Method and system for assessing load outage loss risk in resilient distribution networks

Abstract

The present invention provides a method and system for assessing the risk of load outage loss in a resilient distribution network. The method includes: constructing an extended probability graph model; obtaining a grid topology file and a grid line fault probability table of the resilient distribution network; solving the probability of power on at the load node at each moment in the target period; converting the power on probability of the load node at each moment in the target period into the power off probability of the load node at each moment in the target period, and calculating the load outage loss risk. The method and system for assessing the risk of load outage loss in a resilient distribution network provided by the present invention construct an extended probability graph model and utilize the extended probability graph model to calculate the load outage loss risk. The entire process mainly involves solving a nonlinear set of equations, which greatly improves computational efficiency. At the same time, it can overcome the problem of path duplication in traditional path search methods, and the obtained risk assessment results are more reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid risk assessment, and in particular to a method and system for assessing the risk of load outage losses in a resilient distribution network. Background Art

[0002] The distribution network is the power distribution system that runs from the main grid to the user end and is widely distributed within the power system. Because most of its equipment is medium- and low-voltage, its disaster prevention capabilities are low, and it often suffers greater losses when hit by natural disasters. Therefore, it is necessary to conduct pre-disaster accident evolution simulation and pre-disaster early warning preparation before a disaster occurs, explore the occurrence mechanism of extreme weather events and the characteristics of their impact on the power grid, and especially analyze the weak links in the system. A resilient distribution network is one that can perceive the operating status of the power grid, coordinate internal and external resources of the grid, proactively predict and prepare for various disturbances, proactively defend against them, and quickly restore important power loads. Pre-disaster analysis of a resilient distribution network is typically divided into several major parts, including equipment failure modeling, outage loss analysis, and disaster scenario generation.

[0003] In outage loss analysis, a path-searching algorithm is often used to calculate load outage probability. This method, typically based on a depth-first search, finds all paths from each load to the power node. The outage probability of the load node is then calculated as the probability that all power supply paths are out of service. This method does not involve power flow calculations and only uses an abstract graphical model for analysis. However, this method is complex in its calculation process, and paths often overlap, leading to duplication in the probability calculation and low reliability of the outage loss risk assessment results.

[0004] Therefore, an efficient and accurate load outage loss risk assessment method is urgently needed to solve the above problems. Summary of the Invention

[0005] The present invention provides a method and system for assessing the risk of load outage loss in a resilient distribution network, which are used to solve the defects of the load outage loss assessment method in the prior art, such as complex calculation and low reliability of the results.

[0006] In a first aspect, the present invention provides a method for assessing the risk of load outage losses in a resilient distribution network, comprising:

[0007] Constructing an extended probability graph model; wherein the extended probability graph model is used to solve the power-on probability of the target node based on the power-on probability of the surrounding nodes and connected lines;

[0008] Obtaining a grid topology file and a grid line failure probability table of the resilient distribution network; wherein the grid topology file includes load information, node information, and line information; and the grid line failure probability table includes the cumulative outage probability of each line at each time point;

[0009] Converting the cumulative outage probability of each line at each time point into the power-on probability of the line at each moment in the target period, and inputting the load information, node information, line information, and the power-on probability of the line at each moment in the target period into the extended probability graph model to obtain the power-on probability of the load node at each moment in the target period;

[0010] The power-on probability of the load node at each moment in the target period is converted into the power-off probability of the load node at each moment in the target period, and the load outage loss risk is calculated based on the power-off probability of the load node at each moment in the target period.

[0011] According to a method for assessing load outage loss risk in a resilient distribution network provided by the present invention, the construction process of the extended probability graph model includes:

[0012] The resilient distribution network is abstracted into node-type devices and line-type devices; the node-type devices include power sources and loads, and the line-type devices include power supply lines and transfer lines;

[0013] Setting association information between the node-type device and the line-type device; wherein the association information includes: the power supply node has a power probability of 1, and the load node draws power from its surrounding nodes and depends on the line connected to it;

[0014] A node power probability equation group is established based on the association information; wherein, the node power probability equation group uses the power probability of the connected lines as a known quantity and the power probability of the target node and its surrounding nodes as unknown variables.

[0015] According to a method for assessing the risk of load outage loss in a resilient distribution network provided by the present invention, the node power-on probability equation group is:

[0016]

[0017] Among them, P(e i ) is the probability that node i is powered, Ω S is the power node set, Ω i is the set of adjacent nodes of node i, Ω l is the set of load nodes, P(e j→i ) represents the probability of power being present in the direction of line j→i.

[0018] According to a method for assessing load outage loss risk in a resilient distribution network provided by the present invention, the process of calculating the power-on probability of a load node at each moment in a target period includes:

[0019] Inputting the load information, node information, line information, and the power-on probability of the line at each moment in the target period into the extended probability graph model to obtain a power-on probability equation group of the load node at each moment;

[0020] Constructing a corresponding Jacobian matrix according to the power probability equation group of the load node at each moment;

[0021] Based on the Jacobian matrix, the probability of power supply at the load node at each moment in the target period is obtained by solving the Newton iteration method.

[0022] According to a method for assessing the risk of load outage loss in a resilient distribution network provided by the present invention, the probability of power supply at a load node at each moment in the target period is converted into the probability of power loss at the load node at each moment in the target period. The conversion formula is:

[0023]

[0024] in, represents the power failure probability of the load node at time t, P t (e i ) represents the probability of power being available at the load node at time t.

[0025] According to a method for assessing load outage loss risk in a resilient distribution network provided by the present invention, the load outage loss risk is calculated as follows:

[0026]

[0027] Among them, R is the risk of loss due to outage of the entire network load, P i is the load power of load node i, W i is the loss of the unit power load outage at load node i, represents the power failure probability of the load node at time t, Ω l is the set of load nodes.

[0028] In a second aspect, the present invention further provides a resilient distribution network load outage loss risk assessment system, comprising:

[0029] A model building module is used to build an extended probability graph model; wherein the extended probability graph model is used to solve the power-on probability of the target node based on the power-on probability of the surrounding nodes and connected lines;

[0030] A data acquisition module is configured to acquire a grid topology file and a grid line failure probability table of the resilient distribution network; wherein the grid topology file includes load information, node information, and line information; and the grid line failure probability table includes the cumulative outage probability of each line at each time point;

[0031] a probability solving module, configured to convert the cumulative outage probability of each line at each time point into the probability of the line being energized at each moment in the target period, input the load information, node information, line information, and the probability of the line being energized at each moment in the target period into the extended probability graph model, and solve for the probability of the load node being energized at each moment in the target period;

[0032] The risk solving module is used to convert the probability of power supply of the load node at each moment in the target period into the probability of power outage of the load node at each moment in the target period, and calculate the load outage loss risk based on the power outage probability of the load node at each moment in the target period.

[0033] In a third aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for assessing the risk of load outage loss in a resilient distribution network as described above are implemented.

[0034] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described methods for assessing the risk of load outage losses in a resilient distribution network.

[0035] In a fifth aspect, the present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for assessing the risk of load outage losses in a resilient distribution network.

[0036] The method and system for assessing the risk of load outage loss in a resilient distribution network provided by the present invention construct an extended probability graph model and utilize the extended probability graph model to calculate the risk of load outage loss. The entire process mainly involves solving a set of nonlinear equations, which greatly improves the computational efficiency. At the same time, it can overcome the problem of path duplication in traditional path search methods, and the obtained risk assessment results are more reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 It is a flow chart of the method for assessing the risk of load outage loss in a resilient distribution network provided by the present invention;

[0039] Figure 2 is the extended probability graph of the abstracted node case;

[0040] Figure 3 It is a schematic diagram of the execution logic of the inner and outer process pools;

[0041] Figure 4 It is a structural diagram of the resilient distribution network load outage loss risk assessment system provided by the present invention;

[0042] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0043] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0044] Figure 1 The present invention provides a method for assessing the risk of load outage losses in a resilient distribution network, including:

[0045] S110: Constructing an extended probability graph model; wherein the extended probability graph model is used to solve the power-on probability of the target node based on the power-on probability of the surrounding nodes and connected lines.

[0046] With the development of information systems and the increasing complexity of distribution network structures, modern distribution networks are gradually exhibiting the interdependent characteristics of complex systems. For example, the power supply of load nodes is related to the reliability of power supply nodes and power supply lines; the control of transfer lines depends on the reliability of the control system communication link. Probabilistic graphical models are a general framework for constructing and utilizing probabilistic models of complex systems. In extreme weather scenarios, equipment such as power lines and distributed power sources (DGs) can be out of service according to probabilistic models. Whether a load node is out of service is related to whether the power supply path to the power source is out of service, which can be characterized by random variables. Because load outages are driven by complex logical relationships, using probabilistic graphical models to infer load outage probabilities can make the assessment of node power loss and other losses efficient and compact.

[0047] Probabilistic graphical models are generally divided into two categories: one using directed acyclic graphs (DAGs), known as Bayesian networks, and the other using undirected cyclic graphs (Markov networks). Resilient distribution networks have clear power supply directions, making directed graphs suitable. However, complex logical relationships exist, requiring cyclic graphs. Classical probabilistic graphical models struggle to fully describe these relationships.

[0048] Drawing on the local dependency concept of the classic probabilistic graphical model, this embodiment abstracts the resilient distribution network into two types of devices: node-type devices and line-type devices, and uses a set of equations to solve the probability of load power being available.

[0049] The construction process of the extended probability graph model in this embodiment is described in detail below:

[0050] First, the equipment in the distribution network is divided into two categories: node-type equipment and line-type equipment. Line-type equipment includes power supply lines and transfer lines. In the resilient distribution network, nodes can be divided into power nodes and load nodes according to the type of equipment connected to the busbar.

[0051] If we ignore bus failures, we can assume that the power supply in the distribution network is highly reliable, and its normal operation is independent of surrounding nodes (i.e., no failures). However, whether a load node has power depends on the power supply status of surrounding nodes. For a node to have power, at least one of its surrounding nodes must be able to supply it with power through the line between the two nodes.

[0052] In other words, the extended probabilistic graph model assumes that the power supply node can reliably supply power with a power supply probability of 1; the load node needs to obtain power from the surrounding nodes and depends on the lines connected to the node.

[0053] At a certain moment, the probability that the power node has power is:

[0054] P(e i )=1,i∈Ω s (1)

[0055] Among them, e i Indicates the power status of node i, P(e i ) is the probability that node i is powered, Ω S A set of power nodes.

[0056] The load node requires that the surrounding nodes have electricity and the surrounding lines can supply power normally. The probability of power supply is expressed as:

[0057]

[0058] Among them, Ω i is the set of adjacent nodes of node i. Adjacent nodes are nodes that can be reached from node i through only one edge. l is the set of load nodes, P(e j→i ) represents the probability of power supply in the direction of line j→i, that is, the probability that the line from node j to i is fault-free and the power supply direction is from j to i.

[0059] When calculating, it should be noted that P(e j→i ) and P(e i→j) represent different meanings, which is very important in loop network calculations. Combining the calculation formulas for the power supply node and the load node power probability, we can obtain the node power probability equation group, namely:

[0060]

[0061] Among them, P(e i ) is the probability that node i is powered, Ω S is the power node set, Ω i is the set of adjacent nodes of node i, Ω l is the set of load nodes, P(e j→i ) represents the probability of power supply in the direction of line j→i, which is given by the disaster failure probability model and is a known quantity.

[0062] S120: Obtain a grid topology file and a grid line failure probability table of the resilient distribution network; wherein the grid topology file includes load information, node information, and line information, and the grid circuit failure probability table includes the cumulative outage probability of each line at each time point.

[0063] It is understandable that the power grid topology file specifically includes the distribution network node number, node power, node unit power value data, as well as line number, line head end, end node number, whether it is a transfer line and other information.

[0064] S130: Convert the cumulative outage probability of each line at each time point into the power-on probability of the line at each moment in the target period, and input the load information, node information, line information and the power-on probability of the line at each moment in the target period into the extended probability graph model to solve for the power-on probability of the load node at each moment in the target period.

[0065] Since the extended probabilistic graphical model takes the probability of nodes being powered on as the variable to be solved, the problem of solving the probability of loads being powered on can be transformed into the problem of solving a set of nonlinear equations.

[0066] x i =P(e i ) is the variable to be solved in the equation, and the following system of equations is obtained:

[0067]

[0068] The number of unknowns in this system of equations is equal to the number of equations. The Newton iteration method can be used to solve this nonlinear system of equations. When using the Newton iteration method to solve this system of equations, the Jacobian matrix of the system of equations needs to be given. The calculation expressions of each term of the Jacobian matrix are given below. For the power node, we have:

[0069]

[0070] For load node i, it is necessary to determine whether node j is adjacent to node i. The expression is:

[0071]

[0072] Since the Jacobian matrix expression is known and easy to calculate, the Newton-Raphson method can be used to iteratively solve the nonlinear equations to obtain the probability of power supply at the load node at each moment in the target period.

[0073] S140: Convert the power-on probability of the load node at each moment in the target period into the power-off probability of the load node at each moment in the target period, and calculate the load outage loss risk based on the power-off probability of the load node at each moment in the target period.

[0074] It can be understood that the conversion formula used to convert the power-on probability of the load node at each moment in the target period into the power-off probability of the load node at each moment in the target period is:

[0075]

[0076] in, represents the power failure probability of the load node at time t, P t (e i ) represents the probability of power being available at the load node at time t.

[0077] The risk assessment indicators considered in this embodiment are the system equipment loss rate and load loss rate. To facilitate calculation, this embodiment abstracts the devices in the system into two categories: nodes and lines. For different devices, the system equipment loss rate is defined as the ratio of all devices in the power grid to failure, which is the expected cumulative time failure probability of each device. Its formula is expressed as:

[0078]

[0079] Among them, Ω brh Indicates line type equipment, Ω node Indicates a node-type device. represents the probability of device i failure at time t.

[0080] For a load, whether it loses power depends only on the power supply status of the node where it is located. Therefore, the load loss rate of the entire network is defined as the ratio of the expected load outage loss, and its formula is expressed as:

[0081]

[0082] Among them, P(e s |τ=t) represents the probability of load s being energized at time t, P s Indicates the power of load s, W s Indicates the value of load s.

[0083] In extreme weather scenarios, if the line repair and restoration process is not considered, any fault that occurs at any time will remain. The existence of the fault will cause system load loss, which accumulates over time. If the expected value of the node power outage loss is used to represent the system risk, the risk is defined as follows:

[0084]

[0085] Assume that there is no failure in the system at the initial moment, that is Then further expand the above formula to obtain:

[0086]

[0087] Thus, the calculation formula for the load outage loss risk in this embodiment is:

[0088]

[0089] Among them, R is the risk of loss due to outage of the entire network load, P i is the load power of load node i, W i is the loss of the unit power load outage at load node i, represents the power failure probability of the load node at time t, Ω l is the set of load nodes.

[0090] Therefore, it is only necessary to calculate the outage probability of each node at each moment. The risk calculation result is related to the analysis time, reflecting the time accumulation of risk.

[0091] The advantages of the method for assessing the risk of load outage loss in a resilient distribution network provided by the present invention are described in detail below through an example.

[0092] In order to verify the feasibility and correctness of the risk assessment method for load outage loss in a resilient distribution network, this embodiment takes a simple example for analysis. The example topology diagram is as follows: Figure 2 The example information is shown in Table 1 and Table 2:

[0093] Table 1 Node example information

[0094]

[0095]

[0096] Table 2 Line calculation example information

[0097] Example line number Line direction Line normal probability 1 1→2 0.9 2 2→3 0.9 3 4→5 0.9 4 2→5 0.45 5 5→2 0.45

[0098] First, use the path search method to calculate:

[0099] For nodes 1 and 4, the outage probability is 0. For node 2, its power supply paths are 1-2 and 4-5-2, and the probability of power outage on each path is 0.1 and 1-0.9×0.45=0.595, respectively. Therefore, the probability of power outage at node 2 is 0.1×0.595=0.0595, and the probability of power supply is 0.9405.

[0100] Node 5 has a similar power supply path, so the outage probability of node 5 is also 0.0595. For node 3, since its power supply path involves repeated path calculations, the outage probability of node 2 is used for calculation, resulting in P(e3) = P(e2) × 0.9 = 0.8465.

[0101] The path search algorithm needs to calculate sequentially according to the node number. The calculation between nodes is basically decoupled and cannot reflect the interdependence of nodes in disaster scenarios. Therefore, the calculated probability value may be too small. The results calculated based on the extended probability graph model are shown in Table 3:

[0102] Table 3 Calculation results of five node examples

[0103]

[0104]

[0105] It is not difficult to see that the resilient distribution network load outage loss risk assessment method provided by the embodiment of the present invention is more accurate than the system risk obtained by using the path search method.

[0106] Considering that the number of nodes to be analyzed in actual power grids is large and the topological relationships are complex, it is a large-scale problem and is still solved based on the Newton iteration method. However, the dimension of the Jacobian matrix in each step of the Newton iteration method is huge, and the calculation of the matrix inverse takes up a lot of computing resources, which is inefficient. In particular, each iteration step takes a long time to solve the incremental equation J(Δx) = F. The adjacency matrix of the actual power grid is relatively sparse, and specific methods for sparse matrices should be considered to improve computational performance. The solution methods for linear systems described by sparse matrices can be divided into two categories: direct methods and indirect methods. Direct methods usually refer to solution methods using methods such as decomposition and Gaussian elimination, while indirect methods are usually iterative methods.

[0107] For the system of equations Ax=b, the direct method solution steps are as follows:

[0108] Step 1: Symbolic analysis to obtain the permutation sorting matrices P and Q.

[0109] Step 2: Numerical decomposition to obtain L and U matrices that satisfy the decomposition relationship PAQ=LU.

[0110] Step 3: Substitute forward and back to solve the two triangular equations, i.e. Ly=P Tb,U(Q T x)=y.

[0111] The built-in spsolve function in the scipy sparse matrix package can be used to calculate the incremental equations in the sparse case. This method uses the UMFPACK solver to solve the problem. The UMFPACK solver is based on the multi-wavefront method and uses the characteristics of sparse matrices to obtain a series of dense sub-matrices (wavefronts). It converts the LU decomposition into operations such as assembly, elimination, and update of these wavefronts. Since the wavefront is a small-scale dense matrix, it can directly call high-performance matrix calculation libraries such as BLAS to improve parallelism.

[0112] For large-scale problems, high-performance servers are often used for computing. To further improve efficiency, a multi-process approach can be used to fully utilize computing resources and achieve high-speed computation. In engineering practice, some power grids assume reliable communications and often only care about the topological characteristics of the grid itself. Therefore, the solution of node outage probability can be decoupled in terms of timing, with the timing characteristics being reflected only in the input cumulative outage probability. In this case, node outage probabilities can be calculated in parallel. For different substations of the system being analyzed, since the substations have no or weak connections, the power supply between substations can be ignored when calculating node outage probabilities. Therefore, decoupling and parallel computing can also be performed, further improving computational efficiency.

[0113] This embodiment implements parallel computing based on the commonly used CPython. Due to the existence of the global interpreter lock (GIL), multithreading cannot bring effective performance improvement. The multiprocessing package can use subprocesses instead of threads for computing, which can effectively bypass the GIL and make full use of the processor's computing power. The multiprocessing package uses the daemon mode to start the subprocess by default, that is, when the main process exits, the subprocess also exits; while the concurrent.futures package uses the non-daemon mode to start the subprocess by default, and the running status of the main process does not affect the subprocess. The combination of the two can cope with different computing needs of the inner and outer layers. The inner and outer layers execute logic such as Figure 3 See attached. Figure 3 , the double-layer parallel execution logic is as follows:

[0114] After the outer site-level process pool assigns substation computation tasks, the substation process pool then allocates single-time risk computation tasks for the full time section of the system being analyzed. The process pool blocks until all risk computation tasks complete, retrieves the probability calculation results for each time section, performs risk assessment, and writes them to the data file, completing the substation computation task. The outer process pool is implemented as a non-daemon process, allowing tasks in the inner layer to terminate automatically without waiting for the results of the inner layer's execution. The inner process pool, however, uses a daemon process, which waits until the full time section calculation is complete before conducting risk analysis, ensuring the reliability of the computation results.

[0115] To verify the computational efficiency of the aforementioned parallel computing method, this example divides the parallel pool into a time layer (inner layer) and a site layer (outer layer). A parallel computing test was conducted on a computing server that met the computing configuration requirements, using 29 substations in a real power grid, totaling 31,146 nodes. The number of time sections was 20 in each case. The results are shown in Tables 4 and 5 below.

[0116] Table 4 Time layer parallel acceleration test data

[0117]

[0118]

[0119] Table 5 System layer parallel acceleration test data

[0120] Computing scenarios Parallel computing time (s) Serial calculation time (s) Speedup Minor disaster scenarios 14.2144 39.2468 2.7611 Severe disaster scenarios 54.0669 81.4187 1.5059

[0121] It should be noted that in this embodiment, the time layer parallelism is implemented using a process pool. From the data in Tables 4 and 5 above, it can be seen that the inner and outer process pools greatly improve the calculation speed of risk assessment. In particular, for the analysis of multiple sites and multiple time sections, the use of parallel computing can achieve a nearly tenfold acceleration effect, greatly improving the calculation efficiency and having engineering application value.

[0122] The resilient distribution network load outage loss risk assessment system provided by the present invention is described below. The resilient distribution network load outage loss risk assessment system described below and the resilient distribution network load outage loss risk assessment method described above can be referenced to each other.

[0123] Figure 4 The present invention provides a resilient distribution network load outage loss risk assessment system, including:

[0124] The model construction module 410 is used to construct an extended probability graph model. The extended probability graph model is used to solve the power-on probability of the target node based on the power-on probability of the surrounding nodes and connected lines.

[0125] Data acquisition module 420 is used to obtain a grid topology file and a grid line failure probability table of the resilient distribution network; wherein the grid topology file includes load information, node information, and line information, and the grid line failure probability table includes the cumulative outage probability of each line at each time point;

[0126] The probability solving module 430 is used to convert the cumulative outage probability of each line at each time point into the power-on probability of the line at each time point in the target period. The load information, node information, line information, and the power-on probability of the line at each time point in the target period are input into the extended probability graph model to solve for the power-on probability of the load node at each time point in the target period.

[0127] The risk solving module 440 is used to convert the power-on probability of the load node at each moment in the target period into the power-off probability of the load node at each moment in the target period, and calculate the load outage loss risk based on the power-off probability of the load node at each moment in the target period.

[0128] Specifically, in this embodiment, the process of constructing the extended probability graph model by the model construction module 410 includes:

[0129] First, the resilient distribution network is abstracted into node-type devices and line-type devices. Node-type devices include power sources and loads, while line-type devices include power supply lines and transfer lines.

[0130] Then, the association information between the node type devices and the line type devices is set; wherein the association information includes: the power supply node has a power probability of 1, and the load node draws power from its surrounding nodes and depends on the lines connected to it;

[0131] Finally, a set of node power-on probability equations is established based on the association information to obtain an extended probabilistic graphical model. In this set of node power-on probability equations, the power-on probabilities of connected lines are used as known variables, and the power-on probabilities of the target node and its surrounding nodes are used as unknown variables.

[0132] Specifically, in this embodiment, the process of the probability solving module 430 calculating the power-on probability of the load node at each time in the target period includes:

[0133] First, the load information, node information, line information, and the power-on probability of the line at each moment in the target period are input into the extended probabilistic graphical model to obtain the power-on probability equations of the load nodes at each moment.

[0134] Then, according to the probability equations of the load nodes being powered at each moment, the corresponding Jacobian matrix is ​​constructed;

[0135] Finally, based on the Jacobian matrix, the Newton iteration method is used to solve the probability of power supply at the load node at each moment in the target period.

[0136] It can be seen that the resilient distribution network load outage loss risk assessment system provided by the embodiment of the present invention uses the extended probability graph model constructed by the model construction module to realize the calculation of load outage loss risk. The entire process mainly involves the solution process of the nonlinear equation group, and the calculation efficiency is greatly improved. At the same time, it can overcome the problem of path duplication in traditional path search methods, and the obtained risk assessment results are more accurate and the system reliability is higher.

[0137] Figure 5 An example of a physical structure diagram of an electronic device is shown below. Figure 5As shown, the electronic device may include: a processor (processor) 510, a communication interface (Communications Interface) 520, a memory (memory) 530 and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call the logic instructions in the memory 530 to execute the resilient distribution network load outage loss risk assessment method, which includes: constructing an extended probability graph model; wherein the extended probability graph model is used to solve the energized probability of the target node based on the energized probability of the surrounding nodes and the connected lines; obtaining the grid topology file and the grid line failure probability table of the resilient distribution network; wherein the grid topology file contains load information, node information and line information, and the grid circuit failure probability table contains the cumulative outage probability of each line at each time point; converting the cumulative outage probability of each line at each time point into the energized probability of the line at each moment in the target period, and inputting the load information, node information, line information and the energized probability of the line at each moment in the target period into the extended probability graph model to solve for the energized probability of the load node at each moment in the target period; converting the energized probability of the load node at each moment in the target period into the power-off probability of the load node at each moment in the target period, and calculating the load outage loss risk based on the power-off probability of the load node at each moment in the target period.

[0138] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0139] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the resilient distribution network load outage loss risk assessment method provided by the above-mentioned methods, the method including: constructing an extended probability graph model; wherein the extended probability graph model is used to solve the energized probability of the target node based on the energized probability of the surrounding nodes and the connected lines; obtaining the grid topology file and the grid line failure probability table of the resilient distribution network; wherein the grid topology file contains load information, node information and line information, and the grid circuit failure probability table contains the cumulative outage probability of each line at each time point; converting the cumulative outage probability of each line at each time point into the energized probability of the line at each moment in the target period, and inputting the load information, node information, line information and the energized probability of the line at each moment in the target period into the extended probability graph model to solve for the energized probability of the load node at each moment in the target period; converting the energized probability of the load node at each moment in the target period into the power-off probability of the load node at each moment in the target period, and calculating the load outage loss risk based on the power-off probability of the load node at each moment in the target period.

[0140] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the resilient distribution network load outage loss risk assessment method provided by the above-mentioned methods, the method comprising: constructing an extended probability graph model; wherein the extended probability graph model is used to solve the energized probability of the target node based on the energized probability of the surrounding nodes and the connected lines; obtaining a grid topology file and a grid line failure probability table of the resilient distribution network; wherein the grid topology file contains load information, node information and line information, and the grid circuit failure probability table contains the cumulative outage probability of each line at each time point; converting the cumulative outage probability of each line at each time point into the energized probability of the line at each moment in the target period, and inputting the load information, node information, line information and the energized probability of the line at each moment in the target period into the extended probability graph model to solve for the energized probability of the load node at each moment in the target period; converting the energized probability of the load node at each moment in the target period into the power-off probability of the load node at each moment in the target period, and calculating the load outage loss risk based on the power-off probability of the load node at each moment in the target period.

[0141] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0142] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for assessing the risk of load outage loss in a resilient distribution network, characterized in that: include: Constructing an extended probability graph model; wherein the extended probability graph model is used to solve the power-on probability of the target node based on the power-on probability of the surrounding nodes and connected lines; Obtaining a grid topology file and a grid line failure probability table of the resilient distribution network; wherein the grid topology file includes load information, node information, and line information; and the grid line failure probability table includes the cumulative outage probability of each line at each time point; Converting the cumulative outage probability of each line at each time point into the power-on probability of the line at each moment in the target period, and inputting the load information, node information, line information, and the power-on probability of the line at each moment in the target period into the extended probability graph model to obtain the power-on probability of the load node at each moment in the target period; Converting the probability of power on at the load node at each moment in the target period into the probability of power off at the load node at each moment in the target period, and calculating the load outage loss risk based on the power off probability of the load node at each moment in the target period; The construction process of the extended probability graph model includes: The resilient distribution network is abstracted into node-type devices and line-type devices; the node-type devices include power sources and loads, and the line-type devices include power supply lines and transfer lines; Setting association information between the node-type device and the line-type device; wherein the association information includes: the power supply node has a power probability of 1, and the load node draws power from its surrounding nodes and depends on the line connected to it; A node power probability equation group is established based on the association information; wherein the node power probability equation group uses the power probability of the connected lines as a known variable and the power probability of the target node and its surrounding nodes as unknown variables; wherein the node power probability equation group is: Among them, P(e i ) is the probability that node i is powered, Ω S is the power node set, Ω i is the set of adjacent nodes of node i, Ω l is the set of load nodes, P(e j→i ) represents the probability of power being present in the direction of line j→i.

2. A method for assessing the risk of load outage loss in a resilient distribution network according to claim 1, characterized in that: The calculation process of the power supply probability of the load node at each moment in the target period includes: Inputting the load information, node information, line information, and the power-on probability of the line at each moment in the target period into the extended probability graph model to obtain a power-on probability equation group of the load node at each moment; Constructing a corresponding Jacobian matrix according to the power probability equation group of the load node at each moment; Based on the Jacobian matrix, the probability of power supply at the load node at each moment in the target period is obtained by solving the Newton iteration method.

3. A method for assessing the risk of load outage loss in a resilient distribution network according to claim 1, characterized in that: The power-on probability of the load node at each moment in the target period is converted into the power-off probability of the load node at each moment in the target period. The conversion formula is: in, represents the power failure probability of the load node at time t, P t (e i ) represents the probability of power being available at the load node at time t.

4. A method for assessing the risk of load outage loss in a resilient distribution network according to claim 1, characterized in that: The calculation formula for the load outage loss risk is: Among them, R is the risk of loss due to outage of the entire network load, P i is the load power of load node i, W i is the loss of the unit power load outage at load node i, represents the power failure probability of the load node at time t, Ω l is the set of load nodes.

5. A resilient distribution network load outage loss risk assessment system, characterized in that: include: A model building module is used to build an extended probability graph model; wherein the extended probability graph model is used to solve the power-on probability of the target node based on the power-on probability of the surrounding nodes and connected lines; A data acquisition module is configured to acquire a grid topology file and a grid line failure probability table of the resilient distribution network; wherein the grid topology file includes load information, node information, and line information; and the grid line failure probability table includes the cumulative outage probability of each line at each time point; a probability solving module, configured to convert the cumulative outage probability of each line at each time point into the probability of the line being energized at each moment in the target period, input the load information, node information, line information, and the probability of the line being energized at each moment in the target period into the extended probability graph model, and solve for the probability of the load node being energized at each moment in the target period; a risk solving module, configured to convert the probability of power on at the load node at each moment in the target period into the probability of power off at the load node at each moment in the target period, and calculate the load outage loss risk based on the power off probability of the load node at each moment in the target period; The construction process of the extended probability graph model includes: The resilient distribution network is abstracted into node-type devices and line-type devices; the node-type devices include power sources and loads, and the line-type devices include power supply lines and transfer lines; Setting association information between the node-type device and the line-type device; wherein the association information includes: the power supply node has a power probability of 1, and the load node draws power from its surrounding nodes and depends on the line connected to it; Establishing a node power-on probability equation group based on the association information; wherein the node power-on probability equation group uses the power-on probability of the connected lines as a known variable and the power-on probability of the target node and its surrounding nodes as unknown variables; Among them, the node power probability equation group is: Among them, P(e i ) is the probability that node i is powered, Ω S is the power node set, Ω i is the set of adjacent nodes of node i, Ω l is the set of load nodes, P(e j→i ) represents the probability of power being present in the direction of line j→i.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for assessing the risk of load outage loss in a resilient distribution network as described in any one of claims 1 to 4 are implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for assessing the risk of load outage loss in a resilient distribution network as claimed in any one of claims 1 to 4 are implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for assessing the risk of load outage loss in a resilient distribution network as claimed in any one of claims 1 to 4 are implemented.

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