CN116244937A - Method and system for identifying key element of main gateway facing extreme event - Google Patents

Method and system for identifying key element of main gateway facing extreme event Download PDF

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CN116244937A
CN116244937A CN202310140334.8A CN202310140334A CN116244937A CN 116244937 A CN116244937 A CN 116244937A CN 202310140334 A CN202310140334 A CN 202310140334A CN 116244937 A CN116244937 A CN 116244937A
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赵龙
杨立超
刘海涛
田鑫
杨思
高效海
杨斌
王男
张丽娜
魏佳
魏鑫
邱轩宇
张玉跃
袁振华
马力
程佩芬
孟祥飞
李淑杨
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Abstract

The invention discloses a method and a system for identifying a main gateway key element facing an extreme event, and mainly relates to the technical field of extreme event prediction. The method comprises the following steps: based on the load importance degree and the remedial measures of the power system for coping with the extreme events, taking the maximum value of the system loss of the power failure caused by the extreme events as a first objective function; modeling the operation of the power system by using a direct current power flow model and establishing element constraints; based on a first objective function, power system operation modeling and element constraint, establishing an upper layer of robust model and a lower layer of robust model, and converting the two layers of robust models into main problems and sub-problems; solving the main problem and the sub-problem and outputting a key element combination in the power system; the method for determining the importance degree ordering of each element in the element combination through the repair sequence of the elements has the advantages that: the method solves the problem that the Monte Carlo simulation method cannot generate corresponding scenes due to small occurrence probability of events, and simultaneously identifies key element combinations in the power transmission network.

Description

Method and system for identifying key element of main gateway facing extreme event
Technical Field
The invention relates to the technical field of extreme event prediction, in particular to a method and a system for identifying a main gateway key element facing an extreme event.
Background
At present, extreme events such as global climate change, natural disasters and the like frequently occur in small probability high risk events, and the like, so that huge threat is generated to the safety of a power grid, and the social and economic development is seriously influenced.
The electric power system can become a main energy source of society, the safety and stability of the electric power system directly affect the continuous and stable growth of national economy, and the electric power safety is a key ring in national energy safety. The traditional power system reliability index is only aimed at the conditions of large probability and small loss time, such as N-1 or N-2, and cannot cope with the attack of an extreme event, so that the establishment of an elastic power system with high recovery force, which can cope with the extreme event, is urgent, and has great significance for guaranteeing the power supply of users and the national energy safety.
When facing extreme events, the elastic power system can prepare and prevent the power system before the extreme events, resist, absorb, respond and adapt to the negative influence of the extreme events on the power system during the event, and the system functions can be quickly restored to normal level after the extreme events. Therefore, the construction of the elastic power system is performed in three stages, i.e., in advance, in the middle of the event, and after the event, and many students have studied the restoration force of the power system. In the existing research, before an extreme event occurs, the power system evaluates the restoring force of the power system according to indexes such as load loss rate, robustness and restoring speed, the positions of elements such as a distributed generator, an energy storage, a transformer substation and the like are optimized, the capability of the system for maintaining the connectivity of the system is improved, parts of elements are strengthened in advance, the resistance of the system to the extreme event is improved, the failure rate of the elements in the event is reduced, and the robustness of the system is improved. In addition, aiming at predictable disasters such as typhoons, heavy rainfall and the like, the power system can also schedule flexible resources such as mobile energy storage vehicles, maintenance teams and the like in advance so as to prepare for a subsequent recovery process. After an extreme event, the power system can optimize flexible resources such as a dispatching maintenance team, a distributed generator, a renewable energy source, energy storage, an electric vehicle and the like to improve recovery efficiency, so that the power system is quickly recovered to a normal level. Whether the prior site selection and reinforcement or the subsequent maintenance planning and arrangement are carried out, the importance of the elements is required to be considered, the power system can only strengthen the key elements of the system in advance due to limited resources, the key elements in the system can be repaired preferentially in the subsequent recovery process, the key elements in the system are identified, the method has guiding significance for the prior reinforcement and the subsequent recovery, the method is the basis for constructing an elastic power system, the existing element identification method mainly carries out a large number of simulations through a Monte Carlo method, then samples to generate scenes, and the elements in the system are evaluated according to the methods such as PageRa-nk, copeland sequencing, and the like, so that the key elements are identified.
However, the existing key element identification method is performed based on a monte carlo simulation method, the generation of a scene is related to the occurrence probability of an event, the extreme event is a small probability event, the monte carlo simulation method is difficult or even impossible to generate a scene under the extreme event, the lack of the scene can cause the identified key element to be not necessarily a real key element of the system, and therefore, how to identify the whole scene is a problem to be solved urgently. In addition, the importance of multiple element combinations may be greater than the sum of the importance of individual elements, so it is also significant to identify key element combinations.
Disclosure of Invention
The invention aims to provide a main gateway key element identification method facing extreme events, which solves the problem that a Monte Carlo simulation method cannot generate corresponding scenes due to low occurrence probability of the events, and identifies key element combinations in a power transmission network.
The invention aims to achieve the aim, and the aim is achieved by the following technical scheme:
the method comprises the following steps:
s1: based on the load importance degree and the remedial measures of the power system for coping with the extreme events, taking the maximum value of the system loss of the power failure caused by the extreme events as a first objective function;
s2: modeling the operation of the power system by using a direct current power flow model and establishing element constraints;
s3: based on a first objective function, power system operation modeling and element constraint, establishing an upper layer of robust model and a lower layer of robust model, and converting the two layers of robust models into main problems and sub-problems;
s4: solving the main problem and the sub-problem and outputting a key element combination in the power system;
s5: the importance ranking of each element in the element combination is determined by the repair order of the elements.
Preferably, the first objective function is:
Figure SMS_1
wherein N is l For line set, N g For the generator set, N b For the combination of points, W is the weight of the importance of the load, P shed,j Representing the cut load of node j, v is a 0/1 variable, W gen Is the start-stop variable of the generator.
Preferably, the step S2 specifically includes: modeling the operation of the power system by using a direct current power flow model, and describing the states of system elements by introducing 0/1 variables, wherein the specific form is as follows:
Figure SMS_2
Figure SMS_3
Figure SMS_4
Figure SMS_5
Figure SMS_6
Figure SMS_7
wherein pi (j) is a line set starting from node j, delta (j) is a line set ending from node j, and P l For active power flowing on line l, P G,j For generator output at node j, P L,j For the load of node j, B ij For susceptance of the line between node i and node j,
Figure SMS_8
for the capacity of line l, q l In the state of line l, θ i Representing the phase angle of node i, +.>
Figure SMS_9
And->
Figure SMS_10
The minimum output and the maximum output of the generator g are respectively;
the element constraint establishment is specifically as follows: the built model considers the component damage and network reconstruction problems, and uses 0/1 variable, the states of the transmission network lines are:
q l ≤w l ,l∈N l (8)
q l ≤v l ,l∈N l (9)
wherein the variables v and w represent the damaged state and the reconstructed state of the line respectively, the equation (8) represents that the line cannot transmit power after the human exits, and the equation (9) represents that the line cannot transmit power when the line is damaged.
Preferably, the formula (3) is a nonlinear constraint, and the linearization constraint is performed by using a large M method, and the specific expression is as follows:
Figure SMS_11
Figure SMS_12
Figure SMS_13
Figure SMS_14
where M is a significant number.
Preferably, the step S3 specifically includes: considering the influence of extreme events on a power transmission network element, taking remedial measures such as network reconstruction and the like, establishing a two-layer robust model, and searching for the worst scene in the whole scene; in the upper model, according to a reconstruction scheme made by the power system, a component damage scheme is determined, so that the loss load of the system is maximized, in the lower model, a transmission network operator uses network reconstruction to reduce the influence of component damage on the system, so that the loss load of the system is minimized, and a C & CG algorithm is used for solving the two layers of built robust models, so that the two layers of models are decomposed into main problems and sub-problems.
Preferably, the solving the main problem is specifically: the maximum system load loss is used as a main problem objective function, the decision variable is a damage scheme of a line, and the main problem calculates the damage scheme with the maximum loss, and the specific form is as follows:
obj:maxα (14)
Figure SMS_15
Figure SMS_16
Figure SMS_17
Figure SMS_18
Figure SMS_19
Figure SMS_20
Figure SMS_21
Figure SMS_22
Figure SMS_23
Figure SMS_24
Figure SMS_25
wherein beta is 1 To beta 12 Is a dual variable, n v For the number of line damages, alpha is a main problem objective function of the dual problem after the dual is taken from the inner layer problem;
the solving of the sub-problem is specifically as follows: and taking the minimum value of the system load loss amount as a sub-problem objective function, reducing the influence of element damage to the minimum through network reconstruction and generator start-stop by an operator, and calculating an optimal reconstruction strategy, wherein the specific form is as follows:
obj:minη (26)
s.t.(2)，(4)-(8)，(10)-(13)
Figure SMS_26
where η is the sub-problem objective function.
Preferably, the determining the importance ranking of each element in the element combination according to the repair sequence of the elements specifically includes: in order to order importance of elements in a combination, a mixed integer linear programming model is established to describe a post recovery process, a maintenance team is scheduled to maintain damaged elements, the maintenance sequence of the elements is determined, and elements with high importance are preferentially repaired, wherein the mixed integer linear programming model comprises a system operation constraint and a maintenance team scheduling constraint, and the system operation constraint is specifically formed as follows:
min(∑ t∈T ∑ j∈V P shed,j,t ·Δt) (28)
Figure SMS_27
Figure SMS_28
Figure SMS_29
Figure SMS_30
Figure SMS_31
Figure SMS_32
the specific form of the maintenance team scheduling constraint is as follows:
∑ j∈DN\{dp} x dp,j -∑ j∈DN\{dp} x j,dp ＝1 (35)
∑ j∈DN\{dp′} x dp′,j -∑ j∈DN\{dp′} x j,dp′ ＝-1 (36)
Figure SMS_33
Figure SMS_34
Q dp ＝1 (39)
Figure SMS_35
Figure SMS_36
Figure SMS_37
Figure SMS_38
Figure SMS_39
Figure SMS_40
Figure SMS_41
Figure SMS_42
wherein x is a maintenance path decision variable which is a 0/1 variable, dp represents a maintenance station, Q i Representing the repair order of the damaged element i, N is the number of damaged elements, AT i Rt for the time of arrival of the repair team at the damaged element i i For the repair time of element i, tt i,j For the transit time between element i and element j, τ i,t And r i,t The variables for the repair state of the element are 0/1, and epsilon is a small number.
The main gateway key element identification system for the extreme event comprises a data acquisition module, a model building module and an analysis processing module, wherein the data acquisition module is used for acquiring the importance degree of a load and the remedial measures of the power system for the extreme event, and the maximum value of the system loss caused by the power failure due to the extreme event is used as a first objective function; the model building module is used for modeling the operation of the power system by using the direct current power flow model and building element constraint, and is also used for building an upper layer of robust model and a lower layer of robust model based on a first objective function, the operation modeling of the power system and the element constraint, and converting the two layers of robust models into main problems and sub-problems; the analysis processing module is used for solving the main problem and the sub-problem and outputting a key element combination in the power system, and is also used for determining the importance ranking of each element in the element combination through the repair sequence of the elements.
Preferably, the data acquisition module, the model building module and the analysis processing module are connected through data.
Compared with the prior art, the invention has the beneficial effects that:
1. the method establishes two layers of robust models based on a direct current power flow model, considers remedial measures such as power transmission network reconstruction and the like, uses 0/1 variable to represent an element damage scheme and a line reconstruction strategy, and describes the final running state of an element. Fully considering all scenes caused by extreme events, searching for the scene with the largest loss, solving the problem that the Monte Carlo simulation method cannot generate corresponding scenes due to small occurrence probability of the events, and identifying key element combinations in a power transmission network.
2. The invention can accurately acquire the optimal element maintenance sequence and the contribution degree of each element recovery to the system load recovery in the recovery process by establishing the post-maintenance team scheduling model, and sorts the importance of each damaged element, thereby identifying the most critical element.
3. The invention solves the established two-layer robust model by using the C & CG algorithm, can obtain the worst scene possibly encountered by the system in a smaller iteration number and a shorter solving time, improves the calculation efficiency of the model on the premise of ensuring that the solved result is the global optimal solution, and provides an efficient tool for power transmission network operators.
Drawings
Fig. 1 is a flow chart of the present invention.
FIG. 2 is a schematic diagram of two layers of robust model constraints.
Fig. 3 is a robust model solution flow diagram.
FIG. 4 is a flow chart of a key element identification method.
Detailed Description
The invention will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present invention and are not intended to limit the scope of the present invention. Further, it will be understood that various changes or modifications may be made by those skilled in the art after reading the teachings of the invention, and such equivalents are intended to fall within the scope of the invention as defined herein.
As shown in fig. 1, a method for reinforcing a main network element considering renewable energy sources in extreme weather according to the present invention is implemented by the following steps:
s1: taking the importance degree of the load and the remedial measures of the power system for coping with the extreme events into consideration, wherein the maximum system loss of power failure caused by the extreme events is an objective function;
s2: modeling the operation of the power system by using a direct current power flow model, and describing the states of system elements by introducing 0/1 variables;
s3: and considering the influence of extreme events on the power transmission network element, taking remedial measures such as network reconstruction and the like, establishing a two-layer robust model, and searching for the worst scene in the full scene. In the upper model, determining an element damage scheme according to a reconstruction scheme made by the power system, so that the loss load of the system is maximized, and in the lower model, a transmission network operator uses network reconstruction to reduce the influence of element damage on the system and minimize the loss load of the system;
s4: converting the two-layer robust model into a main problem and a sub-problem by using a Column constraint generation (Column-and-Constraint Generation) algorithm, and obtaining an element damage scene with the largest loss through iterative solution, wherein the damaged element combination is a key element combination in the system;
s5: based on the damage scene of the element with the largest loss, a mixed integer linear programming model is established to describe the recovery process after that, a maintenance team is scheduled to repair the damaged element, and the importance ranking of each element in the element combination is determined through the repair sequence of the element.
The two-layer robust model established by the invention takes remedial measures such as network reconstruction and the like into account, and finds the worst scene caused by an extreme event by taking the maximum system loss as an objective function, wherein the constraints contained in the model are shown in figure 2 and comprise power balance constraint, line capacity constraint, load loss constraint, power supply output constraint, phase angle constraint and element state constraint. And solving the built mixed two-layer robust model by using a C & CG algorithm. The mathematical form of each part of the two-layer robust model is as follows:
(1) First objective function:
Figure SMS_43
wherein N is l For line set, N g For the generator set, N b For node set, W is load importance weight, P shed,j Representing the cut load of node j, v is a 0/1 variable representing whether the line is damaged, w is a reconstruction variable of the line, also a 0/1 variable if 0 represents that the line is manually taken out of operation, w gen Is the start-stop variable of the generator.
(2) Direct current power flow model:
the invention aims at the key element problems of a power transmission network, essentially belongs to planning problems, and does not need to consider reactive power and other problems, so a direct current power flow model is used for describing the system operation, and the specific form is as follows:
Figure SMS_44
Figure SMS_45
Figure SMS_46
Figure SMS_47
Figure SMS_48
Figure SMS_49
wherein pi (j) is a line set starting from node j, delta (j) is a line set ending from node j, and P l For active power flowing on line l, P G,j For generator output at node j, P L,j For the load of node j, B ij For susceptance of the line between node i and node j,
Figure SMS_50
for the capacity of line l, q l In the state of line l, θ i Representing the phase angle of node i, +.>
Figure SMS_51
And->
Figure SMS_52
Minimum output and maximum output of generator g respectivelyThe force, formula (2) is the power balance constraint, formula (3) represents the relation between the power flowing on the line and the phase angles at two ends of the line, formula (4) limits the power flowing on the line, formula (5) limits the load of the cut load to be not larger than the load of the node, formula (6) limits the output of the generator, and formula (7) limits the phase angle difference at two ends of the line.
(3) Element state constraints:
the built model considers the problems of element damage and network reconstruction, and uses 0/1 variables v and w to represent the damaged state and the reconstruction state of the line respectively, and the states of the transmission network line are as follows:
q l ≤w l ,l∈N l (8)
q l ≤v l ,l∈N l (9)
wherein the variables v and w represent the damaged state and the reconstructed state of the line respectively, the formula (8) represents that the line cannot transmit power after the human exits, the formula (9) represents that the line cannot transmit power when damaged, and q l When 0, the line l is out of operation, and when 1, the line l is normal operation.
(4) Linearization:
the built model of the invention contains nonlinear constraint (3), and a general commercial solver cannot solve the nonlinear constraint, so that the nonlinear constraint is required to be linearized by using a large M method, and the specific form after linearization is shown as follows:
Figure SMS_53
Figure SMS_54
Figure SMS_55
Figure SMS_56
where M is a significant number.
(5) Model solving:
the two-layer robust model established by the method contains a large number of 0/1 variables, the solution space is complex and the solution is difficult, so the method uses a C & CG algorithm to solve the established model, the two-layer model is decomposed into a Main Problem (MP) and a sub-problem (SP), and the problems are respectively as follows:
major problems: the objective function is that the attack effect is maximized, namely the system load loss is maximized, the decision variable is a damage scheme of the line, and the main problem calculates the damage scheme with the largest loss and transmits the damage scheme to the sub-problems. The inner layer problem is combined with the outer layer problem into a single layer after dual, and the specific form of MP is as follows:
obj:maxα (14)
Figure SMS_57
Figure SMS_58
Figure SMS_59
Figure SMS_60
Figure SMS_61
Figure SMS_62
Figure SMS_63
Figure SMS_64
Figure SMS_65
Figure SMS_66
/>
Figure SMS_67
wherein beta is 1 To beta 12 Is a dual variable, n v For the number of line damages, alpha is a main problem objective function of the dual problem after the dual is taken from the inner layer problem;
sub-problems: the sub-problem is a minimization problem, the objective function is that the system load loss is minimum, the influence of element damage is minimized by an operator through network reconstruction and generator start-stop, and the optimal reconstruction strategy is calculated. The specific form of SP is as follows:
obj:minη (26)
s.t.(2)，(4)-(8)，(10)-(13)
Figure SMS_68
where η is the sub-problem objective function.
The specific solving process is shown in fig. 2, and the solving steps are as follows:
1) Setting lb= - +, ub= ++, m=1, initializing
Figure SMS_69
And->
Figure SMS_70
2) Solving MP to obtain the optimal solution
Figure SMS_71
Updating ub=min { UB, α };
3) Will be
Figure SMS_72
Transferring to SP for solving to obtain optimal solution +.>
Figure SMS_73
Updating LB max {LB,∑P shed,j }；
4) If ub+.lb, go back to step 2), and m=m+1, will
Figure SMS_74
Passing to the MP, introducing a new set of variables at the MP and generating a new set of constraints, and outputting the result if ub=lb.
(6) Element importance ranking:
as shown in fig. 4, after solving the two layers of robust models, a key element combination in the system can be obtained, in order to order the importance of the elements in the combination, a mixed integer linear programming model description post recovery process is established, a maintenance team is scheduled to maintain the damaged elements, the maintenance sequence of the elements is determined, and the importance of the elements which are preferentially repaired is high. The specific form of the recovery model is as follows:
system operation constraints:
min(∑ t∈T ∑ j∈V P shed,j,t ·Δt) (28)
Figure SMS_75
Figure SMS_76
Figure SMS_77
Figure SMS_78
Figure SMS_79
Figure SMS_80
the maintenance team scheduling constraints:
∑ j∈DN\{dp} x dp,j -∑ j∈DN\{dp} x j,dp ＝1
(35)
∑ j∈DN\{dp′} x dp′,j -∑ j∈DN\{dp′} x j,dp′ ＝-1 (36)
Figure SMS_81
Figure SMS_82
Q dp ＝1 (39)
Figure SMS_83
Figure SMS_84
Figure SMS_85
Figure SMS_86
Figure SMS_87
Figure SMS_88
Figure SMS_89
Figure SMS_90
wherein x is a maintenance path decision variable which is a 0/1 variable, dp represents a maintenance station, Q i Representing the repair order of the damaged element i, N is the number of damaged elements, AT i Rt for the time of arrival of the repair team at the damaged element i i For the repair time of element i, tt i,j For the transit time between element i and element j, τ i,t And r i,t For the components repair state variables, 0/1, ε is a small number, equations (35) and (36) are used to ensure that the repair team will arrive at each damaged component from the repair station and finally return to the repair station, equations (37) and (38) ensure that the repair team will leave the component after repair, equations (39) and (40) are used to prevent the occurrence of unconnected subgraphs while determining the repair order of the component, equations (41) and (42) determine the time for the repair team to arrive at each damaged component, equations (43) - (46) determine the repair state of each damaged component at each time, and equation (47) is a change in the component state. And solving the maintenance team scheduling model, and obtaining the maintenance sequence of the damaged element to obtain the importance ranking of the element.

Claims (9)

1. The method for identifying the main gateway key element facing the extreme event is characterized by comprising the following steps of:
s1: based on the load importance degree and the remedial measures of the power system for coping with the extreme events, taking the maximum value of the system loss of the power failure caused by the extreme events as a first objective function;
s2: modeling the operation of the power system by using a direct current power flow model and establishing element constraints;
s3: based on a first objective function, power system operation modeling and element constraint, establishing an upper layer of robust model and a lower layer of robust model, and converting the two layers of robust models into main problems and sub-problems;
s4: solving the main problem and the sub-problem and outputting a key element combination in the power system;
s5: the importance ranking of each element in the element combination is determined by the repair order of the elements.
2. The method for identifying a primary gateway key element for an extreme event according to claim 1, wherein the first objective function is:
Figure QLYQS_1
wherein N is l For line set, N g For the generator set, N b For the combination of points, W is the weight of the importance of the load, P shed,j Representing the cut load of node j, v is a 0/1 variable, W gen Is the start-stop variable of the generator.
3. The method for identifying a primary gateway key element for an extreme event according to claim 1, wherein the step S2 specifically comprises: modeling the operation of the power system by using a direct current power flow model, and describing the states of system elements by introducing 0/1 variables, wherein the specific form is as follows:
Figure QLYQS_2
Figure QLYQS_3
Figure QLYQS_4
Figure QLYQS_5
Figure QLYQS_6
Figure QLYQS_7
wherein pi (j) is a line set starting from node j, delta (j) is a line set ending from node j, and P l For active power flowing on line l, P G,j For generator output at node j, P L,j For the load of node j, B ij For susceptance of the line between node i and node j,
Figure QLYQS_8
for the capacity of line l, q l In the state of line l, θ i Representing the phase angle of node i, +.>
Figure QLYQS_9
And
Figure QLYQS_10
the minimum output and the maximum output of the generator g are respectively;
the element constraint establishment is specifically as follows: the built model considers the component damage and network reconstruction problems, and uses 0/1 variable, the states of the transmission network lines are:
q l ≤w l ,l∈N l (8)
q l ≤v l ,l∈N l (9)
wherein the variables v and w represent the damaged and reconstructed states of the line respectively, the constraint (8) represents that the line cannot transmit power after the human exit, and the constraint (9) represents that the line cannot transmit power when damaged.
4. The method for identifying a primary gateway key element for an extreme event according to claim 3, wherein the formula (3) is a nonlinear constraint, and the linear constraint is performed by using a large M method, and the specific expression is as follows:
Figure QLYQS_11
Figure QLYQS_12
Figure QLYQS_13
Figure QLYQS_14
where M is a significant number.
5. The method for identifying a primary gateway key element for an extreme event according to claim 1, wherein the step S3 specifically comprises: considering the influence of extreme events on a power transmission network element, taking remedial measures such as network reconstruction and the like, establishing a two-layer robust model, and searching for the worst scene in the whole scene; in the upper model, according to a reconstruction scheme made by the power system, a component damage scheme is determined, so that the loss load of the system is maximized, in the lower model, a transmission network operator uses network reconstruction to reduce the influence of component damage on the system, so that the loss load of the system is minimized, and a C & CG algorithm is used for solving the two layers of built robust models, so that the two layers of models are decomposed into main problems and sub-problems.
6. The method for identifying a primary gateway key element for an extreme event according to claim 1, wherein the solving the primary problem is specifically: the maximum system load loss is used as a main problem objective function, the decision variable is a damage scheme of a line, and the main problem calculates the damage scheme with the maximum loss, and the specific form is as follows:
obj:maxα(14)
s.t.∑ i∈N* (1-v l )≤n v (15)
Figure QLYQS_15
Figure QLYQS_16
Figure QLYQS_17
Figure QLYQS_18
Figure QLYQS_19
Figure QLYQS_20
Figure QLYQS_21
Figure QLYQS_22
Figure QLYQS_23
Figure QLYQS_24
wherein beta is 1 To beta 12 Is a dual variable, n v For the number of line damages, alpha is a main problem objective function of the dual problem after the dual is taken from the inner layer problem;
the solving of the sub-problem is specifically as follows: and taking the minimum value of the system load loss amount as a sub-problem objective function, reducing the influence of element damage to the minimum through network reconstruction and generator start-stop by an operator, and calculating an optimal reconstruction strategy, wherein the specific form is as follows:
obj:minη(26)s.t.(2)，(4)-(8)，(10)-(13)
Figure QLYQS_25
where η is the sub-problem objective function.
7. The method for identifying a primary gateway key element for an extreme event according to claim 1, wherein the determining the importance ranking of each element in the element combination according to the repair order of the elements is specifically: in order to order importance of elements in a combination, a mixed integer linear programming model is established to describe a post recovery process, a maintenance team is scheduled to maintain damaged elements, the maintenance sequence of the elements is determined, and elements with high importance are preferentially repaired, wherein the mixed integer linear programming model comprises a system operation constraint and a maintenance team scheduling constraint, and the system operation constraint is specifically formed as follows:
min(∑ t∈T ∑ j∈V P shed,j,t ·Δt) (28)
Figure QLYQS_26
Figure QLYQS_27
Figure QLYQS_28
Figure QLYQS_29
Figure QLYQS_30
Figure QLYQS_31
the specific form of the maintenance team scheduling constraint is as follows:
∑ j∈DN\{dp} x dp,j -∑ j∈DN\{dp} x j,dp ＝1 (35)
∑ j∈DN\{dp′} x dp′,pj -∑ j∈DN\{dp′} x j,dp′ ＝-1 (36)
Figure QLYQS_32
Figure QLYQS_33
Q dp ＝1 (39)
Figure QLYQS_34
Figure QLYQS_35
Figure QLYQS_36
Figure QLYQS_37
Figure QLYQS_38
Figure QLYQS_39
Figure QLYQS_40
Figure QLYQS_41
wherein x is a maintenance path decision variable which is a 0/1 variable, dp represents a maintenance station, Q i Representing the repair order of the damaged element i, N is the number of damaged elements, AT i Rt for the time of arrival of the repair team at the damaged element i i For the repair time of element i, tt i,j For the transit time between element i and element j, τ i,t And r i,t The variables for the repair state of the element are 0/1, and epsilon is a small number.
8. The main gateway key element identification system for the extreme event is characterized by comprising a data acquisition module, a model building module and an analysis processing module, wherein the data acquisition module is used for acquiring the importance degree of a load and the remedial measures of the power system on the extreme event, and the maximum value of the system loss caused by the power failure due to the extreme event is used as a first objective function; the model building module is used for modeling the operation of the power system by using the direct current power flow model and building element constraint, and is also used for building an upper layer of robust model and a lower layer of robust model based on a first objective function, the operation modeling of the power system and the element constraint, and converting the two layers of robust models into main problems and sub-problems; the analysis processing module is used for solving the main problem and the sub-problem and outputting a key element combination in the power system, and is also used for determining the importance ranking of each element in the element combination through the repair sequence of the elements.
9. The system of claim 8, wherein the data acquisition module, the modeling module, and the analysis processing module are coupled via data.
CN202310140334.8A 2023-02-17 2023-02-17 Method and system for identifying key element of main gateway facing extreme event Pending CN116244937A (en)

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