Community group resource emergency scheduling method based on ice disaster pre-disaster prediction and dynamic scheduling

Abstract

The application discloses a community group resource emergency scheduling method based on ice disaster pre-disaster prediction and dynamic scheduling, and belongs to the technical field of disaster treatment, and comprises the following steps: S1, a unified failure rate calculation model of a power distribution network under ice disaster is established; S2, the path and influence area of the ice disaster are predicted based on historical data and meteorological information, and disaster area division is performed; and S3, resource scheduling strategies are formulated for each divided disaster area.The application adopts the community group resource emergency scheduling method based on ice disaster pre-disaster prediction and dynamic scheduling, through scientific pre-disaster prediction and reasonable resource area division, pre-disaster scheduling and flexible scheduling in the disaster, the disaster resistance and recovery efficiency of the community under the extreme ice disaster situation are effectively improved, a systematic emergency solution is provided for coping with the extreme ice disaster, a theoretical basis and practical guidance are provided for the flexible configuration of community energy, and the self-maintenance ability and recovery flexibility of the community under the disaster condition are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of disaster response technology, and in particular to a community-based emergency resource dispatching method based on pre-disaster prediction and dynamic scheduling of ice storms. Background Technology

[0002] Currently, how to restore and manage power and water supply systems under natural disasters such as ice storms is an increasingly hotly debated research issue. Existing technologies mainly focus on the following aspects:

[0003] (1) Post-disaster emergency dispatch: Many existing technologies focus on emergency dispatch after a disaster, with an emphasis on how to quickly dispatch repair teams to repair damaged power transmission lines and water pipelines. These technologies are usually based on emergency repair optimization dispatch algorithms, aiming to reduce the time of power and water outages for residents through optimal paths and rapid response strategies.

[0004] (2) Enhanced resilience of distribution networks: Some existing studies have begun to explore how to cope with disasters by enhancing the resilience of distribution networks, such as by increasing the redundancy of distribution lines and strengthening the anti-icing capabilities of transmission lines to reduce the impact of disasters on the power grid.

[0005] (3) Mobile energy storage and water storage systems: Existing technologies have also proposed solutions for providing temporary power and water to communities after disasters using mobile energy storage vehicles and mobile water storage vehicles. The focus of these technologies is on vehicle scheduling and route optimization to ensure that vehicles can quickly reach disaster areas and provide the necessary resources.

[0006] Although existing technologies have made some progress in post-disaster emergency dispatch and resource supply, there are still some significant shortcomings and deficiencies in dealing with long-lasting and wide-ranging disasters such as ice storms:

[0007] (1) Insufficient pre-disaster scheduling: Most existing technologies only carry out emergency response after a disaster and lack pre-disaster scheduling planning. This results in a relatively slow scheduling response after a disaster, and the supply of resources cannot quickly cover the affected communities. Ice storms often last for a long time and may cause large-scale power and water supply system interruptions. The lack of timeliness in post-disaster scheduling is the main shortcoming of existing technologies.

[0008] (2) Insufficient consideration of dynamic changes during the disaster: The impact of ice storms is not limited to the post-disaster period. During the disaster, the power grid and water supply may be interrupted at any time. Existing technologies lack flexible dispatching schemes during disasters and cannot adequately cope with changes in road conditions and supply conditions during disasters.

[0009] (3) Lack of user satisfaction measurement standards: Existing dispatch schemes rarely consider users' requirements for the timeliness of resource supply. Especially when the disaster impact is large, the waiting time for residents to receive resources is too long, which seriously affects user satisfaction. However, the existing dispatch system does not have a clear mechanism to maximize user satisfaction.

[0010] (4) Route optimization is limited by changes in the impact of disasters: Most existing route optimization techniques only consider the optimal route under fixed conditions and do not fully consider the real-time impact of extreme weather such as ice storms on the transportation network. Roads covered by ice and snow are prone to congestion or slowdown, and existing technologies have failed to effectively combine dynamic changes in road conditions to adjust the route selection for resource scheduling. Summary of the Invention

[0011] The purpose of this invention is to provide a community-based emergency resource scheduling method based on pre-disaster prediction and dynamic scheduling of ice storms. Through scientific pre-disaster prediction, reasonable resource area division, pre-disaster scheduling, and flexible scheduling during the disaster, it can effectively improve the community's disaster resistance and recovery efficiency under extreme ice storm scenarios. It provides a systematic emergency solution for coping with extreme ice storms, and provides theoretical basis and practical guidance for the flexible allocation of community energy. It has good scalability and adaptability, can cope with ice storm challenges of different scales, and significantly improve the community's self-sustaining ability and recovery resilience under disaster conditions.

[0012] To achieve the above objectives, this invention provides a community-based emergency resource scheduling method based on pre-disaster prediction and dynamic scheduling of ice storms, comprising the following steps:

[0013] S1. Establish a unified failure rate calculation model for the power distribution network under freezing disasters;

[0014] S2. Based on historical data and meteorological information, predict the path and affected area of ​​the ice storm, and divide the disaster area.

[0015] S3. Develop resource allocation strategies for the designated disaster areas.

[0016] Preferably, in S1, a conductor failure rate model and a tower failure rate model are established respectively, and a unified failure rate calculation model is derived by combining the conductor failure rate model and the tower failure rate model.

[0017] Preferably, in the conductor failure rate model, the icing thickening rate of the conductor is... It increases proportionally to the rainfall rate and wind speed, as shown in the following formula:

[0018] ;

[0019] In the formula, Indicates the density of ice; This indicates the density of liquid water; Indicates the area wind speed; This represents the rainfall rate in region m; Indicates the area The liquid water content in saturated air, ;

[0020] Ice load per unit length of line for:

[0021] ;

[0022] In the formula, D is the diameter of the cable;

[0023] Wind load per unit length of line for:

[0024] ;

[0025] In the formula, C is a constant coefficient, with a value of 6.964 × 10⁻⁶. -3 S is the span factor, and its calculation formula is as follows:

[0026] ;

[0027] When ice accumulates on transmission lines, the load on the lines is a combination of the ice load in the vertical direction due to the weight of the ice and the wind load in the horizontal direction. Therefore, the combined load is the sum of the wind load and the ice load. for:

[0028] ;

[0029] Corresponding tower failure rate for:

[0030] .

[0031] Preferably, in the tower failure rate model, the tower loads include ice loads, wind loads, and unbalanced forces;

[0032] When the wind load, ice load, and unbalanced tension load from the lines on both sides of the tower exceed its upper limit, the tower will collapse, and the total load on the tower will be... Defined as:

[0033] + + ;

[0034] Ice load The formula is:

[0035] ;

[0036] in, The combination of cable diameter and ice thickness reflects the change in effective area; Indicates the density of ice; Indicates the thickness of the ice layer; The length of the tower or conductor;

[0037] Wind load The formula is:

[0038] ;

[0039] in, express ; This is the cross-sectional area formed by the diameter of the conductor and the thickness of the ice layer.

[0040] This represents the unbalanced forces experienced by the tower under extreme ice storm conditions:

[0041] ;

[0042] in, and It is a quadratic function of ice load and wind load; The number of conductors indicates the number of conductors connected to the tower.

[0043] Corresponding tower failure rate for:

[0044] .

[0045] Preferably, a unified failure rate calculation model for line segment ij is derived by combining the failure rates of conductors and towers. for:

[0046] .

[0047] Preferably, in S3, the resource scheduling strategy first needs to purchase electricity and fresh water resources with the goal of minimizing costs, then deploy electricity and fresh water resources in advance based on the predicted situation of the disaster area, and finally schedule the mobile energy storage vehicle and mobile water storage vehicle to the optimal route based on the actual disaster situation.

[0048] Preferably, the objective function for the cost of purchasing electricity and freshwater resources is:

[0049] ;

[0050] in, Indicates the total amount of freshwater resources. This represents the total cost of purchasing freshwater resources; Indicates the total amount of electricity resources. This represents the total cost of purchasing electricity resources; and These represent the allocation amounts of freshwater and electricity resources, respectively. This indicates the allocation cost of freshwater and electricity resources; and These represent the number of freshwater vehicles and energy storage vehicles deployed, respectively. This represents the deployment cost of mobile energy storage vehicles and mobile water storage vehicles.

[0051] Preferably, when pre-deploying electricity and freshwater resources, the objective function for the deployment locations of electricity and water resources is:

[0052] ;

[0053] ;

[0054] in, The objective function for pre-disaster location of charging stations; The objective function for pre-disaster reservoir location; For all faulty load nodes; For 0-1 variables, if the load node If there is a charging station nearby, then ,otherwise , This is a 0-1 variable; if the mobile energy storage vehicle can restore power to the node, then... Otherwise, it is 0; For load nodes The weight coefficients reflect the importance of the nodes; For charging station To the node The shortest travel time; To the reservoir n To the node i The shortest travel time.

[0055] The constraints are:

[0056] ;

[0057] ;

[0058] ;

[0059] ;

[0060] ;

[0061] ;

[0062] .

[0063] Preferably, a spatiotemporal dynamic scheduling model is established to optimize the route scheduling of mobile energy storage vehicles and mobile water storage vehicles based on the actual disaster situation. Specifically, the nodes of mobile energy storage vehicles and mobile water storage vehicles are... With nodes Travel time between and equivalent travel distance With actual vehicle speed The relationship is represented as:

[0064] ;

[0065] In the formula, The ideal speed under zero traffic conditions; It indicates the degree of congestion in the transportation network under disaster conditions, and is related to the severity of the disaster and traffic flow;

[0066] By introducing equivalent travel distance To represent the travel distance under disaster conditions: ;

[0067] In the formula, For nodes The geographical distance between node n and node n under normal circumstances;

[0068] Further, the travel time under disaster conditions is:

[0069] ;

[0070] For each possible path from node m to node n Calculate the travel time for all paths, select the optimal path with the shortest travel time, and finally obtain the travel time for the optimal route as follows:

[0071] ;

[0072] A comprehensive satisfaction model based on response time is proposed, as follows:

[0073] ;

[0074] ;

[0075] in, This refers to the corresponding time for community s, specifically the time from when the community experiences a water and power outage until resources are restored. This is the maximum response time allowed by the system; if this time is exceeded, user satisfaction will drop to zero. It is a community i User satisfaction, representing community residents' satisfaction with the timeliness of services during disaster emergency response; It is an adjustment coefficient used to control the degree to which response time affects user satisfaction;

[0076] We set a goal to maximize overall user satisfaction by minimizing the response time of each community. The objective function is:

[0077] ;

[0078] Where n is the number of communities. For the community i The weights;

[0079] At the same time, the following constraints must be met:

[0080] ;

[0081] ;

[0082] ;

[0083] .

[0084] Preferably, the spatial state scheduling constraints for mobile energy storage vehicles are:

[0085] ;

[0086] ;

[0087] ;

[0088] ;

[0089] ;

[0090] ;

[0091] ;

[0092] The spatiotemporal transfer scheduling constraints for mobile water storage vehicles are:

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] At a water storage station, mobile water storage vehicles transport water between reservoirs, with the following constraints:

[0099] ;

[0100] ;

[0101] ;

[0102] .

[0103] Therefore, the beneficial effects of the above-mentioned community-based emergency resource scheduling method based on pre-disaster prediction and dynamic scheduling of ice disasters in this invention are as follows:

[0104] (1) Pre-disaster prediction and pre-scheduling: Based on historical data and meteorological information, this invention predicts the possible paths and affected areas of ice storms, analyzes the impact range of ice storms in advance, and rationally allocates the pre-scheduling locations of mobile energy storage vehicles and water storage vehicles, significantly shortening the post-disaster resource response time. This strategy effectively reduces power and water supply interruptions during disasters and improves the community's rapid response capability to ice storms.

[0105] (2) Dynamic scheduling and route optimization during and after disasters: This invention considers the impact of ice storms on transportation networks and proposes a route optimization method based on real-time road conditions to dynamically adjust the scheduling routes of mobile energy storage vehicles and water storage vehicles, ensuring that resources can reach disaster-stricken communities in a timely manner. Compared with traditional static route selection, this method improves the flexibility and efficiency of emergency scheduling and has significant advantages under conditions of continuous changes in disasters.

[0106] (3) Maximizing User Satisfaction: This invention introduces a user satisfaction model that comprehensively considers the response time and satisfaction level of resource supply, and optimizes scheduling with the goal of maximizing user satisfaction. By reducing the waiting time for community users to access resources and rationally allocating resources to meet the needs of different communities, it significantly improves the satisfaction of users after a disaster and the fairness of resource supply.

[0107] (4) Cost optimization and resource utilization: During the pre-disaster scheduling process, this invention reduces resource waste and unnecessary scheduling costs by optimizing the purchase, allocation, and scheduling costs of electricity and water resources. This method not only improves resource utilization efficiency but also reduces the overall economic cost of emergency scheduling and enhances the system's economy.

[0108] (5) System resilience and adaptability: The scheduling system designed in this invention has good scalability and adaptability, and can flexibly adjust resource deployment and scheduling strategies according to ice disasters of different scales. At the same time, the introduction of mobile energy storage and water storage systems provides stronger resilience for community survival during disasters and enhances the system's ability to continue operating under extreme conditions.

[0109] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0110] Figure 1 This is a step diagram of an embodiment of the community group resource emergency dispatching method based on pre-disaster prediction and dynamic scheduling of ice disasters of the present invention;

[0111] Figure 2 This is a network topology diagram for community resource allocation and defense planning based on ice disaster prediction;

[0112] Figure 3 This is a flowchart of the emergency resource dispatch system during an ice storm.

[0113] Figure 4 This is a schematic diagram of community resource zoning and mobile energy storage vehicle dispatching during an ice storm;

[0114] Figure 5 This is a schematic diagram showing the optimized vehicle route from the ice storm emergency resource depot to community nodes;

[0115] Figure 6 This is a schematic diagram of vehicle transportation modes and timelines in emergency dispatching during ice storms. Detailed Implementation

[0116] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0117] The severe impact of extreme ice storms on infrastructure such as power and water supply is receiving increasing attention, especially in the face of persistent and widespread extreme weather events. Enhancing the disaster resilience and recovery efficiency of communities is crucial. To address these challenges, this invention proposes a community-based emergency resource scheduling method based on pre-disaster prediction and dynamic scheduling of ice storms, which significantly improves the emergency response and recovery capabilities of communities under extreme ice storm scenarios.

[0118] This method combines key technologies such as pre-disaster prediction and pre-scheduling, dynamic scheduling and path optimization, and user satisfaction models to ensure that communities can quickly obtain electricity and water resources during disasters, significantly shortening post-disaster response time and improving the timeliness of resource supply and user satisfaction. Specifically, this method integrates mobile energy storage devices (such as vehicle-mounted batteries integrating converters and controllers) to provide rapid power support to communities during disasters, stabilizing the operation of the power distribution system. Simultaneously, mobile water storage devices (such as mobile water trucks) are used for freshwater resource distribution to meet the basic living needs of community residents. The flexible scheduling of these devices ensures that resources can be delivered quickly and safely to affected communities in disaster situations, guaranteeing their basic living needs.

[0119] Regarding resource allocation, this method proposes scientific pre-disaster prediction and pre-scheduling strategies. By analyzing historical data and meteorological information, it predicts the possible paths and impact ranges of ice storms, and deploys mobile energy storage and water storage equipment in areas with a high probability of disaster in advance. Pre-disaster deployment includes rationally selecting the locations of charging stations and reservoirs to minimize the total travel time from high-priority nodes to low-priority nodes, thereby rapidly replenishing power and water resources after a disaster and reducing post-disaster response delays. In addition, in-disaster scheduling employs a path optimization method based on real-time road conditions to dynamically adjust the scheduling paths of mobile devices, ensuring that resources are delivered to communities in need in the shortest possible time under complex road conditions.

[0120] To further improve the quality of life for community residents after disasters, this method introduces a user satisfaction model. Based on the response time and satisfaction level of resource supply, it maximizes the satisfaction of community residents during disasters. This model significantly reduces the time residents wait for resources by optimizing response time and meets the needs of different communities through reasonable resource allocation. Therefore, while improving the efficiency of emergency resource dispatch, it enhances residents' living experience during disasters and significantly improves the overall quality and effectiveness of emergency response.

[0121] Furthermore, this method focuses on the system's economy and sustainability. By optimizing the cost of pre-disaster resource allocation and scheduling, it reduces unnecessary scheduling expenses, improves resource utilization efficiency, and lowers the overall system operating costs. Through multi-energy coordination and priority supply strategies, it ensures that the needs of critical nodes are met first during disasters, thereby achieving fairness and effectiveness in overall resource supply.

[0122] Example 1

[0123] like Figure 1 As shown, the community-based emergency resource scheduling method based on pre-disaster prediction and dynamic scheduling of ice storms in this embodiment specifically includes the following steps:

[0124] S1. Establish a unified failure rate calculation model for the power distribution network under freezing disasters.

[0125] Ice disasters not only evolve over time, but also exhibit significant dynamic characteristics in space. The causes of transmission line faults mainly include: (1) ice load, which is the vertical force generated by the accumulation of ice and snow on the transmission line; and (2) wind load, which is the horizontal force generated by the wind on the transmission line.

[0126] This method establishes separate failure rate models for conductors and towers, and then derives a unified failure rate calculation model by combining the conductor failure rate model and the tower failure rate model.

[0127] During prolonged ice storms, overhead power lines are particularly vulnerable to icing and subsequent failures. However, underground heat pipes are generally unaffected by ice storms, making research on icing failures of above-ground transmission lines especially important.

[0128] First, a conductor failure rate model was established, along with the conductor icing thickening rate. It increases proportionally to the rainfall rate and wind speed, as shown in the following formula:

[0129]

[0130] In the formula, Indicates the density of ice; This indicates the density of liquid water; Indicates the area wind speed; This represents the rainfall rate in region m; Indicates the area The liquid water content in saturated air, .

[0131] Ice load per unit length of line for:

[0132]

[0133] In the formula, D is the diameter of the cable.

[0134] Wind load per unit length of line for:

[0135]

[0136] In the formula, C is a constant coefficient, with a value of 6.964 × 10⁻⁶. -3 S is the span factor, and its calculation formula is as follows:

[0137]

[0138] When ice accumulates on transmission lines, the load on the lines is a combination of the ice load in the vertical direction due to the weight of the ice and the wind load in the horizontal direction. Therefore, the combined load is the sum of the wind load and the ice load. for:

[0139]

[0140] Corresponding tower failure rate for:

[0141]

[0142] Secondly, a tower failure rate model was established, with the tower loads including ice load, wind load, and unbalanced force.

[0143] Similar to conductor faults, when wind loads, ice loads, and unbalanced tension loads from the lines on both sides of the tower exceed their upper limits, the tower will collapse, and the total load on the tower will increase. Defined as:

[0144] + +

[0145] Ice load The formula is:

[0146]

[0147] in, The combination of cable diameter and ice thickness reflects the change in effective area; Indicates the density of ice; Indicates the thickness of the ice layer; This refers to the length of the tower or conductor.

[0148] Wind load The formula is:

[0149]

[0150] in, express ; This is the cross-sectional area formed by the diameter of the conductor and the thickness of the ice layer.

[0151] This represents the unbalanced forces experienced by the tower under extreme ice storm conditions:

[0152]

[0153] in, and It is a quadratic function of ice load and wind load; The number of conductors indicates the number of conductors connected to the tower.

[0154] Corresponding tower failure rate for:

[0155]

[0156] A unified failure rate calculation model for line segment ij is derived by combining the failure rates of conductors and towers. for:

[0157]

[0158] S2. Based on historical data and meteorological information, predict the path and affected area of ​​the ice storm, and divide the disaster area.

[0159] Under extreme ice storm conditions, the electricity, water, and heating supplies of community residents may be severely impacted. To ensure the basic living needs of community residents, the integrated energy system for the community cluster must possess the following characteristics:

[0160] A: In the event of a temporary power outage, the system can meet the basic electricity and fresh water needs of community residents for a certain period of time;

[0161] B: To reduce interdependence between communities, all communities are integrated into a large energy-sharing system to ensure smooth passage between communities;

[0162] C: In each area, a reservoir and a power station are pre-planned to ensure that the electricity and freshwater needs of all communities are effectively met through reasonable resource allocation.

[0163] To effectively plan for disaster prevention before the onset of freezing weather, historical data and meteorological information are used to predict the possible paths and affected areas of ice storms, thereby formulating corresponding prevention strategies. Specifically, through predictive analysis of the impact of ice storms, transmission lines are divided into dispatchable and non-dispatchable lines, and potentially affected areas are identified.

[0164] In this embodiment, as Figure 2 As shown, according to the forecast results, the ice storm will mainly affect three areas: Area 1: including the transmission lines from node 1 to node 9 and from node 19 to node 22; Area 2: covering the transmission lines from node 9 to node 18; Area 3: including the transmission lines from node 23 to node 33.

[0165] Based on this, corresponding resource allocation strategies were developed for each region to ensure efficient resource allocation before and during the disaster, and to minimize the negative impact of ice storms on the community power grid.

[0166] S3. Develop resource allocation strategies for the designated disaster areas.

[0167] like Figure 3 As shown, the resource scheduling strategy first requires purchasing electricity and fresh water resources with the goal of minimizing costs, then deploying electricity and fresh water resources in advance based on the predicted situation in the disaster area, and finally scheduling mobile energy storage vehicles and mobile water storage vehicles according to the actual disaster situation.

[0168] Referring to the IEEE-33 node system, where multiple nodes are assumed to be community nodes, reasonable resource deployment is carried out before an ice storm occurs to accelerate the response speed of post-disaster dispatch. For grid managers, the goal is to minimize the costs of freshwater, electricity, mobile energy storage vehicles, and mobile water storage vehicles by optimizing resource allocation.

[0169] The objective function for the cost of purchasing electricity and freshwater resources is:

[0170]

[0171] in, Indicates the total amount of freshwater resources. This represents the total cost of purchasing freshwater resources; Indicates the total amount of electricity resources. This represents the total cost of purchasing electricity resources; and These represent the allocation amounts of freshwater and electricity resources, respectively. This indicates the allocation cost of freshwater and electricity resources; and These represent the number of freshwater vehicles and energy storage vehicles deployed, respectively. The deployment cost of mobile energy storage vehicles and mobile water storage vehicles includes fixed costs such as vehicle purchase and maintenance.

[0172] To enhance the disaster resilience of communities, this method optimizes the location and quantity of pre-disaster resource deployment to ensure rapid response to resource needs after a disaster. The goal is to achieve efficient resource allocation and energy management at the lowest economic cost while meeting community resource needs by minimizing the overall cost function. Through systematic mathematical modeling and optimization analysis, this method constructs a resource scheduling scheme that combines economy and emergency response capabilities, minimizing the burden and cost of post-disaster scheduling, prioritizing the restoration of critical load nodes, and significantly improving the overall disaster resilience and recovery efficiency of the community.

[0173] In order to enable mobile energy storage vehicles and mobile water storage vehicles to quickly replenish resources from resource stations and restore the resource needs of critical load nodes as much as possible, the pre-disaster positioning of charging stations and reservoirs needs to be rationally selected to minimize the total travel time from high-weight nodes to low-weight nodes.

[0174] The objective function for the deployment location of power and water resources is:

[0175] ;

[0176] ;

[0177] in, The objective function for pre-disaster location of charging stations; The objective function for pre-disaster reservoir location; For all faulty load nodes; For 0-1 variables, if the load node If there is a charging station nearby, then ,otherwise , This is a 0-1 variable; if the mobile energy storage vehicle can restore power to the node, then... Otherwise, it is 0; For load nodes The weighting coefficients reflect the importance of the nodes; For charging station To the node The shortest travel time; To the reservoir n To the node i The shortest travel time.

[0178] To achieve effective dispatch of mobile energy storage vehicles and mobile water storage vehicles to various regions during ice storms and to meet the electricity and water resource needs of all regions, the following formula describes its constraints:

[0179]

[0180]

[0181]

[0182]

[0183]

[0184]

[0185]

[0186] The first formula represents the overall constraint on the quantity of resources, where This refers to the total number of mobile water storage vehicles. This represents the total number of energy storage vehicles. The second formula describes how the resource needs of each region should be met, where... This indicates the water supply capacity of a single water storage vehicle. The first formula represents the power supply capacity of a single energy storage vehicle. The third and fourth formulas represent the unique allocation and capacity limitations of the vehicles in each time period. The fifth and sixth formulas describe the conditions for meeting the electricity and water resource needs of various regions, where... and Representing regions The demand for electricity and water resources is in short supply. This represents the fixed amount of resources each vehicle can carry. The seventh formula represents the transportation distance and time constraints, indicating the travel time for each vehicle. The maximum driving time must not be exceeded. .

[0187] In integrated power grid and transportation network systems, the scheduling status of mobile energy storage vehicles is jointly determined by their charging / discharging status and transportation status, exhibiting significant spatiotemporal coupling characteristics. To effectively address complex situations in disaster scenarios, a spatiotemporal dynamic scheduling model is established to comprehensively consider the travel time of mobile energy storage devices between node m and node n. Traffic conditions and equipment installation and configuration time The model establishes a spatiotemporal relationship between resources and energy supply. By building this model, we can better optimize route selection and scheduling strategies, ensuring that resources can be delivered to disaster-stricken areas efficiently and in a timely manner during disasters. It can also effectively optimize route selection and energy supply timeliness during transportation, ensuring that resources can be delivered to disaster-stricken areas quickly and accurately.

[0188] like Figure 4 The diagram illustrates the time-space scheduling scheme for a mobile multi-energy storage system during and after a disaster. Under the impact of the disaster, multiple power lines suffered varying degrees of damage from ice storms. Mobile energy storage vehicles and mobile water storage vehicles were dynamically dispatched among multiple community nodes to ensure the basic water and electricity needs of community residents. Through flexible scheduling among different nodes, resource allocation became more balanced and efficient, significantly enhancing the self-sustaining capacity and resilience of the community energy system under disaster conditions. This method introduces the following optimization strategies:

[0189] Route planning and dynamic scheduling optimization: Due to potential road closures or traffic restrictions caused by ice storms, the route selection for mobile energy storage devices needs to be adjusted in real time based on changes in traffic conditions to ensure the optimal route is chosen under complex road conditions, delivering resources to target communities as quickly as possible. By considering the congestion level of the traffic network, vehicle travel time and efficiency are optimized.

[0190] Multi-energy coordination and priority supply: During disasters, different communities may have significantly different needs for electricity, heat, and water resources. Therefore, this approach introduces a multi-energy coordination strategy to ensure that the needs of critical nodes are prioritized during dispatching, especially for key facilities such as hospitals and fire stations, thereby improving the fairness and effectiveness of overall supply.

[0191] Because transportation networks are susceptible to disasters, the actual travel time of mobile energy storage vehicles on the same roads may vary depending on the interaction time and environmental influences during a disaster. To ensure the reliability of emergency mobile energy storage devices under disaster conditions, this method considers the impact of disaster conditions such as congestion and snow accumulation on traffic conditions, and uses traffic congestion parameters to reflect the spatial relationship between the actual speed of mobile energy storage and the equivalent traffic distance.

[0192] The travel speed of mobile energy storage vehicles and mobile water storage vehicles is significantly affected under disaster conditions; therefore, it is necessary to consider the degree of traffic network congestion in disaster scenarios. Estimate its speed based on actual traffic conditions. Travel time between node m and node n. and equivalent travel distance With actual vehicle speed The relationship can be represented as:

[0193]

[0194] In the formula, The ideal speed under zero traffic conditions; This parameter represents the degree of congestion in a transportation network under disaster conditions and is related to the severity of the disaster and traffic flow. In applications, it can be estimated by assessing the disaster status of the transportation network. In disaster scenarios, actual distances are affected not only by geographical distance but also by deteriorating road conditions. This is addressed by introducing an equivalent travel distance. To represent the travel distance under disaster conditions:

[0195]

[0196] In the formula, For nodes The geographical distance between node n and the road surface under normal conditions, taking into account the impact of disasters on road conditions, can be further expressed as the travel time under disaster conditions:

[0197]

[0198] For each possible path from node m to node n Calculate the travel time for all paths, select the optimal path with the shortest travel time, and finally obtain the travel time for the optimal route as follows:

[0199]

[0200] Therefore, the transportation network between community j and community k can be simplified into an optimal travel route, such as... Figure 5 As shown.

[0201] Because under extreme conditions, user satisfaction models are mainly determined by the timeliness of energy and fresh water supply and the degree of satisfaction with energy and fresh water supply, a comprehensive satisfaction model based on response time is proposed to explore the impact of response time on user satisfaction, as follows:

[0202]

[0203]

[0204] in, This refers to the corresponding time for community s, specifically the time from when the community experiences a water and power outage until resources are restored. This is the maximum response time allowed by the system; if this time is exceeded, user satisfaction will drop to zero. It is a community i User satisfaction, representing community residents' satisfaction with the timeliness of services during disaster emergency response; It is an adjustment coefficient used to control the degree to which response time affects user satisfaction.

[0205] To optimize user satisfaction across all communities, a maximization of overall user satisfaction is set, achieved by minimizing the response time of each community. The optimization objective function is:

[0206]

[0207] Where n is the number of communities. For the community i The weight.

[0208] At the same time, the following constraints must be met:

[0209]

[0210]

[0211]

[0212]

[0213] The first formula represents the response time of each mobile energy storage vehicle c. Not exceeding the maximum permitted time The second formula states that the response time of a high-priority vehicle should be less than or equal to the response time of a low-priority vehicle. This ensures that priority is followed when allocating resources. The third formula ensures that the battery state of each mobile energy storage vehicle c remains at the minimum permissible value at time t. and maximum allowed value The fourth formula represents the battery state change of the mobile energy storage vehicle c between time t and t+1, taking into account the charging power. and power usage This constraint ensures that the vehicle can effectively manage the battery state to complete the task.

[0214] After receiving a dispatch instruction, if the mobile energy storage vehicle needs to move from node m to node n for charging or discharging operations, it must first select a route based on the congestion level of the traffic network under the disaster context to achieve the shortest travel time. From node m to node n. Before reaching node n, the mobile energy storage vehicle needs to move from node m to node n while maintaining a valid connection. The following are the spatial state scheduling constraints for the mobile energy storage vehicle:

[0215]

[0216]

[0217]

[0218] The first formula guarantees the mobile energy storage vehicle At any given moment Can only be in one node or The first formula ensures the uniqueness of its location; the second formula states that the transmission time must not be less than h, which is used to constrain the time stability of the mobile energy storage vehicle during charging and discharging switching, ensuring that the expected minimum time requirement is met during the switching process; the third formula ensures that the mobile energy storage vehicle c will not appear at multiple nodes simultaneously during the transmission time.

[0219] During inter-node charging and discharging scheduling, the mobile energy storage vehicle c needs to adjust its output power according to its energy status to support the power supply system. Simultaneously, the following constraints must also be met:

[0220]

[0221]

[0222]

[0223]

[0224] The first formula defines the mobile energy storage vehicle. The charging and discharging power is controlled to ensure that the charging and discharging power does not exceed the maximum power at any time. The second formula specifies mobile energy storage vehicles. Only charging or discharging operations can be performed at any given time; the third formula describes the mobile energy storage vehicle. The relationship between the state of charge and its charging and discharging power ensures that at time 10:00, the state of charge is equal to the charging and discharging power. The battery state is reasonably adjusted based on the previous moment and the power input and output; the fourth formula indicates that the state of charge of the mobile energy storage vehicle c must be kept between the allowable minimum and maximum values ​​to ensure that the battery is not overcharged or over-discharged.

[0225] Compared to the charging and discharging of mobile energy storage vehicles, the loading and unloading of water may be faster. Without loss of generality, we assume that the sum of the loading and unloading times for water is 1 hour.

[0226] like Figure 6 As shown, this diagram illustrates a typical dispatching process for mobile water storage vehicles during disaster emergencies. The diagram uses different time steps (e.g., t, t+1, t+2, ..., t+h, t+h+1, t+h+2) to illustrate the state changes of the mobile water storage vehicle during water loading, transportation, and unloading. Different colors and rectangles in the diagram represent different tasks performed by the mobile water storage vehicle; the color intensity reflects the priority of the tasks and the dispatching mode, ensuring a reasonable supply of water resources to meet the needs of emergency situations.

[0227] Complete transportation modes (such as) Figure 6 The darker areas depict the continuous transportation process of mobile water storage vehicles over extended periods, primarily used for rapid response to water resource needs in target areas. The segmented transportation and operation modes illustrate situations where the mobile water storage vehicle needs to make multiple stops for loading or unloading water during transport, increasing flexibility and the ability to meet the needs of multiple sites.

[0228] To better reflect the scheduling process of mobile energy storage vehicle c under disaster emergency conditions, this method describes the transfer constraints and different operational states in the time and space dimensions, helping to optimize resource allocation and task response. The specific spatiotemporal transfer scheduling constraints for the mobile water storage vehicle are as follows:

[0229]

[0230]

[0231]

[0232]

[0233]

[0234] The first formula represents the total operation time limit of the mobile water storage vehicle within a certain time period t, ensuring that the water storage vehicle completes transportation and operation within the specified time. The second formula ensures that the task execution order is reasonable, guaranteeing that loading, transportation, and unloading are performed in the correct sequence. The third formula represents the scheduling strategy that prioritizes high-priority tasks. For task sets i and j within the same time period t, if task i has a higher priority than task j, the system should execute task i first. The fourth formula limits the total time for loading and unloading tasks of the water storage vehicle to no more than a threshold. This ensures that task scheduling time is effectively controlled; the fifth formula guarantees that transportation time is based on actual distance. It dynamically adjusts based on path conditions.

[0235] In a reservoir, mobile water storage vehicles can transport water between nodes. The constraints are as follows:

[0236]

[0237]

[0238]

[0239]

[0240] The first formula indicates that the water filling and discharging power of the mobile water storage vehicle is physically limited and cannot exceed the maximum loading and unloading rate of the energy storage vehicle; the second formula indicates that the water capacity of the mobile water storage vehicle needs to be updated according to the filling or discharging operation to ensure that the water resources meet the actual needs; the third formula limits the water capacity of the energy storage vehicle to its minimum and maximum limits; and the fourth formula indicates that the mobile water storage vehicle can only travel along one route or stop at a certain community to fill or discharge water in each time period.

[0241] The specific process for solving the problem using the community-based emergency resource scheduling method based on pre-disaster prediction and dynamic scheduling of ice storms proposed in this embodiment is as follows:

[0242] (1) Calculation of unified failure rate model and generation of parameters: Based on meteorological data and historical disaster data, predict the ice disaster path and its possible impact on community nodes. Parameters include the electricity and water resource requirements of each community.

[0243] (2) Pre-disaster resource pre-scheduling optimization: The community partitioning algorithm Fast Newman algorithm (with the optimization objective of minimizing pre-scheduling costs) is used to partition the IEEE 33-node system and perform pre-disaster resource optimization to ensure the reasonable allocation of resource vehicles, reservoirs and charging stations.

[0244] (3) Route optimization and vehicle scheduling time calculation: Based on real-time traffic information, the driving route of the vehicle is dynamically adjusted to avoid high-risk areas, select the optimal route, and then the vehicle scheduling time is obtained based on the vehicle's movement time and resource loading and unloading time.

[0245] (4) Evaluation of user satisfaction: Calculate the user satisfaction of each community after the disaster based on the vehicle dispatch time. Maximize user satisfaction by minimizing the vehicle dispatch time in step (3) so that residents' resources can be replenished as soon as possible.

[0246] Therefore, this invention adopts the above-mentioned emergency scheduling method for community resources based on pre-disaster prediction and dynamic scheduling of ice disasters. Through scientific pre-disaster prediction and reasonable resource area division, pre-disaster scheduling and flexible scheduling during the disaster, it effectively improves the disaster resistance and recovery efficiency of communities under extreme ice disaster scenarios. It not only provides a systematic emergency solution for coping with extreme ice disasters, but also provides a theoretical basis and practical guidance for the flexible allocation of community energy. It has good scalability and adaptability, can cope with ice disaster challenges of different scales, and significantly improves the self-sustaining capacity and recovery resilience of communities under disaster conditions.

[0247] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A community-based emergency resource dispatching method based on pre-disaster prediction and dynamic scheduling of ice storms, characterized in that: Includes the following steps: S1. Establish a unified failure rate calculation model for the power distribution network under freezing disasters; S2. Based on historical data and meteorological information, predict the path and affected area of ​​the ice storm, and divide the disaster area. S3. Develop resource allocation strategies for the various disaster-affected areas; In S3, the resource scheduling strategy first needs to purchase electricity and fresh water resources with the goal of minimizing costs, then deploy electricity and fresh water resources in advance based on the predicted situation of the disaster area, and finally schedule the mobile energy storage vehicle and mobile water storage vehicle to the optimal route based on the actual disaster situation. When pre-planning the deployment of electricity and freshwater resources, the objective function for the deployment locations of electricity and water resources is: ； ； in, The objective function for pre-disaster location of charging stations; The objective function for pre-disaster reservoir location; For the set of all load nodes; For 0-1 variables, if the load node If there is a charging station nearby, then ,otherwise , This is a 0-1 variable; if the mobile energy storage vehicle can restore power to the node, then... Otherwise, it is 0; For load nodes The weight coefficients reflect the importance of the nodes; For charging station to load node The shortest travel time; To the reservoir n to load node i The shortest travel time; The constraints are: ； ； ； ； ； ； 。 2. The community-based emergency resource dispatching method based on pre-disaster prediction and dynamic scheduling of ice storms according to claim 1, characterized in that: In S1, a conductor failure rate model and a tower failure rate model are established respectively, and a unified failure rate calculation model is derived by combining the conductor failure rate model and the tower failure rate model.

3. The community-based emergency resource dispatching method based on pre-disaster prediction and dynamic scheduling of ice storms as described in claim 2, characterized in that: The line segment derived by combining the failure rates of conductors and towers ij Unified Failure Rate Calculation Model for: 。 4. The community-based emergency resource dispatching method based on pre-disaster prediction and dynamic scheduling of ice storms according to claim 1, characterized in that, The objective function for the cost of purchasing electricity and freshwater resources is: ； in, Indicates the total amount of freshwater resources. This represents the total cost of purchasing freshwater resources; Indicates the total amount of electricity resources. This represents the total cost of purchasing electricity resources; and These represent the allocation amounts of freshwater and electricity resources, respectively. This indicates the allocation cost of freshwater and electricity resources; and These represent the number of freshwater vehicles and energy storage vehicles deployed, respectively. This represents the deployment cost of mobile energy storage vehicles and mobile water storage vehicles.

5. The community-based emergency resource dispatching method based on pre-disaster prediction and dynamic scheduling of ice storms according to claim 1, characterized in that: A spatiotemporal dynamic scheduling model is established to optimize the route scheduling of mobile energy storage vehicles and mobile water storage vehicles based on the actual disaster situation. Specifically, the mobile energy storage vehicle nodes... and mobile water storage vehicle nodes Travel time between and equivalent travel distance With actual vehicle speed The relationship is represented as: ； In the formula, the equivalent travel distance is introduced. To indicate the travel distance under disaster conditions, For mobile energy storage vehicle nodes and mobile water storage vehicle nodes Geographical distance under normal circumstances; Actual vehicle speed for: ； In the formula, The ideal speed under zero traffic conditions; It indicates the degree of congestion in the transportation network under disaster conditions, and is related to the severity of the disaster and traffic flow; Further, the travel time under disaster conditions is: ； For mobile energy storage vehicle nodes and mobile water storage vehicle nodes Each path Calculate all paths Given the travel time, we select the optimal route to minimize the travel time. The final optimal travel time is: ； A comprehensive satisfaction model based on response time is proposed, as follows: ； ； in, It is a community s The corresponding time refers to the time from when the community's water and power are cut off until resources are obtained; This is the maximum response time allowed by the system; if this time is exceeded, user satisfaction will drop to zero. It is a community s User satisfaction, representing community residents' satisfaction with the timeliness of services during disaster emergency response; It is an adjustment coefficient used to control the degree to which response time affects user satisfaction; We set a goal to maximize overall user satisfaction by minimizing the response time of each community. The objective function is: ； in, K For the number of communities, For the community s The weights; At the same time, the following constraints must be met: ； ； ； 。

Images