CN112952823A - Low-voltage power distribution network fault recovery method for distributed power supply output uncertainty - Google Patents

Low-voltage power distribution network fault recovery method for distributed power supply output uncertainty Download PDF

Info

Publication number
CN112952823A
CN112952823A CN202110320982.2A CN202110320982A CN112952823A CN 112952823 A CN112952823 A CN 112952823A CN 202110320982 A CN202110320982 A CN 202110320982A CN 112952823 A CN112952823 A CN 112952823A
Authority
CN
China
Prior art keywords
node
power
fault recovery
distribution network
power supply
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110320982.2A
Other languages
Chinese (zh)
Inventor
徐长宝
肖辅盛
马文磊
张辉
冯义
周详
徐宏伟
周强
晋斌
李鹏程
孟悦恒
王雷
戴雯菊
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.)
Guizhou Power Grid Co Ltd
Original Assignee
Guizhou Power Grid 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 Guizhou Power Grid Co Ltd filed Critical Guizhou Power Grid Co Ltd
Priority to CN202110320982.2A priority Critical patent/CN112952823A/en
Publication of CN112952823A publication Critical patent/CN112952823A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures
    • 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]

Landscapes

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

Abstract

The invention discloses a low-voltage power distribution network fault recovery method for uncertainty of distributed power output, which comprises the steps of analyzing uncertainty of distributed power output based on an affine theory, and establishing a power distribution network fault recovery model taking maximum power loss load recovery as a target function and system operation feasibility as a constraint condition; converting the original quadratic term constraint into a linear solvable form according to a piecewise linear approximation idea, and decomposing the converted fault recovery model into a main problem and a sub problem by using a decomposition algorithm for iterative solution; and modifying the IEEE33 node power distribution test system and carrying out simulation analysis on the fault recovery model based on distributed power access. After the original fault recovery method is improved, under the condition that the output power of the distributed power supply is in the strongest fluctuation, the method can recover more power-losing loads and effectively resist the influence of uncertainty of the distributed power supply on the final fault recovery decision.

Description

Low-voltage power distribution network fault recovery method for distributed power supply output uncertainty
Technical Field
The invention relates to the technical field of low-voltage power distribution network fault recovery, in particular to a low-voltage power distribution network fault recovery method with uncertain distributed power output.
Background
The low-voltage distribution network fault recovery module is used as a core function of distribution automation, and power supply can be quickly and effectively recovered to a power loss load area of a power distribution network after the power distribution network fails through network local reconstruction operation after the power distribution network fails, so that system power failure loss is reduced.
In recent years, the accuracy and effectiveness of a traditional power distribution network fault recovery scheme are affected to a certain degree by high-penetration access of distributed power supplies such as photovoltaic power and wind power, the influence of system uncertainty on a recovery strategy cannot be reduced by the traditional deterministic fault recovery method, and the problem that the feasibility and effectiveness of the power distribution network fault recovery scheme under the condition of high-penetration distributed power supply access are to be solved urgently in power distribution network operation control is solved.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides a low-voltage distribution network fault recovery method with uncertain distributed power supply output, which can solve the problem that the feasibility and effectiveness of a distribution network fault recovery scheme under the condition of high-permeability distributed power supply access influence the operation control of a distribution network.
In order to solve the technical problems, the invention provides the following technical scheme: analyzing the uncertain output quantity of the distributed power supply based on an affine theory, and establishing a power distribution network fault recovery model taking the maximum power loss load recovery as a target function and the system operation feasibility as a constraint condition; converting the original quadratic term constraint into a linear solvable form according to a piecewise linear approximation idea, and decomposing the converted fault recovery model into a main problem and a sub problem by using a decomposition algorithm for iterative solution; and modifying the IEEE33 node power distribution test system and carrying out simulation analysis on the fault recovery model based on distributed power access.
The invention relates to a preferable scheme of a low-voltage distribution network fault recovery method with uncertainty of distributed power supply output, wherein the method comprises the following steps: establishing the fault recovery model may include,
obj.
Figure BDA0002992979060000021
Figure BDA0002992979060000022
where Ω is the topological solution set that satisfies the radial constraint, LoutA system power-loss load node set is obtained;
Figure BDA0002992979060000023
the active load of the power loss node i is expressed in the form of affine number,
Figure BDA0002992979060000026
for a rated power value, Δ X is the maximum deviation from the predicted value, ε ∈ [ -1, +1]For uncertainty disturbance factor, epsiloni,LTo cause the load node i to inject a perturbation factor, ε, of uncertain poweri,GIn order to cause uncertain disturbance factors of the injection power of a distributed power supply node i, delta is an uncertain set of the injection power of the node, ik is a branch in the network with i as a head end node and k as a tail end node, and alphaikAnd betaikAll represent the state information of the switches on the branch ik, where { α }ik,βik0 means that the switch on branch ik is in the off state, whereas αik,βik1 represents that the switch on the branch ik is in a closed state, etaiFor determining whether node i has recovered its power supply, ηi1 indicates that node i has regained power supply, and ηiAnd 0 indicates that the power supply of the node i is not recovered.
The invention relates to a preferable scheme of a low-voltage distribution network fault recovery method with uncertainty of distributed power supply output, wherein the method comprises the following steps: the constraint conditions comprise node voltage injection power balance, branch circuit capacity constraint, node voltage upper and lower limit constraint and radial network operation constraint; the node voltage injection power is balanced as follows,
Figure BDA0002992979060000024
wherein, PikAnd QikActive and reactive power, r, respectively, at the head end of the branch ikikAnd xikThe set theta (k) is the head end node set of the branch circuit taking the node k as the end node in the network, and the set gamma (k) is the tail end node set of the branch circuit taking the node k as the head end node, UiIs the voltage amplitude of node i, PkAnd QkRespectively the net injection amount of active power and reactive power of the node k.
The invention relates to a preferable scheme of a low-voltage distribution network fault recovery method with uncertainty of distributed power supply output, wherein the method comprises the following steps: the branch capacity constraints include the number of branch capacity constraints,
Figure BDA0002992979060000025
wherein the content of the first and second substances,
Figure BDA0002992979060000031
the maximum active power and the maximum reactive power allowed to flow through the branch ik are respectively; the node voltage upper and lower bound constraints include,
Figure BDA0002992979060000032
wherein the content of the first and second substances,U i＝0.95(p.u.)，
Figure BDA0002992979060000033
the radial network operational constraints include that,
Figure BDA0002992979060000034
wherein, betaik1 denotes that node k is the parent node of node i, βik0 denotes that node k is a child node of node i, and n (i) denotes a set of all nodes connected to node i.
The invention relates to a preferable scheme of a low-voltage distribution network fault recovery method with uncertainty of distributed power supply output, wherein the method comprises the following steps: the transformation comprises that a quadratic function form of the fault recovery model utilizes a linear approximation curve based on the piecewise linear approximation idea to enable the quadratic function to carry out linear approximation Hua processing for the first time,
Figure BDA0002992979060000035
wherein M, N is the total cross section number of the active power and reactive power secondary term of the branch ik after the piecewise linearization respectively,
Figure BDA0002992979060000036
are the slopes of the corresponding functions of the section t,
Figure BDA0002992979060000037
are respectively (P)ik)2、(Qik)2The value of the corresponding function on the section t.
The invention relates to a preferable scheme of a low-voltage distribution network fault recovery method with uncertainty of distributed power supply output, wherein the method comprises the following steps: the iterative solution comprises the steps of defining initial upper and lower boundary values as LB ═ infinity and UB +∞respectively, determining the iterative times k of the algorithm as 0, and determining the convergence criterion as ζ; the original problem is decomposed into the main problem and the sub-problems according to the C & CG algorithm, as follows,
obj:
Figure BDA0002992979060000041
Figure BDA0002992979060000042
wherein the content of the first and second substances,
Figure BDA0002992979060000043
respectively representing the active power and the reactive power of the distributed power supply under the strongest fluctuation scene obtained by the sub-problem solution,
Figure BDA0002992979060000044
respectively representing the active power and the reactive power of the load under the strongest fluctuation scene obtained by solving the subproblems, and obtaining the optimization result of the main problem when the algorithm is solved to the (k + 1) th time
Figure BDA0002992979060000045
And the optimal target value F of the main problemMMore, moreLower bound LB ═ LB, F for the new whole modelM}。
The invention relates to a preferable scheme of a low-voltage distribution network fault recovery method with uncertainty of distributed power supply output, wherein the method comprises the following steps: given an optimization result a according to the main problemik、βikSolving the sub-problem, including,
obj:
Figure BDA0002992979060000046
Figure BDA0002992979060000047
Figure BDA0002992979060000051
Figure BDA0002992979060000052
wherein, the above formula is dual problem constraint and relaxation condition constraint added in the sub-problem solving process, and when the k +1 th time is reached, the optimization result of the sub-problem is obtained
Figure BDA0002992979060000053
And the optimal target value F of the sub-problemSPUpdating the upper bound UB { UB, F } of the whole modelSP}。
The invention relates to a preferable scheme of a low-voltage distribution network fault recovery method with uncertainty of distributed power supply output, wherein the method comprises the following steps: judging whether the upper and lower bounds output by the fault recovery model meet UB-LB ≤ η, if so, finishing the algorithm iteration and outputting the current optimal fault recovery scheme, otherwise, adding the dual problem constraint and the relaxation constraint condition to the main problem, and continuing the iteration until the condition UB-LB ≤ η is met;
Figure BDA0002992979060000054
the invention relates to a preferable scheme of a low-voltage distribution network fault recovery method with uncertainty of distributed power supply output, wherein the method comprises the following steps: the simulation analysis comprises the steps that photovoltaic power stations with the rated capacity of 320kW are connected to the 18 nodes and the 25 nodes, and the maximum deviation of the relative predicted value of each photovoltaic power station is 25 kW; fan power stations with the rated capacity of 355kW are connected to the 22 node and the 33 node, and the maximum deviation of the relative predicted value of each fan power station is 40 kW; each feeder line in the network is provided with a section switch, and the initial state is set to be a closed state; the junctor switches added between two nodes in the network comprise 7-21, 24-28 and 14-31, and the initial states are all disconnected; and writing a fault recovery algorithm program based on a Matlab R2017b simulation platform by combining a PC with a main frequency of 3.5GHz and a memory of 32GB, and installing a YALMIP mixed integer optimization tool box in Matlab software to solve the linear optimization problem.
The invention has the beneficial effects that: after the original fault recovery method is improved, compared with the existing fault recovery method, the distributed power source recovery method has obvious advantages under the condition that the output power of the distributed power source is in the strongest fluctuation, can recover more power-losing loads, and effectively resists the influence of the uncertainty of the distributed power source on the final fault recovery decision.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic diagram of a solving flow of a power distribution network fault recovery model based on a C & CG algorithm of a low-voltage power distribution network fault recovery method for distributed power supply output uncertainty according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a modified IEEE33 node system of a method for recovering from a fault in a low-voltage distribution network with distributed power output uncertainty according to an embodiment of the present invention;
fig. 3 is a schematic diagram of the results of 100N-1 fault scans in the method for recovering a low-voltage distribution network from distributed power source output uncertainty according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1, a first embodiment of the present invention provides a method for recovering a fault of a low-voltage distribution network with distributed power output uncertainty, including:
s1: analyzing the output uncertainty of the distributed power supply based on the affine theory, and establishing a power distribution network fault recovery model taking the maximum power loss load recovery as a target function and the system operation feasibility as a constraint condition. It should be noted that establishing the fault recovery model includes:
obj.
Figure BDA0002992979060000071
Figure BDA0002992979060000072
where Ω is the topological solution set that satisfies the radial constraint, LoutFor system power-off load node set
Figure BDA0002992979060000073
To form a table in the form of affine numbersThe active load of the power-down node i is shown,
Figure BDA0002992979060000074
for a rated power value, Δ X is the maximum deviation from the predicted value, ε ∈ [ -1, +1]For uncertainty disturbance factor, epsiloni,LTo cause the load node i to inject a perturbation factor, ε, of uncertain poweri,GIn order to cause uncertain disturbance factors of the injection power of a distributed power supply node i, delta is an uncertain set of the injection power of the node, ik is a branch in the network with i as a head end node and k as a tail end node, and alphaikAnd betaikAll represent the state information of the switches on the branch ik, where { α }ik,βik0 means that the switch on branch ik is in the off state, whereas αik,βik1 represents that the switch on the branch ik is in a closed state, etaiFor determining whether node i has recovered its power supply, ηi1 indicates that node i has regained power supply, and ηiAnd 0 indicates that the power supply of the node i is not recovered.
Further, the constraint conditions include:
node voltage injection power balance, branch capacity constraint, node voltage upper and lower limit constraint and radial network operation constraint;
the node voltage injection power is balanced as follows,
Figure BDA0002992979060000081
wherein, PikAnd QikActive and reactive power, r, respectively, at the head end of the branch ikikAnd xikThe set theta (k) is the head end node set of the branch circuit taking the node k as the end node in the network, and the set gamma (k) is the tail end node set of the branch circuit taking the node k as the head end node, UiIs the voltage amplitude of node i, PkAnd QkRespectively the net injection amount of active power and reactive power of the node k;
the branch capacity constraints include the number of branches,
Figure BDA0002992979060000082
wherein the content of the first and second substances,
Figure BDA0002992979060000083
the maximum active power and the maximum reactive power allowed to flow through the branch ik are respectively;
the constraints on the upper and lower limits of the node voltage include,
Figure BDA0002992979060000084
wherein the content of the first and second substances,U i＝0.95(p.u.)，
Figure BDA0002992979060000085
the radial network operational constraints include,
Figure BDA0002992979060000091
wherein, betaik1 denotes that node k is the parent node of node i, βik0 denotes that node k is a child node of node i, and n (i) denotes a set of all nodes connected to node i.
S2: and converting the original quadratic term constraint into a linear solvable form according to a piecewise linear approximation idea, and decomposing the converted fault recovery model into a main problem and a subproblem by using a decomposition algorithm for iterative solution. In this step, it should be noted that the transformation includes:
based on the idea of piecewise linear approximation, the quadratic function form of the fault recovery model utilizes a linear approximation curve to make the quadratic function perform linear approximation Hua processing for the first time,
Figure BDA0002992979060000092
wherein M, N are the active power of branch ikThe total section number of the quadratic terms of the rate and the reactive power after the piecewise linearization,
Figure BDA0002992979060000093
are the slopes of the corresponding functions of the section t,
Figure BDA0002992979060000094
are respectively (P)ik)2、(Qik)2The value of the corresponding function on the section t;
further, the iterative solution includes:
defining initial upper and lower limit values as LB ═ infinity and UB ∞ infinity respectively, the iteration number k of the algorithm is 0, and the convergence criterion is ζ;
the original problem is decomposed into a main problem and a sub problem according to the C & CG algorithm, as follows,
obj:
Figure BDA0002992979060000101
Figure BDA0002992979060000102
wherein the content of the first and second substances,
Figure BDA0002992979060000103
respectively representing the active power and the reactive power of the distributed power supply under the strongest fluctuation scene obtained by the sub-problem solution,
Figure BDA0002992979060000104
respectively representing the active power and the reactive power of the load under the strongest fluctuation scene obtained by solving the subproblems, and obtaining the optimization result of the main problem when the algorithm is solved to the (k + 1) th time
Figure BDA0002992979060000105
And the optimal target value F of the main problemMAnd updating the lower bound value LB ═ LB, F of the whole modelM}；
In particular, given according to the main problemOptimization result alphaik、βikThe sub-problem is solved by a method comprising,
obj:
Figure BDA0002992979060000106
Figure BDA0002992979060000107
Figure BDA0002992979060000111
Figure BDA0002992979060000112
wherein, the above formula is dual problem constraint and relaxation condition constraint added in the sub-problem solving process, and when the k +1 th time is reached, the optimization result of the sub-problem is obtained
Figure BDA0002992979060000113
And the optimal target value F of the sub-problemSPUpdating the upper bound UB { UB, F } of the whole modelSP}；
Judging whether the upper and lower bounds output by the fault recovery model meet UB-LB ≤ eta, if so, finishing the algorithm iteration, and outputting the current optimal fault recovery scheme, otherwise, adding dual problem constraint and relaxation constraint conditions into the main problem, and continuing the iteration until the condition UB-LB is satisfied ≤ eta;
Figure BDA0002992979060000114
s3: the IEEE33 node power distribution test system is modified and a simulation analysis is performed on a fault recovery model based on distributed power access. It should be further noted that the simulation analysis includes:
photovoltaic power stations with rated capacity of 320kW are connected to the 18 nodes and the 25 nodes, and the maximum deviation of the relative predicted value of each photovoltaic power station is 25 kW;
fan power stations with the rated capacity of 355kW are connected to the 22 node and the 33 node, and the maximum deviation of the relative predicted value of each fan power station is 40 kW;
each feeder line in the network is provided with a section switch, and the initial state is set to be a closed state;
the junctor switches added between two nodes in the network comprise 7-21, 24-28 and 14-31, and the initial states are all disconnected;
and writing a fault recovery algorithm program based on a Matlab R2017b simulation platform by combining a PC with a main frequency of 3.5GHz and a memory of 32GB, and installing a YALMIP mixed integer optimization tool box in Matlab software to solve the linear optimization problem.
Generally speaking, a power distribution network is located at the tail end of a power system, is directly oriented to vast power users, and is a key link for guaranteeing the power supply quality and the power quality of users, however, in recent years, along with the high penetration access of distributed power sources such as photovoltaic power sources and wind power sources, the accuracy and the effectiveness of a traditional power distribution network fault recovery scheme are affected to a certain extent.
Preferably, a power distribution network fault recovery module in the traditional power distribution network fault recovery method is used as a core function of power distribution automation, and power supply can be quickly and effectively recovered to a power loss load area of a power distribution network after a fault occurs through network local reconstruction operation after the power distribution network fails, so that system power failure loss is reduced; the power distribution network fault recovery model provided by the invention considers the access of a distributed power supply in a low-voltage power distribution network, expresses all power in the network fault recovery model by affine numbers, searches the worst fluctuation scene in a given system injection power uncertain range, and provides an optimal fault recovery method.
Preferably, the method of the invention considers the relevant work of the power distribution network fault recovery of the uncertainty of the distributed power supply output, analyzes the uncertainty of the distributed power supply output through the affine theory, and establishes a power distribution network fault recovery model taking the maximum power loss load recovery as a target function and the system operation reliability as a constraint condition on the basis; in order to accurately solve the fault recovery model, original quadratic term constraint is converted into a linear solvable form based on a piecewise linear approximation idea, and meanwhile, a column-and-constraints generation (C & CG) algorithm is adopted to decompose the converted fault recovery model into a main problem and a sub problem for iterative solution; the verification of the example simulation method shows that compared with the existing deterministic fault recovery method, the fault recovery method can recover more power-losing loads under the severe fluctuation scene of the distributed power supply and has higher engineering application value.
Example 2
Referring to fig. 2, in order to verify the advantage of the power distribution network fault recovery method provided by the invention in the aspect of resisting system uncertainty compared with the existing deterministic fault recovery method, a modified IEEE33 node test system is preset that a short-circuit fault occurs in a branch 9-10, and the fault is isolated and then loses power supply for a load of 1062 kW; based on the fault scene, the fault recovery method and the existing deterministic fault recovery method are respectively adopted to carry out load power supply recovery, in order to analyze the influence of the uncertainty of the distributed power supply power on the system power supply recovery result, two power fluctuation conditions are designed based on the test scene to carry out power supply recovery comparison, and the two power fluctuation conditions are respectively as follows: no fluctuation condition: the output power of the distributed power supply is kept at a rated value under the condition of the fluctuation; ② worst fluctuation situation: under the fluctuation condition, all input power of the photovoltaic power station and the fan power station is the fluctuation upper limit of the photovoltaic power station and the fan power station.
Table 1: and comparing the results of deterministic fault recovery and robust fault recovery.
Figure BDA0002992979060000131
Referring to table 1, it can be seen intuitively that, in the case of no fluctuation of the output power of the distributed power supply, although the switch open/close given by the method of the present invention is different from that given by the existing fault recovery method, the system load recovery power supply based on the two fault recovery methods is the same, when the output power of the distributed power supply is in the strongest fluctuation condition, the method of the present invention has an obvious advantage compared with the existing fault recovery method, because the voltage levels of some heavy load feeders are obviously reduced under the fluctuation condition, at this time, the reconstruction strategy given by the existing deterministic fault recovery method makes the partial voltage in the network no longer satisfy the safety constraint, the operation mode of the current fault recovery strategy must be satisfied by performing appropriate load shedding processing, and the resulting load loss recovery amount is less than the load loss amount generated by the method of the present invention.
Preferably, the embodiment further adopts an N-1 fault branch scanning detection method to verify the effectiveness of the power supply recovery strategy of the power distribution network containing uncertain interference of node injection power, and assuming that each fault scanning process is performed under the condition that the fluctuation of the node injection power is the strongest, and only one branch in the network has a ground short circuit fault, then a relay protection device in the power distribution automation system can effectively and timely isolate the fault and start to implement power supply recovery of a load.
Preferably, the simulation is performed with 100N-1 fault scanning detections, the load loss caused by the fault after each scanning test and the load recovery results of the two fault recovery methods are shown in fig. 3, and the specific statistical results are shown in the following table.
Table 2: two failure recovery results are shown in the table for 100N-1 scans.
Recovery method Number of failures Success rate Total load recovery (kW)
Methods of the invention 0 100％ 39.58
Existing methods 13 87％ 27.93
Referring to table 2, the probability of success recovery of the method of the present invention is 100%, while the probability of success recovery of the power loss load by the existing deterministic fault recovery method is only 87%, and the overall effect of the method of the present invention is better in terms of the total load recovery amount, so that the method of the present invention has certain advantages in terms of system uncertainty resistance compared with the existing deterministic fault recovery method.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.

Claims (9)

1. A low-voltage distribution network fault recovery method with uncertain distributed power supply output is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
analyzing the output uncertainty of the distributed power supply based on an affine theory, and establishing a power distribution network fault recovery model with the maximum loss load recovery as a target function and the system operation feasibility as a constraint condition;
converting the original quadratic term constraint into a linear solvable form according to a piecewise linear approximation idea, and decomposing the converted fault recovery model into a main problem and a sub problem by using a decomposition algorithm for iterative solution;
and modifying the IEEE33 node power distribution test system and carrying out simulation analysis on the fault recovery model based on distributed power access.
2. The method of claim 1 for fault recovery in a low voltage distribution network of distributed power output uncertainty, wherein: establishing the fault recovery model may include,
Figure FDA0002992979050000011
Figure FDA0002992979050000012
where Ω is the topological solution set that satisfies the radial constraint, LoutA system power-loss load node set is obtained;
Figure FDA0002992979050000013
the active load of the power loss node i is expressed in the form of affine number,
Figure FDA0002992979050000014
for a rated power value, Δ X is the maximum deviation from the predicted value, ε ∈ [ -1, +1]For uncertainty disturbance factor, epsiloni,LTo cause the load node i to inject a perturbation factor, ε, of uncertain poweri,GIn order to cause uncertain disturbance factors of the injection power of a distributed power supply node i, delta is an uncertain set of the injection power of the node, ik is a branch in the network with i as a head end node and k as a tail end node, and alphaikAnd betaikAll represent the state information of the switches on the branch ik, where { α }ik,βik0 means that the switch on branch ik is in the off state, whereas αik,βik1 denotes a branchik upper switch in closed state, etaiFor determining whether node i has recovered its power supply, ηi1 indicates that node i has regained power supply, and ηiAnd 0 indicates that the power supply of the node i is not recovered.
3. The method for recovering from a fault in a low-voltage distribution network of distributed power supply uncertainty according to claim 1 or 2, characterized in that: the constraint conditions comprise node voltage injection power balance, branch circuit capacity constraint, node voltage upper and lower limit constraint and radial network operation constraint;
the node voltage injection power is balanced as follows,
Figure FDA0002992979050000015
wherein, PikAnd QikActive and reactive power, r, respectively, at the head end of the branch ikikAnd xikThe set theta (k) is the head end node set of the branch circuit taking the node k as the end node in the network, and the set gamma (k) is the tail end node set of the branch circuit taking the node k as the head end node, UiIs the voltage amplitude of node i, PkAnd QkRespectively the net injection amount of active power and reactive power of the node k.
4. The method of claim 3 for fault recovery in a low voltage distribution network of distributed power output uncertainty, wherein: the branch capacity constraints include the number of branch capacity constraints,
Figure FDA0002992979050000021
wherein the content of the first and second substances,
Figure FDA0002992979050000022
the maximum active power and the maximum reactive power allowed to flow through the branch ik are respectively;
the node voltage upper and lower bound constraints include,
Figure FDA0002992979050000023
wherein, Ui＝0.95(p.u.)，
Figure FDA0002992979050000024
The radial network operational constraints include that,
Figure FDA0002992979050000025
wherein, betaik1 denotes that node k is the parent node of node i, βik0 denotes that node k is a child node of node i, and n (i) denotes a set of all nodes connected to node i.
5. The method for recovering from a fault in a low-voltage distribution network of distributed power supply uncertainty according to claim 1 or 4, characterized in that: the transformation comprises the steps of (1) transforming,
based on the piecewise linear approximation idea, the quadratic function form of the fault recovery model utilizes a linear approximation curve to make the quadratic function perform linear approximation Hua processing for the first time,
Figure FDA0002992979050000026
wherein M, N is the total cross section number of the active power and reactive power secondary term of the branch ik after the piecewise linearization respectively,
Figure FDA0002992979050000027
are the slopes of the corresponding functions of the section t,
Figure FDA0002992979050000028
are respectively (P)ik)2、(Qik)2The value of the corresponding function on the section t.
6. The method of claim 5 for fault recovery in a low voltage distribution network of distributed power output uncertainty, wherein: the iterative solution includes the iterative solution including a solution of,
defining initial upper and lower limit values as LB ═ infinity and UB ∞ infinity respectively, the iteration number k of the algorithm is 0, and the convergence criterion is ζ;
the original problem is decomposed into the main problem and the sub-problems according to the C & CG algorithm, as follows,
Figure FDA0002992979050000031
Figure FDA0002992979050000032
wherein the content of the first and second substances,
Figure FDA0002992979050000033
respectively representing the active power and the reactive power of the distributed power supply under the strongest fluctuation scene obtained by the sub-problem solution,
Figure FDA0002992979050000034
respectively representing the active power and the reactive power of the load under the strongest fluctuation scene obtained by solving the subproblems, and obtaining the optimization result of the main problem when the algorithm is solved to the (k + 1) th time
Figure FDA0002992979050000035
And the optimal target value F of the main problemMAnd updating the lower bound value LB ═ LB, F of the whole modelM}。
7. The distributed power supply of claim 6 having an uncertainty outputThe qualitative low-voltage distribution network fault recovery method is characterized by comprising the following steps: given an optimization result a according to the main problemik、βikSolving the sub-problem, including,
Figure FDA0002992979050000041
Figure FDA0002992979050000042
Figure FDA0002992979050000043
Figure FDA0002992979050000044
wherein, the above formula is dual problem constraint and relaxation condition constraint added in the sub-problem solving process, and when the k +1 th time is reached, the optimization result of the sub-problem is obtained
Figure FDA0002992979050000045
And the optimal target value F of the sub-problemSPUpdating the upper bound UB { UB, F } of the whole modelSP}。
8. The method of claim 7 for fault recovery in a low voltage distribution network of distributed power output uncertainty, wherein: also comprises the following steps of (1) preparing,
judging whether the upper and lower bounds output by the fault recovery model meet UB-LB ≤ η, if yes, finishing the algorithm iteration, and outputting the current optimal fault recovery scheme, otherwise, adding the dual problem constraint and the relaxation constraint condition to the main problem, and continuing the iteration until the condition UB-LB ≤ η is met;
Figure FDA0002992979050000051
9. the method of claim 8 for fault recovery in a low voltage distribution network of distributed power output uncertainty, wherein: the simulation analysis includes the steps of,
photovoltaic power stations with rated capacity of 320kW are connected to the 18 nodes and the 25 nodes, and the maximum deviation of the relative predicted value of each photovoltaic power station is 25 kW;
fan power stations with the rated capacity of 355kW are connected to the 22 node and the 33 node, and the maximum deviation of the relative predicted value of each fan power station is 40 kW;
each feeder line in the network is provided with a section switch, and the initial state is set to be a closed state;
the junctor switches added between two nodes in the network comprise 7-21, 24-28 and 14-31, and the initial states are all disconnected;
and writing a fault recovery algorithm program based on a Matlab R2017b simulation platform by combining a PC with a main frequency of 3.5GHz and a memory of 32GB, and installing a YALMIP mixed integer optimization tool box in Matlab software to solve the linear optimization problem.
CN202110320982.2A 2021-03-25 2021-03-25 Low-voltage power distribution network fault recovery method for distributed power supply output uncertainty Pending CN112952823A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110320982.2A CN112952823A (en) 2021-03-25 2021-03-25 Low-voltage power distribution network fault recovery method for distributed power supply output uncertainty

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110320982.2A CN112952823A (en) 2021-03-25 2021-03-25 Low-voltage power distribution network fault recovery method for distributed power supply output uncertainty

Publications (1)

Publication Number Publication Date
CN112952823A true CN112952823A (en) 2021-06-11

Family

ID=76228095

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110320982.2A Pending CN112952823A (en) 2021-03-25 2021-03-25 Low-voltage power distribution network fault recovery method for distributed power supply output uncertainty

Country Status (1)

Country Link
CN (1) CN112952823A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114665479A (en) * 2022-05-24 2022-06-24 武汉大学 Power distribution network power supply recovery method and system considering network reconfiguration

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110676838A (en) * 2019-08-27 2020-01-10 华北电力大学 Fault self-adaptive robust optimization recovery method for flexible power distribution system
CN111478358A (en) * 2019-11-06 2020-07-31 南瑞集团有限公司 Power distribution network robust recovery decision method considering distributed power supply uncertainty

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110676838A (en) * 2019-08-27 2020-01-10 华北电力大学 Fault self-adaptive robust optimization recovery method for flexible power distribution system
CN111478358A (en) * 2019-11-06 2020-07-31 南瑞集团有限公司 Power distribution network robust recovery decision method considering distributed power supply uncertainty

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
刘刚等: "含分布式电源接入的配电网故障恢复方法", 《电测与仪表》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114665479A (en) * 2022-05-24 2022-06-24 武汉大学 Power distribution network power supply recovery method and system considering network reconfiguration
CN114665479B (en) * 2022-05-24 2022-08-30 武汉大学 Power distribution network power supply recovery method and system considering network reconfiguration

Similar Documents

Publication Publication Date Title
Bernabeu et al. Methodology for a security/dependability adaptive protection scheme based on data mining
Bahadoorsingh et al. Minimization of voltage sag costs by optimal reconfiguration of distribution network using genetic algorithms
CN111478358A (en) Power distribution network robust recovery decision method considering distributed power supply uncertainty
CN109004679B (en) Renewable energy source accessed power grid safety and stability monitoring method
Xie et al. Reliability-oriented networking planning for meshed VSC-HVDC grids
CN107123988A (en) One kind considers that the uncertain power failure network load of amount of recovery recovers Second-order cone programming method
CN112952823A (en) Low-voltage power distribution network fault recovery method for distributed power supply output uncertainty
Guo et al. Risk‐based many‐objective configuration of power system fault current limiters utilising NSGA‐III
Wang et al. Distributed Newton‐based voltage control method for high‐penetration PV generation cluster in active distribution networks
Li et al. Optimization method of skeleton network partitioning scheme considering resilience active improvement in power system restoration after typhoon passes through
Li et al. Probabilistic load flow calculation with Latin hypercube sampling applied to grid-connected induction wind power system
CN113346484B (en) Power distribution network elastic lifting method and system considering transient uncertainty
Ahmed et al. Probabilistic load flow analysis for large scale radial distribution systems
Song et al. Short-circuit analysis of grid-connected PV power plants considering inverter limits
CN109932617B (en) Self-adaptive power grid fault diagnosis method based on deep learning
CN112688313A (en) Active power distribution network reliability assessment method based on matrix modeling
Lotfi et al. An enhanced evolutionary algorithm for providing energy management schedule in the smart distribution network
CN115495986A (en) Self-healing distributed network reconstruction method for power distribution network
Cai et al. Multistage bilevel planning model of energy storage system in urban power grid considering network reconfiguration
CN112886597B (en) Radial power distribution network affine power flow analytic calculation method considering new energy output uncertainty
Hatziargyriou et al. On-line dynamic security assessment of power systems in large islands with high wind power penetration
Zhang et al. Multi-time scale adaptive reactive power and voltage control of distribution network based on random forest algorithm
Zhang et al. Robust optimization of the active distribution network involving risk assessment
Chen et al. The Isolated Grid Black-Start Scheme Evaluation Method Based on DEA/TOPSIS
Alvarez et al. Multi-year stochastic transmission network expansion planning considering line uprating

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