A multi-mobile resource power distribution network load restoration method based on traffic network simplification

Abstract

This invention discloses a multi-mobile resource distribution network load restoration method based on a simplified transportation network. First, multi-source data is collected, including input distribution network line fault conditions, transportation network topology, and mobile resource configuration parameters. Then, the original transportation network is simplified according to the functional characteristics of line repair teams, mobile energy storage, and V2G, resulting in simplified transportation networks for each resource. Finally, based on the simplified transportation networks for each resource, a multi-mobile resource collaborative scheduling model is constructed with the objective of maximizing the weighted load restoration of the distribution network. Constraints are introduced for the collaborative optimization scheduling of line repair teams, mobile energy storage, and V2G to achieve distribution network load restoration. Load loss is reduced through the collaborative complementarity among multiple mobile resources.

Description

Technical Field

[0001] This invention relates to the field of post-disaster power distribution network load restoration technology, specifically a multi-mobility resource power distribution network load restoration method based on a simplified transportation network. Background Technology

[0002] Extreme weather events are one of the main causes of power system damage. In recent years, climate change and global warming have triggered numerous natural disasters, severely damaging power distribution network infrastructure and often leading to widespread power outages. Such power outages not only cause enormous property losses but can also endanger lives. Therefore, as a crucial component of urban energy systems, power distribution networks must strengthen their post-disaster load recovery capabilities and improve system resilience and reliability to ensure energy security and minimize the significant economic and social losses caused by power outages.

[0003] In recent years, with the large-scale development of electric vehicles, vehicle-to-grid (V2G) technology has received widespread attention in the field of power distribution network load restoration. As a mobile power source, electric vehicles can provide flexible power support during power distribution network load restoration. At the same time, the number of electric vehicles in use is increasing year by year, and the pace of adoption is accelerating, laying a solid foundation for the application of V2G technology. Against this backdrop, V2G technology continues to develop, enabling electric vehicles to provide emergency power support to power distribution networks after disasters.

[0004] Transportation networks are a crucial foundation for scheduling mobile resources. Many methods remain limited to distribution network reconfiguration and scheduling, failing to fully utilize key factors such as network connectivity and shortest paths to guide mobile resource scheduling. They also lack targeted simplification of the transportation network based on the functional characteristics of different mobile resources, unnecessarily increasing path planning complexity and thus limiting scheduling efficiency. Secondly, existing distribution network load restoration methods mostly focus on single mobile resources, and the coordinated scheduling of multiple mobile resources, including line repair teams, mobile energy storage, and V2G, has not yet been fully integrated within a unified restoration framework.

[0005] In view of this, a multi-mobile resource distribution network load restoration method based on a simplified transportation network is proposed. A simplified transportation network model is constructed according to the functional characteristics of different mobile resources, which improves the scheduling efficiency of mobile resources while reducing the scale and computational complexity of the transportation network. Based on this, the line repair teams, mobile energy storage, and V2G in the transportation network are coordinated and optimized for scheduling to minimize distribution network load loss, thereby improving the post-disaster power supply restoration level and system resilience of the distribution network. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and propose a multi-mobile resource distribution network load restoration method based on a simplified transportation network. Based on the coordinated scheduling of line repair teams, mobile energy storage, and V2G, the repair teams play a decisive role in the load restoration process by repairing the damaged lines, while mobile energy storage and V2G provide auxiliary power support to the power outage area before the damaged lines are repaired. The synergy and complementarity among multiple mobile resources reduce load loss.

[0007] To achieve the above objectives, the technical solution specifically adopted by the present invention is as follows:

[0008] A method for load restoration of multi-mobility resource distribution networks based on simplified transportation networks includes the following steps:

[0009] Step 1: Collect multi-source data. The multi-source data includes the input of power distribution network line fault status, traffic network topology and mobile resource configuration parameters. The mobile resource configuration parameters include the moving speed and repair time of the line repair team, the moving speed, charging and discharging power and charge of mobile energy storage, and the moving speed and charge of electric vehicles in V2G grid connection.

[0010] Step 2: Simplify the original traffic network according to the functional characteristics of the line repair team, mobile energy storage, and V2G respectively, and obtain the simplified traffic network corresponding to each resource;

[0011] Step 3: Based on the simplified transportation network corresponding to each resource, construct a multi-mobile resource collaborative scheduling model with the goal of maximizing the weighted load recovery of the distribution network, and introduce constraints for the collaborative optimization scheduling of line repair teams, mobile energy storage and V2G to realize the load recovery of the distribution network.

[0012] Preferably, the power distribution line fault information includes the location and number of power distribution line faults, and the traffic network topology includes the connection relationships between traffic network nodes and the actual road length.

[0013] As a preferred option, the specific process of simplifying the original traffic network corresponding to the line repair team in step 2 is as follows: take the road nodes corresponding to the nodes at both ends of each damaged line as the target driving nodes of the repair team, use the shortest path algorithm to calculate the shortest path between the repair team's station node and each target driving node, and between each target driving node, delete the road nodes and paths that do not belong to any shortest path to obtain the preliminary simplified traffic network, then retain only the repair team's station node and target driving nodes in the preliminary simplified traffic network, delete the remaining road nodes, and obtain the simplified traffic network corresponding to the line repair team.

[0014] As a preferred option, the specific process of simplifying the original traffic network corresponding to mobile energy storage in step 2 is as follows: the shortest path algorithm is used to calculate the shortest path between the mobile energy storage parking node and each charging and discharging station node, and between each charging and discharging station node. Road nodes and paths that do not belong to any shortest path are deleted to obtain a preliminary simplified traffic network. Then, only the mobile energy storage parking node and each charging and discharging station node in the preliminary simplified traffic network are retained, and the remaining road nodes are deleted to obtain the simplified traffic network corresponding to mobile energy storage.

[0015] As a preferred option, the specific process of simplifying the original traffic network corresponding to V2G in step 2 is as follows: taking the road nodes corresponding to the V2G station as the center, the traffic network area where the travel time of the electric vehicle from the initial position to the V2G station does not exceed the preset maximum travel time is divided into the aggregation area of ​​the V2G station. Only the roads and nodes in the aggregation area are retained, and the remaining road nodes and paths are deleted to obtain the simplified traffic network corresponding to V2G.

[0016] As a preferred option, the objective of maximizing the weighted load recovery of the distribution network in step 3 is specifically to divide the distribution network load into first-level, second-level, and third-level loads and assign corresponding weights, with the first-level load having the highest weight. The sum of the products of the restored active load of the unrestored load nodes at each time point and the corresponding weight is calculated, with the goal of maximizing the total value of the sum of these products.

[0017] Preferably, the constraints include distribution network operation constraints, line repair team dispatch constraints, damaged line repair constraints, mobile energy storage dispatch and operation constraints, and V2G dispatch constraints.

[0018] Preferably, the power distribution network operation constraints include power balance constraints, line capacity constraints, and voltage safety constraints. The power balance constraint ensures that the active and reactive power injection and output of each node in the power distribution network are balanced. The line capacity constraint ensures that the apparent power of each line in the power distribution network does not exceed its capacity limit. The voltage safety constraint ensures that the square of the voltage amplitude of each node in the power distribution network is within a preset upper and lower limit range.

[0019] Preferably, the scheduling constraints for the line repair team are as follows: the line repair team can only be in one of two states at any given time, namely, stationed repair or traveling; the repair team can only depart from its base node; when stationed for repair, it can only be located at the endpoint node corresponding to the damaged line; when the repair team moves between different damaged lines, it needs to consume the travel time of the corresponding path, and the team is in a traveling state during the transfer process; the maintenance constraints for the damaged line are as follows: the damaged line must be continuously repaired within the corresponding preset repair time; the line remains in a fault state until the repair is completed; only after the repair team completes the preset time of continuous repair will the damaged line switch to normal operation.

[0020] Preferably, the mobile energy storage scheduling constraints are as follows: mobile energy storage can only be in one of two states at any given time, either stationary or moving. When stationary, it can only be located at its parking node or charging / discharging station node. When moving, it can only be transferred between charging / discharging station nodes, and the transfer process requires the corresponding path travel time. The number of mobile energy storage connected to the same charging / discharging station node at any given time does not exceed the preset maximum allowable number. When mobile energy storage travels from a charging / discharging station node to a target node, it requires the corresponding path travel time. The mobile energy storage operation constraints include: the charging and discharging states of mobile energy storage are mutually exclusive; the active power of charging and discharging does not exceed its respective maximum limit; the state of charge is always between the preset maximum and minimum state of charge; and the state of charge changes dynamically with the charging and discharging power and charging / discharging efficiency.

[0021] Preferably, the V2G scheduling constraints include: the initial state of charge of the electric vehicles participating in V2G aggregation conforms to the characteristics of a normal distribution; the state of charge of the electric vehicles changes dynamically with the discharge power and discharge efficiency; and the power supply of the V2G station does not exceed the maximum power supply of the station and is related to the proportion of the number of aggregated electric vehicles.

[0022] This invention has the following characteristics and beneficial effects:

[0023] This invention addresses the aforementioned technical problems through a technical solution of **"differentiated simplification of transportation networks + unified collaborative scheduling of multiple mobile resources + comprehensive constraint control + weighted load priority recovery"**, achieving significant improvements in scheduling efficiency, recovery level, operational safety, and emergency adaptability of distribution network post-disaster load recovery. Specific beneficial effects are as follows:

[0024] Significantly reduce scheduling computational complexity and improve mobile resource scheduling efficiency.

[0025] Based on the functional characteristics and scheduling requirements of line repair teams, mobile energy storage, and V2G, the original transportation network is simplified in a differentiated manner. The core nodes and paths required for scheduling of each resource are retained, and all redundant transportation elements are eliminated. This reduces the computational load of path planning and scheduling decisions from the source, significantly improves the scheduling computation efficiency and decision-making speed of multiple mobile resources, and perfectly meets the emergency timeliness requirements of the distribution network after disaster load restoration.

[0026] Build a unified and collaborative scheduling framework to fully leverage the complementary advantages of multiple mobile resources.

[0027] For the first time, line repair teams, mobile energy storage, and V2G are integrated into a unified distribution network load restoration optimization framework to achieve coordinated and optimized scheduling among the three: with the line repair by the repair teams to achieve permanent load restoration as the core, mobile energy storage and V2G provide medium- and short-term power support and local temporary power supply respectively during the repair period. The three work together to form a load restoration system of "temporary power support + permanent line repair", effectively tapping the complementary value of multiple mobile resources, significantly improving the overall load restoration efficiency of the distribution network, and significantly reducing post-disaster load loss.

[0028] Establish a comprehensive constraint and control system to ensure the safe operation and scientific scheduling of the distribution network and mobile resources.

[0029] A comprehensive constraint system is constructed, encompassing constraints on distribution network operation, line repair team scheduling and maintenance, mobile energy storage scheduling and operation, and V2G scheduling. This system not only strictly adheres to the operational rules of distribution network power balance, line capacity, and voltage safety to ensure safe and stable operation during distribution network restoration, but also aligns with the physical characteristics and actual operational requirements of each mobile resource to ensure the feasibility and scientific validity of the scheduling plan, avoiding secondary faults or resource scheduling failures caused by improper scheduling.

[0030] Achieve weighted load priority restoration to accurately guarantee the power supply needs of critical loads.

[0031] The system adopts a tiered approach of "Level 1, Level 2, and Level 3 loads" with differentiated weights. With the objective function of maximizing the weighted load recovery, it mandates the priority restoration of Level 1 loads, such as those essential for people's livelihoods and important public facilities. This approach aligns with the actual engineering needs of emergency recovery of the distribution network after a disaster, enhances the practicality and emergency support capabilities of the distribution network load restoration, and ensures the essential power demand under extreme fault scenarios.

[0032] Enhance the resilience and disaster resistance of the power distribution network system and adapt to fault recovery in multiple scenarios.

[0033] The technical solution of this invention is applicable to various distribution network line fault scenarios, including small-scale and large-scale ones. It can quickly solve scheduling schemes on simulation platforms such as Matlab, and the simplified traffic network and collaborative scheduling model have good versatility and scalability, adapting to the post-disaster load restoration needs of distribution networks of different regions and scales. Through an efficient load restoration process, it significantly shortens the power outage time of distribution network faults, enhances the disaster resistance and system resilience of the distribution network in the face of sudden faults such as extreme weather, and reduces the losses caused by power outages to the economy and society.

[0034] The technical solution is highly practical and easy to implement and promote.

[0035] The multi-source data acquisition, traffic network simplification, and collaborative scheduling model construction of this invention are all based on parameters that are actually available in engineering and mature algorithms (shortest path algorithm). No special hardware equipment is required; it can be achieved simply through software algorithm optimization. The technical solution has low implementation cost and low operation difficulty, and is easy to implement and promote in the actual operation and maintenance of power systems, with broad engineering application prospects. Attached Figure Description

[0036] Figure 1 The flowchart illustrates a method for restoring multi-mobile resource distribution network loads based on a simplified transportation network, as proposed in this embodiment of the invention.

[0037] Figure 2 This is a simplified process diagram of the traffic network based on the line repair team in this embodiment.

[0038] Figure 3 This is a simplified process diagram of the transportation network based on mobile energy storage in this embodiment.

[0039] Figure 4 This is a simplified process diagram of the V2G-based traffic network in this embodiment.

[0040] Figure 5 This is a diagram showing the mobile energy storage scheduling results in this embodiment.

[0041] Figure 6 This is a diagram showing the V2G scheduling results in this embodiment.

[0042] Figure 7 This is a diagram showing the weighted load recovery curve of the distribution network in this embodiment. Detailed Implementation

[0043] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0044] A load restoration method for multi-mobility resource distribution networks based on simplified transportation networks, such as... Figure 1 As shown, it includes the following steps:

[0045] Step 1: Collect multi-source data. The multi-source data includes input distribution network line fault information, traffic network topology, and mobile resource configuration parameters. The distribution network line fault information includes the location and number of distribution network line faults. The traffic network topology includes the connection relationships between traffic network nodes and the actual road length. The mobile resource configuration parameters include the moving speed and repair time of the line repair team, the moving speed, charging and discharging power, and charge of mobile energy storage, as well as the moving speed and charge of electric vehicles in V2G grid connection.

[0046] Understandably, under the influence of extreme weather, faults in distribution network lines may cause some nodes to lose connection with substations, creating power outage areas. In order to repair damaged lines and restore load in power outage areas, it is necessary to input the location information of the damaged distribution network lines.

[0047] Furthermore, regarding the traffic network topology, in this embodiment, the traffic network is represented as a graph. , which indicates Node set, Let represent the set of edges. The connectivity between nodes and the corresponding actual road lengths are described by the adjacency matrix, as shown in the following equation.

[0048]

[0049] In the formula, Represents the elements in the adjacency matrix. and Represents a node With nodes The actual road distance between them.

[0050] Step 2: Simplify the original transportation network according to the functional characteristics of the line repair team, mobile energy storage, and V2G to obtain the simplified transportation network corresponding to each resource.

[0051] In this embodiment, the original traffic network is simplified based on the functional characteristics of different mobile resources. For the simplified traffic network of the line repair team, the shortest path algorithm is used to calculate the shortest paths between the repair team's base node and each target travel node, as well as between each target travel node, and other paths are deleted. Based on this, only the repair team's base node and target travel nodes are retained, and other road nodes are deleted. For the simplified traffic network of mobile energy storage, the shortest path algorithm is used to calculate the shortest paths between the parking node and each charging / discharging station node, as well as between charging / discharging station nodes, and other paths are deleted. Only the mobile energy storage parking node and charging / discharging station node are retained, and other road nodes are deleted. For the simplified traffic network of V2G, V2G aggregation areas are divided with the road nodes corresponding to V2G stations as the center; only the roads and nodes within the aggregation area are retained, and other road nodes and paths are deleted.

[0052] Traffic network simplification includes traffic network simplification based on line repair teams, traffic network simplification based on mobile energy storage, and traffic network simplification based on V2G. Its specific content is as follows:

[0053] like Figure 2As shown, the traffic network simplification based on the line repair team involves two steps: Line repair team scheduling primarily focuses on the repair of damaged basic lines, while the repair of connecting switch lines is not included in the path planning decision. First, the road nodes corresponding to the nodes at both ends of each damaged line are considered as the target travel nodes for the repair team. Then, a shortest path algorithm is used to calculate candidate shortest paths between the line repair team's base node and each target travel node, as well as between each target travel node, and road nodes and paths not belonging to any candidate shortest path are deleted, thus obtaining a preliminary simplified traffic network. Second, based on this preliminary simplified traffic network, the line repair team's base node and target travel nodes are retained, while the remaining road nodes are deleted, finally obtaining the simplified traffic network based on the line repair team.

[0054] like Figure 3 As shown, the traffic network simplification based on mobile energy storage is as follows: First, the shortest path algorithm is used to calculate the shortest paths between the mobile energy storage parking nodes and each charging / discharging station node, as well as between each charging / discharging station node. Road nodes and paths that do not belong to any shortest path are deleted, resulting in a preliminary simplified traffic network. Second, based on this preliminary simplified traffic network, the mobile energy storage parking nodes and each charging / discharging station node are retained, while the remaining road nodes are deleted, finally obtaining the simplified traffic network based on mobile energy storage.

[0055] like Figure 4 As shown, the V2G-based transportation network is simplified: unlike mobile energy storage systems, the battery capacity of a single electric vehicle is relatively small. Therefore, V2G is considered a local, temporary power support resource and does not participate in cross-regional dispatch. When the faulty area is reconnected to the main grid, the V2G resources in that area cease discharging service. In this invention, a corresponding aggregation area is defined for each V2G station. When the travel time of an electric vehicle to a certain V2G station meets the time threshold condition given by the following formula, the electric vehicle is considered to belong to the aggregation area of ​​that station and will travel to that station to provide power support.

[0056]

[0057] In the formula, This indicates the maximum allowed electric vehicle driving time within the V2G aggregation area; This indicates the travel time required for an electric vehicle to travel from its initial location to a V2G station.

[0058] Centered on the road nodes corresponding to V2G stations, traffic network areas that meet the aggregation conditions constitute a V2G aggregation region. Based on this, only this aggregation region is retained, while the remaining road nodes and paths are deleted, thus obtaining a simplified V2G-based traffic network.

[0059] Step 3: Based on the simplified transportation network corresponding to each resource, construct a multi-mobile resource collaborative scheduling model with the goal of maximizing the weighted load recovery of the distribution network. Introduce constraints for the collaborative optimization scheduling of line repair teams, mobile energy storage, and V2G to achieve distribution network load recovery. The constraints include distribution network operation constraints, line repair team scheduling constraints, damaged line maintenance constraints, mobile energy storage scheduling and operation constraints, and V2G scheduling constraints.

[0060] The objective of the multi-mobile resource collaborative scheduling model is to maximize the weighted load recovery of the distribution network. Specifically, the distribution network load is divided into primary, secondary, and tertiary loads and assigned corresponding weights, with primary loads having the highest weight. The sum of the products of the restored active power of the non-restored load nodes and their corresponding weights is calculated at each time point, with the goal of maximizing the total value of this sum of products. The formula is as follows:

[0061]

[0062] In the formula, Represents a node The weights are used to reflect the load restoration priority; the loads are divided into first-level, second-level, and third-level loads, with first-level loads having the highest restoration priority; Indicates at time node The active power load has been restored; This represents the total number of time periods; Indicates a single time step; This represents the set of nodes that have not yet recovered their load.

[0063] Furthermore, the operating constraints of the distribution network include power balance constraints, line capacity constraints, and voltage safety constraints. The power balance constraint ensures that the active and reactive power injection and output at each node of the distribution network remain balanced, as expressed below:

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] In the formula, and Representing time respectively line The active and reactive power flow on the surface; and Representing time respectively From substation to node Injected active and reactive power; and These represent the active power of charging and discharging mobile energy storage, respectively. This indicates the power supply capacity of the V2G site; and These represent the upper limits of active and reactive power injection, respectively; Represents a node Load power at the location.

[0070] The line capacity constraint is that the apparent power of each line in the distribution network does not exceed its upper capacity limit, as expressed below:

[0071]

[0072]

[0073]

[0074]

[0075] In the formula, Indicates the line The upper limit of apparent power capacity.

[0076] The voltage safety constraint requires that the square of the voltage amplitude at each node of the distribution network be within a preset upper and lower limit range, as expressed below:

[0077]

[0078]

[0079]

[0080] In the formula, Represents a node The square of the voltage amplitude; and Representing nodes respectively The upper and lower limits of the square of the voltage amplitude.

[0081] Furthermore, the scheduling constraints for the line repair team are as follows: At any given time, the line repair team can only be in one of two states: stationed repair or moving. The repair team can only depart from its base node. When stationed for repair, it can only be located at the endpoint node corresponding to the damaged line. When the repair team moves between different damaged lines, it needs to consume the travel time of the corresponding path, and it is in a moving state during the transfer process. The expression is as follows:

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] In the formula, This represents the set of emergency repair teams, and its index is... ; This represents the set of nodes where the emergency repair team is stationed, and its index is... ; This represents the set of damaged lines, with the index being... and ; This represents the set of endpoint nodes of the damaged line, with index . ; It is a Boolean variable, when the repair team At any moment Stopped at the node corresponding to the damaged line The value is 1 when emergency repairs are being carried out. It is a Boolean variable, when the repair team The value is 1 when the vehicle is in motion on the transportation network. This indicates that the repair team is on the damaged line. and The travel time required when transferring between them.

[0088] The maintenance constraints for damaged lines are as follows: damaged lines must be repaired continuously within the corresponding preset repair time. The line remains in a fault state until the repair is completed. Only after the repair team completes the preset continuous repair time will the damaged line switch to normal operation. The expression is as follows:

[0089]

[0090]

[0091]

[0092]

[0093]

[0094] In the formula, Indicates the repair time for the damaged line; Let it be a Boolean variable, representing the time at which the damage occurs. The state.

[0095] Furthermore, the mobile energy storage scheduling constraints are as follows: mobile energy storage can only be in one of two states at any given time: stationary or moving. When stationary, it can only be located at its parking node or charging / discharging station node. When moving, it can only be transferred between charging / discharging station nodes, and the transfer process consumes the travel time of the corresponding path. The number of mobile energy storage devices connected to the same charging / discharging station node at any given time does not exceed the preset maximum allowable number. When mobile energy storage devices travel from a charging / discharging station node to a target node, it consumes the travel time of the corresponding path, as expressed below:

[0096]

[0097]

[0098]

[0099]

[0100]

[0101] In the formula, This represents a collection of mobile energy storage devices, with the index being... ; This represents the set of charging and discharging station nodes, with its index being... ; This is a set of parking point nodes for mobile energy storage systems, with the index being... ; As a Boolean variable, when mobile energy storage At any moment Stop at node The value is 1 at this time. As a Boolean variable, when mobile energy storage The value is 1 when traveling between charging and discharging station nodes; This indicates the maximum number of mobile energy storage systems allowed to connect to the same charging and discharging station; Indicates mobile energy storage From charging and discharging station nodes Drive to the node Assume the required travel time.

[0102] Furthermore, the operational constraints of mobile energy storage include: the charging and discharging states of mobile energy storage are mutually exclusive; the active power of charging and discharging does not exceed its respective maximum limit; the state of charge is always between the preset maximum and minimum charge; and the state of charge changes dynamically with the charging and discharging power and charging and discharging efficiency, as expressed below:

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] In the formula, and These are Boolean variables, representing mobile energy storage. At the node The charging and discharging states at the point; when When, it indicates mobile energy storage At any moment In charging state; when This indicates that the mobile energy storage is in a discharging state. and These represent mobile energy storage. The active power of charging and discharging; and These represent mobile energy storage. The maximum charging and discharging active power; Indicates mobile energy storage At any moment The state of charge; and These represent the maximum and minimum charge capacity of the mobile energy storage, respectively. and These represent the charging efficiency and discharging efficiency of mobile energy storage, respectively.

[0110] Finally, the V2G scheduling constraints include: the initial state of charge of electric vehicles participating in V2G aggregation conforms to the characteristics of a normal distribution; the state of charge of electric vehicles changes dynamically with discharge power and discharge efficiency; and the power supply of V2G stations does not exceed the maximum power supply of the station and is related to the proportion of the number of aggregated electric vehicles.

[0111] Specifically, V2G scheduling constraints include initial state of charge constraints for electric vehicles, state of charge change constraints for electric vehicles, and V2G power supply constraints.

[0112] Initial state of charge constraints for electric vehicles:

[0113]

[0114]

[0115] In the formula, This represents the set of electric vehicles participating in V2G aggregation, and its index is... ; and Parameters representing the normal distribution; This indicates the initial state of charge of the electric vehicle; This indicates the rated battery capacity of an electric vehicle.

[0116] Constraints on the state of charge change of electric vehicles:

[0117]

[0118]

[0119] In the formula, Indicates the time of electric vehicle The state of charge; This indicates the active power of an electric vehicle's discharge. This indicates the discharge efficiency of an electric vehicle.

[0120] V2G power supply constraints:

[0121]

[0122]

[0123]

[0124] In the formula, A proportionality coefficient representing the total number of electric vehicles; Represents a node Maximum power supply of V2G; Represents a node V2G at time Power supply capacity.

[0125] Finally, the constructed multi-mobility resource cooperative scheduling model is solved within each scheduling time period. First, an initial time period is set. A multi-mobility resource collaborative scheduling model for load recovery is constructed for the current time period, and the Gurobi optimizer is called to solve the model to obtain the optimal scheduling result for the multi-mobility resources in this time period. Then, it is determined whether the current time period has reached its termination period. If not reached, the update period will be... The scheduling model for the next time period is constructed and solved based on the scheduling results of the previous time period. If the termination time period is reached, the solution process ends, thus obtaining the multi-mobility resource scheduling results for each time period of the entire recovery process.

[0126] To demonstrate the effectiveness of this method in simplifying the traffic network and coordinating multi-resource scheduling, this embodiment selects two typical distribution network line fault scenarios—large-scale and small-scale—and conducts simulation tests respectively. This embodiment of the invention verifies the multi-mobile resource distribution network load restoration method based on traffic network simplification using simulation on the Matlab platform, and constructs a multi-mobile resource cooperative scheduling model using the Yalmip toolkit, solving the model by calling the Gurobi optimizer. In this embodiment, the scheduling time interval is set to 5 minutes, the total scheduling duration is 4 hours, and the corresponding number of scheduling periods is [number missing]. .

[0127] In a large-scale fault scenario, the traffic network simplification method based on the mobile resource functional characteristics proposed in this invention is simulated and analyzed to verify the effectiveness of the method in simplifying the original traffic network. The traffic network simplification process of the line repair team is as follows: Figure 2 As shown: Figure 2 Figure (1) shows a distribution network containing several faulty lines. Figure 2 In Figure (2), the endpoints of the faulty line are mapped to road nodes in the traffic network. Figure 2 Figure (3) shows a preliminary simplified transportation network. Figure 2 Figure (4) shows the final simplified transportation network. The process of simplifying the transportation network for mobile energy storage is as follows: Figure 3 As shown: Figure 3 Figure (1) shows the mobile energy storage parking points and charging / discharging stations deployed in the distribution network, such as... Figure 3 As shown in Figure (2), the corresponding nodes have been mapped to the traffic network. Figure 3 As shown in Figure (3), all roads that do not belong to any shortest path are eliminated. Figure 3 Figure (4) shows the final simplified traffic network. The traffic network simplification process of V2G is as follows: Figure 4 As shown: Figure 4 Figure (1) shows a distribution network containing multiple V2G sites, with corresponding nodes in Figure 4 The V2G aggregation area is mapped to the traffic network in Figure (2) of Figure 4. The final simplified traffic network is shown in Figure (3) of Figure 4. Figure 4 As shown in Figure (4).

[0128] The proposed multi-mobile resource cooperative scheduling method was simulated and verified under a small-scale fault scenario to demonstrate its effectiveness in power distribution network restoration. After solving the multi-mobile resource cooperative scheduling model using an optimizer, the scheduling results for each time period can be obtained. The scheduling results for mobile energy storage and V2G in each time period are shown below. Figure 5 and Figure 6 As shown. In At that time, mobile energy storage is connected to charging and discharging stations to support power supply to the faulty area. V2G in It reaches maximum output power at a certain time and then maintains a stable power supply. At that time, the repair team arrived at the location of the faulty line and began line repairs. At that time, the line repair team completed the line repair, the damaged line returned to normal operation, and subsequently the power outage area was fully restored. The weighted load recovery curve of the distribution network is shown below. Figure 7 As shown, three key events significantly impacted the load restoration process: first, the V2G reached its maximum output power; second, the mobile energy storage arrived at the outage area and began reverse power supply support; and finally, the line repair team completed the line repair.

[0129] This invention simplifies the transportation network by differentiating mobile resource functional characteristics, effectively eliminating redundant nodes and paths in the original transportation network, significantly reducing the computational complexity of multi-mobile resource scheduling, and improving the efficiency and speed of scheduling calculations. Simultaneously, by constructing a unified multi-mobile resource collaborative scheduling model, it achieves collaborative optimization scheduling of line repair teams, mobile energy storage, and V2G, fully leveraging the complementary advantages of these three types of resources. During the repair of damaged lines, temporary power supply through mobile energy storage and V2G minimizes load loss, and permanent load restoration is achieved after line repairs are completed. This significantly improves the level and efficiency of post-disaster load recovery in the distribution network, enhances the system resilience and disaster resistance of the distribution network, and is applicable to various distribution network line fault scenarios caused by extreme weather, showing broad application prospects.

[0130] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for load restoration of a multi-mobility resource distribution network based on a simplified transportation network, characterized in that, Includes the following steps: Step 1: Collect multi-source data. The multi-source data includes the input of power distribution network line fault status, traffic network topology and mobile resource configuration parameters. The mobile resource configuration parameters include the moving speed and repair time of the line repair team, the moving speed, charging and discharging power and charge of mobile energy storage, and the moving speed and charge of electric vehicles in V2G grid connection. Step 2: Simplify the original traffic network according to the functional characteristics of the line repair team, mobile energy storage, and V2G respectively, and obtain the simplified traffic network corresponding to each resource; Step 3: Based on the simplified transportation network corresponding to each resource, construct a multi-mobile resource collaborative scheduling model with the goal of maximizing the weighted load recovery of the distribution network. Introduce constraints for the collaborative optimization scheduling of line repair teams, mobile energy storage, and V2G. By solving the multi-mobile resource collaborative scheduling model with introduced constraints, output the multi-mobile resource scheduling results for the current time period to achieve distribution network load recovery.

2. The multi-mobility resource distribution network load restoration method based on simplified transportation network as described in claim 1, characterized in that, The power distribution line fault information includes the location and number of power distribution line faults, and the traffic network topology includes the connection relationships between traffic network nodes and the actual road length.

3. The multi-mobility resource distribution network load restoration method based on simplified transportation network as described in claim 1, characterized in that, The specific process of simplifying the original traffic network corresponding to the line repair team in step 2 is as follows: take the road nodes corresponding to the nodes at both ends of each damaged line as the target driving nodes of the repair team, use the shortest path algorithm to calculate the shortest path between the repair team's station node and each target driving node, and between each target driving node, respectively, delete the road nodes and paths that do not belong to any shortest path to obtain the preliminary simplified traffic network, then only retain the repair team's station node and target driving nodes in the preliminary simplified traffic network, delete the remaining road nodes, and obtain the simplified traffic network corresponding to the line repair team.

4. The multi-mobility resource distribution network load restoration method based on simplified transportation network as described in claim 1, characterized in that, The specific process of simplifying the original traffic network corresponding to mobile energy storage in step 2 is as follows: the shortest path algorithm is used to calculate the shortest path between the mobile energy storage parking node and each charging and discharging station node, and between each charging and discharging station node. Road nodes and paths that do not belong to any shortest path are deleted to obtain a preliminary simplified traffic network. Then, only the mobile energy storage parking node and each charging and discharging station node in the preliminary simplified traffic network are retained, and the remaining road nodes are deleted to obtain the simplified traffic network corresponding to mobile energy storage.

5. The multi-mobility resource distribution network load restoration method based on simplified transportation network as described in claim 1, characterized in that, The specific process of simplifying the original traffic network corresponding to V2G in step 2 is as follows: taking the road nodes corresponding to the V2G station as the center, the traffic network area where the travel time of the electric vehicle from the initial position to the V2G station does not exceed the preset maximum travel time is divided into the aggregation area of ​​the V2G station. Only the roads and nodes in the aggregation area are retained, and the remaining road nodes and paths are deleted to obtain the simplified traffic network corresponding to V2G.

6. The multi-mobility resource distribution network load restoration method based on simplified transportation network as described in claim 1, characterized in that, The objective of maximizing the weighted load recovery of the distribution network in step 3 is specifically to divide the distribution network load into first-level, second-level, and third-level loads and assign corresponding weights, with first-level loads having the highest weight. The sum of the products of the restored active load of the unrestored load nodes at each time point and their corresponding weights is calculated, with the goal of maximizing the total value of the sum of these products.

7. The multi-mobility resource distribution network load restoration method based on simplified transportation network as described in claim 1, characterized in that, The constraints include distribution network operation constraints, line repair team dispatch constraints, damaged line repair constraints, mobile energy storage dispatch and operation constraints, and V2G dispatch constraints.

8. The multi-mobility resource distribution network load restoration method based on simplified transportation network as described in claim 7, characterized in that, The power distribution network operation constraints include power balance constraints, line capacity constraints, and voltage safety constraints. The power balance constraint ensures that the active and reactive power injection and output of each node in the power distribution network are balanced. The line capacity constraint ensures that the apparent power of each line in the power distribution network does not exceed its capacity limit. The voltage safety constraint ensures that the square of the voltage amplitude of each node in the power distribution network is within the preset upper and lower limits.

9. The multi-mobility resource distribution network load restoration method based on simplified transportation network as described in claim 7, characterized in that, The scheduling constraints for the line repair team are as follows: at any given time, the line repair team can only be in one of two states: stationed repair or moving. The repair team can only depart from its base node. When stationed for repair, it can only be located at the endpoint node corresponding to the damaged line. When the repair team moves between different damaged lines, it needs to consume the travel time of the corresponding path, and it is in a moving state during the transfer process. The maintenance constraints for the damaged line are as follows: the damaged line must be continuously repaired within the corresponding preset repair time. The line remains in a fault state until the repair is completed. Only after the repair team completes the preset time of continuous repair will the damaged line switch to normal operation.

10. The multi-mobility resource distribution network load restoration method based on simplified transportation network as described in claim 7, characterized in that, The mobile energy storage scheduling constraint is that mobile energy storage can only be in one of two states, either stationary or moving, at any given time. When stationary, it can only be located at its parking node or charging / discharging station node. When moving, it can only be transferred between charging / discharging station nodes, and the transfer process requires the travel time of the corresponding path. The number of mobile energy storage devices connected to the same charging and discharging station node at any given time shall not exceed the preset maximum allowable number. When mobile energy storage devices travel from the charging and discharging station node to the target node, they shall consume the corresponding path travel time. The operational constraints of the mobile energy storage include: the charging and discharging states of the mobile energy storage are mutually exclusive; the active power of charging and discharging does not exceed its respective maximum limit; the state of charge is always between the preset maximum and minimum state of charge; and the state of charge changes dynamically with the charging and discharging power and charging and discharging efficiency.

11. The multi-mobility resource distribution network load restoration method based on simplified transportation network as described in claim 7, characterized in that, The V2G scheduling constraints include: the initial state of charge of electric vehicles participating in V2G aggregation conforms to the characteristics of a normal distribution; the state of charge of electric vehicles changes dynamically with discharge power and discharge efficiency; and the power supply of V2G stations does not exceed the maximum power supply of the station and is related to the proportion of the number of aggregated electric vehicles.

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