Power grid evaluation method based on extreme weather

Abstract

The invention discloses a power grid evaluation method based on extreme weather, which comprises the following steps: taking the extreme weather of typhoon as a representative event, and firstly, comprehensively considering the wind speed characteristic and the electric pole operation state when the typhoon lands; establishing a power distribution network pole fault correction model under the typhoon disaster condition according to the relationship between the pole load and the pole bending strength; providing a load shedding risk index reflecting the power supply capability of the power distribution network after the fault, and calculating the load shedding risk when the typhoon disaster occurs by using a Monte Carlo state sampling method; and finally, verifying the effectiveness of the method by using an IEEE33 system example. The power grid risk assessment method based on the time sequence Monte Carlo simulation method has the advantages that the risk of the power grid under the typhoon disaster can be reasonably assessed, and the safe operation level of the power distribution network is improved through risk early warning.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system dispatching, and in particular relates to a power grid evaluation method based on extreme weather. Background Art

[0002] In recent years, the increasingly frequent extreme weather has brought severe challenges to the stable operation of the power system. Compared with the transmission network with strong reliability and high redundancy, the distribution network grid structure is relatively weak and more susceptible to changes in the external environment. Compared with the traditional reliability assessment, the risk assessment of the distribution network not only considers the failure probability of the components, but also needs to comprehensively consider the fault recovery process after the accident. Therefore, quantitative analysis of the risk level of distribution network operation under extreme weather will help power grid dispatchers understand the operation trend of the distribution network after encountering extreme weather, find out the weak links in the distribution network, and formulate corresponding emergency response strategies.

[0003] At present, most of the power grid performance analysis under extreme weather conditions at home and abroad adopts non-sequential methods to screen fault scenarios. For example, Yan Yuping and Dong Xin et al. use state sampling method to select fault scenarios to evaluate the risk operation level of distribution network under lightning disasters and other conditions. The non-sequential method has fast calculation speed, but ignores the dynamic development process of distribution network operation status under extreme weather attacks. The power outage and restoration process of distribution network under natural disasters is highly random and is affected by factors such as meteorological changes, component failure and maintenance status, and load transfer. At the same time, with the widespread access of DG in the distribution network, when the distribution network encounters serious faults and some loads lose the support of the main grid power supply, DG can form a temporary island with the adjacent nodes to restore power supply to the critical loads, which is an important link in the risk assessment process of the distribution network. Summary of the invention

[0004] In order to solve the technical problems existing in the background technology, the present invention aims to provide a power grid assessment method based on extreme weather. Taking typhoon as a representative extreme weather event, the wind speed characteristics and the operating status of the poles when the typhoon lands are comprehensively considered, and a distribution network pole fault correction model under typhoon disaster conditions is established according to the relationship between the pole load and the pole bending strength; then a load shedding risk index reflecting the power supply capacity of the distribution network after a fault is proposed, and the Monte Carlo state sampling method is used to calculate the load shedding risk when a typhoon disaster occurs; finally, the effectiveness of the method is verified using an IEEE33 system example.

[0005] In order to solve the technical problem, the technical solution of the present invention is:

[0006] A power grid assessment method based on extreme weather conditions includes the following steps:

[0007] S1: For typhoons, a representative extreme weather event, a vulnerability curve of power grid equipment components is constructed. The time, intensity, and path changes of typhoons are determined through meteorological forecast data, and the failure probability and fault repair time of equipment components are evaluated;

[0008] S2: A two-stage scheme of "search + adjustment" is adopted to evaluate the load loss of the distribution network under various fault conditions. In the search stage, the path search algorithm is used to search for load nodes connected to the main grid and DG to determine the number of islands; in the adjustment stage, the power balance of the islands is checked with the minimum load reduction as the objective function to ensure that each island meets the basic power balance.

[0009] S3: A risk assessment method for active distribution networks based on the time-series Monte Carlo simulation method is proposed. By setting different grid resource configuration scenarios and calculating the respective risk assessment indicators, an accurate assessment of the power outage risk level of the active distribution network under typhoon weather can be achieved.

[0010] Preferably, the overhead line failure rate model based on the vulnerability curve in step S1 is determined specifically as follows:

[0011]

[0012] Where: P f is the failure probability of the line under the action of a typhoon; v is the wind speed borne by the transmission line; V is the design value of the wind speed of the line. When the actual wind speed v borne by the line is less than the design value of the wind speed V, the failure probability is 0; when the wind speed v borne by the line is greater than 2 times the design value of the wind speed 2V, the failure probability is 1; in other states, the failure probability of the line is exponentially related to the wind speed.

[0013] Preferably, the typhoon wind field model in step S1 is determined specifically as follows:

[0014] During the typhoon landing, the wind speed that the transmission line is subjected to can be determined by factors such as the wind field model, the typhoon path, and the moving speed of the typhoon center. The present invention uses the Batts model commonly used in engineering practice to model the typhoon. The instantaneous wind speed calculation values ​​at each point in the wind field are as follows:

[0015]

[0016] Where: R max Indicates the maximum wind speed radius; represents the wind speed at the maximum wind speed radius; r represents the distance from the typhoon center; x represents the attenuation coefficient of the typhoon intensity, which generally takes a value between 0.5 and 0.7, and is taken as 0.6 in the present invention.

[0017] Compared with the scale of typhoon wind farms, the length of distribution lines is relatively short, so it can be assumed that all lines on the same feeder are subject to the same wind speed. Combining weather forecast data and the geographical location of the power grid, the wind speed v and failure probability P of each transmission line in the grid at each time during the typhoon can be obtained. f .

[0018] Preferably, the repair time modeling of the faulty component in step S1 is specifically as follows:

[0019] The repair time of faulty components in extreme weather is related to many factors, including weather intensity, location of faulty components, degree of damage to components, etc., which is a relatively complex model. Since this paper only uses the repair time to make a pre-evaluation of the operation risk of the distribution network, and does not involve the actual scheduling arrangements after the accident, a simple recovery model is used to simulate the repair time of faulty components. The specific formula is as follows:

[0020] t r =c+η (3)

[0021] Where: t r represents the total repair time of each component, and the constant c represents the minimum repair time of the faulty component, including the time when the fault is detected by the dispatcher and the time when the maintenance personnel are dispatched; the random variable η obeys the exponential distribution with parameter λ. In the present invention, the constant c is 3h and λ is 0.2h.

[0022] Under the influence of extreme weather, large-scale faults often occur in the distribution network, and maintenance resources are relatively limited in a short period of time. Therefore, the present invention adopts the following repair order: when the number of faults is less than the number of maintenance teams, personnel can be dispatched at the same time to repair the faults. If the number of faults is more than the number of maintenance teams, repairs are carried out in order of the time when the faults occurred.

[0023] Preferably, the "search phase" in step S2 is specifically determined as follows:

[0024] DFS is used to search for islands in the distribution network after a fault. The specific steps are as follows:

[0025] (1) Select the distribution network node connected to the substation as the initial root node, search along a path based on DFS, and save the searched nodes in the stack.

[0026] (2) Pop the nodes in the stack one by one according to the "last in, first out" principle. If there is an untraversed path to the popped node, it is regarded as the new root node and returns to step (1) for searching. Otherwise, the popping process continues until all nodes in the stack are popped.

[0027] (3) After the first two steps, all popped-up nodes form an island system. If the island contains all the nodes of the distribution network, the search is complete. If there are untraversed nodes in the distribution network, a node is randomly selected as the root node and returns to step (1).

[0028] The whole search process is illustrated by taking a 9-node power distribution system, where a fault occurs between nodes 3 and 4 and the whole system is divided into two independent parts.

[0029] (a) Starting from node 1 connected to the substation, the search is performed, and nodes 2, 6, and 7 are stacked in sequence. After reaching node 7, there is no adjacent unvisited node, and step (1) is terminated;

[0030] (b) In step (2), node 7 is popped out first. Since node 7 is only connected to node 6 which has been put into the stack, node 6 and node 2 are popped out. Among the nodes adjacent to node 2, there is node 3 which has not been put into the stack. Therefore, node 2 is regarded as the new root node and the process returns to step (1).

[0031] (c) After that, nodes 2 and 3 are pushed into the stack in this order. Node 3 has no other adjacent nodes, and the process goes to step (2). Nodes 3, 2, and 1 are popped out in this order. At this point, all nodes in the stack are popped out, and nodes 1, 2, 3, 6, and 7 form the first island.

[0032] (d) After entering step (3), there are still nodes in the system that have not been traversed. Node 4 is randomly selected as the root node to start the search, and the search returns to step (1). Finally, nodes 4, 5, 8, and 9 form the second island, and the search ends.

[0033] Preferably, the "adjustment phase" in step S2 is specifically determined as follows:

[0034] After the island search, the system is divided into the main distribution system and several island systems. The main distribution system is directly connected to the substation and can obtain sufficient power support, so there is generally no need to reduce the load. For the island system with DG, due to the capacity limitation of DG, in order to maintain the power balance of the island system, it is necessary to reduce the conventional load and ensure the stable power supply of important loads. The present invention constructs the following load reduction model with the goal of minimizing the weighted load shedding to simulate the operation process of the island system.

[0035]

[0036] Where: N represents the total number of isolated nodes; E represents the set of isolated branches; P Gi and Q Gi Respectively represent the active and reactive power of DG; P Li and Q Li Respectively represent the load active and reactive demands; P CiIndicates load reduction active power; w i represents the load weight; α i Indicates the load power factor angle; V i represents the node voltage amplitude; V0 represents the reference power; r ij and x ij Represents branch resistance and reactance respectively; P Gmax Indicates the upper limit of DG power; S ijmax Indicates the line transmission power limit; V min and V max is the node voltage limit. The constraints are node power balance constraint, relationship constraint between voltage and line parameters and power flow, load reduction constraint, DG output constraint, power flow safety constraint, and voltage safety constraint.

[0037] This model is a linear programming model and can be solved using a commercial solver. The above model is constructed and solved for all DG islands to obtain the total load reduction P for each island. i C For an island system without DG, all loads in the system cannot be restored to power. The load reduction of the entire distribution system is calculated by the following formula:

[0038]

[0039] Where: N D represents the number of island systems containing DG; C L It represents the total load demand in the island system without DG.

[0040] Preferably, the power grid risk assessment index in step S3 is specifically:

[0041] When the distribution network is affected by extreme weather disasters, a large number of power equipment are damaged, which in turn causes large-scale power outages, causing the load power of the distribution system to be reduced. As the repair process progresses, the load gradually returns to normal levels. Therefore, the probability of each fault scenario occurring under typhoon weather and the corresponding system load removal ratio are used to characterize the risk level of the distribution system. The specific formula is as follows:

[0042]

[0043] Where: E(·) represents the expected value; P c (t) represents the system load reduction power during the typhoon attack; P l (t) represents the normal load level of the system under the fault-free state; M represents the number of Monte Carlo simulations; p mIndicates the probability of scenario m. The index R reflects the impact of the typhoon on the load power of the distribution system, and its value is between [0, 1]. The closer its value is to 1, the higher the system risk level is, and the closer its value is to 0, the lower the system risk level is.

[0044] Preferably, the power grid risk assessment process in step S3 is specifically as follows:

[0045] When conducting risk assessment of the power grid under typhoon weather, the present invention focuses on two issues: first, the failure probability of overhead lines will change in real time with the movement of the typhoon, and its fault state will also change with the investment of maintenance resources; second, the process simulation of the power grid using DG to restore power supply after a fault occurs.

[0046] Based on the above considerations, the present invention proposes a power grid risk assessment method based on time series Monte Carlo simulation, and the specific steps are as follows:

[0047] (1) Before a typhoon passes through, information such as the typhoon center location, moving path direction, wind speed, and maximum wind speed radius can be obtained through meteorological forecast data. Combined with the geographical location information of the power grid, the wind speed that each overhead line will withstand during the typhoon can be obtained. The failure probability prediction value of the line at each time point can be obtained through the vulnerability curve of the component.

[0048] (2) Generate a set of fault scenarios based on the Monte Carlo simulation method. Generate a random number [0, 1] and compare it with the line fault probability. If the random number is less than the fault probability, the line is determined to be faulty and the line repair time is generated. If there are sufficient maintenance resources, the line will immediately enter the repair process after the fault, and the line will return to normal after the repair time. If there is no idle maintenance team, the repairs will be carried out in the order of the fault occurrence.

[0049] (3) Under each fault scenario, island division and load reduction are performed based on the grid response model proposed in step S2, and the total amount of grid load reduction under each fault scenario is calculated.

[0050] (4) Enter the next time step, update the typhoon center position and wind speed, and repeat steps (1), (2), and (3) until the typhoon passes and the power grid is no longer affected by the typhoon.

[0051] (5) According to the load reduction of the power grid at each moment, the load curve of the distribution system during the typhoon period can be obtained, thereby evaluating the risk level of the system.

[0052] The present invention also provides a power grid risk assessment system under extreme weather conditions, which can be used to implement the above-mentioned power grid risk assessment method, specifically comprising: a fault probability calculation module, an island number search module, a load adjustment module and a risk assessment module.

[0053] Failure probability calculation module: For typhoons, a representative extreme weather event, the vulnerability curve of power grid equipment components is constructed, and the time, intensity, and path changes of typhoons are determined through meteorological forecast data to evaluate the failure probability of equipment components;

[0054] Island number search module: In the search phase, the path search algorithm is used to search for load nodes connected to the main grid and DG to determine the number of islands;

[0055] Load adjustment module: In the adjustment phase, the objective function is to minimize load reduction and perform power balance check on isolated islands to ensure that each isolated island meets basic power balance.

[0056] Risk assessment module: A risk assessment method for active distribution networks based on the time-series Monte Carlo simulation method is proposed. By setting different grid resource configuration scenarios and calculating the respective risk assessment indicators, an accurate assessment of the power outage risk level of the active distribution network under typhoon weather can be achieved.

[0057] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for assessing power grid risks under extreme weather conditions when executing the program.

[0058] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for assessing power grid risks under extreme weather conditions is implemented.

[0059] Compared with the prior art, the advantages of the present invention are:

[0060] Taking typhoon as a representative event of extreme weather, the Monte Carlo method is used to simulate the operation status of the power grid, and the "search + adjustment" two-stage power grid operation response model is used to calculate the load reduction status of the power grid in various fault scenarios, so as to obtain the risk index of the power grid. The proposed risk assessment method can accurately reflect the risk level of system load power outage during typhoon attacks. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a flow chart of power grid risk assessment in an embodiment of the present invention;

[0062] Figure 2 is a schematic diagram of a power grid structure and a typhoon attack process in an embodiment of the present invention;

[0063] Figure 3 is a vulnerability curve diagram of an overhead line in a power grid according to an embodiment of the present invention;

[0064] Figure 4is a graph showing changes in wind speed and fault probability of overhead power lines in an embodiment of the present invention;

[0065] Figure 5 It is a graph of power grid load in various scenarios according to an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The specific implementation mode of the present invention is described below in conjunction with embodiments:

[0067] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to match the contents disclosed in the specification so that people familiar with this technology can understand and read them, and are not used to limit the conditions under which the present invention can be implemented. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0068] At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" cited in this specification are only for the convenience of description and are not used to limit the scope of implementation of the present invention. Changes or adjustments to their relative relationships should be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0069] Embodiment 1:

[0070] The present invention provides a power grid evaluation method based on extreme weather conditions, the specific process is as follows: Figure 1 shown.

[0071] The specific application steps are as follows:

[0072] Taking the IEEE 33-node system as an example, the risk assessment of the power grid under typhoon weather is carried out. The system contains 33 load nodes, 32 lines, and a total active load of 3715kW. All lines are overhead lines. The system contains 3 emergency generators as DGs with a power capacity of 600kW, which are deployed at nodes 8, 24, and 30 respectively. There are 3 maintenance teams, and at most 3 faulty lines are repaired in the same time period. Assuming that the power grid is located in the southeast coastal area, the typhoon takes the 2019 "Lekima" typhoon as an example, and the typhoon data comes from the China Meteorological Administration Typhoon Network. In this example, the simulation step length of wind speed and overhead line fault probability prediction is 15 minutes. The typhoon data time step provided by the information is 3 hours, and linear interpolation is used to fill in the blank data. Figure 2The figure is a schematic diagram of the power grid structure and the typhoon invasion process. 2019-08-09 17:00 is taken as the initial time of the typhoon, the initial coordinates of the typhoon center are the origin (0,0), and it moves along the O1-O3 path with a moving speed of 15km / h. The five-pointed star in the figure is the geographical location of the power grid, with coordinates of (100, 100)km. During the movement of the typhoon after landing, its wind force will gradually weaken over time, so the maximum wind speed will gradually decrease, and the maximum wind speed radius will gradually increase. Table 1 shows some characteristic parameter data of Typhoon Lekima, where the maximum wind speed and central air pressure are provided by website data, and the maximum wind speed radius is calculated by the Batts model.

[0073] Table 1- Data on some characteristic parameters of Typhoon Lekima

[0074]

[0075] Since the spatial scale of the power grid is much smaller than the typhoon wind path, in this example, it is assumed that all overhead lines are subjected to the same wind speed during a typhoon. Assuming that the wind speed design value of the overhead lines in the power grid is 30m / s, the vulnerability curve of the overhead lines in the power grid is obtained from formula (1). Figure 3 shown.

[0076] According to the maximum wind speed, maximum wind speed radius, moving path and geographical location of the typhoon, combined with the Batts model of the typhoon wind field and the vulnerability curve of the overhead power line, the wind speed and fault probability changes of the line at different times can be calculated. The calculation results are as follows: Figure 4 shown.

[0077] Depend on Figure 4 It can be seen that due to the geographical location of the power grid being on the moving path of the typhoon, when the typhoon gradually approaches the power grid from the initial stage, the wind speed borne by the overhead line increases. When the power grid is located at the maximum wind speed radius of the typhoon for the first time, the wind speed it bears reaches the maximum value, and then gradually decreases. As the typhoon moves, the wind speed on the line drops to the minimum and then gradually increases until the power grid is located at the maximum wind speed radius of the typhoon again, and the line wind speed reaches a maximum value again. Therefore, during the entire typhoon period, the wind speed variation curve borne by the overhead line of the power grid presents two peaks. The failure probability of the line is positively correlated with the wind speed borne, so the time-varying curve of the failure probability of the overhead line also presents a double peak shape.

[0078] In order to verify the feasibility of the proposed risk assessment method, risk assessment is performed on power grids under different resource configurations. The number of Monte Carlo simulations is set to 500, the single simulation time step is 1 hour, and the hourly failure rate of overhead lines is taken as the average of the four failure probability prediction values ​​in this period. The specific scenarios are as follows:

[0079] Scenario 1: Emergency resource allocation is performed based on the number of DGs and maintenance teams described previously.

[0080] Scenario 2: Based on Scenario 1, two more emergency generators are added and deployed at nodes 15 and 21 respectively. At this time, there are a total of five emergency generators in the power grid.

[0081] Scenario 3: Based on Scenario 1, the wind speed design value of the power grid overhead lines is increased to 35m / s.

[0082] Scenario 4: Based on Scenario 1, three more maintenance teams are added, and the power grid can repair six faulty lines at the same time.

[0083] After simulation calculation, Figure 5 The changes in load curves of distribution systems under different scenarios are described.

[0084] Depend on Figure 5 The system load curve of scenario 1 shows that the typhoon began to damage the distribution lines at 4h, and the system operating load level dropped rapidly. At 9h, the system load loss reached the most serious level, and the operating load was only 1140kW, which was 30.7% of the normal operating load level. At 13h, after the power grid was out of the maximum wind speed radius of the typhoon for the second time, the typhoon gradually moved away from the power grid and no longer damaged the distribution lines. As the repair process continued, the system operating load gradually recovered until the system was fully restored to the normal operating level at 46h.

[0085] The system load curves of Scenario 2 and Scenario 3 have roughly the same trend as that of Scenario 1, with the main differences being the minimum load operating level and the full recovery time. Compared with Scenario 1, the measures of Scenario 2 and Scenario 3 have effectively reduced the maximum load shedding of the system. Scenario 2 increases the number of emergency generators, so that more island loads can be supported by emergency generators when the severity of line faults is roughly the same as that of Scenario 1. Scenario 3 minimizes system load losses and recovers to fully normal operating levels earlier by improving the wind resistance of overhead lines and reducing the number of faulty lines.

[0086] Depend on Figure 5It can be seen that the biggest difference between scenario 4 and the other three scenarios in terms of system load curve is the load recovery speed due to the increase in the number of maintenance teams. Before 9 hours, the system load curve of scenario 4 is almost the same as that of scenario 1. After 9 hours, the load level of scenario 4 increases rapidly, and the recovery slope of the load curve is much greater than that of the other three scenarios. The reason for this phenomenon is that once a typhoon lands in a region, it will destroy the lines in the regional power grid in a very short time. At this time, maintenance resources cannot play their role. As the typhoon moves away from the power grid, the number of line failures no longer increases. At this time, more maintenance teams will significantly improve the system load recovery level.

[0087] According to the operating status of the system during the typhoon attack, the power grid risk index is calculated using formula (6). The calculation results are shown in Table 2.

[0088] Table 2 - Grid risk assessment results

[0089]

[0090] From the data in Table 2, we can see that for this typhoon weather, the risk index R value of the power grid under scenario 1 is 0.2763. By increasing the number of emergency power generation vehicles and maintenance teams, and improving the wind resistance of overhead lines, these measures can effectively reduce the system risk level. Compared with scenario 1, the risk levels of scenario 2, scenario 3, and scenario 4 are reduced by 22.1%, 62.2%, and 46.8%, respectively. Among them, improving the wind resistance of overhead lines is the most effective measure.

[0091] At the same time, the variance coefficients of the power grid risk indicators in each scenario are all less than 1%, indicating that the simulation results have converged and the number of Monte Carlo simulations selected as 500 is reasonable.

[0092] It can be seen from the above results that the method proposed in the present invention can accurately reflect the impact of different resource configurations of the system on the risk level of the power grid, and realize accurate assessment of the power outage risk level of the active distribution network under typhoon weather.

[0093] Embodiment 2:

[0094] The present invention provides a power grid risk assessment system under extreme weather conditions, which can be used to implement the above-mentioned power grid risk assessment method under extreme weather conditions, and specifically includes: a fault probability calculation module, an island number search module, a load adjustment module and a risk assessment module.

[0095] Fault probability calculation module: For typhoons, a representative extreme weather event, the vulnerability curve of power grid equipment components is constructed, and the time, intensity, and path changes of typhoons are determined through meteorological forecast data, and the failure probability and fault repair time of equipment components are evaluated;

[0096] Island number search module: In the search phase, the path search algorithm is used to search for load nodes connected to the main grid and DG to determine the number of islands;

[0097] Load adjustment module: In the adjustment phase, the objective function is to minimize load reduction and perform power balance check on isolated islands to ensure that each isolated island meets basic power balance.

[0098] Risk assessment module: A risk assessment method for active distribution networks based on the time-series Monte Carlo simulation method is proposed. By setting different grid resource configuration scenarios and calculating the respective risk assessment indicators, an accurate assessment of the power outage risk level of the active distribution network under typhoon weather can be achieved.

[0099] Embodiment 3:

[0100] The present invention provides a terminal device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in this embodiment can be used for the operation of the power grid risk assessment method under extreme weather, including the following steps:

[0101] S1: For typhoons, a representative extreme weather event, a vulnerability curve of power grid equipment components is constructed. The time, intensity, and path changes of typhoons are determined through meteorological forecast data, and the failure probability and fault repair time of equipment components are evaluated;

[0102] S2: A two-stage scheme of "search + adjustment" is adopted to evaluate the load loss of the power grid under various fault conditions. In the search stage, the path search algorithm is used to search for load nodes connected to the main grid and DG to determine the number of islands; in the adjustment stage, the power balance of the islands is checked with the minimum load reduction as the objective function to ensure that each island meets the basic power balance.

[0103] S3: A risk assessment method for active distribution networks based on the time-series Monte Carlo simulation method is proposed. By setting different grid resource configuration scenarios and calculating the respective risk assessment indicators, an accurate assessment of the power outage risk level of the active distribution network under typhoon weather can be achieved.

[0104] Embodiment 4:

[0105] The present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0106] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the method for assessing power grid risk under extreme weather conditions in the above-mentioned embodiment; the processor may load and execute the following steps:

[0107] S1: For typhoons, a representative extreme weather event, a vulnerability curve of power grid equipment components is constructed. The time, intensity, and path changes of typhoons are determined through meteorological forecast data, and the failure probability and fault repair time of equipment components are evaluated;

[0108] S2: A two-stage scheme of "search + adjustment" is adopted to evaluate the load loss of the power grid under various fault conditions. In the search stage, the path search algorithm is used to search for load nodes connected to the main grid and DG to determine the number of islands; in the adjustment stage, the power balance of the islands is checked with the minimum load reduction as the objective function to ensure that each island meets the basic power balance.

[0109] S3: A risk assessment method for active distribution networks based on the time-series Monte Carlo simulation method is proposed. By setting different grid resource configuration scenarios and calculating the respective risk assessment indicators, an accurate assessment of the power outage risk level of the active distribution network under typhoon weather can be achieved.

[0110] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0114] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the implementation methods of the present invention, and should be understood that the protection scope of the present invention is not limited to such special statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

[0115] The preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

[0116] Many other changes and modifications may be made without departing from the concept and scope of the present invention.It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A power grid assessment method based on extreme weather conditions, characterized in that: The method comprises: Construct a vulnerability curve for power grid equipment components under typhoons, a representative extreme weather event. Use meteorological forecast data to determine the time, intensity, and path of typhoons, and assess the failure probability and repair time of equipment components. Construct a power grid response model to evaluate the load loss of the distribution network under various fault conditions. In the search phase, use the path search algorithm to search for load nodes connected to the main grid and DG to determine the number of islands. In the adjustment phase, use the minimum load reduction as the objective function to perform power balance verification on the islands. The active distribution network risk assessment method based on the time-series Monte Carlo simulation method is used to set different distribution network resource configuration scenarios, calculate the respective risk assessment indicators, and accurately assess the power outage risk level of the active distribution network under typhoon weather.

2. A power grid assessment method based on extreme weather conditions according to claim 1, characterized in that: The overhead line failure rate model based on the fragility curve is determined as follows: Where: P f is the failure probability of the line under the action of a typhoon; v is the wind speed borne by the transmission line; V is the design value of the wind speed of the line; when the actual wind speed v borne by the line is less than the design value of the wind speed V, the failure probability is 0; when the wind speed v borne by the line is greater than 2 times the design value of the wind speed 2V, the failure probability is 1; in other states, the failure probability of the line is exponentially related to the wind speed; The preset Batts model is used to model the typhoon and determine the typhoon wind field model V. The instantaneous wind speed calculation values ​​at each point in the wind field are as follows: Where: R max Indicates the maximum wind speed radius; V Rmax represents the wind speed at the maximum wind speed radius; r represents the distance from the typhoon center; x represents the attenuation coefficient of the typhoon intensity; By ensuring that all lines on the same feeder are subject to the same wind speed, combined with weather forecast data and the geographical location of the power grid, the wind speed v and failure probability P of each transmission line in the grid at each time during the typhoon can be obtained. f .

3. A power grid assessment method based on extreme weather conditions according to claim 1, characterized in that: The repair time of the faulty component is modeled as follows: The maintenance time is used to make a pre-evaluation of the operation risk of the distribution network, and the recovery model is used to simulate the repair time of the faulty components. The specific formula is as follows: t r =c+η(3) Where: t r represents the total repair time of each component, and the constant c represents the minimum repair time of the faulty component, including the time when the fault is detected by the dispatcher and the time when the maintenance personnel are dispatched; The random variable η follows an exponential distribution with parameter λ; When the number of faults is less than the number of maintenance teams, personnel can be dispatched simultaneously to repair the faults. If the number of faults is greater than the number of maintenance teams, repairs will be carried out in the order of the time when the faults occurred.

4. The method for evaluating a power grid under extreme weather conditions according to claim 1, characterized in that: The search phase is determined as follows: DFS is used to search for islands in the distribution network after a fault. The specific steps are as follows: Step S21: Select the distribution network node connected to the substation as the initial root node, search along a path based on DFS, and save the searched node in the stack; Step S22: pop the nodes in the stack one by one according to the last-in-first-out principle. If the popped node has an untraversed path, it is regarded as a new root node and returns to step S21 for searching. Otherwise, the popping process continues until all nodes in the stack are popped out. Step S23: After the first two steps, all popped-up nodes form an island system. If the island contains all the nodes in the distribution network, the search is completed; if there are untraversed nodes in the distribution network, a node is randomly selected as the root node and the process returns to step S21.

5. The method for evaluating power grid under extreme weather conditions according to claim 1, characterized in that: The adjustment phase is determined as follows: After the island search, the system is divided into the main distribution system and multiple island systems. The main distribution system is directly connected to the substation and can obtain sufficient power support without load reduction. For the island system with DG, due to the capacity limitation of DG, in order to maintain the power balance of the island system, it is necessary to reduce the conventional load and ensure the stable power supply of important loads. The following load reduction model is constructed with the goal of minimizing the weighted load shedding to simulate the operation process of the island system: Where: N represents the total number of isolated nodes; E represents the set of isolated branches; P Gi and Q Gi Respectively represent the active and reactive power of DG; P Li and Q Li Respectively represent the load active and reactive demands; P Ci Indicates load reduction active power; w i represents the load weight; α i Indicates the load power factor angle; V i represents the node voltage amplitude; V0 represents the reference power; r ij and x ij Represents branch resistance and reactance respectively; P Gmax Indicates the upper limit of DG power; S ijmax Indicates the line transmission power limit; V min and V max is the node voltage limit, and the constraints are node power balance constraint, relationship constraint between voltage and line parameters and power flow, load reduction constraint, DG output constraint, power flow safety constraint, and voltage safety constraint; The model is a linear programming model that can be solved using a commercial solver. The above model is constructed and solved for all DG islands to obtain the total load reduction P for each island. i C For an island system without DG, all loads in the system cannot resume power supply, and the load reduction of the entire distribution system is calculated by the following formula: Where: N D represents the number of island systems containing DG; C L It represents the total load demand in the island system without DG.

6. The method for evaluating power grid under extreme weather conditions according to claim 1, characterized in that: The grid risk assessment indicators are specifically: The probability of each fault scenario occurring under typhoon weather and the corresponding system load shedding ratio are used to characterize the risk level of the distribution system. The specific formula is as follows: Where: E(·) represents the expected value; P c (t) represents the system load reduction power during the typhoon attack; P l (t) represents the normal load level of the system under the fault-free state; M represents the number of Monte Carlo simulations; p m represents the probability of occurrence of scenario m; The index R reflects the impact of typhoons on the load power of the distribution system. Its value is between [0, 1]. The closer its value is to 1, the higher the system risk level is, and the closer its value is to 0, the lower the system risk level is.

7. The method for evaluating power grid under extreme weather conditions according to claim 1, characterized in that: The specific process of power grid risk assessment is as follows: Before a typhoon passes through, the location of the typhoon center, the direction of the moving path, the wind speed and the maximum wind speed radius can be obtained through meteorological forecast data. Combined with the geographical location information of the distribution network, the wind speed that each overhead line will withstand during the typhoon can be obtained. The failure probability prediction value of the line at each time point can be obtained through the vulnerability curve of the component; Generate a fault scenario set based on the Monte Carlo simulation method, generate a random number [0, 1] and compare it with the line fault probability. If the random number is less than the fault probability, the line is judged to have a fault, and the line repair time is generated at the same time; If there are sufficient maintenance resources, the line will immediately start the repair process after the fault, and the line will return to normal after the repair time. If there is no idle maintenance team, the repair will be carried out in the order of the fault occurrence; Under each fault scenario, island division and load reduction are performed based on the power grid response model, and the total load reduction of the distribution network under each fault scenario is calculated; Enter the next time step, update the typhoon center position and wind speed, and repeat the above steps until the typhoon passes and the distribution network is no longer affected by the typhoon; The load curve of the distribution system during the typhoon passage is obtained according to the load reduction of the distribution network at each moment, so as to evaluate the risk level of the system.

8. A power grid risk assessment system under extreme weather conditions, characterized in that: The system is applied to the power grid risk assessment method under extreme weather conditions as described in any one of claims 1 to 7, and the system comprises: a fault probability calculation module, an island number search module, a load adjustment module and a risk assessment module; Failure probability calculation module: For typhoons, a representative extreme weather event, the vulnerability curve of power grid equipment components is constructed, and the time, intensity, and path changes of typhoons are determined through meteorological forecast data to evaluate the failure probability of equipment components; Island number search module: In the search phase, the path search algorithm is used to search for load nodes connected to the main grid and DG to determine the number of islands; Load adjustment module: In the adjustment phase, the power balance of the island is checked with the minimum load reduction as the objective function; Risk assessment module: The risk assessment method of active distribution network using the time series Monte Carlo simulation method is used to set different grid resource configuration scenarios, calculate the respective risk assessment indicators, and accurately assess the power outage risk level of the active distribution network under typhoon weather.

9. A computer device, characterized in that: The invention comprises 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 method for assessing the risk of power grid under extreme weather conditions as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored on a computer-readable storage medium, and when the program is executed by a processor, the method for assessing power grid risk under extreme weather conditions described in any one of claims 1 to 7 is implemented.

Images