Evaluation method of multi-failure recovery process of distribution system after disaster considering maintenance strategy

Abstract

The application discloses a kind of distribution system post-disaster multiple fault recovery process evaluation method considering maintenance strategy, solve the deficiency of prior art, including the following steps: S1, determine fault scene and maintenance parameter, specifically: S101, determine distribution system network topology parameter, including: branch information, user information and power information;S102, determine the fault element set in distribution system after disaster, obtain system fault scene, analysis and statistics power outage load, obtain the initial state before system starts recovery;S103, determine the number of maintenance team in system and the maintenance time of single element;S2, establish distribution system multiple fault recovery process evaluation index system;S3, the maintenance attribute of fault element is optimized, determine the best maintenance strategy, according to the power-on time of each load and user in recovery process according to the best maintenance strategy, load recovery curve and user recovery curve are obtained, then each recovery process evaluation index is calculated.

Description

Technical Field

[0001] This invention relates to the field of power distribution system technology, and in particular to an assessment method for the post-disaster recovery process of power distribution systems considering maintenance strategies. Background Technology

[0002] Against the backdrop of "dual carbon" goals and energy transition, people's reliance on electricity is constantly increasing. Even brief power outages can have a significant impact on users, thus raising their demands for power supply continuity. In the power system, the distribution system connects the transmission system and users. The reliability of the distribution system directly affects the reliability of users' electricity supply; over 80% of power outages are caused by the distribution system. Traditional reliability assessments focus on individual component failures and repair processes that occur during the daily operation of the distribution system. However, in recent years, the frequent occurrence of severe weather events such as typhoons, ice storms, earthquakes, tsunamis, and man-made attacks has led to situations where, unlike traditional reliability assessments, the distribution system experiences significant disturbances and multiple component failures simultaneously, resulting in power outages for numerous users. Due to limited repair personnel and vehicles, it is impossible to repair all faulty components simultaneously. Therefore, appropriate maintenance strategies need to be developed, prioritizing the repair of critical components to achieve the goal of quickly restoring the lost load. Assessing the recovery process of power distribution systems after extreme events and analyzing the losses and recovery status after a disaster provides theoretical reference for governments, power grids, and users to adopt response strategies. Currently, existing post-disaster recovery assessments use Monte Carlo simulations, which involve multiple samplings until convergence, resulting in significant computational time. Therefore, improving the efficiency of assessing the recovery process of power distribution systems with multiple faults after a disaster, while simultaneously obtaining optimal repair strategies for faulty components, is a worthwhile research topic. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of existing technologies, such as the inability to promptly and effectively repair critical components when power distribution systems fail under extreme events, resulting in low repair efficiency. This invention provides an evaluation method for the post-disaster recovery process of power distribution systems that considers maintenance strategies.

[0004] The objective of this invention is achieved through the following technical solution: An assessment method for the post-disaster multi-fault recovery process of power distribution systems, considering maintenance strategies, includes the following steps: S1, determine the fault scenario and maintenance parameters, specifically: S101, Determine the network topology parameters of the power distribution system, including: branch information, user information and power supply information; S102, determine the set of faulty components in the power distribution system after the disaster, obtain the system fault scenario, analyze and statistically analyze the power outage load, and obtain the initial state before the system begins to recover; S103, determine the number of maintenance teams in the system and the maintenance time for individual components; S2, Establish an evaluation index system for the recovery process of multiple faults in the power distribution system; S3 optimizes the maintenance attributes of the faulty components, determines the optimal maintenance strategy, and determines the power restoration time for each load and user during the restoration process based on the optimal maintenance strategy. Based on this, the load restoration curve and user restoration curve are obtained, and then the evaluation index of each restoration process is calculated.

[0005] Preferably, S2 is specifically: A multi-fault recovery process evaluation index system for power distribution systems is established based on three aspects: disaster severity, recovery efficiency, and dynamic curves: S201. The disaster severity-related indicators include: Real-time power outage load: RPNS t This represents the load size at time t during the power outage, where i is the load node number, and L... i Let Q be the load size of the i-th load node. t Let t be the set of load nodes that are experiencing power outages at time t; Number of users experiencing real-time power outages: RCNS t N represents the number of users experiencing power outages at time t. i Let i be the number of users at the i-th load node; Total power outage amount: CPNS represents the total power outage amount across all load nodes of the system during the entire post-disaster recovery process, U i Let be the outage duration of the i-th load node, and Q be the set of all load nodes that have experienced outages. Total number of households affected by power outages: CCNS represents the cumulative number of households experiencing power outages across all load nodes of the system during the entire post-disaster recovery process; S202. Recovery efficiency related indicators include: System power-on time: SPRT represents the total system power restoration time, which is the time spent from the start of post-disaster repairs until all loads are powered back. Average power restoration time per user: ACPRT represents the average time required to restore power to all users in the system who experienced power outages. Power loss load recovery speed: PRS represents the system's recovery speed of power loss load, that is, the amount of load that is restored to power per unit time; Power recovery speed for users experiencing power outages: CRS represents the recovery speed of users who have lost power in the system, that is, the number of users whose power is restored per unit time; S203. The dynamic recovery curve specifically includes the load recovery curve and the user recovery curve.

[0006] Preferably, determining the optimal maintenance strategy in S3 specifically involves: Objective function: Minimizing the cumulative power outage amount (CPNS) is chosen as the objective function, and the decision variable is the state of component j at time t. The formula for calculating the cumulative power outage amount (CPNS) is as follows: Among them, l ti The load lost at node i; Constraints: Define a load node power supply path matrix S, where the number of rows and columns of matrix S are I and J, respectively, where I is the number of load nodes and J is the number of lines. The element s in the i-th row and j-th column of matrix S... ij Indicates line b j Is it a load node n? i The power supply path, s ij When it is 1, it indicates line B. j For load node N i The power supply path, s ij When it is 0, it indicates line B. j Not load node N i The power supply path is as follows. The power supply path matrix for the load node is: From a row perspective, the element in row 4 is

[11010] , indicating that the power supply path for load node 4 is {b1,b2,b3}. A fault in any of these three lines will cause a power outage for load node 4. Only when all three lines are operating normally can load node 4 use power normally. From a column perspective, the element in column 2 is

[01111] . T This indicates that when line 2 fails, it will cause power outages at four load nodes, load nodes 2-5.

[0007] Define the line state vector b at time t. t It consists of J elements, b tj Indicates line B j work status, b tj A value of 1 indicates line B. tj Normal work, b tj A value of 0 indicates line B. tj Fault. Assuming all components fail after the disaster, the initial state vector of the system is: For a load node, it can only use power normally when all lines in the power supply path are working properly. If any line in the power supply path fails, the load node will be in a power outage state. Therefore, the power consumption status of each node can be determined based on the load node power supply path matrix and line state vector. Since the objective function is to minimize load loss, the constraints can be transformed into inequalities. When a component fails on the power supply path of a load node, the load node loses power, and the power outage occurs. i Greater than the electrical load L under normal conditions of this node i After linearizing this constraint, the expression is as follows: By optimizing the solution of the line state vector at each time step, the optimal maintenance strategy can be obtained.

[0008] As a preferred option, after constraining the power outage load, it is also necessary to constrain the number of maintenance team resources, specifically:

[0009] The beneficial effects of this invention are as follows: This invention can evaluate the recovery process of a power distribution system after multiple faults caused by extreme events, analyze the damage and recovery status of the power distribution system, and provide theoretical reference for governments, power grid departments, and users. The first step of this invention establishes an evaluation process for the recovery of a power distribution system after a disaster involving multiple faults, and establishes a set of evaluation index systems for the recovery process of a power distribution system involving multiple faults to measure the severity of the disaster and the recovery speed of the power distribution system. The second step uses a 0-1 integer programming algorithm to optimize the maintenance strategy, obtain the optimal repair sequence of faulty components, and enable the lost load to restore power supply as quickly as possible. Simultaneously, it calculates the evaluation indexes for the recovery process of a power distribution system involving multiple faults proposed in the previous step. This method uses an analytical approach, which greatly improves computational efficiency compared to the Monte Carlo simulation method used in existing methods. Furthermore, this method, while evaluating the recovery process of the power distribution system, also provides the optimal repair sequence of faulty components, improving the recovery speed of the power distribution system after a disaster. Attached Figure Description

[0010] Figure 1 This is a flowchart of the present invention; Figure 2 This is a schematic diagram of the load recovery curve; Figure 3 This is a diagram illustrating the user's recovery curve; Figure 4 This is a schematic diagram of a simple power distribution system with a load node power supply path matrix; Figure 5 This is a modified topology diagram of the IEEE 37-node test system; Figure 6 This is the load recovery curve for scenario 1; Figure 7 This is a user recovery curve for scenario 1; Figure 8 This is the load recovery curve for scenario 2; Figure 9 This is the user recovery curve for scenario 2. Detailed Implementation

[0011] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0012] Example: An assessment method for the post-disaster recovery process of multiple faults in a power distribution system, considering maintenance strategies, with the flowchart shown below. Figure 1 As shown, this method first determines the power distribution system fault scenario based on the faulty components, and then determines information such as network topology, the number of maintenance resources, and maintenance time. It establishes an evaluation index system from three aspects: damage severity, recovery efficiency, and dynamic process. A 0-1 integer programming algorithm is used to optimize the maintenance sequence of the faulty components to obtain the optimal maintenance strategy. Based on the component repair process, corresponding evaluation indicators are calculated, and the system's damage severity and recovery efficiency are analyzed. This scheme includes the following steps: S1, determine the fault scenario and maintenance parameters, specifically: S101, Determine the network topology parameters of the power distribution system, including: branch information, user information and power supply information; S102, determine the set of faulty components in the power distribution system after the disaster, obtain the system fault scenario, analyze and statistically analyze the power outage load, and obtain the initial state before the system begins to recover; S103, determine the number of maintenance teams in the system and the maintenance time for individual components; S2, Establish an evaluation index system for the recovery process of multiple faults in the power distribution system; S3 optimizes the maintenance attributes of the faulty components, determines the optimal maintenance strategy, and determines the power restoration time for each load and user during the restoration process based on the optimal maintenance strategy. Based on this, the load restoration curve and user restoration curve are obtained, and then the evaluation index of each restoration process is calculated.

[0013] Specifically, S2 is: A multi-fault recovery process evaluation index system for power distribution systems is established based on three aspects: disaster severity, recovery efficiency, and dynamic curves: S201. The disaster severity-related indicators include: Real-time power outage load: RPNS t This represents the load size at time t during the power outage, where i is the load node number, and L... i Let Q be the load size of the i-th load node. t Let t be the set of load nodes that are experiencing power outages at time t; Number of users experiencing real-time power outages: RCNS t N represents the number of users experiencing power outages at time t. i Let i be the number of users at the i-th load node; Total power outage amount: CPNS represents the total power outage amount across all load nodes of the system during the entire post-disaster recovery process, U i Let be the outage duration of the i-th load node, and Q be the set of all load nodes that have experienced outages. Total number of households affected by power outages: CCNS represents the cumulative number of households experiencing power outages across all load nodes of the system during the entire post-disaster recovery process; S202. Recovery efficiency related indicators include: System power-on time: SPRT represents the total system power restoration time, which is the time spent from the start of post-disaster repairs until all loads are powered back. Average power restoration time per user: ACPRT represents the average time required to restore power to all users in the system who experienced power outages. Power loss load recovery speed: PRS represents the system's recovery speed of power loss load, that is, the amount of load that is restored to power per unit time; Power recovery speed for users experiencing power outages: CRS represents the recovery speed of users who have lost power in the system, that is, the number of users whose power is restored per unit time; S203. The dynamic recovery curve specifically includes the load recovery curve and the user recovery curve.

[0014] Load recovery curve A schematic diagram of the load recovery curve is shown below. Figure 2 As shown, the horizontal axis represents time, and the vertical axis represents the load restored. The curve represents the cumulative load restored over time. From this graph, we can analyze the disaster severity and recovery efficiency indicators related to the load mentioned earlier. The horizontal dashed line at the top of the graph represents the load size of the initial power outage, and the distance from a point on the curve to the initial power outage load dashed line represents the real-time power outage load (RPNS). t The area of ​​the shaded part represents the cumulative power outage amount CPNS during the entire recovery process, the time corresponding to the end of the curve is the system power restoration time SPRT, and the slope of the line connecting the beginning and end of the curve represents the power loss load recovery speed PRS. User recovery curve. A schematic diagram of the user recovery curve is shown below. Figure 3 As shown, the horizontal axis represents time, and the vertical axis represents the number of users whose power has been restored. The curve represents the cumulative number of users whose power has been restored over time. From this graph, we can analyze the disaster severity and restoration efficiency indicators related to users mentioned earlier. The horizontal dashed line at the top of the graph represents the number of users whose power was initially cut off, and the distance from a point on the curve to the dashed line representing the number of users whose power was initially cut off represents the real-time number of users whose power was cut off (RCNS). t The area of ​​the shaded region represents the cumulative number of households experiencing power outages during the entire restoration process (CCNS). The time corresponding to the end of the curve is the system power restoration time (SPRT). The slope of the line connecting the beginning and end of the curve represents the power loss load restoration speed (CRS).

[0015] The determination of the optimal maintenance strategy in S3 specifically involves: Objective function: Minimizing the cumulative power outage amount (CPNS) is chosen as the objective function, and the decision variable is the state of component j at time t. The formula for calculating the cumulative power outage amount (CPNS) is as follows: Among them, l ti The load lost at node i; Constraints: Define a load node power supply path matrix S, where the number of rows and columns of matrix S are I and J, respectively, where I is the number of load nodes and J is the number of lines. The element s in the i-th row and j-th column of matrix S... ij Indicates line bj Is it a load node n? i The power supply path, s ij When it is 1, it indicates line B. j For load node N i The power supply path, s ij When it is 0, it indicates line B. j Not load node N i The power supply path. Figure 4 Taking the simple power distribution system shown as an example, under this topology, the power supply path matrix of the load nodes is as follows: From a row perspective, the element in row 4 is

[11010] , indicating that the power supply path for load node 4 is {b1,b2,b3}. A fault in any of these three lines will cause a power outage for load node 4. Only when all three lines are operating normally can load node 4 use power normally. From a column perspective, the element in column 2 is

[01111] . T This indicates that when line 2 fails, it will cause power outages at four load nodes, load nodes 2-5.

[0016] Define the line state vector b at time t. t It consists of J elements, b tj Indicates line B j work status, b tj A value of 1 indicates line B. tj Normal work, b tj A value of 0 indicates line B. tj Fault. Figure 4 Taking the simple power distribution system shown as an example, assuming all components fail after a disaster, the initial state vector of the system is: For a load node, it can only use power normally when all lines in the power supply path are working properly. If any line in the power supply path fails, the load node will be in a power outage state. Therefore, the power consumption status of each node can be determined based on the load node power supply path matrix and line state vector. Since the objective function is to minimize load loss, the constraints can be transformed into inequalities. When a component fails on the power supply path of a load node, the load node loses power, and the power outage occurs. i Greater than the electrical load L under normal conditions of this node i After linearizing this constraint, the expression is as follows: By optimizing the solution of the line state vector at each time step, the optimal maintenance strategy can be obtained.

[0017] After constraining the power outage load, constraints on the number of maintenance team resources are also required, specifically:

[0018] The method of the present invention will be further illustrated below with specific examples.

[0019] A modified IEEE-37 node test system is used as a case study, such as... Figure 5 As shown, the original system had 37 load nodes, 35 lines, and 1 transformer. To facilitate the demonstration of the method proposed in this paper, the following modifications were made: load node 799 was changed to a low-voltage substation node, i.e., the power supply node of the distribution network; and the transformer between load nodes 709 and 775 was replaced with line L35. The load capacity and number of users of each load node are shown in Table 1.

[0020] Table 1 701 30.4 85 713 37.61 88 732 48.5 130 702 18.61 48 714 31.51 74 733 5.98 10 703 38.84 103 718 18.89 50 734 7.49 15 704 26.39 65 720 12.17 30 735 12.45 23 705 12.58 30 722 15.36 42 736 35.12 58 706 29.58 72 724 29.37 70 737 11.03 18 707 31.09 75 725 33.82 80 738 46.84 145 708 22.57 20 727 43.9 125 740 7.41 12 709 43.08 128 728 41.18 110 741 31.86 90 710 12.57 23 729 35.71 89 742 12.73 32 711 48.23 140 730 43.98 114 744 19.04 54 712 21.06 52 731 24.38 56 775 40.6 108 To determine the fault scenarios, the case study uses two fault scenarios for evaluation. The first scenario randomly selects 10 lines as fault lines to serve as the initial fault scenario after a disaster in the distribution network. The selected fault lines are L4, L7, L14, L16, L19, L22, L23, L28, L32, and L36. The second scenario assumes that all lines in the system are faulty.

[0021] The number of maintenance teams and the repair time were determined. There are a total of 2 maintenance teams, and the repair time for each line is 5 hours.

[0022] (1) Scene 1 The optimal maintenance strategy for faulty components was obtained using a 0-1 integer programming algorithm, as shown in Table 2.

[0023] Table 2 1 L4, L36 2 L14, L22 3 L28, L32 4 L7, L19 5 L16, L23 The load recovery curve and the user recovery curve are as follows: Figure 6 and Figure 7 As shown in Table 3, the evaluation indicators for the recovery process proposed in S2 were calculated.

[0024] Table 3 result 9472.1 4660 25 10.2778 39.2772 98.56 (2) Scene 2 The optimal maintenance strategy for faulty components was obtained using a 0-1 integer programming algorithm, as shown in Table 4.

[0025] Table 4 1 L1, L36 10 L12, L24 2 L4, L6 11 L14, L28 3 L5, L27 12 L29, L31 4 L3, L35 13 L20, L32 5 L15, L17 14 L19, L30 6 L26, L33 15 L16, L23 7 L22, L34 16 L2, L10 8 L7, L8 17 L9, L13 9 L11, L25 18 L18, L21 The load recovery curve and the user recovery curve are as follows: Figure 8 and Figure 9 As shown in Table 5, the evaluation indicators for the recovery process proposed in S2 were calculated.

[0026] Table 5 result 40190.9 40190.9 90 47.5 10.9103 27.3778 The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Other variations and modifications are possible without departing from the technical solutions described in the claims.

Claims

1. An assessment method for the post-disaster multi-fault recovery process of power distribution systems considering maintenance strategies, characterized by: Includes the following steps: S1, determine the fault scenario and maintenance parameters, specifically: S101, Determine the network topology parameters of the power distribution system, including: branch information, user information and power supply information; S102, determine the set of faulty components in the power distribution system after the disaster, obtain the system fault scenario, analyze and statistically analyze the power outage load, and obtain the initial state before the system begins to recover; S103, determine the number of maintenance teams in the system and the maintenance time for individual components; S2, Establish an evaluation index system for the recovery process of multiple faults in the power distribution system; S3 optimizes the maintenance attributes of the faulty component and determines the optimal maintenance strategy, specifically by selecting the strategy that minimizes the cumulative power outage amount. As the objective function, the decision variable is the element. exist Given the state at any given time, since the objective function is to minimize load loss, the constraints can be transformed into inequalities. When a component fails on the power supply path of a load node, that load node loses power, and the power outage occurs to the load. Greater than the electrical load of this node under normal conditions After linearizing this constraint, the expression is as follows: Define the power supply path matrix for load nodes , The number of rows and columns of the matrix are respectively and , The number of load nodes, For the number of lines, The first in the matrix Line 1 Column elements Indicates the line Is it a load node? The power supply path, When it is 1, it indicates the line For load nodes The power supply path, When it is 0, it indicates the line Not a load node The power supply path, Indicates the line work status, A value of 1 indicates a line. Normal work, A value of 0 indicates a line. Fault, For load nodes Lost load, For load nodes The power load under normal conditions; by optimizing the line state vector at each time point, the optimal maintenance strategy can be obtained; after constraining the power outage load, the constraint of the number of maintenance team resources is also required, specifically: ； Based on the optimal maintenance strategy, the power restoration time for each load and user during the restoration process is determined, and load restoration curves and user restoration curves are obtained accordingly. Then, evaluation indicators for each restoration process are calculated.

2. The method for evaluating the post-disaster multi-fault recovery process of a power distribution system considering maintenance strategies, as described in claim 1, is characterized in that... Specifically, S2 is: A multi-fault recovery process evaluation index system for power distribution systems is established based on three aspects: disaster severity, recovery efficiency, and dynamic curve. S201, the indicators related to the severity of the disaster include: Real-time power outage load: express The size of the load that is constantly experiencing power outages. Number the load nodes. For the first The load size of each load node, for A set of load nodes that experience power outages at any given time; Number of users experiencing real-time power outages: express The number of users experiencing constant power outages. For the first Number of users per load node; Total power outage amount: This represents the total power outage amount across all load nodes in the system during the entire post-disaster recovery process. For the first The duration of power outages at each load node, It is the set of all load nodes that have experienced power outages; Total number of households affected by power outages: This represents the cumulative number of households experiencing power outages across all load nodes in the system during the entire post-disaster recovery process. S202. Recovery efficiency related indicators include: System power-on time: This indicates the time required for the system to fully restore power, that is, the time taken from the start of post-disaster repairs to the restoration of power to all loads. Average power restoration time per user: This represents the average time required to restore power to all users experiencing power outages in the system. Power loss load recovery speed: This indicates the recovery speed of the system's power-loss load, that is, the amount of load that is restored to power per unit time; Power recovery speed for users experiencing power outages: This indicates the recovery speed for users who have experienced a power outage, i.e., the number of users whose power is restored per unit of time. S203. The dynamic recovery curve specifically includes the load recovery curve and the user recovery curve.

3. The method for evaluating the post-disaster multi-fault recovery process of a power distribution system considering maintenance strategies, as described in claim 1, is characterized in that... The determination of the optimal maintenance strategy in S3 specifically involves: Objective function: Select the minimum cumulative power outage amount As the objective function, the decision variable is the element. exist Real-time status, cumulative power outage amount The calculation formula is: Constraints: The power supply path matrix for the load nodes is as follows: Assuming all components fail after the disaster, the initial state vector of the system is: For a load node, it can only use power normally when all lines in the power supply path are working properly. If any line in the power supply path fails, the load node will be in a power outage state. Therefore, the power consumption status of each node can be determined based on the load node power supply path matrix and line state vector. Since the objective function is to minimize load loss, the constraints can be transformed into inequalities. When a component fails on the power supply path of a load node, the load node loses power, and the power outage occurs to the load. Greater than the electrical load of this node under normal conditions .

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