CN108808745B - Dynamic reactive power optimization method for active power distribution network - Google Patents

Dynamic reactive power optimization method for active power distribution network Download PDF

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CN108808745B
CN108808745B CN201810638683.1A CN201810638683A CN108808745B CN 108808745 B CN108808745 B CN 108808745B CN 201810638683 A CN201810638683 A CN 201810638683A CN 108808745 B CN108808745 B CN 108808745B
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方瑞明
谢少棠
王黎
尚荣艳
彭长青
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • 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
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Abstract

The invention provides a dynamic reactive power optimization method of an active power distribution network, which comprises the steps of obtaining initial data of the active power distribution network; determining an objective function of reactive power optimization; processing the load rate; processing the distributed power output; calculating optimal configuration data of each time period; and performing static reactive power optimization on the active power distribution network, setting two indexes to judge whether the device needs to act, and finally obtaining a dynamic reactive power optimization scheme of the power distribution network. The method considers the time-varying property of the load and the fluctuation of the output of the distributed power supply, segments the time-varying property of the load and the fluctuation of the output of the distributed power supply in units of hours, processes DG output and load rate curves of one day in the future, performs static reactive power optimization in each time period to obtain corresponding optimal configuration data, and judges the action condition of the device in the adjacent time period by setting two index thresholds.

Description

Dynamic reactive power optimization method for active power distribution network
Technical Field
The invention relates to the technical field of power systems, in particular to a dynamic reactive power optimization method of an active power distribution network considering a network loss index and a voltage offset index.
Background
The distributed power supply grid connection can generate certain impact on the power flow of the power distribution network, and the impact is not small. When the DG with output fluctuation is merged into the power distribution network, the DG with output fluctuation possibly affects the power distribution network to different degrees all the time, and corresponding reactive power optimization measures are needed to be adopted to optimize the system in consideration of the influence, so that the stability and the safety are improved. The research angle focuses on the improvement of the optimization algorithm, and the dynamic reactive power optimization strategy of the active power distribution network is researched, so that the dynamic reactive power optimization method has practical application value.
In order to be closer to the actual situation of the operation of the power system, when the reactive power optimization is performed on the power distribution network, the action time constraint of the reactive power compensation device, the fluctuation of the DG output and the load variability should be added into the research, so that the practical significance can be achieved. Therefore, the dynamic reactive power optimization strategy of the active power distribution network is researched, a simplified mode is adopted, the action times of the optimization device are reduced on the premise of reducing the network loss and improving the voltage level, and the dynamic reactive power optimization strategy has good applicability.
Disclosure of Invention
The invention aims to simplify the method on the basis of ensuring the economy and stability of the system, provides a dynamic reactive power optimization method of the active power distribution network, reduces the action times of the device all day, has reasonability and objectivity, and has reference significance for the research of the dynamic reactive power optimization strategy of the active power distribution network.
The invention adopts the following technical scheme:
a dynamic reactive power optimization method for an active power distribution network comprises the following steps:
a. acquiring initial data of the active power distribution network;
b. determining an objective function of reactive power optimization;
c. processing the load rate;
d. processing the distributed power output;
e. calculating optimal configuration data of each time period;
f. performing static reactive power optimization on the active power distribution network, and setting constraint conditions of two variables to judge whether the device needs to act or not to obtain the whole dynamic reactive power optimization scheme;
in step a, the acquiring initial data of the active power distribution network includes: collecting data of each node and each branch, and acquiring the load rate of the whole day and the output condition of the distributed power supply;
in the step b, the reactive power optimization objective function comprises the active network loss f of the power distribution network1And a voltage offset f2The expression is as follows:
Figure BDA0001701678540000021
Figure BDA0001701678540000022
in the above formula, UiAnd UjThe voltage amplitudes of the node i and the node j are respectively; gijIs the conductance of the i-j branch; thetaijIs the phase angle difference of the voltages at node i and node j; n is a set formed by all nodes of the power distribution network,
Figure BDA0001701678540000023
maximum allowed voltage offset value for node i;
Figure BDA0001701678540000024
is the ideal voltage of node i;
in the step f, the total objective function expression of the active power distribution network for static reactive power optimization is as follows:
minf＝ω1f1+ω2f2 (3)
in the above formula, f is the total objective function of reactive power optimization, omega1、ω2Is a weight value and has omega1+ω2＝1；
The constraint conditions of the variables comprise active power and reactive power balance constraints of each node and control variable constraints;
the active power and reactive power balance constraints of each node are as follows:
Figure BDA0001701678540000031
Figure BDA0001701678540000032
the control variables are constrained as follows:
Tmin＜T＜Tmax (6)
QC.min＜QC＜QC.max (7)
QDG.min＜QDG＜QDG.max (8)
CT≤CTmax (9)
wherein, PiAnd QiRespectively injecting active power and reactive power into the node; b isijIs the susceptance of the i-j branch; t is the position of the on-load tap changer tapminAnd TmaxRespectively a minimum gear and a maximum gear of the on-load tap changer; qCBeing the reactive capacity of parallel capacitors, Qc.minAnd Qc.maxRespectively the minimum value and the maximum value of the reactive capacity of the parallel capacitor; qDGFor reactive output of distributed power, QDG.minAnd QDG.maxRespectively the minimum value and the maximum value of the reactive output quantity of the distributed power supply; cTIndicating the number of movements of the device throughout the day, CTmaxRepresents the maximum allowable number of device actions on the day.
In a preferred embodiment: in step c, the continuous load in one day is segmented and discretized, and the load is divided into 24 time intervals by the integral median theorem to serve as the calculation load.
In a preferred embodiment: and d, taking the fluctuation of the output power of the distributed power supply into consideration, segmenting the daily output power condition of the distributed power supply, performing discretization treatment, and processing the output power into the output power of 24 time intervals by using an integral median theorem.
In a preferred embodiment: in step e, performing corresponding static reactive power optimization calculation on each time period to obtain a device configuration condition under the optimal reactive power optimization condition in each time period, namely optimal device configuration data.
In a preferred embodiment: in step f, for the first time period, the device adopts the optimal device configuration data of the time period; starting from the second time period, applying the adopted device configuration data in the previous time period to the time period, adjusting reactive power through a DG (distributed generation) to perform reactive power optimization, calculating corresponding active network loss reduction rate and voltage offset reduction rate, correspondingly comparing the two indexes with the active network loss reduction rate and the voltage offset reduction rate under the optimal configuration, respectively setting two threshold values, and determining that the data deviation exceeds an expected value as long as one of the two indexes is greater than a set value of the corresponding threshold value, wherein the device needs to act at the moment; otherwise, the device configuration in the previous time period may be continued, and so on until the 24 th time period, and the process is completed.
In a preferred embodiment: the two thresholds, one refers to a network loss reduction rate, and the other refers to a voltage offset reduction rate;
taking the current time as t, the time of the last time period as t-1, wherein the network loss reduction rate refers to the ratio of the network loss reduction after reactive optimization to the network loss before reactive optimization, and the voltage offset reduction rate refers to the ratio of the voltage offset reduction after reactive optimization to the voltage offset before reactive optimization;
respectively carrying out static reactive power optimization in each time interval to obtain corresponding optimal reactive power optimization configuration, and after carrying out reactive power optimization in the current time interval t, expressing the loss reduction rate of the network under the optimal configuration as delta Pt% and the voltage offset reduction rate is represented by Δ UtPercent, the network loss reduction rate obtained by substituting the configuration of the last period of time t-1 into the current time t is delta Pt'%, the voltage offset reduction rate is delta Ut'%, the threshold is set to M1And M2. The setting of the two threshold values is related to the actual requirement of a decision maker and is set according to the actual situation, and the two values in the patent are both 2.5 percent, thereby meeting the requirement of an engineering example. If one of the two following equations is satisfied, the reactive power optimization device in the time interval needs to act:
|ΔPt'％-ΔPt％|＞M1 (10)
|ΔUt'％-ΔUt％|＞M2 (11)
if the two formulas (10) and (11) are not satisfied, the reactive power optimization device configuration in the t-1 time period can be used in the t time period, and adjustment is not needed.
As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following advantages:
by processing the real-time changing load factor and the active power output of the distributed power supply, adopting a simplified dynamic reactive power optimization strategy, based on the static reactive power optimization of time intervals, and judging whether the reactive power optimization device acts or not by setting two threshold values for comparative analysis, the method can effectively reduce the whole network active power loss of the power grid, improve the node voltage level, be beneficial to the safe and economic operation of the power grid, greatly reduce the action times of the reactive power optimization device on the basis, accord with the expectation and have certain practical application value.
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Fig. 1 is a flow chart of a dynamic reactive power optimization method of an active power distribution network according to the present invention;
Detailed Description
The invention provides a dynamic reactive power optimization method of an active power distribution network, which divides the load rate and DG output condition of one day into 24 time periods for research by processing the load and the active power output of a distributed power supply, adopts a simplified dynamic reactive power optimization strategy, is based on the static reactive power optimization of the time periods, judges and analyzes whether a reactive power device needs to act or not by setting two threshold values, applies the method to a calculation example, and can effectively reduce the network loss and improve the node voltage level through simulation research, greatly reduces the action times of the device on the premise of ensuring the economy and stability of the system, and has certain practical application value.
An evaluation method for accessing a distributed power supply with consideration of reactive power optimization to a power distribution network is shown in fig. 1, and comprises the following steps:
a. acquiring initial data of the active power distribution network;
b. determining an objective function of reactive power optimization;
c. processing the load rate;
d. processing the distributed power output;
e. calculating optimal configuration data of each time period;
f. and performing static reactive power optimization on the active power distribution network, setting two indexes to judge whether the device needs to act, and finally obtaining the whole dynamic reactive power optimization scheme.
In step a, the pair of obtaining initial data of the active power distribution network includes: and collecting data of each node and each branch, and acquiring the load rate and the output condition of the distributed power supply.
In the step b, the reactive power optimization objective function comprises the active network loss f of the power distribution network1And a voltage offset f2The expression is as follows:
Figure BDA0001701678540000061
Figure BDA0001701678540000062
in the above formula, UiAnd UjThe voltage amplitudes of the node i and the node j are respectively; gijIs the conductance of the i-j branch; thetaijIs the phase angle difference of the voltages at node i and node j; n is a set of all nodes of the system,
Figure BDA0001701678540000063
maximum allowed voltage offset value for node i;
Figure BDA0001701678540000064
is the ideal voltage for node i.
The reactive power optimization total objective function expression of the power distribution network is as follows:
minf＝ω1f1+ω2f2 (25)
in the above formula, f is a reactive power optimization general objective letterNumber f1For power distribution network active network loss, f2Is the voltage offset, ω1、ω2Is a weight value and has omega1+ω2＝1。
The active power and reactive power balance constraints of each node are as follows:
Figure BDA0001701678540000065
Figure BDA0001701678540000071
the control variables are constrained as follows:
Tmin＜T＜Tmax (28)
QC.min＜QC＜QC.max (29)
QDG.min＜QDG＜QDG.max (30)
CT≤CTmax (31)
wherein, PiAnd QiRespectively injecting active power and reactive power into the node; b isijIs the susceptance of the i-j branch; t is the position of the on-load tap changer tapminAnd TmaxRespectively a minimum gear and a maximum gear of the on-load tap changer; qcBeing the reactive capacity of parallel capacitors, Qc.minAnd Qc.maxRespectively the minimum value and the maximum value of the reactive capacity of the parallel capacitor; qDGFor reactive output of distributed power, QDG.minAnd QDG.maxRespectively the minimum value and the maximum value of the reactive output quantity of the distributed power supply; cTIndicating the number of movements of the device throughout the day, CTmaxRepresents the maximum allowable number of device actions on the day.
In step c, the continuous load in one day is segmented and discretized, and the load is divided into 24 time intervals by the integral median theorem to serve as the calculation load.
And d, taking the fluctuation of the output power of the distributed power supply into consideration, and processing the daily output power condition of the distributed power supply into the output power of 24 time intervals.
And e, performing corresponding static reactive power optimization calculation on each time period to obtain data configured by the optimal reactive power optimization device in each time period.
In step f, for the first time period, the device adopts the optimal configuration data of the time period. Starting from the second time period, the device configuration in the previous time period is used to the time period, reactive power output is adjusted through DG to carry out reactive power optimization, corresponding active network loss reduction rate and voltage offset reduction rate are calculated, the two indexes are correspondingly compared with the active network loss reduction rate and the voltage offset reduction rate under the optimal configuration, two threshold values are respectively set, and if one index is greater than the set value of the corresponding threshold value, the data deviation is judged to exceed the expected value, and at the moment, the device needs to act; otherwise, the device configuration in the previous time period may be continued, and so on until the 24 th time period, and the process is completed.
The two thresholds mentioned above, one refers to the rate of reduction of the network loss and the other refers to the rate of reduction of the voltage offset.
Taking the current time as t, the time of the last time period as t-1, wherein the network loss reduction rate refers to the ratio of the network loss reduction (the difference value of the network loss before and after the reactive optimization) after the reactive optimization to the network loss before the reactive optimization, and the voltage offset reduction rate refers to the ratio of the voltage offset reduction (namely the difference value of the voltage offset before and after the reactive optimization) after the reactive optimization to the voltage offset before the reactive optimization.
Respectively carrying out static reactive power optimization in each time interval to obtain corresponding optimal reactive power optimization configuration, and after carrying out reactive power optimization in the current time interval t, expressing the loss reduction rate of the network under the optimal configuration as delta Pt% and the voltage offset reduction rate is represented by Δ UtPercent, the network loss reduction rate obtained by substituting the configuration of the last period of time t-1 into the current time t is delta Pt'%, the voltage offset reduction rate is delta Ut'%, the threshold is set to M1And M2. Wherein, the setting of the two threshold values is related to the actual requirement of the decision maker according to the actual situationThe setting is that both values in the patent are 2.5%, which meets the requirement of engineering example. If one of the two following equations is satisfied, the reactive power optimization device in the time interval needs to act:
|ΔPt'％-ΔPt％|＞M1 (32)
|ΔUt'％-ΔUt％|＞M2 (33)
if the two formulas (32) and (33) are not satisfied, the reactive power optimization device configuration in the t-1 time period can be used in the t time period, and adjustment is not needed.
Two set indexes, wherein one index is the active network loss reduction rate, the other index is the voltage offset reduction rate, and the starting points of the two indexes correspond to the economic problem and the safety stability problem respectively, so that the reactive power optimization result can meet the limitation of constraint conditions, the action times of the device are reduced, the economic efficiency and the stability of the optimization scheme can be guaranteed as much as possible, and the reactive power optimization method has certain practical application value.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (4)

1. A dynamic reactive power optimization method of an active power distribution network is characterized by comprising the following steps:
a. acquiring initial data of the active power distribution network;
b. determining an objective function of reactive power optimization;
c. processing the load rate;
d. processing the distributed power output;
e. calculating optimal configuration data of each time period;
f. performing static reactive power optimization on the active power distribution network, and setting constraint conditions of two variables to judge whether the device needs to act or not to obtain the whole dynamic reactive power optimization scheme;
in step a, the acquiring initial data of the active power distribution network includes: collecting data of each node and each branch, and acquiring the load rate of the whole day and the output condition of the distributed power supply;
in the step b, the reactive power optimization objective function comprises the active network loss f of the power distribution network1And a voltage offset f2The expression is as follows:
Figure FDA0003087959240000011
Figure FDA0003087959240000012
in the above formula, UiAnd UjThe voltage amplitudes of the node i and the node j are respectively; gijIs the conductance of the i-j branch; thetaijIs the phase angle difference of the voltages at node i and node j; n is a set formed by all nodes of the power distribution network,
Figure FDA0003087959240000013
maximum allowed voltage offset value for node i;
Figure FDA0003087959240000014
is the ideal voltage of node i;
in the step f, the total objective function expression of the active power distribution network for static reactive power optimization is as follows:
minf＝ω1f1+ω2f2 (3)
in the above formula, f is the total objective function of reactive power optimization, omega1、ω2Is a weight value and has omega1+ω2＝1；
The constraint conditions of the variables comprise active power and reactive power balance constraints of each node and control variable constraints;
the active power and reactive power balance constraints of each node are as follows:
Figure FDA0003087959240000021
Figure FDA0003087959240000022
the control variables are constrained as follows:
Tmin＜T＜Tmax (6)
QC.min＜QC＜QC.max (7)
QDG.min＜QDG＜QDG.max (8)
CT≤CTmax (9)
wherein, PiAnd QiRespectively injecting active power and reactive power into the node; b isijIs the susceptance of the i-j branch; t is the position of the on-load tap changer tapminAnd TmaxRespectively a minimum gear and a maximum gear of the on-load tap changer; qCBeing the reactive capacity of parallel capacitors, Qc.minAnd Qc.maxRespectively the minimum value and the maximum value of the reactive capacity of the parallel capacitor; qDGFor reactive output of distributed power, QDG.minAnd QDG.maxRespectively the minimum value and the maximum value of the reactive output quantity of the distributed power supply; cTIndicating the number of movements of the device throughout the day, CTmaxThe maximum value of the allowable action times of the device on the day is represented;
in step f, for the first time period, the device adopts the optimal device configuration data of the time period; starting from the second time period, applying the adopted device configuration data in the previous time period to the time period, adjusting reactive power through a DG (distributed generation) to perform reactive power optimization, calculating corresponding active network loss reduction rate and voltage offset reduction rate, correspondingly comparing the two indexes with the active network loss reduction rate and the voltage offset reduction rate under the optimal configuration, respectively setting two threshold values, and determining that the data deviation exceeds an expected value as long as one of the two indexes is greater than a set value of the corresponding threshold value, wherein the device needs to act at the moment; otherwise, continuing the device configuration in the previous time period, and repeating the operation until the 24 th time period, and finishing the treatment;
the two thresholds, one refers to a network loss reduction rate, and the other refers to a voltage offset reduction rate;
taking the current time period as t, the last time period as t-1, wherein the network loss reduction rate refers to the ratio of the network loss reduction after reactive power optimization to the network loss before reactive power optimization, and the voltage offset reduction rate refers to the ratio of the voltage offset reduction after reactive power optimization to the voltage offset before reactive power optimization;
respectively carrying out static reactive power optimization in each time interval to obtain corresponding optimal reactive power optimization configuration, and after carrying out reactive power optimization in the current time interval t, expressing the loss reduction rate of the network under the optimal configuration as delta Pt% and the voltage offset reduction rate is represented by Δ UtPercent, the network loss reduction rate obtained by substituting the configuration of the last period of time t-1 into the current period of time t is delta Pt'%, the voltage offset reduction rate is delta Ut'%, the threshold is set to M1And M2(ii) a Wherein, both thresholds are taken as 2.5%; if one of the two following equations is satisfied, the reactive power optimization device in the time interval needs to act:
|ΔP′t％-ΔPt％|＞M1 (10)
|ΔU′t％-ΔUt％|＞M2 (11)
if the two formulas (10) and (11) are not satisfied, the reactive power optimization device configuration in the t-1 time period can be used in the t time period, and adjustment is not needed.
2. The dynamic reactive power optimization method for the active power distribution network according to claim 1, wherein in step c, continuous loads in one day are segmented, discretized and divided into 24 time intervals by the integral median theorem as the calculation loads.
3. The dynamic reactive power optimization method for the active power distribution network according to claim 1, wherein in the step d, the fluctuation of the distributed power supply output is taken into consideration, the daily output power condition of the distributed power supply is segmented and discretized, and the output power is processed into 24 periods of output power by using an integral median theorem.
4. The dynamic reactive power optimization method of the active power distribution network according to claim 1, wherein in step e, the corresponding static reactive power optimization calculation is performed for each time segment to obtain the device configuration condition under the optimal reactive power optimization condition in each time segment, that is, the optimal device configuration data.
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CN112994028B (en) * 2021-05-24 2021-07-23 广东电网有限责任公司中山供电局 Virtual power plant reactive voltage rapid adjustment method and device
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20050093893A (en) * 2004-03-19 2005-09-23 학교법인 명지학원 Overcurrent protective relaying method for bus interconnected with distributed generations
CN102289566A (en) * 2011-07-08 2011-12-21 浙江大学 Multiple-time-scale optimized energy dispatching method for micro power grid under independent operation mode
CN102868161A (en) * 2012-10-23 2013-01-09 四川大学 Optimization method of network variable structure with distributed type power supply distribution system
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
CN106169766A (en) * 2016-08-09 2016-11-30 国网浙江德清县供电公司 Dynamic reactive method of adjustment containing distributed power source power distribution network
CN106329546A (en) * 2016-09-12 2017-01-11 东南大学 Active power distribution network distributed type reactive optimizing method based on equal network loss increment rate
CN107394797A (en) * 2017-08-30 2017-11-24 华侨大学 A kind of appraisal procedure for the distributed power source access power distribution network for considering idle work optimization
CN107947206A (en) * 2017-12-20 2018-04-20 武汉大学 Three-phase distribution net Multipurpose Optimal Method based on improved NSGA II algorithms

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140148966A1 (en) * 2012-11-29 2014-05-29 Magdy Salama Decentralized Volt/VAR Control for Advanced Distribution Automation Systems

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20050093893A (en) * 2004-03-19 2005-09-23 학교법인 명지학원 Overcurrent protective relaying method for bus interconnected with distributed generations
CN102289566A (en) * 2011-07-08 2011-12-21 浙江大学 Multiple-time-scale optimized energy dispatching method for micro power grid under independent operation mode
CN102868161A (en) * 2012-10-23 2013-01-09 四川大学 Optimization method of network variable structure with distributed type power supply distribution system
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
CN106169766A (en) * 2016-08-09 2016-11-30 国网浙江德清县供电公司 Dynamic reactive method of adjustment containing distributed power source power distribution network
CN106329546A (en) * 2016-09-12 2017-01-11 东南大学 Active power distribution network distributed type reactive optimizing method based on equal network loss increment rate
CN107394797A (en) * 2017-08-30 2017-11-24 华侨大学 A kind of appraisal procedure for the distributed power source access power distribution network for considering idle work optimization
CN107947206A (en) * 2017-12-20 2018-04-20 武汉大学 Three-phase distribution net Multipurpose Optimal Method based on improved NSGA II algorithms

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
A Fully Distributed Reactive Power Optimization and Control Method for Active Distribution Networks;Weiye Zheng,etc.;《IEEE TRANSACTIONS ON SMART GRID》;20160331;第7卷(第2期);第1021-1033页 *
计及分布式电源与电容器协调的配电网日前无功计划;谭煌 等;《电网技术》;20140930;第38卷(第9期);第2590-2597页 *

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