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

Abstract

The invention discloses a community group resource emergency scheduling method based on pre-disaster prediction and dynamic scheduling of an ice disaster, and belongs to the technical field of disaster situation processing, and the method comprises the following steps: S1, building a unified fault rate calculation model of a power distribution network under the ice disaster; s2, predicting a path and an influence area of an ice disaster based on historical data and meteorological information, and carrying out disaster situation area division; and S3, making a resource scheduling strategy for each divided disaster area. According to the community group resource emergency scheduling method based on ice disaster pre-disaster prediction and dynamic scheduling, through scientific pre-disaster prediction, reasonable resource region division, pre-disaster pre-scheduling and in-disaster flexible scheduling, the disaster resistance and recovery efficiency of a community in an extreme ice disaster situation are effectively improved; a systematized emergency solution is provided for dealing with extreme ice disasters, theoretical basis and practical guidance are provided for elastic configuration of community energy, and the self-maintaining capacity and recovery elasticity of a community under the disaster condition are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster handling, and in particular to a community group resource emergency dispatching method based on pre-ice disaster prediction and dynamic dispatching. Background Art

[0002] At present, how to restore and dispatch power supply and water supply systems under natural disasters such as ice disasters 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 the emphasis on how to quickly dispatch repair teams to repair damaged power transmission lines and water pipelines. Such technologies are usually based on optimized dispatch algorithms for emergency repairs, aiming to reduce the duration of power and water outages for residents through optimal paths and rapid response strategies.

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

[0005] (3) Mobile energy storage and water storage systems: Existing technologies have also proposed solutions to provide temporary power and water supply to communities through mobile energy storage vehicles and mobile water storage vehicles after disasters. This type of technology focuses on vehicle scheduling and route optimization to ensure that vehicles can quickly reach the disaster area and provide the required resources.

[0006] Although existing technologies have made some progress in post-disaster emergency dispatch and resource supply, there are still some significant defects and shortcomings in dealing with disasters such as ice disasters that last for a long time and have a wide range of impacts:

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

[0008] (2) Insufficient consideration of dynamic changes during disasters: The impact of ice disasters is not limited to the aftermath of the disaster. During the disaster, the power grid and water resources may be interrupted at any time. Existing technologies lack flexible scheduling solutions during disasters and cannot fully respond to changes in road conditions and supply conditions during disasters.

[0009] (3) Lack of user satisfaction measurement standards: Existing dispatching schemes rarely consider users’ requirements for the timeliness of resource supply. Especially when the disaster affects a large area, residents wait too long for resource supply, which seriously affects user satisfaction. However, the existing dispatching system does not have a clear mechanism to maximize user satisfaction.

[0010] (4) Path optimization is limited by the changes in disaster impacts: Most existing path optimization technologies only consider the optimal path under fixed conditions, and do not fully consider the real-time impact of extreme weather such as ice disasters on the transportation network. Roads covered with ice and snow are prone to blockage or slowdown, and existing technologies fail to effectively combine changes in dynamic road conditions to adjust the path selection for resource scheduling. Summary of the invention

[0011] The purpose of the present invention is to provide a community group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch. Through scientific pre-disaster prediction and reasonable resource area division, pre-disaster pre-disaster dispatch and flexible dispatch during the disaster, the community's disaster resistance and recovery efficiency in extreme ice disaster scenarios can be effectively improved, and a systematic emergency solution can be provided for dealing with extreme ice disasters. A theoretical basis and practical guidance are provided for the flexible configuration of community energy. The method has good scalability and adaptability, can cope with the challenges of ice disasters of different scales, and significantly improve the community's self-sustaining ability and recovery resilience under disaster conditions.

[0012] To achieve the above object, the present invention provides a community group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch, comprising the following steps:

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

[0014] S2. Predict the path and affected area of ​​the ice disaster based on historical data and meteorological information, and divide the disaster area;

[0015] S3. Develop resource dispatch strategies for each disaster area.

[0016] Preferably, in S1, a conductor failure rate model and a pole 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 pole tower failure rate model.

[0017] Preferably, in the conductor failure rate model, the conductor ice thickness rate ΔG m It increases in direct proportion to the rainfall rate and wind speed, and the formula is as follows:

[0018]

[0019] Where ρ1 represents the density of ice; ρ2 represents the density of liquid water; v m represents the wind speed in area k; Rm represents the rainfall rate in area m; Z m represents the liquid water content in the saturated air of area k,

[0020] The ice load L1 per unit length of line is:

[0021] L1=9.8×10 -3 ρ ice π(D+ΔG m )ΔG m ;

[0022] Where D is the diameter of the cable;

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

[0024]

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

[0026]

[0027] When ice accumulates on the transmission line, the load on the line is the combination of the ice load generated by the weight of the ice in the vertical direction and the wind load in the horizontal direction. Therefore, the combined load L under the combined action of wind load and ice load is total for:

[0028]

[0029] The corresponding tower failure rate P tension for:

[0030]

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

[0032] When the wind load, ice load and unbalanced tension load from the lines on both sides of the tower exceed their upper limits, the tower will collapse. The total load T IW Defined as:

[0033] T IW =T ice +T wind +T f ;

[0034] Ice load T ice The formula is:

[0035] Tice =π(D+G m )ρ ice G m L tower ;

[0036] Among them, D+G m Combining the cable diameter and ice thickness, it reflects the change in effective area; ρ ice Indicates the density of ice; G m Indicates the thickness of the ice layer; L tower is the length of the tower or conductor;

[0037] Wind load T wind The formula is:

[0038]

[0039] Among them, ρ air Indicates the air density; Indicates the square of wind speed; C D is the drag coefficient; D+2G m is the cross-sectional area composed of the diameter of the wire and the thickness of the ice layer;

[0040] T f Indicates the unbalanced force on the tower under extreme ice disasters:

[0041]

[0042] Among them, F1 and F2 are quadratic functions of ice load and wind load; n p is the number of conductors, indicating the number of conductors connected to the tower;

[0043] The corresponding tower failure rate P f for:

[0044]

[0045] Preferably, the unified failure rate calculation model P of the line segment ij is derived by combining the failure rates of the conductors and towers. ij 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 pre-deploy electricity and fresh water resources based on the predicted situation in the disaster area, and finally schedule the mobile energy storage vehicle and mobile water storage vehicle along the optimal route based on the actual disaster situation.

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

[0049]

[0050] Among them, x w represents the total amount of freshwater resources, c w (x w ) represents the total cost of purchasing fresh water resources; x e represents the total amount of power resources, c e (x e ) represents the total cost of purchasing electricity resources; d w and d e Respectively represent the allocation of fresh water and electricity resources, c d (d w ,d e ) represents the allocation cost of fresh water and electricity resources; r w and r e denote the number of deployed fresh water vehicles and energy storage vehicles, c v (r w ,r e ) represents the deployment cost of mobile energy storage vehicles and mobile water storage vehicles.

[0051] Preferably, when the power and fresh water resources are deployed in advance, the objective function of the power resource and water resource deployment location is:

[0052]

[0053] Among them, G i is the objective function of the pre-disaster positioning of the charging station; G w is the objective function of reservoir pre-disaster positioning; N is the set of all fault load nodes; b i is a 0-1 variable. If there is a charging station at load node i, then b i =1, otherwise b i =0, is a 0-1 variable. If the mobile energy storage vehicle can restore the power supply of the node, Otherwise, 0; γ i is the weight coefficient of load node i, reflecting the importance of the node; is the shortest travel time from charging station m to node i; is the shortest travel time from reservoir n to node i.

[0054] The constraints are:

[0055]

[0056]

[0057] Preferably, a spatiotemporal dynamic scheduling model is established to perform optimal route scheduling for mobile energy storage vehicles and mobile water storage vehicles according to the actual disaster situation, specifically: the travel time between node m and node n of the mobile energy storage vehicle and the mobile water storage vehicle and equivalent travel distance L m,n (t) and actual vehicle speed v new (t) is expressed as:

[0058] v new (t) = v0e -1.7α ;

[0059] Where v0 is the ideal vehicle speed under zero traffic flow conditions; α represents the congestion level of the traffic network under disaster background, which is related to the disaster level and traffic flow;

[0060] By introducing the equivalent travel distance L m,n (t) is used to represent the travel distance under disaster conditions:

[0061]

[0062] Where, L m,n,0 is the geographical distance between node m and node n under normal circumstances;

[0063] It is further stated that the travel time under disaster conditions is:

[0064]

[0065] For each possible path r from node m to node n, calculate the travel time of all paths, select the optimal path with the shortest travel time, and finally get the travel time of the optimal path:

[0066]

[0067] The upper-level model of comprehensive satisfaction based on response time is proposed, as follows:

[0068]

[0069]

[0070] in, is the corresponding time of community s, which refers to the time from when the community loses water and electricity to when it obtains resources; T max is the maximum response time allowed by the system. If this time is exceeded, user satisfaction will drop to zero; S i is the user satisfaction of community i, indicating the satisfaction of community residents with the timeliness of services in disaster emergency response; α is the adjustment coefficient, which is used to control the impact of response time on user satisfaction;

[0071] Set a maximum comprehensive user satisfaction, which is achieved by minimizing the response time of each community. The optimization objective function is:

[0072]

[0073] Where n is the number of communities, w i is the weight of community i;

[0074] At the same time, the constraints must be met:

[0075]

[0076] Preferably, the spatial state scheduling constraint of the mobile energy storage vehicle is:

[0077]

[0078]

[0079] The time and space transfer scheduling constraints of the mobile water storage vehicle are:

[0080]

[0081] At the water storage station, mobile water storage vehicles transport water between reservoirs, with the following constraints:

[0082]

[0083] Therefore, the beneficial effects of the community group resource emergency dispatching method based on pre-ice disaster prediction and dynamic dispatching adopted by the present invention are as follows:

[0084] (1) Pre-disaster prediction and pre-dispatch: Based on historical data and meteorological information, the present invention predicts the possible path and impact area of ​​ice disasters, analyzes the impact range of ice disasters in advance, and reasonably allocates the pre-dispatch positions 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 ability to respond quickly to ice disasters.

[0085] (2) Dynamic dispatch and route optimization during and after disasters: This paper considers the impact of ice disasters on the transportation network and proposes a route optimization method based on real-time road conditions to dynamically adjust the dispatch routes of mobile energy storage vehicles and water storage vehicles to ensure that resources can reach the affected communities in a timely manner. Compared with traditional static route selection, this method improves the flexibility and efficiency of emergency dispatch and has significant advantages under conditions of continuous changes in disasters.

[0086] (3) Maximizing user satisfaction: This paper introduces a user satisfaction model, 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 and rationally allocating resources to meet the needs of different communities, the satisfaction of post-disaster users and the fairness of resource supply are significantly improved.

[0087] (4) Cost optimization and resource utilization: In the pre-disaster dispatch process, the present invention reduces resource waste and unnecessary dispatch costs by optimizing the purchase, allocation and dispatch costs of electricity and water resources. This method not only improves resource utilization efficiency, but also reduces the economic cost of overall emergency dispatch and enhances the economy of the system.

[0088] (5) System flexibility and adaptability: The dispatching system designed by the present invention has good scalability and adaptability, and can flexibly adjust resource deployment and dispatching strategies according to ice disasters of different scales. At the same time, the introduction of mobile energy storage and water storage systems provides greater flexibility for community survival during disasters and enhances the system's ability to continue operating under extreme conditions.

[0089] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 It is a step diagram of an embodiment of a community group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch of the present invention;

[0091] Figure 2 It is a network topology diagram of community resource scheduling and defense planning based on ice disaster prediction;

[0092] Figure 3 It is the flow chart of the resource emergency dispatch system under ice disaster;

[0093] Figure 4 This is a schematic diagram of community resource zoning and mobile energy storage vehicle dispatching during ice disasters;

[0094] Figure 5 It is a schematic diagram of vehicle route optimization from the ice disaster emergency resource library to the community node;

[0095] Figure 6 It is a schematic diagram of vehicle transportation mode and timeline in ice disaster emergency dispatch. DETAILED DESCRIPTION

[0096] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0097] The serious impact of extreme ice disasters on infrastructure such as electricity and water supply has attracted increasing attention. In particular, in the face of persistent and widespread extreme weather events, it is crucial to improve the community's disaster resistance and recovery efficiency. To meet such challenges, this paper proposes a community group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch, which can significantly enhance the community's emergency response and recovery capabilities in extreme ice disaster scenarios.

[0098] This method combines key technologies such as pre-disaster prediction and pre-scheduling, dynamic scheduling and path optimization, and user satisfaction models, aiming to ensure that communities can quickly obtain electricity and water resources during disasters, significantly shorten post-disaster response time, and improve the timeliness of resource supply and user satisfaction. Specifically, this method integrates mobile energy storage devices (such as on-board batteries with integrated converters and controllers) to provide communities with rapid power support when disasters occur to stabilize the operation of the distribution system. At the same time, mobile water storage equipment (such as mobile water storage trucks) is used to distribute fresh water resources to meet the basic living needs of community residents. The flexible scheduling of these devices ensures that in disaster scenarios, resources can be delivered to disaster-stricken communities quickly and safely to ensure the basic living security of the community.

[0099] In terms of resource scheduling, this method proposes a scientific pre-disaster prediction and pre-scheduling strategy. By analyzing historical data and meteorological information, the possible path and impact range of the ice disaster are predicted, and mobile energy storage and water storage equipment are deployed in advance in areas with a higher probability of disaster. Pre-disaster deployment includes the reasonable selection of the location of charging stations and reservoirs to minimize the total driving time from high-priority nodes to low-priority nodes, so as to quickly replenish electricity and water sources after the disaster occurs and reduce post-disaster response delays. In addition, the dispatch during the disaster adopts a path optimization method based on real-time road conditions to dynamically adjust the dispatch path of mobile devices to ensure that resources are delivered to the required communities in the shortest time under complex road conditions.

[0100] In order to further improve the quality of life of community residents after disasters, this method introduces a user satisfaction model to maximize the satisfaction of community residents in disasters based on the response time and satisfaction of resource supply. This model significantly reduces the time residents spend waiting for resources by optimizing the response time, and meets the needs of different communities through reasonable resource allocation, thereby improving the efficiency of emergency resource scheduling, enhancing the life experience of residents in disasters, and significantly improving the quality and effectiveness of the overall emergency response.

[0101] In addition, this method is also committed to the economy and sustainability of the system. Through the cost optimization of the scheduling and allocation of pre-disaster resources, unnecessary scheduling costs can be reduced, resource utilization efficiency can be improved, and the operating cost of the overall system can be reduced. Through multi-energy coordination and priority supply strategies, key node needs can be met first during disasters, thereby achieving fairness and effectiveness in the overall resource supply.

[0102] Embodiment 1

[0103] The community group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch in this embodiment specifically includes the following steps:

[0104] S1. Establish a unified failure rate calculation model for distribution networks under freezing disasters.

[0105] Ice disasters not only evolve continuously in the time dimension, but also have significant dynamic characteristics in the space dimension. The main causes of transmission line failures include: (1) ice load, which is the vertical force generated by the accumulation of ice and snow on the transmission line; (2) wind load, which is the horizontal force generated by wind on the transmission line.

[0106] This method establishes a conductor failure rate model and a tower failure rate model respectively, and derives a unified failure rate calculation model by combining the conductor failure rate model and the tower failure rate model.

[0107] During long ice storms, overhead power lines are particularly susceptible to ice accumulation and failure. However, underground heat pipes are usually not directly affected by ice storms, which makes the study of ice failure of aboveground transmission lines particularly important.

[0108] First, a conductor failure rate model was established, and the conductor ice thickening rate ΔG m It increases in direct proportion to the rainfall rate and wind speed, and the formula is as follows:

[0109]

[0110] Where ρ1 represents the density of ice; ρ2 represents the density of liquid water; v m represents the wind speed in area k; R m represents the rainfall rate in area m; Z m represents the liquid water content in the saturated air of area k,

[0111] The ice load L1 per unit length of line is:

[0112] L1=9.8×10 -3 ρ ice π(D+ΔG m )ΔG m

[0113] Where D is the diameter of the cable.

[0114] Wind load per unit length of line L w for:

[0115]

[0116] In the formula, C is a constant coefficient, and its value is 6.964 10 -3 ; S is the span factor, and its calculation formula is as follows:

[0117]

[0118] When ice accumulates on the transmission line, the load on the line is the combination of the ice load generated by the weight of the ice in the vertical direction and the wind load in the horizontal direction. Therefore, the combined load L under the combined action of wind load and ice load is total for:

[0119]

[0120] The corresponding tower failure rate P tension for:

[0121]

[0122] Secondly, a tower failure rate model was established. The loads on the tower include ice load, wind load and unbalanced force.

[0123] Similar to conductor failure, when the wind load, ice load and unbalanced tension load from the lines on both sides of the tower exceed their upper limits, the tower will collapse. The total load T IW Defined as:

[0124] T IW =T ice +T wind +T f

[0125] Ice load T ice The formula is:

[0126] T ice =π(D+G m )ρ ice G m L tower

[0127] Among them, D+G m Combining the cable diameter and ice thickness, it reflects the change in effective area; ρ ice Indicates the density of ice; G m Indicates the thickness of the ice layer; L tower is the length of the tower or conductor.

[0128] Wind load T wind The formula is:

[0129]

[0130] Among them, ρ air Indicates the air density; Indicates the square of wind speed; C D is the drag coefficient; D+2G m It is the cross-sectional area composed of the diameter of the wire and the thickness of the ice layer.

[0131] T f Indicates the unbalanced force on the tower under extreme ice disasters:

[0132]

[0133] Among them, F1 and F2 are quadratic functions of ice load and wind load; n p is the number of conductors, indicating the number of conductors connected to the tower.

[0134] The corresponding tower failure rate P f for:

[0135]

[0136] The unified failure rate calculation model P of line segment ij is derived by combining the failure rates of conductors and towers. ij for:

[0137]

[0138] S2. Predict the path and affected area of ​​the ice disaster based on historical data and meteorological information, and divide the disaster area.

[0139] Under extreme ice disaster conditions, the electricity, water and heating supply of community residents may be severely impacted. In order to ensure the basic living needs of community residents, the community group integrated energy system must have the following characteristics:

[0140] 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;

[0141] B: To reduce the interdependence between communities, all communities are integrated into a large energy sharing system to ensure smooth access between communities;

[0142] C: Preset a reservoir and a power station in each area to ensure that the electricity and fresh water needs of all communities are effectively met through reasonable resource scheduling.

[0143] In order to carry out effective disaster prevention planning before the freezing disaster arrives, the possible path and affected area of ​​the ice disaster are predicted based on historical data and meteorological information, so as to formulate corresponding prevention strategies. Specifically, through the prediction and analysis of the impact of the ice disaster, the transmission lines are divided into dispatchable lines and non-dispatchable lines, and the potential disaster-stricken areas are identified.

[0144] In this embodiment, Figure 2 As shown, according to the prediction results, the ice disaster will mainly affect three areas: Area 1: including transmission lines from node 1 to node 9, and node 19 to node 22; Area 2: covering transmission lines between node 9 and node 18; Area 3: including transmission lines from node 23 to node 33.

[0145] On this basis, corresponding resource scheduling strategies were formulated for each region to ensure efficient allocation of resources before and during disasters and to minimize the negative impact of ice disasters on community power supply networks.

[0146] S3. Develop resource dispatch strategies for each disaster area.

[0147] like Figure 3 As shown in the figure, 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 according to the predicted situation in the disaster area, and finally schedule the mobile energy storage vehicles and mobile water storage vehicles on the optimal route according to the actual disaster situation.

[0148] Referring to the IEEE-33 node system, multiple nodes in the system are assumed to be community nodes, and reasonable resource deployment is carried out before the ice disaster occurs to speed up the response speed of post-disaster dispatch. For power grid managers, the goal is to minimize the cost of fresh water, electricity, mobile energy storage vehicles, and mobile water storage vehicles by optimizing resource allocation.

[0149] The objective function of the cost of purchasing electricity and fresh water resources is:

[0150]

[0151] Among them, x w represents the total amount of freshwater resources, c w (x w ) represents the total cost of purchasing fresh water resources; x e represents the total amount of power resources, c e (x e ) represents the total cost of purchasing power resources; d w and d e Respectively represent the allocation of fresh water and electricity resources, c d (d w ,d e ) represents the allocation cost of fresh water and electricity resources; r w and r e denote the number of deployed fresh water vehicles and energy storage vehicles, c v (r w ,r e) represents the deployment cost of mobile energy storage vehicles and mobile water storage vehicles, including fixed costs such as vehicle purchase, operation and maintenance.

[0152] In order to enhance the community's disaster resilience, this method optimizes the location and quantity of pre-disaster resource deployment to ensure that resource needs can be responded to quickly after a disaster occurs. The goal is to achieve efficient resource allocation and energy management at the lowest economic cost while meeting the community's resource needs by minimizing the comprehensive cost function. Through systematic mathematical modeling and optimization analysis, this method constructs a resource scheduling solution that is both economical and has emergency response capabilities, minimizes the burden and cost of post-disaster scheduling, prioritizes the restoration of key load nodes, and significantly improves the community's overall disaster resilience and recovery efficiency.

[0153] 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 key load nodes as much as possible, the pre-disaster pre-positioning of charging stations and reservoirs requires the reasonable selection of the layout locations of charging stations and reservoirs to minimize the total driving time from high-weight nodes to low-weight nodes.

[0154] The objective function of the deployment location of power resources and water resources is:

[0155]

[0156] Among them, G i is the objective function of the pre-disaster positioning of the charging station; G w is the objective function of reservoir pre-disaster positioning; N is the set of all fault load nodes; b i is a 0-1 variable. If there is a charging station at load node i, then b i =1, otherwise b i =0, is a 0-1 variable. If the mobile energy storage vehicle can restore the power supply of the node, Otherwise, 0; γ i is the weight coefficient of load node i, reflecting the importance of the node; is the shortest travel time from charging station m to node i; is the shortest travel time from reservoir n to node i.

[0157] In order to achieve effective dispatch of mobile energy storage vehicles and mobile water storage vehicles to various regions during ice disasters and meet the power and water resource needs of all regions, the following formula describes its constraints:

[0158]

[0159]

[0160] The first formula represents the overall constraint on the number of resources, where Xsum is the total number of mobile water storage vehicles, Y sum is the total number of energy storage vehicles. The second formula describes how the resource demand of each region should be met, where H a Indicates the water supply capacity of a single water storage truck, F b The third and fourth formulas represent the unique allocation and capacity limit of the vehicle in each time period. The fifth and sixth formulas describe the conditions for meeting the power and water resource needs of each region, where and They represent the shortage of electricity and water resources in region r, respectively, and W is the fixed resource that each vehicle can carry. The seventh formula represents the transportation distance and time constraints, which represents the travel time of each vehicle. The set maximum travel time T must not be exceeded max .

[0161] In the integrated system of power grid and transportation network, the dispatching state of mobile energy storage vehicles is determined by its charging and discharging state and transportation state, which has obvious spatiotemporal coupling characteristics. In order to effectively deal with the complex situation under disaster scenarios, a spatiotemporal dynamic dispatching model is established to comprehensively consider the traffic travel time of mobile energy storage equipment between node m and node n. Traffic status and equipment installation and configuration time By establishing this model, we can better optimize the path selection and scheduling strategy to ensure that resources can be efficiently and timely delivered to the disaster-stricken areas during disasters. We can also effectively optimize the path selection and energy supply timeliness during the transportation process to ensure that resources can be delivered to the disaster-stricken areas quickly and accurately.

[0162] like Figure 4 The figure shows the time-space scheduling scheme of the mobile multi-energy storage system during and after a disaster. Under the influence of the disaster, multiple power lines were damaged to varying degrees by the ice disaster. Mobile energy storage vehicles and mobile water storage vehicles were dynamically scheduled between multiple community nodes to ensure the basic water and electricity needs of community residents. Through flexible scheduling between different nodes, the allocation of resources becomes more balanced and efficient, which significantly enhances the self-sustaining ability of the community energy system in disaster situations and the scientificity and practicality of the resilience recovery scheme. This method introduces the following optimization strategies:

[0163] Path planning and dynamic scheduling optimization: As ice disasters may cause road closures or restricted access, the path selection of mobile energy storage equipment needs to be adjusted in real time according to changes in traffic conditions to ensure that the optimal path can be selected under complex road conditions and resources can be delivered to the target community at the fastest speed. By considering the congestion level of the traffic network, the travel time and efficiency of vehicles are optimized.

[0164] Multi-energy coordination and priority supply: During disasters, the demand for electricity, heat and water resources in different communities may vary significantly. Therefore, this method introduces a multi-energy coordination strategy to ensure that the needs of key nodes are prioritized during the scheduling process, especially for key facilities such as hospitals and firefighting, to improve the fairness and effectiveness of the overall supply.

[0165] Since the traffic network is easily affected by disasters, emergencies may cause differences in the actual travel time of mobile energy storage vehicles on the same road under different interaction periods and environmental influences during disasters. In order to ensure the reliability of emergency mobile energy storage equipment under disaster background, this method considers the impact of disaster conditions such as congestion and snow accumulation on traffic conditions, and reflects the spatial relationship between the actual speed of mobile energy storage and the equivalent traffic journey through traffic congestion parameters.

[0166] The travel speed of mobile energy storage vehicles and mobile water storage vehicles will be significantly affected under disaster conditions, so it is necessary to estimate their speed based on the traffic network congestion degree α and actual traffic conditions under the disaster scenario. and equivalent travel distance L m,n (t) and actual vehicle speed v new The relationship between (t) can be expressed as:

[0167] v new (t) = v0e -1.7α

[0168] Where v0 is the ideal vehicle speed under zero traffic flow conditions; α represents the degree of congestion of the traffic network under disaster conditions, which is related to the degree of disaster and traffic flow. In applications, this parameter can be estimated by evaluating the disaster status of the traffic network. In disaster scenarios, the actual distance is not only affected by the geographical distance, but also by the deterioration of road conditions. By introducing the equivalent travel distance L m,n (t) is used to represent the travel distance under disaster conditions:

[0169]

[0170] Where, L m,n,0 is the geographical distance between node m and node n under normal circumstances. Considering the impact of disasters on road conditions, the travel time under disaster conditions can be further expressed as:

[0171]

[0172] For each possible path r from node m to node n, calculate the travel time of all paths, select the optimal path with the shortest travel time, and finally get the travel time of the optimal path:

[0173]

[0174] Therefore, the traffic network between community j and community k can be simplified into an optimal moving route, such as Figure 5 shown.

[0175] Under extreme conditions, the user satisfaction model is mainly determined by the timeliness of energy and fresh water supply and the satisfaction of energy and fresh water supply. In order to explore the impact of response time on user satisfaction, a comprehensive satisfaction upper-level model based on response time is proposed, as follows:

[0176]

[0177] in, is the corresponding time of community s, which refers to the time from when the community loses water and electricity to when it obtains resources; T max is the maximum response time allowed by the system. If this time is exceeded, user satisfaction will drop to zero; S i is the user satisfaction of community i, which indicates the satisfaction of community residents with the timeliness of services in disaster emergency response; α is the adjustment coefficient, which is used to control the impact of response time on user satisfaction.

[0178] In order to optimize the user satisfaction of all communities, a maximum comprehensive user satisfaction is set, which is achieved by minimizing the response time of each community. The optimization objective function is:

[0179]

[0180] Where n is the number of communities, w i is the weight of community i.

[0181] At the same time, the constraints must be met:

[0182]

[0183] The first formula represents the response time of each mobile energy storage vehicle c: Not exceeding the maximum allowed time T max The second formula indicates that the response time of a high priority vehicle should be less than or equal to the response time of a low priority vehicle (P c1 >P c2 ) to ensure that the priority order is followed when allocating resources. The third formula ensures that the battery state of each mobile energy storage vehicle c at time t is maintained at the minimum allowable value and the 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 P charge and power used P use,This constraint ensures that the vehicle can effectively manage the battery state to complete the mission.

[0184] After the mobile energy storage vehicle receives the dispatch instruction, if it needs to move from node m to node n for charging or discharging, it must first select a path based on the congestion level of the traffic network under the disaster background to achieve the shortest travel time. From node m to node n. Before the mobile energy storage vehicle reaches node n, it needs to move from node m to node n in an effective connection state. The following are the spatial state scheduling constraints of the mobile energy storage vehicle:

[0185]

[0186] Among them, the first formula ensures that the mobile energy storage vehicle c can only be in one node m or n at any time t, ensuring its position uniqueness; the second formula indicates 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 requirements are met during the switching process; the third formula ensures that the mobile energy storage vehicle c will not appear in multiple nodes at the same time during the transmission time.

[0187] During the charging and discharging scheduling between nodes, the mobile energy storage vehicle c needs to adjust the output power according to its power state to support the power supply system. At the same time, the following constraints must be met:

[0188]

[0189] Among them, the first formula limits the charging and discharging power of the mobile energy storage vehicle c, ensuring that its charging and discharging power at any time does not exceed the maximum power The second formula stipulates that the mobile energy storage vehicle c can only perform charging or discharging operations at any time; the third formula describes the relationship between the charge state of the mobile energy storage vehicle c and its charging and discharging power, ensuring that the battery state at time t is reasonably adjusted according to the previous moment and the power input and output; the fourth formula indicates that the charge state of the mobile energy storage vehicle c must be maintained between the minimum and maximum values ​​allowed to ensure that the battery is not over-charged and over-discharged.

[0190] Compared with the charging and discharging of the mobile energy storage vehicle, the loading and unloading of water may be faster. Without loss of generality, it is assumed that the sum of the loading and unloading time of water is 1h.

[0191] like Figure 6As shown in the figure, the typical dispatching process of a mobile water storage vehicle in a disaster emergency is shown. The figure shows the state changes of the mobile water storage vehicle during water filling, transportation and unloading through different time steps (such as t, t+1, t+2, ..., t+h, t+h+1, t+h+2). The different colors and rectangular blocks in the figure represent different tasks performed by the mobile water storage vehicle. The priority and dispatching mode of the task are reflected by the depth of color to reasonably meet the demand for water resources supply in emergency situations.

[0192] Complete transport mode (such as Figure 6 The dark part) shows the process of continuous transportation of mobile water storage vehicles over a long period of time, which is mainly used to quickly respond to the water resource needs of the target area. The segmented transportation and operation mode shows that the mobile water storage vehicle needs to stop multiple times to fill or unload water during transportation to increase flexibility and the ability to respond to multi-site needs.

[0193] In order to better reflect the dispatching process of mobile energy storage vehicle c in disaster emergency, this method describes the transfer restrictions and different operating states in time and space dimensions to help optimize resource allocation and task response. The specific time and space transfer dispatching constraints of mobile water storage vehicles are:

[0194]

[0195] Among them, 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 order of task execution is reasonable, ensuring that loading, transportation and unloading are carried out in the correct order; the third formula represents the priority of completing high-priority tasks in the scheduling strategy. For task sets i and j in the same time period t, if the priority of task i is higher than that of task j, the system should give priority to task i; the fourth formula limits the total time of the loading and unloading tasks of the water storage vehicle to no more than the threshold h, to ensure that the task scheduling time is effectively controlled; the fifth formula ensures that the transportation time is based on the actual distance D m,n Dynamically adjust to the path conditions.

[0196] At the reservoir, mobile water storage vehicles can transport water between nodes. The constraints are as follows:

[0197]

[0198] Among them, the first formula indicates that the water resource filling and discharging power of the mobile water storage vehicle is subject to physical limitations 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 amount of water resources adapts to actual demand; the third formula limits the water capacity of the energy storage vehicle to within its minimum and maximum limits; the fourth formula indicates that the mobile water storage vehicle can only follow one route in each time period, or can only stop and fill or discharge water in a certain community.

[0199] The specific process of solving the problem by applying the community group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch proposed in this embodiment is as follows:

[0200] (1) Calculation of the unified failure rate model and generation of parameters: Based on meteorological data and historical disaster data, the path of the ice disaster and its possible impact on community nodes are predicted. The parameters include the electricity and water resource requirements of each community.

[0201] (2) Pre-disaster resource pre-scheduling optimization: The IEEE33 node system is divided using the community partitioning algorithm Fast Newman algorithm (the optimization goal is to minimize the pre-scheduling cost) to perform pre-disaster resource optimization to ensure the rational allocation of resource vehicles, reservoirs, and charging stations.

[0202] (3) Path optimization and vehicle dispatch time calculation: Based on real-time traffic information, the vehicle's driving path is dynamically adjusted to avoid high-risk areas and select the optimal path. The vehicle dispatch time is then calculated based on the vehicle's movement time and resource loading and unloading time.

[0203] (4) User satisfaction evaluation: The user satisfaction of each community after the disaster is calculated based on the vehicle dispatch time. The vehicle dispatch time is minimized in step (3) to maximize user satisfaction, so that residents’ resources can be replenished as soon as possible.

[0204] Therefore, the present invention adopts the above-mentioned community group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch. Through scientific pre-disaster prediction and reasonable resource area division, pre-disaster pre-disaster dispatch and flexible dispatch during the disaster, it effectively improves the community's disaster resistance and recovery efficiency in extreme ice disaster scenarios. It not only provides a systematic emergency solution for dealing with extreme ice disasters, but also provides a theoretical basis and practical guidance for the flexible configuration of community energy. It has good scalability and adaptability, can cope with the challenges of ice disasters of different scales, and significantly improves the community's self-sustaining ability and recovery resilience under disaster conditions.

[0205] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A community resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch, characterized in that: The following steps are involved: S1. Establish a unified failure rate calculation model for distribution network under freezing disasters; S2. Predict the path and affected area of ​​the ice disaster based on historical data and meteorological information, and divide the disaster area; S3. Develop resource dispatch strategies for each disaster area.

2. The community group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch according to claim 1 is characterized by: 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 group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch according to claim 2 is characterized by: In the conductor failure rate model, the conductor ice thickening rate ΔG m It increases in direct proportion to the rainfall rate and wind speed, and the formula is as follows: Where ρ1 represents the density of ice; ρ2 represents the density of liquid water; v m represents the wind speed in area k; R m represents the rainfall rate in area m; Z m represents the liquid water content in the saturated air of area k, The ice load L1 per unit length of line is: L1=9.8×10 -3 r ice π(D+ΔG m )ΔG m ; Where D is the diameter of the cable; Wind load per unit length of line L w for: In the formula, C is a constant coefficient, and its value is 6.964×10 -3 ; S is the span factor, and its calculation formula is as follows: When ice accumulates on the transmission line, the load on the line is the combination of the ice load generated by the weight of the ice in the vertical direction and the wind load in the horizontal direction. Therefore, the combined load L under the combined action of wind load and ice load is total for: The corresponding tower failure rate P tension for:

4. The community group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch according to claim 2 is characterized by: In the tower failure rate model, the loads on the tower include ice load, wind load and unbalanced force; When the wind load, ice load and unbalanced tension load from the lines on both sides of the tower exceed their upper limits, the tower will collapse. The total load T IW Defined as: T IW =T ice +T wind +T f ; Ice load T ice The formula is: T ice =π(D+G m )r ice G m L tower ; Among them, D+G m Combining the cable diameter and ice thickness, it reflects the change in effective area; ρ ice Indicates the density of ice; G m Indicates the thickness of the ice layer; L tower is the length of the tower or conductor; Wind load T wind The formula is: Among them, ρ air Indicates the air density; Indicates the square of wind speed; C D is the drag coefficient; D+2G m is the cross-sectional area composed of the diameter of the wire and the thickness of the ice layer; T f Indicates the unbalanced force on the tower under extreme ice disasters: Among them, F1 and F2 are quadratic functions of ice load and wind load; n p is the number of conductors, indicating the number of conductors connected to the tower; The corresponding tower failure rate P f for:

5. The community group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch according to claim 2 is characterized by: The unified failure rate calculation model P of line segment ij is derived by combining the failure rates of conductors and towers. ij for:

6. The community group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch according to claim 1 is characterized by: 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 in the disaster area, and finally schedule the mobile energy storage vehicles and mobile water storage vehicles on the optimal route based on the actual disaster situation.

7. The community group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch according to claim 6 is characterized in that: The objective function of the cost of purchasing electricity and fresh water resources is: Among them, x w represents the total amount of freshwater resources, c w (x w ) represents the total cost of purchasing fresh water resources; x e represents the total amount of power resources, c e (x e ) represents the total cost of purchasing power resources; d w and d e Respectively represent the allocation of fresh water and electricity resources, c d (d w ,d e ) represents the allocation cost of fresh water and electricity resources; r w and r e denote the number of deployed fresh water vehicles and energy storage vehicles, c v (r w ,r e ) represents the deployment cost of mobile energy storage vehicles and mobile water storage vehicles.

8. The community group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch according to claim 6 is characterized by: When electricity and fresh water resources are deployed in advance, the objective function of the deployment location of electricity resources and water resources is: Among them, G i is the objective function of the pre-disaster positioning of the charging station; G w is the objective function of reservoir pre-disaster positioning; N is the set of all fault load nodes; b i is a 0-1 variable. If there is a charging station at load node i, then b i =1, otherwise b i =0, is a 0-1 variable. If the mobile energy storage vehicle can restore the power supply of the node, Otherwise, 0; γ i is the weight coefficient of load node i, reflecting the importance of the node; is the shortest travel time from charging station m to node i; is the shortest travel time from reservoir n to node i. The constraints are:

9. The community group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch according to claim 6 is characterized by: A spatiotemporal dynamic scheduling model is established to schedule the mobile energy storage vehicle and mobile water storage vehicle on the optimal route according to the actual disaster situation. Specifically, the travel time between node m and node n of the mobile energy storage vehicle and mobile water storage vehicle is: and equivalent travel distance L m,n (t) and actual vehicle speed v new (t) is expressed as: v new (t)=v0e -1.7α ; Where v0 is the ideal vehicle speed under zero traffic flow conditions; α represents the congestion level of the traffic network under disaster background, which is related to the disaster level and traffic flow; By introducing the equivalent travel distance L m,n (t) is used to represent the travel distance under disaster conditions: Where, L m,n,0 is the geographical distance between node m and node n under normal circumstances; It is further stated that the travel time under disaster conditions is: For each path r from node m to node n, calculate the travel time of all paths r, select the optimal path with the shortest travel time, and finally get the travel time of the optimal path: The upper-level model of comprehensive satisfaction based on response time is proposed, as follows: in, is the corresponding time of community s, which refers to the time from when the community loses water and electricity to when it obtains resources; T max is the maximum response time allowed by the system. If this time is exceeded, user satisfaction will drop to zero; S i is the user satisfaction of community i, indicating the satisfaction of community residents with the timeliness of services in disaster emergency response; α is the adjustment coefficient, which is used to control the impact of response time on user satisfaction; Set a maximum comprehensive user satisfaction, which is achieved by minimizing the response time of each community. The optimization objective function is: Where n is the number of communities, w i is the weight of community i; At the same time, the constraints must be met:

10. The community group resource emergency dispatch method based on pre-ice disaster prediction and dynamic dispatch according to claim 9 is characterized by: The spatial state scheduling constraints of the mobile energy storage vehicle are: The time-space transfer scheduling constraints of the mobile water storage vehicle are: At the water storage station, mobile water storage vehicles transport water between reservoirs, with the following constraints:

Images