CN111490552A - Reactive power optimization method for power distribution network - Google Patents

Reactive power optimization method for power distribution network Download PDF

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CN111490552A
CN111490552A CN202010431382.9A CN202010431382A CN111490552A CN 111490552 A CN111490552 A CN 111490552A CN 202010431382 A CN202010431382 A CN 202010431382A CN 111490552 A CN111490552 A CN 111490552A
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reactive power
particle
power
value
snop
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CN111490552B (en
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叶影
唐丹红
陈云峰
陈龙
蒋陈忠
李晨
沈杰士
杨晓林
唐江
汤衡
汤波
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Shanghai Electric Power University
State Grid Shanghai Electric Power Co Ltd
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Shanghai Electric Power University
State Grid Shanghai 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
    • H02J3/1821—Arrangements for adjusting, eliminating or compensating reactive power in networks using shunt compensators
    • H02J3/1835—Arrangements for adjusting, eliminating or compensating reactive power in networks using shunt compensators with stepless control
    • H02J3/1842—Arrangements for adjusting, eliminating or compensating reactive power in networks using shunt compensators with stepless control wherein at least one reactive element is actively controlled by a bridge converter, e.g. active filters
    • H02J3/1857—Arrangements for adjusting, eliminating or compensating reactive power in networks using shunt compensators with stepless control wherein at least one reactive element is actively controlled by a bridge converter, e.g. active filters wherein such bridge converter is a multilevel converter
    • 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
    • H02J3/381—Dispersed generators
    • 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

A reactive power optimization method of a power distribution network aims at the power distribution network containing an SNOP and a DG, a reactive power optimization model taking the minimum of system active network loss and reactive power exchange with a superior power grid as objective functions is established, gears of an O L TC are optimized in a first stage, action frequency constraint conditions of the O L TC are ignored, gears of an on-load tap changer O L TC in each period are solved by using a mixed integer particle swarm algorithm, gears of the on-load tap changer O L TC in each period in one day are obtained by using a clustering algorithm, active network loss and reactive power are optimized in a second stage, gears of the on-load tap changer O L TC in each period in one day are used as known values, and the active power of a first converter VSC1, the reactive power of a first converter VSC1, the reactive power of a second converter VSC2 and the reactive power of a distributed power supply DG in an intelligent soft switch SNOP are solved by using a standard particle swarm algorithm.

Description

Reactive power optimization method for power distribution network
Technical Field
The invention relates to a two-stage reactive power optimization method for a power distribution network, which takes SNOP and DG regulation capabilities into consideration.
Background
With the access of a Distributed Generation (DG) to a traditional power distribution network, the current situation that the voltage of a radiation type power distribution network is reduced along the direction from a power supply point to a branch terminal node is changed, and reactive voltage control variables are increased in multiples. In recent years, the difficulty in solving the reactive power optimization problem of the power distribution network is increased by an intelligent Soft Switch (SNOP) installed on a power distribution network tie line, because the SNOP can change the active power flow directions at two ends of the tie line and two VSCs of the converter can flexibly output or absorb reactive power. With the continuous improvement of the DG permeability and the wide application of the SNOP device, the development of the research on the reactive power optimization of the power distribution network considering the SNOP and DG reactive power regulation capacity has certain practical significance.
Disclosure of Invention
The invention provides a reactive power optimization method for a power distribution network, which aims at carrying out two-stage reactive power optimization on the power distribution network containing SNOP and DG, can optimize reactive power distribution of the power distribution network and reduce network loss, and has better convergence and optimization capability.
In order to achieve the purpose, the invention provides a reactive power optimization method of a power distribution network, aiming at the power distribution network comprising an intelligent soft switch SNOP and a distributed power supply DG, a reactive power optimization model taking the minimum of the system active network loss and the reactive power of a superior power grid as objective functions is established, two-stage optimization is carried out on the reactive power optimization model, the first stage optimizes the gears of an on-load tap changer O L TC, the constraint condition of action frequency limitation in one day of the on-load tap changer O L TC is ignored, the mixed integer particle swarm algorithm is used for solving the gears of the on-load tap changer O L TC in each period, the clustering algorithm is used for obtaining the gears of each period in one day of the on-load tap changer O L TC, the second stage optimizes the active network loss and the reactive power, the gears of each period in one day of the on-load tap changer O L TC are used as known values, and the standard particle swarm algorithm is used for solving the active power of a first VSC1, the first VSC1, the reactive power of a second converter 2 and.
The objective function is as follows:
Figure BDA0002500653290000021
wherein f is the fitness value, Pt,lossIs the active network loss at time t, P0,lossFor the initial active network loss of the system at peak load, Pt,refAnd Qt,refRespectively providing active power and reactive power for a superior power grid at the moment t; n is the study period; omegaDGReactive power combination, omega, for a distributed generator DGSOPThe active power and the reactive power of the intelligent soft switch SNOP are combined;
the constraint conditions are as follows:
and (3) system power flow constraint: f (P)i,Qi,Ui)＝0
Node voltage constraint: u shapei,min≤Ui≤Ui,max
Branch flow constraint:
Figure BDA0002500653290000022
power constraint of intelligent soft switch SNOP:
P1(t)+P2(t)＝0
Figure BDA0002500653290000023
Figure BDA0002500653290000024
in the formula, P1(t) and Q1(t) the active power and the reactive power of the first converter VSC1 in the intelligent soft switch SNOP in a t period; p2(t) and Q2(t) the active power and the reactive power of the second converter VSC2 in the intelligent soft switch SNOP in a t period; s1maxAnd S2maxThe capacity of two current transformers in the intelligent soft switch SNOP;
power constraint of distributed generator DG:
Figure BDA0002500653290000025
Figure BDA0002500653290000026
Figure BDA0002500653290000027
in the formula (I), the compound is shown in the specification,
Figure BDA0002500653290000031
respectively, the real active power and the reactive power of the ith distributed power supply DG, wherein
Figure BDA0002500653290000032
The value of (1) depends on the condition of the fan and the photovoltaic resource corresponding to the distributed power supply DG; thetaminAllowing an angle corresponding to a minimum power factor for the distributed power supply DG;
Figure BDA0002500653290000033
is the capacity of the distributed power supply DG;
gear adjustment limit constraint of the on-load tap changer O L TC:
TCmin≤TCt≤TCmax∩TC∈Z
NTC≤NTCmax
in the formula, Pi、QiTotal active and reactive power, U, injected for node iiIs the voltage of node i, Ui,minAnd Ui,maxMinimum and maximum allowed voltage of node I, IijAnd Iij,maxIs the current amplitude and the current amplitude upper limit, TC, of the branch between the node i and the node jtFor the on-load tap changer O L TC position at time t, TCminAnd TCmaxIs the minimum gear and the maximum gear of an on-load tap changer O L TC, Z is an integer set, NTCAnd NTCmaxThe actual number of actions and the maximum allowable number of actions of the on-load tap changer O L TC per day are respectively.
The method for optimizing the gear position of the on-load tap changer O L TC in the first stage comprises the following steps:
s2.1, initializing population scale, iteration times, particle initial position and speed, particle position limit value and speed limit value, and carrying out comparison on the gear of the on-load tap changing transformer O L TC and the active power P of the first converter VSC1 of the intelligent soft switch SNOP1Reactive power Q of the first converter VSC1 and the second converter VSC21And Q2Encoding the reactive power of the distributed generation DG;
setting particle position limits:
the maximum value and the minimum value of the particle positions representing the TC gear of the on-load tap changing transformer O L TC are respectively set as TCmaxAnd TCminIndicates the active power P of the intelligent soft switch SNOP1(t) the maximum and minimum values of the particle positions are S1maxand-S1maxExpressing intelligent soft switch SNOP reactive power Q1(t) the maximum and minimum values of the particle positions are S1maxand-S1maxExpressing intelligent soft switch SNOP reactive power Q2(t) the maximum and minimum values of the particle positions are S2maxand-S2maxDenotes the reactive power Q of the distributed power supply DGDGRespectively of particle positions of
Figure BDA0002500653290000034
And
Figure BDA0002500653290000035
setting particle velocity limit ranges of [ -0.2 × (position maximum-position minimum), 0.2 × (position maximum-position minimum) ];
s2.2, decoding the positions of the particles into the gear of the on-load tap changer O L TC and the active power P of the first converter VSC1 of the intelligent soft switch SNOP1Reactive power Q of the first converter VSC1 and the second converter VSC21And Q2Performing load flow calculation on the reactive power of the distributed generation DG to enable the reactive power to meet system load flow constraint, node voltage constraint and branch load flow constraint, and calculating the fitness value of each particle according to a target function;
s2.3, counting the extreme value of each particle and the extreme value of the group, wherein the extreme value of each particle is the historical minimum value of the fitness value of each particle, and the extreme value of the group is the historical minimum value of all the particles;
s2.4, updating the position and the speed of the particle swarm, and correcting the updated position and speed according to the integer constraint, the particle position limit value and the particle speed limit value;
the location update and velocity update formulas are as follows:
Figure BDA0002500653290000041
Figure BDA0002500653290000042
in the formula, c1、c2Weights for local and global optimization directions; r is1、r2Is two [0-1 ]]A random number in between;
Figure BDA0002500653290000043
and
Figure BDA0002500653290000044
for the nth iteration the position and velocity of the id-th particle,
Figure BDA0002500653290000045
for the individual extremum, p, of the id particle of the nth iterationngdIs the nth iteration population extreme value;
s2.5, judging whether the iteration times are reached, if not, skipping to the S2.2, and if so, performing the S2.6;
s2.6, counting the gears of the on-load tap changing transformer O L TC in each time period, initializing each gear into a cluster, combining the clusters of adjacent same gears into a cluster, and counting the number of the combined clusters to be NTC；
S2.7, calculating the evaluation function value after the combination of each two adjacent clusters, and selecting the scheme with the minimum evaluation function for combination;
evaluation function f of the clustersclusterThe definition is as follows:
Figure BDA0002500653290000046
in the formula, K is a clustering number and is a value of the maximum allowable action times N of the on-load tap changing transformer O L TCTCmax，JiFor the ith cluster, TCtIs a cluster JiGear at time intermediate t, TCiThe value of the gear of the ith cluster is equal to the mean value of all gears in the cluster and is rounded up nearby;
step S2.8, after combination, if the cluster number NTC≤NTCmaxIf yes, jumping to the step S2.9, otherwise, continuing to the step S2.7;
and S2.9, calculating the average value of the gears in each cluster, rounding corresponding integers to be the gears of the on-load tap changer O L TC in the corresponding time period, and calculating a cluster evaluation function value.
The second stage active network loss and reactive power optimization method comprises the following steps:
s3.1, initializing population scale, iteration times, particle initial position and speed, particle position limit value and speed limit value, and carrying out active power P on VSC1 of first converter of intelligent soft switch SNOP1Reactive power Q of the first converter VSC1 and the second converter VSC21And Q2Encoding the reactive power of the distributed generation DG;
setting particle position limits:
the maximum value and the minimum value of the particle positions representing the TC gear of the on-load tap changing transformer O L TC are respectively set as TCmaxAnd TCminIndicates the active power P of the intelligent soft switch SNOP1(t) the maximum and minimum values of the particle positions are S1maxand-S1maxExpressing intelligent soft switch SNOP reactive power Q1(t) the maximum and minimum values of the particle positions are S1maxand-S1maxExpressing intelligent soft switch SNOP reactive power Q2(t) the maximum and minimum values of the particle positions are S2maxand-S2maxDenotes the reactive power Q of the distributed power supply DGDGRespectively of particle positions of
Figure BDA0002500653290000051
And
Figure BDA0002500653290000052
setting particle velocity limit ranges of [ -0.2 × (position maximum-position minimum), 0.2 × (position maximum-position minimum) ];
s3.2, decoding the positions of the particles into gears and intelligence of the on-load tap changer O L TCActive power P of first converter VSC1 capable of soft switching SNOP1Reactive power Q of the first converter VSC1 and the second converter VSC21And Q2Performing load flow calculation on the reactive power of the distributed generation DG to enable the reactive power to meet system load flow constraint, node voltage constraint and branch load flow constraint, and calculating the fitness value of each particle according to a target function;
s3.3, counting the extreme value of each particle and the extreme value of the group, wherein the extreme value of each particle is the historical minimum value of the fitness value of each particle, and the extreme value of the group is the historical minimum value of all the particles;
s3.4, updating the position and the speed of the particle swarm, and correcting the updated position and speed according to the integer constraint, the particle position limit value and the particle speed limit value;
the location update and velocity update formulas are as follows:
Figure BDA0002500653290000053
Figure BDA0002500653290000054
in the formula, c1、c2Weights for local and global optimization directions; r is1、r2Is two [0-1 ]]A random number in between;
Figure BDA0002500653290000055
and
Figure BDA0002500653290000056
for the nth iteration the position and velocity of the id-th particle,
Figure BDA0002500653290000057
for the individual extremum, p, of the id particle of the nth iterationngdIs the nth iteration population extreme value;
and S3.5, judging whether the iteration times are reached, if not, skipping to the step S3.2, and if so, determining that the particle position corresponding to the group extreme value is the optimal solution.
Aiming at a power distribution network containing SNOP and DG, the invention establishes a reactive power optimization model taking the minimum of system active network loss and reactive power exchange power with a superior power grid as a target function, limits the action times of an on-load tap changer O L TC in one day as a constraint condition, and carries out two-stage optimization on the reactive power optimization model, wherein the first stage optimizes the gear of the O L TC, and the second stage optimizes the active network loss and the reactive power, the SNOP equipment is flexible to control, and the reactive power distribution of the power distribution network can be optimized and the network loss can be reduced by matching with the DG.
Drawings
Fig. 1 is a typical configuration diagram of the intelligent soft switch SNOP.
Fig. 2 is a schematic diagram of the operation range of the converter VSC in the intelligent soft switch SNOP.
Fig. 3 is a flowchart of a power distribution network reactive power optimization method provided by the invention.
Fig. 4 is a schematic diagram of an improved IEEE33 node system in accordance with an embodiment of the present invention.
FIG. 5 is a graph of load fluctuation, fan and photovoltaic output in one embodiment of the present invention.
FIG. 6 is a diagram of the O L TC shift change in one embodiment of the present invention.
FIG. 7 is a result of objective function optimization in one embodiment of the invention.
FIG. 8 is a diagram illustrating the result of optimization of SNOP control variables in an embodiment of the present invention.
FIG. 9 is a diagram illustrating the results of DG control variable optimization in one embodiment of the invention.
FIG. 10 is a diagram illustrating the convergence of a particle swarm algorithm in one embodiment of the invention.
Detailed Description
The preferred embodiment of the present invention will be described in detail below with reference to fig. 1 to 10.
The reactive power optimization method comprises the following steps of solving optimization results of different time periods according to load, fluctuation of resources such as a fan, photovoltaic and the like, and meeting time relevance, such as limitation of shift times of an on-load tap changer (O L TC) in one day.
Traditional distribution network closed loop design and open loop operation, radial network realizes rack reconsitution and load under the fault condition of normal operating through the interconnection switch and changes the confession. SNOP can replace a tie switch, realize the exchange of active power between two feeders, and provide certain voltage reactive support. A typical structure of SNOP is shown in fig. 1. The reactive power optimization problem belongs to the scene of normal operation state, in the embodiment, the SNOP adopts PQ-VdcQ control mode, three control variables: active power output P of the first converter VSC1 flowing to the second converter VSC21Reactive power Q of VSC1 and VSC21And Q2The schematic operating range diagram is shown in fig. 2, and the power constraint formulas are as formulas (1) to (3).
P1(t)+P2(t)＝0(1)
Figure BDA0002500653290000071
Figure BDA0002500653290000072
In the formula, P1(t) and Q1(t) is the active power and the reactive power of the first converter VSC1 in the t period SNOP; p2(t) and Q2(t) is the active power and the reactive power of the second converter VSC2 in the t period SNOP; s1maxAnd S2maxThe capacity of two converters is SNOP.
The distributed power supply connected into the power grid through the converter can provide active power and can emit or absorb reactive power according to the operation requirement of the system. The converter can be cut off by active powerThe reactive output is realized, and the large-capacity converter can realize additional reactive support under the condition of not losing the active output. The grid operation unit generally requires that a converter type distributed power supply connected to a grid can realize advanced or delayed operation, and the power factor can be within [ cos theta ]min，1]The range is continuously adjustable. Capacity of distributed power converter
Figure BDA0002500653290000073
Should satisfy
Figure BDA0002500653290000074
In the formula (I), the compound is shown in the specification,
Figure BDA0002500653290000075
the maximum active power of the ith DG. The operation constraint of the distributed power supply is shown in mathematical expressions (4) and (5), and the capacity constraint condition of the converter is the same as the formula (2).
Figure BDA0002500653290000076
Figure BDA0002500653290000077
In the formula (I), the compound is shown in the specification,
Figure BDA0002500653290000078
respectively, the real active power and the reactive power of the ith distributed power supply DG, wherein
Figure BDA0002500653290000079
The value of (c) depends on the condition of the fan and the photovoltaic resource corresponding to the DG; thetaminAllowing for an angle for the distributed power supply that corresponds to the minimum power factor.
The reactive power optimization of the power distribution network needs to consider that the active network loss of the system is minimum, and meanwhile, the power distribution network should meet the principle of 'reactive power in-situ balance', namely, the reactive power exchanged between a root node of the power distribution network and a superior power network should be as less as possible. Therefore, as shown in fig. 3, the invention provides a reactive power optimization of a power distribution networkThe method comprises the steps of establishing a reactive power optimization model taking the minimum system active network loss and the minimum reactive power exchange power with a superior power grid as objective functions for a power distribution network containing SNOP and DG, limiting the action times of an on-load tap changer O L TC in one day as constraint conditions, carrying out two-stage optimization on the reactive power optimization model, optimizing the gear of the O L TC in the first stage, neglecting the action time constraint conditions of O L TC, solving the gear of the on-load tap changer O L TC in each period by using a mixed integer particle swarm algorithm, obtaining the action scheme of the on-load tap changer O L TC by using a clustering algorithm, obtaining the gear of the O L TC in each period in one day, optimizing the active network loss and the reactive power in the second stage, taking the gear of the on-load tap changer O L TC in each period in one day as a known value, and solving the running states of the SNOP and DG by using a standard particle swarm1Reactive power Q of VSC1 and VSC21And Q2DG reactive power QDG。
Specifically, the present invention comprises the steps of:
step S1, establishing a reactive power optimization model taking the system active power network loss and the reactive power exchange power with the upper-level power network minimum as objective functions, wherein the active power P of the SNOPSOPAnd reactive power QSOPDG reactive power QDGAnd the gear TC of the on-load tap changer (O L TC) is a control variable.
In order to realize the unification of dimensions, per unit processing is performed on two targets, that is, a per unit value with a unit of 1 is obtained by dividing a named value by a reference value, and a constructed target function can be expressed as:
Figure BDA0002500653290000081
wherein f is the fitness value, Pt,lossIs the active network loss at time t, P0,lossFor the initial active network loss of the system at peak load, Pt,refAnd Qt,refRespectively providing active power and reactive power for a superior power grid at the moment t; n is a study period, and in the present embodiment, when hours are taken as study intervals, n is 24; omegaDGReactive power combination, omega, being DGSOPIs the active and reactive power combination of SNOP. In summary, the first part of equation (6) is the ratio of the real-time grid loss to the initial active grid loss, and the second part is the ratio of the absolute value of the reactive power and the apparent power provided by the upper-level grid.
QDGIs to represent the reactive power of a certain DG, with emphasis on representing this value as a variable; omegaDGThe reactive power combination of each DG in the solution that minimizes the objective function f emphasizes that this value is the optimization variable of the objective function, which is a series of specific values when the objective function is optimal. One is a univariate and one is a set of variables.
P1Refers to the active power, Q, of VSC1 of a particular SNOP1And Q2Refers to the reactive power of VSC1 and VSC2 of a specific SNOP, and the set of the two is QSOP。ΩSOPActive and reactive sets referring to multiple SNOPs, i.e. P of multiple SNOPsSOPAnd QSOPAnd (4) collecting.
The constraint conditions mainly consider system power flow constraint (see formula (7)), node voltage constraint (see formula (8)), branch power flow constraint (see formula (9)), SNOP and DG operation constraint (see formulas (1) - (5)), and gear adjustment constraint (see formulas (10) - (11)).
f(Pi,Qi,Ui)＝0 (7)
Ui,min≤Ui≤Ui,max(8)
Figure BDA0002500653290000091
TCmin≤TCt≤TCmax∩TC∈Z (10)
NTC≤NTCmax(11)
In the formula, Pi、QiTotal active and reactive power, U, injected for node iiIs the voltage of node i, Ui,minAnd Ui,maxMinimum and maximum allowed voltage of node I, IijAnd Iij,maxIs the current amplitude and the current amplitude upper limit, TC, of the branch between the node i and the node jtAt time t, O L TC, TCminAnd TCmaxMinimum gear and maximum gear of O L TC, Z is integer set, NTCAnd NTCmaxThe actual number of actions and the maximum allowed number of actions per day of O L TC, respectively.
The reactive optimization model established above is a mixed integer programming problem (MIP) with time coupling.
When reactive power optimization is carried out, the gear of the O L TC and the reactive power output of the SNOP and the DG have a coupling relation, and a two-stage reactive power optimization method is adopted for processing the action time limit of the O L TC in one day, namely a constraint condition (11).
And S2, optimizing in the first stage, neglecting the constraint condition (11), solving the optimization scheme of each time interval by adopting a mixed integer particle swarm algorithm, and clustering the gear number of the O L TC of each time interval to obtain an O L TC action scheme of one whole day.
For gear of O L TC, active power P of VSC11Reactive power Q of VSC1 and VSC21And Q2DG reactive power QDGCoding is carried out, the gear of O L TC in each time interval is calculated by adopting a mixed integer particle swarm algorithm, and the position updating formula and the speed updating formula are as follows:
Figure BDA0002500653290000101
Figure BDA0002500653290000102
in the formula, c1、c2Weights for local and global optimization directions; r is1、r2Is two [0-1 ]]A random number in between;
Figure BDA0002500653290000103
and
Figure BDA0002500653290000104
for the nth iteration the position and velocity of the id-th particle,
Figure BDA0002500653290000105
is the nth iterationIndividual extreme, p, for the id particlengdSetting the maximum value and the minimum value of the particle positions according to the formulas (1) - (5) and (10), specifically setting the maximum value and the minimum value of the particle positions of the gear TC of the on-load tap changing transformer (O L TC) as TC respectivelymaxAnd TCminRepresents the SNOP active power P1(t) the maximum and minimum values of the particle positions are S1maxand-S1maxIs representative of SNOP reactive power Q1(t) the maximum and minimum values of the particle positions are S1maxand-S1maxIs representative of SNOP reactive power Q2(t) the maximum and minimum values of the particle positions are S2maxand-S2maxDenotes the reactive power Q of DGDGRespectively of particle positions of
Figure BDA0002500653290000107
And
Figure BDA0002500653290000108
Figure BDA0002500653290000109
the gear of O L TC in each time interval can be obtained by the calculation, the gear value of partial time is adjusted to ensure that the integral action times meet the limiting condition, and a clustering algorithm is adopted to realize the purposeclusterThe definition is as follows:
Figure BDA0002500653290000106
in the formula, K is a clustering number and can be O L TC maximum allowable action number NTCmax，JiFor the ith cluster, TCtIs a cluster JiGear at time intermediate t, TCiThe value of the gear of the ith cluster is equal to the mean value of all gears in the cluster and is rounded up nearby.
Further, the first stage optimization specifically comprises the following steps:
and S2.1, initializing a population, initializing the population scale, the iteration number, the initial position and the speed of the particle, and setting the particle position limit value and the speed limit value according to the formulas (1) to (5) and (10), wherein the particle speed limit value ranges from-0.2 × (position maximum value-position minimum value) to 0.2 × (position maximum value-position minimum value).
When the initial position of the particles is initialized, the gear of O L TC and the active power P of VSC1 are adjusted1Reactive power Q of VSC1 and VSC21And Q2DG reactive power QDGThe 1 st value of the particle position represents the O L TC position, and the 2 nd value represents the P of VSC1 for SNOP11And by analogy, namely determining the physical meaning of each value of the particle position is the encoding process, and the value range of each value of the particle position, namely the particle position limit value, can be determined after the determination.
Step S2.2, decoding the positions of the particles into a gear of O L TC and running states of SNOP and DG (the running states of SNOP and DG are active power P of VSC11Reactive power Q of VSC1 and VSC21And Q2DG, and DG reactive power) and performs load flow calculation so that the constraint conditions (7) - (9) are satisfied. The fitness value of each particle is calculated according to equation (6).
And S2.3, counting the extreme value of the particle individual and the extreme value of the group. After each iteration of the particle swarm algorithm, the fitness value of each particle can be calculated through a formula (6), the extreme value of each particle is the historical minimum value of the fitness value of the particle after each iteration, and the population extreme value is the historical minimum value of all the particles.
And S2.4, updating the position and the speed of the particle swarm according to the formulas (12) and (13). The updated position and velocity are corrected based on the integer constraint, the position limit, and the velocity limit.
And S2.5, judging whether a convergence condition is met (whether the convergence condition reaches the iteration times or not), if not, skipping to the step S2.2, and if so, performing the step S2.6.
S2.6, counting gears of each time interval O L TC, initializing each gear into a cluster, combining the clusters of adjacent same gears into a cluster, and counting the combined clustersNumber of clusters NTC。
And S2.7, calculating the evaluation function value after the two adjacent clusters are combined according to the formula (14), selecting the scheme with the minimum evaluation function for combination, and if a plurality of schemes exist, randomly selecting one scheme for combination.
Step S2.8, after combination, if the cluster number NTC≤NTCmaxThen jump to step S2.9, otherwise proceed to step S2.7.
And S2.9, calculating the average value of the gears in each cluster, rounding the corresponding integer to be the gear of O L TC in the corresponding time period, and calculating a cluster evaluation function value.
Note that the clustering process (step S2.6 to step S2.9) may be run multiple times, and the optimal clustering scheme is selected according to the final cluster evaluation function value.
And S3, optimizing in the second stage, namely solving the running states of the SNOP and the DG by adopting a standard particle swarm algorithm by taking the O L TC gear as a known value.
The second stage optimization specifically comprises the following steps:
and S3.1, initializing a population, initializing the population scale, the iteration number, the initial position and the speed of the particle, and setting the particle position limit value and the speed limit value according to the formulas (1) to (5) and (10), wherein the particle speed limit value ranges from-0.2 × (position maximum value-position minimum value) to 0.2 × (position maximum value-position minimum value).
When the initial position of the particle is initialized, the active power P to the VSC11Reactive power Q of VSC1 and VSC21And Q2DG reactive power QDGAnd (6) coding is carried out.
Step S3.2, decode the position of the particle into the running state of SNOP and DG (i.e. the active power P of VSC1, the running state of SNOP and DG)1Reactive power Q of VSC1 and VSC21And Q2DG, and DG reactive power) and performs load flow calculation so that the constraint conditions (7) - (9) are satisfied. The fitness value of each particle is calculated according to equation (6).
And S3.3, counting the extreme value of the particle individual and the group extreme value. After each iteration of the particle swarm algorithm, the fitness value of each particle can be calculated through a formula (6), the extreme value of each particle is the historical minimum value of the fitness value of the particle after each iteration, and the population extreme value is the historical minimum value of all the particles.
And step S3.4, updating the position and the speed of the particle swarm according to the formulas (12) and (13). The updated position and velocity are corrected based on the position limit and velocity limit.
And S3.5, judging whether a convergence condition is met (the convergence condition is set to be whether the iteration times are reached), if not, skipping to the step S3.2, if so, decoding the particle positions corresponding to the group extremum to obtain parameter values of the SNOP and the DG in the power distribution network, wherein the parameter values are optimal control variables.
The solution is performed using a standard particle swarm algorithm, the steps are similar to steps S2.1 to S2.5 of the first stage optimization process, except that integer constraints are not considered.
As shown in FIG. 4, in one embodiment of the present invention, a modified IEEE33 node system is used, allowing voltage range [0.9, 1.1], node 1 connected to O L TC, adjustable gear of + -8, each adjustable gear of 1.25%, setting O L TC tap allowed number of actions 6 times a day, tie line is replaced by two SNOPs, tie line from node 12 to node 22 is replaced by SNOP1, tie line from node 25 to node 29 is replaced by SNOP2, load fluctuation situation, wind turbine and photovoltaic output curve is shown in FIG. 5, wherein load is residential load, load peak is concentrated at 19:00-23: 00. OP and specific parameters are shown in Table 1, wherein capacity of two VSCs of SNOP is 300 kW, power factor of VA is continuously adjustable within [0.9, 1], system OP does not consider SNDG, peak tap and when not adjusted, original grid loss is 202.7 when load is applied.
TABLE 1
SNOP(VSC1--VSC2) Draught fan (WT) Photovoltaic (PV)
Position of 12—22、25—29 10、16、30 7、13、27
Capacity of 300kVA--300kVA 500kW 400kW
In the particle swarm optimization, the population size is 100, the iteration number is 80 generations, c1＝c 22. If the position range of the particle is [ x ]min，xmax]The particle velocity range is then set to [ -0.2 (x)max-xmin),0.2(xmax-xmin)]。
O L TC gear optimization, wherein the original gear of O L TC in each time period obtained in the first stage optimization is shown by a dotted line in figure 6, the gear is influenced by fluctuation of fan and photovoltaic resources and load conditions, such as 7:00-17:00 fans and photovoltaic resources are relatively rich, the load is at a middle level, the gear is integrally lower for better absorbing new energy, and f is obtained through clustering algorithm calculationclusterThe minimum value is 5, the gear position at 5 is correspondingly shifted down by 2, shifted up by 1 at 7, shifted down by 1 at 17 and shifted up by 1 at 18, and the clustered O L TC gear positions are shown by solid lines in fig. 6.
Optimizing active network loss and reactive power: in the second stage of optimization, the objective function is optimized by mutual cooperation of SNOP and DG, and the optimization result is shown in fig. 7. As can be seen from the dotted lines in the figure, the network loss at each moment is reduced to 3.53% at least (moment 10), and even when the load is at a peak (moment 21), the network loss is reduced to 94.9%, so that the load is small as a whole, and the loss reduction effect is more obvious when the fan and photovoltaic resources are rich, for example, the network loss is reduced by more than 80% from 1:00 to 17: 00. As can be seen from the horizontal line in the figure, the ratio of the reactive power and the apparent power exchanged with the upper grid as a whole is small, the value range is [0, 0.335], that is, the reactive power exchanged with the upper grid is small, and the corresponding power factor interval is [0.942, 1 ]. The reactive power exchanged with the superior power grid at the ratio of 7:00-16:00 is basically equal to 0, the fan and photovoltaic resources at the time are rich, sufficient reactive power output is provided, the local balance of the reactive power is realized, and the active loss of the superior power grid caused by the transmission of the reactive power is reduced.
The control variables corresponding to the above optimization results are shown in fig. 8 and 9. The active power transmitted by the SNOP takes the flow direction of the small-number node to the large-number node as the positive direction, and the small-number node side corresponds to the VSC 1. As can be seen from fig. 8, the active power transmitted in SNOP changes direction with load, wind and photovoltaic resource conditions, for example, the active power of SNOP1 flows from node 22 to node 12 at 7:00-16:00, the active power is opposite at the rest of the time, the active power of SNOP2 flows from node 29 to node 25 only at 11, and the active power is opposite at the rest of the time. Two VSCs corresponding to the two SNOPs respectively send out reactive power at the full time. As can be seen from fig. 9, the wind turbine and the photovoltaic are both generating reactive power, and most of the time, the generated reactive power is identical to the real power trend of the DG in fig. 5, and only the PV2 connected to the node 13 and the WT2 connected to the node 16 reduce the reactive output because the reactive power is more sufficient at the peak time of the wind turbine and the photovoltaic resources, as shown by the cross-hatched line and the dotted line in fig. 9.
Single-time section analysis and convergence analysis: taking 12 hours as an example, at this time, the total load active power is 2.48MW, the reactive power is 1.53MVAr, the active power emitted by a single photovoltaic is 294.9kW, the active power emitted by a single fan is 298.3kW, and the convergence of the second-stage particle swarm algorithm at this time is shown in fig. 10. The algorithm is quickly approached to the optimal value in the first eight iteration processes, and converges in 15 iterations, so that the optimization capability and the convergence are better.
The DG minimum allowable power factor is 0.9, and the maximum value of reactive power which can be emitted or absorbed by the photovoltaic and the wind turbine at the moment is 142.8kVAr and 144.5kVAr respectively. The active power network loss at the moment is 10.1kW, and the reactive power provided by the upper-level power network is 0.0 kVAr. The optimum combination of output powers of SNOP and DG is shown in table 2, where the bold part is to reach the upper limit value. The results show that with a load of 12 hours and wind and photovoltaic resource conditions, when the objective function is formula (6), the higher the reactive power emitted by SNOP and DG is, the better, but the reactive power should be balanced locally and compensated nearby.
TABLE 2
Figure BDA0002500653290000141
The method comprises the following steps of establishing a model objective function, namely minimizing system network loss and performing reactive power exchange with a superior power grid, fully considering the operation constraint of the SNOP and the DG and the constraint of O L TC tap positions and the like, and solving the model by using a two-stage reactive power optimization method, wherein the improved IEEE33 node system is used as an example for verification, and the result shows that SNOP equipment is flexible to control, can optimize reactive power distribution of the power distribution network and reduce network loss by matching with the DG, and has better convergence and optimization searching capability.
The reactive power optimization problem of the power distribution network comprising the multi-terminal SNOP can be developed in future research. In addition, the economic research of the SNOP is relatively deficient at present, the SNOP optimization configuration and operation of the whole life cycle can be developed, and the most economic SNOP installation quantity, capacity and position can be solved.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.

Claims (4)

1. A reactive power optimization method for a power distribution network is characterized in that a reactive power optimization model taking system active network loss and minimum reactive exchange power with a superior power grid as objective functions is established for the power distribution network comprising an intelligent soft switch SNOP and a distributed power supply DG, two-stage optimization is carried out on the reactive power optimization model, the gears of an on-load tap changer O L TC are optimized in the first stage, constraint conditions of action frequency limitation of the on-load tap changer O L TC in one day are ignored, the gears of the on-load tap changer O L TC in each period are solved by using a mixed integer particle swarm algorithm, the gears of the on-load tap changer O L TC in each period in one day are obtained by using a clustering algorithm, the active network loss and the reactive power are optimized in the second stage, the gears of the on-load tap changer O L TC in each period in one day are used as known values, and the active power of a first converter VSC1, the reactive power of the first converter VSC1, the reactive power of a second VSC2 and the reactive power supply DG in.
2. The reactive power optimization method for the power distribution network according to claim 1, wherein the objective function is:
Figure FDA0002500653280000011
wherein f is the fitness value, Pt,lossIs the active network loss at time t, P0,lossFor the initial active network loss of the system at peak load, Pt,refAnd Qt,refRespectively providing active power and reactive power for a superior power grid at the moment t; n is the study period; omegaDGReactive power combination, omega, for a distributed generator DGSOPThe active power and the reactive power of the intelligent soft switch SNOP are combined;
the constraint conditions are as follows:
and (3) system power flow constraint: f (P)i,Qi,Ui)＝0
Node voltage constraint: u shapei,min≤Ui≤Ui,max
Branch flow constraint:
Figure FDA0002500653280000012
power constraint of intelligent soft switch SNOP:
P1(t)+P2(t)＝0
Figure FDA0002500653280000021
Figure FDA0002500653280000022
in the formula, P1(t) and Q1(t) the active power and the reactive power of the first converter VSC1 in the intelligent soft switch SNOP in a t period; p2(t) and Q2(t) the active power and the reactive power of the second converter VSC2 in the intelligent soft switch SNOP in a t period; s1maxAnd S2maxThe capacity of two current transformers in the intelligent soft switch SNOP;
power constraint of distributed generator DG:
Figure FDA0002500653280000023
Figure FDA0002500653280000024
Figure FDA0002500653280000025
in the formula (I), the compound is shown in the specification,
Figure FDA0002500653280000026
respectively, the real active power and the reactive power of the ith distributed power supply DG, wherein
Figure FDA0002500653280000027
The value of (1) depends on the condition of the fan and the photovoltaic resource corresponding to the distributed power supply DG; thetaminAllowing an angle corresponding to a minimum power factor for the distributed power supply DG;
Figure FDA0002500653280000028
is the capacity of the distributed power supply DG;
gear adjustment limit constraint of the on-load tap changer O L TC:
TCmin≤TCt≤TCmax∩TC∈Z
NTC≤NTCmax
in the formula, Pi、QiTotal active and reactive power, U, injected for node iiIs the voltage of node i, Ui,minAnd Ui,maxMinimum and maximum allowed voltage of node I, IijAnd Iij,maxIs the current amplitude and the current amplitude upper limit, TC, of the branch between the node i and the node jtFor the on-load tap changer O L TC position at time t, TCminAnd TCmaxIs the minimum gear and the maximum gear of an on-load tap changer O L TC, Z is an integer set, NTCAnd NTCmaxThe actual number of actions and the maximum allowable number of actions of the on-load tap changer O L TC per day are respectively.
3. The reactive power optimization method for the power distribution network according to claim 2, wherein the method for optimizing the on-load tap changer O L TC gear in the first stage comprises the following steps:
s2.1, initializing population scale, iteration times, particle initial position and speed, particle position limit value and speed limit value, and carrying out comparison on the gear of the on-load tap changing transformer O L TC and the active power P of the first converter VSC1 of the intelligent soft switch SNOP1Reactive power Q of the first converter VSC1 and the second converter VSC21And Q2Encoding the reactive power of the distributed generation DG;
setting particle position limits:
the maximum value and the minimum value of the particle positions representing the TC gear of the on-load tap changing transformer O L TC are respectively set as TCmaxAnd TCminIndicates the active power P of the intelligent soft switch SNOP1(t) the maximum and minimum values of the particle positions are S1maxand-S1maxExpressing intelligent soft switch SNOP reactive power Q1(t) the maximum and minimum values of the particle positions are S1maxand-S1maxExpressing intelligent soft switch SNOP reactive power Q2(t) the maximum and minimum values of the particle positions are S2maxand-S2maxDenotes the reactive power Q of the distributed power supply DGDGRespectively of particle positions of
Figure FDA0002500653280000031
Figure FDA0002500653280000032
And
Figure FDA0002500653280000033
setting particle velocity limit ranges of [ -0.2 × (position maximum-position minimum), 0.2 × (position maximum-position minimum) ];
s2.2, decoding the positions of the particles into the gear of the on-load tap changer O L TC and the active power P of the first converter VSC1 of the intelligent soft switch SNOP1Reactive power Q of the first converter VSC1 and the second converter VSC21And Q2Performing load flow calculation on the reactive power of the distributed generation DG to enable the reactive power to meet system load flow constraint, node voltage constraint and branch load flow constraint, and calculating the fitness value of each particle according to a target function;
s2.3, counting the extreme value of each particle and the extreme value of the group, wherein the extreme value of each particle is the historical minimum value of the fitness value of each particle, and the extreme value of the group is the historical minimum value of all the particles;
s2.4, updating the position and the speed of the particle swarm, and correcting the updated position and speed according to the integer constraint, the particle position limit value and the particle speed limit value;
the location update and velocity update formulas are as follows:
Figure FDA0002500653280000034
Figure FDA0002500653280000035
in the formula, c1、c2Weights for local and global optimization directions; r is1、r2Is two [0-1 ]]A random number in between;
Figure FDA0002500653280000036
and
Figure FDA0002500653280000037
for the nth iteration the position and velocity of the id-th particle,
Figure FDA0002500653280000038
for the individual extremum, p, of the id particle of the nth iterationngdIs the nth iteration population extreme value;
s2.5, judging whether the iteration times are reached, if not, skipping to the S2.2, and if so, performing the S2.6;
s2.6, counting the gears of the on-load tap changing transformer O L TC in each time period, initializing each gear into a cluster, combining the clusters of adjacent same gears into a cluster, and counting the number of the combined clusters to be NTC；
S2.7, calculating the evaluation function value after the combination of each two adjacent clusters, and selecting the scheme with the minimum evaluation function for combination;
evaluation function f of the clustersclusterThe definition is as follows:
Figure FDA0002500653280000041
in the formula, K is a clustering number and is a value of the maximum allowable action times N of the on-load tap changing transformer O L TCTCmax，JiFor the ith cluster, TCtIs a cluster JiGear at time intermediate t, TCiThe value of the gear of the ith cluster is equal to the mean value of all gears in the cluster and is rounded up nearby;
step S2.8, after combination, if the cluster number NTC≤NTCmaxIf yes, jumping to the step S2.9, otherwise, continuing to the step S2.7;
and S2.9, calculating the average value of the gears in each cluster, rounding corresponding integers to be the gears of the on-load tap changer O L TC in the corresponding time period, and calculating a cluster evaluation function value.
4. A method for reactive power optimization in a power distribution network according to claim 3, wherein the second step of optimizing the active network loss and reactive power comprises the steps of:
s3.1, initializing population scale, iteration times, particle initial position and speed, particle position limit value and speed limit value, and carrying out active power P on VSC1 of first converter of intelligent soft switch SNOP1Reactive power Q of the first converter VSC1 and the second converter VSC21And Q2Encoding the reactive power of the distributed generation DG;
setting particle position limits:
representing active power P of intelligent soft switch SNOP1(t) the maximum and minimum values of the particle positions are S1maxand-S1maxExpressing intelligent soft switch SNOP reactive power Q1(t) the maximum and minimum values of the particle positions are S1maxand-S1maxExpressing intelligent soft switch SNOP reactive power Q2(t) the maximum and minimum values of the particle positions are S2maxand-S2maxDenotes the reactive power Q of the distributed power supply DGDGRespectively of particle positions of
Figure FDA0002500653280000042
And
Figure FDA0002500653280000043
set particle velocity limit ranges [ -0.2 × (position maximum-position minimum), 0.2 × (position maximum-position minimum)]；
S3.2, decoding the positions of the particles into the gear of the on-load tap changer O L TC and the active power P of the first converter VSC1 of the intelligent soft switch SNOP1Reactive power Q of the first converter VSC1 and the second converter VSC21And Q2Performing load flow calculation on the reactive power of the distributed generation DG to enable the reactive power to meet system load flow constraint, node voltage constraint and branch load flow constraint, and calculating the fitness value of each particle according to a target function;
s3.3, counting the extreme value of each particle and the extreme value of the group, wherein the extreme value of each particle is the historical minimum value of the fitness value of each particle, and the extreme value of the group is the historical minimum value of all the particles;
s3.4, updating the position and the speed of the particle swarm, and correcting the updated position and speed according to the integer constraint, the particle position limit value and the particle speed limit value;
the location update and velocity update formulas are as follows:
Figure FDA0002500653280000051
Figure FDA0002500653280000052
in the formula, c1、c2Weights for local and global optimization directions; r is1、r2Is two [0-1 ]]A random number in between;
Figure FDA0002500653280000053
and
Figure FDA0002500653280000054
is the nth timeIterating the position and velocity of the id-th particle,
Figure FDA0002500653280000055
for the individual extremum, p, of the id particle of the nth iterationngdIs the nth iteration population extreme value;
and S3.5, judging whether the iteration times are reached, if not, skipping to the step S3.2, and if so, determining that the particle position corresponding to the group extreme value is the optimal solution.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111900765A (en) * 2020-08-27 2020-11-06 广东电网有限责任公司 Dynamic reactive power planning method and related device for wind power access system
CN112561136A (en) * 2020-11-30 2021-03-26 郑州轻工业大学 Power distribution network dynamic reactive power optimization method based on fuzzy time clustering
CN112909993A (en) * 2021-01-21 2021-06-04 山东大学 Three-phase current unbalance compensation method for medium-voltage photovoltaic power generation system
CN113363988A (en) * 2021-06-08 2021-09-07 南方电网科学研究院有限责任公司 Power grid operation optimization method, device and medium based on-load tap changer

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000116003A (en) * 1998-09-29 2000-04-21 Kansai Electric Power Co Inc:The Method for controlling reactive voltage power
CN103280821A (en) * 2013-06-25 2013-09-04 国家电网公司 Multi-period dynamic reactive power optimization method of intelligent power distribution system
CN104821605A (en) * 2015-04-13 2015-08-05 国家电网公司 Active safety correction method based on improved particle swarm optimization algorithm
CN105740973A (en) * 2016-01-25 2016-07-06 天津大学 Mixed integer cone programming based intelligent distribution system synthetic voltage reactive power optimization method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000116003A (en) * 1998-09-29 2000-04-21 Kansai Electric Power Co Inc:The Method for controlling reactive voltage power
CN103280821A (en) * 2013-06-25 2013-09-04 国家电网公司 Multi-period dynamic reactive power optimization method of intelligent power distribution system
CN104821605A (en) * 2015-04-13 2015-08-05 国家电网公司 Active safety correction method based on improved particle swarm optimization algorithm
CN105740973A (en) * 2016-01-25 2016-07-06 天津大学 Mixed integer cone programming based intelligent distribution system synthetic voltage reactive power optimization method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
A HYBRID OPTIMIZATION ALGORITHM FOR DISTRIBUTION NETWORK COORDINATED OPERATION WITH SNOP BASED ON SIMULATED ANNEALING AND CONIC PR: "风电场并网穿透功率极限研究" *
XUZHU DONG: "A Hybrid Optimization Algorithm for Distribution Network Coordinated Operation with SNOP Based on Simulated Annealing and Conic Programming" *
陈垚煜: "考虑传统调控手段的配电网SOP 选址定容方法" *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111900765A (en) * 2020-08-27 2020-11-06 广东电网有限责任公司 Dynamic reactive power planning method and related device for wind power access system
CN111900765B (en) * 2020-08-27 2022-03-04 广东电网有限责任公司 Dynamic reactive power planning method and related device for wind power access system
CN112561136A (en) * 2020-11-30 2021-03-26 郑州轻工业大学 Power distribution network dynamic reactive power optimization method based on fuzzy time clustering
CN112909993A (en) * 2021-01-21 2021-06-04 山东大学 Three-phase current unbalance compensation method for medium-voltage photovoltaic power generation system
CN112909993B (en) * 2021-01-21 2022-07-05 山东大学 Three-phase current unbalance compensation method for medium-voltage photovoltaic power generation system
CN113363988A (en) * 2021-06-08 2021-09-07 南方电网科学研究院有限责任公司 Power grid operation optimization method, device and medium based on-load tap changer

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