Collaborative power restoration method and system for distribution network after disasters

Abstract

The present invention discloses a method and system for collaborative power restoration of a distribution network after a disaster. The method includes obtaining real-time supply and demand status data and network topology constraint information, analyzing and extracting a set of key load nodes and boundary conditions of power flow distribution to construct a resource allocation model, and obtaining an optimized allocation strategy by integrating power flow analysis and multi-objective optimization method; combining temporary equipment access parameters, determining a temporary power supply network configuration and capacity allocation plan, and then planning a deployment path of the temporary power supply configuration to deploy a temporary power supply network for collaborative power restoration of the distribution network. It can quickly evaluate the supply and demand status in a post-disaster environment, optimize resource allocation, ensure power supply to key loads, improve the restoration efficiency and stability of the distribution network, and provide an effective solution for the rapid restoration of the post-disaster power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular, to a method and system for collaborative power restoration of a distribution network after a disaster. Background Art

[0002] As the lifeblood of modern social operation, the stability and recovery ability of the power system under extreme disasters are directly related to social security and sustainable economic development. Especially in areas such as Haicheng Mountain Island with complex geographical environments and limited resources, the distribution network not only has to cope with physical damage caused by natural disasters but also meet the urgent demand for power supply after the disaster. However, extreme disasters often lead to extensive damage to power infrastructure, and the recovery methods of the distribution network are particularly crucial in terms of efficiency and adaptability. How to quickly restore power supply in the case of resource scarcity and ensure the basic operation of society has become a key research area.

[0003] Currently, the restoration of the post-disaster distribution network mainly relies on preset static scheduling schemes or single-objective optimization strategies. However, these methods are difficult to adapt to the dynamically changing post-disaster scenarios. For example, static schemes are difficult to adapt to real-time supply and demand fluctuations, while single-objective optimization is prone to ignoring load differences or network topology constraints, resulting in insufficient power supply for critical users or uneven resource allocation. In addition, the access of mobile power sources and emergency equipment lacks systematic coordination and management, exacerbating the instability of the temporary power supply network. This significantly restricts the reasonable allocation of post-disaster power resources and the system restoration efficiency. Therefore, how to dynamically optimize the allocation ratio of power resources based on real-time supply and demand conditions and network topology constraints after extreme disasters, while achieving critical load guarantee, maximizing social benefits, and stable operation of the temporary power supply network, has become a key issue for the collaborative restoration method of the distribution network in Haicheng Mountain Island after the disaster. Summary of the Invention

[0004] The present invention provides a method and system for collaborative power restoration of a distribution network after a disaster. Based on real-time supply and demand conditions and network topology constraints, it dynamically optimizes the allocation ratio of power resources. At the same time, with the goals of critical load guarantee, maximizing social benefits, and stable operation of the temporary power supply network, it can quickly evaluate the supply and demand situation in the post-disaster environment, optimize resource allocation, ensure power supply for critical loads, improve the restoration efficiency and stability of the distribution network, and provide an effective solution for the rapid restoration of the post-disaster power system.

[0005] To achieve the above object, an embodiment of the present invention provides a method for collaborative power restoration of a distribution network after a disaster, including:

[0006] Obtain the real-time supply and demand data and network topology information of the post-disaster distribution network in the target area, extract and analyze the real-time supply and demand data, and determine the current load rating parameters of the distribution network; wherein, the real-time supply and demand data includes load demand data and power supply data;

[0007] Calculate the priority weight of each load node according to the load rating parameters, load historical data, and user type to determine the set of critical load nodes of the distribution network; calculate the feasible paths of the power flow of the distribution network according to the network topology information, and determine the boundary conditions of the power flow distribution of the distribution network;

[0008] Construct a resource allocation model according to the boundary conditions of the power flow distribution and the set of critical load nodes. Use the resource allocation model to determine the first resource allocation strategy through the real-time supply and demand data;

[0009] Construct a multi-objective optimization model according to the resource allocation efficiency, social benefits, and the boundary conditions of the power flow distribution; use the multi-objective optimization model to optimize the first resource allocation strategy to obtain the second resource allocation strategy; wherein, the multi-objective optimization model includes a comprehensive objective function and objective constraint conditions;

[0010] Determine the temporary power supply configuration of the distribution network according to the power flow distribution data in the second resource allocation strategy, the priority weight, and the access parameters of the temporary equipment;

[0011] Determine the power supply redundancy of each critical load node according to the resource allocation ratio in the second resource allocation strategy and the output power of the temporary power supply configuration to obtain the capacity allocation strategy of the distribution network;

[0012] Plan the deployment path of the temporary power supply configuration according to the power flow distribution data and voltage stability constraint parameters in the second resource allocation strategy, and the information of the temporary power supply configuration to perform coordinated power restoration on the distribution network.

[0013] As an improvement of the above solution, after planning the deployment path of the temporary power supply configuration, the method further includes:

[0014] Analyze the current load demand data according to the capacity allocation plan and the temporary power supply configuration to obtain the critical load guarantee rate and network stability index. Analyze the critical load guarantee rate and network stability index through a simulation algorithm to obtain the recovery efficiency evaluation result;

[0015] Update the load rating parameters and the resource allocation ratio according to the recovery efficiency evaluation result to adjust the resource allocation model and generate a third resource allocation strategy;

[0016] Plan the deployment path of the adjusted temporary power supply configuration according to the third resource allocation strategy to perform coordinated power restoration on the distribution network.

[0017] As an improvement to the above solution, obtaining the real-time supply and demand data and network topology information of the post-disaster distribution network in the target area, extracting and analyzing the real-time supply and demand data, and determining the current load rating parameters of the distribution network, including:

[0018] Obtaining the real-time supply and demand data and network topology information of the post-disaster distribution network in the target area, using the time series analysis method to analyze the change trend of the real-time supply and demand data, and obtaining the load fluctuation characteristics and the corresponding power supply status;

[0019] Calculating the matching deviation between the load fluctuation characteristics and the corresponding power supply status, determining the current supply and demand imbalance area and degree of the distribution network, so as to obtain the current supply and demand matching deviation distribution of the distribution network;

[0020] According to the supply and demand matching deviation distribution, determining the current load rating parameters of the distribution network.

[0021] As an improvement to the above solution, according to the load rating parameters, load historical data and user types, calculating the priority weight of each load node to determine the key load node set of the distribution network; according to the network topology information, calculating the feasible path of the power flow of the distribution network, and determining the boundary conditions of the power flow distribution of the distribution network, including:

[0022] According to the load rating parameters, load historical data and user types, calculating the priority weight of each load node;

[0023] Taking all load nodes with the priority weight greater than the preset load weight threshold as the key load node set of the distribution network;

[0024] According to the line connection status and damaged node location information in the network topology information, calculating the feasible path of the power flow of the distribution network through the topology analysis algorithm;

[0025] According to the feasible path, determining the boundary constraint conditions of the power flow, and obtaining the boundary conditions of the power flow distribution of the distribution network according to the boundary constraint conditions and the line connection status.

[0026] As an improvement to the above solution, constructing a resource allocation model according to the power flow distribution boundary conditions and the key load node set, and using the resource allocation model to determine the first resource allocation strategy through the real-time supply and demand data, including:

[0027] Constructing a resource allocation model according to the power flow distribution boundary conditions and the key load node set; wherein, the resource allocation model includes an allocation objective function and allocation constraint conditions;

[0028] Input the real-time supply and demand data into the resource allocation model. According to the real-time supply and demand data, the allocation objective function, and the allocation constraint conditions, use an optimization algorithm to iteratively adjust the resource allocation ratio among each load node until the resource allocation model converges, and then obtain the first resource allocation strategy.

[0029] As an improvement to the above solution, construct a multi-objective optimization model based on the resource allocation efficiency, social benefits, and the power flow distribution boundary conditions; use the multi-objective optimization model to optimize the first resource allocation strategy to obtain the second resource allocation strategy, including:

[0030] Construct the comprehensive objective function of the multi-objective optimization model according to the resource allocation efficiency and social benefits, and establish the objective constraint conditions of the multi-objective optimization model according to the power flow distribution boundary conditions and the actual operation constraints;

[0031] According to the real-time supply and demand data, the comprehensive objective function, and the objective constraint conditions, use an optimization algorithm to iteratively adjust the first resource allocation strategy until the multi-objective optimization model converges, and then obtain the second resource allocation strategy.

[0032] As an improvement to the above solution, analyze the current load demand data according to the capacity allocation plan and the temporary power supply configuration to obtain the critical load guarantee rate and the network stability index, and analyze the critical load guarantee rate and the network stability index through a simulation algorithm to obtain the recovery efficiency evaluation result, including:

[0033] According to the load demand data, calculate the critical load guarantee rate for each through the allocated capacity of the capacity allocation strategy and the power supply capacity of the temporary power supply configuration;

[0034] According to the load demand data, evaluate the network stability index through the network topology structure and power transmission situation of the temporary power supply configuration;

[0035] Analyze the critical load guarantee rate and the network stability index through a simulation algorithm to obtain the recovery efficiency evaluation result.

[0036] As an improvement to the above solution, update the load rating parameters and the resource allocation ratio according to the recovery efficiency evaluation result to adjust the resource allocation model and generate the third resource allocation strategy, including:

[0037] According to the recovery efficiency evaluation result, obtain the deviation feedback data, analyze the deviation feedback data, and obtain the distribution characteristics of the deviation feedback data;

[0038] Update the load rating parameters according to the distribution characteristics, and optimize the resource allocation ratio according to the updated load rating parameters to obtain the optimized allocation parameters;

[0039] Iteratively adjust the resource allocation model according to the optimized allocation parameters to generate a third resource allocation strategy.

[0040] To achieve the above object, an embodiment of the present invention provides a coordinated power restoration system for a distribution network after a disaster, including:

[0041] A load rating parameter determination module, configured to obtain real-time supply and demand data and network topology information of a post-disaster distribution network in a target area, extract and analyze the real-time supply and demand data, and determine the current load rating parameters of the distribution network; wherein, the real-time supply and demand data includes load demand data and power supply data;

[0042] A distribution boundary condition determination module, configured to calculate the priority weight of each load node according to the load rating parameters, load historical data, and user types to determine the set of critical load nodes of the distribution network; calculate the feasible path of the power flow of the distribution network according to the network topology information, and determine the boundary conditions of the power flow distribution of the distribution network;

[0043] A first allocation strategy determination module, configured to construct a resource allocation model according to the power flow distribution boundary conditions and the set of critical load nodes, and use the resource allocation model to determine a first resource allocation strategy through the real-time supply and demand data;

[0044] A second allocation strategy determination module, configured to construct a multi-objective optimization model according to resource allocation efficiency and social benefits, and the power flow distribution boundary conditions; use the multi-objective optimization model to optimize the first resource allocation strategy to obtain a second resource allocation strategy; wherein, the multi-objective optimization model includes a comprehensive objective function and objective constraint conditions;

[0045] A temporary power supply configuration determination module, configured to determine the temporary power supply configuration of the distribution network according to the power flow distribution data in the second resource allocation strategy, the priority weight, and the access parameters of temporary equipment;

[0046] A capacity allocation strategy determination module, configured to determine the power supply redundancy of each critical load node according to the resource allocation ratio in the second resource allocation strategy and the output power of the temporary power supply configuration to obtain the capacity allocation strategy of the distribution network;

[0047] A first deployment path planning module, configured to plan the deployment path of the temporary power supply configuration according to the power flow distribution data and voltage stability constraint parameters in the second resource allocation strategy, and the information of the temporary power supply configuration, so as to perform coordinated power restoration on the distribution network.

[0048] As an improvement of the above solution, the post-disaster distribution network collaborative power restoration system further includes:

[0049] An efficiency evaluation result acquisition module, configured to analyze the current load demand data according to the capacity allocation scheme and the temporary power supply configuration, obtain the critical load guarantee rate and the network stability index, and analyze the critical load guarantee rate and the network stability index through a simulation algorithm to obtain a restoration efficiency evaluation result;

[0050] A third allocation strategy generation module, configured to update the load rating parameter and the resource allocation ratio according to the restoration efficiency evaluation result, so as to adjust the resource allocation model and generate a third resource allocation strategy;

[0051] A second deployment path planning module, configured to plan the deployment path of the adjusted temporary power supply configuration according to the third resource allocation strategy, so as to perform collaborative power restoration on the distribution network.

[0052] Compared with the prior art, a post-disaster distribution network collaborative power restoration method and system disclosed in an embodiment of the present invention obtain real-time supply and demand condition data and network topology constraint information, analyze and extract a critical load node set and a power flow distribution boundary condition to construct a resource allocation model, fuse power flow analysis and a multi-objective optimization method to obtain an optimized allocation strategy; combine temporary device access parameters to determine a temporary power supply network configuration and a capacity allocation scheme, and then plan the deployment path of the temporary power supply configuration to deploy a temporary power supply network to perform collaborative power restoration on the distribution network. It can quickly evaluate the supply and demand situation in a post-disaster environment, optimize resource allocation, ensure the power supply of critical loads, improve the restoration efficiency and stability of the distribution network, and provide an effective solution for the rapid restoration of the post-disaster power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a schematic flowchart of a post-disaster distribution network collaborative power restoration method provided by an embodiment of the present invention;

[0054] Figure 2 is a schematic structural diagram of a post-disaster distribution network collaborative power restoration system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0056] It should be noted that the terms "including" and "specific" of the present invention, as well as any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0057] Please refer to Figure 1 , Figure 1 , which is a schematic flowchart of a method for collaborative power restoration of a distribution network after a disaster provided by an embodiment of the present invention. The method for collaborative power restoration of a distribution network after a disaster includes:

[0058] S1. Obtain the real-time supply-demand data and network topology information of the post-disaster distribution network in the target area, extract and analyze the real-time supply-demand data, and determine the current load rating parameters of the distribution network; wherein, the real-time supply-demand data includes load demand data and power supply data;

[0059] S2. Calculate the priority weight of each load node according to the load rating parameters, load historical data, and user types to determine the set of critical load nodes of the distribution network; calculate the feasible paths of the power flow of the distribution network according to the network topology information, and determine the boundary conditions of the power flow distribution of the distribution network;

[0060] S3. Construct a resource allocation model according to the boundary conditions of the power flow distribution and the set of critical load nodes, and use the resource allocation model to determine the first resource allocation strategy through the real-time supply-demand data;

[0061] S4. Construct a multi-objective optimization model according to the resource allocation efficiency, social benefits, and the boundary conditions of the power flow distribution; use the multi-objective optimization model to optimize the first resource allocation strategy to obtain the second resource allocation strategy; wherein, the multi-objective optimization model includes a comprehensive objective function and objective constraint conditions;

[0062] S5. Determine the temporary power supply configuration of the distribution network according to the power flow distribution data, the priority weight, and the access parameters of the temporary equipment in the second resource allocation strategy;

[0063] S6. Determine the power supply redundancy of each critical load node according to the resource allocation ratio in the second resource allocation strategy and the output power of the temporary power supply configuration to obtain the capacity allocation strategy of the distribution network;

[0064] S7. According to the power flow distribution data and voltage stability constraint parameters in the second resource allocation strategy, and the information of the temporary power supply configuration, plan the deployment path of the temporary power supply configuration to perform coordinated power restoration on the distribution network.

[0065] Exemplarily, the method for coordinated power restoration of a distribution network according to an embodiment of the present invention is implemented by a distribution network power restoration server, and the distribution network power restoration server can interact with target users for information. The distribution network power restoration server obtains real-time supply and demand data (load demand data and power supply data) through sensors and smart meters, and obtains network topology information (line connection status and damaged node location of the distribution network) through the distribution network geographic information system; uses a time series analysis method to extract and analyze the real-time supply and demand data to determine the current load rating parameters of the distribution network; calculates the priority weight of each load node according to the load rating parameters, load historical data, and user types (such as industrial users, commercial users, residential users), and takes all load nodes corresponding to the priority weight greater than a preset load weight threshold as the key load node set of the distribution network. It can be understood that the load weight threshold can be set as needed; according to the network topology information, calculate the feasible path of the power flow of the distribution network and determine the boundary conditions of the power flow distribution of the distribution network; determine the allocation objective function and allocation constraint conditions according to the power flow distribution boundary conditions and the key load node set to construct a resource allocation model; iteratively adjust the parameters of the resource allocation model through real-time supply and demand data until the resource allocation model converges to obtain the first resource allocation strategy; construct the comprehensive objective function of the multi-objective optimization model according to the resource allocation efficiency and social benefits, and establish the objective constraint conditions of the multi-objective optimization model according to the power flow distribution boundary conditions and actual operation constraints; according to the real-time supply and demand data, the comprehensive objective function, and the objective constraint conditions, use an optimization algorithm to iteratively adjust the first resource allocation strategy until the multi-objective optimization model converges to obtain the second resource allocation strategy; determine the temporary power supply configuration of the distribution network according to the power flow distribution data, the priority weight, and the access parameters of the temporary equipment in the second resource allocation strategy; determine the power supply redundancy of each key load node according to the resource allocation ratio and the output power of the temporary power supply configuration in the second resource allocation strategy to obtain the capacity allocation strategy of the distribution network; according to the power flow distribution data and voltage stability constraint parameters in the second resource allocation strategy, and the information of the temporary power supply configuration, plan the deployment path of the temporary power supply configuration to perform coordinated power restoration on the distribution network. The embodiment of the present invention can quickly evaluate the supply and demand situation in the post-disaster environment, optimize resource allocation, ensure the power supply of key loads, improve the restoration efficiency and stability of the distribution network, and provide an effective solution for the rapid restoration of the post-disaster power system.

[0066] Further, after planning the deployment path of the temporary power supply configuration, the method further includes:

[0067] S8. Analyze the current load demand data according to the capacity allocation scheme and the temporary power supply configuration to obtain the critical load guarantee rate and the network stability index. Analyze the critical load guarantee rate and the network stability index through a simulation algorithm to obtain the restoration efficiency evaluation result;

[0068] S9. Update the load rating parameters and the resource allocation ratio according to the restoration efficiency evaluation result to adjust the resource allocation model and generate a third resource allocation strategy;

[0069] S10. Plan the deployment path of the adjusted temporary power supply configuration according to the third resource allocation strategy to perform coordinated power restoration on the distribution network.

[0070] Specifically, step S1 includes:

[0071] S11. Obtain the real-time supply and demand data and network topology information of the post-disaster distribution network in the target area, and use the time series analysis method to analyze the change trend of the real-time supply and demand data to obtain the load fluctuation characteristics and the corresponding power supply status;

[0072] S12. Calculate the matching deviation between the load fluctuation characteristics and the corresponding power supply status, determine the current supply and demand imbalance area and degree of the distribution network to obtain the current supply and demand matching deviation distribution of the distribution network;

[0073] S13. Determine the current load rating parameters of the distribution network according to the supply and demand matching deviation distribution.

[0074] Specifically, in step S11, the real-time supply and demand data is obtained through sensors and smart meters, the load demand data and the power supply data are extracted to obtain an initial data set. The initial data set is processed by the time series analysis method to analyze the change trend of the load demand in the fluctuation time to obtain the load fluctuation characteristics and the corresponding power supply status.

[0075] Specifically, in step S12, according to the comparison between the load fluctuation characteristics and the power supply status, the matching deviation between the load fluctuation characteristics and the corresponding power supply status is calculated to determine the imbalance area and the degree of imbalance. The matching deviation is processed by a clustering algorithm to generate a matching deviation distribution map, and the spatial distribution of the supply-demand imbalance is obtained. If the degree of imbalance exceeds the preset threshold, the frequency of obtaining real-time supply-demand data is adjusted according to the deviation distribution within the imbalance area, and high-frequency real-time supply-demand data is obtained. The time series analysis is updated based on the high-frequency real-time supply-demand data to determine whether the change in load demand tends to be stable, and the supply-demand matching optimization result is obtained. Based on the spatial distribution of the deviation distribution and the supply-demand matching optimization result, the dynamic change trend of the final imbalance area is determined.

[0076] Exemplarily, in the real-time monitoring of the post-disaster distribution network, first, load demand and power supply data are collected in real time through sensors and smart meters deployed at key nodes. For example, the load demands recorded by smart meters in a certain area within 15 minutes are 1200 kW, 1250 kW, and 1300 kW respectively, while the power supply data are 1100 kW, 1150 kW, and 1200 kW. Using time series analysis methods, such as the autoregressive integrated moving average model (ARIMA), these data are modeled and predicted. Suppose the prediction result of the ARIMA model shows that the load demand will increase to 1350 kW within the next 30 minutes, while the power supply can only be maintained at 1220 kW. By calculating the supply-demand deviation, the current supply-demand imbalance area is determined as the area downstream of a certain substation, and the deviation value is 130 kW. Using a spatial interpolation algorithm (such as Kriging interpolation), the deviation value is distributed throughout the distribution network to generate a supply-demand matching deviation distribution map. The deviation distribution map shows that the deviation value in the area downstream of a certain substation is 130 kW, while the deviation values in other areas fluctuate within the range of ±50 kW. Based on these analysis results, the power dispatching strategy can be adjusted in real time, such as by starting a standby power supply or adjusting the load distribution, to alleviate the supply-demand imbalance problem.

[0077] Specifically, in step S13, the supply-demand matching deviation is used as the current load rating parameter of the distribution network.

[0078] Specifically, step S2 includes:

[0079] S21, calculating the priority weight of each load node according to the load rating parameter, load historical data, and user type;

[0080] S22, taking all load nodes whose priority weight is greater than the preset load weight threshold as the key load node set of the distribution network;

[0081] S23. Calculate the feasible paths of the power flow of the distribution network through a topology analysis algorithm according to the line connection status and damaged node location information in the network topology information;

[0082] S24. Determine the boundary constraint conditions of the power flow according to the feasible paths, and obtain the boundary conditions of the power flow distribution of the distribution network according to the boundary constraint conditions and the line connection status.

[0083] Exemplarily, calculate the priority weights of each load node by using load rating parameters, load historical data and user types to obtain a preliminary weight distribution. If the preliminary weight distribution exceeds a preset threshold, it is marked as a critical load, and the preliminary weight distribution is processed by a clustering algorithm to obtain a set of critical load nodes. Extract the operation status data of the load nodes according to the set of critical load nodes, and update the priority weights by using time series analysis to obtain a dynamic weight distribution. Compare the supply-demand matching deviation distribution with the dynamic weight distribution to determine whether the load node is in an unbalanced state, and obtain an unbalanced node identifier. Adjust the data acquisition frequency for the unbalanced node identifier to obtain high-frequency operation status data, and obtain an updated set of load node states. Recalculate the priority weights according to the updated set of load node states, and verify the weight change trend by comparing historical data to obtain an optimized set of critical load nodes.

[0084] In the distribution network, the priority weights of each load node can be calculated by the Analytic Hierarchy Process (AHP) combined with historical data and user types. For example, the historical load data of a certain area shows that the annual electricity consumption of industrial load nodes is 5000 MWh, that of commercial load nodes is 3000 MWh, and that of residential load nodes is 2000 MWh. Through the AHP algorithm, the weights of industrial, commercial and residential loads are set to 5, 3 and 2 respectively. According to the user type, an outage loss factor is introduced, and the loss factors of industrial, commercial and residential loads are 10, 5 and 1 respectively. Through weighted calculation, the priority weight of the industrial load node is 5×10 = 50, that of the commercial load node is 3×5 = 15, and that of the residential load node is 2×1 = 2. If the preset threshold is 3, the industrial load node and the commercial load node are marked as critical loads to form a set of critical load nodes. To optimize the calculation of the priority weights, a fuzzy comprehensive evaluation method can be introduced to correct the weights by combining multi-dimensional indicators such as the geographical location, power supply reliability and user importance of the load nodes. For example, an industrial load node located in the core area of the city has a high requirement for power supply reliability, and its weight is increased from 5 to 6 through the fuzzy comprehensive evaluation method. Finally, the set of critical load nodes can be applied to optimize the power dispatching strategy to ensure the power supply reliability of the core loads.

[0085] Exemplarily, obtain the network topology data stored in the distribution network geographic information system, extract the line connection status and damaged node location information, and obtain the initial topology description data. Process the initial topology description data using a topology analysis algorithm, calculate the set of feasible paths of the power flow, and obtain the path distribution data. Extract the boundary constraint conditions of the power flow through the path distribution data, and combine with the line connection status to obtain the power flow distribution boundary data. If the power flow distribution boundary data exceeds the preset threshold, mark the damaged nodes as high-risk nodes to obtain the high-risk node set. Extract the real-time status data of the corresponding nodes according to the high-risk node set, use time series analysis to update the path distribution data, and obtain the dynamic path distribution data. Compare the dynamic path distribution data with the initial topology description data to determine whether the damaged nodes affect the power flow stability, and obtain the stability identification data. Adjust the power flow calculation parameters according to the stability identification data, and process the dynamic path distribution data using a support vector machine algorithm to obtain the optimized power flow distribution boundary data.

[0086] For example, the geographic information system data of a distribution network area shows that the total line length is 150 kilometers, of which the damaged line length is 10 kilometers, mainly concentrated between node A and node B. Using the depth-first search algorithm (DFS) to analyze the network topology, it is found that a feasible power flow path is formed between node C, node D, and node E, with a maximum transmission capacity of 50 MW. Through the power flow calculation model, combined with the line impedance parameters, the power flow distribution from node C to node E is calculated to be 35 MW, and the power flow distribution from node D to node E is 15 MW, meeting the line capacity constraints. Use the minimum spanning tree algorithm (Prim algorithm) in graph theory to optimize the network, generate a new path, and include node F in the power flow distribution range, so that the power flow distribution from node F to node E increases to 20 MW. By introducing a linear programming model, combined with the line load rate and node voltage stability, optimize the power flow distribution boundary conditions to ensure that the power flow distribution from node A to node B does not exceed 30 MW, and the power flow distribution from node C to node E is stable within 40 MW. Finally, input the optimized power flow distribution boundary conditions into the power dispatching system to provide data support for subsequent power dispatching decisions.

[0087] Specifically, step S3 includes:

[0088] S31, construct a resource allocation model according to the power flow distribution boundary conditions and the critical load node set; wherein, the resource allocation model includes an allocation objective function and allocation constraint conditions;

[0089] S32. Input the real-time supply and demand data into the resource allocation model. According to the real-time supply and demand data, the allocation objective function, and the allocation constraint conditions, use an optimization algorithm to iteratively adjust the resource allocation ratios among load nodes until the resource allocation model converges, and then obtain the first resource allocation strategy.

[0090] Exemplarily, use the power flow distribution boundary conditions and the set of critical load nodes as the core input parameters of the model. The power flow distribution boundary conditions include information such as the feasible paths of the power flow, the transmission capacity limits of each path, and the voltage stability constraints. These conditions define the transmission range and capacity of electric power in the distribution network. The set of critical load nodes clarifies the load nodes that need to be prioritized for power supply during the resource allocation process, and the priority weights reflect the importance of different nodes. Set the objective function of the resource allocation model according to actual needs. Usually, the objectives are to maximize the power supply guarantee rate for critical loads, maximize social benefits, and minimize power transmission losses, etc. On the premise of meeting the power supply requirements of critical loads, improve the operation efficiency and economic benefits of the entire distribution network as much as possible and reduce unnecessary energy waste. In addition to the constraints in the power flow distribution boundary conditions, other constraint factors need to be considered, such as the total power supply constraint, the power limits of each line, and the equipment capacity limits, etc. Ensure that during the resource allocation process, unreasonable situations such as power supply exceeding the actual capacity or equipment overload do not occur. Input the real-time collected supply and demand data into the constructed resource allocation model. These data include the real-time load demands of each load node and the total amount and distribution of the current power supply, enabling the model to perform resource allocation calculations based on the actual power supply and demand situation. The model uses an optimization algorithm such as the gradient descent method for iterative calculations. In each iteration, according to the current resource allocation situation, the objective function, and the constraint conditions of the model, continuously adjust the allocation ratios of electric power resources among load nodes. At the initial allocation, a preliminary allocation may be made according to certain experience or simple rules, and then gradually optimized through iteration to make the allocation result more in line with the actual needs and objective requirements. After multiple iterative calculations, when the model converges to a solution that meets the requirements, the first resource allocation strategy is obtained. This strategy clarifies how to allocate limited electric power resources to each load node under the current real-time supply and demand data and network conditions, especially the power supply guarantee measures for critical load nodes, such as the specific allocation strategies of the electric power quantity and power supply path allocated to each critical load node.

[0091] For example, in a regional distribution network, the real-time supply and demand data include the load demands and voltage states of 10 critical load nodes, and the total load is 100 MW. Through monitoring data, it is found that the load of a certain node suddenly increases to 20 MW, exceeding the expectation, and the real-time supply and demand data need to be adjusted.

[0092] In a possible implementation, the system updates the real-time supply and demand data according to the node priority and load changes. The adjusted real-time total load is shown as 110 MW, and the newly added load is allocated to the backup path. When extracting the power flow distribution information from the adjusted real-time supply and demand data, combined with preset boundary conditions such as the line capacity upper limit of 50 MW and voltage stability requirements, feasible paths can be effectively screened. For example, the power flow distribution information of a certain path from node G to node H shows that the transmission power is 30 MW, which does not exceed the boundary conditions. It can be understood that this method optimizes the power transmission efficiency by gradually adjusting the resource allocation ratio. For example, the initial allocation ratio may be 60% for node G and 40% for node H, and after iteration, it is adjusted to 50% and 50%, making the line load more balanced and the ratio distribution data tend to be stable. Preferably, if the load rate of node G is 80% and the voltage deviation is 5%, and the status value exceeds the preset threshold of 70%, it is marked as an abnormal node. After the abnormal node set is updated, the real-time supply and demand data is adjusted accordingly, and the limiting conditions of the abnormal nodes are added. In one embodiment, the support vector machine algorithm can be used to classify abnormal nodes and normal nodes. After optimizing the generation of real-time supply and demand data, the resource allocation strategy shows that the load of node G needs to be reduced to 15 MW to ensure system stability. The power flow from node G to node H is adjusted to 25 MW, and the backup path shares 10 MW.

[0093] It should be noted that this method predicts the future trend through historical power flow data and generates the first resource allocation strategy. Specifically, if the historical data shows that the load of node H increases by 5 MW over time, the strategy will reserve additional capacity to ensure reliable long-term operation.

[0094] In a possible implementation, if the capacity of node G decreases due to equipment aging, the system gives an early warning through abnormal node marking, and the optimized strategy tilts resources to other paths. This method not only improves the adaptability of the power flow distribution but also reduces the overload risk of critical load nodes. Preferably, the iterative adjustment of the constrained path set can also provide real-time basis for power dispatching to ensure the efficiency and safety of resource allocation. It can be understood that the combination of the gradient descent method and the support vector machine algorithm makes the resource allocation strategy more accurate. For example, in a certain iterative calculation, the path capacity utilization rate increases from 70% to 85%, and the number of abnormal nodes decreases by 30%. In one embodiment, linear regression analysis can also reveal the seasonal changes in the power flow distribution, providing a reference for long-term planning. This multi-level analysis and optimization significantly enhance the operation stability and resource utilization efficiency of the distribution network.

[0095] Specifically, step S4 includes:

[0096] S41. Construct a comprehensive objective function of the multi-objective optimization model according to the resource allocation efficiency and social benefits, and establish the objective constraint conditions of the multi-objective optimization model according to the power flow distribution boundary conditions and actual operation constraints;

[0097] S42. According to the real-time supply and demand data, the comprehensive objective function and the objective constraint conditions, use an optimization algorithm to iteratively adjust the first resource allocation strategy until the multi-objective optimization model converges, and obtain the second resource allocation strategy.

[0098] Exemplarily, a comprehensive objective function is set by comprehensively considering the resource allocation efficiency, social benefits, and power flow distribution boundary conditions. In terms of resource allocation efficiency, the goal is to maximize the effective utilization of electric power resources, reduce energy waste, and improve the overall power supply efficiency; at the social benefits level, the focus is on the guarantee of critical loads, ensuring the power supply to important users, and incorporating the maximization of the critical load guarantee rate into the objective function; combined with the power flow distribution boundary conditions, maintaining the stable operation of the power system, meeting voltage stability constraints and line capacity limits, etc. are taken as goals, such as minimizing voltage fluctuations and line overload risks as one of the goals. In addition to the constraints included in the power flow distribution boundary conditions (such as line transmission capacity limits, node voltage stability ranges, etc.), other actual operation constraints also need to be considered. The total power supply constraint ensures that the total allocated power does not exceed the power generation and supply capabilities of the system; the equipment capacity constraint guarantees that the loads of each equipment (such as transformers, generators, etc.) do not exceed their rated capacities; and the load demand constraint meets the basic electricity consumption needs of each load node. The above objective function and constraint conditions are integrated to construct a multi-objective optimization model. In actual construction, methods such as the linear weighted method and the goal programming method may be used to comprehensively process multiple goals, enabling the model to seek a balance among multiple goals and obtain a relatively optimal solution. The first resource allocation strategy is used as the initial solution of the multi-objective optimization model, and real-time resource allocation efficiency data, social benefits evaluation indicators, and relevant information on power flow distribution boundary conditions are input. These data provide the basis and foundation for model optimization, enabling the model to adjust the initial strategy according to the actual situation. An appropriate optimization algorithm is used to solve the multi-objective optimization model, such as the genetic algorithm, particle swarm optimization algorithm, etc. These algorithms simulate natural evolution or swarm intelligence behavior, and continuously search for and improve the first resource allocation strategy on the premise of meeting the constraint conditions, gradually finding a better resource allocation strategy. During the iteration process, the algorithm will continuously adjust the parameters of resource allocation according to the objective function of the model, such as the power supply amount and power supply path of each load node, to improve resource allocation efficiency and social benefits while ensuring the stable operation of the power system. When the optimization algorithm converges to a solution that meets the preset conditions (such as the objective function value no longer has obvious improvement, the number of iterations reaches the upper limit, etc.), this solution is the optimized second resource allocation strategy. Compared with the first resource allocation strategy, this strategy has better performance in terms of resource allocation efficiency, social benefits, and power system stability, and can more reasonably allocate electric power resources to meet the restoration needs of the post-disaster distribution network.

[0099] For example, by combining power flow analysis and multi-objective optimization method, first extract the voltage stability constraints from the boundary conditions of power flow distribution, set the node voltage fluctuation range to ±5% to ensure system stability. By collecting the current power grid data, the total load demand of a regional power grid is 150 MW, and the load demands of key nodes A, B, and C are 50 MW, 40 MW, and 30 MW respectively, and the remaining 30 MW is borne by other nodes. The available resources on the power generation side include the output of thermal power units of 100 MW, the output of hydropower units of 40 MW, and the output of energy storage systems of 10 MW. Using the linear programming algorithm, with minimizing carbon emissions and maximizing social benefits as the objective functions, combined with voltage stability constraints and line transmission capacity limits, optimize the calculation. The initial optimization results show that the thermal power unit is allocated an output of 80 MW, the hydropower unit is allocated an output of 40 MW, the energy storage system is allocated an output of 10 MW, and the remaining 20 MW of load is met by increasing the output of the thermal power unit. In the further optimization process, introduce multi-objective weight coefficients, combine the power supply priorities of nodes A, B, and C, and adjust the resource allocation ratio, so that the load satisfaction rate of node A is increased to 98%, and the load satisfaction rates of nodes B and C reach 95% and 92% respectively. Finally, through multiple rounds of optimization calculations, an optimized resource allocation plan is generated, in which the output of the thermal power unit is adjusted to 90 MW, the output of the hydropower unit remains 40 MW, and the output of the energy storage system is adjusted to 10 MW, ensuring the power supply reliability of key nodes and the overall stability of the system, while achieving the goals of minimizing carbon emissions and maximizing social benefits.

[0100] Specifically, in the step S5, eigenvalues are obtained from the power flow distribution data, and the support vector machine algorithm is used to process the priorities of critical load nodes to obtain load classification data. Stability-related indicators are extracted from the load classification data, and combined with the equipment location information, the temporary equipment adjustment requirements are determined to obtain equipment adjustment data. The network topology structure is updated according to the equipment adjustment data, and the linear programming algorithm is used to process the joint adjustment parameters to obtain topology adjustment data. For the topology adjustment data, the trend of the power flow distribution change is obtained. If the change trend exceeds the preset threshold, it is marked as an abnormal state to obtain abnormal marking data. The stability correction requirements are extracted from the abnormal marking data, and the time series analysis is used to adjust the index extraction results to obtain corrected index data. The allocation scheme is updated through the corrected index data, and it is judged whether the configuration determination conditions are met to obtain the network configuration after joint adjustment. By obtaining the temporary equipment parameter data, the equipment type, power capacity, and voltage level information are extracted from it to generate an equipment feature matrix. According to the geographical coordinates of the mobile power source and emergency equipment, the distances from the existing power grid nodes are calculated, and the nearest node is selected as the potential access point. The network topology structure is analyzed by the graph theory algorithm to identify critical nodes and weak links, and a network vulnerability assessment model is established. If the load rate of the access point exceeds the preset threshold, the equipment allocation scheme is adjusted, and the sub-optimal access point is selected until the load rate requirement is met. The power flow calculation method is used to simulate the network operation state after the temporary equipment is connected, and the node voltage and line power are calculated. For the power flow calculation results, it is judged whether there is an overload or under-voltage situation. If so, it is marked as an unstable state. According to the stability judgment results and equipment characteristics, the temporary power supply network configuration is optimized.

[0101] For example, based on the optimized power flow distribution eigenvalues, the eigenvalue decomposition algorithm is used to extract the power flow stability indicators of critical nodes. The voltage eigenvalues of nodes D, E, and F are 92, 88, and 85 respectively, indicating that the stability of node D is the best. Combining the priority weights of critical load nodes, the priorities of nodes D, E, and F are 6, 3, and 1 respectively. The overall power flow stability of the system is calculated to be 78 by the weighted average method, which is lower than the set threshold of 85. To achieve network topology optimization, the temporary equipment location information is introduced, and two temporary transformers are deployed near nodes E and F respectively, and their output powers are adjusted to 20MW and 15MW. The line connections are re-planned by the graph theory algorithm, and the line capacity between node E and node D is increased to 50MW, and the line capacity between node F and node D is increased to 40MW. Using the improved particle swarm optimization algorithm, with the goal of minimizing line losses and maximizing power flow stability, the network configuration is optimized. Finally, the adjusted network topology is obtained, where the voltage eigenvalue of node E is increased to 91, the voltage eigenvalue of node F is increased to 89, and the overall power flow stability of the system reaches 86, meeting the design requirements.

[0102] For example, capacity and location information are extracted from the mobile power supply and emergency equipment database, and data mining techniques are used to screen out equipment with a capacity greater than 30 MW and within 5 km of the target area. Among them, mobile power supply A has a capacity of 35 MW and is located 3 km northeast of node G, and mobile power supply B has a capacity of 40 MW and is located 4 km southeast of node H. Through network topology constraints, the equipment is matched to access nodes, and the Dijkstra algorithm is used to calculate the shortest path from the equipment to the target node. It is determined that mobile power supply A accesses node G and mobile power supply B accesses node H. Combining with power flow stability analysis, the Newton-Raphson method is used for power flow calculation, and the voltage eigenvalue of node G is obtained as 89, and the voltage eigenvalue of node H is 90, both of which meet the requirement of the stability threshold of 85. According to the equipment output parameters, the output of mobile power supply A is set to 30 MW, and the output of mobile power supply B is set to 35 MW. The linear programming algorithm is used to optimize the line load distribution, and the line capacity between node G and node H is increased to 45 MW, and the line capacity between node G and node D is increased to 50 MW. Based on the power flow stability index, the Monte Carlo simulation method is used to evaluate the reliability of the temporary power supply network, and the overall system stability is obtained as 87, which meets the design requirements.

[0103] Specifically, in step S6, the mapping relationship between the resource allocation ratio and the output power is obtained through the optimization strategy, the linear regression algorithm is used to determine the correlation strength, and the power allocation benchmark of the temporary equipment is obtained. The priority weight data is extracted from the critical load nodes, and after sorting by weight, it is matched with the emergency equipment capacity information to determine the capacity demand distribution of the load nodes. According to the capacity demand distribution and the power allocation benchmark of the temporary equipment, the power supply redundancy of each load node is calculated to obtain the redundancy evaluation result. If the redundancy evaluation result is lower than the preset threshold, the resource allocation ratio is adjusted, and the emergency equipment capacity is rematched to obtain an updated allocation plan. The power coverage rate of the critical load nodes is calculated through the updated allocation plan, and the Monte Carlo algorithm is used to simulate multiple allocation scenarios to determine the optimal power allocation combination. According to the optimal power allocation combination and the priority weight of the load nodes, the operation parameters of the temporary equipment are adjusted to obtain the final capacity allocation strategy.

[0104] It should be noted that in the second resource allocation strategy, by associating the resource allocation ratio with the output power of the temporary equipment, key parameters are first extracted from the priority weights of the critical load nodes and matched with the emergency equipment capacity data.

[0105] For example, the priority weight of the critical load node X is 8, the capacity of the emergency equipment C is 25 MW, and the capacity of the emergency equipment D is 30 MW. Through the weighted average algorithm, the power supply redundancy of node X is calculated to be 2, meeting the requirement of the minimum redundancy of 0. Using the multi-objective optimization algorithm, with minimizing the power supply cost as the objective function and combining the equipment capacity and load demand constraints, it is determined that the output power of equipment C is 20 MW and the output power of equipment D is 25 MW to ensure the power supply reliability of the critical load node. To improve the accuracy of the allocation scheme, the analytic hierarchy process is introduced to classify the critical load nodes. Nodes X, Y, and Z are respectively classified as high, medium, and low priorities, with weights of 8, 6, and 4. Combining with the emergency equipment capacity data, the power supply demands of each node are predicted through a linear regression model. The predicted demand of node Y is 15 MW, and the predicted demand of node Z is 10 MW. According to the prediction results, the output powers of equipment C and D are dynamically adjusted to ensure that the power supply redundancy of the high-priority node X is always higher than 2, and the redundancies of the medium- and low-priority nodes are maintained at 0 and above 8 respectively. Finally, through the genetic algorithm to optimize the equipment output allocation, the output of equipment C is adjusted to 18 MW, and the output of equipment D is adjusted to 22 MW. Combining with the line capacity limit, the shortest path algorithm is used to optimize the power transmission path to ensure the overall stability and economy of the power supply network.

[0106] Specifically, in step S7, obtain the topological structure information of the temporary power supply network, including the number of nodes, line connection relationships and parameters, and construct a network model. Calculate the voltage distribution and line power flow of each node according to the network model, use the Newton-Raphson method to solve the power flow equation, and obtain the initial power flow distribution result. Through the sensitivity analysis method, calculate the influence degree of each node on the system stability, and identify the critical nodes and weak lines. For the critical nodes and weak lines, set the voltage stability constraint conditions and power balance constraint conditions, and construct an optimization model. Use the genetic algorithm to solve the optimization model to obtain the optimal access position and capacity configuration scheme of the temporary equipment that meet the constraint conditions. According to the optimal configuration scheme, calculate the static voltage stability margin index of the network, and judge whether the system stability meets the requirements. If not, adjust the constraint conditions and re-optimize and solve. Based on the final temporary equipment configuration scheme, plan the deployment path of the temporary equipment, and determine the installation order and connection method.

[0107] By analyzing the characteristic values of the power flow distribution of the temporary power supply network and combining the voltage stability constraint parameters, judge the stability margin of the network operation. For example, the power flow characteristic value of node A is 2, node B is 8, and node C is 5. Based on these data, use the Newton-Raphson algorithm to calculate the voltage deviation of each node, and obtain that the voltage deviation of node A is 03, node B is 05, and node C is 02, meeting the voltage stability constraint conditions.

[0108] Combined with the power flow stability index, the sensitivity analysis method is used to evaluate the impact of the temporary equipment access node on the network stability. For example, the sensitivity index of node D is 12, that of node E is 08, and that of node F is 15. Through comparative analysis, node F is determined as the optimal access point. On this basis, the Dijkstra algorithm in graph theory is used to optimize the deployment path of the temporary equipment with the goal of minimizing line losses. For example, the shortest path from node F to node A is path 1 with a loss of 5 MW, the shortest path to node B is path 2 with a loss of 3 MW, and the shortest path to node C is path 3 with a loss of 4 MW. Finally, path 2 is selected as the main deployment path to ensure the stability and economy of the network operation.

[0109] Specifically, step S8 includes:

[0110] S81, according to the load demand data, calculate each critical load guarantee rate through the allocated capacity of the capacity allocation strategy and the power supply capacity of the temporary power supply configuration;

[0111] S82, according to the load demand data, evaluate the network stability index through the network topology structure and power transmission situation of the temporary power supply configuration;

[0112] S83, analyze the critical load guarantee rate and the network stability index through a simulation algorithm to obtain the recovery efficiency evaluation result.

[0113] Exemplarily, according to the load demand data, use a data acquisition tool to extract the load change characteristics of each node to obtain the load distribution state. According to the load distribution state, calculate each critical load guarantee rate through the allocated capacity of the capacity allocation strategy and the power supply capacity of the temporary power supply configuration; compare the critical load guarantee rate with a preset threshold. If it is lower than the threshold, adjust the capacity allocation strategy to obtain updated allocation parameters. Through the updated allocation parameters, obtain the topology adjustment information of the temporary power supply network configuration and determine the change trend of network stability. According to the change trend, use a simulation algorithm to calculate the dynamic distribution characteristics of the network stability index and judge whether the system meets the power balance requirement. If it does not meet the power balance requirement, optimize the network configuration through a linear programming algorithm to obtain the adjusted recovery efficiency parameters. According to the adjusted recovery efficiency parameters, determine the stable state of the system operation and obtain the recovery efficiency evaluation result.

[0114] For example, first, extract the updated load demand data from the real-time supply and demand situation. For example, the load demand at Node X is 120 MW, at Node Y is 80 MW, and at Node Z is 150 MW. Through the Monte Carlo simulation method, combined with the load fluctuation characteristics, generate 1000 random scenarios and analyze the critical load guarantee rate. For example, at a 95% confidence level, the critical load guarantee rate reaches 95%. Then, use the particle swarm optimization algorithm with the network stability index as the objective function for optimization calculation. If the optimized network stability index is 92, which meets the preset threshold of 90. Based on the simulation results, use the support vector machine model to evaluate the system recovery efficiency. The system recovery efficiency predicted by the model is 93%, and compared with the actual recovery efficiency of 91%, the error is only 2%. Finally, through Bayesian network analysis, determine the key factors affecting the system recovery efficiency, such as load fluctuation, network topology, and equipment failure rate, providing a basis for subsequent optimization. The whole process is automatically processed through information technology to ensure the accuracy and reliability of the analysis results.

[0115] Specifically, step S9 includes:

[0116] S91, according to the evaluation result of the recovery efficiency, obtain the deviation feedback data, analyze the deviation feedback data, and obtain the distribution characteristics of the deviation feedback data;

[0117] S92, update the load rating parameters according to the distribution characteristics, and optimize the resource allocation ratio according to the updated load rating parameters to obtain the optimized allocation parameters;

[0118] S93, iteratively adjust the resource allocation model according to the optimized allocation parameters to generate the third resource allocation strategy.

[0119] Exemplarily, obtain the deviation feedback data from the system recovery efficiency evaluation result, use statistical tools to process the deviation feedback data, and obtain the distribution characteristics of the feedback data. Judge the change of the load importance through the distribution characteristics, use the adaptive algorithm to update the load rating parameters, and determine the adjustment value of the load rating parameters. Obtain the updated demand for resource allocation according to the adjustment value, optimize the resource allocation ratio through the linear programming algorithm to obtain the optimized allocation parameters. Iteratively adjust the resource allocation model for the optimized allocation parameters, obtain the reallocation information of the electric power resources, and determine the allocation scheme of the electric power resources. Extract the characteristic data of the allocation result from the allocation scheme, and judge whether the allocation result meets the balance requirement through comparative analysis to obtain the determination value of the balance state. If the determination value is lower than the preset threshold, update the resource allocation model by adjusting the allocation parameters to obtain the adjusted allocation result. Update the configuration information of the electric power resources according to the adjusted allocation result, and judge the consistency of the configuration information through data verification to obtain the generation of the third resource allocation strategy.

[0120] For example, based on the evaluation results of the system recovery efficiency, the differences between the load distribution and the actual demand are identified through deviation feedback data. For example, the load deviation of Node A is 5 MW, that of Node B is 3 MW, and that of Node C is 7 MW. The adaptive filtering algorithm is used to process the deviation data, and combined with the historical load characteristics, the load importance rating is updated. If the importance rating of Node A is adjusted from 8 to 85, that of Node B from 7 to 75, and that of Node C from 9 to 92. Based on the updated load rating parameters, the genetic algorithm is used to optimize the power resource allocation ratio, and the objective function is to minimize the deviation and maximize the resource utilization rate. If after optimization, the resource allocation ratio of Node A is adjusted from 30% to 32%, that of Node B from 25% to 27%, and that of Node C from 45% to 47%. The optimization results are input into the dynamic power resource allocation model, and combined with the real-time supply and demand data and the network topology structure, the third resource allocation strategy is generated. For example, the allocated power of Node A is 100 MW, that of Node B is 80 MW, and that of Node C is 120 MW. Through the automated processing technology, the real-time and accuracy of the allocation scheme are ensured, providing support for the stable operation of the power system.

[0121] Specifically, in the step S10, a temporary power supply network is deployed through the third resource allocation strategy, the operation instructions are extracted from the temporary device access parameters, and the remote control system is used to adjust the device output power and access status to obtain the adjusted device operation data. The real-time status of the critical load is obtained from the adjusted device operation data, and the data processing tool is used to analyze the change trend of the real-time status to determine the guarantee rate of the critical load. For the guarantee rate of the critical load, if the guarantee rate reaches the preset threshold, the stable operation status of the distribution network is judged through logical verification to obtain the stability data of the distribution network. According to the stability data of the distribution network, the support vector machine algorithm is used to analyze the allocation efficiency of the power resources to determine the optimization requirements of the temporary power supply network. The instruction parameters of the remote control system are adjusted through the optimization requirements to obtain the updated output power and access status, and the dynamic adjustment information of the distribution network is obtained. The characteristic data of the distribution network status is extracted from the dynamic adjustment information, and the clustering algorithm is used to analyze the distribution law of the characteristic data to judge the collaborative recovery ability of the distribution network. For the collaborative recovery ability, the statistical tool is used to process the characteristic data to determine the final configuration state of the power resources and obtain the stable operation plan of the temporary power supply network.

[0122] Exemplarily, when deploying a temporary power supply network according to the third resource allocation strategy, operating instructions such as voltage, frequency, and power factor are extracted from the access parameters of temporary devices. If the voltage instruction of node D is 15 kV, the frequency is 50 Hz, and the power factor is 95. Through the remote control system, combined with real-time monitoring data, the fuzzy control algorithm is used to adjust the output power and access status of the devices. If the output power of node D is adjusted from 50 MW to 55 MW, node E from 40 MW to 45 MW, and node F from 60 MW to 65 MW. During the adjustment process, based on the load data collected in real time, the Kalman filter algorithm is used to analyze the guarantee rate of critical loads. The guarantee rate of node D is increased from 85% to 92%, node E from 80% to 88%, and node F from 90% to 95%. When the guarantee rate of critical loads reaches the expected threshold (such as 90%), the system automatically determines that it enters the stable operation state and generates the stable operation state parameters of the distribution network. When the voltage of node D is stable at 15 kV ± 2 kV, the frequency is stable at 50 Hz ± 1 Hz, and the power factor is stable at 95 ± 02, through the automated processing technology, the stable operation state parameters are compared and analyzed with the historical data to ensure that the restoration process of the distribution network is consistent with the expected goal.

[0123] A method for collaborative power restoration of a distribution network based on post-disaster conditions disclosed in an embodiment of the present invention, by obtaining real-time supply and demand condition data and network topology constraint information, analyzing and extracting a set of critical load nodes and boundary conditions of power flow distribution to construct a resource allocation model, and integrating power flow analysis and multi-objective optimization method to obtain an optimized allocation strategy; combining the access parameters of temporary devices to determine the configuration and capacity allocation scheme of the temporary power supply network, and then planning the deployment path of the temporary power supply configuration to deploy the temporary power supply network for collaborative power restoration of the distribution network. It can quickly evaluate the supply and demand conditions in the post-disaster environment, optimize resource allocation, ensure power supply to critical loads, improve the restoration efficiency and stability of the distribution network, and provide an effective solution for the rapid restoration of the post-disaster power system.

[0124] See Figure 2 , Figure 2 is a schematic structural diagram of a collaborative power restoration system 10 for a distribution network based on post-disaster conditions provided by an embodiment of the present invention. The collaborative power restoration system 10 for a distribution network based on post-disaster conditions includes:

[0125] A load rating parameter determination module 11, configured to obtain real-time supply and demand data and network topology information of a post-disaster distribution network in a target area, extract and analyze the real-time supply and demand data, and determine the current load rating parameters of the distribution network; wherein, the real-time supply and demand data includes load demand data and power supply data;

[0126] The distribution boundary condition determination module 12 is configured to calculate the priority weight of each load node according to the load rating parameter, load historical data, and user type, so as to determine the set of critical load nodes of the distribution network; according to the network topology information, calculate the feasible paths of the power flow of the distribution network, and determine the boundary conditions of the power flow distribution of the distribution network;

[0127] The first allocation strategy determination module 13 is configured to construct a resource allocation model according to the power flow distribution boundary condition and the set of critical load nodes, and use the resource allocation model to determine the first resource allocation strategy through the real-time supply and demand data;

[0128] The second allocation strategy determination module 14 is configured to construct a multi-objective optimization model according to the resource allocation efficiency, social benefits, and the power flow distribution boundary condition; use the multi-objective optimization model to optimize the first resource allocation strategy to obtain the second resource allocation strategy; wherein, the multi-objective optimization model includes a comprehensive objective function and objective constraint conditions;

[0129] The temporary power supply configuration determination module 15 is configured to determine the temporary power supply configuration of the distribution network according to the power flow distribution data in the second resource allocation strategy, the priority weight, and the access parameters of the temporary equipment;

[0130] The capacity allocation strategy determination module 16 is configured to determine the power supply redundancy of each critical load node according to the resource allocation ratio in the second resource allocation strategy and the output power of the temporary power supply configuration, so as to obtain the capacity allocation strategy of the distribution network;

[0131] The first deployment path planning module 17 is configured to plan the deployment path of the temporary power supply configuration according to the power flow distribution data and voltage stability constraint parameters in the second resource allocation strategy, and the information of the temporary power supply configuration, so as to perform coordinated power restoration on the distribution network.

[0132] Further, the distribution network coordinated power restoration system 10 based on after-disaster also includes:

[0133] The efficiency evaluation result acquisition module is configured to analyze the current load demand data according to the capacity allocation scheme and the temporary power supply configuration to obtain the critical load guarantee rate and network stability index, and analyze the critical load guarantee rate and network stability index through a simulation algorithm to obtain the restoration efficiency evaluation result;

[0134] The third allocation strategy generation module is configured to update the load rating parameter and the resource allocation ratio according to the restoration efficiency evaluation result, so as to adjust the resource allocation model and generate the third resource allocation strategy;

[0135] The second deployment path planning module is configured to plan the deployment path of the adjusted temporary power supply configuration according to the third resource allocation strategy, so as to perform collaborative power restoration on the distribution network.

[0136] A distribution network collaborative power restoration system 10 provided by an embodiment of the present invention can implement all the processes of the distribution network collaborative power restoration method based on disasters in the above embodiment. The functions and achieved technical effects of each module in the system are respectively the same as those of the distribution network collaborative power restoration method based on disasters in the above embodiment, and will not be elaborated here.

[0137] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A collaborative power restoration method for a distribution network after a disaster, characterized in that, Including: Obtain the real-time supply-demand data and network topology information of the post-disaster distribution network in the target area, extract and analyze the real-time supply-demand data, and determine the current load rating parameters of the distribution network; wherein, the real-time supply-demand data includes load demand data and power supply data; Calculate the priority weight of each load node according to the load rating parameters, load historical data, and user types to determine the set of critical load nodes of the distribution network; according to the network topology information, calculate the feasible paths of the power flow of the distribution network, and determine the boundary conditions of the power flow distribution of the distribution network; Construct a resource allocation model based on the boundary conditions of the power flow distribution and the set of critical load nodes, and use the resource allocation model to determine the first resource allocation strategy through the real-time supply-demand data; Construct a multi-objective optimization model according to the resource allocation efficiency, social benefits, and the boundary conditions of the power flow distribution; use the multi-objective optimization model to optimize the first resource allocation strategy to obtain the second resource allocation strategy; wherein, the multi-objective optimization model includes a comprehensive objective function and objective constraint conditions; Determine the temporary power supply configuration of the distribution network according to the power flow distribution data, the priority weight, and the access parameters of the temporary equipment in the second resource allocation strategy; Determine the power supply redundancy of each critical load node according to the resource allocation ratio and the output power of the temporary power supply configuration in the second resource allocation strategy, and determine the capacity allocation strategy of the distribution network according to the power supply redundancy and the priority weight of each critical load node; Plan the deployment path of the temporary power supply configuration according to the power flow distribution data, voltage stability constraint parameters, and the information of the temporary power supply configuration in the second resource allocation strategy to perform coordinated power restoration on the distribution network.

2. The collaborative power restoration method for a distribution network based on post-disaster conditions according to claim 1, wherein After planning the deployment path of the temporary power supply configuration, the method further includes: Analyze the current load demand data according to the capacity allocation strategy and the temporary power supply configuration to obtain the critical load guarantee rate and network stability index, and analyze the critical load guarantee rate and network stability index through a simulation algorithm to obtain the restoration efficiency evaluation result; Update the load rating parameters and the resource allocation ratio according to the restoration efficiency evaluation result to adjust the resource allocation model and generate the third resource allocation strategy; Plan the deployment path of the adjusted temporary power supply configuration according to the third resource allocation strategy to perform coordinated power restoration on the distribution network.

3. The collaborative power restoration method for a distribution network based on post-disaster conditions according to claim 1, characterized in that, The obtaining the real-time supply-demand data and network topology information of the post-disaster distribution network in the target area, extracting and analyzing the real-time supply-demand data, and determining the current load rating parameters of the distribution network includes: Obtain the real-time supply-demand data and network topology information of the post-disaster distribution network in the target area, and use the time series analysis method to analyze the change trend of the real-time supply-demand data to obtain the load fluctuation characteristics and the corresponding power supply status; Calculate the matching deviation between the load fluctuation characteristics and the corresponding power supply status, determine the current supply-demand imbalance area and degree of the distribution network to obtain the current supply-demand matching deviation distribution of the distribution network; Determine the current load rating parameters of the distribution network according to the deviation distribution of the supply-demand matching.

4. The collaborative power restoration method for a distribution network based on post-disaster conditions as claimed in claim 1, wherein, Calculate the priority weight of each load node according to the load rating parameters, load historical data and user types to determine the set of critical load nodes of the distribution network; according to the network topology information, calculate the feasible paths of the power flow of the distribution network, and determine the boundary conditions of the power flow distribution of the distribution network, including: Calculate the priority weight of each load node according to the load rating parameters, load historical data and user types; Take all load nodes corresponding to the priority weight greater than the preset load weight threshold as the set of critical load nodes of the distribution network; Calculate the feasible paths of the power flow of the distribution network through a topology analysis algorithm according to the line connection status and damaged node location information in the network topology information; Determine the boundary constraint conditions of the power flow according to the feasible paths, and obtain the boundary conditions of the power flow distribution of the distribution network according to the boundary constraint conditions and the line connection status.

5. The collaborative power restoration method for the distribution network based on the post-disaster situation according to claim 1, wherein, Construct a resource allocation model according to the boundary conditions of the power flow distribution and the set of critical load nodes, and use the resource allocation model to determine the first resource allocation strategy through the real-time supply-demand data, including: Construct a resource allocation model according to the boundary conditions of the power flow distribution and the set of critical load nodes; wherein, the resource allocation model includes an allocation objective function and allocation constraint conditions; Input the real-time supply-demand data into the resource allocation model, and use an optimization algorithm to iteratively adjust the resource allocation ratio between each load node according to the real-time supply-demand data, the allocation objective function and the allocation constraint conditions until the resource allocation model converges, and obtain the first resource allocation strategy.

6. The collaborative power restoration method for a distribution network based on post-disaster conditions according to claim 1, wherein Construct a multi-objective optimization model according to the resource allocation efficiency and social benefits, and the boundary conditions of the power flow distribution; Optimize the first resource allocation strategy using the multi-objective optimization model to obtain the second resource allocation strategy, including: Construct an integrated objective function of the multi-objective optimization model according to the resource allocation efficiency and social benefits, and establish the objective constraint conditions of the multi-objective optimization model according to the boundary conditions of the power flow distribution and the actual operation constraints; Use an optimization algorithm to iteratively adjust the first resource allocation strategy according to the real-time supply-demand data, the integrated objective function and the objective constraint conditions until the multi-objective optimization model converges, and obtain the second resource allocation strategy.

7. The collaborative power restoration method for a distribution network based on post-disaster conditions according to claim 2, wherein Analyze the current load demand data according to the capacity allocation strategy and the temporary power supply configuration to obtain the critical load guarantee rate and the network stability index, and analyze the critical load guarantee rate and the network stability index through a simulation algorithm to obtain the recovery efficiency evaluation result, including: Calculate the critical load guarantee rate of each according to the load demand data, the allocated capacity of the capacity allocation strategy and the power supply capacity of the temporary power supply configuration; Evaluate the network stability index according to the load demand data, the network topology structure and the power transmission situation of the temporary power supply configuration. Analyze the critical load guarantee rate and the network stability index through a simulation algorithm to obtain a recovery efficiency evaluation result.

8. The collaborative power restoration method for a distribution network after a disaster according to claim 2, wherein, Updating the load rating parameter and the resource allocation ratio according to the recovery efficiency evaluation result to adjust the resource allocation model and generate a third resource allocation strategy includes: According to the recovery efficiency evaluation result, obtain deviation feedback data, analyze the deviation feedback data, and obtain the distribution characteristics of the deviation feedback data; Update the load rating parameter according to the distribution characteristics, optimize the resource allocation ratio according to the updated load rating parameter, and obtain the optimized allocation parameter; Iteratively adjust the resource allocation model according to the optimized allocation parameter to generate a third resource allocation strategy.

9. A collaborative power restoration system for a distribution network after a disaster, characterized in that, Including: A load rating parameter determination module, configured to obtain real-time supply and demand data and network topology information of a post-disaster distribution network in a target area, extract and analyze the real-time supply and demand data, and determine the current load rating parameter of the distribution network; wherein, the real-time supply and demand data includes load demand data and power supply data; A distribution boundary condition determination module, configured to calculate the priority weight of each load node according to the load rating parameter, load historical data, and user type to determine the set of critical load nodes of the distribution network; according to the network topology information, calculate the feasible path of the power flow of the distribution network, and determine the power flow distribution boundary condition of the distribution network; A first allocation strategy determination module, configured to construct a resource allocation model according to the power flow distribution boundary condition and the set of critical load nodes, and use the resource allocation model to determine a first resource allocation strategy through the real-time supply and demand data; A second allocation strategy determination module, configured to construct a multi-objective optimization model according to the resource allocation efficiency, social benefits, and the power flow distribution boundary condition; use the multi-objective optimization model to optimize the first resource allocation strategy to obtain a second resource allocation strategy; wherein, the multi-objective optimization model includes a comprehensive objective function and objective constraint conditions; A temporary power supply configuration determination module, configured to determine the temporary power supply configuration of the distribution network according to the power flow distribution data, the priority weight, and the access parameters of temporary equipment in the second resource allocation strategy; A capacity allocation strategy determination module, configured to determine the power supply redundancy of each critical load node according to the resource allocation ratio and the output power of the temporary power supply configuration in the second resource allocation strategy, and determine the capacity allocation strategy of the distribution network according to the power supply redundancy and priority weight of each critical load node; A first deployment path planning module, configured to plan the deployment path of the temporary power supply configuration according to the power flow distribution data, voltage stability constraint parameters, and the information of the temporary power supply configuration in the second resource allocation strategy to perform coordinated power restoration on the distribution network.

10. The collaborative power restoration system for a distribution network based on post-disaster conditions as claimed in claim 9, wherein, It also includes: An efficiency evaluation result acquisition module, which is used to analyze the current load demand data according to the capacity allocation strategy and the temporary power supply configuration, obtain the critical load guarantee rate and the network stability index, and analyze the critical load guarantee rate and the network stability index through a simulation algorithm to obtain the restoration efficiency evaluation result; A third allocation strategy generation module, which is used to update the load rating parameter and the resource allocation ratio according to the restoration efficiency evaluation result, so as to adjust the resource allocation model and generate a third resource allocation strategy; A second deployment path planning module, which is used to plan the deployment path of the adjusted temporary power supply configuration according to the third resource allocation strategy, so as to perform coordinated power restoration on the distribution network.

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