CN111585288A - Multi-target dynamic reactive power optimization method for power distribution network based on analytic hierarchy process - Google Patents

Multi-target dynamic reactive power optimization method for power distribution network based on analytic hierarchy process Download PDF

Info

Publication number
CN111585288A
CN111585288A CN202010520743.7A CN202010520743A CN111585288A CN 111585288 A CN111585288 A CN 111585288A CN 202010520743 A CN202010520743 A CN 202010520743A CN 111585288 A CN111585288 A CN 111585288A
Authority
CN
China
Prior art keywords
reactive power
optimization
node
function
judgment matrix
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.)
Pending
Application number
CN202010520743.7A
Other languages
Chinese (zh)
Inventor
罗培
孙吉浩
李倩如
彭伊爽
张颖
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.)
Xiangtan University
Original Assignee
Xiangtan University
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 Xiangtan University filed Critical Xiangtan University
Priority to CN202010520743.7A priority Critical patent/CN111585288A/en
Publication of CN111585288A publication Critical patent/CN111585288A/en
Pending legal-status Critical Current

Links

Images

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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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

The invention provides a multi-target dynamic reactive power optimization method for a power distribution network based on an analytic hierarchy process, which comprises the following steps: determining a target function and a constraint condition of the multi-target dynamic reactive power optimization of the power distribution network; normalizing each target function to obtain a comprehensive optimization function; evaluating the importance degree of each target function according to the concept of an analytic hierarchy process; constructing a judgment matrix according to the evaluation result of each objective function; calculating the weight of each objective function according to the constructed judgment matrix, and carrying out consistency check; and determining coefficients of each objective function based on the weight calculation result, and performing reactive power optimization. The model established by the invention can effectively reduce the network loss, improve the voltage quality, optimize the capacity of the reactive power compensation device and provide optimization schemes with different optimization requirements for decision makers.

Description

Multi-target dynamic reactive power optimization method for power distribution network based on analytic hierarchy process
Technical Field
The invention relates to the technical field of power systems, in particular to a multi-target dynamic reactive power optimization method for a power distribution network.
Background
With the increasing expansion of the scale of the power system, the network loss and the power quality problem are more and more concerned. The reactive power optimization of the power system can reduce the loss of the power grid and improve the quality of the electric energy, so that the power grid can run safely, stably and economically. The reactive power optimization aims to reduce the network loss as much as possible on the basis of ensuring that the voltage meets the requirement of the electric energy quality. In the traditional reactive power optimization, the network loss is taken as an objective function, and indexes such as voltage deviation, input capacity of each reactive power compensation device and the like are taken as constraint conditions. With the increasingly complex structure of the power system, the reactive power optimization of a single target cannot meet the requirements of the modern power system, and the optimization target of the reactive power optimization fully considers the economy and the safety of the power distribution network. Therefore, it is urgently needed to establish a reactive power optimization method, consider a plurality of optimization objectives at the same time, and provide an optimization result when an operator attaches different importance to a plurality of objective functions.
Disclosure of Invention
According to the multi-objective dynamic reactive power optimization method for the power distribution network based on the analytic hierarchy process, the established model can effectively reduce the network loss, improve the voltage quality, optimize the capacity of a reactive power compensation device and provide optimization schemes with different optimization requirements for decision makers.
In order to achieve the purpose, the invention adopts the following technical scheme that the method comprises the following steps:
determining a target function and a constraint condition of the multi-target dynamic reactive power optimization of the power distribution network;
normalizing each target function to obtain a comprehensive optimization function;
according to the concept of the analytic hierarchy process, the importance degree of each objective function is evaluated, and a judgment matrix is constructed according to the evaluation result;
calculating the weight of each objective function according to the constructed judgment matrix, and carrying out consistency check;
and determining the weight coefficient of each objective function based on the weight calculation result, and performing reactive power optimization.
Further, the objective function is:
minimum active network loss:
Figure BDA0002531946460000011
wherein, PlossIs provided withLoss of power network, UiAnd UjVoltage amplitudes, G, of nodes i and j, respectivelyk(i,j)And thetaijRespectively the conductance and the phase difference between the node i and the node j, and m is the total number of branches;
the voltage deviation is minimum:
Figure BDA0002531946460000021
wherein, UdIs a voltage deviation, UjIs the actual voltage of node j, UjNIs the rated voltage of the node j, and n is the total number of the nodes;
the input capacity of the reactive power compensation device is minimum:
Figure BDA0002531946460000022
wherein Q istotalThe total capacity put into the reactive power compensator, whose size may characterize the investment, Q, of the reactive power compensatorCiFor the input capacity of the i-th reactive power compensator, NCThe total number of the reactive compensation devices;
the constraint conditions are as follows:
and (3) restraining a power flow equation:
Figure BDA0002531946460000023
Figure BDA0002531946460000024
wherein, PGiAnd QGiActive and reactive power, P, injected for generator and DG, respectivelyLiAnd QLiActive and reactive power, Q, respectively, consumed by the loadCiReactive power, G, input for reactive power compensation devicesijAnd BijConductance and susceptance between node i and node j, respectively;
node voltage constraint:
Uimin≤Ui≤Uimax(6)
wherein, UiIs the voltage amplitude of node i, UimaxAnd UiminThe upper limit and the lower limit of the voltage amplitude of the node i are respectively;
reactive power output range constraint of DG:
QDGmin≤QDG≤QDGmax(7)
wherein Q isDGReactive power, Q, supplied to DGDGmax、QDGminRespectively providing an upper limit and a lower limit of reactive power which can be provided by the DG;
reactive power output range constraint of SVC:
QSVCmin≤QSVC≤QSVCmax(8)
wherein Q isSVCReactive power, Q, put into SVCSVCmax、QSVCminRespectively the upper limit and the lower limit of the SVC reactive power output range.
Further, the normalization processing and comprehensive optimization function includes:
the normalization processing process comprises the following steps:
Figure BDA0002531946460000031
Figure BDA0002531946460000032
Figure BDA0002531946460000033
in the formula: f. of1、f2、f3The values are respectively normalized values of active network loss, voltage deviation and reactive power compensation device capacity. Ploss,min、Ud,min、Qtotal,minThe minimum values of the active network loss, the voltage deviation and the reactive power compensation device capacity are obtained when the active network loss, the voltage deviation and the reactive power compensation device capacity are independently optimized by taking the active network loss, the voltage deviation and the reactive power compensation device capacity as a single target. Ploss,max、Ud,maxFor large active network loss and voltage deviation without reactive power optimizationIs small. Qtotal,maxThe total capacity of the reactive compensation equipment.
Establishing a comprehensive optimization function of
F＝ω1f1+ω2f2+ω3f3(12)
In the formula: omega1、ω2、ω3Is a weight coefficient, ω1+ω2+ω3F is a comprehensive optimization function, and the closer the value is to 1, the better the reactive optimization scheme is satisfied. The weights of the three objective functions can be divided into omega according to the magnitude relation of the importance degrees1＞ω2＞ω3，ω1＞ω3＞ω2，ω2＞ω1＞ω3，ω2＞ω3＞ω1，ω3＞ω1＞ω2，ω3＞ω2＞ω1，ω3＝ω2＝ω1Seven kinds of the Chinese herbal medicines.
Further, the evaluating the importance degree of each objective function and constructing a judgment matrix according to the evaluation result includes:
let F be the overall target, ai、ajRepresenting factor, aijDenotes aiTo ajThe relative importance values of (a) are scaled by the values 1-9, and the scales 1, 3, 5, 7, 9 correspond to five different levels of importance of equal importance, slightly important, significantly important, strongly important, and extremely important, respectively, with 2, 4, 6, 8 between the five levels of importance. The constructed judgment matrix is:
Figure BDA0002531946460000041
further, the calculating the weight of each objective function and performing consistency check includes:
according to the judgment matrix, p β ═ λmaxβ finding the maximum feature root λmaxThe corresponding eigenvector β is normalized to obtain the weight ω of β1、ω2、ω3The size of (2).
The weight assignment result is not necessarily reasonable, and it is necessary to perform a consistency check on the judgment matrix. The formula is as follows:
Figure BDA0002531946460000042
in the formula: CR is the random consistency ratio of the judgment matrix, and RI is the average consistency index of the judgment matrix. CI is the general consistency index of the judgment matrix.
Figure BDA0002531946460000043
In the formula, n is the index number.
If CR of the matrix P is judged to be less than 0.1, P has satisfactory consistency, otherwise, elements in the matrix need to be adjusted to meet consistency check.
The multi-objective dynamic reactive power optimization method for the power distribution network based on the analytic hierarchy process can effectively reduce the network loss, improve the voltage quality, optimize the capacity of a reactive power compensation device and provide optimization schemes with different optimization requirements for decision makers.
Drawings
FIG. 1 is a schematic diagram of a multi-objective dynamic reactive power optimization method of a power distribution network based on an analytic hierarchy process;
Detailed Description
So that the manner in which the features and advantages of the invention are attained and can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the appended drawings.
A multi-target dynamic reactive power optimization method of a power distribution network based on an analytic hierarchy process is shown in figure 1 and comprises the following steps:
step S101, determining a target function and a constraint condition of multi-target dynamic reactive power optimization of the power distribution network;
step S102, normalizing each target function to obtain a comprehensive optimization function;
step S103, evaluating the importance degree of each objective function according to the concept of the analytic hierarchy process, and constructing a judgment matrix according to the evaluation result;
step S104, calculating the weight of each objective function according to the constructed judgment matrix, and carrying out consistency check;
and step S105, determining the weight coefficient of each objective function based on the weight calculation result, and performing reactive power optimization.
The specific implementation method of step S101 is: the method is characterized in that the minimum input capacity of an active network loss, a voltage deviation and a reactive power compensation device is taken as a target function, and the constraint conditions of a power flow equation, a node voltage, a DG reactive power output range, an SVC reactive power output range, a capacitor bank reactive power output range and a capacitor bank maximum daily switching frequency are taken as constraint conditions.
The objective function is:
minimum active network loss:
Figure BDA0002531946460000051
wherein, PlossFor active network loss, UiAnd UjVoltage amplitudes, G, of nodes i and j, respectivelyk(i,j)And thetaijRespectively the conductance and the phase difference between the node i and the node j, and m is the total number of branches;
the voltage deviation is minimum:
Figure BDA0002531946460000052
wherein, UdIs a voltage deviation, UjIs the actual voltage of node j, UjNIs the rated voltage of the node j, and n is the total number of the nodes;
the input capacity of the reactive power compensation device is minimum:
Figure BDA0002531946460000053
wherein Q istotalThe total capacity of the reactive power compensation device is used, the size of the total capacity can represent the investment of the reactive power compensation device,QCifor the input capacity of the i-th reactive power compensator, NCThe total number of the reactive compensation devices;
the constraint conditions are as follows:
and (3) restraining a power flow equation:
Figure BDA0002531946460000061
Figure BDA0002531946460000062
wherein, PGiAnd QGiActive and reactive power, P, injected for generator and DG, respectivelyLiAnd QLiActive and reactive power, Q, respectively, consumed by the loadCiReactive power, G, input for reactive power compensation devicesijAnd BijConductance and susceptance between node i and node j, respectively;
node voltage constraint:
Uimin≤Ui≤Uimax(6)
wherein, UiIs the voltage amplitude of node i, UimaxAnd UiminThe upper limit and the lower limit of the voltage amplitude of the node i are respectively;
reactive power output range constraint of DG:
QDGmin≤QDG≤QDGmax(7)
wherein Q isDGReactive power, Q, supplied to DGDGmax、QDGminRespectively providing an upper limit and a lower limit of reactive power which can be provided by the DG;
reactive power output range constraint of SVC:
QSVCmin≤QSVC≤QSVCmax(8)
wherein Q isSVCReactive power, Q, put into SVCSVCmax、QSVCminRespectively the upper limit and the lower limit of the SVC reactive power output range.
The specific implementation method of step S102 is:
the normalization process is as follows:
Figure BDA0002531946460000063
Figure BDA0002531946460000064
Figure BDA0002531946460000071
in the formula: f. of1、f2、f3The values are respectively normalized values of active network loss, voltage deviation and reactive power compensation device capacity. Ploss,min、Ud,min、Qtotal,minThe minimum values of the active network loss, the voltage deviation and the reactive power compensation device capacity are obtained when the active network loss, the voltage deviation and the reactive power compensation device capacity are independently optimized by taking the active network loss, the voltage deviation and the reactive power compensation device capacity as a single target. Ploss,max、Ud,maxThe values of the active network loss and the voltage deviation which are not subjected to reactive power optimization are shown. Qtotal,maxThe total capacity of the reactive compensation equipment.
Establishing a comprehensive optimization function of
F＝ω1f1+ω2f2+ω3f3(12)
In the formula: omega1、ω2、ω3Is a weight coefficient, ω1+ω2+ω3F is a comprehensive optimization function, and the closer the value is to 1, the better the reactive optimization scheme is satisfied. The weights of the three objective functions can be divided into omega according to the magnitude relation of the importance degrees1＞ω2＞ω3，ω1＞ω3＞ω2，ω2＞ω1＞ω3，ω2＞ω3＞ω1，ω3＞ω1＞ω2，ω3＞ω2＞ω1，ω3＝ω2＝ω1Seven kinds of the Chinese herbal medicines.
The specific implementation method of step S103 is:
let F be the overall target, ai、ajRepresenting factor, aijDenotes aiTo ajThe relative importance values of (a) are scaled by the values 1-9, and the scales 1, 3, 5, 7, 9 correspond to five different levels of importance of equal importance, slightly important, significantly important, strongly important, and extremely important, respectively, with 2, 4, 6, 8 between the five levels of importance. The constructed judgment matrix is:
Figure BDA0002531946460000072
the specific implementation method of step S104 is:
according to the judgment matrix, p β ═ λmaxβ finding the maximum feature root λmaxThe corresponding eigenvector β is normalized to obtain the weight ω of β1、ω2、ω3The size of (2).
The weight assignment result is not necessarily reasonable, and it is necessary to perform a consistency check on the judgment matrix. The formula is as follows:
Figure BDA0002531946460000073
in the formula: CR is the random consistency ratio of the judgment matrix, and RI is the average consistency index of the judgment matrix. CI is the general consistency index of the judgment matrix.
Figure BDA0002531946460000081
In the formula, n is the index number.
If CR of the matrix P is judged to be less than 0.1, P has satisfactory consistency, otherwise, elements in the matrix need to be adjusted to meet consistency check.
Taking three schemes of importance degree of network loss, voltage deviation, network loss and reactive compensation device capacity (scheme 1), voltage deviation, network loss, reactive compensation device capacity (scheme 2) and reactive compensation device capacity, network loss and voltage deviation (scheme 3) as examples, a judgment matrix is constructed as follows:
the decision matrix of scheme 1 is
Figure BDA0002531946460000082
The decision matrix of scheme 2 is
Figure BDA0002531946460000083
The decision matrix of scheme 3 is
Figure BDA0002531946460000084
After calculation, the weight and the CR of each objective function are finally obtained as follows:
in the scheme 1, the weight coefficients corresponding to three objective functions of the network loss, the voltage deviation and the reactive power compensation device capacity are respectively as follows: omega1＝0.649，ω2＝0.279，ω3The size of CR is 0.072, 0.062.
In the scheme 2, the weight coefficients corresponding to three objective functions of the network loss, the voltage deviation and the reactive power compensation device capacity are respectively as follows: omega1＝0.279，ω2＝0.649，ω3The size of CR is 0.072, 0.062.
In the scheme 2, the weight coefficients corresponding to three objective functions of the network loss, the voltage deviation and the reactive power compensation device capacity are respectively as follows: omega1＝0.258，ω2＝0.105，ω30.637, the size of CR is 0.037.
It can be seen that the CR values for the three schemes are less than 0.1, so the three schemes all pass the consistency check, and the weight assignment is reasonable.
And substituting the weight coefficients of the three schemes into the comprehensive optimization function to perform reactive power optimization to obtain reactive power optimization results under three different schemes. The optimization method can adopt mathematical optimization methods such as an interior point method, a gradient descent method and the like, and can also adopt artificial intelligence algorithms such as a particle swarm algorithm, a genetic algorithm and the like, and the specific solving method is not limited in the embodiment.
The multi-objective dynamic reactive power optimization method for the power distribution network based on the analytic hierarchy process can effectively reduce the active network loss, improve the voltage quality, optimize the capacity of a reactive power compensation device and provide optimization schemes with different optimization requirements for decision makers.
Finally, the method of the present application is only a preferred embodiment and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (5)

1. A multi-target dynamic reactive power optimization method for a power distribution network based on an analytic hierarchy process is characterized by comprising the following steps:
determining a target function and a constraint condition of the multi-target dynamic reactive power optimization of the power distribution network;
normalizing each target function to obtain a comprehensive optimization function;
according to the concept of the analytic hierarchy process, the importance degree of each objective function is evaluated, and a judgment matrix is constructed according to the evaluation result;
calculating the weight of each objective function according to the constructed judgment matrix, and carrying out consistency check;
and determining the weight coefficient of each objective function based on the weight calculation result, and performing reactive power optimization.
2. The analytic hierarchy process-based multi-objective dynamic reactive power optimization method for the power distribution network of claim 1, wherein the objective function is:
minimum active network loss:
Figure FDA0002531946450000011
wherein, PlossFor active network loss, UiAnd UjVoltage amplitudes, G, of nodes i and j, respectivelyk(i,j)And thetaijRespectively the conductance and the phase difference between the node i and the node j, and m is the total number of branches;
the voltage deviation is minimum:
Figure FDA0002531946450000012
wherein, UdIs a voltage deviation, UjIs the actual voltage of node j, UjNIs the rated voltage of the node j, and n is the total number of the nodes;
the input capacity of the reactive power compensation device is minimum:
Figure FDA0002531946450000013
wherein Q istotalThe total capacity put into the reactive power compensator, whose size may characterize the investment, Q, of the reactive power compensatorCiFor the input capacity of the i-th reactive power compensator, NCThe total number of the reactive compensation devices;
the constraint conditions are as follows:
and (3) restraining a power flow equation:
Figure FDA0002531946450000014
Figure FDA0002531946450000015
wherein, PGiAnd QGiActive and reactive power, P, injected for generator and DG, respectivelyLiAnd QLiActive and reactive power, Q, respectively, consumed by the loadCiReactive power, G, input for reactive power compensation devicesijAnd BijConductance and susceptance between node i and node j, respectively;
node voltage constraint:
Uimin≤Ui≤Uimax(6)
wherein, UiIs the voltage amplitude of node i, UimaxAnd UiminThe upper limit and the lower limit of the voltage amplitude of the node i are respectively;
reactive power output range constraint of DG:
QDGmin≤QDG≤QDGmax(7)
wherein Q isDGReactive power, Q, supplied to DGDGmax、QDGminRespectively providing an upper limit and a lower limit of reactive power which can be provided by the DG;
reactive power output range constraint of SVC:
QSVCmin≤QSVC≤QSVCmax(8)
wherein Q isSVCReactive power, Q, put into SVCSVCmax、QSVCminRespectively the upper limit and the lower limit of the SVC reactive power output range.
3. The analytic hierarchy process-based multi-objective dynamic reactive power optimization method for the power distribution network according to claim 1, wherein the normalization processing and comprehensive optimization function comprises:
the normalization processing process comprises the following steps:
Figure FDA0002531946450000021
Figure FDA0002531946450000022
Figure FDA0002531946450000023
wherein f is1、f2、f3The values are respectively normalized values of active network loss, voltage deviation and reactive power compensation device capacity. Ploss,min、Ud,min、Qtotal,minThe minimum values of the active network loss, the voltage deviation and the reactive power compensation device capacity are obtained when the active network loss, the voltage deviation and the reactive power compensation device capacity are independently optimized by taking the active network loss, the voltage deviation and the reactive power compensation device capacity as a single target. Ploss,max、Ud,maxThe values of the active network loss and the voltage deviation which are not subjected to reactive power optimization are shown. Qtotal,maxThe total capacity of the reactive compensation equipment.
Establishing a comprehensive optimization function of
F＝ω1f1+ω2f2+ω3f3(12)
In the formula: omega1、ω2、ω3Is a weight coefficient, ω1+ω2+ω3F is a comprehensive optimization function, and the closer the value is to 1, the better the reactive optimization scheme is satisfied. The weights of the three objective functions can be divided into omega according to the magnitude relation of the importance degrees1＞ω2＞ω3，ω1＞ω3＞ω2，ω2＞ω1＞ω3，ω2＞ω3＞ω1，ω3＞ω1＞ω2，ω3＞ω2＞ω1，ω3＝ω2＝ω1Seven kinds of the Chinese herbal medicines.
4. The multi-objective dynamic reactive power optimization method for the power distribution network based on the analytic hierarchy process of claim 1, wherein the evaluating the importance degree of each objective function and constructing a judgment matrix according to the evaluation result comprises:
let F be the overall target, ai、ajRepresenting factor, aijDenotes aiTo ajThe relative importance values of (a) are scaled by the values 1-9, and the scales 1, 3, 5, 7, 9 correspond to five different levels of importance of equal importance, slightly important, significantly important, strongly important, and extremely important, respectively, with 2, 4, 6, 8 between the five levels of importance. The constructed judgment matrix is:
Figure FDA0002531946450000031
5. the multi-objective dynamic reactive power optimization method for the power distribution network based on the analytic hierarchy process, as claimed in claim 1, wherein the calculating weights of the objective functions and performing consistency check includes:
according to a decision matrix, ispβ＝λmaxβ finding the maximum feature root λmaxThe corresponding eigenvector β is normalized to obtain the weight ω of β1、ω2、ω3The size of (2).
The weight assignment result is not necessarily reasonable, and it is necessary to perform a consistency check on the judgment matrix. The formula is as follows:
Figure FDA0002531946450000032
in the formula: CR is the random consistency ratio of the judgment matrix, and RI is the average consistency index of the judgment matrix. CI is the general consistency index of the judgment matrix.
Figure FDA0002531946450000033
In the formula, n is the index number.
If CR of the matrix P is judged to be less than 0.1, P has satisfactory consistency, otherwise, elements in the matrix need to be adjusted to meet consistency check.
CN202010520743.7A 2020-06-10 2020-06-10 Multi-target dynamic reactive power optimization method for power distribution network based on analytic hierarchy process Pending CN111585288A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010520743.7A CN111585288A (en) 2020-06-10 2020-06-10 Multi-target dynamic reactive power optimization method for power distribution network based on analytic hierarchy process

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010520743.7A CN111585288A (en) 2020-06-10 2020-06-10 Multi-target dynamic reactive power optimization method for power distribution network based on analytic hierarchy process

Publications (1)

Publication Number Publication Date
CN111585288A true CN111585288A (en) 2020-08-25

Family

ID=72112854

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010520743.7A Pending CN111585288A (en) 2020-06-10 2020-06-10 Multi-target dynamic reactive power optimization method for power distribution network based on analytic hierarchy process

Country Status (1)

Country Link
CN (1) CN111585288A (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113937829A (en) * 2021-11-16 2022-01-14 华北电力大学 Active power distribution network multi-target reactive power control method based on D3QN
CN115423351A (en) * 2022-09-21 2022-12-02 国网山东省电力公司淄博供电公司 Multi-target active reconstruction method for power distribution network based on analytic hierarchy process and genetic algorithm
CN117892558A (en) * 2024-03-14 2024-04-16 西安现代控制技术研究所 Construction method of ultra-remote guidance rocket multidisciplinary dynamic optimization model

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110956266A (en) * 2019-06-06 2020-04-03 国网辽宁省电力有限公司 Multi-power-supply power system multi-target optimization scheduling method based on analytic hierarchy process
CN111064235A (en) * 2019-12-17 2020-04-24 湘潭大学 Day-ahead dynamic reactive power optimization method for active power distribution network

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110956266A (en) * 2019-06-06 2020-04-03 国网辽宁省电力有限公司 Multi-power-supply power system multi-target optimization scheduling method based on analytic hierarchy process
CN111064235A (en) * 2019-12-17 2020-04-24 湘潭大学 Day-ahead dynamic reactive power optimization method for active power distribution network

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
PEI LUO: "Multi-objective Reactive Power Optimization for Distribution System with Distributed Generators" *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113937829A (en) * 2021-11-16 2022-01-14 华北电力大学 Active power distribution network multi-target reactive power control method based on D3QN
CN115423351A (en) * 2022-09-21 2022-12-02 国网山东省电力公司淄博供电公司 Multi-target active reconstruction method for power distribution network based on analytic hierarchy process and genetic algorithm
CN117892558A (en) * 2024-03-14 2024-04-16 西安现代控制技术研究所 Construction method of ultra-remote guidance rocket multidisciplinary dynamic optimization model

Similar Documents

Publication Publication Date Title
CN111585288A (en) Multi-target dynamic reactive power optimization method for power distribution network based on analytic hierarchy process
CN104779630B (en) A kind of mixed energy storage system capacity collocation method for stabilizing wind power output power fluctuation
Sarma et al. Optimal selection of capacitors for radial distribution systems using plant growth simulation algorithm
CN103914741A (en) Line loss intelligent evaluation and assistant decision-making system for power distribution network
CN107103433B (en) Distributed power supply absorption capacity calculation method based on hierarchical partition idea
CN111064235A (en) Day-ahead dynamic reactive power optimization method for active power distribution network
CN109638810A (en) A kind of energy storage method and system for planning based on electric power system transient stability
Xu Hybrid GWO and CS Algorithm for UPQC Positioning in the Power Distribution Network
CN105576653B (en) A kind of 220kV sections power network power supply capacity optimization method
Wartana et al. Optimal integration of wind energy with a shunt-FACTS controller for reductions in electrical power loss
CN116581759A (en) Power transmission section active correction control method, system and equipment
Sayed et al. Congestion management in power system based on optimal load shedding using grey wolf optimizer
CN113363991B (en) Tidal current control method based on comprehensive sensitivity
Reddy et al. Optimal placement of interline power flow controller (IPFC) to enhance voltage stability
Easwaramoorthy et al. Solution of Optimal Power Flow Problem Incorporating Various FACTS Devices
Youssef et al. Optimal allocation and size of appropriate compensation devices for voltage stability enhancement of power systems
CN110957767B (en) Method and device for treating power quality of microgrid
CN113241793A (en) Prevention control method for power system with IPFC (intelligent power flow controller) considering wind power scene
Popovtsev et al. N-1 Based Optimal Placement of SVC Using Elitist Genetic Algorithm in Terms of Multiobjective Problem Statement
Hao et al. Reactive Power Optimization of Distribution Network with Distributed Generators by Improved Evolutionary Programming Algorithm
CN115693681B (en) Improved power distribution network partitioning method, optimization algorithm and regulation planning scheme
CN112821452B (en) Multifunctional steady-state power regulation and control method and system of flexible multi-state switch
Prasad et al. Optimal placement of shunt capacitor with VCPI to improve voltage profile using Mi power
CN112054515B (en) Receiving-end power grid DC receiving capacity evaluation method based on multi-objective optimization
CN112467746B (en) Power distribution network optimization method considering out-of-limit risk

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
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20200825

WD01 Invention patent application deemed withdrawn after publication