodes PiAnd reactive power Qi, i=1,2 ..., m, wherein m is the quantity of the power distribution network part of nodes of known load power data；
Step 2, using being pushed forward, the load power that back substitution power flow algorithm calculates power distribution network residue node using formula (1) is active Power PiAnd reactive power Qi, i=m+1,2 ..., n, wherein n is power distribution network node total number:
In formula, PsAnd QsThe total active power and reactive power of power distribution network are supplied for substation bus bar s,With Active loss and reactive loss for full electric network, KiFor the power partition coefficient of distribution transformer, sought by formula (2):
In formula, SiFor the rated capacity of transformer i；
Electric current is pushed forward iterative for formula (3) in (n+1)th step iteration；Pushing back for node voltage is iterative for formula (4):
In formula, node j is the father node of node i, and node k is the child node of node i, CiIt is the collection of the child node of node i It closes, UiAnd UjIt is the voltage of node i and node j, rijAnd xijThe impedance of branch, P between node i, jijAnd QijBetween node i, j The power that branch flows through, PiAnd QiFor the load power of node i, PikAnd QikThe power that branch flows through between node i, k；
Step 3, the output power X of power supply is decision variable in a distributed manner, is constraint with each node voltage and branch current Condition, determining makes distribution network line that the smallest objective function be lost:
Node voltage constraint condition is formula (5):
Umin≤Ui(X)≤Umax (5)
In formula, UiIt (X) is node voltage of the node i when distributed power source output power is X；UminAnd UmaxArbitrarily to save The minimum allowable value and maximum permissible value of point voltage；
Branch current constraint condition is formula (6):
0≤Iij(X)≤Iijmax (6)
In formula, Iij(X) between node i, j branch electric current, IijmaxThe maximum current allowed to flow through for branch road；
Distribution network line is calculated using formula (7), and P is lostloss(X):
Ploss(X)=∑ △ Pij(X)=∑ Iij(X)2xij (7)
In formula, Δ Pij(X) the branch active power loss between node i, j；Electric current Iij(X) by the way that distributed generation resource is added Forward-backward sweep method afterwards calculates；
Step 4 establishes penalty function according to constraint condition, and combined objective function determines fitness function:
According to node voltage constraint condition, penalty function p is established using formula (8)1(X)；According to branch current constraint condition, adopt Penalty function p is established with formula (9)2(X):
In formula, k1(X) and k2It (X) is penalty coefficient；
Fitness function F (X) is established using formula (10):
F (X)=A-Ploss(X)-p1(X)-p2(X) (10)
In formula, A is constant；
Step 5 solves the power configuration for obtaining each distributed generation resource according to following steps using particle swarm algorithm:
1. initializing a group particle: the dimension of particle is consistent with the distributed generation resource number that need to optimize power, particle I-th dimension degree initial position [0, PDGi] in random, wherein PDGiIt is micro- for the output power limit value of i-th of distributed generation resource The initial velocity of grain is random in [- 1,1]；
2. evaluating the fitness of each particle using fitness function: the output power of each distributed generation resource is configured X It is updated in fitness function F (X) as decision variable, calculates the fitness size of each particle；
3. to each particle in kth generation, the desired positions that its fitness is lived through with itIt makes comparisons, if compared with It is good, then as current desired positions
4. to each particle in kth generation, by its fitness and global desired positions experiencedIt makes comparisons, if compared with It is good, then as global desired positions
5. the speed and position to particle are updated, more new formula uses formula are as follows:
Wherein: c1And c2For aceleration pulse；Rand () and Rand () is two random letters changed in [0,1] range Number；
6. judging whether to reach preset maximum algebra Gmax, if it is not, return step is 2.；If so, most by the final overall situation Good positionAs optimal solution, the optimal output power configuration of each distributed generation resource is obtained.
Further embodiment is: in above-mentioned step one, the load data of the power distribution network node of acquisition is 24 hours one day Interior active power and reactive power every 1 hour record；The step 2 is into step 5, and related calculating is for every One period carries out, each period is 1 hour, and using the load data of record as the period in average value.
Further embodiment is: step in above-mentioned step five 5. in, aceleration pulse c1And c2Value c1=c2= 1.49445。
Further embodiment is: step in above-mentioned step five 6. in, preset maximum algebra Gmax=200.
The present invention has the effect of positive: the distributed power source output power optimization of the invention based on particle swarm algorithm is matched Set method, it is suitable for distributed generation resource access distribution network, for distributed generation resource access power distribution network provide one kind with Reduce the distributed power source output power Optimal Configuration Method that line loss is target；This method is optimization mesh with loss minimization Mark, optimizes configuration using output power of the particle swarm algorithm to each distributed generation resource, can be effectively reduced power distribution network Line loss；Meanwhile the present invention is when calculating line loss, it is contemplated that the payload of part of nodes is unknown, known real-time by utilizing Load data, using back substitution power flow algorithm is pushed forward, meter estimates the payload of remaining node, and carries out line loss calculation, possess compared with High precision.
Detailed description of the invention
Fig. 1 is a kind of distribution network topology used by the embodiment of the present invention.
Specific embodiment
The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
(embodiment 1)
The distributed power source output power Optimal Configuration Method based on particle swarm algorithm of the present embodiment, with shown in FIG. 1 Power distribution network is illustrated.Fig. 1 is a radial distribution network topology, and 0~11 marked in figure is power distribution network node, total to save Points are n, are connected to photovoltaic power generation unit DG1 in node 3, are connected to wind power generating set DG2 in node 6, predict two distributions Power supply power output limit value in different time periods in one day is respectively PDG1And PDG2.The load of feeder line head end and part of nodes in Fig. 1 Power optimizes distributed generation resource in output power in different time periods it is known that now with the minimum target of Line Loss of Distribution Network System, Method therefor specifically includes the following steps:
Step 1 obtains the load data of the topological structure of power distribution network, the impedance parameter of each branch and part of nodes (m) PiAnd Qi(i=1,2 ..., m).The load data of node is in 24 hours one day every the active power of 1 hour record and idle Power.
Step 2 calculates the load power P of remaining nodeiAnd Qi(i=m+1,2 ..., n):
It is calculated using back substitution power flow algorithm is pushed forward, the load power P of remaining nodeiAnd Qi(i=m+1,2 ..., n) Expression formula are as follows:
In formula, PsAnd QsThe total active power and reactive power of power distribution network are supplied for substation bus bar s,WithFor The active loss of full electric network and reactive loss, K when n-th calculatesiFor the power partition coefficient of distribution transformer, ask as the following formula It takes:
In formula, SiFor the rated capacity (kVA) of transformer i.
Electric current is pushed forward iterative are as follows:
Pushing back for node voltage is iterative are as follows:
In formula, node j is the father node of node i, and node k is the child node of node i, CiIt is the collection of the child node of node i It closes, UiAnd UjIt is the voltage of node i and node j, rijAnd xijThe impedance of branch, P between node i, jijAnd QijBetween node i, j The power that branch flows through, PiAnd QiFor the load power of node i, PikAnd QikThe power that branch flows through between node i, k.
Setting the value being initially lost is 0, and start node voltage is power distribution network voltage rating, can be more smart by iterative formula Really calculate the load power of remaining node.
Step 3, using the output power matrix of distributed generation resource in certain time period as decision variable X, with each node voltage It is constraint condition with branch current, determining makes distribution network line that the smallest objective function f=min (P be lostloss(X)):
Node voltage constraint condition are as follows:
Umin≤Ui(X)≤Umax
In formula, UiIt (X) is node voltage of the node i when distributed power source output power is X；UminAnd UmaxArbitrarily to save The minimum allowable value and maximum permissible value of point voltage.
Branch current constraint condition are as follows:
0≤Iij(X)≤Iijmax
In formula, Iij(X) between node i, j branch electric current, IijmaxThe maximum current allowed to flow through for branch road.
The calculation method of distribution network line loss are as follows:
Ploss(X)=∑ △ Pij(X)=∑ Iij(X)2xij
In formula, Δ Pij(X) the branch active power loss between node i, j；Electric current Iij(X) by the way that distributed generation resource is added Forward-backward sweep method afterwards calculates, and the output power for accessing the distributed generation resource of power distribution network is considered as negative load power to handle.
Step 4 establishes penalty function according to constraint condition, and combined objective function determines fitness function.
According to different constraint condition, it is as follows to establish penalty function:
In formula, k1(X) and k2It (X) is penalty coefficient；
Fitness function F (X) are as follows:
F (X)=A-Ploss(X)-p1(X)-p2(X)
In formula, A is constant.
Step 5 is solved using particle swarm algorithm, obtains the power configuration of each distributed generation resource, specific steps Are as follows:
1. initializing a group particle (population size 30): the dimension of particle and the distributed generation resource number that power need to be optimized Be consistent, particle i-th dimension degree initial position [0, PDGi] in random, wherein PDGiFor the defeated of i-th distributed generation resource Power limit out, the initial velocity of particle are random in [- 1,1]；
2. evaluating the fitness of each particle using fitness function: by the decision variable X (output of each distributed generation resource Power configuration) it is updated in fitness function F (X), calculate the fitness size of each particle；
3. to each particle in kth generation, the desired positions that its fitness is lived through with itIt makes comparisons, if compared with It is good, then as current desired positions
4. to each particle in kth generation, by its fitness and global desired positions experiencedIt makes comparisons, if compared with It is good, then as global desired positions
5. the speed and position to particle are updated, more new formula are as follows:
Wherein: c1And c2For aceleration pulse, c can use herein1=c2=1.49445, rand () and Rand () be two [0,1] random function changed in range；
6. such as not up to termination condition (default maximum algebra Gmax=200), then return step is 2..
Final global desired positionsFor the optimal solution of the algorithm, available each distributed generation resource is in one day accordingly Output power configuration in different time periods.
Above embodiments are the explanations to a specific embodiment of the invention, rather than limitation of the present invention, related technology The technical staff in field without departing from the spirit and scope of the present invention, can also make various transformation and variation and obtain To corresponding equivalent technical solution, therefore all equivalent technical solutions should be included into patent protection model of the invention It encloses.

Claims (4)

1. a kind of distributed power source output power Optimal Configuration Method based on particle swarm algorithm, it is characterised in that: including following Step:
Step 1 obtains the load active-power P of the topological structure of power distribution network, the impedance parameter of each branch and part of nodesiAnd nothing Function power Qi, i=1,2 ..., m, wherein m is the quantity of the power distribution network part of nodes of known load power data；
Step 2, using be pushed forward back substitution power flow algorithm using formula (1) calculate power distribution network residue node load power active-power Pi And reactive power Qi, i=m+1,2 ..., n, wherein n is power distribution network node total number:
In formula, PsAnd QsThe total active power and reactive power of power distribution network are supplied for substation bus bar s,WithIt is complete The active loss of power grid and reactive loss, KiFor the power partition coefficient of distribution transformer, sought by formula (2):
In formula, SiFor the rated capacity of transformer i；
Electric current is pushed forward iterative for formula (3) in (n+1)th step iteration；Pushing back for node voltage is iterative for formula (4):
In formula, node j is the father node of node i, and node k is the child node of node i, CiIt is the set of the child node of node i, Ui And UjIt is the voltage of node i and node j, rijAnd xijThe impedance of branch, P between node i, jijAnd QijThe branch stream between node i, j The power crossed, PiAnd QiFor the load power of node i, PikAnd QikThe power that branch flows through between node i, k；
Step 3, the output power X of power supply is decision variable in a distributed manner, is constraint item with each node voltage and branch current Part, determining makes distribution network line that the smallest objective function be lost:
Node voltage constraint condition is formula (5):
Umin≤Ui(X)≤Umax (5)
In formula, UiIt (X) is node voltage of the node i when distributed power source output power is X；UminAnd UmaxFor arbitrary node electricity The minimum allowable value and maximum permissible value of pressure；
Branch current constraint condition is formula (6):
0≤Iij(X)≤Iijmax (6)
In formula, Iij(X) between node i, j branch electric current, IijmaxThe maximum current allowed to flow through for branch road；
Distribution network line is calculated using formula (7), and P is lostloss(X):
Ploss(X)=∑ △ Pij(X)=∑ Iij(X)2xij (7)
In formula, Δ Pij(X) the branch active power loss between node i, j；Electric current Iij(X) before passing through after distributed generation resource is added Push away back substitution method calculating；
Step 4 establishes penalty function according to constraint condition, and combined objective function determines fitness function:
According to node voltage constraint condition, penalty function p is established using formula (8)1(X)；According to branch current constraint condition, using formula (9) penalty function p is established2(X):
In formula, k1(X) and k2It (X) is penalty coefficient；
Fitness function F (X) is established using formula (10):
F (X)=A-Ploss(X)-p1(X)-p2(X) (10)
In formula, A is constant；
Step 5 solves the power configuration for obtaining each distributed generation resource according to following steps using particle swarm algorithm:
1. initializing a group particle: the dimension of particle is consistent with the distributed generation resource number that need to optimize power, and particle is i-th The initial position of dimension is [0, PDGi] in random, wherein PDGiFor the output power limit value of i-th of distributed generation resource, particle just Beginning speed is random in [- 1,1]；
2. evaluating the fitness of each particle using fitness function: using the output power of each distributed generation resource configuration X as Decision variable is updated in fitness function F (X), calculates the fitness size of each particle；
3. to each particle in kth generation, the desired positions P that its fitness is lived through with iti kIt makes comparisons, if preferably, As current desired positions Pi k；
4. to each particle in kth generation, by its fitness and global desired positions experiencedIt makes comparisons, if preferably, Then as global desired positions
5. the speed and position to particle are updated, more new formula uses formula are as follows:
Wherein: c1And c2For aceleration pulse；Rand () and Rand () is two random functions changed in [0,1] range；
6. judging whether to reach preset maximum algebra Gmax, if it is not, return step is 2.；If so, by the best position of the final overall situation It setsAs optimal solution, the optimal output power configuration of each distributed generation resource is obtained.
2. the distributed power source output power Optimal Configuration Method according to claim 1 based on particle swarm algorithm, special Sign is: in the step one, the load data of the power distribution network node of acquisition is in 24 hours one day every 1 hour record Active power and reactive power；The step 2 is into step 5, and related calculating is carried out for each period, often One period is 1 hour, and using the load data of record as the period in average value.
3. the distributed power source output power Optimal Configuration Method according to claim 1 based on particle swarm algorithm, special Sign is: step in the step five 5. in, aceleration pulse c1And c2Value c1=c2=1.49445.
4. the distributed power source output power Optimal Configuration Method according to claim 1 based on particle swarm algorithm, special Sign is: step in the step five 6. in, preset maximum algebra Gmax=200.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112084626A (en) * 2020-08-06 2020-12-15 国网浙江省电力有限公司嘉兴供电公司 Distributed photovoltaic access-based power distribution network reactive compensation configuration capacity calculation method
CN114094573A (en) * 2021-11-15 2022-02-25 国家电网有限公司 Distributed power source node arrangement optimization method in power distribution network

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102832625A (en) * 2011-06-13 2012-12-19 重庆市电力公司教育培训中心 Mathematical model for optimal configuration of power distribution network filtering devices
CN103310065A (en) * 2013-06-25 2013-09-18 国家电网公司 Intelligent distribution network reconstruction method concerning distributed power generation and energy storage unit
CN103400207A (en) * 2013-08-01 2013-11-20 天津大学 Operation optimization method for power distribution network comprising schedulable distributed power supply
CN105071433A (en) * 2015-07-31 2015-11-18 贵州大学 Optimal configuration scheme of distributed power supply
CN105844348A (en) * 2016-03-22 2016-08-10 国网宁夏电力公司石嘴山供电公司 Distributed power supply optimization configuration method
CN106099964A (en) * 2016-06-16 2016-11-09 南京工程学院 A kind of energy-storage system participates in active distribution network runing adjustment computational methods
US20170133851A1 (en) * 2015-11-06 2017-05-11 Tsinghua University Method and device for controlling active distribution network

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102832625A (en) * 2011-06-13 2012-12-19 重庆市电力公司教育培训中心 Mathematical model for optimal configuration of power distribution network filtering devices
CN103310065A (en) * 2013-06-25 2013-09-18 国家电网公司 Intelligent distribution network reconstruction method concerning distributed power generation and energy storage unit
CN103400207A (en) * 2013-08-01 2013-11-20 天津大学 Operation optimization method for power distribution network comprising schedulable distributed power supply
CN105071433A (en) * 2015-07-31 2015-11-18 贵州大学 Optimal configuration scheme of distributed power supply
US20170133851A1 (en) * 2015-11-06 2017-05-11 Tsinghua University Method and device for controlling active distribution network
CN105844348A (en) * 2016-03-22 2016-08-10 国网宁夏电力公司石嘴山供电公司 Distributed power supply optimization configuration method
CN106099964A (en) * 2016-06-16 2016-11-09 南京工程学院 A kind of energy-storage system participates in active distribution network runing adjustment computational methods

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
何頔: "考虑分布式电源的配电网优化规划研究", 《硕士论文.工程科技II辑》 *
刘晓春: "《雷达天线罩电性能设计技术》", 31 January 2017, 航空工业出版社 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112084626A (en) * 2020-08-06 2020-12-15 国网浙江省电力有限公司嘉兴供电公司 Distributed photovoltaic access-based power distribution network reactive compensation configuration capacity calculation method
CN114094573A (en) * 2021-11-15 2022-02-25 国家电网有限公司 Distributed power source node arrangement optimization method in power distribution network

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