CN114156867A - Power distribution network interval network reconstruction model optimization method and device - Google Patents

Power distribution network interval network reconstruction model optimization method and device Download PDF

Info

Publication number
CN114156867A
CN114156867A CN202111332341.5A CN202111332341A CN114156867A CN 114156867 A CN114156867 A CN 114156867A CN 202111332341 A CN202111332341 A CN 202111332341A CN 114156867 A CN114156867 A CN 114156867A
Authority
CN
China
Prior art keywords
model
branch
power distribution
interval
distribution network
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.)
Granted
Application number
CN202111332341.5A
Other languages
Chinese (zh)
Other versions
CN114156867B (en
Inventor
席嫣娜
吴文传
张隽
张宏宇
吴越强
刘海涛
刘庆时
王彬
王冠楠
韦凌霄
孙宏斌
郭庆来
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.)
Tsinghua University
State Grid Corp of China SGCC
State Grid Jibei Electric Power Co Ltd
State Grid Beijing Electric Power Co Ltd
Original Assignee
Tsinghua University
State Grid Corp of China SGCC
State Grid Jibei Electric Power Co Ltd
State Grid Beijing 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 Tsinghua University, State Grid Corp of China SGCC, State Grid Jibei Electric Power Co Ltd, State Grid Beijing Electric Power Co Ltd filed Critical Tsinghua University
Priority to CN202111332341.5A priority Critical patent/CN114156867B/en
Publication of CN114156867A publication Critical patent/CN114156867A/en
Application granted granted Critical
Publication of CN114156867B publication Critical patent/CN114156867B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution 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
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Economics (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • Health & Medical Sciences (AREA)
  • Educational Administration (AREA)
  • Power Engineering (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The disclosure relates to a power distribution network interval network reconstruction model optimization method and device, and belongs to the field of power distribution network interval network reconstruction of a power system. The method comprises the following steps: establishing a deterministic network reconstruction model of the power distribution network; converting the deterministic network reconstruction model into an interval optimization model; converting the interval optimization model into a deterministic optimization model; relaxing the deterministic optimization model to obtain a relaxed deterministic optimization model; and solving the relaxed deterministic optimization model to obtain an optimal switching result of the branch of the power distribution network, and completing the reconstruction optimization of the network among the power distribution networks. The method can avoid the statistical demands of a large amount of historical data and subjective assumed distribution of a probability method and a fuzzy number theory method, can comprehensively reflect the real condition of the operation of the power distribution network, and has high application value.

Description

Power distribution network interval network reconstruction model optimization method and device
Technical Field
The disclosure belongs to the field of power distribution network interval network reconstruction of a power system, and particularly relates to a power distribution network interval network reconstruction model optimization method and device.
Background
The power distribution network reconstruction is to enable the power distribution network to operate in a more reliable and economic mode by changing the topology structure of the power distribution network, so that the method has very important significance for the stable operation of a power system. Because the power distribution network reconstruction model belongs to the mixed integer nonlinear programming problem, the solution is very difficult, and the solution process is easy to fail. Different from the reconstruction of a deterministic network of a power distribution network, when the uncertainty injection power exists in the power distribution network, the reconstruction has the following two problems:
(1) deterministic network reconstruction methods have difficulty dealing with power injection uncertainty information. When the power distribution network system operates, the load and the distributed power generation have strong randomness and fluctuation, so that the optimal structure of the network changes along with different time points. Therefore, how to process the uncertainty information of power injection is necessary to calculate the comprehensive optimal network structure.
(2) The existing method for processing the uncertainty information of the power distribution network system is difficult to apply; currently, there are two general methods for processing uncertainty information of network reconstruction: probabilistic models and fuzzy number models. However, both methods need to obtain an accurate probability distribution or probability distribution of uncertain information, and are difficult to obtain in practical application.
In summary, in order to ensure that the safety operation and loss after reconstruction are optimal, the power distribution network reconstruction method must have the characteristics of reliability, rapidness and convenience in application, and the problems of fluctuation and uncertainty of the injection power of the power distribution network are mainly solved. Therefore, it is necessary to research a power distribution network reconstruction model interval optimization method based on an interval mathematical theory with the aim of improving reliability of power distribution network uncertainty reconstruction.
Disclosure of Invention
The purpose of the disclosure is to provide a power distribution network interval network reconstruction model optimization method and device for overcoming the defects in the prior art. The method can avoid the statistical demands of a large amount of historical data and subjective assumed distribution of a probability method and a fuzzy number theory method, can comprehensively reflect the real condition of the operation of the power distribution network, and has high application value.
The first aspect of the disclosure is that an embodiment provides a power distribution network interval network reconstruction model optimization method, including:
establishing a deterministic network reconstruction model of the power distribution network;
converting the deterministic network reconstruction model into an interval optimization model;
converting the interval optimization model into a deterministic optimization model;
relaxing the deterministic optimization model to obtain a relaxed deterministic optimization model;
and solving the relaxed deterministic optimization model to obtain an optimal switching result of the branch of the power distribution network, and completing the reconstruction optimization of the network among the power distribution networks.
In a specific embodiment of the present disclosure, the establishing a deterministic network reconstruction model of a power distribution network includes:
1-1) establishing an objective function of the model, wherein the expression is as follows:
Figure BDA0003349244640000021
wherein,
Figure BDA0003349244640000022
the resistance of the s phase in the branch ij;
Figure BDA0003349244640000023
the s phase is the square of the current in branch ij, and s is { a, b, c };
1-2) determining constraints of the model, including:
the switching state of the branch of the power distribution network is restricted;
xij∈{0,1} (2)
wherein x isijFor the switching state of the branch ij, 1 represents that the branch ij is switched in;
distflow power flow constraint of the power distribution network;
Figure BDA0003349244640000024
Figure BDA0003349244640000025
wherein,
Figure BDA0003349244640000026
respectively the active power and the reactive power of the s-phase in the branch ij;
Figure BDA0003349244640000027
respectively the active injection power and the reactive injection power of the s-phase at the node j;
Figure BDA0003349244640000028
the current amplitude of the s phase in the branch ij;
Figure BDA0003349244640000029
reactance for phase s in branch ij; vi s,
Figure BDA00033492446400000210
The voltage amplitude of s phase at the node i, j is squared, and s is { a, b, c };
radial running condition constraint of the power distribution network;
Figure BDA00033492446400000211
wherein N isnodeThe total number of the nodes of the power distribution network is; n is a radical ofrootThe number of the power generation nodes is; k (i) is a set of nodes connected to node i;
the safety operation condition of the power distribution network is restricted;
Figure BDA0003349244640000031
wherein,
Figure BDA0003349244640000032
the upper limit of the current amplitude of the s phase in the branch ij is set; vl,VuRespectively, a lower limit and an upper limit of the squared node voltage magnitude.
In a specific embodiment of the present disclosure, the converting the deterministic network reconstruction model into an interval optimization model includes:
2-1) reconstructing state variables in the model for deterministic network
Figure BDA0003349244640000033
Vi sRespectively established as corresponding interval numbers, as shown in formula (7):
Figure BDA0003349244640000034
wherein,
Figure BDA0003349244640000035
the lower limit and the upper limit of the current amplitude of the s phase in the branch ij are respectively; i sV,
Figure BDA0003349244640000036
the lower limit and the upper limit of the square of the voltage amplitude of the s phase at the node i are respectively set;
Figure BDA0003349244640000037
the lower limit and the upper limit of the active power of the s phase in the branch ij are respectively;
Figure BDA0003349244640000038
the lower limit and the upper limit of the reactive power of the s phase in the branch ij are respectively;
Figure BDA0003349244640000039
respectively is the lower limit and the upper limit of the active injection power of the s phase at the node j;
Figure BDA00033492446400000310
the lower limit and the upper limit of reactive injection power of the s phase at the node j are respectively set, and s is { a, b, c };
2-2) converting the deterministic network reconstruction model into the following interval optimization model:
Figure BDA00033492446400000311
s.t.xij∈{0,1} (9)
Figure BDA00033492446400000312
Figure BDA00033492446400000313
Figure BDA0003349244640000041
Figure BDA0003349244640000042
in a specific embodiment of the present disclosure, the deterministic optimization model expression is as follows:
Figure BDA0003349244640000043
s.t.xij∈{0,1} (15)
Figure BDA0003349244640000044
Figure BDA0003349244640000045
Figure BDA0003349244640000046
Figure BDA0003349244640000047
Figure BDA0003349244640000048
Figure BDA0003349244640000049
wherein (C)RDMAn RDM form representing the corresponding parameter;
Figure BDA00033492446400000410
the RDM variable corresponding to the current amplitude of the s-phase in the branch ij is obtained;
Figure BDA00033492446400000411
the RDM variable corresponding to the current amplitude of the s-phase in the branch ij is obtained;
Figure BDA00033492446400000412
the RDM variable corresponding to the voltage amplitude of the s phase at the node i;
Figure BDA0003349244640000051
injecting an RDM variable of reactive power at node j for the s-phase;
Figure BDA0003349244640000052
the RDM variable corresponding to the active power of the s-phase in the branch ij;
Figure BDA0003349244640000053
for the RDM variable corresponding to the reactive power of the s-phase in branch ij, s ═ a, b, c.
In a specific embodiment of the present disclosure, the relaxing the deterministic optimization model to obtain a relaxed deterministic optimization model includes:
4-1) convex relaxation of the nonlinear term using the large M method for the last three terms in equation (20):
Figure BDA0003349244640000054
Figure BDA0003349244640000055
Figure BDA0003349244640000056
wherein M is a positive number;
4-2) rewrite equations (16), (17) as:
Figure BDA0003349244640000057
Figure BDA0003349244640000058
Figure BDA0003349244640000059
Figure BDA00033492446400000510
relaxing a voltage equation by a large M method;
when either branch is disconnected, equation (28) is rewritten as:
Figure BDA00033492446400000511
subscripts i and j are a head end node and a tail end node of the disconnected branch;
4-3) calculating and transforming through the RDM interval to obtain a lower bound function and an upper bound function of the formula (14) as shown in the following formula:
Figure BDA00033492446400000512
in a specific embodiment of the present disclosure, solving the relaxed deterministic optimization model to obtain an optimal switching result of the branch of the power distribution network, and completing the network reconfiguration optimization between power distribution networks includes:
5-1) making all branch powers of the power distribution network be greater than 0, and then the lower limit values of the active power, the reactive power and the current square of each branch of the s-phase are all 0, and the expression is as follows:
Figure BDA0003349244640000061
order:
Figure BDA0003349244640000062
according to equation (29), then:
M＝Vu-Vl (39)
let the upper limit of the branch power and the square of the current be expressed as follows:
Figure BDA0003349244640000063
introducing equations (37) - (40) into the deterministic optimization model, converting the deterministic optimization model into a linear model;
5-2) solving the linear model to obtain an optimal switching result of the branch of the power distribution network; the method comprises the following specific steps:
5-2-1) solving the linear model converted in the step 5-1) according to the upper and lower limits of the interval variable in the step 5-1) to obtain the branch of the power distribution networkSwitching state xij；
5-2-2) solving that the switching state of the branch of the power distribution network is x based on a linear relaxation contraction iterative algorithmijLower interval power flow and corresponding interval network loss
Figure BDA0003349244640000064
5-2-3) newly adding the constraint on the linear model converted in the step 5-1) as follows:
Figure BDA0003349244640000065
obtaining an updated linear model;
solving the updated linear model to obtain an updated branch switching state x of the power distribution networkij；
And (3) judging: if the updated switching state of the branch of the power distribution network is not changed compared with the switching state of the branch of the power distribution network obtained in the step 5-2-1), turning to the step 5-2-4); otherwise, returning to the step 5-2-2);
5-2-4) output xijAnd as an optimal result of branch switching of the power distribution network, finishing network reconstruction optimization of the power distribution network interval.
In a specific embodiment of the present disclosure, the linear relaxation contraction iterative algorithm includes:
5-2-2-1) writing equation (27) as a non-linear equation as shown below:
g(αx)-C1＝0 (41)
equations (25) and (26) are written as linear equations as shown below:
Kαx-C2＝0 (42)
wherein the vector αxIncluded
Figure BDA0003349244640000071
K is a constant matrix, and C1 and C2 are constant vectors;
obtaining an optimization model shown as the following formula:
Figure BDA0003349244640000072
5-2-2-2) converting the optimized model obtained in the step 5-2-2-1) into a linear shrinkage model, wherein the specific method comprises the following steps:
equation (41) is written as follows:
Figure BDA0003349244640000073
wherein ε ∈ [ a ]x]，H＝Jg( xα)，b＝-(Jg(ε)-Jg( xα))(αx- xα)-g( xα)+Jg( xα) xα+C1；
Order:
Β＝-(Jg(αx)-Jg( xα))(αx- xα)-g( xα)+Jg( xα) xα+C1 (45)
wherein, Jg(.) is the Jacobian matrix of equations;
wherein,
Figure BDA0003349244640000074
Figure BDA0003349244640000075
and xαis a vector alphaxThe upper and lower limits of (a) and (b),
Figure BDA0003349244640000076
andBthe upper limit and the lower limit of the interval variable B;
iteratively updating by equation (46)
Figure BDA0003349244640000077
AndB：
Figure BDA0003349244640000078
equation (43) is solved by the following linear shrinkage model:
Figure BDA0003349244640000079
5-2-2-3) obtaining a calculation result of the interval power flow by solving the linear shrinkage model obtained in the step 5-2-2-2); the method comprises the following specific steps:
5-2-2-3-1) order branch power
Figure BDA00033492446400000710
Is in the initial range of [ -M, M]Let us order
Figure BDA00033492446400000711
Has an initial range of [ V ]l,Vu]Let the branch current
Figure BDA00033492446400000712
Is in the initial range of [ -M, M]；
5-2-2-3-2) solving the linear shrinkage model formula (47) to obtain min/max alphaxAnd the branch switching state is xij；
5-2-2-3-3) judging the result of the step 5-2-2-3-2):
if [ alpha ] isx]The interval width is not reduced any more, then the min/max alpha obtained in the step 5-2-2-3-2)xI.e. the final solution of the linear shrinkage model to obtain alphaxTurning to the step 5-2-2-3-4) for the minimum value and the maximum value of each variable in the step; otherwise, the [ alpha ] obtained according to the step 5-2-2-3-2)x]Renewal of H, BETA, K, C1,C2Then returning to the step 5-2-2-3-2);
5-2-2-3-4) dividing the RDM interval variable according to equation (20)
Figure BDA0003349244640000081
Figure BDA0003349244640000082
Change back to interval variable
Figure BDA0003349244640000083
And finishing the interval load flow calculation.
The second aspect of the present disclosure is to provide an apparatus for optimizing a power distribution network interval network reconstruction model, including:
the deterministic network reconstruction model building module is used for building a deterministic network reconstruction model of the power distribution network;
the interval optimization model construction module is used for converting the deterministic network reconstruction model into an interval optimization model;
the deterministic model construction module is used for converting the interval optimization model into a deterministic optimization model;
the deterministic optimization model relaxation module is used for relaxing the deterministic optimization model to obtain a relaxed deterministic optimization model;
and the power distribution network interval network reconstruction optimization module is used for solving the relaxed deterministic optimization model to obtain an optimal switching result of the power distribution network branches and complete power distribution network interval network reconstruction optimization.
A third aspect of the present disclosure is an embodiment of an electronic device, including:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform a method of power distribution inter-grid network reconfiguration model optimization as described above.
A fourth aspect of the present disclosure is an embodiment of the present disclosure, which provides a computer-readable storage medium storing computer instructions, where the computer instructions are configured to cause the computer to execute the above-mentioned method for optimizing a network reconstruction model between power distribution networks.
The characteristics and the beneficial effects of the disclosure are as follows:
1. the method has the characteristics of reliability, rapidness and convenient application.
2. The method provides a power distribution network RDM interval reconstruction model according to the theorem of upper and lower function boundary optimization of interval optimization, and the model can comprehensively consider the fluctuation of injection power and new energy in a power distribution network interval.
3. The disclosure provides a method for solving a power distribution network RDM interval reconstruction model by using an RDM-MILP Contractor. The method has the advantages of easy implementation and high calculation efficiency, and is expected to be applied to the actual operation of the power distribution network on line.
4. The interval method can avoid the statistical demands of a large amount of historical data and subjective assumed distribution of a probability method and a fuzzy number theory method, can comprehensively reflect the real condition of the operation of the power distribution network, and has high application value.
Drawings
Fig. 1 is an overall flowchart of a power distribution network interval network reconstruction model optimization method in the embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The method for optimizing the power distribution network interval network reconstruction model provided in the embodiment of the first aspect of the disclosure has the overall flow shown in fig. 1, and includes the following steps:
1) and establishing a traditional deterministic network reconstruction model of the power distribution network.
In one embodiment of the present disclosure, a specific process for establishing a traditional deterministic network reconstruction model of a power distribution network includes:
1-1) establishing an objective function of a model;
the objective function of the power distribution network reconstruction is the minimum of the network loss, and under the condition of neglecting three-phase coupling, the expression of the objective function is as follows:
Figure BDA0003349244640000091
wherein,
Figure BDA0003349244640000092
the resistance of the s phase in the branch ij;
Figure BDA0003349244640000093
the s phase is the square of the current in leg ij, and s ═ a, b, c.
1-2) determining the constraint conditions of the model, specifically as follows:
the switching state of the branch of the power distribution network is restricted;
xij∈{0,1} (2)
wherein x isijFor the switching state of the branch ij, 1 represents that the branch ij is switched in.
Distflow power flow constraint of the power distribution network;
Figure BDA0003349244640000094
Figure BDA0003349244640000095
wherein,
Figure BDA0003349244640000101
respectively the active power and the reactive power of the s-phase in the branch ij;
Figure BDA0003349244640000102
respectively the active injection power and the reactive injection power of the s-phase at the node j;
Figure BDA0003349244640000103
the current amplitude of the s phase in the branch ij;
Figure BDA0003349244640000104
reactance for phase s in branch ij; vi s,
Figure BDA0003349244640000105
The voltage amplitude of s phase at node i, j squared, s ═ a, b, c, respectively.
Radial running condition constraint of the power distribution network;
Figure BDA0003349244640000106
wherein N isnodeThe total number of the nodes of the power distribution network is; n is a radical ofrootThe number of the power generation nodes is; k (i) is a set of nodes connected to node i.
The safety operation condition of the power distribution network is restricted;
Figure BDA0003349244640000107
wherein,
Figure BDA0003349244640000108
the upper limit of the current amplitude of the s phase in the branch ij is set; vl,VuRespectively, a lower limit and an upper limit of the squared node voltage magnitude.
2) Converting the traditional deterministic network reconstruction model established in the step 1) into an interval optimization model;
in an embodiment of the present disclosure, a specific process of converting the traditional deterministic network reconstruction model established in step 1) into an interval optimization model includes:
2-1) due to the load of the distribution network and the existence of uncertainty factors of the generator, aiming at the state variables in the model in the step 1)
Figure BDA0003349244640000109
Vi sRespectively established as corresponding interval numbers, as shown in formula (7):
Figure BDA00033492446400001010
wherein,
Figure BDA00033492446400001011
the lower limit and the upper limit of the current amplitude of the s phase in the branch ij are respectively; i sV,
Figure BDA00033492446400001012
the lower limit and the upper limit of the square of the voltage amplitude of the s phase at the node i are respectively set;
Figure BDA00033492446400001013
the lower limit and the upper limit of the active power of the s phase in the branch ij are respectively;
Figure BDA00033492446400001014
the lower limit and the upper limit of the reactive power of the s phase in the branch ij are respectively;
Figure BDA00033492446400001015
respectively is the lower limit and the upper limit of the active injection power of the s phase at the node j;
Figure BDA00033492446400001016
the lower limit and the upper limit of the reactive injection power of the s phase at the node j are respectively, and s is { a, b, c }.
2-2) converting the traditional deterministic network reconstruction model established in the step 1) into the following interval optimization model:
Figure BDA0003349244640000111
s.t.xij∈{0,1} (9)
Figure BDA0003349244640000112
Figure BDA0003349244640000113
Figure BDA0003349244640000114
Figure BDA0003349244640000115
3) converting the interval optimization model in the step 2) into a deterministic optimization model through RDM interval operation;
in one embodiment of the present disclosure, the deterministic optimization model is expressed as follows:
Figure BDA0003349244640000116
s.t.xij∈{0,1} (15)
Figure BDA0003349244640000117
Figure BDA0003349244640000118
Figure BDA0003349244640000119
Figure BDA00033492446400001110
Figure BDA0003349244640000121
Figure BDA0003349244640000122
wherein (C)RDMAn RDM form representing the corresponding parameter;
Figure BDA0003349244640000123
the RDM variable corresponding to the current amplitude of the s-phase in the branch ij is obtained;
Figure BDA0003349244640000124
the RDM variable corresponding to the current amplitude of the s-phase in the branch ij is obtained;
Figure BDA0003349244640000125
the RDM variable corresponding to the voltage amplitude of the s phase at the node i;
Figure BDA0003349244640000126
injecting an RDM variable of reactive power at node j for the s-phase;
Figure BDA0003349244640000127
the RDM variable corresponding to the active power of the s-phase in the branch ij;
Figure BDA0003349244640000128
for the RDM variable corresponding to the reactive power of the s-phase in branch ij, s ═ a, b, c.
4) Relaxing the deterministic optimization model obtained in the step 3), and relaxing the relaxed deterministic optimization model;
in a specific embodiment of the present disclosure, a specific process of relaxing the deterministic optimization model obtained in step 3) includes:
4-1) solving is difficult because the model in the step 3) is a nonlinear programming model. A linear relaxation approach is therefore utilized in one embodiment of the present disclosure to address this issue. Convex relaxation of the nonlinear term is carried out on the last three terms in the non-convex constraint equation (20) containing the switching variables in the deterministic optimization model by using a large M method:
Figure BDA0003349244640000129
Figure BDA00033492446400001210
Figure BDA00033492446400001211
wherein, M is a relatively large positive value, so that the value should be reasonably selected in order to avoid enlarging the optimization space and affecting the calculation efficiency of the optimization algorithm. The value of M in one specific example of the present disclosure is 10.
4-2) after the nonlinear terms are relaxed, the power flow constraint equations (16) and (17) can be rewritten as follows:
Figure BDA00033492446400001212
Figure BDA00033492446400001213
Figure BDA0003349244640000131
Figure BDA0003349244640000132
after processing, the voltage constraint becomes an inequality constraint because the constraints of the head end voltage and the tail end voltage no longer exist after one branch is disconnected. Therefore, the voltage equation needs to be relaxed by the large M method.
When one branch is disconnected, the voltage constraint equation (28) can be rewritten as:
Figure BDA0003349244640000133
subscripts i and j are a head end node and a tail end node of the disconnected branch;
4-3) after calculation and transformation are carried out on the RDM interval, the lower bound function and the upper bound function of the target function formula (14) are shown as follows:
Figure BDA0003349244640000134
therefore, finding the optimal solution of the original target becomes finding the appropriate one
Figure BDA0003349244640000135
Let the lower bound function flowAnd an upper bound function fupWhile achieving the optimum. From the above two functional expressions, the lower bound function flowInvolving a function f of upper boundupTherefore, in practice we only need to find fupThe optimal solution of (a). But due to fupThe objective function of (2) includes a non-linear term
Figure BDA0003349244640000136
It is not easy to directly solve. To this end, the embodiments of the present disclosure propose a mixed integer linear programming shrinkage method (MILP contractor) to solve.
5) Solving the model relaxed in the step 4) to obtain an optimal switching result of the branch of the power distribution network, and completing the reconstruction optimization of the network among the power distribution networks.
In an embodiment of the present disclosure, solving the model relaxed in step 4) adopts a mixed integer linear programming shrinkage method (MILP contractor), which includes the following specific processes:
5-1) according to the characteristics of the power distribution network, assuming that the power of all branches of the power distribution network is greater than 0, it can be defined that the lower limit values of the active power, the reactive power and the square of the current of each branch of the s-phase are all 0, namely:
Figure BDA0003349244640000137
according to the definition of the upper limit and the lower limit of the voltage, the following definitions are given:
Figure BDA0003349244640000138
according to formula (29), M may be:
M＝Vu-Vl (39)
the upper limit value of the branch power and the current square is given according to the inequality of the nonlinear relaxation as follows:
Figure BDA0003349244640000141
introducing the equations (37) to (40) into the deterministic optimization model of the step 3), and converting the deterministic optimization model of the step 3) into a linear model, wherein the linear model is not only easy to implement, but also improves the calculation efficiency.
5-2) step 5-1) is actually to relax the feasible domain of the upper bound function of the RDM interval function to [0, M ], wherein M is a large positive number, and the value of a specific embodiment of the disclosure is 10. Therefore some conservation of the process results is inevitable. Therefore, one embodiment of the present disclosure proposes an RDM-MILP container method, which iteratively reduces the [0, M ] space, thereby avoiding the conservative problem of the result. The specific process is as follows:
5-2-1) solving the linear model converted in the step 5-1) according to the upper and lower limits of the interval variable given in the step 5-1) to obtain the branch switching state x of the power distribution networkij。
5-2-2) solving that the switching state of the branch of the power distribution network is x based on a linear relaxation contraction iterative algorithmijThe interval power flow of the structure is given, and the interval network loss of the structure is given
Figure BDA0003349244640000142
The linear relaxation contraction iterative algorithm comprises the following specific steps:
5-2-2-1) writing equation (27) as a non-linear equation as shown below:
g(αx)-C1＝0 (41)
equations (25) and (26) are written as linear equations as shown below:
Kαx-C2＝0 (42)
wherein the vector αxContains all the following RDM variables
Figure BDA0003349244640000143
K is a constant matrix, and C1 and C2 are constant vectors.
Therefore, the converted distribution network interval power flow calculation model in the form of RDM is converted into an optimization model shown as the following formula:
Figure BDA0003349244640000144
5-2-2-2) converting the optimized model obtained in the step 5-2-2-1) into a linear shrinkage model; the specific method comprises the following steps:
the non-linear equation (41) is written as follows:
Figure BDA0003349244640000145
wherein ε ∈ [ a ]x]，H＝Jg( xα)，b＝-(Jg(ε)-Jg( xα))(αx- xα)-g( xα)+Jg( xα) xα+C1；
The following interval variable vectors are defined:
Β＝-(Jg(αx)-Jg( xα))(αx- xα)-g( xα)+Jg( xα) xα+C1 (45)
wherein, Jg(.) is the Jacobian matrix of equations;
due to the fact that
Figure BDA0003349244640000151
Then
Figure BDA0003349244640000152
Wherein
Figure BDA0003349244640000153
And xαis a vector alphaxThe upper and lower limits of (a) and (b),
Figure BDA0003349244640000154
andBthe upper limit and the lower limit of the interval variable B; iteratively updating by equation (46)
Figure BDA0003349244640000155
AndB：
Figure BDA0003349244640000156
equation (43) is solved by the following linear shrinkage model:
Figure BDA0003349244640000157
5-2-2-3) obtaining a calculation result of the interval power flow by solving the linear shrinkage model obtained in the step 5-2-2-2); the method comprises the following specific steps:
5-2-2-3-1) gives the branch power
Figure BDA0003349244640000158
Is in the initial range of [ -M, M]In one embodiment of the present disclosure, M is 10. Giving node voltage
Figure BDA0003349244640000159
Has an initial range of [ V ]l,Vu]Here, take Vl＝0.8，Vu1.2. Giving branch current
Figure BDA00033492446400001510
Is in the initial range of [ -M, M]In one specific embodiment of the present disclosure, M is 10;
5-2-2-3-2) solving the linear shrinkage model formula (47) to obtain min/max alphaxAnd the branch switching state is xij；
5-2-2-3-3) judging the result of the step 5-2-2-3-2):
if [ alpha ] isx]The interval width is not reduced any more, then the min/max alpha is obtained in the step 5-2-2-3-2)xThat is, the final solution of the linear shrinkage model, and then alpha is obtainedxTurning to the step 5-2-2-3-4) for the minimum value and the maximum value of each RDM variable; otherwise, the [ alpha ] obtained according to the step 5-2-2-3-2)x]Renewal of H, BETA, K, C1,C2And then returns to step 5-2-2-3-2) again.
5-2-2-3-4) dividing the RDM interval variable according to equation (20)
Figure BDA00033492446400001511
Figure BDA00033492446400001512
Go back to the conventional interval variable
Figure BDA00033492446400001513
And finishing the interval load flow calculation.
5-2-3) newly adding constraint on the linear model converted in the step 5-1)
Figure BDA00033492446400001514
Then solving the linear model to obtain an updated branch switching state x of the power distribution networkij。
If the updated switching state of the branch of the power distribution network is not changed compared with the result of the step 5-2-1), turning to the step 5-2-4), otherwise, turning to the step 5-2-2), and solving the updated xijAnd (5) the following interval trend.
5-2-4) output xijAnd as an optimal result of branch switching of the power distribution network, finishing network reconstruction optimization of the power distribution network interval.
An embodiment of a second aspect of the present disclosure provides an optimization device for a power distribution network interval network reconstruction model, including:
the deterministic network reconstruction model building module is used for building a deterministic network reconstruction model of the power distribution network;
the interval optimization model construction module is used for converting the deterministic network reconstruction model into an interval optimization model;
the deterministic model construction module is used for converting the interval optimization model into a deterministic optimization model;
the deterministic optimization model relaxation module is used for relaxing the deterministic optimization model to obtain a relaxed deterministic optimization model;
and the power distribution network interval network reconstruction optimization module is used for solving the relaxed deterministic optimization model to obtain an optimal switching result of the power distribution network branches and complete power distribution network interval network reconstruction optimization.
In an embodiment of a third aspect of the present disclosure, an electronic device includes:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform a method of power distribution inter-grid network reconfiguration model optimization as described above.
A fourth aspect of the present disclosure is directed to a computer-readable storage medium storing computer instructions for causing a computer to execute the method for optimizing an inter-distribution network reconstruction model of a power distribution network according to the foregoing claims.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device is caused to execute a power distribution network interval network reconfiguration model optimization method of the embodiment.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware that is related to instructions of a program, and the program may be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.

Claims (10)

1. A power distribution network interval network reconstruction model optimization method is characterized by comprising the following steps:
establishing a deterministic network reconstruction model of the power distribution network;
converting the deterministic network reconstruction model into an interval optimization model;
converting the interval optimization model into a deterministic optimization model;
relaxing the deterministic optimization model to obtain a relaxed deterministic optimization model;
and solving the relaxed deterministic optimization model to obtain an optimal switching result of the branch of the power distribution network, and completing the reconstruction optimization of the network among the power distribution networks.
2. The optimization method according to claim 1, wherein the establishing of the deterministic network reconstruction model of the power distribution network comprises:
1-1) establishing an objective function of the model, wherein the expression is as follows:
Figure FDA0003349244630000011
wherein,
Figure FDA0003349244630000012
the resistance of the s phase in the branch ij;
Figure FDA0003349244630000013
the s phase is the square of the current in branch ij, and s is { a, b, c };
1-2) determining constraints of the model, including:
the switching state of the branch of the power distribution network is restricted;
xij∈{0,1} (2)
wherein x isijFor the switching state of the branch ij, 1 represents that the branch ij is switched in;
distflow power flow constraint of the power distribution network;
Figure FDA0003349244630000014
Figure FDA0003349244630000015
wherein,
Figure FDA0003349244630000016
respectively the active power and the reactive power of the s-phase in the branch ij;
Figure FDA0003349244630000017
respectively the active injection power and the reactive injection power of the s-phase at the node j;
Figure FDA0003349244630000018
the current amplitude of the s phase in the branch ij;
Figure FDA0003349244630000019
reactance for phase s in branch ij;
Figure FDA00033492446300000110
the voltage amplitude of s phase at the node i, j is squared, and s is { a, b, c };
radial running condition constraint of the power distribution network;
Figure FDA0003349244630000021
wherein N isnodeThe total number of the nodes of the power distribution network is; n is a radical ofrootThe number of the power generation nodes is; k (i) is a set of nodes connected to node i;
the safety operation condition of the power distribution network is restricted;
Figure FDA0003349244630000022
wherein,
Figure FDA0003349244630000023
the upper limit of the current amplitude of the s phase in the branch ij is set; vl,VuRespectively, a lower limit and an upper limit of the squared node voltage magnitude.
3. The optimization method of claim 2, wherein said converting the deterministic network reconstruction model into an interval optimization model comprises:
2-1) for the determinationState variables in a model for sexual network reconstruction
Figure FDA0003349244630000024
Respectively establishing corresponding interval numbers as shown in formula (7):
Figure FDA0003349244630000025
wherein,
Figure FDA0003349244630000026
the lower limit and the upper limit of the current amplitude of the s phase in the branch ij are respectively;
Figure FDA0003349244630000027
the lower limit and the upper limit of the square of the voltage amplitude of the s phase at the node i are respectively set;
Figure FDA0003349244630000028
the lower limit and the upper limit of the active power of the s phase in the branch ij are respectively;
Figure FDA0003349244630000029
the lower limit and the upper limit of the reactive power of the s phase in the branch ij are respectively;
Figure FDA00033492446300000210
respectively is the lower limit and the upper limit of the active injection power of the s phase at the node j;
Figure FDA00033492446300000211
the lower limit and the upper limit of reactive injection power of the s phase at the node j are respectively set, and s is { a, b, c };
2-2) converting the deterministic network reconstruction model into the following interval optimization model:
Figure FDA00033492446300000212
s.t. xij∈{0,1} (9)
Figure FDA0003349244630000031
Figure FDA0003349244630000032
Figure FDA0003349244630000033
Figure FDA0003349244630000034
4. the optimization method of claim 3, wherein the deterministic optimization model is expressed as follows:
Figure FDA0003349244630000035
s.t.xij∈{0,1} (15)
Figure FDA0003349244630000036
Figure FDA0003349244630000037
Figure FDA0003349244630000038
Figure FDA0003349244630000039
Figure FDA0003349244630000041
Figure FDA0003349244630000042
wherein (C)RDMAn RDM form representing the corresponding parameter;
Figure FDA0003349244630000043
the RDM variable corresponding to the current amplitude of the s-phase in the branch ij is obtained;
Figure FDA0003349244630000044
the RDM variable corresponding to the current amplitude of the s-phase in the branch ij is obtained;
Figure FDA0003349244630000045
the RDM variable corresponding to the voltage amplitude of the s phase at the node i;
Figure FDA0003349244630000046
injecting an RDM variable of reactive power at node j for the s-phase;
Figure FDA0003349244630000047
the RDM variable corresponding to the active power of the s-phase in the branch ij;
Figure FDA0003349244630000048
for the RDM variable corresponding to the reactive power of the s-phase in branch ij, s ═ a, b, c.
5. The optimization method of claim 4, wherein the relaxing the deterministic optimization model to obtain a relaxed deterministic optimization model comprises:
4-1) convex relaxation of the nonlinear term using the large M method for the last three terms in equation (20):
Figure FDA0003349244630000049
Figure FDA00033492446300000410
Figure FDA00033492446300000411
wherein M is a positive number;
4-2) rewrite equations (16), (17) as:
Figure FDA00033492446300000412
Figure FDA00033492446300000413
Figure FDA00033492446300000414
Figure FDA00033492446300000415
relaxing a voltage equation by a large M method;
when either branch is disconnected, equation (28) is rewritten as:
Figure FDA0003349244630000051
subscripts i and j are a head end node and a tail end node of the disconnected branch;
4-3) calculating and transforming through the RDM interval to obtain a lower bound function and an upper bound function of the formula (14) as shown in the following formula:
Figure FDA0003349244630000052
6. the optimization method according to claim 5, wherein the relaxed deterministic optimization model is solved to obtain optimal switching results of the branches of the power distribution network, and the inter-distribution-network reconstruction optimization is completed, and the method comprises the following steps:
5-1) making all branch powers of the power distribution network be greater than 0, and then the lower limit values of the active power, the reactive power and the current square of each branch of the s-phase are all 0, and the expression is as follows:
Figure FDA0003349244630000053
order:
Figure FDA0003349244630000054
according to equation (29), then:
M＝Vu-Vl (39)
let the upper limit of the branch power and the square of the current be expressed as follows:
Figure FDA0003349244630000055
introducing equations (37) - (40) into the deterministic optimization model, converting the deterministic optimization model into a linear model;
5-2) solving the linear model to obtain an optimal switching result of the branch of the power distribution network; the method comprises the following specific steps:
5-2-1) solving the linear model converted in the step 5-1) according to the upper and lower limits of the interval variable in the step 5-1) to obtain the branch switching state x of the power distribution networkij；
5-2-2) solving that the switching state of the branch of the power distribution network is x based on a linear relaxation contraction iterative algorithmijLower interval power flow and corresponding interval network loss
Figure FDA0003349244630000056
5-2-3) newly adding the constraint on the linear model converted in the step 5-1) as follows:
Figure FDA0003349244630000057
obtaining an updated linear model;
solving the updated linear model to obtain an updated branch switching state x of the power distribution networkij；
And (3) judging: if the updated switching state of the branch of the power distribution network is not changed compared with the switching state of the branch of the power distribution network obtained in the step 5-2-1), turning to the step 5-2-4); otherwise, returning to the step 5-2-2);
5-2-4) output xijAnd as an optimal result of branch switching of the power distribution network, finishing network reconstruction optimization of the power distribution network interval.
7. The optimization method according to claim 6, wherein the linear relaxation contraction iterative algorithm comprises:
5-2-2-1) writing equation (27) as a non-linear equation as shown below:
g(αx)-C1＝0 (41)
equations (25) and (26) are written as linear equations as shown below:
Kαx-C2＝0 (42)
wherein the vector αxIncluded
Figure FDA0003349244630000061
K is a constant matrix, and C1 and C2 are constant vectors;
obtaining an optimization model shown as the following formula:
Figure FDA0003349244630000062
5-2-2-2) converting the optimized model obtained in the step 5-2-2-1) into a linear shrinkage model, wherein the specific method comprises the following steps:
equation (41) is written as follows:
Figure FDA0003349244630000063
wherein ε ∈ [ a ]x]，H＝Jg( xα)，b＝-(Jg(ε)-Jg( xα))(αx- xα)-g( xα)+Jg( xα) xα+C1；
Order:
Β＝-(Jg(αx)-Jg( xα))(αx- xα)-g( xα)+Jg( xα) xα+C1 (45)
wherein, Jg(.) is the Jacobian matrix of equations;
wherein,
Figure FDA0003349244630000064
Figure FDA0003349244630000065
and xαis a vector alphaxThe upper and lower limits of (a) and (b),
Figure FDA0003349244630000066
and B is the upper and lower limits of the interval variable B;
iteratively updating by equation (46)
Figure FDA0003349244630000067
AndB：
Figure FDA0003349244630000068
equation (43) is solved by the following linear shrinkage model:
Figure FDA0003349244630000071
5-2-2-3) obtaining a calculation result of the interval power flow by solving the linear shrinkage model obtained in the step 5-2-2-2); the method comprises the following specific steps:
5-2-2-3-1) order branch power
Figure FDA0003349244630000072
Figure FDA0003349244630000073
Is in the initial range of [ -M, M]Let us order
Figure FDA0003349244630000074
Has an initial range of [ V ]l,Vu]Let the branch current
Figure FDA0003349244630000075
Is in the initial range of [ -M, M]；
5-2-2-3-2) solving the linear shrinkage model formula (47) to obtain min/max alphaxAnd the branch switching state is xij；
5-2-2-3-3) judging the result of the step 5-2-2-3-2):
if [ alpha ] isx]The interval width is not reduced any more, then the min/max alpha obtained in the step 5-2-2-3-2)xI.e. the final solution of the linear shrinkage model to obtain alphaxTurning to the step 5-2-2-3-4) for the minimum value and the maximum value of each variable in the step; otherwise, the [ alpha ] obtained according to the step 5-2-2-3-2)x]Renewal of H, BETA, K, C1,C2Then returning to the step 5-2-2-3-2);
5-2-2-3-4) dividing the RDM interval variable according to equation (20)
Figure FDA0003349244630000076
Figure FDA0003349244630000077
Change back to interval variable
Figure FDA0003349244630000078
And finishing the interval load flow calculation.
8. The utility model provides a distribution network interval network reconfiguration model optimization device which characterized in that includes:
the deterministic network reconstruction model building module is used for building a deterministic network reconstruction model of the power distribution network;
the interval optimization model construction module is used for converting the deterministic network reconstruction model into an interval optimization model;
the deterministic model construction module is used for converting the interval optimization model into a deterministic optimization model;
the deterministic optimization model relaxation module is used for relaxing the deterministic optimization model to obtain a relaxed deterministic optimization model;
and the power distribution network interval network reconstruction optimization module is used for solving the relaxed deterministic optimization model to obtain an optimal switching result of the power distribution network branches and complete power distribution network interval network reconstruction optimization.
9. An electronic device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform the method for optimizing an inter-distribution grid network reconstruction model according to any of the preceding claims 1 to 7.
10. A computer-readable storage medium storing computer instructions for causing a computer to perform the method for optimizing an inter-distribution network reconstruction model for a power distribution network according to any one of claims 1 to 7.
CN202111332341.5A 2021-11-11 2021-11-11 Optimization method and device for power distribution network interval network reconstruction model Active CN114156867B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111332341.5A CN114156867B (en) 2021-11-11 2021-11-11 Optimization method and device for power distribution network interval network reconstruction model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111332341.5A CN114156867B (en) 2021-11-11 2021-11-11 Optimization method and device for power distribution network interval network reconstruction model

Publications (2)

Publication Number Publication Date
CN114156867A true CN114156867A (en) 2022-03-08
CN114156867B CN114156867B (en) 2024-04-09

Family

ID=80459500

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111332341.5A Active CN114156867B (en) 2021-11-11 2021-11-11 Optimization method and device for power distribution network interval network reconstruction model

Country Status (1)

Country Link
CN (1) CN114156867B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114865602A (en) * 2022-05-05 2022-08-05 国网安徽省电力有限公司 5G communication and improved DTW-based power distribution network differential protection algorithm

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108173265A (en) * 2018-01-15 2018-06-15 国网吉林省电力有限公司电力科学研究院 A kind of power distribution network network reconstruction method based on linearisation trend
CN112003330A (en) * 2020-09-02 2020-11-27 浙江浙能技术研究院有限公司 Adaptive control-based microgrid energy optimization scheduling method
CN113141011A (en) * 2021-04-15 2021-07-20 国家电网有限公司 Power distribution network interval load flow calculation method based on relative distance measure
CN113364054A (en) * 2021-05-17 2021-09-07 国家电网有限公司 Power distribution network interval network reconstruction model optimization method based on second-order cone relaxation method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108173265A (en) * 2018-01-15 2018-06-15 国网吉林省电力有限公司电力科学研究院 A kind of power distribution network network reconstruction method based on linearisation trend
CN112003330A (en) * 2020-09-02 2020-11-27 浙江浙能技术研究院有限公司 Adaptive control-based microgrid energy optimization scheduling method
CN113141011A (en) * 2021-04-15 2021-07-20 国家电网有限公司 Power distribution network interval load flow calculation method based on relative distance measure
CN113364054A (en) * 2021-05-17 2021-09-07 国家电网有限公司 Power distribution network interval network reconstruction model optimization method based on second-order cone relaxation method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
徐俊俊;吴在军;周力;成晟;李培帅;窦晓波;: "考虑分布式电源不确定性的配电网鲁棒动态重构", 中国电机工程学报, no. 16, 8 April 2018 (2018-04-08), pages 87 - 97 *
胡伟;杨梓俊;荆江平;吴奕;韩锋;汪超群;: "计及N-1安全和网络重构的配网可开放容量评估", 现代电力, no. 02, 10 April 2020 (2020-04-10), pages 19 - 26 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114865602A (en) * 2022-05-05 2022-08-05 国网安徽省电力有限公司 5G communication and improved DTW-based power distribution network differential protection algorithm

Also Published As

Publication number Publication date
CN114156867B (en) 2024-04-09

Similar Documents

Publication Publication Date Title
Christakou et al. AC OPF in radial distribution networks–Part I: On the limits of the branch flow convexification and the alternating direction method of multipliers
Zhao et al. Coordinated restoration of transmission and distribution system using decentralized scheme
Nassar et al. A novel load flow algorithm for islanded AC/DC hybrid microgrids
US8849614B2 (en) System and method for monitoring and managing three-phase power flows in electrical transmission and distribution networks
KR20220100975A (en) Cable route planning methods, systems, media and electronic devices in wind farms
Sur et al. A modified holomorphic embedding method based hybrid AC-DC microgrid load flow
CN114156867A (en) Power distribution network interval network reconstruction model optimization method and device
CN114140022A (en) Multi-virtual power plant distributed dynamic economic dispatching method and system
CN114597902B (en) Power flow calculation method of power system based on convex relaxation
Khatua et al. Voltage stability enhancement using VSC-OPF including wind farms based on Genetic algorithm
Pan et al. Load flow calculation for droop‐controlled islanded microgrids based on direct Newton–Raphson method with step size optimisation
CN110556854B (en) Static harmonic analysis method and system for grid-connected point of flexible direct-current power transmission system
Yang et al. An MILP-based optimal power and gas flow in electricity-gas coupled networks
da Silva et al. Tabu search applied to transmission expansion planning considering losses and interruption costs
Li et al. A multi-objective stochastic-information gap decision model for soft open points planning considering power fluctuation and growth uncertainty
Gatta et al. Analytical methods for series compensation of a transmission line
Hagh et al. Load flow analysis of radial and weakly meshed distribution systems including distributed generations
CN113364054A (en) Power distribution network interval network reconstruction model optimization method based on second-order cone relaxation method
Fenu et al. Evaluating complex network indices for vulnerability analysis of a territorial power grid
CN116093995B (en) Multi-target network reconstruction method and system for power distribution system
Chagas et al. The sequential power flow in complex plane for solving the VSC-MTDC hybrid AC/DC transmission grids
Huang et al. A review on applications of holomorphic embedding methods
Yun et al. Warm‐start piecewise linear approximation‐based solution for load pick‐up problem in electrical distribution system
Yuan et al. Power Flow Analysis Using Graph based Combination of Iterative Methods and Vertex Contraction Approach
Rifai et al. Load Flow Analysis In Radial Distribution Network Using Backward/Forward Sweep 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