CN110474368B - Sequential layered optimization method for recovery of black start network elements of DG (distributed generation) auxiliary power distribution network - Google Patents

Sequential layered optimization method for recovery of black start network elements of DG (distributed generation) auxiliary power distribution network Download PDF

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CN110474368B
CN110474368B CN201910729857.XA CN201910729857A CN110474368B CN 110474368 B CN110474368 B CN 110474368B CN 201910729857 A CN201910729857 A CN 201910729857A CN 110474368 B CN110474368 B CN 110474368B
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朱炜锋
毛晓明
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Guangdong University of Technology
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

The invention provides a sequential layered optimization method for recovery of black start network elements of a DG auxiliary power distribution network, which comprises the following steps: acquiring system parameters; establishing an optimization model objective function, and minimizing power failure loss; setting power standby constraint and determining relevant parameters of an optimization model; performing outer layer optimization by taking the DG and load recovery sequence as optimization variables to obtain a line recovery sequence initial solution; performing inner-layer optimization by taking the initial solution of the line recovery sequence as an optimization variable to obtain the optimal recovery sequence of the line with the minimum power failure loss; and alternately optimizing the DG, the load and the line recovery sequence, outputting the variable DG, the load and line optimal recovery sequence and the corresponding power failure loss, and finishing the layered optimization of the recovery sequence. The hierarchical optimization method provided by the invention reduces the complexity and the calculated amount of an optimization model by utilizing the thought of hierarchical optimization; and obtaining a line recovery sequence initial solution according to the DG and the load recovery sequence so as to improve the optimization effect, improve the optimization efficiency and avoid a large number of infeasible solutions.

Description

Sequential layered optimization method for black start network element recovery of DG (distributed generation) auxiliary power distribution network
Technical Field
The invention relates to the technical field of black start of power systems, in particular to a layered optimization method for a black start network element recovery sequence of a DG auxiliary power distribution network.
Background
The black start of the power system generally adopts a mode of recovering the main network frame firstly and then recovering the power distribution network, and the load power failure time is longer. In recent years, a large amount of Distributed Generation (DG) is connected to a power distribution network in a multipoint and Distributed mode, the DG has the advantages of short starting time, high response speed, flexible control mode and the like, the DG is used for assisting black start of the power distribution network, the load power failure time can be greatly reduced, and a new way is provided for the black start of the power distribution network.
Because the output of the DG at the initial stage of black start recovery may not meet the power requirements of all loads, it is necessary to optimize the recovery sequence of DG, load and line in order to speed up the recovery process and reduce the power loss. From the mathematical perspective, when the DG assists in black start of the power distribution network, the DG, load and line recovery sequence optimization problem is a mixed integer nonlinear programming problem with high variable dimension and complex constraint, and the solving difficulty is high.
At present, the industry often uses the restoration sequence of DG, load and line as optimization variables, and adopts an intelligent optimization algorithm to directly solve through random search, which has the following disadvantages: 1) The optimization variables are more, and the optimization efficiency is low; 2) The element recovery order is directly optimized in a random search mode, and an infeasible solution which does not meet the requirement of network connectivity often occurs in the optimization process.
Disclosure of Invention
The invention provides a layered optimization method for a recovery sequence of elements of a DG auxiliary power distribution network, which aims to overcome the technical defects that the existing intelligent optimization algorithm is adopted to optimize the recovery sequence of the elements of the network, the optimization variables are more, the optimization efficiency is low, and the infeasible solution which does not meet the requirement of network connectivity often occurs.
In order to solve the technical problems, the technical scheme of the invention is as follows:
the DG auxiliary power distribution network black start network element recovery sequence layered optimization method comprises the following steps:
s1: acquiring parameters of a power distribution system;
s2: establishing an optimization model objective function, and minimizing power failure loss;
s3: setting system power standby constraint and determining relevant parameters of an optimization model;
s4: performing outer layer optimization by taking the DG and load recovery sequence as an optimization variable to obtain a line recovery sequence initial solution meeting the network connectivity requirement;
s5: performing inner-layer optimization by taking the obtained initial solution of the line recovery sequence as an optimization variable to obtain an optimal line recovery sequence and judging whether the iteration times are reached, if so, executing the step S6; if not, executing the step S4 and continuing iterative computation;
s6: and outputting the variable DG of the optimal line recovery sequence with the minimum power failure loss, the load, the optimal line recovery sequence and the corresponding power failure loss, and finishing the layered optimization of the network element recovery sequence.
The parameters of the power distribution system obtained in the step S1 include a DG type, a maximum output, a start time, a start power and a climbing capability; line parameters; type, size and unit outage loss of node load.
In step S2, the optimization model objective function is specifically expressed as:
Figure GDA0003910476360000011
Figure GDA0003910476360000012
Figure GDA0003910476360000021
in the formula: minF represents minimum loss of power outage; n is a radical of hydrogen G 、N L And N BR Respectively representing DG, load and line number; the value range of the elements in X is [1, N ] G +N L ]The numerical value represents DG and load recovery sequence; the value range of the element in Y is [1, N ] BR ]The numerical value represents the line restoration sequence; p is L,i Power (kW) representing load i; f. of i (t L,i ) The unit power failure loss (yuan/kW) of the load i and the power failure time t of the load i are shown L,i Related to the load type; t is t L,i Related to X and Y.
In step S3, the system power backup constraint is specifically expressed as:
Figure GDA0003910476360000022
in the formula, P G,j,t And P Gcost,j,t Respectively representing the output and the service power of the unit j at the time t; α represents the system reserve power percentage;
the determining of the relevant parameters of the optimization model comprises the following steps: allowing simultaneous recovery of a maximum number of lines k brmax (ii) a Time for line restoration T br (ii) a The model solves the time step delta t; black start recovery investigation time T max (ii) a Outer layer iterationMaximum number of maxgen X Inner layer iteration maximum number maxgen Y (ii) a Number N of neighbor solutions in outer layer and inner layer k 、N l 。
Wherein, in the step S4, the outer layer optimization specifically includes the following steps:
s41: randomly generating a DG, a load recovery sequence X best Let the current optimal objective function F best ＝10 9 Current number of outer iterations gen X ＝1；
S42: for X by means of tabu search algorithm best Performing a neighborhood search to generate N k Each neighbor solution X neighbor Enabling an outer-layer neighbor solution pointer k =0;
s43: let k = k +1, take the kth neighbor solution X neighbor (k)；
S44: according to the restoration sequence of DG and load, self-adaptively obtaining line restoration sequence Y best 。
Wherein, the step S44 specifically includes the following steps:
s441: let the current time t =0, record the DG recovery time with black start capability as t;
s442: calculating the shortest recovery path l of the next DG or load to be recovered according to the recovery sequence of the DG and the load by means of a Dijkstra algorithm min,i And a minimum recovery time t min,i ；
S443: if t + t min,i <T max Step S444 is performed; if not, recording the recovery time of all the unrecovered lines as T max Executing the step S5;
s444: if it is determined to be (t + t) min,i ) Is the next DG or load recovered at that time, is the system power reserve constraint met? If yes, recording the current time and the element recovery time as (t + t) min,i ) (ii) a Otherwise, judging that the signal is (t + t) min,i + Δ t) whether the system power reserve constraint is satisfied? If yes, recording the current time and the element recovery time as (t + t) min,i + Δ t), otherwise consider the next time step again; if at T max The power standby constraint of the system is not satisfied at all times, and the recovery time of the current unrecovered element is recordedIs T max And executes step S447; (ii) a
S445: according to the line connection relation and the line recovery time T br Recording the recovery path l min,i The recovery time of each section of line;
s446: in the recovery process of the element, if the number of the input lines in each time step delta t is less than k brmax In the process, the line on the recovery path through which the subsequent element to be recovered passes is recovered, and the recovery time of the recovered line is recorded;
s447: if the recovery time of all lines or the recovery time of all DGs and loads are determined, executing step S5; otherwise, step S442 is executed.
Wherein the inner layer optimization in the step S5 specifically includes the steps of:
s501: for Y by means of tabu search algorithm best Performing a neighborhood search to generate N l Each neighbor solution Y neighbor Let the inner-layer neighbor solution pointer l =0;
s502: let l = l +1, take the l-th neighbor solution Y neighbor (l)；
S503: let t =0, record the DG recovery time with black start capability as t;
s504: t = T + Δ T, if T = T max Step S509 is executed; otherwise, determine if all lines have been restored at time t? If yes, go to step S507;
s505: judging time t, Y neighbor (l) Is the bus at one end of the next line to be restored? If yes, recording the line recovery time as t; otherwise, step S509 is executed;
s506: is it determined whether the line recovered at time t is maximized? If not, executing step S505;
s507: determine whether the next DG to be restored or the bus on which the load is located has already restored power supply and meets the system power backup constraint at time t? If yes, recording the recovery time of the element as t; otherwise, executing step S504;
s508: is it determined whether all DG and the restoration time of the load are recorded? If yes, go to step S510; otherwise, executing step S507;
s509: recording the recovery time of all current unrecovered DGs and loads as T max ；
S510: calculating the power failure loss F (X) neighbor (k),Y neighbor (l) If F (X) neighbor (k),Y neighbor (l))<F best Then F is best ＝F(X neighbor (k),Y neighbor (l))，X best ＝X neighbor (k)，Y best ＝Y neighbor (l)；
S511: if l is not equal to N l Step S502 is executed; otherwise, go to step S512;
S512：gen Y ＝gen Y +1 if gen Y ≤maxgen Y If yes, go to step S501; otherwise, step S513 is executed;
s513: if k is not equal to N k Executing step S43, otherwise, executing step S514;
S514：gen X ＝gen X +1 if gen X ≤maxgen X Executing step S42; otherwise, output X best ，Y best ，F(X best ,Y best )。
In the scheme, the method provided by the invention is divided into two layers, wherein the outer layer takes the restoration sequence of DG and load as an optimization variable, and the inner layer takes the restoration sequence of a line as an optimization variable; the outer layer provides a line recovery sequence initial solution meeting the network connectivity requirement for the inner layer, and the inner layer searches for an optimal line recovery sequence by taking the minimum power failure loss as a target; and alternately and iteratively updating the outer layer and the inner layer through a tabu search algorithm, and finally obtaining a recovery scheme with the minimum power failure loss.
In the scheme, the outer layer realizes iterative update of a DG and load recovery sequence, and the inner layer realizes iterative update of a line recovery sequence, so that the variable dimension can be reduced, and the solution space is reduced; the steps S441 to S447 are to adaptively restore the line according to the known DG and load restoration sequence, coordinate the relationship between the outer layer variable and the inner layer variable, and are the key to realize the hierarchical optimization; and S5, optimizing to obtain the optimal line recovery sequence according to the DG, the load recovery sequence and the line self-adaptive recovery sequence given by the outer layer, and greatly reducing the generation probability of infeasible solutions.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the DG auxiliary power distribution network black start network element recovery sequence layered optimization method provided by the invention reduces the complexity and the calculated amount of an optimization model by using the idea of layered optimization; according to the known DG and load recovery sequence, the initial solution of the line recovery sequence meeting the network connectivity requirement is obtained in advance, so that the optimization effect is improved, the optimization efficiency is improved, and a large number of infeasible solutions are avoided.
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FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a diagram of a model of a 69-node power distribution system including DGs.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
As shown in fig. 1, the DG auxiliary power distribution network black start network element recovery sequence layered optimization method includes the following steps:
s1: acquiring parameters of a power distribution system;
s2: establishing an optimization model objective function, and minimizing power failure loss;
s3: setting system power standby constraint and determining relevant parameters of an optimization model;
s4: performing outer layer optimization by taking the DG and load recovery sequence as an optimization variable to obtain a line recovery sequence initial solution meeting the network connectivity requirement;
s5: performing inner-layer optimization by taking the obtained initial solution of the line recovery sequence as an optimization variable to obtain an optimal line recovery sequence and judging whether the iteration times are reached, if so, executing the step S6; if not, executing the step S4 and continuing iterative computation;
s6: and outputting the variable DG of the optimal line recovery sequence with the minimum power failure loss, the load, the optimal line recovery sequence and the corresponding power failure loss, and finishing the layered optimization of the network element recovery sequence.
More specifically, the power distribution system parameters obtained in step S1 include a DG type, a maximum output, a start time, a start power, and a climbing capability; line parameters; the type, size and unit outage loss of node load.
More specifically, in step S2, the optimization model objective function is specifically expressed as:
Figure GDA0003910476360000041
Figure GDA0003910476360000042
Figure GDA0003910476360000051
in the formula: minF represents minimized loss of power outage; n is a radical of G 、N L And N BR Respectively representing DG, load and line number; the value range of the element in X is [1, N ] G +N L ]The numerical value represents DG and load recovery sequence; the value range of the element in Y is [1, N ] BR ]The numerical value represents the line restoration sequence; p is L,i Power (kW) representing load i; f. of i (t L,i ) The unit power failure loss (yuan/kW) of the load i and the power failure time t of the load i are shown L,i Related to the load type; t is t L,i Related to X and Y.
More specifically, in step S3, the system power backup constraint is specifically expressed as:
Figure GDA0003910476360000052
in the formula, P G,j,t And P Gcost,j,t Respectively representing the output and the service power of the unit j at the moment t; α represents the system reserve power percentage;
the determining of relevant parameters of the optimization model comprises the following steps: allowing simultaneous recovery of maximum number of lines k brmax (ii) a Line recovery time T br (ii) a The model solves the time step delta t; black start recovery time of investigation T max (ii) a Maximum number of outer iterations maxgen X Inner layer maximum number of iterations maxgen Y (ii) a Number N of neighbor solutions in outer layer and inner layer k 、N l 。
More specifically, the outer layer optimization in step S4 specifically includes the following steps:
s41: randomly generating a DG, a load recovery sequence X best Let the current optimal objective function F best ＝10 9 Current number of outer iterations gen X ＝1；
S42: by tabu search algorithm, for X best Performing a neighborhood search to generate N k Neighbor solution X neighbor And let the outer neighbor solution pointer k =0;
s43: let k = k +1, take the kth neighbor solution X neighbor (k)；
S44: according to the restoration sequence of DG and load, self-adaptively obtaining line restoration sequence Y best 。
More specifically, the step S44 specifically includes the following steps:
s441: let the current time t =0, record the DG recovery time with black start capability as t;
s442: calculating the shortest recovery path l of the next DG or load to be recovered according to the recovery sequence of the DG and the load by means of a Dijkstra algorithm min,i And a minimum recovery time t min,i ；
S443: if t + t min,i <T max Step S444 is performed; if not, all the current nodes areThe recovery time of the unrecovered line is recorded as T max Executing the step S5;
s444: if it is determined that (t + t) min,i ) Is the next DG or load recovered at a time, is the system power reserve constraint met? If yes, recording the current time and the element recovery time as (t + t) min,i ) (ii) a Otherwise, judging that the signal is (t + t) min,i + Δ t) whether the system power reserve constraint is met? If yes, recording the current time and the element recovery time as (t + t) min,i + Δ t), otherwise the next time step is considered; if at T max The power standby constraint of the system is not satisfied at all times, and the recovery time of the current unrecovered element is recorded as T max And executes step S447;
s445: according to the line connection relation and the line recovery time T br Recording the recovery path l min,i The recovery time of each section of line;
s446: in the recovery process of the element, if the number of the input lines in each time step delta t is less than k brmax In the process, the line on the recovery path through which the subsequent element to be recovered passes is recovered, and the recovery time of the recovered line is recorded;
s447: if the recovery time of all lines or the recovery time of all DGs and loads are determined, executing step S5; otherwise, step S442 is executed.
More specifically, the inner layer optimization in step S5 specifically includes the following steps:
s501: for Y by means of tabu search algorithm best Performing a neighborhood search to generate N l Neighbor solution Y neighbor Let the inner-layer neighbor solution pointer l =0;
s502: let l = l +1, take the l-th neighbor solution Y neighbor (l)；
S503: let t =0, record the DG recovery time with black start capability as t;
s504: t = T + Δ T, if T = T max Step S509 is executed; otherwise, determine if all lines have been restored at time t? If yes, go to step S507;
s505: determine time t, Y neighbor (l) In the next placeIs the bus at one end of the line to be restored? If yes, recording the line recovery time as t; otherwise, step S509 is executed;
s506: is it determined whether the line recovered at time t is maximized? If not, executing step S505;
s507: determine whether the next DG to be restored or the bus on which the load is located has already restored power supply and meets the system power backup constraint at time t? If yes, recording the recovery time of the element as t; otherwise, executing step S504;
s508: is it determined whether all DG and the restoration time of the load are recorded? If yes, go to step S510; otherwise, go to step S507;
s509: recording the recovery time of all current unrecovered DGs and loads as T max ；
S510: calculating the power failure loss F (X) neighbor (k),Y neighbor (l) If F (X) neighbor (k),Y neighbor (l))<F best Then F is best ＝F(X neighbor (k),Y neighbor (l))，X best ＝X neighbor (k)，Y best ＝Y neighbor (l)；
S511: if l is not equal to N l Step S502 is executed; otherwise, go to step S512;
S512：gen Y ＝gen Y +1 if gen Y ≤maxgen Y Then, go to step S501; otherwise, step S513 is executed;
s513: if k is not equal to N k Executing step S43, otherwise, executing step S514;
S514：gen X ＝gen X +1 if gen X ≤maxgen X Executing step S42; otherwise, output X best ，Y best ，F(X best ,Y best )。
In the specific implementation process, the method provided by the invention is divided into two layers, wherein the outer layer takes the restoration sequence of DG and load as an optimization variable, and the inner layer takes the restoration sequence of a line as an optimization variable; the outer layer provides a line recovery sequence initial solution meeting the network connectivity requirement for the inner layer, and the inner layer searches the optimal recovery sequence of the line by taking the minimum power failure loss as a target; and alternately and iteratively updating the outer layer and the inner layer through a tabu search algorithm, and finally obtaining a recovery scheme with the minimum power failure loss.
In a specific implementation process, the outer layer realizes iterative update of DG and load recovery sequences, and the inner layer realizes iterative update of line recovery sequences, so that the variable dimension can be reduced, and the solution space can be reduced; the steps S441 to S447 are to adaptively restore the line according to the known DG and load restoration sequence, coordinate the relationship between the outer layer variable and the inner layer variable, and are the key to realize the hierarchical optimization; and S5, optimizing to obtain the optimal line recovery sequence according to the DG, the load recovery sequence and the line self-adaptive recovery sequence given by the outer layer, and greatly reducing the generation probability of infeasible solutions.
Example 2
More specifically, based on embodiment 1, as shown in fig. 2, a 69-node power distribution system including a DG is taken as an example, and the DG connected to the node 15 is a black-start power supply with a self-start capability; the DG type and parameters are shown in appendix 1, and the system load and line parameters are shown in appendix 2.
In the specific implementation process, the unit power failure loss of the five typical electrical loads shown in the table 1 in various power failure times is adopted to calculate the power failure loss of the power distribution system in the black start recovery process.
TABLE 1 subscriber blackout loss classification (Yuan/kW)
Figure GDA0003910476360000071
In the process of calculating the power failure loss, if the power failure time is between two time intervals, the corresponding function value can be obtained by utilizing linear interpolation.
In the implementation process, the maximum number k of lines is allowed to be recovered simultaneously brmax =4; system standby power percentage α =10%; time for line restoration T br =1min; model solving time step delta T =1min, and black start recovery investigation time T max =60min; maximum number of outer iterations maxgen X =200, inner layer stackMaximum number of generations maxgen Y =100; number of outer and inner neighbor solutions N k ＝40、N l =40. According to the steps of the invention, the restoration sequence of DG, load and line and the corresponding power failure loss are obtained as shown in Table 2:
TABLE 2 results of the calculation of the present invention
Figure GDA0003910476360000072
Figure GDA0003910476360000081
In the specific implementation process, under the same calculation conditions, the calculation of the above-mentioned calculation example is performed by using the existing direct optimization method, and the results are shown in table 3:
TABLE 3 direct optimization calculation
Figure GDA0003910476360000091
Figure GDA0003910476360000101
By comparing table 2 and table 3, the calculation efficiency of the method of the invention is improved by 60.58% and the calculation result is optimized by 1.2% compared with the direct optimization method.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. This need not be, nor should it be exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Attached table 1 DG power output characteristics in 69 node power distribution system
Figure GDA0003910476360000102
Figure GDA0003910476360000111
Attached table 2 node power distribution system load and line parameters
Figure GDA0003910476360000112
Figure GDA0003910476360000121
Figure GDA0003910476360000131
Figure GDA0003910476360000141
Figure GDA0003910476360000151
Figure GDA0003910476360000161
Regarding the selection of the load type: in order to make the test system have certain representativeness, the invention mainly considers the following five types of loads: the important loads of residence, government, business, small industry and industry are respectively corresponding to the load types 1-5 in the table.

Claims (3)

  1. A DG auxiliary power distribution network black start network element recovery sequence layered optimization method is characterized by comprising the following steps:
    s1: acquiring parameters of a power distribution system; the parameters of the power distribution system obtained in the step S1 comprise DG type, maximum output, starting time, starting power and climbing capacity; line parameters; the type and size of node load and unit power failure loss;
    s2: establishing an optimization model objective function, and minimizing power failure loss;
    the optimization model objective function is specifically expressed as:
    Figure FDA0003910476350000011
    Figure FDA0003910476350000012
    Figure FDA0003910476350000013
    in the formula: minF represents minimum loss of power outage; n is a radical of hydrogen G 、N L And N BR Respectively representing DG, load and line number; the value range of the elements in X is [1, N ] G +N L ]The numerical value represents DG and load recovery sequence; the value range of the element in Y is [1, N ] BR ]The numerical value represents the line restoration sequence; p is L,i Represents the power of load i in kW; f. of i (t L,i ) Represents the unit power failure loss of the load i, f i Is t L,i In units of yuan/kW, and the power-off time t of the load i L,i Related to the load type; t is t L,i Related to X, Y;
    s3: setting system power standby constraint and determining relevant parameters of an optimization model;
    the system power reserve constraints are specifically expressed as:
    Figure FDA0003910476350000014
    in the formula, P G,j,t And P Gcost,j,t Respectively representing the output and the service power of the unit j at the moment t; p is L,i,t Indicating the magnitude of the load i at time t; α represents the system reserve power percentage;
    the determining of relevant parameters of the optimization model comprises the following steps: allowing simultaneous recovery of a maximum number of lines k brmax (ii) a Line recovery time T br (ii) a The model solves the time step delta t; black start recovery time of investigation T max (ii) a Maximum number of outer iterations maxgen X Inner layer maximum number of iterations maxgen Y (ii) a Number N of outer-layer and inner-layer neighbor solutions k 、N l ；
    S4: performing outer layer optimization by taking the DG and load recovery sequence as an optimization variable to obtain a line recovery sequence initial solution meeting the network connectivity requirement;
    the outer layer optimization specifically comprises the following steps:
    s41: randomly generating a DG, a load recovery sequence X best Let the current optimal objective function F best ＝10 9 Current number of outer iterations gen X ＝1；
    S42: for X by means of tabu search algorithm best Performing a neighborhood search to generate N k Neighbor solution X neighbor And let the outer neighbor solution pointer k =0;
    s43: let k = k +1, take the kth neighbor solution X neighbor (k)；
    S44: according to the restoration sequence of DG and load, self-adaptively obtaining line restoration sequence Y best ；
    S5: performing inner layer optimization by taking the obtained initial solution of the line recovery sequence as an optimization variable to obtain an optimal line recovery sequence and judging whether the iteration times are reached, if so, executing the step S6; if not, executing the step S4 and continuing iterative computation;
    s6: and outputting the variable DG of the optimal line recovery sequence with the minimum power failure loss, the load, the optimal line recovery sequence and the corresponding power failure loss, and finishing the layered optimization of the network element recovery sequence.
  2. 2. The DG auxiliary power distribution network black start network element recovery sequence layered optimization method of claim 1, wherein: the step S44 specifically includes the following steps:
    s441: let the current time t =0, record the DG recovery time with black start capability as t;
    s442: calculating the shortest recovery path l of the next DG or load to be recovered according to the recovery sequence of the DG and the load by means of a Dijkstra algorithm min,i And a minimum recovery time t min,i ；
    S443: if t + t min,i <T max Step S444 is performed; if not, recording the recovery time of all the unrecovered lines as T max Executing the step S5;
    s444: if it is determined that (t + t) min,i ) Is the next DG or load recovered at a time, is the system power reserve constraint met? If yes, recording the current time and the element recovery time as (t + t) min,i ) (ii) a Otherwise, judging that the signal is (t + t) min,i + Δ t) whether the system power reserve constraint is satisfied? If yes, recording the current time and the element recovery time as (t + t) min,i + Δ t), otherwise the next time step is considered; if at T max The standby constraint of system power is still not satisfied at all times, and the recovery time of the current unrecovered element is recorded as T max And executes step S447;
    s445: according to the line connection relation and the line recovery time T br Recording the recovery path l min,i The recovery time of each section of line;
    s446: in the recovery process of the element, if the number of the input lines in each time step delta t is less than k brmax In the process, the line on the recovery path of the subsequent element to be recovered is recovered, and the recovery time of the recovered line is recorded;
    s447: if the recovery time of all lines or the recovery time of all DGs and loads has been determined, step S5 is executed, otherwise, step S442 is executed.
  3. 3. The DG auxiliary power distribution network black start network element recovery sequence layered optimization method of claim 2, wherein: the inner layer optimization in step S5 specifically includes the following steps:
    s501: by means of tabu search algorithm, for Y best Performing a neighborhood search to generate N l Neighbor solution Y neighbor Let the inner-layer neighbor solution pointer l =0;
    s502: let l = l +1, take the l-th neighbor solution Y neighbor (l)；
    S503: let t =0, record the DG recovery time with black start capability as t;
    s504: t = T + Δ T, if T = T max Step S509 is executed; otherwise, determine if all lines have been restored at time t? If yes, go to step S507;
    s505: judging time t, Y neighbor (l) Is the bus at one end of the next line to be restored? If yes, recording the line recovery time as t; otherwise, step S509 is executed;
    s506: is it determined whether the line recovered at time t is maximized? If not, executing step S505;
    s507: determine whether the next DG to be restored or the bus on which the load is located has already restored power supply and meets the system power backup constraint at time t? If yes, recording the recovery time of the element as t; otherwise, executing step S504;
    s508: is it determined whether all DG and the restoration time of the load are recorded? If yes, go to step S510; otherwise, go to step S507;
    s509: recording the recovery time of all current unrecovered DGs and loads as T max ；
    S510: calculating the power failure loss F (X) neighbor (k),Y neighbor (l) If F (X) neighbor (k),Y neighbor (l))<F best Then F is best ＝F(X neighbor (k),Y neighbor (l))，X best ＝X neighbor (k)，Y best ＝Y neighbor (l)；
    S511: if l is not equal to N l Step S502 is executed; otherwise, go to step S512;
    S512：gen Y ＝gen Y +1 if gen Y ≤maxgen Y Then, go to step S501; otherwise, go to step S513;
    s513: if k is not equal to N k Executing step S43, otherwise, executing step S514;
    S514：gen X ＝gen X +1 if gen X ≤maxgen X Executing step S42; otherwise, output X best ，Y best ，F(X best ,Y best )。
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Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101158987A (en) * 2007-11-15 2008-04-09 浙江大学 Nesting optimizing method for solving heat exchange network integration
CN103280817A (en) * 2013-05-24 2013-09-04 武汉大学 Reactive balance area determining method based on tabu search
CN104573882A (en) * 2015-02-11 2015-04-29 上海宝钢节能环保技术有限公司 Comprehensive optimization method for clean circulating cooling water system based on hierarchical nested algorithm
CN105337278A (en) * 2015-12-14 2016-02-17 国网浙江省电力公司 Method for network reconstruction double-layer optimization based on node importance evaluation matrix
CN107017622A (en) * 2017-04-12 2017-08-04 长沙理工大学 The multiple faults multiple target of distribution containing DG combined optimization repairing recovery policy is asked for
CN107862405A (en) * 2017-10-27 2018-03-30 广东电网有限责任公司电力调度控制中心 The power system rack reconstruction and optimization method of meter and microgrid as black starting-up power supply
CN109286204A (en) * 2018-11-21 2019-01-29 广东电网有限责任公司 A kind of distribution network black starting-up reconstructing method based on minimum expectation loss of outage
CN109659974A (en) * 2018-12-12 2019-04-19 广东电网有限责任公司 A kind of heuristic distribution network black starting-up reconstructing method based on TABU search
CN109995028A (en) * 2019-04-09 2019-07-09 国网山东省电力公司济南供电公司 A kind of Distribution Network Failure restoration methods and system based on optimal recovery sequence

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB201218342D0 (en) * 2012-10-12 2012-11-28 Univ Leuven Kath Method and system for distributing and/or controlling an energy flow taking into account constraints relating to the electricity network

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101158987A (en) * 2007-11-15 2008-04-09 浙江大学 Nesting optimizing method for solving heat exchange network integration
CN103280817A (en) * 2013-05-24 2013-09-04 武汉大学 Reactive balance area determining method based on tabu search
CN104573882A (en) * 2015-02-11 2015-04-29 上海宝钢节能环保技术有限公司 Comprehensive optimization method for clean circulating cooling water system based on hierarchical nested algorithm
CN105337278A (en) * 2015-12-14 2016-02-17 国网浙江省电力公司 Method for network reconstruction double-layer optimization based on node importance evaluation matrix
CN107017622A (en) * 2017-04-12 2017-08-04 长沙理工大学 The multiple faults multiple target of distribution containing DG combined optimization repairing recovery policy is asked for
CN107862405A (en) * 2017-10-27 2018-03-30 广东电网有限责任公司电力调度控制中心 The power system rack reconstruction and optimization method of meter and microgrid as black starting-up power supply
CN109286204A (en) * 2018-11-21 2019-01-29 广东电网有限责任公司 A kind of distribution network black starting-up reconstructing method based on minimum expectation loss of outage
CN109659974A (en) * 2018-12-12 2019-04-19 广东电网有限责任公司 A kind of heuristic distribution network black starting-up reconstructing method based on TABU search
CN109995028A (en) * 2019-04-09 2019-07-09 国网山东省电力公司济南供电公司 A kind of Distribution Network Failure restoration methods and system based on optimal recovery sequence

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Optimal allocation of dynamic VAR sources using zoning-based distributed optimization algorithm;Xiaoming Mao et al.;《Electrical Power and Energy Systems》;20190621;第113卷;952-962 *
电力系统网络重构的多目标双层优化策略;张璨等;《电力系统自动化》;20140410;第38卷(第10期);29-38 *

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