CN111490552B - Reactive power optimization method for power distribution network - Google Patents

Reactive power optimization method for power distribution network Download PDF

Info

Publication number
CN111490552B
CN111490552B CN202010431382.9A CN202010431382A CN111490552B CN 111490552 B CN111490552 B CN 111490552B CN 202010431382 A CN202010431382 A CN 202010431382A CN 111490552 B CN111490552 B CN 111490552B
Authority
CN
China
Prior art keywords
reactive power
power
particle
snop
gear
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010431382.9A
Other languages
Chinese (zh)
Other versions
CN111490552A (en
Inventor
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
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai University of Electric Power
State Grid Shanghai Electric Power Co Ltd
Original Assignee
Shanghai University of Electric Power
State Grid Shanghai Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai University of Electric Power, State Grid Shanghai Electric Power Co Ltd filed Critical Shanghai University of Electric Power
Priority to CN202010431382.9A priority Critical patent/CN111490552B/en
Publication of CN111490552A publication Critical patent/CN111490552A/en
Application granted granted Critical
Publication of CN111490552B publication Critical patent/CN111490552B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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

Landscapes

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

Abstract

A reactive power optimization method of a power distribution network aims at a power distribution network comprising SNOP and DG, a reactive power optimization model taking the minimum system active network loss and reactive power exchange with an upper power network as objective functions is established, the gear of an OLTC is optimized in a first stage, the action frequency constraint condition of the OLTC is ignored, the gear of an on-load voltage-regulating transformer OLTC in each period is solved by utilizing a mixed integer particle swarm algorithm, the gear of the on-load voltage-regulating transformer OLTC in each period in one day is obtained by adopting a clustering algorithm, the active network loss and the reactive power are optimized in a second stage, the gear of the on-load voltage-regulating transformer OLTC in each period in one day is used as a known value, and the active power of a first converter VSC1, the reactive power of a second converter VSC2 and the reactive power of a distributed power source DG in an intelligent soft switch SNOP are solved by adopting a standard particle swarm algorithm. The invention can optimize reactive power distribution of the power distribution network and reduce network loss, and has better convergence and optimizing capability.

Description

Reactive power optimization method for power distribution network
Technical Field
The invention relates to a two-stage reactive power optimization method of a power distribution network, which takes SNOP and DG regulation capability into account.
Background
Along with the distributed power supplies (Distributed Generation, DG) are connected into the traditional power distribution network, the current situation that the voltage of the radiation type power distribution network drops along the direction from a power supply point to a branch terminal node is changed, and reactive voltage control variables are increased in multiple. In recent years, intelligent soft switches (Soft Normally Open Point, sno) mounted on the distribution network tie-lines increase the difficulty in solving the reactive power optimization problem of the distribution network, because the SNOP not only can change the active power flow direction at both ends of the tie-lines, but also can flexibly output or absorb reactive power by two converters VSC. Along with the continuous improvement of DG permeability and the wide application of SNOP devices, the development of power distribution network reactive power optimization research considering SNOP and DG reactive power adjustment capability has a 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 comprising SNOP and DG, can optimize reactive power distribution of the power distribution network and reduce network loss, and has better convergence and optimizing capability.
In order to achieve the above purpose, the invention provides a reactive power optimization method of a power distribution network, which aims at the power distribution network comprising intelligent soft switches SNOP and distributed power sources DG, a reactive power optimization model taking the minimum active network loss and reactive power exchange with an upper power network as objective functions is established, the reactive power optimization model is subjected to two-stage optimization, the first-stage optimization of the gear of an on-load voltage regulating transformer OLTC, the constraint condition of the limit of the number of actions in one day of the on-load voltage regulating transformer OLTC is ignored, the gear of the on-load voltage regulating transformer OLTC in each period is solved by utilizing a mixed integer particle swarm algorithm, the gear of each period in one day of the on-load voltage regulating transformer OLTC is obtained by utilizing a clustering algorithm, the active network loss and reactive power are optimized in the second stage, the gear of each period in one day of the on-load voltage regulating transformer OLTC is taken as a known value, and the active power of a first converter VSC1, the reactive power of a second converter VSC2 and the reactive power of the distributed power sources DG are solved by utilizing a standard particle swarm algorithm.
The objective function is as follows:
wherein f is a fitness value, P t,loss Is the active network loss at the time t, P 0,loss For the initial active network loss of the system at peak load, P t,ref And Q t,ref Active power and reactive power provided by an upper power grid at t time respectively; n is the study period; omega shape DG Reactive power combination, Ω for distributed power supply DG SOP Active and reactive power combinations for intelligent soft switching SNOP;
the constraint conditions are as follows:
and (3) constraint of system tide: f (P) i ,Q i ,U i )＝0
Node voltage constraint: u (U) i,min ≤U i ≤U i,max
Branch tidal current constraint:
power constraint of intelligent soft switch SNOP:
P 1 (t)+P 2 (t)＝0
wherein P is 1 (t) and Q 1 (t) is the active power and reactive power of the first converter VSC1 in the t-period intelligent soft switch sno; p (P) 2 (t) and Q 2 (t) is the active power and reactive power of the second converter VSC2 in the t-period intelligent soft switch SNOP; s is S 1max And S is 2max The capacity of two converters in the intelligent soft switch SNOP;
power constraint of distributed power DG:
in the method, in the process of the invention,the actual active power and reactive power of the ith distributed power source DG, respectively, whereinThe value of (1) depends on the condition of the fan and the photovoltaic resource corresponding to the distributed power supply DG; θ min Allowing an angle corresponding to the minimum power factor for the distributed power supply DG; />Is the capacity of the distributed power source DG;
gear adjustment limit constraint for on-load step-down transformer OLTC:
TC min ≤TC t ≤TC max ∩TC∈Z
N TC ≤N TCmax
wherein P is i 、Q i Total active and reactive power injected for node i, U i For the voltage of node i, U i,min And U i,max For minimum and maximum allowed voltages of node I, I ij And I ij,max For the current amplitude and upper current amplitude limit of the branch between the node i and the node j, TC t For the gear position of an on-load voltage regulating transformer OLTC at time t, TC min And TC max For the minimum gear and the maximum gear of the on-load voltage regulating transformer OLTC, Z is an integer set, N TC And N TCmax The actual operation times and the maximum allowed operation times of the on-load voltage regulating transformer OLTC in one day are respectively.
The first-stage method for optimizing the on-load voltage-regulating transformer OLTC gear comprises the following steps:
step S2.1, initializing population size, iteration number, particle initial position and speed, particle position limit and speed limit, active power P of first converter VSC1 of intelligent soft switch SNOP for gear of on-load voltage regulating transformer OLTC 1 Reactive power Q of the first converter VSC1 and the second converter VSC2 1 And Q 2 Coding reactive power of the distributed power supply DG;
setting particle position limit values:
particle position maximum and minimum values representing on-load voltage regulating transformer OLTC gear TC are respectively set as TC max And TC min Representing the active power P of the intelligent soft switch SNOP 1 The maximum and minimum values of the particle position of (t) are S 1max and-S 1max Representing intelligent soft switch SNOP reactive power Q 1 The maximum and minimum values of the particle position of (t) are S 1max and-S 1max Representing intelligenceSoft-switchable SNOP reactive power Q 2 The maximum and minimum values of the particle position of (t) are S 2max and-S 2max Reactive power Q representing distributed power source DG DG The maximum and minimum values of the particle positions of (2) are respectivelyAnd->
Setting a particle speed limit range: [ -0.2× (position maximum-position minimum), 0.2× (position maximum-position minimum) ];
step S2.2 decoding the position of the particles into the gear of the on-load step-up transformer OLTC and the active power P of the first converter VSC1 of the intelligent soft switch SNOP 1 Reactive power Q of the first converter VSC1 and the second converter VSC2 1 And Q 2 Reactive power of the distributed power supply DG is subjected to load flow calculation, so that the reactive power meets system load flow constraint, node voltage constraint and branch load flow constraint, and the fitness value of each particle is calculated according to an objective function;
s2.3, counting extremum and population extremum of individual particles, wherein the extremum of the individual particles is the historical minimum value of fitness value of the particles, and the population extremum 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 integer constraint, particle position limit and particle speed limit;
the location update and velocity update formulas are as follows:
wherein, c 1 、c 2 For local seekingWeights of the optimal direction and the global optimal direction; r is (r) 1 、r 2 Two [0-1 ]]Random numbers in between;and->Position and velocity of the id-th particle for the nth iteration, < >>For the individual extremum, p, of the nth iteration, the id-th particle ngd The population extremum is the nth iteration;
step S2.5, judging whether iteration times are reached, if not, jumping to step S2.2, and if so, executing step S2.6;
step S2.6, counting gears of the on-load voltage-regulating transformer OLTC in each period, initializing each gear into one cluster, combining clusters adjacent to the same gear into one cluster, and counting the combined clusters as N TC ；
S2.7, calculating an evaluation function value after combining each two adjacent clusters, and selecting a scheme with the minimum evaluation function for combining;
evaluation function f of clustering cluster The definition is as follows:
wherein K is a cluster number, and the value is N which is the maximum allowable action times of an on-load voltage regulating transformer OLTC TCmax ，J i For the ith cluster, TC t For clustering J i Shift position at middle t moment, TC i The value of the gear is equal to the average value of all gears in the ith cluster and is rounded nearby;
step S2.8, merging and then determining the clustering number N TC ≤N TCmax The step is skipped to the step S2.9, otherwise, the step S2.7 is continued;
and S2.9, calculating an average value of gears in each cluster, rounding the corresponding integer to be the gear of the on-load voltage-regulating transformer OLTC in the corresponding time period, and calculating a cluster evaluation function value.
The second-stage active power loss and reactive power optimizing method comprises the following steps:
step S3.1, initializing population size, iteration number, particle initial position and velocity, particle position limit and velocity limit, active power P of first converter VSC1 of intelligent soft switch SNOP 1 Reactive power Q of the first converter VSC1 and the second converter VSC2 1 And Q 2 Coding reactive power of the distributed power supply DG;
setting particle position limit values:
particle position maximum and minimum values representing on-load voltage regulating transformer OLTC gear TC are respectively set as TC max And TC min Representing the active power P of the intelligent soft switch SNOP 1 The maximum and minimum values of the particle position of (t) are S 1max and-S 1max Representing intelligent soft switch SNOP reactive power Q 1 The maximum and minimum values of the particle position of (t) are S 1max and-S 1max Representing intelligent soft switch SNOP reactive power Q 2 The maximum and minimum values of the particle position of (t) are S 2max and-S 2max Reactive power Q representing distributed power source DG DG The maximum and minimum values of the particle positions of (2) are respectivelyAnd->
Setting a particle speed limit range: [ -0.2× (position maximum-position minimum), 0.2× (position maximum-position minimum) ];
step S3.2 decoding the position of the particles into the gear of the on-load step-up transformer OLTC and the active power P of the first converter VSC1 of the intelligent soft switch SNOP 1 Reactive power Q of the first converter VSC1 and the second converter VSC2 1 And Q 2 Reactive power of the distributed power supply DG is subjected to load flow calculation, so that the reactive power meets system load flow constraint, node voltage constraint and branch load flow constraint, and the fitness value of each particle is calculated according to an objective function;
s3.3, counting extremum and population extremum of individual particles, wherein the extremum of the individual particles is the historical minimum value of fitness value of the particles, and the population extremum 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 integer constraint, particle position limit and particle speed limit;
the location update and velocity update formulas are as follows:
wherein, c 1 、c 2 Weights for the local and global optimization directions; r is (r) 1 、r 2 Two [0-1 ]]Random numbers in between;and->Position and velocity of the id-th particle for the nth iteration, < >>For the individual extremum, p, of the nth iteration, the id-th particle ngd The population extremum is the nth iteration;
and S3.5, judging whether the iteration times are reached, if not, jumping to the step S3.2, and if so, judging that the particle positions corresponding to the population extremum are the optimal solutions.
The invention establishes a reactive power optimization model which takes the minimum system active network loss and reactive power exchange with an upper power grid as objective functions aiming at a power distribution network comprising SNOP and DG, takes the limit of the number of actions in one day of an on-load voltage regulating transformer OLTC as a constraint condition, optimizes the reactive power optimization model in two stages, optimizes the gear of the OLTC in the first stage, optimizes the active network loss and the reactive power in the second stage, has flexible SNOP equipment control, and can optimize the reactive power distribution of the power distribution network and reduce the network loss by matching with DG.
Drawings
Fig. 1 is a typical block diagram of an intelligent soft switch SNOP.
Fig. 2 is a schematic diagram of the operating range of the converter VSC in the intelligent soft switch sno.
Fig. 3 is a flowchart of a reactive power optimization method of a power distribution network provided by the invention.
Fig. 4 is a schematic diagram of an improved IEEE33 node system in 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 OLTC gear change in an embodiment of the present invention.
FIG. 7 is an objective function optimization result in one embodiment of the invention.
Fig. 8 is a schematic diagram of optimization results of SNOP control variables in an embodiment of the present invention.
Fig. 9 is a schematic diagram of DG controlled variable optimization results in one embodiment of the invention.
Fig. 10 is a schematic diagram of convergence of a particle swarm algorithm in an embodiment of the invention.
Detailed Description
The following describes a preferred embodiment of the present invention with reference to fig. 1 to 10.
Due to the fluctuation of the load, the fan, the photovoltaic and other resources, the reactive power optimization problem needs to solve the optimization results of different time periods, and meanwhile, the time relevance, such as the limit of the shift frequency of one day of an on-load voltage regulating transformer (OLTC), is met. In terms of objective functions, the minimum network loss, the optimal reactive power economy, the minimum running risk and the combination of the objectives are mainly considered. The related research shows that the access of SNOP in the power distribution network can obviously improve the voltage level of the load point and reduce the network loss, and has important effect on reactive power optimization. The difficulty of reactive power optimization of a distribution network comprising SNOP and DG is to coordinate the active and reactive power of a plurality of SNOP and DG, and meanwhile, the optimization of multiple time scales should be satisfied.
The traditional distribution network is designed in a closed loop and operated in an open loop, and the radial network realizes grid reconstruction during normal operation and load transfer under a fault state through a connecting switch. The SNOP can replace a tie switch, realizes active power exchange between two feeder lines and provides a certain voltage reactive support. A typical structure of the SNOP is shown in fig. 1. The reactive power optimization problem belongs to the scene of normal operation state, in the embodiment, SNOP adopts PQ-V dc Q control mode, the control variable has three: active power output P of the first converter VSC1 flowing to the second converter VSC2 1 Reactive power Q of VSC1 and VSC2 1 And Q 2 The operation range is schematically shown in fig. 2, and the power constraint formulas are shown in formulas (1) to (3).
P 1 (t)+P 2 (t)＝0(1)
Wherein P is 1 (t) and Q 1 (t) is the active power and reactive power of the first converter VSC1 in the period SNOP; p (P) 2 (t) and Q 2 (t) is the active power and reactive power of the second converter VSC2 in the period SNOP; s is S 1max And S is 2max Is the capacity of the two converters of the SNOP.
The distributed power supply connected to the power grid through the converter can provide active power and simultaneously send or absorb reactive power according to the running requirement of the system. Reactive output can be realized through active power reduction of the converter, and the high-capacity converterAdditional reactive support can also be achieved without loss of active output. The power grid operation units generally require that the converter type distributed power supply connected to the power grid can realize leading or lagging operation, and the power factor can be controlled in [ cos theta ] min ，1]Continuously adjustable in range. Capacity of distributed power converterShould satisfy->In (1) the->Is 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 that of the formula (2).
In the method, in the process of the invention,the actual active power and reactive power of the ith distributed power source DG, respectively, whereinThe value of (1) depends on the condition of the fan and the photovoltaic resource corresponding to DG; θ min The angle corresponding to the minimum power factor is allowed for the distributed power supply.
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 on-site balancing, namely, the reactive power exchanged between the root node of the power distribution network and the upper power network should be as little as possible. Thus, as shown in FIG. 3, the present invention provides a kitAccording to the power grid reactive power optimization method, a reactive power optimization model taking the minimum system active network loss and reactive power exchange with an upper power grid as objective functions is established for a power distribution network comprising SNOP and DG, the limitation of the number of actions in one day of an on-load voltage-regulating transformer OLTC is taken as a constraint condition, the reactive power optimization model is subjected to two-stage optimization, the gear of the OLTC is optimized in the first stage, the constraint condition of the number of actions of the OLTC is ignored, the gear of the on-load voltage-regulating transformer OLTC in each period is solved by utilizing a mixed integer particle swarm algorithm, the action scheme of the on-load voltage-regulating transformer OLTC is obtained by adopting a clustering algorithm, the gear of each period of the on-load voltage-regulating transformer OLTC is obtained, the active network loss and reactive power are optimized in the second stage, the gear of each period of the on-load voltage-regulating transformer OLTC is taken as a known value, and the running state of the SNOP and DG is solved by adopting a standard particle swarm algorithm, namely the active power P of VSC1 1 Reactive power Q of VSC1 and VSC2 1 And Q 2 Reactive power Q of DG DG 。
Specifically, the invention comprises the following steps:
s1, establishing a reactive power optimization model taking the minimum system active network loss and reactive power exchange power with an upper power grid as objective functions, wherein the active power P of the SNOP SOP And reactive power Q SOP Reactive power Q of DG DG And a gear position TC of an on-load voltage regulating transformer (OLTC) is used as a control variable.
In order to achieve dimension unification, the two targets are subjected to per unit processing, namely, a nominal value is divided by a reference value to obtain a per unit value with a unit of 1, and a constructed objective function can be expressed as:
wherein f is a fitness value, P t,loss Is the active network loss at the time t, P 0,loss For the initial active network loss of the system at peak load, P t,ref And Q t,ref Active power and reactive power provided by an upper power grid at t time respectively; n is the study period, in this example with hours as study interval, then n=24; omega shape DG Is DGReactive power combination, Ω SOP Is the active and reactive power combination of the SNOP. To sum up, the first part of the formula (6) is the ratio of the real-time network loss to the initial active network loss, and the second part is the ratio of the absolute value of reactive power and apparent power provided by the upper power grid.
Q DG Is reactive power representing a certain DG, focusing on representing this value as a variable; omega shape DG The reactive power combination of each DG in the solution that minimizes the objective function f emphasizes that this value is the optimal variable for the objective function, which is a series of specific values when the objective function is optimal. One is a single variable and one is a set of variables.
P 1 Refers to the active power, Q, of VSC1 of a particular SNOP 1 And Q 2 Refers to reactive power of VSC1 and VSC2 of a specific SNOP, and the set of the two is Q SOP 。Ω SOP Refers to active and reactive sets of SNOP, i.e. P of SNOP SOP And Q SOP And (5) collecting.
The constraint conditions mainly consider the system power flow constraint (formula (7)), the node voltage constraint (formula (8)), the branch power flow constraint (formula (9)), the SNOP and DG operation constraints (formulas (1) - (5)), and the gear adjustment limits (formulas (10) - (11)).
f(P i ,Q i ,U i )＝0 (7)
U i,min ≤U i ≤U i,max (8)
TC min ≤TC t ≤TC max ∩TC∈Z (10)
N TC ≤N TCmax (11)
Wherein P is i 、Q i Total active and reactive power injected for node i, U i For the voltage of node i, U i,min And U i,max For minimum and maximum allowed voltages of node I, I ij And I ij,max For the current amplitude and upper current amplitude limit of the branch between the node i and the node j, TC t For the OLTC gear at time t, TC min And TC max Is the minimum gear and the maximum gear of the OLTC, Z is an integer set, N TC And N TCmax The actual number of actions and the maximum allowed number of actions per day for OLTC, respectively.
The reactive optimization model established above is a mixed integer programming problem (MIP) with time coupling.
During reactive power optimization, the gear of the OLTC 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 limit of the action times in one day of the OLTC, namely a constraint condition (11).
Step S2, optimizing in the first stage, neglecting constraint conditions (11), solving an optimization scheme of each period by adopting a mixed integer particle swarm algorithm, and clustering gear numbers of the OLTC of each period to obtain an OLTC action scheme of the whole day.
Active power P for gear, VSC1 of OLTC 1 Reactive power Q of VSC1 and VSC2 1 And Q 2 Reactive power Q of DG DG Coding, and calculating gear positions of the OLTC in each period by adopting a mixed integer particle swarm algorithm, wherein the position updating and speed updating formulas are as follows:
wherein, c 1 、c 2 Weights for the local and global optimization directions; r is (r) 1 、r 2 Two [0-1 ]]Random numbers in between;and->Position and velocity of the id-th particle for the nth iteration, < >>For the individual extremum, p, of the nth iteration, the id-th particle ngd Is the population extremum for the nth iteration. Setting maximum and minimum values of particle positions according to formulas (1) - (5), (10), specifically: particle position maxima and minima representing on-load step-down transformer (OLTC) gear TC are set to TC, respectively max And TC min Representing SNOP active power P 1 The maximum and minimum values of the particle position of (t) are S 1max and-S 1max Represents SNOP reactive power Q 1 The maximum and minimum values of the particle position of (t) are S 1max and-S 1max Represents SNOP reactive power Q 2 The maximum and minimum values of the particle position of (t) are S 2max and-S 2max Representing reactive power Q of DG DG The maximum and minimum of the particle positions of (2) are +.>And->
The gear of the OLTC in each period can be obtained through the calculation, the integral action times meet the limiting condition by adjusting the gear value of part of time, and the aim is achieved by adopting a clustering algorithm. Evaluation function f of clustering cluster The definition is as follows:
wherein K is a cluster number, and can be given as the maximum allowable number of actions N of OLTC TCmax ，J i For the ith cluster, TC t For clustering J i Shift position at middle t moment, TC i The value of the gear of the ith cluster is equal to the average value of all gears in the cluster and is rounded nearby.
Further, the first-stage optimization specifically comprises the following steps:
and S2.1, initializing a population. Initializing population scale, iteration times, initial particle position and speed, particle position limit and speed limit. The particle position limit is set according to formulas (1) - (5), (10), the particle speed limit range is [ -0.2× (position maximum-position minimum), 0.2× (position maximum-position minimum) ].
When the initial position of the particles is initialized, the active power P of the VSC1 and the gear of the OLTC are controlled 1 Reactive power Q of VSC1 and VSC2 1 And Q 2 Reactive power Q of DG DG Encoding is performed. The 1 st value of the particle position represents the OLTC gear and the 2 nd value represents the P of VSC1 of SNOP1 1 And so on, namely determining the physical meaning of each value of the particle position is the coding 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 position of the particle into the gear of the OLTC and the operating states of SNOP and DG (the operating states of SNOP and DG, i.e. the active power P of VSC1 1 Reactive power Q of VSC1 and VSC2 1 And Q 2 Reactive power of DG), and performing power flow calculation so as to satisfy constraint conditions (7) - (9). And calculating the fitness value of each particle according to the formula (6).
And S2.3, counting extremum and population extremum of the particle individuals. After each iteration of the particle swarm algorithm, the fitness value of each particle can be calculated through the formula (6), the extremum of each particle is the historical minimum value of the fitness value of each particle after each iteration, and the population extremum is the historical minimum value of all particles.
And S2.4, updating the position and the speed of the particle swarm according to formulas (12) and (13). And correcting the updated position and speed according to the integer constraint, the position limit and the speed limit.
And step S2.5, judging whether a convergence condition is met (whether the convergence condition is set to reach the iteration times or not), if not, jumping to the step S2.2, and if so, executing the step S2.6.
Step S2.6, counting gears of the OLTC in each period, initializing each gear into a cluster, combining clusters of adjacent identical gears into a cluster, and counting the combined clusters as N TC 。
And S2.7, calculating the evaluation function value after combining each two adjacent clusters according to a formula (14), selecting a scheme with the minimum evaluation function for combining, and if a plurality of schemes exist, randomly selecting one scheme for combining.
Step S2.8, merging and then determining the clustering number N TC ≤N TCmax The process jumps to step S2.9, otherwise the process continues to step S2.7.
And S2.9, calculating an average value of gears in each cluster, rounding the gears with corresponding integers as the gears of the OLTC in the corresponding time period, and calculating a cluster evaluation function value.
Note that the clustering process (steps S2.6 to S2.9) may be run multiple times, and an optimal clustering scheme is selected according to the final cluster evaluation function value.
And step S3, optimizing in a second stage, taking the OLTC gear as a known value, and solving the running states of SNOP and DG by adopting a standard particle swarm algorithm.
The second-stage optimization specifically comprises the following steps:
and S3.1, initializing a population. Initializing population scale, iteration times, initial particle position and speed, particle position limit and speed limit. The particle position limit is set according to formulas (1) - (5), (10), the particle speed limit range is [ -0.2× (position maximum-position minimum), 0.2× (position maximum-position minimum) ].
Active power P to VSC1 when initializing the initial position of the particle 1 Reactive power Q of VSC1 and VSC2 1 And Q 2 Reactive power Q of DG DG Encoding is performed.
Step S3.2 decoding the position of the particle into the operating states of SNOP and DG (SNOP and DG operating states, i.e. active power P of VSC1 1 Reactive power Q of VSC1 and VSC2 1 And Q 2 Reactive power of DG), and performing power flow calculation so as to satisfy constraint conditions (7) - (9). According toEquation (6) calculates fitness values of the respective particles.
And S3.3, counting extremum and population extremum of the particle individuals. After each iteration of the particle swarm algorithm, the fitness value of each particle can be calculated through the formula (6), the extremum of each particle is the historical minimum value of the fitness value of each particle after each iteration, and the population extremum is the historical minimum value of all particles.
And S3.4, updating the position and the speed of the particle swarm according to formulas (12) and (13). And correcting the updated position and speed according to the position limit value and the speed limit value.
And S3.5, judging whether a convergence condition is met (whether the convergence condition is set to reach the iteration times or not), if not, jumping to the step S3.2, and if so, decoding the particle positions corresponding to the population extremum to obtain parameter values of SNOP and DG in the power distribution network, wherein the parameter values are the optimal control variables.
The solution is performed using a standard particle swarm algorithm, the steps being similar to steps S2.1 to S2.5 of the first stage optimization procedure, except that integer constraints are not considered.
In one embodiment of the invention, as shown in FIG. 4, a modified IEEE33 node system is used, the allowable voltage range is [0.9,1.1], node 1 is connected to an OLTC, the adjustable gear is + -8, 1.25% per gear can be adjusted, and the allowed number of times of operation of the OLTC tap is set to 6 a day. The tie-line is replaced with two SNOPs, the tie-line from node 12 to node 22 is replaced with SNOP1, and the tie-line from node 25 to node 29 is replaced with SNOP 2. The load fluctuation, fan and photovoltaic output curves are shown in fig. 5, wherein the load is a resident load, and the load peaks are concentrated at 19:00-23:00. The specific parameters of the SNOP and DG are shown in table 1, wherein the capacities of the two converters VSC of the SNOP are 300kva, and the power factor of DG is continuously adjustable within the range of [0.9,1 ]. When the system does not consider the SNOP, DG and the tap is not adjusted, the original net loss is 202.7kW at peak load.
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 algorithm, the population scale is 100, the iteration number is 80 generations, c 1 ＝c 2 =2. If the position range of the particles is [ x ] min ，x max ]The particle velocity range is set to [ -0.2 (x max -x min ),0.2(x max -x min )]。
OLTC gear optimization: the original gear of the OLTC obtained in the first-stage optimization is shown by a dotted line in fig. 6, the gear is influenced by fluctuation of fan and photovoltaic resources and load conditions, for example, the fan and the photovoltaic resources are relatively rich in 7:00-17:00, the load is at an intermediate level, and the gear is low in whole in order to better absorb new energy. F is calculated by a clustering algorithm cluster The minimum value is 5, the gear at 5 is correspondingly adjusted down by 2, up by 1, down by 1 and up by 1, respectively, at 17, and at 18, and clustered OLTC gears are shown as solid lines in fig. 6.
Active network loss and reactive power optimization: in the second-stage optimization, the optimization of the objective function is realized through the interaction of SNOP and DG, and the optimization result is shown in figure 7. As can be seen from the broken line in the figure, the network loss at each moment is reduced to 3.53 percent (moment 10) at the lowest, even if the load is high (moment 21), the network loss is reduced to 94.9 percent, the load is smaller as a whole, and the loss reducing effect is more obvious when the fan and the photovoltaic resources are rich, for example, the network loss is reduced by more than 80 percent from 1:00 to 17:00. As can be seen from the horizontal line at the middle point of the graph, the ratio of the reactive power exchanged with the upper power grid as a whole to the apparent power is smaller, the numerical range is [0,0.335], namely the reactive power exchanged with the upper power grid is smaller, and the corresponding power factor interval is [0.942,1]. The reactive power exchanged with the upper power grid at the ratio of 7:00-16:00 is basically equal to 0, at this time, the section fans and the photovoltaic resources are rich, enough reactive power output is provided, the on-site balancing of reactive power is realized, and the active loss of the upper power grid caused by the reactive power transmission is reduced.
The control variables corresponding to the above-described optimization result 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 side of the small-number node is correspondingly VSC1. As can be seen from fig. 8, the active power transmitted in the SNOP changes direction with the load, fan 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 directions are opposite at the rest of time, the active power of SNOP2 flows from node 29 to node 25 only at 11, and the rest of time is opposite. The two VSCs corresponding to the two SNOPs respectively emit reactive power at all times. As can be seen from fig. 9, both the blower and the photovoltaic are generating reactive power, and most of the reactive power generated by the blower and the photovoltaic are consistent with the trend of the active power of DG in fig. 5, only PV2 connected to node 13 and WT2 connected to node 16 reduce the reactive output at peak time of the blower and the photovoltaic resources because the reactive power is sufficient, as shown by the horizontal line and the dashed line with squares in fig. 9, respectively.
Single-moment section analysis and convergence analysis: taking 12 hours as an example, the total active power of the load 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 the moment is shown in FIG. 10. The algorithm quickly approaches to the optimal value in the previous eight iterative processes, converges in 15 times, and has better optimizing capability and convergence.
DG minimum allowable power factor is 0.9, then at this point the photovoltaic fan can emit or absorb reactive power at maximum values of 142.8kVAr and 144.5kVAr, respectively. The active power loss at the moment is 10.1kW, and the reactive power provided by the upper power grid is 0.0kVAr. The optimum combinations of the output powers of the SNOP and DG are shown in table 2, in which the thickened portions reach the upper limit value. The results show that with the load at 12 and the fan and photovoltaic resource conditions, when the objective function is formula (6), the reactive power emitted by the SNOP and the DG is not higher and better, but should be balanced and compensated nearby in situ.
TABLE 2
The SNOP and DG are connected to increase the difficulty in solving the reactive power optimization problem of the power distribution network, and meanwhile, the running state of the power distribution network can be greatly optimized by setting a reasonable target. The model objective function established by the invention is to minimize the system network loss and reactive exchange with the upper power grid, and fully considers the operation constraint of SNOP and DG and the constraint of the OLTC tap position and the like. The model is solved by using a two-stage reactive power optimization method, and verification is performed by taking an improved IEEE33 node system as an example, and the result shows that SNOP equipment is flexible to control, and can optimize reactive power distribution of a power distribution network and reduce network loss by matching with DG.
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 SNOP is currently lacking, the SNOP optimizing configuration and operation of the whole life cycle can be carried out, and the most economical SNOP installation quantity, capacity and position are solved.
While the present invention has been described in detail through the foregoing description of the preferred embodiment, it should be understood that the foregoing description is not to be considered as limiting the invention. Many modifications and substitutions of the present invention will become apparent to those of ordinary skill in the art upon reading the foregoing. Accordingly, the scope of the invention should be limited only by the attached claims.

Claims (1)

1. A reactive power optimization method for a power distribution network is characterized by establishing a reactive power optimization model taking the minimum system active network loss and the minimum reactive power exchange power with an upper power network as objective functions for the power distribution network comprising an intelligent soft switch SNOP and a distributed power source DG, performing two-stage optimization on the reactive power optimization model, optimizing the gear of an on-load voltage-regulating transformer OLTC in the first stage, and ignoring the on-load voltage-regulating transformer OLTC
Solving the gear of the on-load voltage-regulating transformer OLTC in each period by utilizing a mixed integer particle swarm algorithm under the constraint condition of action frequency limitation in one day, obtaining the gear of the on-load voltage-regulating transformer OLTC in each period in one day by adopting a clustering algorithm, optimizing active network loss and reactive power in the second stage, taking the gear of the on-load voltage-regulating transformer OLTC in each period in one day as a known value, and solving the active power of the first converter VSC1, the reactive power of the second converter VSC2 and the reactive power of the distributed power source DG in the intelligent soft switch SNOP by adopting a standard particle swarm algorithm;
the objective function is as follows:
wherein f is a fitness value, P t,loss Is the active network loss at the time t, P 0,loss For the initial active network loss of the system at peak load, P t,ref And Q t,ref Active power and reactive power provided by an upper power grid at t time respectively; n is the study period; omega shape DG Reactive power combination, Ω for distributed power supply DG SOP Active and reactive power combinations for intelligent soft switching SNOP;
the constraint conditions are as follows:
and (3) constraint of system tide: f (P) i ,Q i ,U i )＝0
Node voltage constraint: u (U) i,min ≤U i ≤U i,max
Branch tidal current constraint:
power constraint of intelligent soft switch SNOP:
P 1 (t)+P 2 (t)＝0
wherein P is 1 (t) and Q 1 (t) is the active power and reactive power of the first converter VSC1 in the t-period intelligent soft switch sno; p (P) 2 (t) and Q 2 (t) is the active power and reactive power of the second converter VSC2 in the t-period intelligent soft switch SNOP; s is S 1max And S is 2max The capacity of two converters in the intelligent soft switch SNOP;
power constraint of distributed power DG:
in the middle of，The actual active power and reactive power of the i-th distributed power source DG, respectively, wherein +.>The value of (1) depends on the condition of the fan and the photovoltaic resource corresponding to the distributed power supply DG; θ min Allowing an angle corresponding to the minimum power factor for the distributed power supply DG; />Is the capacity of the distributed power source DG;
gear adjustment limit constraint for on-load step-down transformer OLTC:
TC min ≤TC t ≤TC max ∩TC∈Z
N TC ≤N TCmax
wherein P is i 、Q i Total active and reactive power injected for node i, U i For the voltage of node i, U i,min And U i,max For minimum and maximum allowed voltages of node I, I ij And I ij,max For the current amplitude and upper current amplitude limit of the branch between the node i and the node j, TC t For the gear position of an on-load voltage regulating transformer OLTC at time t, TC min And TC max For the minimum gear and the maximum gear of the on-load voltage regulating transformer OLTC, Z is an integer set, N TC And N TCmax The actual action times and the maximum allowed action times of the on-load voltage regulating transformer OLTC in one day are respectively;
the first-stage method for optimizing the on-load voltage-regulating transformer OLTC gear comprises the following steps:
step S2.1, initializing population size, iteration number, particle initial position and speed, particle position limit and speed limit, active power P of first converter VSC1 of intelligent soft switch SNOP for gear of on-load voltage regulating transformer OLTC 1 Reactive power of the first converter VSC1 and the second converter VSC2Rate Q 1 And Q 2 Coding reactive power of the distributed power supply DG;
setting particle position limit values:
particle position maximum and minimum values representing on-load voltage regulating transformer OLTC gear TC are respectively set as TC max And TC min Representing the active power P of the intelligent soft switch SNOP 1 The maximum and minimum values of the particle position of (t) are S 1max and-S 1max Representing intelligent soft switch SNOP reactive power Q 1 The maximum and minimum values of the particle position of (t) are S 1max and-S 1max Representing intelligent soft switch SNOP reactive power Q 2 The maximum and minimum values of the particle position of (t) are S 2max and-S 2max Reactive power Q representing distributed power source DG DG The maximum and minimum values of the particle positions of (2) are respectively And->
Setting a particle speed limit range: [ -0.2× (position maximum-position minimum), 0.2× (position maximum-position minimum) ];
step S2.2 decoding the position of the particles into the gear of the on-load step-up transformer OLTC and the active power P of the first converter VSC1 of the intelligent soft switch SNOP 1 Reactive power Q of the first converter VSC1 and the second converter VSC2 1 And Q 2 Reactive power of the distributed power supply DG is subjected to load flow calculation, so that the reactive power meets system load flow constraint, node voltage constraint and branch load flow constraint, and the fitness value of each particle is calculated according to an objective function;
s2.3, counting extremum and population extremum of individual particles, wherein the extremum of the individual particles is the historical minimum value of fitness value of the particles, and the population extremum 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 integer constraint, particle position limit and particle speed limit;
the location update and velocity update formulas are as follows:
wherein, c 1 、c 2 Weights for the local and global optimization directions; r is (r) 1 、r 2 Two [0-1 ]]Random numbers in between;and->Position and velocity of the id-th particle for the nth iteration, < >>For the individual extremum, p, of the nth iteration, the id-th particle ngd The population extremum is the nth iteration;
step S2.5, judging whether iteration times are reached, if not, jumping to step S2.2, and if so, executing step S2.6;
step S2.6, counting gears of the on-load voltage-regulating transformer OLTC in each period, initializing each gear into one cluster, combining clusters adjacent to the same gear into one cluster, and counting the combined clusters as N TC ；
S2.7, calculating an evaluation function value after combining each two adjacent clusters, and selecting a scheme with the minimum evaluation function for combining;
evaluation function f of clustering cluster The definition is as follows:
wherein K is a cluster number, and the value is N which is the maximum allowable action times of an on-load voltage regulating transformer OLTC TCmax ，J i For the ith cluster, TC t For clustering J i Shift position at middle t moment, TC i The value of the gear is equal to the average value of all gears in the ith cluster and is rounded nearby;
step S2.8, merging and then determining the clustering number N TC ≤N TCmax The step is skipped to the step S2.9, otherwise, the step S2.7 is continued;
s2.9, calculating an average value of gears in each cluster, rounding the corresponding integer to be the gear of the on-load voltage-regulating transformer OLTC in the corresponding time period, and calculating a cluster evaluation function value; the second-stage active power loss and reactive power optimizing method comprises the following steps:
step S3.1, initializing population size, iteration number, particle initial position and velocity, particle position limit and velocity limit, active power P of first converter VSC1 of intelligent soft switch SNOP 1 Reactive power Q of the first converter VSC1 and the second converter VSC2 1 And Q 2 Coding reactive power of the distributed power supply DG;
setting particle position limit values:
representing intelligent soft switch SNOP active power P 1 The maximum and minimum values of the particle position of (t) are S 1max and-S 1max Representing intelligent soft switch SNOP reactive power Q 1 The maximum and minimum values of the particle position of (t) are S 1max and-S 1max Representing intelligent soft switch SNOP reactive power Q 2 The maximum and minimum values of the particle position of (t) are S 2max and-S 2max Reactive power Q representing distributed power source DG DG The maximum and minimum values of the particle positions of (2) are respectivelyAnd->Setting a particle speed limit range: [ -0.2× (position maximum-position minimum), 0.2× (position maximum-position minimum)]；
Step S3.2 decoding the position of the particles into the gear of the on-load step-up transformer OLTC and the active power P of the first converter VSC1 of the intelligent soft switch SNOP 1 Reactive power Q of the first converter VSC1 and the second converter VSC2 1 And Q 2 Reactive power of the distributed power supply DG is subjected to load flow calculation, so that the reactive power meets system load flow constraint, node voltage constraint and branch load flow constraint, and the fitness value of each particle is calculated according to an objective function;
s3.3, counting extremum and population extremum of individual particles, wherein the extremum of the individual particles is the historical minimum value of fitness value of the particles, and the population extremum 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 integer constraint, particle position limit and particle speed limit;
the location update and velocity update formulas are as follows:
wherein, c 1 、c 2 Weights for the local and global optimization directions; r is (r) 1 、r 2 Two [0-1 ]]Random numbers in between;and->Position and velocity of the id-th particle for the nth iteration, < >>For the individual extremum, p, of the nth iteration, the id-th particle ngd The population extremum is the nth iteration;
and S3.5, judging whether the iteration times are reached, if not, jumping to the step S3.2, and if so, judging that the particle positions corresponding to the population extremum are the optimal solutions.
CN202010431382.9A 2020-05-20 2020-05-20 Reactive power optimization method for power distribution network Active CN111490552B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010431382.9A CN111490552B (en) 2020-05-20 2020-05-20 Reactive power optimization method for power distribution network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010431382.9A CN111490552B (en) 2020-05-20 2020-05-20 Reactive power optimization method for power distribution network

Publications (2)

Publication Number Publication Date
CN111490552A CN111490552A (en) 2020-08-04
CN111490552B true CN111490552B (en) 2023-07-25

Family

ID=71811360

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010431382.9A Active CN111490552B (en) 2020-05-20 2020-05-20 Reactive power optimization method for power distribution network

Country Status (1)

Country Link
CN (1) CN111490552B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
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
CN113363988B (en) * 2021-06-08 2022-12-06 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 Programming.风电场并网穿透功率极限研究.《华东电力》.2014,全文. *
Xuzhu Dong.A Hybrid Optimization Algorithm for Distribution Network Coordinated Operation with SNOP Based on Simulated Annealing and Conic Programming.《2016 IEEE Power and Energy Society General Meeting (PESGM)》.2016,全文. *
陈垚煜.考虑传统调控手段的配电网SOP 选址定容方法.《高电压技术》.2020,全文. *

Also Published As

Publication number Publication date
CN111490552A (en) 2020-08-04

Similar Documents

Publication Publication Date Title
CN111490552B (en) Reactive power optimization method for power distribution network
CN107301472B (en) Distributed photovoltaic planning method based on scene analysis method and voltage regulation strategy
Senjyu et al. Optimal distribution voltage control and coordination with distributed generation
CN108306298B (en) Design method for flexible multi-state switch to be connected to power distribution network
CN107979092A (en) It is a kind of to consider distributed generation resource and the power distribution network dynamic reconfiguration method of Sofe Switch access
CN110690702B (en) Active power distribution network optimal scheduling and operation method considering comprehensive bearing capacity
Erlich et al. Optimal dispatch of reactive sources in wind farms
CN107069776B (en) Energy storage look-ahead distributed control method for smooth microgrid tie line power
CN109687469B (en) Intelligent soft switching voltage control method for active power distribution network based on opportunity constraint planning
CN103441506A (en) Method for multi-target coordination reactive power optimization control of distributed wind farm in different time scales
CN116388302B (en) Active-reactive power combined optimization method for power distribution network for coordinating network side resources
Li et al. Active distribution network active and reactive power coordinated dispatching method based on discrete monkey algorithm
CN110350600A (en) A kind of flexible multimode switch regulation method promoting distributed generation resource consumption
CN108155649A (en) A kind of consideration probabilistic distribution network structure Fuzzy Programmings of DG
CN113178868B (en) Voltage optimization method for flexible interconnection power distribution system
CN116345466A (en) Two-stage power flow optimization method of active power distribution network considering distribution network reconstruction
CN115933370A (en) Converter control method and system based on PID control
CN113762622B (en) Virtual power plant access point and capacity optimization planning method
CN111193295A (en) Distribution network flexibility improvement robust optimization scheduling method considering dynamic reconfiguration
Zhang et al. Ensemble learning for optimal active power control of distributed energy resources and thermostatically controlled loads in an islanded microgrid
CN110380407B (en) Power distribution network operation optimization method considering agricultural electric irrigation and drainage loads
CN117060499A (en) Control method, device, equipment and storage medium for wind, light and water multi-energy complementary operation
CN116865270A (en) Optimal scheduling method and system for flexible interconnection power distribution network containing embedded direct current
CN113890110B (en) AC/DC hybrid energy system based on energy router and operation optimization method thereof
CN110867902A (en) Power generation prediction-based micro-grid distributed power supply de-centering optimized operation method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant