Distribution network restoration method, device, equipment and storage medium under waterlogging disaster

Abstract

This application discloses a method, apparatus, device, and storage medium for restoring a distribution network under a flood disaster. The method includes: determining the failure probability of i lines based on the wind speed corresponding to each line in i lines in the distribution network under a flood disaster; determining the status of i lines based on the i line failure probabilities; determining the failure probability of j nodes based on the waterlogging depth of each distribution node in the distribution network under a flood disaster; determining the status of j nodes based on the j node failure probabilities; determining y distribution network restoration plans based on the i line status and the j node status; determining the total value of each of the y distribution network restoration plans to obtain y total values; and selecting one distribution network restoration plan from the y distribution network restoration plans as a target distribution network restoration plan based on the y total values. This application is conducive to improving the accuracy of distribution network restoration plans.

Description

Technical Field

[0001] The present application relates to the technical field of improving the resilience of distribution networks, and in particular to a method, device, equipment, and storage medium for restoring distribution networks under urban flooding disasters. Background Art

[0002] In recent years, low-probability, high-loss extreme events, such as urban flooding, have posed a significant threat to the safe and stable operation of power systems, making the development of resilient power systems a pressing need. Urban distribution networks are directly related to load supply, and improving distribution network resilience has become a key research topic to address the issue of ensuring load supply during extreme events. Energy storage, as a resource with time-shifting flexibility, is widely used in resilience-enhancing strategies. Existing research has largely focused on mobile energy storage systems (MESS), which are essentially chemical batteries. This research includes determining the location and capacity of pre-disaster MESS deployments, as well as path selection and scheduling after disasters.

[0003] However, in the existing distribution network post-disaster recovery methods, the line faults of the distribution network are often analyzed empirically and a distribution network recovery plan is formulated, and the accuracy of the distribution network recovery plan is low. Summary of the Invention

[0004] In order to solve the above-mentioned problems existing in the prior art, the embodiments of the present application provide a method, apparatus, device and storage medium for restoring a distribution network under a flood disaster. Based on the wind speed corresponding to each line segment in the i-section line of the distribution network under the flood disaster, the line status of each line segment is predicted. Then, based on the water depth of each distribution node in the j-section line of the distribution network under the flood disaster, the node status of each distribution node is predicted. Finally, based on the line status of each line segment and the node status of each distribution node, y distribution network restoration plans are determined. From the y distribution network restoration plans, a distribution network restoration plan with the highest total value is selected as the target distribution network restoration plan, thereby improving the accuracy of the distribution network restoration plan.

[0005] In a first aspect, an embodiment of the present application provides a method for restoring a distribution network under a flood disaster, comprising:

[0006] Obtain the wind speed corresponding to each section of an i-section line in a distribution network under a flood disaster to obtain i wind speeds, and obtain the water depth of each of j distribution nodes in the distribution network under the flood disaster to obtain j water depths; the distribution network is coupled to a heat network through compressed air energy storage and a heat pump, a first distribution node among the j distribution nodes is connected to a compressor of the compressed air energy storage, a second distribution node among the j distribution nodes is connected to a steam turbine of the compressed air energy storage, a heat source node of the heat network is connected to a heat storage system of the compressed air energy storage and to the heat pump, and the heat source node is any one of the n heating nodes of the heat network; the water depth is the depth of the distribution equipment of the distribution node corresponding to the water depth immersed in water; i and n are both integers greater than 1, and j=i+1;

[0007] Determine, based on the i wind speeds, a line failure probability corresponding to each of the i line sections, to obtain i line failure probabilities;

[0008] Determine the line status of each line in the i line segments according to the i line failure probabilities to obtain i line statuses;

[0009] Determine, based on the j flooding depths, a node failure probability corresponding to each of the j power distribution nodes, to obtain j node failure probabilities;

[0010] Determine the node status of each of the j power distribution nodes according to the j node failure probabilities to obtain j node statuses;

[0011] Determining y distribution network restoration plans based on the i line states and the j node states; the distribution network restoration plans are used to allocate the restored electrical load of each of the j distribution nodes and the restored thermal load of each of the n heating nodes; y is an integer greater than 1;

[0012] Determining the total value of each of the y distribution network restoration plans to obtain y total values; the total value refers to the value of the total restored electrical load of the distribution network and the total restored thermal load of the heating network in the distribution network restoration plan corresponding to the total value;

[0013] According to the y total values, a distribution network restoration scheme is selected from the y distribution network restoration schemes as a target distribution network restoration scheme.

[0014] In a second aspect, an embodiment of the present application provides a distribution network restoration device, comprising:

[0015] An acquisition unit is configured to obtain the wind speed corresponding to each section of an i-section line in a distribution network under a flood disaster, thereby obtaining i wind speeds, and to obtain the water immersion depth of each of j distribution nodes in the distribution network under the flood disaster, thereby obtaining j water immersion depths; the distribution network is coupled to a heat network through compressed air energy storage and a heat pump, a first distribution node among the j distribution nodes is connected to a compressor of the compressed air energy storage, a second distribution node among the j distribution nodes is connected to a steam turbine of the compressed air energy storage, a heat source node of the heat network is connected to a heat storage system of the compressed air energy storage, and to the heat pump, the heat source node being any one of n heating nodes of the heat network; the water immersion depth is the depth of the distribution equipment of the distribution node corresponding to the water immersion depth; i and n are both integers greater than 1, and j=i+1;

[0016] a processing unit, configured to determine, based on the i wind speeds, a line failure probability corresponding to each of the i line segments, to obtain i line failure probabilities;

[0017] Determine the line status of each line in the i line segments according to the i line failure probabilities to obtain i line statuses;

[0018] Determine, based on the j flooding depths, a node failure probability corresponding to each of the j power distribution nodes, to obtain j node failure probabilities;

[0019] Determine the node status of each of the j power distribution nodes according to the j node failure probabilities to obtain j node statuses;

[0020] Determining y distribution network restoration plans based on the i line states and the j node states; the distribution network restoration plans are used to allocate the restored electrical load of each of the j distribution nodes and the restored thermal load of each of the n heating nodes; y is an integer greater than 1;

[0021] Determining the total value of each of the y distribution network restoration plans to obtain y total values; the total value refers to the value of the total restored electrical load of the distribution network and the total restored thermal load of the heating network in the distribution network restoration plan corresponding to the total value;

[0022] According to the y total values, a distribution network restoration scheme is selected from the y distribution network restoration schemes as a target distribution network restoration scheme.

[0023] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device performs the method described in the first aspect.

[0024] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method as described in the first aspect.

[0025] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program product is operable to enable a computer to execute the method described in the first aspect.

[0026] The implementation of the embodiments of the present application has the following beneficial effects:

[0027] In an embodiment of the present application, the wind speed corresponding to each of i sections of a distribution network under a flooding disaster is first obtained, obtaining i wind speeds, and the water depth of each of j distribution nodes in the distribution network under a flooding disaster is obtained, obtaining j water depths. Then, based on the i wind speeds, the line failure probability corresponding to each of the i sections is determined, obtaining i line failure probabilities. Based on the i line failure probabilities, the line status of each of the i sections can be determined, obtaining i line failure statuses. Further, based on the j water depths, the node failure probability corresponding to each of the j distribution nodes can be determined, obtaining j node failure probabilities. Based on the j node failure probabilities, the node status of each of the j distribution nodes can be determined, obtaining j node statuses. Further, based on the i line statuses and the j node statuses, y distribution network restoration plans can be determined. The total value of each of the y distribution network restoration plans is determined, obtaining y total values. Finally, based on the y total values, one distribution network restoration scheme can be selected from the y distribution network restoration schemes as the target distribution network restoration scheme. Thus, the line status of each line segment in the distribution network can be predicted based on the wind speed corresponding to that segment, and the node status of each distribution node in the distribution network can be predicted based on the water depth of each distribution node in the distribution network. Based on the line status of each line segment and the node status of each distribution node in the distribution network, y distribution network restoration schemes can be determined. Each distribution network restoration scheme is related to the line status of each line segment and the node status of each distribution node, improving the accuracy of the distribution network restoration scheme. Furthermore, by determining the total value of each of the y distribution network restoration schemes, the target distribution network restoration scheme with the highest value for restoring electric and thermal loads can be selected from the y distribution network restoration schemes, further improving the accuracy of the distribution network restoration scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0029] Figure 1 A schematic diagram of an application scenario of a method for restoring a distribution network under a flood disaster provided by an embodiment of the present application;

[0030] Figure 2 A flow chart of a method for restoring a distribution network under a flood disaster provided in an embodiment of the present application;

[0031] Figure 3A schematic diagram of a distribution network restoration device provided in an embodiment of the present application;

[0032] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0034] The terms "first," "second," "third," and "fourth," etc., in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, rather than to describe a specific order. In addition, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0035] References herein to "embodiments" mean that a particular feature, result, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0036] First, see Figure 1 , Figure 1 This is a schematic diagram of an application scenario of a method for restoring a distribution network under a flood disaster provided by an embodiment of the present application. Figure 1 The scenario shown includes a distribution network, compressed air energy storage (CAES), a heating network, heat pumps, and processing equipment. The distribution network is coupled to the heating network via CAES and heat pumps.

[0037] In the embodiment of the present application, the distribution network refers to a power network that receives power from the transmission network or power plant and transmits the power to power users through distribution equipment. The distribution network includes j distribution nodes, wherein the first distribution node among the j distribution nodes is connected to the compressor of the CAES, such as Figure 1The device connected to the "M" node is the compressor. The second of the j distribution nodes is connected to the steam turbine of the CAES, as shown in Figure 1 The device connected to the "G" node in the compressed air energy storage shown is the steam turbine, such as Figure 1 The "G" node in the distribution network shown is the second distribution node. Any two distribution nodes in the distribution network are connected by a line segment, forming a total of i lines, where i is an integer greater than 1 and j = i + 1. It should be noted that the j distribution nodes also include generator nodes. When the lines are operating normally, the distribution network load is supplied by the generator nodes. In the event of a line or node fault in the distribution network, CAES is used to supply power to the faulty node.

[0038] In the embodiments of the present application, CAES is a relatively mature energy storage technology that converts electricity into high-pressure air and stores it in the form of potential energy through electric motors and compressors. When power is insufficient, the high-pressure air drives a steam turbine and generator to output electricity. Heat exchange is required for both air cooling during compression and air heating during expansion. In the embodiments of the present application, CAES can be a non-supplementary compressed air energy storage system, including a compressor, a steam turbine, an air storage chamber, and a heat storage system. When there is excess electricity in the distribution network, the excess electricity is driven by the motor to drive the compressor, compressing air and storing it in the air storage chamber. During the compression process, heat is transferred to the heat storage system. When the distribution network experiences a line failure or distribution node failure due to flooding, causing a distribution node to disconnect from the main grid, CAES can drive the disconnected distribution node to form a local microgrid. At this time, the air storage chamber absorbs heat from the heat storage system, causing the high-pressure air in the air storage chamber to expand. The expanded gas drives the steam turbine and generator, thereby transmitting electricity to the distribution network to supply the disconnected distribution node.

[0039] In the embodiment of the present application, the heat network is a heat supply network formed for user nodes with heat supply demand. The heat network includes n heat supply nodes, where n is an integer greater than 1. The heat source node of the heat network is connected to the heat storage system of the compressed air energy storage and is connected to the heat pump. The heat source node is any one of the n heat supply nodes, such as Figure 1 The heat load of the shaded nodes in the heat network shown is mainly supplied by the heat source nodes. The heat of the heat source nodes is mainly supplied by the heat storage system and the heat pump, wherein the heat pump can also provide heat to the heat storage system. Each heating node is connected by two pipes, namely the water supply pipe and the return pipe, as shown in Figure 1 As shown in the figure, the arrows pointing outward from the heat source node represent the flow direction of the liquid in the supply pipe, and the arrows pointing from other heating nodes to the heat source node represent the flow direction of the liquid in the return pipe. The heat pump draws electricity from the distribution grid and pumps the liquid from the heat source node into the supply pipe. Through circulation through the supply and return pipes, the heat from the heat source node is distributed to each heating node.

[0040] In an embodiment of the present application, the processing device is mainly used to obtain data such as the wind speed corresponding to each section of the line, the flooding depth corresponding to each distribution node, etc. when the distribution network experiences a disaster, and predict the line status of each section of the line and the node status of each distribution node based on the above data, so as to determine the distribution network recovery plan. Among them, the processing device can be a server for data processing and data storage functions, including: application server, virtual private server (Virtual Private Server, VPS), database server, etc. The processing device can be a processor for data processing, including: central processing unit (CPU), digital signal processor (DSP), microprocessor (MPU), etc. Among them, the processing device can also obtain real-time working data of the distribution network, compressed air energy storage and heating network, such as the electric load of the distribution network, the electric power of the compressor in the compressed air energy storage, the thermal load of the heating network, etc., which are not limited here.

[0041] It's important to note that after a flood disaster, some lines and nodes in the distribution network may experience failures. These network failures can affect the operation of heat pumps and the thermal storage system in compressed air energy storage, leading to insufficient heat supply in the heating network. Therefore, a distribution network restoration plan is necessary to maximize the effectiveness of restoring the supply of electricity and heat loads after a flood disaster. Existing distribution network restoration plans are typically designed based on technical experience, resulting in limited accuracy.

[0042] To this end, in the distribution network restoration method under waterlogging disasters provided in the embodiments of the present application, the processing device obtains the wind speed corresponding to each of the i sections of the distribution network under waterlogging disasters, obtaining i wind speeds, and obtains the water depth of each of the j distribution nodes of the distribution network under waterlogging disasters, obtaining j water depths; the water depth is the depth of the distribution equipment of the distribution node corresponding to the water depth immersed in water;

[0043] The processing device determines the line failure probability corresponding to each line segment in the i-segment line based on the i wind speeds, and obtains the i-line failure probability;

[0044] The processing device determines the line status of each line in the i-section line according to the failure probability of the i-section line, and obtains the i-section line status;

[0045] The processing device determines the node failure probability corresponding to each of the j distribution nodes according to the j flooding depths, and obtains the j node failure probabilities;

[0046] The processing device determines the node state of each of the j power distribution nodes according to the j node failure probabilities, thereby obtaining j node states;

[0047] The processing device determines y distribution network restoration plans based on i line states and j node states; the distribution network restoration plans are used to allocate the restored electric load of each of the j distribution nodes and the restored thermal load of each of the n heating nodes; y is an integer greater than 1;

[0048] The processing device determines the total value of each of the y distribution network restoration plans to obtain y total values; the total value refers to the value of the total restored electrical load of the distribution network and the total restored thermal load of the thermal network in the distribution network restoration plan corresponding to the total value;

[0049] The processing device selects a distribution network restoration scheme from the y distribution network restoration schemes as the target distribution network restoration scheme according to the y total values.

[0050] As can be seen, in the above scenario, the processing equipment can determine the line failure probability of each i-section line in the distribution network under a flood disaster based on the wind speed of each section, thereby determining the line status of each section based on the line failure probability of each section. Then, based on the flooding depth of each of the j distribution nodes, the node failure probability of each distribution node is determined, thereby determining the node status of each distribution node based on the node failure probability of each distribution node. Based on the line status of each section and the node status of each distribution node, y distribution network restoration plans can be determined. Each distribution network restoration plan is determined by the line status and node status of the distribution network, improving the accuracy of the distribution network restoration plan. Furthermore, based on the total value of the restored electrical load and restored thermal load of each distribution network restoration plan, a target distribution network restoration plan can be determined from the y distribution network restoration plans, further improving the accuracy of the distribution network restoration plan.

[0051] See Figure 2 , Figure 2 This is a flow chart of a method for restoring a distribution network under a flood disaster provided in an embodiment of the present application. The method is applied to the above scenario and includes but is not limited to the following steps:

[0052] 201: Obtain the wind speed corresponding to each line segment in the i-th line segment of the distribution network under the waterlogging disaster, obtaining i wind speeds, and obtain the flooding depth of each distribution node in the j-th distribution node of the distribution network under the waterlogging disaster, obtaining j flooding depths.

[0053] In this embodiment of the present application, the immersion depth is the depth of the distribution equipment at the distribution node submerged in water. Distribution nodes may include substations, distribution rooms, etc. Distribution equipment may include transformers, circuit breakers, switchgear, and other equipment.

[0054] Specifically, multiple wind speed sensors can be installed on each of the i segments of a distribution network to detect the wind speed on that segment. A processing device can obtain multiple sub-wind speeds detected by the multiple wind speed sensors in each segment, with each wind speed sensor detecting a sub-wind speed. The average of the multiple sub-wind speeds is used as the wind speed corresponding to that segment. Based on the above processing method, i wind speeds are obtained. Each of the j distribution nodes in the distribution network is equipped with a liquid level sensor to detect the water depth of each distribution node. The processing device can obtain the water depth of each of the j distribution nodes from the liquid level sensor of that distribution node, thereby obtaining j water depths.

[0055] As you can understand, flooding disasters are often accompanied by strong winds. High wind speeds can cause line failures in the distribution network. Furthermore, during flooding, some of the j distribution nodes may be deeply flooded, causing damage to the distribution equipment at those nodes. Therefore, the processing equipment obtains the wind speed corresponding to each line segment and the flood depth corresponding to each distribution node to predict potential line and node failures in the distribution network.

[0056] 202: Determine the line failure probability corresponding to each line segment in the i line segments based on the i wind speeds, and obtain the i line failure probabilities.

[0057] In this embodiment of the present application, line failures in a distribution network are primarily related to the wind speed of that line segment. The conductors and towers within the line are susceptible to damage due to the stress of high wind speeds. The processing device can determine the line failure probability for each of the i line segments based on i wind speeds, thereby obtaining i line failure probabilities.

[0058] For example, each section of i lines is erected on k towers, where k is an integer greater than 1. Determining the line failure probability corresponding to each section of the i lines based on i wind speeds to obtain the i line failure probabilities may include:

[0059] According to i wind speeds, determine the conductor failure probability corresponding to each line segment in i lines, and obtain the i conductor failure probability;

[0060] According to i wind speeds, determine the tower damage probability of k towers corresponding to each line section i, and obtain x tower damage probabilities;

[0061] According to the probability of i conductor failure and x tower damage, the probability of i line failure is obtained.

[0062] Wherein, k and x are integers greater than 1, and x=i*k. It can be understood that if any one of the conductors and k towers of each line section fails, the line section is determined to have a fault.

[0063] Specifically, the processing device first determines the conductor failure probability corresponding to each section of the i-section line according to i wind speeds, and obtains i conductor failure probabilities. The conductor failure probability can be expressed as the normal distribution of the natural logarithm of the wind speed corresponding to the conductor failure probability. The conductor failure probability can be expressed by formula (1):

[0064]

[0065] Where v represents any one of the i wind speeds, P line (v) represents the conductor failure probability of the line corresponding to the wind speed, v min1 Indicates the minimum wind speed at which the probability of conductor failure increases sharply. max1 Indicates the maximum wind speed that the conductor can withstand. σ1 represents the standard deviation of the natural logarithm of the wind speed that causes conductor failure. m1 The median wind speed that causes conductor failure. line，min Indicates the failure probability of the conductor under normal operation. min1 、v max1 , σ1 and v m1 Both can be pre-set according to actual test results.

[0066] Formula (1) shows that when the wind speed is less than the minimum wind speed at which the conductor failure probability increases sharply, the conductor failure probability is the failure probability during normal operation. When the wind speed is greater than or equal to the minimum wind speed at which the conductor failure probability increases sharply, and less than the maximum wind speed the conductor can withstand, the conductor failure probability follows a normal distribution based on the natural logarithm of the wind speed. When the wind speed is greater than or equal to the maximum wind speed the conductor can withstand, the conductor failure probability is 1. Therefore, the processing device can determine the conductor failure probability for each of the i sections of the line based on i wind speeds, obtaining i conductor failure probabilities.

[0067] Furthermore, the processing device determines the tower damage probability of the k towers corresponding to each section of the i-section line based on the i wind speeds, and obtains x tower damage probabilities. The formula corresponding to the tower damage probability is similar to formula (1). When the wind speed is less than the lowest wind speed when the tower failure probability increases sharply, the tower failure probability is the failure probability during normal operation. Optionally, the failure probability can be 0. When the wind speed is greater than or equal to the lowest wind speed when the tower failure probability increases sharply, and less than the maximum wind speed that the tower can withstand, the tower failure probability is a normal distribution of the natural logarithm of the wind speed. When the wind speed is greater than or equal to the maximum wind speed that the tower can withstand, the tower failure probability is 1. Therefore, the processing device can determine the x tower damage probabilities according to the above method. Each section of the line corresponds to k tower damage probabilities.

[0068] Finally, the processing device determines the line failure probability corresponding to each line segment based on the conductor failure probability corresponding to each line segment and the k tower damage probability in the i line segments, and obtains the i line failure probabilities. The line failure probability of each line segment can be expressed by formula (2):

[0069]

[0070] Among them, P L represents the line failure probability of the line corresponding to the wind speed v, P tow,t represents the tower failure probability of the t-th tower on the line. As can be seen from formula (2), the failure of the conductor and the failure of any tower are independent of each other. A failure of the conductor or any tower indicates a failure in that section of the line. Therefore, the processing device can determine the line failure probability corresponding to each section of the line based on the conductor failure probability corresponding to each section of the line and the k tower damage probabilities, thereby successively determining the line failure probability of each section of the i-section line, and obtaining the i-line failure probability.

[0071] It can be seen that according to the i wind speeds, the conductor failure probability of each section of the i line can be determined, as well as the tower damage probability of the k towers corresponding to each section of the line can be determined. Therefore, according to the conductor failure probability and the k tower damage probability corresponding to each section of the line, the line failure probability corresponding to each section of the line can be determined, and the line status of each conductor section can be determined. According to the line status, a distribution network restoration plan can be formulated to ensure the accuracy of the distribution network restoration plan.

[0072] 203: Determine the line status of each line segment in the i line segments according to the i line failure probabilities, and obtain i line statuses.

[0073] In this embodiment of the present application, the processing device can simulate the line status of each of the i line segments using a Monte Carlo simulation method based on the failure probability of i lines in the distribution network, thereby obtaining i line states. The line states may include: connected to the main network, line failure, line off-grid, etc.

[0074] It is understandable that since the line status of each line segment in the i-segment line is simulated based on the line failure probability of the line segment, the line status corresponding to the line segment may be different from the actual line status. However, it can help technicians more quickly eliminate some lines that are unlikely to have failures, thereby reducing the waste of manpower and resources caused by manual detection and analysis.

[0075] 204: Determine the node failure probability corresponding to each of the j distribution nodes based on the j flooding depths, and obtain j node failure probabilities.

[0076] In an embodiment of the present application, the processing device may determine the node failure probability corresponding to each power distribution node based on the water immersion depth corresponding to the power distribution node.

[0077] Among them, the formula corresponding to the node failure probability of each distribution node is similar to formula (1). When the flooding depth is less than the minimum flooding depth when the node failure probability increases sharply, the node failure probability corresponding to the flooding depth is the failure probability of the distribution node when it is working normally. Optionally, the failure probability can be 0. When the flooding depth is greater than or equal to the minimum flooding depth when the node failure probability increases sharply, and is less than the maximum flooding depth that the distribution node can withstand, the node failure probability corresponding to the flooding depth is a normal distribution of the natural logarithm of the flooding depth. When the flooding depth is greater than or equal to the maximum flooding depth that the distribution node can withstand, the node failure probability is 1. Therefore, the processing device can determine the node failure probability corresponding to each of the j distribution nodes based on the j flooding depths to obtain j node failure probabilities.

[0078] 205: Determine the node status of each of the j distribution nodes according to the j node failure probabilities, and obtain j node statuses.

[0079] In an embodiment of the present application, the processing device can simulate the node status of each of the j distribution nodes using a Monte Carlo simulation method based on the failure probability of the j nodes in the distribution network, thereby obtaining j node states. The node states may include: normal power supply, node failure, node off-grid, etc.

[0080] It is understandable that since the node state of each of the j distribution nodes is simulated based on the node failure probability of the distribution node, the node state corresponding to the distribution node may be different from the actual node state, but it can help technicians eliminate some distribution nodes that are unlikely to fail more quickly, thereby reducing the waste of manpower and resources caused by manual detection and analysis.

[0081] 206: Determine y distribution network restoration plans based on the i line states and the j node states.

[0082] In this embodiment of the present application, a distribution network restoration plan is used to allocate the restored electrical load of each of j distribution nodes and the restored thermal load of each of n heating nodes. y is an integer greater than 1. After determining i line states and j node states, the processing device may determine y distribution network restoration plans that match the i line states and j node states.

[0083] Exemplarily, determining y distribution network restoration plans based on i line states and j node states includes:

[0084] Obtain the path lengths of multiple traffic roads between any two distribution nodes among j distribution nodes;

[0085] Determine the shortest path between any two distribution nodes among j distribution nodes based on the path lengths of multiple traffic roads between any two distribution nodes;

[0086] Get the water depth of the shortest path between any two distribution nodes;

[0087] Determine the travel time between any two distribution nodes among j distribution nodes based on the water depth of the shortest path between any two distribution nodes;

[0088] According to the status of i lines and j nodes, m deployment nodes are determined from the j distribution nodes;

[0089] According to the travel time between any two distribution nodes among the j distribution nodes, the charge and discharge state variables corresponding to each deployment node among the m deployment nodes are determined to obtain m charge and discharge state variables;

[0090] According to the m charging and discharging state variables, establishing first operating constraints corresponding to the m mobile energy storage systems;

[0091] According to the first operating constraint, y distribution network restoration plans are determined.

[0092] In this embodiment of the present application, the water depth refers to the road water depth along the shortest path corresponding to the water depth. m deployment nodes are used to deploy m mobile energy storage systems (MESSs), with one MESS deployed at each deployment node. The MESSs are used to power faulty nodes among j distribution nodes, where m is an integer greater than or equal to a target number and less than or equal to j, where the target number is the number of faulty nodes among the j distribution nodes.

[0093] Specifically, the processing device first obtains the path length of each of the multiple traffic roads between any two of the j distribution nodes. Based on the path length of each of the multiple traffic roads between any two of the j distribution nodes, the shortest path between any two of the j distribution nodes is determined.

[0094] It should be noted that when a line or node failure occurs in the distribution network, CAES and generators alone cannot supply power to all distribution nodes. Therefore, mobile energy storage systems are deployed as emergency power sources in distribution nodes that CAES and generators cannot reach. Therefore, the processing equipment needs to determine the shortest path between any two of the j distribution nodes to determine the travel time between them and thus determine the deployment nodes of the MESS.

[0095] Specifically, the processing device obtains the depth of water on the traffic road corresponding to the shortest path between any two distribution nodes. Each traffic road is provided with a liquid level sensor, and the processing device can directly obtain the water depth corresponding to the shortest path between any two distribution nodes from the liquid level sensor on the traffic road corresponding to the shortest path. It should be noted that in the case of road flooding, the driving speed of the vehicle deployed with the MESS can be determined according to the depth of the water. The driving speed of the vehicle can be expressed by the following formula (3):

[0096]

[0097] Where d represents the depth of water, v M represents the vehicle's speed on the shortest path corresponding to the water depth, v M0 represents the speed of the vehicle when there is no water on the shortest path. a represents half the depth of the water when vehicles are prohibited from passing, and b represents the attenuation coefficient. M0 , a and b can be preset according to actual test results.

[0098] Therefore, based on formula (2), the processing device can determine the vehicle's travel speed on the shortest path between any two distribution nodes based on the water depth of the shortest path between any two distribution nodes. The processing device then obtains the path length of the shortest path between any two distribution nodes. Based on the path length of the shortest path between any two distribution nodes among the j distribution nodes and the vehicle's travel speed on the shortest path between any two distribution nodes, the travel time between any two distribution nodes among the j distribution nodes can be determined.

[0099] Furthermore, the processing device needs to determine m MESSs corresponding to m deployment nodes in j distribution nodes based on i line states and j node states. Exemplarily, determining m deployment nodes in j distribution nodes based on i line states and j node states may include:

[0100] According to i line states, determining the first virtual commodity flow of each of the i line segments, and obtaining i first virtual commodity flows;

[0101] Determine, based on the j node states and the i first virtual commodity flows, a second virtual commodity flow corresponding to each of the j distribution nodes, to obtain j second virtual commodity flows;

[0102] Determine, based on the j second virtual commodity flows, a third virtual commodity flow corresponding to each of the j distribution nodes, to obtain j third virtual commodity flows;

[0103] According to the j third virtual commodity flows, a deployment node of each of the m mobile energy storage systems is determined in the j distribution nodes, thereby obtaining m deployment nodes.

[0104] In this embodiment of the present application, the first virtual commodity flow is used to indicate whether compressed air energy storage is supplying power to the line segment. The second virtual commodity flow is used to indicate whether compressed air energy storage is supplying power to the distribution node corresponding to the second virtual commodity flow. The third virtual commodity flow is used to indicate whether a mobile energy storage system is deployed at the distribution node corresponding to the third virtual commodity flow.

[0105] It should be noted that when some lines and distribution nodes in the distribution network fail, the backup lines are temporarily closed, and the entire distribution network is reconfigured to form multiple microgrids. The number of microgrids is equal to the number of non-faulty distribution nodes minus the number of non-faulty lines, and each microgrid is interconnected. Since each microgrid can only be powered by one power source, which can be a generator or a CAES in a generating state, the number of microgrids is the sum of the number of nodes covered by the generator and the CAES power generation state variable. Therefore, based on the line status of each line segment in the distribution network and the node status of each distribution node, all distribution nodes in the distribution network that can be supplied by generators and CAES, as well as all distribution nodes that cannot be supplied by generators and CAES, can be determined, thereby determining the deployment nodes of each of the m mobile energy storage systems, resulting in m deployment nodes.

[0106] Specifically, the processing device first determines the first virtual commodity flow corresponding to each of the i line segments based on i line states. For example, when the line state is connected to the main grid, indicating that the compressed air energy storage or generator can power the line corresponding to this state, the first virtual commodity flow is a preset value greater than 1. When the line state is a line fault or the line is off-grid, indicating that the compressed air energy storage or generator cannot power the line corresponding to this state, the first virtual commodity flow is 0. Thus, the processing device can determine the first virtual commodity flow corresponding to each of the i line segments based on the i line states, thereby obtaining i first virtual commodity flows.

[0107] Then, the processing device determines a second virtual commodity flow corresponding to each of the j distribution nodes based on the j node states and the i first virtual commodity flows, thereby obtaining j second virtual commodity flows. Due to the connectivity between adjacent distribution nodes, when the first virtual commodity flow is a preset value, the second virtual commodity flow of the two nodes connected by the line corresponding to the first virtual commodity flow is a preset value greater than 1. Furthermore, based on the j node states, it can be determined that when the node state is normal power supply, the second virtual commodity flow corresponding to the node state is a preset value greater than 1. When the node state is node failure or node is off-grid, the second virtual commodity flow corresponding to the node state is 0. Thus, the processing device can determine a second virtual commodity flow corresponding to each of the j distribution nodes based on the j node states and the i first virtual commodity flows, thereby obtaining j second virtual commodity flows.

[0108] Furthermore, after determining the j second virtual commodity flows, the processing device can identify distribution nodes among the j distribution nodes that are not covered by the generator and MESS, i.e., distribution nodes where the second virtual commodity flow is 0. Therefore, the processing device can determine j third virtual commodity flows based on the j second virtual commodity flows to ensure that each distribution node in the distribution network can normally supply power. When the MESS is in the discharging state of a distribution node, the third virtual commodity flow corresponding to the distribution node is a preset value greater than 1. When the MESS is in the charging state of the distribution node, the third virtual commodity flow corresponding to the distribution node is 0. The correspondence between the j third virtual commodity flows and the j second virtual commodity flows can be pre-set based on actual test results. Thus, the processing device can determine the third virtual commodity flow corresponding to each of the j distribution nodes based on the j second virtual commodity flows, thereby obtaining j third virtual commodity flows.

[0109] Finally, based on the j third virtual commodity flows, the deployment node for each of the m mobile energy storage systems is determined from the j distribution nodes, resulting in m deployment nodes. These m deployment nodes include the distribution node with a third virtual commodity flow of 0 and the distribution node with the shortest travel time to the distribution node with a third virtual commodity flow of 0.

[0110] It can be seen that the processing device can determine the first virtual commodity flow for each of the i segments of the line based on the i line states, obtaining i first virtual commodity flows. Then, based on the j node states and the i first virtual commodity flows, the second virtual commodity flow corresponding to each of the j distribution nodes is determined, obtaining j second virtual commodity flows. Furthermore, based on the j second virtual commodity flows, the third virtual commodity flow corresponding to each of the j distribution nodes is determined. Finally, based on the j third virtual commodity flows, the deployment node of each of the m mobile energy storage systems is determined in the j distribution nodes, obtaining m deployment nodes. Thus, by deploying MESS at the m deployment nodes, power can be supplied to distribution nodes that are not covered by generators and CAES, thereby improving the stability of the power supply to the distribution network.

[0111] Furthermore, based on the travel time between any two of the j distribution nodes, the charge-discharge state variable corresponding to each of the m deployment nodes can be determined, resulting in m charge-discharge state variables. For example, the processing device first determines that all faulty nodes and off-grid nodes in the m deployment nodes can be identified based on the third virtual commodity flow corresponding to the m deployment nodes. Then, the charge-discharge state variables corresponding to all faulty nodes and off-grid nodes are determined to be a discharge state value, e.g., -1, and the charge-discharge state variables corresponding to nodes that are operating normally among the m deployment nodes are determined to be a charge state value, e.g., 1.

[0112] Next, based on the m charge-discharge state variables, first operating constraints corresponding to the m mobile energy storage systems can be established. These first operating constraints may include: constraints on the state of charge (SOC) of each of the m mobile energy storage systems, constraints on the active and reactive power of each mobile energy storage system, constraints on the active and reactive output of the MESS corresponding to each of the j distribution nodes, and so on.

[0113] Finally, according to the first operating constraint, y distribution network restoration schemes can be determined.

[0114] Thus, the processing device can determine the travel time between any two distribution nodes among the j distribution nodes through the shortest path between any two distribution nodes among the j distribution nodes and the depth of water accumulation on the shortest path. Then, based on the i line states and the j node states, m deployment nodes are determined among the j distribution nodes. Based on the travel time between any two distribution nodes among the j distribution nodes, the charge and discharge state variables corresponding to each deployment node among the m deployment nodes are determined to obtain m charge and discharge state variables. Based on the m charge and discharge variables, the first operating constraints corresponding to the m mobile energy storage systems are established. Finally, based on the first operating constraints, y distribution network recovery plans can be determined. In this way, the deployment nodes of m MESSs can be determined to help the distribution network restore the power load, and y distribution network recovery plans can be determined based on the first operating constraints of the m MESSs, thereby improving the accuracy of the distribution network recovery plan.

[0115] Exemplarily, determining y distribution network restoration plans according to the first operating constraint may include:

[0116] Obtaining a first air mass flow rate during the air compression process of the compressor; and obtaining a second air mass flow rate during the air expansion process of the steam turbine;

[0117] determining an electrical power of the compressor based on the first air mass flow rate;

[0118] determining an electrical power of the steam turbine based on the second air mass flow rate;

[0119] Determine the thermal power provided by the thermal storage system to the thermal network;

[0120] Establishing a second operating constraint corresponding to the compressed air energy storage based on the electrical power of the compressor, the electrical power of the steam turbine, and the thermal power provided by the thermal storage system to the thermal network;

[0121] Obtaining the supply water temperature of each of the n heating nodes to obtain n supply water temperatures; and obtaining the return water temperature of each of the n heating nodes to obtain n return water temperatures; and obtaining the ambient temperature of each of the n heating nodes to obtain n ambient temperatures;

[0122] According to n water supply temperatures and n ambient temperatures, n water supply heat losses are obtained;

[0123] According to n return water temperatures and n ambient temperatures, n return water heat losses are obtained;

[0124] According to n supply water temperatures, n return water temperatures, n ambient temperatures, n supply water heat losses, and n return water heat losses, a third operation constraint corresponding to the heating network is established;

[0125] Determine y distribution network restoration plans based on the first operating constraint, the second operating constraint, and the third operating constraint.

[0126] In the embodiments of the present application, the compressed air energy storage system is affected by three parameters during operation: air pressure, temperature, and air mass flow rate. When the compressed air energy storage system operates in a constant temperature and constant pressure mode, the electrical power of the equipment in the compressed air energy storage system is only related to the air mass flow rate.

[0127] Specifically, a gas flow detector is provided in each of the compressor and the steam turbine. The processing device can obtain a first air mass flow rate during the air compression process of the compressor from the gas flow detector of the compressor, and obtain a second air mass flow rate during the air expansion process of the steam turbine from the gas flow detector of the steam turbine.

[0128] The processing device then determines the compression level of the compressor or the expansion level of the steam turbine based on the target number of faulty nodes among the j distribution nodes. For example, when the target number is greater than or equal to a preset number, the steam turbine in the CAES is operating. The processing device may determine the difference between the target number and the preset number, and determine the expansion level of the steam turbine based on the difference between the target number and the preset number. When the target number is less than the preset number, the compressor in the CAES is operating. The processing device may determine the difference between the preset number and the target number, and determine the compression level of the compressor based on the difference between the preset number and the target number. The mapping relationship between the difference between the target number and the preset number and the expansion level of the steam turbine can be pre-set based on actual test results. The mapping relationship between the difference between the preset number and the target number and the compression level of the compressor can be pre-set based on actual test results. Based on this, the electrical power of the compressor can be determined based on the first air mass flow rate and the compression level of the compressor. The electrical power of the steam turbine can be determined based on the second air mass flow rate and the expansion level of the steam turbine.

[0129] Furthermore, the processing device can obtain the amount of heat stored in the thermal storage system and, based on the electrical power of the compressor and the electrical power of the steam turbine, determine the amount of heat circulation between the thermal storage system, the compressor, and the steam turbine. Based on the amount of heat circulation between the thermal storage system, the compressor, and the steam turbine, the amount of heat provided by the thermal storage system to the heating network can be determined. Consequently, based on the amount of heat provided by the thermal storage system to the heating network, the thermal power provided by the thermal storage system to the heating network can be determined.

[0130] Finally, based on the compressor power, turbine power, and thermal power provided by the thermal storage system to the heating network, a second operational constraint for the compressed air energy storage can be established. This second operational constraint may include constraints on the compressor's active and reactive power, constraints on the active and reactive power of the turbine, constraints on the air mass flow rate in the air storage chamber, constraints on the thermal power provided by the thermal storage system to the heating network, and constraints on the restored heat load corresponding to each of the n heating nodes. The mapping relationship between the compressor power, turbine power, and thermal power provided by the thermal storage system to the heating network and the second operational constraint can be pre-set based on actual test results.

[0131] Furthermore, the processing device obtains the water supply temperature of each of the n heating nodes in the heating network, obtaining n water supply temperatures. The processing device obtains the return water temperature of each of the n heating nodes, obtaining n return water temperatures. The processing device obtains the ambient temperature of each of the n heating nodes, obtaining n ambient temperatures. It should be noted that the water supply pipe and return water pipe of each heating node are both provided with temperature sensors. The processing device can directly obtain the water supply temperature of each heating node from the temperature sensor of the water supply pipe of the heating node, and obtain the return water temperature of each heating node from the temperature sensor of the return water pipe of the heating node. At the same time, the processing device can obtain the ambient temperature of each heating node from the ambient temperature sensor of the heating node.

[0132] Then, based on n supply water temperatures and n ambient temperatures, n supply water heat losses can be obtained. Based on n return water temperatures and n ambient temperatures, n return water heat losses can be obtained. Each supply water temperature and ambient temperature corresponds to one supply water heat loss. Each return water temperature and ambient temperature corresponds to one return water heat loss. The correspondence between supply water temperature, ambient temperature, and supply water heat loss, as well as the correspondence between return water temperature, ambient temperature, and return water heat loss, can be pre-set based on actual test results.

[0133] Next, a third operational constraint corresponding to the heating network can be established based on the n supply water temperatures, n return water temperatures, n ambient temperatures, n supply water heat losses, and n return water heat losses. This third operational constraint includes constraints on the supply water temperature and return water temperature for each of the n heating nodes, constraints on the supply water heat loss and return water heat loss for each of the n heating nodes, constraints on the supply water flow rate and return water flow rate of the supply water pipes for each of the n heating nodes, and constraints on the electrical power of the heat pumps. The mapping relationship between the n supply water temperatures, n return water temperatures, n ambient temperatures, n supply water heat losses, and n return water heat losses and the third operational constraint can be pre-set based on actual test results.

[0134] Finally, according to the first operating constraint, the second operating constraint and the third operating constraint, y distribution network restoration schemes can be determined.

[0135] It can be seen that the electric power of the compressor can be determined based on the first air mass flow rate during the air compression process of the compressor. The electric power of the steam turbine can be determined based on the second air mass flow rate during the air expansion process of the steam turbine. According to the electric power of the compressor, the electric power of the steam turbine, and the thermal power provided by the heat storage system to the heat network, the second operating constraint corresponding to the compressed air energy storage can be established. According to the supply water temperature, return water temperature, ambient temperature, supply water heat loss, and return water heat loss of each of the n heating nodes in the heat network, the third operating constraint corresponding to the heat network can be established. Therefore, according to the first operating constraints corresponding to the m mobile energy storage systems, the second operating constraints corresponding to the CAES, and the third operating constraints of the heat network, y distribution network recovery plans can be determined, thereby improving the accuracy of the distribution network recovery plan.

[0136] Exemplarily, determining y distribution network restoration plans based on the first operating constraint, the second operating constraint, and the third operating constraint may include:

[0137] Determine, according to the first operating constraint, a first active power and a first reactive power of the mobile energy storage system corresponding to each of the m deployment nodes, to obtain m first active powers and m first reactive powers;

[0138] determining a second active power and a second reactive power of the compressor according to a second operating constraint;

[0139] determining a third active power and a third reactive power of the steam turbine according to the second operation constraint;

[0140] determining the electrical power of the heat pump according to a third operating constraint;

[0141] Establishing a fourth operation constraint corresponding to the distribution network according to the m first active powers, the m first reactive powers, the second active power, the second reactive power, the third active power, the third reactive power, and the electric power of the heat pump;

[0142] According to the second operating constraint, determining y recovery heat loads corresponding to each of the n heating nodes to obtain s recovery heat loads;

[0143] According to the fourth operating constraint, determining y restored electric loads corresponding to each of the j distribution nodes to obtain a restored electric loads;

[0144] According to s restored thermal loads and a restored thermal loads, y distribution network restoration plans are determined.

[0145] In the embodiment of the present application, s = n*y, a = j*y, and each heat source node in each power distribution network restoration scheme corresponds to a restored heat load, and each power distribution node corresponds to a restored power load.

[0146] Specifically, the first operating constraints include constraints on the active power and reactive power of the mobile energy storage system corresponding to each of the m deployment nodes. These constraints define the range of values ​​for the active power and reactive power of the mobile energy storage system corresponding to each deployment node. The processing device may determine, based on the power demand of each of the m deployment nodes, a first active power and a first reactive power of the mobile energy storage system corresponding to each of the m deployment nodes from the first operating constraints, thereby obtaining m first active powers and m first reactive powers.

[0147] Similarly, the second operating constraint includes constraints on the compressor's active power and reactive power, which define a range of values ​​for the compressor's active power and reactive power. The processing device can obtain excess power from the distribution network and, based on the excess power in the distribution network, determine the second active power and second reactive power of the compressor from the second operating constraint. The second operating constraint also includes constraints on the steam turbine's active power and reactive power, which define a range of values ​​for the steam turbine's active power and reactive power. Based on the total power demand of the distribution network, the processing device can determine a third active power and a third reactive power of the steam turbine from a third operating constraint. The heat pump's electrical power is then determined based on the third operating constraint.

[0148] Furthermore, the processing device establishes a fourth operating constraint corresponding to the distribution network based on the m first active powers, the m first reactive powers, the second active powers, the second reactive powers, the third active powers, the third reactive powers, and the power of the heat pump. The fourth operating constraint includes a constraint on the restored power load of each of the j distribution nodes, a constraint on the node voltage of each of the j distribution nodes, and the like.

[0149] Then, because the second operating constraint includes a constraint on the restored heat load corresponding to each of the n heating nodes, the processing device can randomly extract y restored heat loads from this constraint as the y restored heat loads corresponding to each heating node, thereby obtaining s restored heat loads. Because the fourth operating constraint includes a constraint on the restored electrical load for each of the j power distribution nodes, the processing device can randomly extract y restored electrical loads from this constraint as the y restored electrical loads corresponding to each power distribution node, thereby obtaining a restored electrical loads.

[0150] Finally, the processing device associates each of the j distribution nodes with a restored electrical load and each of the n heating nodes with a restored thermal load to determine a distribution network restoration plan. Based on the above method, y distribution network restoration plans can be determined.

[0151] Thus, by establishing a fourth operating constraint for the distribution network and determining, based on the fourth operating constraint, y restored electrical loads corresponding to each of the j distribution nodes, a restored electrical load can be obtained. Based on the second operating constraint, y restored thermal loads corresponding to each of the n heating nodes can be determined, resulting in s restored thermal loads. Based on the s restored thermal loads and the a restored thermal loads, y distribution network restoration plans can be determined. Each distribution network restoration plan is related to the operating status of the distribution network, improving the accuracy of the distribution network restoration plan.

[0152] 207: Determine the total value of each of the y distribution network restoration plans to obtain y total values.

[0153] In the embodiment of the present application, the total value refers to the value of the total restored electric load of the distribution network and the total restored thermal load of the heating network in the distribution network restoration plan corresponding to the total value.

[0154] Exemplarily, determining the total value of each of y distribution network restoration schemes to obtain y total values ​​may include:

[0155] Determine the sum of the restored heat loads of the n heating nodes corresponding to each distribution network restoration plan in the y distribution network restoration plans as the total restored heat load of the distribution network restoration plan, and obtain y total restored heat loads;

[0156] Determine the sum of the restored power loads of the j distribution nodes corresponding to each distribution network restoration plan in the y distribution network restoration plans as the total restored power load of the distribution network restoration plan, and obtain y total restored power loads;

[0157] Obtain the electrical load value coefficient and the thermal load value coefficient;

[0158] Determine a heat load value according to the heat load value coefficient and each of the y total recovery heat loads to obtain y heat load values;

[0159] Determine an electric load value according to the electric load value coefficient and each of the y total restored electric loads to obtain y electric load values;

[0160] Based on y thermal load values ​​and y electrical load values, determine y total values.

[0161] In an embodiment of the present application, the higher the total restored electric load corresponding to the j distribution nodes in the distribution network, the greater the value of the distribution network restoration plan corresponding to the total restored electric load. Similarly, the higher the total restored thermal load of the n heating nodes in the heating network, the greater the value of the distribution network restoration plan corresponding to the total restored thermal load. The processing device can determine the total value of each distribution network restoration plan based on the total restored electric load and the total restored thermal load of each distribution network restoration plan.

[0162] Specifically, the processing device first determines the sum of the restored thermal loads of the n heating nodes corresponding to each of the y distribution network restoration plans as the total restored thermal load of the distribution network restoration plan, obtaining y total restored thermal loads, where each total restored thermal load is the restored thermal load corresponding to the heat source node of the heat network in the distribution network restoration plan. Then, the processing device determines the sum of the restored electrical loads of the j distribution nodes corresponding to each of the y distribution network restoration plans as the total restored electrical load of the distribution network restoration plan, obtaining y total restored electrical loads. The total restored electrical load is supplied by the generator, CAES, and m MESSs.

[0163] Furthermore, the processing device obtains the energy load value coefficient and the thermal load value coefficient. The energy load value coefficient and the thermal load value coefficient can be pre-set according to user requirements. The processing device then multiplies the energy load value coefficient by j to obtain the total energy load value coefficient. The product of the total energy load value coefficient and each of the y total restored energy loads is used as the energy load value corresponding to the total restored energy load, thereby obtaining the y energy load values.

[0164] Furthermore, the processing device multiplies the heat load value coefficient by n to obtain a total heat load value coefficient. The product of the total heat load value coefficient and each of the y total recovery heat loads is used as the heat load value corresponding to the total recovery heat load to obtain y heat load values.

[0165] Finally, the processing device calculates the total value of each distribution network restoration plan, based on the distribution network restoration plan corresponding to each of the y heat load values ​​and the distribution network restoration plan corresponding to each of the y electric load values, by summing the heat load value and electric load value corresponding to each distribution network restoration plan. Based on this, the total value of each of the y distribution network restoration plans can be determined, resulting in y total values.

[0166] Thus, by determining the sum of the restored heat loads of the n heating nodes corresponding to each of the y distribution network restoration plans, y total restored heat loads can be obtained. By determining the sum of the restored electric loads of the j distribution nodes corresponding to each of the y distribution network restoration plans, y total restored electric loads can be obtained. Based on the y total restored heat loads and the heat load value coefficient, y heat load values ​​can be determined. Based on the y total restored electric loads and the electric load value coefficient, y electric load values ​​can be determined. Finally, based on the y heat load values ​​and the y electric load values, y total values ​​corresponding to the y distribution network restoration plans are determined. Thus, the distribution network restoration plan with the highest total value can be determined based on the y total values, thereby improving the accuracy of the distribution network restoration plan.

[0167] 208: According to the y total values, select a distribution network restoration scheme from the y distribution network restoration schemes as the target distribution network restoration scheme.

[0168] In this embodiment of the present application, the processing device can determine the distribution network restoration plan with the highest total value from y distribution network restoration plans based on y total values ​​as the target distribution network restoration plan, so as to maximize the value of the total restored electrical load of the distribution network and the total restored thermal load of the heat network, thereby improving the accuracy of the distribution network restoration plan. Thus, technicians can schedule the distribution network, CAES, m MESSs, and heat pumps based on the distribution network restoration plan, as well as repair faulty lines in i segments and faulty nodes in j distribution nodes, thereby ensuring the stability of the distribution network.

[0169] In summary, in an embodiment of the present application, the wind speed corresponding to each of i segments of a distribution network under a flooding disaster is first obtained, obtaining i wind speeds, and the water depth of each of j distribution nodes in the distribution network under a flooding disaster is obtained, obtaining j water depths. Then, based on the i wind speeds, the line failure probability corresponding to each of the i segments is determined, obtaining i line failure probabilities. Based on the i line failure probabilities, the line status of each of the i segments can be determined, obtaining i line failure statuses. Further, based on the j water depths, the node failure probability corresponding to each of the j distribution nodes can be determined, obtaining j node failure probabilities. Based on the j node failure probabilities, the node status of each of the j distribution nodes can be determined, obtaining j node statuses. Further, based on the i line statuses and the j node statuses, y distribution network restoration plans can be determined. The total value of each of the y distribution network restoration plans is determined, obtaining y total values. Finally, based on the y total values, one distribution network restoration scheme can be selected from the y distribution network restoration schemes as the target distribution network restoration scheme. Thus, the line status of each line segment in the distribution network can be predicted based on the wind speed corresponding to that segment, and the node status of each distribution node in the distribution network can be predicted based on the water depth of each distribution node in the distribution network. Based on the line status of each line segment and the node status of each distribution node in the distribution network, y distribution network restoration schemes can be determined. Each distribution network restoration scheme is related to the line status of each line segment and the node status of each distribution node, improving the accuracy of the distribution network restoration scheme. Furthermore, by determining the total value of each of the y distribution network restoration schemes, the target distribution network restoration scheme with the highest value for restoring electric and thermal loads can be selected from the y distribution network restoration schemes, further improving the accuracy of the distribution network restoration scheme.

[0170] See Figure 3 , Figure 3 Schematic diagram of a power distribution network restoration device provided in an embodiment of the present application. The power distribution network restoration device 300 may be a processing device in any of the above embodiments. Figure 3 As shown, the power distribution network restoration device 300 includes an acquisition unit 301 and a processing unit 302 .

[0171] An acquisition unit 301 is configured to acquire the wind speed corresponding to each of i sections of a distribution network under a waterlogging disaster, obtaining i wind speeds, and to acquire the water depth of each of j distribution nodes in the distribution network under a waterlogging disaster, obtaining j water depths.

[0172] The processing unit 302 is configured to determine the line failure probability corresponding to each line segment in the i line segments according to the i wind speeds, thereby obtaining the i line failure probabilities;

[0173] According to the failure probability of i lines, the line status of each line in the i-section line is determined to obtain i line statuses;

[0174] According to the j flooding depths, determine the node failure probability corresponding to each of the j distribution nodes, and obtain the j node failure probability;

[0175] According to the failure probability of j nodes, the node state of each distribution node in the j distribution nodes is determined to obtain j node states;

[0176] Determine y distribution network restoration plans based on the status of i lines and j nodes; the distribution network restoration plan is used to allocate the restored electrical load of each of the j distribution nodes and the restored thermal load of each of the n heating nodes;

[0177] Determine the total value of each of the y distribution network restoration plans to obtain y total values;

[0178] According to the y total values, a distribution network restoration scheme is selected from the y distribution network restoration schemes as the target distribution network restoration scheme.

[0179] In a possible embodiment, each of i line segments is installed on k towers, where k is an integer greater than 1. In determining the line failure probability corresponding to each of the i line segments based on i wind speeds to obtain the i line failure probabilities, the processing unit 302 is specifically configured to:

[0180] According to i wind speeds, determine the conductor failure probability corresponding to each line segment in i lines, and obtain the i conductor failure probability;

[0181] Based on i wind speeds, determine the tower damage probability of k towers corresponding to each line section i, and obtain x tower damage probabilities; x is an integer greater than 1, x = i * k;

[0182] According to the probability of i conductor failure and x tower damage, the probability of i line failure is obtained.

[0183] In a possible embodiment, in determining y distribution network restoration solutions according to i line states and j node states, the processing unit 302 is specifically configured to:

[0184] Obtain the path lengths of multiple traffic roads between any two distribution nodes among j distribution nodes;

[0185] Determine the shortest path between any two distribution nodes among j distribution nodes based on the path lengths of multiple traffic roads between any two distribution nodes;

[0186] Obtain the water depth of the shortest path between any two distribution nodes; the water depth refers to the road water depth on the shortest path corresponding to the water depth;

[0187] Determine the travel time between any two distribution nodes among j distribution nodes based on the water depth of the shortest path between any two distribution nodes;

[0188] Based on i line states and j node states, m deployment nodes are determined among the j distribution nodes; the m deployment nodes are used to deploy m mobile energy storage systems, with one mobile energy storage system deployed on each deployment node. The mobile energy storage systems are used to supply power to faulty nodes among the j distribution nodes, where m is an integer greater than or equal to a target number and less than or equal to j, where the target number is the number of faulty nodes among the j distribution nodes.

[0189] According to the travel time between any two distribution nodes among the j distribution nodes, the charge and discharge state variables corresponding to each deployment node among the m deployment nodes are determined to obtain m charge and discharge state variables;

[0190] According to the m charging and discharging state variables, establishing first operating constraints corresponding to the m mobile energy storage systems;

[0191] According to the first operating constraint, y distribution network restoration plans are determined.

[0192] In a possible embodiment, in determining y distribution network restoration solutions according to the first operating constraint, the processing unit 302 is specifically configured to:

[0193] Obtaining a first air mass flow rate during the air compression process of the compressor; and obtaining a second air mass flow rate during the air expansion process of the steam turbine;

[0194] determining an electrical power of the compressor based on the first air mass flow rate;

[0195] determining an electrical power of the steam turbine based on the second air mass flow rate;

[0196] Determine the thermal power provided by the thermal storage system to the thermal network;

[0197] Establishing a second operating constraint corresponding to the compressed air energy storage based on the electrical power of the compressor, the electrical power of the steam turbine, and the thermal power provided by the thermal storage system to the thermal network;

[0198] Obtaining the supply water temperature of each of the n heating nodes to obtain n supply water temperatures; and obtaining the return water temperature of each of the n heating nodes to obtain n return water temperatures; and obtaining the ambient temperature of each of the n heating nodes to obtain n ambient temperatures;

[0199] According to n water supply temperatures and n ambient temperatures, n water supply heat losses are obtained;

[0200] According to n return water temperatures and n ambient temperatures, n return water heat losses are obtained;

[0201] According to n supply water temperatures, n return water temperatures, n ambient temperatures, n supply water heat losses, and n return water heat losses, a third operation constraint corresponding to the heating network is established;

[0202] Determine y distribution network restoration plans based on the first operating constraint, the second operating constraint, and the third operating constraint.

[0203] In a possible embodiment, in determining m deployment nodes from j distribution nodes according to i line states and j node states, the processing unit 302 is specifically configured to:

[0204] Determine, based on i line states, a first virtual commodity flow for each of the i line segments, obtaining i first virtual commodity flows; the first virtual commodity flow is used to indicate whether the compressed air energy storage system is supplying power to the line segment;

[0205] Determine, based on the j node states and the i first virtual commodity flows, a second virtual commodity flow corresponding to each of the j distribution nodes, to obtain j second virtual commodity flows, where the second virtual commodity flows are used to indicate whether the compressed air energy storage system supplies power to the distribution node corresponding to the second virtual commodity flow;

[0206] Determine, based on the j second virtual commodity flows, a third virtual commodity flow corresponding to each of the j distribution nodes, to obtain j third virtual commodity flows, where the third virtual commodity flows are used to indicate whether a mobile energy storage system is deployed at the distribution node corresponding to the third virtual commodity flow;

[0207] According to the j third virtual commodity flows, a deployment node of each of the m mobile energy storage systems is determined in the j distribution nodes, thereby obtaining m deployment nodes.

[0208] In a possible embodiment, in determining y distribution network restoration solutions according to the first operating constraint, the second operating constraint, and the third operating constraint, the processing unit 302 is specifically configured to:

[0209] Determine, according to the first operating constraint, a first active power and a first reactive power of the mobile energy storage system corresponding to each of the m deployment nodes, to obtain m first active powers and m first reactive powers;

[0210] determining a second active power and a second reactive power of the compressor according to a second operating constraint;

[0211] determining a third active power and a third reactive power of the steam turbine according to the second operation constraint;

[0212] determining the electrical power of the heat pump according to a third operating constraint;

[0213] Establishing a fourth operation constraint corresponding to the distribution network according to the m first active powers, the m first reactive powers, the second active power, the second reactive power, the third active power, the third reactive power, and the electric power of the heat pump;

[0214] According to the second operating constraint, y recovery heat loads corresponding to each of the n heating nodes are determined to obtain s recovery heat loads; s = n*y;

[0215] According to the fourth operating constraint, y restored electric loads corresponding to each of the j distribution nodes are determined to obtain a restored electric load; a=j*y;

[0216] According to s restored thermal loads and a restored thermal loads, y distribution network restoration plans are determined, wherein each heat source node in each distribution network restoration plan corresponds to a restored thermal load, and each distribution node corresponds to a restored electrical load.

[0217] In a possible embodiment, in determining the total value of each of the y distribution network restoration solutions to obtain the y total values, the processing unit 302 is specifically configured to:

[0218] Determine the sum of the restored heat loads of the n heating nodes corresponding to each distribution network restoration plan in the y distribution network restoration plans as the total restored heat load of the distribution network restoration plan, and obtain y total restored heat loads;

[0219] Determine the sum of the restored power loads of the j distribution nodes corresponding to each distribution network restoration plan in the y distribution network restoration plans as the total restored power load of the distribution network restoration plan, and obtain y total restored power loads;

[0220] Obtain the electrical load value coefficient and the thermal load value coefficient;

[0221] Determine a heat load value according to the heat load value coefficient and each of the y total recovery heat loads to obtain y heat load values;

[0222] Determine an electric load value according to the electric load value coefficient and each of the y total restored electric loads to obtain y electric load values;

[0223] Based on y thermal load values ​​and y electrical load values, determine y total values.

[0224] See Figure 4 , Figure 4This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 4 As shown, electronic device 400 includes a transceiver 401, a processor 402, and a memory 403. These are connected via a bus 404. Memory 403 is used to store computer programs and data and transmit data stored in memory 403 to processor 402. Electronic device 400 may be the power distribution network restoration device 300. Electronic device 400 may also be the processing device of any of the above embodiments.

[0225] The processor 402 is configured to read the computer program in the memory 403 and perform the following operations:

[0226] Obtain the wind speed corresponding to each of the i-section lines in the distribution network under the waterlogging disaster, obtaining i wind speeds, and obtain the flooding depth of each of the j distribution nodes in the distribution network under the waterlogging disaster, obtaining j flooding depths;

[0227] According to i wind speeds, determine the line failure probability corresponding to each line in i sections of the line, and obtain the i line failure probability;

[0228] According to the failure probability of i lines, the line status of each line in the i-section line is determined to obtain i line statuses;

[0229] According to the j flooding depths, determine the node failure probability corresponding to each of the j distribution nodes, and obtain the j node failure probability;

[0230] According to the failure probability of j nodes, the node state of each distribution node in the j distribution nodes is determined to obtain j node states;

[0231] Determine y distribution network restoration plans based on i line states and j node states;

[0232] Determine the total value of each of the y distribution network restoration plans to obtain y total values;

[0233] According to the y total values, a distribution network restoration scheme is selected from the y distribution network restoration schemes as the target distribution network restoration scheme.

[0234] The above mainly introduces the solution of the embodiment of the present application from the perspective of the execution process of the method side. It is understandable that, in order to realize the above functions, the electronic device 400 includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiment provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0235] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement part or all of the steps of any one of the methods described in the above method embodiments.

[0236] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any one of the methods described in the above method embodiments.

[0237] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required for this application.

[0238] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0239] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0240] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0241] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or in the form of software program modules.

[0242] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0243] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program. The program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0244] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for restoring a distribution network under a flood disaster, characterized in that: include: Obtain the wind speed corresponding to each section of an i-section line in a distribution network under a flood disaster to obtain i wind speeds, and obtain the water depth of each of j distribution nodes in the distribution network under the flood disaster to obtain j water depths; the distribution network is coupled to a heat network through compressed air energy storage and a heat pump, a first distribution node among the j distribution nodes is connected to a compressor of the compressed air energy storage, a second distribution node among the j distribution nodes is connected to a steam turbine of the compressed air energy storage, a heat source node of the heat network is connected to a heat storage system of the compressed air energy storage and to the heat pump, and the heat source node is any one of n heating nodes of the heat network; the water depth is the depth to which the distribution equipment of the distribution node corresponding to the water depth is immersed in water; i and n are both integers greater than 1, j = i + 1; Determine, based on the i wind speeds, a line failure probability corresponding to each of the i line sections, to obtain i line failure probabilities; Determine the line status of each line in the i line segments according to the i line failure probabilities to obtain i line statuses; Determine, based on the j flooding depths, a node failure probability corresponding to each of the j power distribution nodes, to obtain j node failure probabilities; Determine the node status of each of the j power distribution nodes according to the j node failure probabilities to obtain j node statuses; Determining y distribution network restoration plans according to the i line states and the j node states; The distribution network restoration plan is used to allocate the restored electrical load of each of the j distribution nodes and the restored thermal load of each of the n heating nodes; y is an integer greater than 1; Determining the total value of each of the y distribution network restoration plans to obtain y total values; the total value refers to the value of the total restored electrical load of the distribution network and the total restored thermal load of the heating network in the distribution network restoration plan corresponding to the total value; According to the y total values, a distribution network restoration scheme is selected from the y distribution network restoration schemes as a target distribution network restoration scheme.

2. The method according to claim 1, characterized in that Each of the i line sections is erected on k towers, where k is an integer greater than 1; and determining, based on the i wind speeds, a line failure probability corresponding to each of the i line sections to obtain i line failure probabilities, includes: Determine, based on the i wind speeds, the conductor failure probability corresponding to each of the i sections of the line, to obtain i conductor failure probabilities; Determine the tower damage probability of k towers corresponding to each of the i sections of the line according to the i wind speeds, and obtain x tower damage probabilities; x is an integer greater than 1, x=i*k; The i line failure probabilities are obtained according to the i conductor failure probabilities and the x tower damage probabilities.

3. The method according to claim 1 or 2, characterized in that The determining y distribution network restoration plans according to the i line states and the j node states includes: Obtaining the path lengths of multiple traffic roads between any two of the j distribution nodes; Determining the shortest path between any two of the j distribution nodes based on the path lengths of the plurality of traffic roads between the any two distribution nodes; Obtaining the water depth of the shortest path between any two power distribution nodes; the water depth refers to the water depth of the road on the shortest path corresponding to the water depth; Determining the travel time between any two of the j distribution nodes according to the water depth of the shortest path between the any two distribution nodes; According to the i line states and the j node states, m deployment nodes are determined from the j distribution nodes; the m deployment nodes are used to deploy m mobile energy storage systems, with one mobile energy storage system deployed on each deployment node, and the mobile energy storage systems are used to supply power to faulty nodes from the j distribution nodes, where m is an integer greater than or equal to a target number and less than or equal to j, and the target number is the number of faulty nodes from the j distribution nodes; Determine, based on the travel time between any two of the j distribution nodes, a charge and discharge state variable corresponding to each of the m deployment nodes, to obtain m charge and discharge state variables; Establishing first operating constraints corresponding to the m mobile energy storage systems according to the m charge and discharge state variables; Determine the y distribution network restoration plans according to the first operating constraint.

4. The method according to claim 3, characterized in that The determining, according to the first operating constraint, the y distribution network restoration plans includes: Acquiring a first air mass flow rate during the air compression process of the compressor; and acquiring a second air mass flow rate during the air expansion process of the steam turbine; determining an electrical power of the compressor based on the first air mass flow rate; determining an electrical power of the steam turbine based on the second air mass flow rate; determining the thermal power provided by the thermal storage system to the thermal network; establishing a second operating constraint corresponding to the compressed air energy storage according to the electric power of the compressor, the electric power of the steam turbine, and the thermal power provided by the thermal storage system to the thermal network; Obtaining the supply water temperature of each of the n heating nodes to obtain n supply water temperatures; and obtaining the return water temperature of each of the n heating nodes to obtain n return water temperatures; and obtaining the ambient temperature of each of the n heating nodes to obtain n ambient temperatures; Obtaining n water supply heat losses according to the n water supply temperatures and the n ambient temperatures; Obtaining n return water heat losses according to the n return water temperatures and the n ambient temperatures; establishing a third operation constraint corresponding to the heating network according to the n supply water temperatures, the n return water temperatures, the n ambient temperatures, the n supply water heat losses, and the n return water heat losses; The y distribution network restoration plans are determined according to the first operating constraint, the second operating constraint, and the third operating constraint.

5. The method according to claim 3, characterized in that The determining m deployment nodes from the j power distribution nodes according to the i line states and the j node states includes: Determine, based on the i line states, a first virtual commodity flow for each of the i line segments, to obtain i first virtual commodity flows; the first virtual commodity flow is used to indicate whether the compressed air energy storage system supplies power to the line segment; Determine, based on the j node states and the i first virtual commodity flows, a second virtual commodity flow corresponding to each of the j distribution nodes, to obtain j second virtual commodity flows, each of which is used to indicate whether the compressed air energy storage system supplies power to the distribution node corresponding to the second virtual commodity flow; Determining, based on the j second virtual commodity flows, a third virtual commodity flow corresponding to each of the j distribution nodes, to obtain j third virtual commodity flows, wherein the third virtual commodity flow is used to indicate whether a mobile energy storage system is deployed in the distribution node corresponding to the third virtual commodity flow; According to the j third virtual commodity flows, a deployment node of each of the m mobile energy storage systems is determined in the j power distribution nodes to obtain the m deployment nodes.

6. The method according to claim 4, characterized in that The determining the y distribution network restoration plans according to the first operating constraint, the second operating constraint, and the third operating constraint includes: Determine, according to the first operating constraint, a first active power and a first reactive power of the mobile energy storage system corresponding to each of the m deployment nodes, to obtain m first active powers and m first reactive powers; determining a second active power and a second reactive power of the compressor according to the second operating constraint; determining a third active power and a third reactive power of the steam turbine according to the second operating constraint; determining the electrical power of the heat pump according to the third operating constraint; Establishing a fourth operation constraint corresponding to the power distribution network according to the m first active powers, the m first reactive powers, the second active power, the second reactive power, the third active power, the third reactive power, and the electric power of the heat pump; According to the second operation constraint, y recovery heat loads corresponding to each of the n heating nodes are determined to obtain s recovery heat loads; s=n*y; According to the fourth operation constraint, y restored electric loads corresponding to each of the j distribution nodes are determined to obtain a restored electric load; a=j*y; The y distribution network restoration plans are determined based on the s restored heat loads and the a restored electric loads, wherein each heat source node in each distribution network restoration plan corresponds to a restored heat load, and each distribution node corresponds to a restored electric load.

7. The method according to claim 6, characterized in that The determining the total value of each of the y distribution network restoration schemes to obtain y total values ​​includes: Determine the sum of the restored heat loads of the n heating nodes corresponding to each distribution network restoration scheme in the y distribution network restoration schemes as the total restored heat load of the distribution network restoration scheme, and obtain y total restored heat loads; Determine the sum of the restored power loads of the j distribution nodes corresponding to each distribution network restoration scheme in the y distribution network restoration schemes as the total restored power load of the distribution network restoration scheme, and obtain y total restored power loads; Obtain the electrical load value coefficient and the thermal load value coefficient; Determine a heat load value according to the heat load value coefficient and each of the y total recovery heat loads to obtain y heat load values; Determine an electric load value according to the electric load value coefficient and each of the y total restored electric loads to obtain y electric load values; The y total values ​​are determined based on the y thermal load values ​​and the y electrical load values.

8. A distribution network restoration device, characterized in that: include: an acquisition unit, configured to acquire the wind speed corresponding to each section of an i-section line in a distribution network under a flood disaster, to obtain i wind speeds, and to acquire the water immersion depth of each of j distribution nodes of the distribution network under the flood disaster, to obtain j water immersion depths; the distribution network is coupled to a heat network through compressed air energy storage and a heat pump, a first distribution node of the j distribution nodes is connected to a compressor of the compressed air energy storage, a second distribution node of the j distribution nodes is connected to a steam turbine of the compressed air energy storage, a heat source node of the heat network is connected to a heat storage system of the compressed air energy storage and to the heat pump, the heat source node being any one of n heating nodes of the heat network; the water immersion depth is the depth to which the distribution equipment of the distribution node corresponding to the water immersion depth is immersed in water; i and n are both integers greater than 1, j = i + 1; a processing unit, configured to determine, based on the i wind speeds, a line failure probability corresponding to each of the i line segments, to obtain i line failure probabilities; Determine the line status of each line in the i line segments according to the i line failure probabilities to obtain i line statuses; Determine, based on the j flooding depths, a node failure probability corresponding to each of the j power distribution nodes, to obtain j node failure probabilities; Determine the node status of each of the j power distribution nodes according to the j node failure probabilities to obtain j node statuses; Determining y distribution network restoration plans according to the i line states and the j node states; The distribution network restoration plan is used to allocate the restored electrical load of each of the j distribution nodes and the restored thermal load of each of the n heating nodes; y is an integer greater than 1; Determining the total value of each of the y distribution network restoration plans to obtain y total values; the total value refers to the value of the total restored electrical load of the distribution network and the total restored thermal load of the heating network in the distribution network restoration plan corresponding to the total value; According to the y total values, a distribution network restoration scheme is selected from the y distribution network restoration schemes as a target distribution network restoration scheme.

9. An electronic device, characterized in that: include: A processor and a memory, the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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