A method and system for constructing a core backbone network based on invulnerability of a power system

Abstract

The application discloses a method and system for constructing a core backbone network architecture based on invulnerability of a power system, and the method comprises the following steps: constructing a corresponding power directed graph according to a topological structure of the power system; statistically processing historical power flow data of the power system based on the power directed graph, and constructing an invulnerability index of the core backbone network architecture; constructing an objective function of the core backbone network architecture by considering the invulnerability index, total length of lines of the core backbone network architecture and connectivity; and optimizing the objective function by using a particle swarm optimization algorithm, so as to obtain an optimal core backbone network architecture of the power system. The invulnerability of the power system is quantitatively evaluated by using the invulnerability evaluation index, and the optimal core backbone network architecture is obtained, so that the operation stability of the power system is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of power system emergency, and particularly relates to a method and system for constructing a core backbone network architecture based on the invulnerability of a power system. BACKGROUND

[0002] The invulnerability of a power system can be understood as the limit ability of the power system to maintain the power supply of key users under unconventional extreme conditions. It contains two connotations: first, under unconventional extreme scenarios, due to the intentional and strong burstiness, the power system cannot take into account the power demand of most users (the "supply" demand), and needs to ensure the power supply of important users (the "heavy" demand). Second, unconventional extreme events often have high damage, and the damage caused by them often cannot be recovered in a short time, so it cannot be described by the resilience of the power system. In order to improve the invulnerability of the power system, the core backbone network architecture of the power system is often constructed to ensure that the relatively important part of the power system network structure, including important loads, important power sources, important lines, etc., can run stably under extreme scenarios.

[0003] There are two existing methods for constructing the core backbone network architecture of the power system. One is based on the importance of the power grid elements, and the importance of the power grid elements is sorted by drawing a directed graph of the power grid topology based on graph theory, and the elements with high importance are included in the backbone network architecture. However, this method only considers the connection relationship and power flow characteristics of the power grid elements, and does not consider the survival properties of the power grid under extreme scenarios, so the core backbone network architecture constructed in this way does not evaluate the survival ability under extreme events from the perspective of invulnerability. The other method is based on the business function of the power grid branch, and important branches are selected by using the cost-benefit method to construct the backbone network architecture. This method only helps to speed up the recovery of the power grid in normal scenarios, and cannot be applied in extreme scenarios, so the applicability is not high. SUMMARY

[0004] The application provides a method and system for constructing a core backbone network architecture based on the invulnerability of a power system, which considers the invulnerability of the power system under extreme scenarios to construct an optimal core backbone network architecture and improve the operation stability of the power system under various scenarios.

[0005] The first aspect of the application provides a method for constructing a core backbone network architecture based on the invulnerability of a power system, which comprises:

[0006] constructing a corresponding power directed graph according to the topological structure of the power system;

[0007] statistically analyzing historical power flow data of the power system based on the power directed graph to construct an invulnerability index of the core backbone network architecture;

[0008] The target function of the core backbone network architecture is constructed by considering the invulnerability index, the total length of lines of the core backbone network architecture and connectivity;

[0009] The optimal core backbone network architecture of the power system is obtained by optimizing the target function through a particle swarm optimization algorithm.

[0010] The above scheme first constructs a power directed graph capable of clearly describing the operation between nodes and lines in the power system based on the topology of the power system. Then, the historical power flow data of the power system is counted through the nodes and lines in the power directed graph, and the invulnerability index of the core backbone network architecture in an extreme scenario is obtained by evaluating the power flow balance of the nodes and lines. On the basis of meeting the invulnerability index as much as possible, the comprehensive optimization of the invulnerability index, the total length of lines of the core backbone network architecture and connectivity is taken as the target to construct the corresponding target function. Because the shorter core backbone network architecture is less likely to be affected by extreme events, the core backbone network architecture with shorter line length is constructed as much as possible under the condition of ensuring that the network architecture has high connectivity and can resist extreme events. Finally, the particle swarm optimization algorithm is used to optimize the target function, and the optimal core backbone network architecture of the power system that can ensure stable operation in an extreme scenario is obtained, thereby ensuring power supply to important users in various scenarios and improving power quality.

[0011] In a possible implementation method of the first aspect, the corresponding power directed graph is constructed according to the topology of the power system, specifically:

[0012] Data of each node of the power system is collected to obtain historical power flow data of the power system;

[0013] The power directed graph is constructed according to the historical power flow data of the power system and the topology;

[0014] The node adjacency matrix of the power directed graph is constructed according to the adjacency relationship between each node in the power directed graph;

[0015] The connectivity of each node in the power directed graph is evaluated based on the node adjacency matrix to obtain the natural connectivity of each node in the power directed graph.

[0016] The above scheme processes the adjacency relationship and connectivity between each node based on the power directed graph, evaluates whether other paths of the node can be used as alternative paths to ensure normal operation of the power system when some paths cannot be connected due to failure in an extreme scenario, and obtains the natural connectivity of each node for evaluating the invulnerability of the power system in an extreme scenario.

[0017] In a possible implementation method of the first aspect, the natural connectivity is specifically:

[0018] wherein the natural connectivity of a node is specifically expressed as:

[0019]

[0020] wherein s i is the natural connectivity of the i th node, N is the total number of nodes, λ i is the eigenroot of the i th node in the node adjacency matrix.

[0021] In a possible implementation method of the first aspect, the invulnerability index of the core backbone network architecture is constructed by statistically processing power system historical power flow data based on the power directed graph, and specifically comprises:

[0022] For the resistance, connectivity, security and recovery of the power system under extreme scenarios, the resistance index, the connectivity index, the security index and the recovery index are constructed by statistically processing power system historical power flow data based on the power directed graph.

[0023] The resistance index comprises a line preservation rate, a power supply preservation rate and a load preservation rate; the connectivity index comprises a network architecture relative tightness, a network architecture relative cohesion, an average power transmission distance and an average power transmission margin; the security index comprises a line stability margin index, a node voltage margin index and a power supply power margin index; and the recovery index comprises a generator standby index, a load recovery degree index and a number of tie lines index.

[0024] The resistance index, the connectivity index, the security index and the recovery index are normalized respectively to obtain the invulnerability index.

[0025] The above scheme designs the invulnerability index from the four aspects of resistance, connectivity, security and recovery, and considers the influence of load loss, power loss and line failure under extreme scenarios on the operation stability of the power system. Through these indexes, the advantages and disadvantages of different network architectures under extreme scenarios can be intuitively evaluated, thereby providing data support for subsequent construction of the optimal core backbone network architecture.

[0026] In a possible implementation method of the first aspect, the invulnerability index specifically comprises:

[0027] The invulnerability index specifically comprises:

[0028]

[0029] wherein S urv is the invulnerability index, ω1, ω2 and ω3 are weight coefficients, η is the number of indexes, ψ i is the combination of the resistance index, the connectivity index, the security index and the recovery index, s iThe natural connectivity of the i-th node is s0, and the connectivity of the initial network frame is s0.

[0030] In a possible implementation method of the first aspect, a target function of the core backbone network frame is constructed by considering the invulnerability index, the total length of lines of the core backbone network frame, and the connectivity, and specifically, the target function is:

[0031] According to the invulnerability index, the total length of lines of the power system, and the switching state of the lines, a target function is constructed with the comprehensive optimization of the invulnerability index, the total length of lines of the core backbone network frame, and the connectivity as the target;

[0032] According to the power flow constraint of the power system and the line constraint of the power system, a constraint condition of the core backbone network frame is constructed.

[0033] In a possible implementation method of the first aspect, the target function and the constraint condition are specifically:

[0034] The target function has a specific expression as follows:

[0035]

[0036] In the formula, F represents the target function, L1 represents the total length of lines of the core backbone network frame, L0 represents the total length of lines of the power system, N represents the total number of lines, xi represents the switching state of the i-th line, Bi represents the impedance of the i-th line, j represents the branch end point sequence number of the core backbone network frame, Q represents the branch end point set of the core backbone network frame, Ψ(j) represents the set of the resistance index, the connectivity index, the security index, and the recovery index, S represents the natural connectivity of the i-th node, and s0 represents the connectivity of the initial network frame. L i xi represents the switching state of the i-th line, Bi represents the impedance of the i-th line, j represents the branch end point sequence number of the core backbone network frame, Q represents the branch end point set of the core backbone network frame, Ψ(j) represents the set of the resistance index, the connectivity index, the security index, and the recovery index, S represents the natural connectivity of the i-th node, and s0 represents the connectivity of the initial network frame. urv The invulnerability index is F.

[0037] The constraint condition has a specific expression as follows:

[0038]

[0039] In the formula, li represents the length of the i-th line, N represents the total number of lines, xi represents the switching state of the i-th line, Bi represents the impedance of the i-th line, j represents the branch end point sequence number of the core backbone network frame, Q represents the branch end point set of the core backbone network frame, Ψ(j) represents the set of the resistance index, the connectivity index, the security index, and the recovery index, S represents the natural connectivity of the i-th node, and s0 represents the connectivity of the initial network frame. i LF li represents the length of the i-th line, N represents the total number of lines, xi represents the switching state of the i-th line, Bi represents the impedance of the i-th line, j represents the branch end point sequence number of the core backbone network frame, Q represents the branch end point set of the core backbone network frame, Ψ(j) represents the set of the resistance index, the connectivity index, the security index, and the recovery index, S represents the natural connectivity of the i-th node, and s0 represents the connectivity of the initial network frame.

[0040] In a possible implementation method of the first aspect, the target function is optimized by using a particle swarm optimization algorithm to obtain an optimal core backbone network frame of the power system, and specifically, the target function is optimized by using the particle swarm optimization algorithm.

[0041] ​​updating the particle swarm variables at a preset updating speed;

[0042] obtaining a current value of the objective function based on the updated particle swarm variables;

[0043] when the updated particle swarm variables satisfy a preset convergence condition, taking the current value as an optimization solution;

[0044] constructing the optimal core backbone network architecture according to the optimization solution.

[0045] In a possible implementation method of the first aspect, the current value of the objective function is obtained based on the updated particle swarm variables, and specifically:

[0046] taking the current value of the objective function as a second result;

[0047] calculating a first result of the objective function using the updated particle swarm variables;

[0048] if the first result is smaller than the second result, taking the first result as the current value of the objective function.

[0049] The second aspect of the application provides a core backbone network architecture construction system based on invulnerability of a power system, and the system comprises a directed graph construction module, an invulnerability index construction module, an objective function construction module, and an optimal core backbone network architecture construction module.

[0050] The directed graph construction module is configured to construct a corresponding power directed graph according to a topological structure of the power system.

[0051] The invulnerability index construction module is configured to statistically analyze historical power flow data of the power system based on the power directed graph, and construct an invulnerability index of the core backbone network architecture.

[0052] The objective function construction module is configured to construct an objective function of the core backbone network architecture by considering the invulnerability index, total length of lines of the core backbone network architecture, and connectivity.

[0053] The optimal core backbone network architecture construction module is configured to optimize the objective function by using a particle swarm optimization algorithm, and obtain an optimal core backbone network architecture of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0054] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0055] Figure 1 This is a schematic diagram of a specific process for constructing a core backbone network based on the resilience of a power system, according to a certain embodiment of this application.

[0056] Figure 2 This is a structural diagram of a core backbone network construction system based on the resilience of power systems, provided in a certain embodiment of this application. Detailed Implementation

[0057] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0058] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0059] First Embodiment

[0060] The core backbone network of a power system comprises relatively important parts of the power system network structure, including critical loads, critical power sources, and critical lines. In the event of extreme events such as severe natural disasters, the core backbone network is often used to maintain power supply to key users, ensuring that the power needs of important users are met even during power system failures, thus maintaining system operational stability. Therefore, the main research objective of this application is to construct an optimal core backbone network that significantly improves the resilience of the power system, enabling it to maintain power supply to important users to the greatest extent possible under both normal and extreme scenarios, and to accelerate the restoration of the grid to normal operation.

[0061] like Figure 1 As shown, Figure 1 This application provides a schematic flowchart of a method for constructing a core backbone network based on the resilience of a power system, according to a certain embodiment. The method for constructing a core backbone network based on the resilience of a power system includes steps S1 to S4, which are detailed below:

[0062] Step S1: Construct the corresponding directed power graph based on the power system topology.

[0063] In this embodiment, data is collected from each node and line of the power system to obtain historical power flow data and draw the topology of the power system. Then, based on the topology, a directed power graph that reflects the topology and power flow characteristics of the power system is constructed.

[0064] For example, a directed power graph is defined as G = (V, E), where G represents the set of nodes V and lines E, and satisfies the condition that nodes υ i ∈V, Line e i =(υ i υ j )∈E, line e i The direction is from node v i to node v j The total number of nodes is N B =|V|, the total number of lines is N L =|E|.

[0065] After defining the directed power graph, a node adjacency matrix is ​​constructed to reflect the adjacency relationships between nodes. The specific expression is as follows:

[0066]

[0067] The eigenvalues ​​of the node adjacency matrix A are λ. i , i = 1, 2, ..., N.

[0068] Based on the directed graph of the power grid, the more different paths that maintain connectivity between any two nodes, the greater the probability that the two nodes can still maintain connectivity through alternative paths when some paths fail. Correspondingly, the overall power grid can be approximated as having better resilience. Therefore, natural connectivity is defined as an index to quantitatively evaluate the redundancy of alternative paths between all nodes in the network structure. The natural connectivity of each node can be obtained from the eigenvalues ​​of the adjacency matrix A, with the specific expression as follows:

[0069]

[0070] In the formula, s i Let λ be the natural connectivity of the i-th node, N be the total number of nodes, and λ be the natural connectivity of the i-th node. i is the eigenvalue of the i-th node in the node adjacency matrix.

[0071] Because the adjacency matrix of the nodes and the natural connectivity between nodes are present in the topology of most power systems, these evaluation indicators have good universality and can be applied to most power grids.

[0072] Optionally, when drawing the directed power graph, this application embodiment only considers the transmission network with a voltage level of 35kV or above and the directly connected nodes, and does not consider the distribution network with a voltage level of 35kV or below and related nodes, and also ignores the ground branches of the power grid.

[0073] Step S2: Based on the power directed graph, perform statistical analysis on the historical power flow data of the power system to construct the resilience index of the core backbone network.

[0074] In this embodiment, the resilience of the power system is quantified from four aspects: resistance, connectivity, security, and resilience. These four aspects are used to construct four different indicators for the core backbone network: resistance, connectivity, security, and resilience, which are used to evaluate the constructed core backbone network and intuitively demonstrate the superiority or inferiority of different network structures.

[0075] To address the resilience, connectivity, security, and recoverability of power systems under extreme scenarios, historical power flow data of the power system are statistically analyzed based on the aforementioned directed power graph to construct resilience, connectivity, security, and recoverability indicators.

[0076] Among them, the resilience indicators include line retention rate, power supply retention rate and load retention rate; the connectivity indicators include the relative density of the grid, the relative cohesion of the grid, the average power transmission distance and the average power transmission margin; the security indicators include the line stability margin, the node voltage margin and the power supply margin; and the resilience indicators include the generator standby index, the load recovery rate index and the number of tie lines index.

[0077] Specifically, the line retention rate, grid relative density, grid relative cohesion, average power transmission distance, and average power transmission margin are derived from power flow data statistics of nodes and lines in the directed power graph.

[0078] The line retention rate D i (1), the specific expression is:

[0079]

[0080] Where, N LF N represents the number of power lines that fail after an extreme event. L This represents the total number of power lines in the power system.

[0081] The power retention rate D i (2), the specific expression is:

[0082]

[0083] In the formula, W BFW represents the amount of power loss in a power system after an extreme event. B This represents the total number of power sources in the power system.

[0084] The load retention rate D i (3), the specific expression is:

[0085]

[0086] Among them, LO BF LO represents the amount of load loss in a power system after an extreme event. B This represents the total number of loads in the power system.

[0087] The relative tightness K of the space frame i (1), the specific expression is:

[0088]

[0089] In the formula, K i This indicates that node i is surrounded by K. i There are t neighboring nodes, and there exists t i Line, C0 is the grid density of the power system, N B N represents the total number of nodes. BF This represents the total number of nodes affected by the extreme event.

[0090] The relative cohesion of the space frame K i (2), the specific expression is:

[0091]

[0092] In the formula, K0 represents the grid cohesion of the power system. This represents the weighted number of sides when the equivalent impedance of the power line is minimized.

[0093] The average power transmission distance K i (3), the specific expression is:

[0094] The average power transmission distance K i (3), the specific expression is:

[0095]

[0096] In the formula, D path (i) represents the electrical distance of the i-th power flow transmission path, and is the sum of the reactance values ​​of all branches on the path.

[0097] The average power transmission margin K i (4), the specific expression is:

[0098]

[0099] In the formula, ΔP path (i) represents the active power margin of the i-th power flow transmission path, P max (i), P path (i) represents the active power over-limit value and real-time value of the i-th power flow transmission path, respectively.

[0100] The generator standby index H i (1), the specific expression is:

[0101]

[0102] In the formula, G max (i), G(i) represents the maximum installed capacity and actual output of the i-th generator, and N... G This represents the number of generators in the power system.

[0103] The load recovery index H i (2), the specific expression is:

[0104]

[0105] In the formula, P m P represents the maximum active power of the current core backbone network, and P represents the actual active power of the current core backbone network.

[0106] The number of connecting lines index H i (3), the specific expression is:

[0107]

[0108] In the formula, N LL L represents the number of interconnecting lines in the current core backbone network, L(i) represents the total length of the i-th interconnecting line, and L0 represents the total length of the power system lines.

[0109] Then, resistance index D was analyzed separately. i Connectivity index K i Safety Index U i and recovery index H i Normalization is performed to form a new normalized index. Among them, Ψ i ∈[0, 1], i = 1, ..., N B It is a collection of resistance indicators, connectivity indicators, security indicators, and recovery indicators.

[0110] Based on the normalized index, a resilience index for the core backbone network of the power system is constructed, and the specific formula is as follows:

[0111]

[0112] In the formula, S urv Here, ω1, ω2, and ω3 are the survivability indicators, η is the number of indicators, and ψ is the weighting coefficient. i A collection of resistance, connectivity, security, and resilience metrics, s i Let be the natural connectivity of the i-th node, and s0 be the connectivity of the initial network structure. The weight coefficients must satisfy the following conditions:

[0113] In this embodiment of the application, η is 10, ||Ψ i || ∞ =max|Ψ i |

[0114] Step S3: Based on the survivability index, construct the objective function of the core backbone network with the goal of minimizing the total length of the core backbone network lines.

[0115] In this embodiment, the designed core backbone network, in addition to meeting the requirements of resilience and high connectivity, also needs to consider the total length of the lines within the network. Generally, shorter core backbone networks are less likely to be affected by extreme events; therefore, core backbone networks with shorter line lengths should be constructed as much as possible.

[0116] Based on the aforementioned resilience indicators, the total length of power system lines, and the line switching status, the objective function is constructed with the goal of achieving a comprehensive optimization of the aforementioned resilience indicators, the total length of the core backbone network lines, and connectivity. The specific expression is as follows:

[0117]

[0118] In the formula, F is the objective function, L1 is the total length of the core backbone network lines, L0 is the total length of the power system lines, and N is the total length of the network lines. L x represents the total number of lines. i Let B(i) represent the switching state of line i, B(i) represent the impedance of line i, j represent the branch endpoint number of the core backbone network, Q represent the set of branch endpoints of the core backbone network, Ψ(j) represent the set of resistance, connectivity, security, and recovery indices, and S represent the switching state of line i. urv This refers to the survivability index.

[0119] The switching status reflects the connectivity of the core backbone network.

[0120] When constructing the optimal core backbone network structure using the objective function, the following constraints also need to be satisfied:

[0121]

[0122] In the formula, l i Let N be the length of line i. LF Let H(x,y)≤θ represent the number of failed lines in the power system after an extreme event, H(x,y)≤θ represent the power flow inequality constraint of the power system, G(x,y)=0 represent the power flow equality constraint of the power system, and Φ(Q) represent the connectivity requirement of the core backbone network.

[0123] Among them, the constraints for constructing the core backbone network are based on the power flow constraints and line constraints of the power system.

[0124] Step S4: The objective function is optimized using the particle swarm optimization algorithm to obtain the optimal core backbone network of the power system.

[0125] In this embodiment of the application, the nodes and edges in the directed power graph are used as particle swarm variables, and the particle swarm variables are updated at a preset update rate.

[0126] For example, based on the directed electrical graph G(V, E), using (V i E i As a particle swarm variable,

[0127] Initialize the particle swarm as (V0, E0), and simultaneously initialize the current minimum value of the objective function as:

[0128]

[0129] The particle swarm variables are updated at preset update rates Δv and Δe, i.e., the update value of the particle swarm variables is set to (V... i +Δυ,E i +Δe), where (Δυ, Δe) represent the velocity values ​​of the set of nodes V and lines E, respectively.

[0130] Then, based on the constraints of the core backbone network, the current value of the objective function is obtained through the updated particle swarm variables. Next, it is determined whether the current value is less than the minimum value of the objective function; if it is, the minimum value of the objective function is updated.

[0131] Specifically, the current value of the objective function is used as the second result; the updated particle swarm variables are used to calculate the first result of the objective function; if the first result is less than the second result, the first result is used as the current value of the objective function.

[0132] Finally, when the updated particle swarm variable satisfies the preset convergence condition, the current value is used as the optimal solution.

[0133] Specifically, the convergence condition can be to determine whether the current particle swarm variable exceeds the maximum grid structure of the power system, i.e., the directed power graph G = (V, E). If it does, it means that the optimization algorithm has been completed and the minimum value of the current objective function is output.

[0134] Based on the optimization solution, construct the optimal core backbone network.

[0135] Implementing the embodiments of this application has the following beneficial effects:

[0136] This application first constructs a directed power graph based on the power system topology, clearly describing the operational status between nodes and lines in the power system. Then, using the nodes and lines in the directed power graph, historical power flow data of the power system is statistically analyzed. By evaluating the power flow balance of nodes and lines, an indicator that can be used to assess the resilience of the core backbone network under extreme scenarios is obtained. While satisfying the resilience indicator as much as possible, a corresponding objective function is constructed with the comprehensive optimization of the resilience indicator, the total length of the core backbone network lines, and connectivity as the goal. Because a shorter core backbone network is less likely to be affected by extreme events, a core backbone network with shorter line lengths is constructed as much as possible while ensuring high connectivity and resilience to extreme events. Finally, a particle swarm optimization algorithm is used to optimize the objective function, obtaining an accurate optimal core backbone network for the power system that can ensure stable operation even under extreme scenarios, ensuring power supply to important users in various scenarios and improving power quality.

[0137] Second Embodiment

[0138] Furthermore, in order to implement the core backbone network construction system based on power system resilience corresponding to the above method embodiments, and to achieve the corresponding functions and technical effects, Figure 2 A structural diagram of a core backbone grid construction system based on power system resilience is provided. For ease of explanation, only the parts relevant to this embodiment are shown. The core backbone grid construction system based on power system resilience provided in this application embodiment includes:

[0139] The directed graph construction module 201 is used to construct the corresponding directed power graph based on the topology of the power system.

[0140] In this embodiment of the application, data is collected from each node of the power system to obtain historical power flow data; the power directed graph is constructed based on the historical power flow data and the topology; the node adjacency matrix of the power directed graph is constructed based on the adjacency relationship between each node in the power directed graph; based on the node adjacency matrix, the connectivity of the paths between each node in the power directed graph is evaluated to obtain the natural connectivity of each node in the power directed graph.

[0141] The resilience index construction module 202 is used to statistically analyze the historical power flow data of the power system based on the power directed graph and construct the resilience index of the core backbone network.

[0142] In this embodiment, to assess the resilience, connectivity, security, and recoverability of a power system under extreme scenarios, historical power flow data of the power system is statistically analyzed based on the directed power graph to construct resilience, connectivity, security, and recoverability indices. These indices are then normalized to obtain the survivability index.

[0143] Among them, the resilience indicators include line retention rate, power supply retention rate and load retention rate; the connectivity indicators include the relative density of the grid, the relative cohesion of the grid, the average power transmission distance and the average power transmission margin; the security indicators include the line stability margin, the node voltage margin and the power supply margin; and the resilience indicators include the generator standby index, the load recovery rate index and the number of tie lines index.

[0144] The objective function construction module 203 is used to construct the objective function of the core backbone network based on the survivability index, with the goal of minimizing the total length of the core backbone network lines.

[0145] In this embodiment of the application, the objective function is constructed with the goal of minimizing the total length of the core backbone network lines, based on the survivability index, the total length of the power system lines, and the switching status of the lines; and the constraint conditions of the core backbone network are constructed based on the power flow constraints and line constraints of the power system.

[0146] The optimal core backbone network construction module 204 is used to optimize the objective function through the particle swarm optimization algorithm to obtain the optimal core backbone network of the power system.

[0147] In this embodiment, the nodes and edges in the directed power graph are used as particle swarm variables, and the particle swarm variables are updated at a preset update rate. Based on the constraints of the core backbone network, the current value of the objective function is obtained through the updated particle swarm variables. When the updated particle swarm variables satisfy the preset convergence conditions, the current value is used as the optimal solution. Based on the optimal solution, the optimal core backbone network is constructed.

[0148] In some embodiments, the directed graph construction module 201 further includes:

[0149] In this embodiment, data is collected from each node and line of the power system to obtain historical power flow data and draw the topology of the power system. Then, based on the topology, a directed power graph that reflects the topology and power flow characteristics of the power system is constructed.

[0150] For example, a directed power graph is defined as G = (V, E), where G represents the set of nodes V and lines E, and satisfies the condition that nodes υ i ∈V, Line e i =(υ i υ j )∈E, line e i The direction is from node v i to node v j The total number of nodes is N B =|B|, the total number of lines is N L =|E|.

[0151] After defining the directed power graph, a node adjacency matrix is ​​constructed to reflect the adjacency relationships between nodes. The specific expression is as follows:

[0152]

[0153] The eigenvalues ​​of the node adjacency matrix A are λ. i , i = 1, 2, ..., N.

[0154] Based on the directed graph of the power grid, the more different paths that maintain connectivity between any two nodes, the greater the probability that the two nodes can still maintain connectivity through alternative paths when some paths fail. Correspondingly, the overall power grid can be approximated as having better resilience. Therefore, natural connectivity is defined as an index to quantitatively evaluate the redundancy of alternative paths between all nodes in the network structure. The natural connectivity of each node can be obtained from the eigenvalues ​​of the adjacency matrix A, with the specific expression as follows:

[0155]

[0156] In the formula, s i Let λ be the natural connectivity of the i-th node, N be the total number of nodes, and λ be the natural connectivity of the i-th node. i is the eigenvalue of the i-th node in the node adjacency matrix.

[0157] Because the adjacency matrix of the nodes and the natural connectivity between nodes are present in the topology of most power systems, these evaluation indicators have good universality and can be applied to most power grids.

[0158] Optionally, when drawing the directed power graph, this application embodiment only considers the transmission network with a voltage level of 35kV or above and the directly connected nodes, and does not consider the distribution network with a voltage level of 35kV or below and related nodes, and also ignores the ground branches of the power grid.

[0159] In some embodiments, the resilience index construction module 202 further includes:

[0160] In this embodiment, the resilience of the power system is quantified from four aspects: resistance, connectivity, security, and resilience. These four aspects are used to construct four different indicators for the core backbone network: resistance, connectivity, security, and resilience, which are used to evaluate the constructed core backbone network and intuitively demonstrate the superiority or inferiority of different network structures.

[0161] To address the resilience, connectivity, security, and recoverability of power systems under extreme scenarios, historical power flow data of the power system are statistically analyzed based on the aforementioned directed power graph to construct resilience, connectivity, security, and recoverability indicators.

[0162] Among them, the resilience indicators include line retention rate, power supply retention rate and load retention rate; the connectivity indicators include the relative density of the grid, the relative cohesion of the grid, the average power transmission distance and the average power transmission margin; the security indicators include the line stability margin, the node voltage margin and the power supply margin; and the resilience indicators include the generator standby index, the load recovery rate index and the number of tie lines index.

[0163] Specifically, the line retention rate, grid relative density, grid relative cohesion, average power transmission distance, and average power transmission margin are derived from power flow data statistics of nodes and lines in the directed power graph.

[0164] The line retention rate D i (1), the specific expression is:

[0165]

[0166] Where, N LF N represents the number of power lines that fail after an extreme event. L This represents the total number of power lines in the power system.

[0167] The power retention rate D i (2), the specific expression is:

[0168]

[0169] In the formula, W BFW represents the amount of power loss in a power system after an extreme event. B This represents the total number of power sources in the power system.

[0170] The load retention rate D i (3), the specific expression is:

[0171]

[0172] Among them, LO BF LO represents the amount of load loss in a power system after an extreme event. B This represents the total number of loads in the power system.

[0173] The relative tightness K of the space frame i (1), the specific expression is:

[0174]

[0175] In the formula, K i This indicates that node i is surrounded by K. i There are t neighboring nodes, and there exists t i Line, C0 is the grid density of the power system, N B N represents the total number of nodes. BF This represents the total number of nodes affected by the extreme event.

[0176] The relative cohesion of the space frame K i (2), the specific expression is:

[0177]

[0178] In the formula, K0 represents the grid cohesion of the power system. This represents the weighted number of sides when the equivalent impedance of the power line is minimized.

[0179] The average power transmission distance K i (3), the specific expression is:

[0180] The average power transmission distance K i (3), the specific expression is:

[0181]

[0182] In the formula, D path (i) represents the electrical distance of the i-th power flow transmission path, and is the sum of the reactance values ​​of all branches on the path.

[0183] The average power transfer margin K i (4), the specific expression is:

[0184]

[0185] In the formula, ΔP path (i) represents the active power margin of the i-th power flow transmission path, P max (i), P path (i) represents the active power over-limit value and real-time value of the i-th power flow transmission path, respectively.

[0186] The generator standby index H i (1), the specific expression is:

[0187]

[0188] In the formula, G max (i), G(i) represents the maximum installed capacity and actual output of the i-th generator, and N... G This represents the number of generators in the power system.

[0189] The load recovery index H i (2), the specific expression is:

[0190]

[0191] In the formula, P m P represents the maximum active power of the current core backbone network, and P represents the actual active power of the current core backbone network.

[0192] The number of connecting lines index H i (3), the specific expression is:

[0193]

[0194] In the formula, N LL L represents the number of interconnecting lines in the current core backbone network, L(i) represents the total length of the i-th interconnecting line, and L0 represents the total length of the power system lines.

[0195] Then, resistance index D was analyzed separately. i Connectivity index K i Safety Index U i and recovery index H i Normalization is performed to form a new normalized index. Among them, Ψ i ∈[0,1],i=1,2,...,N B .

[0196] Based on the normalized index, a resilience index for the core backbone network of the power system is constructed, and the specific formula is as follows:

[0197]

[0198] In the formula, S urv Here, ω1, ω2, and ω3 are the survivability indicators, η is the number of indicators, and ψ is the weighting coefficient. i A collection of resistance, connectivity, security, and resilience metrics, s i Let be the natural connectivity of the i-th node, and s0 be the connectivity of the initial network structure. The weight coefficients must satisfy the following conditions:

[0199] In this embodiment of the application, η is 10, ||Ψ i || ∞ =max|Ψ i |

[0200] In some embodiments, the objective function construction module 203 further includes:

[0201] In this embodiment, the designed core backbone network not only needs to meet the tamper resistance index, but also needs to consider the total length of the lines in the network. Generally speaking, a shorter core backbone network is less likely to be affected by extreme events, so the core backbone network with the shortest possible line length should be constructed.

[0202] Based on the aforementioned resilience indicators, the total length of power system lines, and line switching status, and with the goal of achieving a comprehensive optimization of the resilience indicators, the total length of the core backbone network lines, and connectivity, the objective function is constructed, with the specific expression as follows:

[0203]

[0204] In the formula, F is the objective function, L1 is the total length of the core backbone network lines, L0 is the total length of the power system lines, and N is the total length of the network lines. L x represents the total number of lines. i Let B(i) represent the switching state of line i, B(i) represent the impedance of line i, j represent the branch endpoint number of the core backbone network, Q represent the set of branch endpoints of the core backbone network, Ψ(j) represent the set of resistance, connectivity, security, and recovery indices, and S represent the switching state of line i. urv This refers to the survivability index.

[0205] When constructing the optimal core backbone network structure using the objective function, the following constraints also need to be satisfied:

[0206]

[0207] In the formula, l i Let N be the length of line i. LFLet H(x,y)≤θ represent the number of failed lines in the power system after an extreme event, H(x,y)≤θ represent the power flow inequality constraint of the power system, G(x,y)=0 represent the power flow equality constraint of the power system, and Φ(Q) represent the connectivity requirement of the core backbone network.

[0208] Among them, the constraints for constructing the core backbone network are based on the power flow constraints and line constraints of the power system.

[0209] In some embodiments, the optimal core backbone network construction module 204 further includes:

[0210] In this embodiment of the application, the nodes and edges in the directed power graph are used as particle swarm variables, and the particle swarm variables are updated at a preset update rate.

[0211] For example, based on the directed electrical graph G = (V, E), using (V i E i As a particle swarm variable,

[0212] Initialize the particle swarm as (V0, E0), and simultaneously initialize the current minimum value of the objective function as:

[0213]

[0214] The particle swarm variables are updated at preset update rates Δv and Δe, i.e., the update value of the particle swarm variables is set to (V... i +Δυ,E i +Δe), where (Δυ, Δe) represent the velocity values ​​of the set of nodes V and lines E, respectively.

[0215] Then, based on the constraints of the core backbone network, the current value of the objective function is obtained through the updated particle swarm variables. Next, it is determined whether the current value is less than the minimum value of the objective function; if it is, the minimum value of the objective function is updated.

[0216] Specifically, the current value of the objective function is used as the second result; the updated particle swarm variables are used to calculate the first result of the objective function; if the first result is less than the second result, the first result is used as the current value of the objective function.

[0217] Finally, when the updated particle swarm variable satisfies the preset convergence condition, the current value is used as the optimal solution.

[0218] Specifically, the convergence condition can be to determine whether the current particle swarm variable exceeds the maximum grid structure of the power system, i.e., the directed power graph G = (V, E). If it does, it means that the optimization algorithm has been completed and the minimum value of the current objective function is output.

[0219] Based on the optimization solution, construct the optimal core backbone network.

[0220] Implementing the embodiments of this application has the following beneficial effects:

[0221] This application first constructs a directed power graph based on the power system topology, clearly describing the operational status between nodes and lines in the power system. Then, using the nodes and lines in the directed power graph, historical power flow data of the power system is statistically analyzed. By evaluating the power flow balance of nodes and lines, an indicator that can be used to assess the resilience of the core backbone network under extreme scenarios is obtained. While satisfying the resilience indicator as much as possible, a corresponding objective function is constructed with the comprehensive optimization of the resilience indicator, the total line length of the core backbone network, and connectivity as the goal. Because a shorter core backbone network is less likely to be affected by extreme events, a core backbone network with shorter line lengths is constructed to ensure high connectivity while resisting extreme events. Finally, a particle swarm optimization algorithm is used to optimize the objective function, obtaining an accurate optimal core backbone network for the power system that can ensure stable operation even under extreme scenarios, ensuring power supply to important users in various scenarios and improving power quality.

[0222] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, or improvements made by those skilled in the art within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for constructing a core backbone network based on the resilience of power systems, characterized in that, include: Based on the topology of the power system, construct the corresponding directed power graph; Based on the power directed graph, the historical power flow data of the power system is statistically analyzed to construct the resilience index of the core backbone network. By considering the aforementioned resilience indicators, the total length of the core backbone network lines, and connectivity, an objective function for the core backbone network is constructed. The objective function is specifically expressed as follows: In the formula, F is the objective function, L1 is the total length of the core backbone network lines, L0 is the total length of the power system lines, and N is the total length of the network lines. L x represents the total number of lines. i Let B(i) represent the switching state of line i, B(i) represent the impedance of line i, j represent the branch endpoint number of the core backbone network, Q represent the set of branch endpoints of the core backbone network, Ψ(j) represent the set of resistance, connectivity, security, and recovery indices, and S represent the switching state of line i. urv The aforementioned survivability index; The constraints of the core backbone network are specifically expressed as follows: In the formula, l i Let N be the length of line i. LF H(x,y)≤θ represents the number of failed lines in the power system under extreme scenarios and after being subjected to extreme events. H(x,y)≤θ represents the power flow inequality constraint of the power system. G(x,y)=0 represents the power flow equality constraint of the power system. Φ(Q) represents the connectivity requirement of the core backbone network. The objective function is optimized using the particle swarm optimization algorithm to obtain the optimal core backbone network of the power system.

2. The method for constructing a core backbone network based on the resilience of power systems according to claim 1, characterized in that, The construction of the corresponding directed power graph based on the power system topology is as follows: Data is collected from each node of the power system to obtain historical power flow data. Based on the historical power flow data and the aforementioned topology, the directed power graph is constructed. Based on the adjacency relationships between nodes in the directed power graph, construct the node adjacency matrix of the directed power graph; Based on the node adjacency matrix, the connectivity of the paths between nodes in the directed power graph is evaluated to obtain the natural connectivity of each node in the directed power graph.

3. The method for constructing a core backbone network based on the resilience of power systems according to claim 2, characterized in that, The natural connectivity is specifically defined as follows: The natural connectivity of a node is expressed as follows: In the formula, s i Let λ be the natural connectivity of the i-th node, N be the total number of nodes, and λ be the natural connectivity of the i-th node. i is the eigenvalue of the i-th node in the node adjacency matrix.

4. The method for constructing a core backbone network based on the resilience of power systems according to claim 1, characterized in that, The process of statistically analyzing historical power flow data of the power system based on the directed power graph to construct the resilience index of the core backbone network specifically includes: To address the resilience, connectivity, security, and recoverability of power systems under extreme scenarios, historical power flow data of the power system are statistically analyzed based on the aforementioned directed power graph to construct resilience, connectivity, security, and recoverability indicators. Among them, the resilience indicators include line retention rate, power supply retention rate and load retention rate; the connectivity indicators include relative grid compactness, relative grid cohesion, average power transmission distance and average power transmission margin; the security indicators include line stability margin, node voltage margin and power supply margin; and the resilience indicators include generator standby index, load recovery rate index and number of tie lines index. The resistance index, connectivity index, security index, and resilience index are normalized respectively to obtain the survivability index.

5. The method for constructing a core backbone network based on the resilience of power systems according to claim 4, characterized in that, The specific resilience indicators are as follows: The specific formula for the survivability index is as follows: In the formula, S urv Here, ω1, ω2, and ω3 are the survivability indicators, η is the number of indicators, and ψ is the weighting coefficient. i It is a set of resistance, connectivity, security, and resilience metrics, s i Let s be the natural connectivity of the i-th node, and s0 be the connectivity of the initial network structure.

6. The method for constructing a core backbone network based on the resilience of power systems according to claim 1, characterized in that, The process of optimizing the objective function using a particle swarm optimization algorithm to obtain the optimal core backbone network of the power system is as follows: The nodes and edges in the directed power graph are used as particle swarm variables, and the particle swarm variables are updated at a preset update rate. Based on the constraints of the core backbone network, the current value of the objective function is obtained through the updated particle swarm variables; When the updated particle swarm variable satisfies the preset convergence condition, the current value is used as the optimal solution; Based on the optimization solution, construct the optimal core backbone network.

7. The method for constructing a core backbone network based on the resilience of power systems according to claim 6, characterized in that, The step of obtaining the current value of the objective function through the updated particle swarm variables specifically involves: The current value of the objective function is taken as the second result; The first result of the objective function is calculated using the updated particle swarm variables; If the first result is less than the second result, then the first result is taken as the current value of the objective function.

8. A core backbone grid construction system based on the resilience of power systems, characterized in that, include: Directed graph construction module, survivability index construction module, objective function construction module, and optimal core backbone network construction module; Among them, the directed graph construction module is used to construct the corresponding directed power graph based on the topology of the power system; The resilience index construction module is used to statistically analyze the historical power flow data of the power system based on the power directed graph and construct the resilience index of the core backbone network. The objective function construction module is used to construct the objective function of the core backbone network by taking into account the resilience index, the total length of the core backbone network lines and the connectivity. The objective function is specifically expressed as follows: In the formula, F is the objective function, L1 is the total length of the core backbone network lines, L0 is the total length of the power system lines, and N is the total length of the network lines. L x represents the total number of lines. i Let B(i) represent the switching state of line i, B(i) represent the impedance of line i, j represent the branch endpoint number of the core backbone network, Q represent the set of branch endpoints of the core backbone network, Ψ(j) represent the set of resistance, connectivity, security, and recovery indices, and S represent the switching state of line i. urv The aforementioned survivability index; The constraints of the core backbone network are specifically expressed as follows: In the formula, l i Let N be the length of line i. LF H(x,y)≤θ represents the number of failed lines in the power system under extreme scenarios and after being subjected to extreme events. H(x,y)≤θ represents the power flow inequality constraint of the power system. G(x,y)=0 represents the power flow equality constraint of the power system. Φ(Q) represents the connectivity requirement of the core backbone network. The optimal core backbone network construction module is used to optimize the objective function using the particle swarm optimization algorithm to obtain the optimal core backbone network of the power system.

Images