A method and system for identifying key elements of a main network facing extreme events

Abstract

The application discloses an extreme event-oriented main network key element identification method and system, and mainly relates to the technical field of extreme event prediction. The method comprises the following steps: based on the importance of load and the remedial measures of the power system to cope with extreme events, taking the maximum value of the system loss caused by the power cut of the extreme event as a first objective function; using a direct current flow model to model power system operation and establish element constraints; based on the first objective function, power system operation modeling and element constraints, establishing upper and lower two-layer robust models and converting the two-layer robust models into a main problem and a sub-problem; solving the main problem and the sub-problem and outputting the key element combination in the power system; and determining the importance degree of each element in the element combination through the repair sequence of the element. The application has the beneficial effects that it solves the problem that the Monte Carlo simulation method cannot generate the corresponding scene due to the small event occurrence probability, and identifies the key element combination in the power transmission network.

Description

Technical Field

[0001] This invention relates to the field of extreme event prediction technology, specifically a method and system for identifying key mainnet components in response to extreme events. Background Technology

[0002] Currently, we face extreme events such as global climate change and frequent occurrences of low-probability, high-risk natural disasters, which pose a huge threat to power grid security and seriously affect social and economic development.

[0003] The power system will become the primary energy source for society, and its safety and stability will directly impact the sustained and stable growth of the national economy. Power security is a crucial link in national energy security. Traditional power system reliability indicators only address scenarios with high probability and low loss time, such as "N-1" or "N-2" scenarios, and cannot cope with extreme events. Therefore, establishing a highly resilient power system capable of withstanding extreme events is an urgent priority and of great significance for ensuring power supply to users and national energy security.

[0004] In the face of extreme events, resilient power systems prepare for and prevent such events before they occur, resist, absorb, respond to, and adapt to the negative impacts of these events during their occurrence, and quickly restore system functionality to normal levels after the event. Therefore, the construction of resilient power systems involves three phases: pre-event, during-event, and post-event. Many scholars have studied the resilience of power systems. Existing research indicates that before an extreme event, the power system assesses its resilience based on indicators such as load shedding rate, robustness, and recovery speed. This involves optimizing the location of components such as distributed generators, energy storage, and substations to improve the system's ability to maintain connectivity, strengthening certain components in advance, enhancing the system's resilience to extreme events, reducing component failure rates during events, and improving system robustness. Furthermore, for predictable disasters such as typhoons and heavy rainfall, the power system can pre-dispatch mobile energy storage vehicles and maintenance teams to prepare for the recovery process. After an extreme event, the power system optimizes the dispatch of maintenance teams, distributed generators, renewable energy sources, energy storage, electric vehicles, and other flexible resources to improve recovery efficiency, enabling the power system to quickly return to normal levels. Whether it's pre-selection and reinforcement or post-event maintenance planning, the importance of components must be considered. Due to limited resources, power systems can only reinforce critical components in advance, and important components will be repaired first during post-event recovery. Identifying critical components in the system is instructive for both pre-event reinforcement and post-event recovery, and is the foundation for building resilient power systems. Existing component identification methods mainly involve extensive simulations using the Monte Carlo method, followed by sampling to generate scenarios, and then evaluating components in the system based on methods such as PageRank and Copeland sorting to identify critical components.

[0005] However, existing key component identification methods are based on Monte Carlo simulation. Scene generation is related to the probability of events occurring, and extreme events are inherently low-probability events. Monte Carlo simulation struggles to generate scenarios for extreme events, and this lack of scenario generation means that the identified key components may not be the true key components of the system. Therefore, how to achieve full-scene identification is a pressing issue. Furthermore, the combined importance of multiple components may be greater than the sum of the importance of individual components; therefore, identifying combinations of key components is also highly meaningful. Summary of the Invention

[0006] The purpose of this invention is to provide a method for identifying key components of the main grid in response to extreme events. It solves the problem that Monte Carlo simulation cannot generate corresponding scenarios due to the low probability of event occurrence, and at the same time identifies the combination of key components in the transmission network.

[0007] To achieve the above objectives, the present invention employs the following technical solution:

[0008] Includes the following steps:

[0009] S1: Based on the importance of the load and the remedial measures of the power system to deal with extreme events, the maximum system loss caused by the power outage due to the extreme event is the first objective function;

[0010] S2: Use a DC power flow model to model the operation of the power system and establish component constraints;

[0011] S3: Based on the first objective function, power system operation modeling and component constraints, establish upper and lower robust models and transform these two robust models into the main problem and sub-problems;

[0012] S4: Solve the main problem and sub-problems and output the combination of key components in the power system;

[0013] S5: Determine the importance ranking of each component in the component assembly by the repair order of the components.

[0014] Preferably, the first objective function is:

[0015]

[0016] Where, N l For the set of routes, N g For the generator set, N b The factors are combined, where W is the weight of load importance, and P is the weight of load importance. shed,j This represents the load shedding amount at node j, where v is a 0 / 1 variable, and W... gen This is the start / stop variable for the generator.

[0017] Preferably, step S2 specifically involves: modeling the power system operation using a DC power flow model, and describing the state of system components by introducing 0 / 1 variables, in the following form:

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] Where π(j) is the set of routes starting from node j, δ(j) is the set of routes ending at node j, and P l P represents the active power flowing through line l. G,j For the output of the generator located at node j, P L,j For the load of node j, B ij Let be the susceptance of the line between node i and node j. Let q be the capacity of line l. l Let θ represent the state of line l. i This represents the phase angle of node i. and These are the minimum and maximum outputs of generator g, respectively;

[0025] The establishment of component constraints specifically involves: the established model considering component failure and network reconfiguration issues, using 0 / 1 variables, and the state of the transmission network lines as follows:

[0026] q l ≤w l ,l∈N l (8)

[0027] q l ≤v l ,l∈N l (9)

[0028] In this context, variables v and w represent the damaged state and reconstructed state of the line, respectively. Equation (8) indicates that the line cannot transmit power after being manually removed, and Equation (9) indicates that the line cannot transmit power when it is damaged.

[0029] Preferably, equation (3) is a nonlinear constraint, which is linearized using the Big M method, and its specific form is as follows:

[0030]

[0031]

[0032]

[0033]

[0034] Where M is a very large number.

[0035] Preferably, step S3 specifically involves: considering the impact of extreme events on power grid components and taking into account remedial measures such as network reconfiguration, establishing a two-layer robust model to find the worst-case scenario in the entire scenario; in the upper-layer model, based on the reconfiguration scheme made by the power system, determining the component failure scheme to maximize the system's load loss; in the lower-layer model, power grid operators use network reconfiguration to reduce the impact of component failure on the system to minimize the system's load loss; and using the C&CG algorithm to solve the established two-layer robust model, decomposing the two-layer model into a main problem and sub-problems.

[0036] Preferably, the solution to the main problem specifically involves: taking the maximization of system load loss as the objective function of the main problem, with the line damage schemes as the decision variables, and calculating the damage scheme that maximizes the loss, in the following specific form:

[0037] obj:maxα (14)

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048]

[0049] Where, β 1 To β12 Dual variable, n v Let α be the number of line damages, and let α be the objective function of the principal problem of the dual problem after taking the duality of the inner problem.

[0050] The solution to the subproblem specifically involves: using the minimum system load loss as the objective function of the subproblem, and calculating the optimal reconfiguration strategy by minimizing the impact of component damage through network reconfiguration and generator start-up and shutdown. The specific form is as follows:

[0051] obj:minη (26)

[0052] st(2), (4)-(8), (10)-(13)

[0053]

[0054] Where η is the objective function of the subproblem.

[0055] Preferably, the step of determining the importance ranking of each component in the component combination by the repair order of the components specifically involves: in order to rank the importance of the components within the combination, a mixed-integer linear programming model is established to describe the post-recovery process, a maintenance team is scheduled to repair the damaged components, the repair order of the components is determined, and components with higher importance are repaired first. The mixed-integer linear programming model includes system operation constraints and maintenance team scheduling constraints. The specific form of the system operation constraints is as follows:

[0056] min(∑ t∈T ∑ j∈V P shed,j,t ·Δt) (28)

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063] The specific form of the maintenance team scheduling constraints is as follows:

[0064] ∑ j∈DN\{dp} x dp,j -∑ j∈DN\{dp} x j,dp =1 (35)

[0065] ∑j∈DN\{dp′} x dp′,j -∑ j∈DN\{dp′} x j,dp′ =-1 (36)

[0066]

[0067]

[0068] Q dp =1 (39)

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076]

[0077] Where x is the maintenance route decision variable, which is a 0 / 1 variable, dp represents the maintenance station, and Q i The repair order of damaged component i is represented by N, where N is the number of damaged components, and AT is the repair order of damaged component i. i rt is the time it takes for the repair team to arrive at the damaged component i. i tt is the repair time for component i. i,j τ is the travel time between element i and element j. i,t and r i,t The variables represent the component repair status, all of which are 0 / 1 variables, and ε is a very small number.

[0078] A main grid critical component identification system for extreme events includes a data acquisition module, a model building module, and an analysis and processing module. The data acquisition module is used to collect load importance and remedial measures of the power system to cope with extreme events, with the maximum system loss caused by power outages due to extreme events as the first objective function. The model building module is used to model the operation of the power system using a DC power flow model and establish component constraints. It is also used to establish upper and lower robust models based on the first objective function, the power system operation modeling, and the component constraints, and to transform these two robust models into a main problem and sub-problems. The analysis and processing module is used to solve the main problem and sub-problems and output the combination of critical components in the power system. It is also used to determine the importance ranking of each component in the component combination based on the repair order of the components.

[0079] Preferably, the data acquisition module, model building module, and analysis and processing module are connected via data.

[0080] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0081] 1. The method of this invention establishes a two-layer robust model based on a DC power flow model, considering remedial measures such as transmission network reconfiguration. It uses 0 / 1 variables to represent component failure scenarios and line reconfiguration strategies, describing the final operating state of the components. It fully considers all scenarios caused by extreme events, identifying the scenario with the greatest loss, thus solving the problem that Monte Carlo simulation methods cannot generate corresponding scenarios due to the low probability of event occurrence. Simultaneously, it identifies key component combinations in the transmission network.

[0082] 2. By establishing a post-disaster maintenance team scheduling model, this invention can accurately obtain the optimal component repair sequence and the contribution of each component's repair to the system load recovery during the recovery process, and sort the damaged components by importance, thereby identifying the most critical components.

[0083] 3. This invention uses the C&CG algorithm to solve the established two-layer robust model, which can obtain the worst-case scenario that the system may encounter in fewer iterations and shorter solution time. Under the premise of ensuring that the solution is the global optimal solution, the computational efficiency of the model is improved. The proposed method provides an efficient tool for power transmission network operators. Attached Figure Description

[0084] Figure 1 This is a flowchart of the present invention.

[0085] Figure 2 This is a schematic diagram of the constraints of a two-layer robust model.

[0086] Figure 3 Flowchart for solving the robust model.

[0087] Figure 4Flowchart of the key component identification method. Detailed Implementation

[0088] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined in this application.

[0089] like Figure 1 As shown, the present invention relates to a method for strengthening main grid components considering renewable energy under extreme weather conditions, which is achieved through the following steps:

[0090] S1: Considering the importance of the load and the remedial measures of the power system to deal with extreme events, the objective function is to maximize the system loss caused by power outages due to extreme events;

[0091] S2: The DC power flow model is used to model the operation of the power system, and the state of the system components is described by introducing 0 / 1 variables;

[0092] S3: Considering the impact of extreme events on transmission network components and taking into account remedial measures such as network reconfiguration, a two-layer robust model is established to find the worst-case scenario across all scenarios. In the upper-layer model, based on the reconfiguration scheme implemented by the power system, the component failure scheme is determined to maximize the system's load loss. In the lower-layer model, transmission network operators use network reconfiguration to reduce the impact of component failure on the system, minimizing the system's load loss.

[0093] S4: The column-and-constraint generation algorithm is used to transform the two-layer robust model into a main problem and sub-problems. The component failure scenario with the greatest loss is obtained by iterative solution. The combination of failed components is the combination of key components in the system.

[0094] S5: Based on the scenario of the component with the greatest loss, establish a mixed integer linear programming model to describe the post-event recovery process, schedule the maintenance team to repair the damaged component, and determine the importance ranking of each component in the component combination by the repair order of the component.

[0095] The two-layer robust model established in this invention, considering remedial measures such as network reconstruction, aims to maximize system loss and find the worst-case scenario caused by extreme events. The constraints included in the model are as follows: Figure 2 As shown, it includes power balance constraints, line capacity constraints, load shedding constraints, power output constraints, phase angle constraints, and component state constraints. The C&CG algorithm is used to solve the established two-layer robust model. The mathematical forms of each part of the established two-layer robust model are as follows:

[0096] (1) First objective function:

[0097]

[0098] Where, N l For the set of routes, N g For the generator set, N b Let W be the set of nodes, W be the load importance weight, and P be the load weight. shed,j This represents the load shedding amount at node j. `v` is a 0 / 1 variable representing whether the line is damaged; a value of 0 indicates the line is damaged. `w` is the line reconfiguration variable, also a 0 / 1 variable; a value of 0 indicates the line has been manually taken out of service. gen This is the start / stop variable for the generator.

[0099] (2) DC power flow model:

[0100] This invention addresses the critical component issues of power transmission networks, which are essentially planning problems and do not require consideration of reactive power or other issues. Therefore, a DC power flow model is used to describe system operation, and its specific form is as follows:

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] Where π(j) is the set of routes starting from node j, δ(j) is the set of routes ending at node j, and P l P represents the active power flowing through line l. G,j For the output of the generator located at node j, P L,j For the load of node j, B ij Let be the susceptance of the line between node i and node j. Let q be the capacity of line l. l Let θ represent the state of line l. i This represents the phase angle of node i. and Equation (2) represents the minimum and maximum output of generator g, respectively. Equation (3) represents the power balance constraint, Equation (4) represents the relationship between the power flowing through the line and the phase angle at both ends of the line, Equation (5) restricts the load shedding amount from exceeding the load amount of the node, Equation (6) restricts the generator output, and Equation (7) constrains the phase angle difference at both ends of the line.

[0108] (3) Component state constraints:

[0109] The established model considers component failure and network reconfiguration issues, using 0 / 1 variables v and w to represent the damaged and reconfigured states of the transmission network lines, respectively. The states of the transmission network lines are as follows:

[0110] q l ≤w l ,l∈N l (8)

[0111] q l ≤v l ,l∈N l (9)

[0112] Where, variables v and w represent the damaged state and reconfigured state of the line, respectively; equation (8) indicates that the line cannot transmit power after being manually disconnected; equation (9) indicates that the line cannot transmit power when it is damaged; q l A value of 0 indicates that line 1 has stopped operating, while a value of 1 indicates that it is operating normally.

[0113] (4) Linearization:

[0114] The model established in this invention contains nonlinear constraints (3), which cannot be solved by general commercial solvers. Therefore, the Big M method is needed to linearize the nonlinear constraints. The specific form after linearization is shown in the following equation:

[0115]

[0116]

[0117]

[0118]

[0119] Where M is a very large number.

[0120] (5) Model solution:

[0121] The two-layer robust model established by the method of this invention contains a large number of 0 / 1 variables, resulting in a complex solution space and difficulty in solving it. Therefore, this invention uses the C&CG algorithm to solve the established model, decomposing the two-layer model into a main problem (MP) and subproblems (SP), which are as follows:

[0122] Main Problem: The objective function is to maximize the attack effect, i.e., maximize the system load loss. The decision variable is the line damage scheme. The main problem calculates the damage scheme with the maximum loss and passes it to the subproblems. After dualizing the inner problem, it is merged with the outer problem into a single layer. The specific form of MP is as follows:

[0123] obj:maxα (14)

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] Where, β 1 To β 12 Dual variable, n v Let α be the number of line damages, and let α be the objective function of the principal problem of the dual problem after taking the duality of the inner problem.

[0136] Subproblem: The subproblem is a minimization problem. The objective function is to minimize the system load loss, and the operator minimizes the impact of component damage through network reconfiguration and generator start-up and shutdown, calculating the optimal reconfiguration strategy. The specific form of SP is as follows:

[0137] obj:minη (26)

[0138] st(2), (4)-(8), (10)-(13)

[0139]

[0140] Where η is the objective function of the subproblem.

[0141] The specific solution process is as follows: Figure 2 As shown, the solution steps are as follows:

[0142] 1) Set LB = -∞, UB = +∞, m = 1, and initialize. and

[0143] 2) Solve for MP to obtain the optimal solution. Update UB = min{UB, α};

[0144] 3) The solution is passed to SP for solving to obtain the optimal solution. Update LB max {LB,∑P shed,j};

[0145] 4) If UB≠LB, then return to step 2), and m=m+1, and... Pass it to MP, where a new set of variables is introduced and a new set of constraints is generated. If UB = LB, then the result is output.

[0146] (6) Component importance ranking:

[0147] like Figure 4 As shown, solving the two-layer robust model yields the key component combinations in the system. To rank the components within these combinations by importance, a mixed-integer linear programming model is established to describe the post-recovery process. This model schedules maintenance teams to repair damaged components, determining the repair order and prioritizing the repair of components with higher importance. The specific form of the recovery model is as follows:

[0148] System operating constraints:

[0149] min(∑ t∈T ∑ j∈V P shed,j,t ·Δt) (28)

[0150]

[0151]

[0152]

[0153]

[0154]

[0155]

[0156] Maintenance team dispatch constraints:

[0157] ∑ j∈DN\{dp} x dp,j -∑ j∈DN\{dp} x j,dp =1 (35)

[0158] ∑ j∈DN\{dp′} x dp′,j -∑ j∈DN\{dp′} x j,dp′ =-1 (36)

[0159]

[0160]

[0161] Q dp =1 (39)

[0162]

[0163]

[0164]

[0165]

[0166]

[0167]

[0168]

[0169]

[0170] Where x is the maintenance route decision variable, which is a 0 / 1 variable, dp represents the maintenance station, and Q i The repair order of damaged component i is represented by N, where N is the number of damaged components, and AT is the repair order of damaged component i. i rt is the time it takes for the repair team to arrive at the damaged component i. i tt is the repair time for component i. i,j τ is the travel time between element i and element j. i,t and r i,tThe variables representing the component repair status are all 0 / 1 variables, and ε is a very small number. Equations (35) and (36) ensure that the maintenance team departs from the maintenance station and returns to it. Equations (37) and (38) ensure that the maintenance team reaches each damaged component and leaves after repairing it. Equations (39) and (40) prevent disconnected subgraphs and determine the maintenance order of the components. Equations (41) and (42) determine the time it takes for the maintenance team to reach each damaged component. Equations (43)-(46) determine the repair status of each damaged component at each time point. Equation (47) represents the change in the component status. By solving the maintenance team scheduling model and obtaining the maintenance order of the damaged components, the importance ranking of the components can be obtained.

Claims

1. A method for identifying key mainnet components in response to extreme events, characterized in that, Includes the following steps: S1: Based on the importance of the load and the remedial measures of the power system to deal with extreme events, the maximum system loss caused by the power outage due to the extreme event is the first objective function; S2: Use a DC power flow model to model the operation of the power system and establish component constraints; S3: Based on the first objective function, power system operation modeling and component constraints, establish upper and lower robust models and transform these two robust models into the main problem and sub-problems; S4: Solve the main problem and sub-problems and output the combination of key components in the power system; S5: Determine the importance ranking of each component in the component assembly by the repair order of the components; Step S3 specifically involves: considering the impact of extreme events on transmission network components, taking into account network reconfiguration remedial measures, establishing a two-layer robust model, and finding the worst-case scenario in the entire scenario; in the upper-layer model, based on the reconfiguration scheme made by the power system, determining the component failure scheme to maximize the system's load loss; in the lower-layer model, transmission network operators use network reconfiguration to reduce the impact of component failure on the system to minimize the system's load loss; using the C&CG algorithm to solve the established two-layer robust model, decomposing the two-layer model into a main problem and sub-problems; The solution to the main problem is as follows: taking the maximization of system load loss as the objective function, the main problem calculates the damage scheme that results in the greatest loss, in the following form: Objective function: ; ； ； ； ； ； ； ； ； ； ； ； in, to It is a dual variable. This represents the number of damaged lines. It is the objective function of the principal problem of the dual problem after taking the dual of the inner problem; The solution to the subproblem is as follows: the objective function of the subproblem is the minimum value of the system load loss. The operator minimizes the impact of component damage through network reconfiguration and generator start-up and shutdown, and calculates the optimal reconfiguration strategy.

2. The method for identifying key mainnet components for extreme events according to claim 1, characterized in that, The first objective function is: ； in, For the collection of routes, For generator sets, For a set of nodes, As the weight of load importance, Represents a node The shear load, 0 / 1 variable This is the start / stop variable for the generator.

3. The method for identifying key mainnet components for extreme events according to claim 1, characterized in that, Step S2 specifically involves: modeling the power system operation using a DC power flow model, and introducing 0 / 1 variables to describe the state of system components, in the following form: ； ； ； ； ； ； in, For nodes A set of routes starting from [the origin]. For nodes A collection of routes with the destination as the destination. For the line Active power flowing upstream For the node The generator output, For nodes The load, For nodes With nodes The susceptance of the line between them For the line capacity, For the line state, Represents a node phase angle, and Generators Minimum and maximum output; The establishment of component constraints specifically involves: the established model considering component failure and network reconfiguration issues, using 0 / 1 variables, and the state of the transmission network lines as follows: ；（1） ；（2） Among them, variables and Representing the lines respectively The damaged state and the reconstructed state, constraint (1) means that the line cannot transmit power after being manually removed, and constraint (2) means that the line cannot transmit power when it is damaged.

4. The method for identifying key mainnet components for extreme events according to claim 3, characterized in that, formula To represent nonlinear constraints, we use the Big M method to linearize them, as shown in the following form: ； ； ； ； in, It is a very large number.

5. The method for identifying key mainnet components for extreme events according to claim 1, characterized in that, The method of determining the importance ranking of components in a component combination by the repair order of components specifically involves: To rank the importance of components within the combination, a mixed-integer linear programming model is established to describe the post-recovery process. A maintenance team is scheduled to repair damaged components, and the repair order of the components is determined, prioritizing the repair of components with higher importance. The mixed-integer linear programming model includes system operation constraints and maintenance team scheduling constraints. The specific form of the system operation constraints is as follows: ； ； ； ； ； ； The specific form of the maintenance team scheduling constraints is as follows: ； ； ； ； ； ； ； ； ； ； ； ； ； in, These are maintenance path decision variables, and they are 0 / 1 variables. Representing the repair station, Represents damaged components The repair order, where N is the number of damaged components. The time it takes for the repair team to arrive at the damaged component i. For the repair time of component i, Let i be the travel time between element i and element j. and The variables representing the component's repair status are all 0 / 1 variables. It is a very small number.

6. A mainnet critical component identification system for extreme events, used to implement the mainnet critical component identification method for extreme events as described in any one of claims 1-5, characterized in that, The system includes a data acquisition module, a model building module, and an analysis and processing module. The data acquisition module is used to collect data on the importance of loads and the remedial measures of the power system in response to extreme events, with the maximum system loss caused by a power outage due to an extreme event as the first objective function. The model building module is used to model the operation of the power system using a DC power flow model and establish component constraints. It is also used to establish upper and lower robust models based on the first objective function, the power system operation modeling, and the component constraints, and to transform these two robust models into a main problem and sub-problems. The analysis and processing module is used to solve the main problem and sub-problems and output the combination of key components in the power system. It is also used to determine the importance ranking of each component in the component combination based on the repair order of the components.

7. The system according to claim 6, characterized in that, The data acquisition module, model building module, and analysis and processing module are connected via data.

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