Emergency mobile energy storage dispatching method based on satellite terminal distribution and related device

Abstract

The application provides an emergency mobile energy storage scheduling method based on satellite terminal distribution and related devices, and relates to power system and communication system cooperative emergency recovery technology. Through collecting meteorological, topographic, combustible, power distribution and communication coordinate multi-source disaster data; building a wildfire equipment availability evaluation model, quantifying the survival confidence of power distribution branches and communication links; constructing a communication recovery model of a low-orbit satellite terminal carried by a unmanned aerial vehicle, and constraining networking by algebraic connectivity; establishing a mobile energy storage power distribution recovery model under the premise of communication accessibility, reconstructing power distribution network topology and providing power support; solving the target of maximizing load recovery and minimizing deployment cost, outputting the optimal scheme of satellite terminal distribution, energy storage configuration and power distribution recovery, and improving post-disaster recovery efficiency.

Description

Technical Field

[0001] This application relates to emergency recovery technology for power systems and communication systems, and in particular to an emergency mobile energy storage dispatching method and related devices based on satellite terminal deployment. Background Technology

[0002] In responding to extreme natural disasters such as wildfires and typhoons, power distribution and communication systems are often damaged simultaneously, leading to power outages and communication paralysis, severely restricting post-disaster emergency response capabilities. Existing research mainly focuses on single-system recovery: in the field of power distribution system recovery, methods generally rely on power grid reconfiguration, distributed power dispatching, and the deployment of mobile energy storage systems, using power flow constraints and topology optimization models to achieve fault isolation and load restoration. However, these methods typically implicitly assume that the communication network is operating normally, simplifying or completely ignoring communication conditions, failing to incorporate the substantial constraints of post-disaster communication interruptions on remote control command transmission, real-time data acquisition, and dispatching decisions into the model. For example, when communication links are damaged, distribution nodes cannot receive control signals, causing load restoration schemes based on remote operation to fail, and existing technologies lack quantitative analysis of the correlation mechanism between communication reachability and the recovery capability of distribution nodes. In the field of communication recovery, technical solutions often employ ground communication redundancy backup or emergency equipment such as UAV relays and emergency communication vehicles for temporary support. In recent years, some studies have attempted to combine low-orbit satellites and UAVs to build an integrated space-ground communication network to improve post-disaster coverage and flexibility. However, such optimizations only focus on performance indicators such as communication bandwidth and latency, without considering the dynamic demands of the communication network during the power distribution system restoration process. For example, the real-time data interaction and control command transmission required for power distribution node load restoration lead to a disconnect between communication resource deployment and power restoration tasks. This current state of separate modeling and independent optimization results in a fragmentation between power restoration and communication restoration. It lacks both a constraint mechanism for communication link survival confidence on power distribution branch restoration and a unified scheduling framework for low-orbit satellite terminal deployment, mobile energy storage system configuration, and power distribution network load restoration. Especially in wildfire disaster scenarios, the terrain changes and equipment exposure risks caused by fire spread further exacerbate the constraints of communication accessibility on power distribution node load restoration, preventing the coordinated operation of various emergency resources and ultimately leading to low restoration efficiency and resource waste.

[0003] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0004] This application provides an emergency mobile energy storage dispatching method and related device based on satellite terminal deployment. It has the advantages of overcoming the constraints of communication accessibility on power distribution node load restoration, enabling coordinated dispatching of multiple types of emergency resources, and improving post-disaster recovery efficiency.

[0005] Firstly, the emergency mobile energy storage dispatching method based on satellite terminal deployment provided in this application adopts the following technical solution: An emergency mobile energy storage dispatch method based on satellite terminal deployment includes: Obtain multi-source basic data of the disaster-stricken area, including at least meteorological data, topographic data, combustible load distribution data, geographic coordinate data of power distribution equipment, and geographic coordinate data of communication nodes; Based on the aforementioned multi-source basic data, an equipment availability assessment model for power distribution and communication systems under the influence of wildfire disasters is constructed, and the survival confidence of power distribution branches and communication links is quantified. Based on the equipment availability assessment model, a communication network recovery model based on UAV-equipped low-orbit satellite terminals is constructed after a mountain fire. The UAV is used to deploy the low-orbit satellite terminals to the target communication nodes, and the communication network is recovered with algebraic connectivity as a constraint. Under the constraint of communication network recovery results, a post-disaster recovery model of distribution system based on mobile energy storage system is constructed. Communication reachability is taken as a prerequisite constraint for load recovery of distribution nodes, and mobile energy storage system is introduced to participate in distribution network topology reconfiguration and power support. Construct a collaborative optimization objective function that aims to maximize the load recovery of the power distribution system and minimize the deployment cost of emergency resources; The objective function of the collaborative optimization is solved to generate the optimal recovery scheme, which includes at least a low-orbit satellite terminal deployment scheme, a mobile energy storage system configuration scheme, and a power distribution network load recovery scheme.

[0006] Optionally, constructing the equipment availability assessment model based on the multi-source basic data includes: Based on the meteorological data, the topographic data, and the combustible load distribution data, an elliptical diffusion model is used to characterize the coverage of wildfire disasters on regional space at different stages, generating the fire-affected area. The fire-affected area is mapped to the spatial topology of the power distribution system and the communication system, and the fire exposure of the power distribution branch and the communication link are calculated respectively. By combining fire intensity and equipment sensitivity characteristics, instantaneous failure rate functions for power distribution branches and communication links are constructed respectively; Based on the instantaneous failure rate function, reliability theory is introduced to calculate the cumulative survival confidence of the power distribution branch and communication link throughout the entire wildfire cycle.

[0007] Optionally, constructing a communication network recovery model includes: The initial availability status of the post-disaster ground communication link is determined based on the cumulative survival confidence of the communication link and the preset communication link availability threshold. The post-disaster ground communication network is represented as a graph structure, which includes a set of communication nodes and a set of available ground links. Drones equipped with low-orbit satellite terminals were introduced to provide emergency support to the damaged area. The drones were used to deploy the low-orbit satellite terminals to the designated communication nodes, enabling the nodes to have satellite communication access capabilities. Construct a set of satellite-assisted communication links and establish logical constraints between the satellite links and the deployment status of low-Earth orbit satellite terminals; By integrating terrestrial communication links and satellite-assisted links, an extended communication network topology can be constructed. Based on the extended communication network topology, an algebraic connectivity lower bound constraint is introduced to ensure the global connectivity performance of the restored communication network.

[0008] Optionally, the construction of the power distribution system post-disaster recovery model based on the mobile energy storage system includes: The operating status of the distribution branch is determined based on the cumulative survival confidence of the distribution branch and the preset distribution branch availability threshold. Establish a mapping relationship between communication nodes and power distribution nodes, map the reachability of communication nodes to power distribution nodes, and generate communication reachability variables for power distribution nodes; The communication reachability variable of the power distribution node is used as a prerequisite constraint for load restoration and reactive power compensation of the power distribution node. Establish a distribution network operation model that includes power flow constraints, voltage constraints, safety margin constraints, and radial constraints; Introduce mobile energy storage systems to participate in the restoration of power distribution systems, and establish deployment variables, charge and discharge state variables, and state of charge constraints for mobile energy storage systems.

[0009] Optionally, constructing the collaborative optimization objective function includes: The primary optimization objective is to maximize the total weighted load recovery of the power distribution system, whereby the total weighted load recovery is calculated based on the node load weights and the recovered active load. Minimizing the deployment cost of low-Earth orbit satellite terminals is the second optimization objective; Minimizing the deployment cost of mobile energy storage systems is the third optimization objective; The collaborative optimization objective function is constructed by combining the first optimization objective, the second optimization objective, and the third optimization objective.

[0010] Optionally, solving the collaborative optimization objective function includes: Linearize the nonlinear power flow constraints in the distribution network operation model and transform the branch voltage relationships into inequality constraint forms. The branch capacity constraints in the power distribution network operation model are convexized and transformed into a second-order cone constraint form. The collaborative recovery model, which includes linearization constraints and second-order cone constraints, is uniformly transformed into a mixed-integer second-order cone programming problem. The branch and bound method is used to process the integer variables in the mixed integer second-order cone programming problem, and the interior point method is combined to solve the continuous variables and second-order cone constraints to obtain the optimal recovery scheme.

[0011] Optionally, it also includes: Based on the optimal recovery scheme, a drone deployment path suggestion is generated, which is used to guide the drone to deploy the low-orbit satellite terminal to the target communication node. Based on the optimal recovery scheme, a mobile energy storage system scheduling instruction is generated. The mobile energy storage system scheduling instruction includes at least the deployment location of the mobile energy storage system, the charging and discharging power, and the state of charge management strategy.

[0012] Optionally, the lower bound constraint of algebraic connectivity is determined jointly based on the available resources and service requirements of the communication system, and the lower bound of algebraic connectivity is equal to the product of the adjustment coefficient and the ratio of the effective total bandwidth of the communication network to the average communication demand of the network.

[0013] Optionally, the introduction of mobile energy storage systems to participate in power distribution system restoration includes: Based on the state of charge constraints of the mobile energy storage system, ensure the energy sustainability of the mobile energy storage system during the dispatch cycle; The charging and discharging rate of mobile energy storage systems is limited by the upper limit constraint on the charging and discharging power of mobile energy storage systems. Calculate the actual usable energy of the mobile energy storage system based on its charge and discharge efficiency parameters; Based on the deployment location constraints of the mobile energy storage system, ensure that the mobile energy storage system is only deployed at the power distribution node.

[0014] Secondly, this application provides an emergency mobile energy storage and dispatching device based on satellite terminal deployment, comprising: The data acquisition module is used to acquire multi-source basic data of the disaster-stricken area. The multi-source basic data includes at least meteorological data, topographic data, combustible load distribution data, geographical coordinate data of power distribution equipment, and geographical coordinate data of communication nodes. The model building module is used to construct an equipment availability assessment model for the power distribution system and communication system under the influence of wildfire disasters based on the multi-source basic data, and to quantify the survival confidence of power distribution branches and communication links; The constraint module is used to construct a communication network recovery model based on the equipment availability assessment model after a wildfire, using a drone to deploy the low-orbit satellite terminal to the target communication node, and to restore the communication network with algebraic connectivity as a constraint. The recovery model module is used to construct a post-disaster recovery model of the distribution system based on the mobile energy storage system under the constraints of the communication network recovery results. It takes communication reachability as a prerequisite constraint for the load recovery of distribution nodes and introduces the mobile energy storage system to participate in the topology reconstruction and power support of the distribution network. The objective function module is used to construct a collaborative optimization objective function that aims to maximize the load recovery of the power distribution system and minimize the deployment cost of emergency resources. The calculation module is used to solve the collaborative optimization objective function and generate the optimal recovery scheme. The optimal recovery scheme includes at least a low-orbit satellite terminal deployment scheme, a mobile energy storage system configuration scheme, and a power distribution network load recovery scheme.

[0015] In summary, this application acquires multi-source basic data of the disaster-stricken area, including at least meteorological data, topographic data, combustible material load distribution data, geographic coordinate data of power distribution equipment, and geographic coordinate data of communication nodes. Based on the multi-source basic data, it constructs an equipment availability assessment model for the power distribution and communication systems under the influence of wildfire disasters, quantifying the survival confidence of power distribution branches and communication links. Based on the equipment availability assessment model, it constructs a communication network recovery model based on UAVs carrying low-orbit satellite terminals after wildfires, deploying low-orbit satellite terminals to target communication nodes via UAVs, and restoring the communication network with algebraic connectivity as a constraint. Under the constraint of the communication network recovery results, it constructs a post-disaster recovery model for the power distribution system based on a mobile energy storage system, using communication accessibility as a constraint. This paper addresses the prerequisite constraints for power node load restoration and introduces mobile energy storage systems to participate in distribution network topology reconfiguration and power support. It constructs a collaborative optimization objective function aimed at maximizing distribution system load restoration and minimizing emergency resource deployment costs. Solving this collaborative optimization objective function generates the optimal restoration scheme, which includes at least a low-orbit satellite terminal deployment scheme, a mobile energy storage system configuration scheme, and a distribution network load restoration scheme. By constructing and collaboratively optimizing equipment availability assessment models, communication network restoration models, and distribution system post-disaster recovery models, the paper resolves the constraint of communication accessibility on distribution node load restoration. This approach offers advantages such as addressing the constraint of communication accessibility on distribution node load restoration, enabling collaborative scheduling of multiple types of emergency resources, and improving post-disaster recovery efficiency. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the first embodiment of the emergency mobile energy storage system scheduling method based on satellite terminal deployment in this application; Figure 2 This is a structural block diagram of the first embodiment of the emergency mobile energy storage system dispatching device based on satellite terminal deployment in this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0018] Traditional research on power system restoration and communication system support primarily relies on discrete modeling and independent optimization, lacking a unified cyber-physical collaborative restoration framework. Distribution system restoration methods do not adequately consider the constraints of post-disaster communication interruptions, while communication restoration methods lack collaborative design with the distribution system restoration process. Existing technologies suffer from problems such as the disconnect between power and communication restoration, the absence of a constraint mechanism on power restoration based on communication reachability, and the lack of unified scheduling for various emergency resources, making it difficult to achieve efficient collaborative restoration in wildfire disaster scenarios.

[0019] This application provides an emergency mobile energy storage scheduling method based on satellite terminal deployment, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the emergency mobile energy storage scheduling method based on satellite terminal deployment in this application.

[0020] In this embodiment, the emergency mobile energy storage scheduling method based on satellite terminal deployment includes the following steps: Step S10: Obtain multi-source basic data of the disaster-stricken area. The multi-source basic data includes at least meteorological data, topographic data, combustible material load distribution data, geographical coordinate data of power distribution equipment, and geographical coordinate data of communication nodes.

[0021] For ease of understanding, the following explains some key terms in this embodiment: Multi-source basic data refers to various types of data used to assess disaster impact and plan recovery strategies. It includes at least meteorological data, topographic data, combustible material load distribution data, geographic coordinate data of power distribution equipment, and geographic coordinate data of communication nodes. This data provides fundamental information for disaster simulation, equipment condition assessment, and emergency resource deployment.

[0022] Equipment availability assessment model: This refers to a mathematical model used to quantify the probability or confidence level of various devices (such as power distribution branches and communication links) in power distribution and communication systems maintaining normal operation under the influence of wildfire disasters. The model aims to predict the potential impact of disasters on infrastructure and provide a basis for subsequent recovery decisions.

[0023] Survival confidence level: This refers to the probability that a power distribution branch or communication link will remain functional within a certain period of time under a specific disaster scenario. This indicator reflects the equipment's ability to withstand disasters; a higher value indicates a lower risk of equipment damage.

[0024] Drones carrying low-Earth orbit (LEO) satellite terminals: This refers to using drones as carriers to transport and deploy LEO satellite communication terminals to communication nodes in damaged areas. LEO satellite terminals provide satellite communication access, enabling rapid restoration of regional communication when ground communication facilities are damaged.

[0025] Algebraic connectivity: In graph theory, this is a metric for measuring the robustness of network connectivity, typically represented by the second smallest eigenvalue of the graph's Laplacian matrix. A higher algebraic connectivity indicates stronger network connectivity and better resistance to node or link failures.

[0026] Mobile energy storage systems refer to devices that are mobile and can store and release electrical energy, such as mobile energy storage vehicles. These systems can be rapidly deployed to distribution nodes in need of power after a disaster, providing temporary power support and topology reconfiguration capabilities for the distribution network.

[0027] Distribution network topology reconfiguration refers to adjusting the network's connection structure by changing the on / off states of switches in the distribution network to isolate faulty areas, restore power supply, or optimize operational status. In post-disaster scenarios, topology reconfiguration is often used to bypass damaged lines and transfer loads to available power sources.

[0028] Power support: refers to the provision of active or reactive power by mobile energy storage systems to the distribution network to meet load demand, maintain voltage stability, or support grid operation.

[0029] Collaborative optimization objective function: This refers to an optimization objective that comprehensively considers multiple objectives, such as maximizing the recovery of the power distribution system load and minimizing the deployment cost of emergency resources, and is expressed in mathematical form. By solving this function, the aim is to obtain the optimal recovery strategy under multiple constraints.

[0030] Optimal recovery plan: refers to the set of best strategies obtained by solving the co-optimization objective function, which includes at least the low-Earth orbit satellite terminal deployment plan, the mobile energy storage system configuration plan, and the power distribution network load recovery plan. This plan aims to guide the effective deployment of emergency resources and the rapid recovery of power and communication systems after disasters.

[0031] In practice, multi-source basic data of the post-wildfire area is collected, including meteorological data, topographic data, combustible material load distribution data, geographic coordinate data of power distribution equipment, and geographic coordinate data of communication nodes. Among them, meteorological data includes at least historical wind speed, wind direction, ambient temperature, and air humidity; topographic data includes at least slope and elevation; combustible material load distribution data includes at least fuel type and coverage density; power distribution equipment data includes at least spatial location information of power distribution nodes, power distribution branches, and switching equipment; and communication system data includes at least ground communication nodes, communication link endpoints, and their topological connections.

[0032] Step S20: Based on the multi-source basic data, construct an equipment availability assessment model for the power distribution system and communication system under the influence of wildfire disasters, and quantify the survival confidence of power distribution branches and communication links.

[0033] It should be noted that the construction of the equipment availability assessment model based on the multi-source basic data includes: using the meteorological data, the topographic data, and the combustible material load distribution data, an elliptical diffusion model is used to characterize the coverage of the regional space by the wildfire disaster at different stages, generating the fire-affected area; mapping the fire-affected area to the spatial topology of the power distribution system and the communication system, and calculating the fire exposure of the power distribution branch and the communication link respectively; combining the fire intensity and the equipment sensitivity characteristics, constructing the instantaneous failure rate function of the power distribution branch and the communication link respectively; and based on the instantaneous failure rate function, introducing reliability theory to calculate the cumulative survival confidence of the power distribution branch and the communication link throughout the entire wildfire cycle.

[0034] By employing the aforementioned technical solution, based on meteorological data, topographic data, and combustible material load distribution data, an elliptical diffusion model is used to dynamically characterize the spatial coverage of wildfire disasters at different stages, generating refined fire-affected areas and avoiding static or coarse estimations of the fire's impact range. Subsequently, these dynamic fire-affected areas are precisely mapped to the spatial topology of power distribution and communication systems, calculating the fire exposure of power distribution branches and communication links, and quantifying the degree of threat to infrastructure. Combining fire intensity and equipment sensitivity characteristics, an instantaneous failure rate function is constructed, making equipment failure probability assessment more realistic. Finally, reliability theory is introduced to calculate the cumulative survival confidence of power distribution branches and communication links throughout the entire wildfire cycle. This dynamic and refined assessment method provides more accurate and reliable equipment availability information, laying the foundation for subsequent communication network and power distribution system recovery models, and significantly improving the scientific rigor and effectiveness of emergency recovery plans.

[0035] In practical implementation, the wildfire disaster process is discretized and represented within a time interval. The spatial impact within the area is described using an equivalent model, and an elliptical diffusion model is used to characterize the spatial coverage of the wildfire at different stages. The coordinates of the fire source center are defined as follows: The area affected by the fire at time t is... Represented as: In the formula, The two-dimensional geographic spatial range in which the fire spreads; Let be the wind direction angle at time t; and , respectively, represent the lengths of the half-axis of the fire ellipse in the downwind and crosswind directions; R is the steady-state fire propagation rate; and These are the propagation coefficients in the downwind and crosswind directions, respectively; Let be the wind speed at time t.

[0036] Based on this, the fire-affected area A post-disaster equipment damage assessment model is constructed by mapping the spatial topology of the power distribution system and communication system. Fire exposure functions for power distribution branches and communication links at time t are defined respectively. , This is used to determine whether the corresponding branch or link is within the fire-affected area: In the formula, For distribution branch collection; A set of communication links; The coordinates of the geometric midpoint of the power distribution branch / communication link.

[0037] Furthermore, combining fire intensity and equipment sensitivity characteristics, an instantaneous failure rate function for power distribution branches and communication links is constructed: In the formula, , These are the baseline failure rates for power distribution branches and communication links under normal operating conditions; Let be the fire intensity at time t. , These are the sensitivity coefficients of power distribution branches and communication links to fire exposure, respectively.

[0038] Based on this, reliability theory is introduced, defining the relationship between power distribution branches and communication links within a time interval. Survival function within: Further, throughout the entire wildfire cycle, Cumulative survival confidence level within: In the formula, , These refer to the availability of power distribution branches and communication links throughout the entire spread cycle.

[0039] This result serves as a quantitative description of the post-disaster system status, without involving any proactive recovery measures. It provides an objective basis for subsequent communication network reconstruction based on UAVs carrying low-orbit satellite terminals and distribution network restoration based on mobile energy storage systems.

[0040] Step S30: Based on the equipment availability assessment model, construct a communication network recovery model based on UAV-equipped low-orbit satellite terminals after a mountain fire. Deploy the low-orbit satellite terminals to the target communication nodes using UAVs, and restore the communication network with algebraic connectivity as a constraint.

[0041] It should be noted that constructing the communication network recovery model includes: determining the initial availability status of the post-disaster ground communication links based on the cumulative survival confidence of the communication links and a preset communication link availability threshold; representing the post-disaster ground communication network as a graph structure, which includes a set of communication nodes and a set of available ground links; introducing drones equipped with low-Earth orbit satellite terminals to provide emergency support to the damaged area, and deploying low-Earth orbit satellite terminals to designated communication nodes through drones, enabling the nodes to have satellite communication access capabilities; constructing a set of satellite-assisted communication links and establishing logical constraints between the satellite links and the deployment status of low-Earth orbit satellite terminals; integrating ground communication links and satellite-assisted links to construct an extended communication network topology; and introducing algebraic connectivity lower bound constraints based on the extended communication network topology to ensure the global connectivity performance of the recovered communication network.

[0042] Through the above technical solution, this embodiment can first accurately determine the initial availability status of post-disaster ground communication links based on the cumulative survival confidence of the communication links and a preset communication link availability threshold, providing a reliable benchmark for subsequent recovery decisions. Subsequently, the post-disaster ground communication network is represented as a graph structure, and drones carrying low-Earth orbit (LEO) satellite terminals are introduced to provide emergency support to the damaged areas. The drones deploy LEO satellite terminals to designated communication nodes, enabling these nodes to access satellite communication, thus effectively compensating for the lack of ground communication. Based on this, a set of satellite-assisted communication links is constructed, and logical constraints are established between the satellite links and the deployment status of LEO satellite terminals to ensure the rational and effective use of satellite resources. Finally, ground communication links and satellite-assisted links are integrated to construct an extended communication network topology, and an algebraic connectivity lower bound constraint is introduced to ensure the global connectivity performance of the recovered communication network. This significantly improves the resilience and reliability of the post-disaster communication network, ensuring the effective transmission of critical information, thereby overcoming the problems of inaccurate initial state assessment, insufficient satellite resource integration, and inadequate network connectivity guarantees in post-disaster communication network recovery.

[0043] In practical implementation, a communication network recovery model based on a drone-borne low-orbit satellite terminal is constructed after a mountain fire, including: Accumulated liveness confidence of communication links This is converted to the initial available state of the post-disaster ground communication links. The communication link availability threshold is defined as follows: Then the ground communication link state variables Defined as: In the formula, This indicates the availability of the communication link during the wildfire's spread. It can be reserved as a ground communication link; otherwise, it is 0.

[0044] Based on this, the post-disaster ground communication network is represented as a graph structure. ,in For a set of communication nodes, For all satisfied The set of available ground links consists of communication links. To address localized communication disruptions caused by wildfires, drones equipped with low-Earth orbit (LEO) satellite terminals are introduced to provide emergency support to damaged areas. The drones fly to the airspace above or near the target communication node and deploy the LEO satellite terminal to the designated node, thus enabling the node to access satellite communication. In modeling, this process is equivalent to deploying LEO satellite terminals at communication nodes, and the deployment decision variables should satisfy the following conditions: In the formula, Deploy a low-Earth orbit satellite terminal at communication node i; otherwise, set the value to 0. This represents the upper limit on the number of available low-Earth orbit satellite terminals.

[0045] After completing the deployment of low-Earth orbit satellite terminals, a set of satellite-assisted communication links will be constructed. And define satellite-assisted communication link variables. : The establishment of a satellite link requires that the node end has satellite access capability. That is, a corresponding satellite communication link is only allowed to be established when node i and node j are both deployed with low-Earth orbit satellite terminals, and the following conditions must be met: Furthermore, by integrating terrestrial communication links with satellite-assisted links, the overall communication link state variables during the post-disaster recovery phase can be obtained. This allows for the construction of an extended communication network topology that integrates satellite communication capabilities. Based on overall communication link state variables Construct the adjacency matrix A, node degree matrix D, and Laplace matrix L of the communication network: To ensure that the restored communication network has global information exchange capabilities, and to meet the communication performance requirements of remote control and information exchange in the power distribution system, an algebraic connectivity lower bound constraint based on communication resource constraints is introduced: In the formula, Let L be the second smallest eigenvalue of L, representing the algebraic connectivity of the communication network; This is the lower limit threshold for the connectivity performance of the communication network.

[0046] Furthermore, the lower bound of algebraic connectivity It is determined by both the available resources of the communication system and the service requirements: In the formula, This is an adjustment coefficient used to match the communication quality requirements under different application scenarios; The effective total bandwidth of the communication network; This represents the average network communication demand.

[0047] Step S40: Under the constraints of communication network recovery results, construct a post-disaster recovery model for the distribution system based on mobile energy storage system, take communication reachability as a prerequisite constraint for the load recovery of distribution nodes, and introduce mobile energy storage system to participate in distribution network topology reconfiguration and power support.

[0048] It should be noted that the construction of the power distribution system post-disaster recovery model based on the mobile energy storage system includes: determining the operating status of the power distribution branch based on the cumulative survival confidence of the power distribution branch and the preset availability threshold of the power distribution branch; establishing a mapping relationship between communication nodes and power distribution nodes, mapping the reachability of communication nodes to power distribution nodes, and generating communication reachability variables of power distribution nodes; using the communication reachability variables of power distribution nodes as a prerequisite constraint for load restoration and reactive power compensation of power distribution nodes; establishing a power distribution network operation model including power flow constraints, voltage constraints, safety margin constraints, and radial constraints; introducing the mobile energy storage system to participate in the power distribution system recovery, and establishing deployment variables, charging and discharging state variables, and state of charge constraints of the mobile energy storage system.

[0049] Through the above technical solution, this embodiment overcomes the problem of impractical recovery plans caused by the lack of accurate assessment of network status and effective integration of communication dependencies in the post-disaster recovery of power distribution systems. Specifically, by determining the operating status of distribution branches based on their cumulative survival confidence and preset thresholds, the available distribution infrastructure after a disaster can be accurately identified, providing a reliable physical basis for subsequent recovery. Simultaneously, establishing a mapping relationship between communication nodes and distribution nodes, and using the communication reachability of distribution nodes as a prerequisite constraint for load recovery and reactive power compensation, ensures that only controllable loads are restored, avoiding blind power supply to communication interruption areas, thereby significantly improving the practicality and safety of the recovery plan. Furthermore, establishing a distribution network operation model that includes power flow, voltage, safety margin, and radiation constraints ensures that the restored distribution system can operate safely, stably, and reliably, avoiding secondary accidents caused by violations of power system operation rules. By introducing mobile energy storage systems and establishing constraints on their deployment, charge / discharge states, and state of charge, these systems can be efficiently dispatched, providing flexible power support at critical nodes. This effectively alleviates the power supply pressure on the distribution network after disasters, accelerates the load recovery process, and enhances the overall resilience of the distribution system. These measures work together to make the resulting recovery plan not only theoretically optimal but also more feasible and robust in practice.

[0050] In practical implementation, under the constraint of communication network recovery results, a post-disaster recovery model for the power distribution system based on mobile energy storage systems is constructed to achieve optimized power distribution network load recovery based on communication reachability, including: Based on cumulative survival confidence of distribution branches Communication link state variables The operating status of the power distribution branch is determined.

[0051] Define the availability threshold of the distribution branch as Then the operating state variables of the power distribution branch for: Based on this, in order to apply the communication recovery results to the power distribution system recovery process, a mapping relationship between communication nodes and power distribution nodes is established. : In the formula, For the set of power distribution nodes; If the communication node k can provide communication coverage or control access capability to the distribution node i, then the value is 0; otherwise, it is 0.

[0052] Communication node reachability variables Should meet: Furthermore, communication reachability is mapped to distribution nodes, and the communication reachability variables of the distribution nodes should satisfy: Communication reachability directly determines whether a power distribution node can be monitored by the dispatch center and whether restoration operations can be performed. Therefore, during power restoration, both load restoration and reactive power compensation must meet the following conditions: In the formula, , These represent the active and reactive loads that are restored at distribution node i at time t, respectively. , These represent the maximum active and reactive load demands of distribution node i at time t, respectively.

[0053] Operating state variables of power distribution branches during the recovery phase The following conditions must be met: To characterize the operational domain of the distribution system during disasters, a distribution network operation model incorporating power flow, voltage, safety margin, and radial constraints is established. Based on the DistFlow equations, the model uses binary decision variables to characterize branch state changes, thereby supporting topology adjustments and operational recovery of the distribution network under disaster conditions.

[0054] At any time t, the active and reactive power of node i satisfy a steady-state equilibrium relationship: In the formula, E represents the collection of mobile energy storage systems; , These are the active and reactive power of the distribution branch, respectively. , These are resistance and reactance, respectively. Let be the current in the branch at time t; , These represent the active and reactive power outputs of the mobile energy storage system, respectively.

[0055] At any time t, the voltage at node i must satisfy the balance relationship while also meeting the operational safety boundary: In the formula, Node voltage; , These are the upper and lower limits of the node voltage, respectively; This is the upper limit of the branch current.

[0056] To maintain the tree-like structure of the distribution network, the single-goods flow method is used to characterize system connectivity: In the formula, For isolated state variables of nodes; For virtual stream variables; Virtually inject variables into the nodes; M is a very large constant.

[0057] Based on the above model, mobile energy storage systems are introduced to participate in power distribution system recovery. Variables for mobile energy storage system deployment. The following conditions must be met: To ensure the energy sustainability of mobile energy storage systems, the State of Charge (SOC) of the mobile energy storage system must meet the following requirements: In the formula, For the capacity of mobile energy storage systems; For scheduling step size; , These are the charge and discharge efficiencies, respectively. , These are the charging and discharging powers, respectively. The state of charge of the mobile energy storage system at time t; , These are the upper and lower limits of the state of charge of the mobile energy storage system, respectively. , These represent the charging and discharging states of the mobile energy storage system, respectively. , These represent the upper limits of the charging and discharging power of mobile energy storage systems.

[0058] Step S50: Construct a collaborative optimization objective function with the goal of maximizing the load recovery of the power distribution system and minimizing the deployment cost of emergency resources.

[0059] The collaborative optimization objective function is constructed as follows: the first optimization objective is to maximize the total weighted load recovery of the power distribution system, wherein the total weighted load recovery is calculated based on the node load weight and the recovered active load; the second optimization objective is to minimize the deployment cost of low-orbit satellite terminals; the third optimization objective is to minimize the deployment cost of mobile energy storage systems; and the collaborative optimization objective function is constructed by combining the first optimization objective, the second optimization objective, and the third optimization objective.

[0060] Through the above technical solution, this embodiment can comprehensively consider the key performance indicators and economic costs in the post-disaster recovery process. This multi-objective collaborative optimization method avoids the problems of resource waste or poor recovery effect that may be caused by single-objective optimization. It enables the final recovery plan to effectively control the input of emergency resources while ensuring the priority restoration of critical loads in the disaster-stricken area, thereby achieving a balance between recovery efficiency and economic benefits and significantly improving the scientific nature and effectiveness of post-disaster emergency response.

[0061] In practical implementation, a collaborative optimization objective function is constructed with the goals of maximizing the load restoration of the power distribution system and minimizing the deployment cost of emergency resources. This enables unified decision-making for communication restoration and power restoration, including: In the formula, Node load weights; Weighting coefficient for the deployment cost of low-Earth orbit satellite terminals; Weighting coefficients for the deployment cost of mobile energy storage systems.

[0062] The first item represents the total weighted load recovery of the system during the post-disaster recovery phase; the second item represents the deployment cost of low-orbit satellite terminals; and the third item represents the deployment cost of mobile energy storage systems.

[0063] Step S60: Solve the collaborative optimization objective function to generate the optimal recovery scheme. The optimal recovery scheme includes at least a low-orbit satellite terminal deployment scheme, a mobile energy storage system configuration scheme, and a power distribution network load recovery scheme.

[0064] It should be noted that solving the collaborative optimization objective function includes: The nonlinear power flow constraints in the distribution network operation model are linearized, transforming branch voltage relationships into inequality constraints. The branch capacity constraints in the distribution network operation model are convexized, transforming them into second-order cone constraints. The collaborative recovery model, which includes linearized constraints and second-order cone constraints, is uniformly transformed into a mixed-integer second-order cone programming problem. The branch-and-bound method is used to process the integer variables in the mixed-integer second-order cone programming problem, while the interior-point method is combined to solve for continuous variables and second-order cone constraints, thereby obtaining the optimal recovery scheme.

[0065] In practical implementation, the branch voltage constraints are linearized to address the nonlinear power flow relationships in the power distribution system's operational constraints. The branch voltage relationship is equivalently transformed by introducing a maximum constant M, resulting in the following inequality: Furthermore, the branch capacity constraint formula involved is subjected to convexity processing, transforming it into a second-order cone constraint form:

[0066] Through the above processing, the cooperative recovery model containing multiple types of nonlinear constraints is uniformly transformed into a mixed-integer second-order cone programming (MISOCP) problem: .

[0067] It is understood that this also includes: generating a drone deployment path suggestion based on the optimal recovery scheme, the drone deployment path suggestion being used to guide the drone to deploy the low-orbit satellite terminal to the target communication node; and generating a mobile energy storage system scheduling instruction based on the optimal recovery scheme, the mobile energy storage system scheduling instruction including at least the deployment location of the mobile energy storage system, the charging and discharging power, and the state of charge management strategy.

[0068] In practical implementation, for the MISOCP problem, the branch and bound method is used to handle the integer variables in the model, while the interior point method is combined to solve the continuous variables and the second-order cone constraints. By solving the corresponding second-order cone relaxor problem at each branch node, the feasible region is gradually narrowed until an integer solution satisfying the optimality condition is obtained.

[0069] During the solution process, the formed standard second-order cone constraint model is input into a general optimization solver for calculation. This solver includes mathematical programming tools that support the MISOCP problem. By calling the solver's built-in algorithm module, the model is iteratively solved to obtain the optimal value of the objective function and the corresponding values ​​of the decision variables.

[0070] Through the above technical solution, this embodiment effectively transforms a complex nonlinear optimization problem into an easily solvable mixed-integer second-order cone programming problem. This transformation significantly reduces the computational complexity of the model, enabling rapid and accurate solution of the co-optimization objective function in emergency scenarios. Furthermore, the combined strategy of using the branch-and-bound method to handle integer variables and the interior-point method to solve continuous variables and second-order cone constraints can efficiently search for and determine the global optimal solution, thereby ensuring that the generated optimal recovery scheme has high practicality and reliability in emergency situations, providing a solid decision-making foundation for the deployment of low-Earth orbit satellite terminals, the configuration of mobile energy storage systems, and the restoration of power distribution network loads.

[0071] It should be noted that the lower bound constraint of algebraic connectivity is determined jointly based on the available resources of the communication system and the service requirements. The lower bound of algebraic connectivity is equal to the product of the adjustment coefficient and the ratio of the effective total bandwidth of the communication network to the average communication demand of the network.

[0072] It should be noted that the introduction of mobile energy storage systems to participate in distribution system restoration includes: ensuring the energy sustainability of the mobile energy storage system during the dispatch cycle based on the state of charge constraints of the mobile energy storage system; limiting the charging and discharging rate of the mobile energy storage system based on the upper limit constraints of the charging and discharging power of the mobile energy storage system; calculating the actual available energy of the mobile energy storage system based on the charging and discharging efficiency parameters of the mobile energy storage system; and ensuring that the mobile energy storage system is deployed only at distribution nodes based on the deployment location constraints of the mobile energy storage system.

[0073] Reference Figure 2 , Figure 2 This is a structural block diagram of the first embodiment of the emergency mobile energy storage dispatching device based on satellite terminal deployment in this application.

[0074] like Figure 2 As shown, the emergency mobile energy storage dispatching device based on satellite terminal deployment proposed in this application includes: Data acquisition module 10 is used to acquire multi-source basic data of the disaster-stricken area. The multi-source basic data includes at least meteorological data, topographic data, combustible load distribution data, power distribution equipment geographic coordinate data and communication node geographic coordinate data. The model building module 20 is used to build an equipment availability assessment model of the power distribution system and communication system under the action of wildfire disaster based on the multi-source basic data, and to quantify the survival confidence of power distribution branches and communication links; Constraint module 30 is used to construct a communication network recovery model based on UAV-borne low-orbit satellite terminals after a wildfire, based on the equipment availability assessment model, and to restore the communication network by deploying low-orbit satellite terminals to target communication nodes using UAVs and using algebraic connectivity as a constraint. The recovery model module 40 is used to construct a post-disaster recovery model of the distribution system based on the mobile energy storage system under the constraints of the communication network recovery results. It takes communication reachability as a prerequisite constraint for the load recovery of distribution nodes and introduces the mobile energy storage system to participate in the topology reconstruction and power support of the distribution network. Objective function module 50 is used to construct a collaborative optimization objective function with the goal of maximizing the load recovery of the power distribution system and minimizing the deployment cost of emergency resources; The calculation module 60 is used to solve the collaborative optimization objective function and generate the optimal recovery scheme. The optimal recovery scheme includes at least a low-orbit satellite terminal deployment scheme, a mobile energy storage system configuration scheme, and a power distribution network load recovery scheme.

[0075] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solution of this application. In specific applications, those skilled in the art can make settings as needed, and this application does not impose any restrictions on this.

[0076] This embodiment acquires multi-source basic data of the disaster-stricken area, including at least meteorological data, topographic data, combustible material load distribution data, geographic coordinate data of power distribution equipment, and geographic coordinate data of communication nodes. Based on the multi-source basic data, an equipment availability assessment model for the power distribution system and communication system under the influence of wildfire disasters is constructed, quantifying the survival confidence of power distribution branches and communication links. Based on the equipment availability assessment model, a communication network recovery model based on UAV-borne low-orbit satellite terminals is constructed after the wildfire. The UAV deploys the low-orbit satellite terminals to the target communication nodes, and the communication network is restored with algebraic connectivity as a constraint. Under the constraint of the communication network recovery results, a post-disaster recovery model of the power distribution system based on a mobile energy storage system is constructed, taking communication accessibility as the basis for power distribution node recovery. This paper addresses the preconditions for load restoration at distribution nodes and introduces mobile energy storage systems to participate in distribution network topology reconfiguration and power support. It constructs a collaborative optimization objective function aimed at maximizing distribution system load restoration and minimizing emergency resource deployment costs. Solving this collaborative optimization objective function generates the optimal restoration scheme, which includes at least a low-orbit satellite terminal deployment scheme, a mobile energy storage system configuration scheme, and a distribution network load restoration scheme. By constructing and collaboratively optimizing equipment availability assessment models, communication network restoration models, and distribution system post-disaster recovery models, the paper resolves the constraint of communication accessibility on distribution node load restoration. This approach offers advantages such as addressing the constraint of communication accessibility on distribution node load restoration, enabling collaborative scheduling of multiple types of emergency resources, and improving post-disaster recovery efficiency.

[0077] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this application. In practical applications, those skilled in the art can select some or all of it to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0078] In addition, for technical details not described in detail in this embodiment, please refer to the emergency mobile energy storage scheduling method based on satellite terminal deployment provided in any embodiment of this application, which will not be repeated here.

[0079] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0080] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0081] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application. The above are only preferred embodiments of this application and do not limit the patent scope of this application. All equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. An emergency mobile energy storage dispatching method based on satellite terminal deployment, characterized in that, include: Obtain multi-source basic data of the disaster-stricken area, including at least meteorological data, topographic data, combustible load distribution data, geographic coordinate data of power distribution equipment, and geographic coordinate data of communication nodes; Based on the aforementioned multi-source basic data, an equipment availability assessment model for power distribution and communication systems under the influence of wildfire disasters is constructed, and the survival confidence of power distribution branches and communication links is quantified. Based on the equipment availability assessment model, a communication network recovery model based on UAV-equipped low-orbit satellite terminals is constructed after a mountain fire. The UAV is used to deploy the low-orbit satellite terminals to the target communication nodes, and the communication network is recovered with algebraic connectivity as a constraint. Under the constraint of communication network recovery results, a post-disaster recovery model of distribution system based on mobile energy storage system is constructed. Communication reachability is taken as a prerequisite constraint for load recovery of distribution nodes, and mobile energy storage system is introduced to participate in distribution network topology reconfiguration and power support. Construct a collaborative optimization objective function that aims to maximize the load recovery of the power distribution system and minimize the deployment cost of emergency resources; The objective function of the collaborative optimization is solved to generate the optimal recovery scheme, which includes at least a low-orbit satellite terminal deployment scheme, a mobile energy storage system configuration scheme, and a power distribution network load recovery scheme.

2. The method according to claim 1, characterized in that, The construction of the equipment availability assessment model based on the multi-source basic data includes: Based on the meteorological data, the topographic data, and the combustible load distribution data, an elliptical diffusion model is used to characterize the coverage of wildfire disasters on regional space at different stages, generating the fire-affected area. The fire-affected area is mapped to the spatial topology of the power distribution system and the communication system, and the fire exposure of the power distribution branch and the communication link are calculated respectively. By combining fire intensity and equipment sensitivity characteristics, instantaneous failure rate functions for power distribution branches and communication links are constructed respectively; Based on the instantaneous failure rate function, reliability theory is introduced to calculate the cumulative survival confidence of the power distribution branch and communication link throughout the entire wildfire cycle.

3. The method according to claim 1, characterized in that, Constructing a communication network recovery model includes: The initial availability status of the post-disaster ground communication link is determined based on the cumulative survival confidence of the communication link and the preset communication link availability threshold. The post-disaster ground communication network is represented as a graph structure, which includes a set of communication nodes and a set of available ground links. Drones equipped with low-orbit satellite terminals were introduced to provide emergency support to the damaged area. The drones were used to deploy the low-orbit satellite terminals to the designated communication nodes, enabling the nodes to have satellite communication access capabilities. Construct a set of satellite-assisted communication links and establish logical constraints between the satellite links and the deployment status of low-Earth orbit satellite terminals; By integrating terrestrial communication links and satellite-assisted links, an extended communication network topology can be constructed. Based on the extended communication network topology, an algebraic connectivity lower bound constraint is introduced to ensure the global connectivity performance of the restored communication network.

4. The method according to claim 1, characterized in that, The construction of the power distribution system post-disaster recovery model based on mobile energy storage system includes: The operating status of the distribution branch is determined based on the cumulative survival confidence of the distribution branch and the preset distribution branch availability threshold. Establish a mapping relationship between communication nodes and power distribution nodes, map the reachability of communication nodes to power distribution nodes, and generate communication reachability variables for power distribution nodes; The communication reachability variable of the power distribution node is used as a prerequisite constraint for load restoration and reactive power compensation of the power distribution node. Establish a distribution network operation model that includes power flow constraints, voltage constraints, safety margin constraints, and radial constraints; Introduce mobile energy storage systems to participate in the restoration of power distribution systems, and establish deployment variables, charge and discharge state variables, and state of charge constraints for mobile energy storage systems.

5. The method according to claim 1, characterized in that, Constructing the collaborative optimization objective function includes: The primary optimization objective is to maximize the total weighted load recovery of the power distribution system, whereby the total weighted load recovery is calculated based on the node load weights and the recovered active load. Minimizing the deployment cost of low-Earth orbit satellite terminals is the second optimization objective; Minimizing the deployment cost of mobile energy storage systems is the third optimization objective; The collaborative optimization objective function is constructed by combining the first optimization objective, the second optimization objective, and the third optimization objective.

6. The method according to claim 1, characterized in that, Solving the collaborative optimization objective function includes: Linearize the nonlinear power flow constraints in the distribution network operation model and transform the branch voltage relationships into inequality constraint forms. The branch capacity constraints in the power distribution network operation model are convexized and transformed into a second-order cone constraint form. The collaborative recovery model, which includes linearization constraints and second-order cone constraints, is uniformly transformed into a mixed-integer second-order cone programming problem. The branch and bound method is used to process the integer variables in the mixed integer second-order cone programming problem, and the interior point method is combined to solve the continuous variables and second-order cone constraints to obtain the optimal recovery scheme.

7. The method according to claim 1, characterized in that, Also includes: Based on the optimal recovery scheme, a drone deployment path suggestion is generated, which is used to guide the drone to deploy the low-orbit satellite terminal to the target communication node. Based on the optimal recovery scheme, a mobile energy storage system scheduling instruction is generated. The mobile energy storage system scheduling instruction includes at least the deployment location of the mobile energy storage system, the charging and discharging power, and the state of charge management strategy.

8. The method according to claim 1, characterized in that, The lower bound constraint of algebraic connectivity is determined jointly based on the available resources and service requirements of the communication system. The lower bound of algebraic connectivity is equal to the product of the adjustment coefficient and the ratio of the effective total bandwidth of the communication network to the average communication demand of the network.

9. The method according to claim 1, characterized in that, The introduction of mobile energy storage systems to participate in power distribution system restoration includes: Based on the state of charge constraints of the mobile energy storage system, ensure the energy sustainability of the mobile energy storage system during the dispatch cycle; The charging and discharging rate of mobile energy storage systems is limited by the upper limit constraint on the charging and discharging power of mobile energy storage systems. Calculate the actual usable energy of the mobile energy storage system based on its charge and discharge efficiency parameters; Based on the deployment location constraints of the mobile energy storage system, ensure that the mobile energy storage system is only deployed at the power distribution node.

10. An emergency mobile energy storage dispatching device based on satellite terminal deployment, characterized in that, include: The data acquisition module is used to acquire multi-source basic data of the disaster-stricken area. The multi-source basic data includes at least meteorological data, topographic data, combustible load distribution data, geographical coordinate data of power distribution equipment, and geographical coordinate data of communication nodes. The model building module is used to construct an equipment availability assessment model for the power distribution system and communication system under the influence of wildfire disasters based on the multi-source basic data, and to quantify the survival confidence of power distribution branches and communication links; The constraint module is used to construct a communication network recovery model based on the equipment availability assessment model after a wildfire, using a drone to deploy the low-orbit satellite terminal to the target communication node, and to restore the communication network with algebraic connectivity as a constraint. The recovery model module is used to construct a post-disaster recovery model of the distribution system based on the mobile energy storage system under the constraints of the communication network recovery results. It takes communication reachability as a prerequisite constraint for the load recovery of distribution nodes and introduces the mobile energy storage system to participate in the topology reconstruction and power support of the distribution network. The objective function module is used to construct a collaborative optimization objective function that aims to maximize the load recovery of the power distribution system and minimize the deployment cost of emergency resources. The calculation module is used to solve the collaborative optimization objective function and generate the optimal recovery scheme. The optimal recovery scheme includes at least a low-orbit satellite terminal deployment scheme, a mobile energy storage system configuration scheme, and a power distribution network load recovery scheme.

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