A method and device for post-disaster scenario-oriented virtual power plant collaborative repair and energy optimization scheduling based on mobile energy storage of electric vehicles

Abstract

The application discloses a kind of post-disaster scene-oriented virtual power plant collaborative repair and energy optimization scheduling method and device based on electric vehicle mobile energy storage.The method includes electric vehicles with bidirectional charging and discharging capabilities into the virtual power plant node set as mobile energy storage units;Energy accessibility model and repair task priority matrix are constructed;Virtual power plant energy-task matching matrix is established to realize the dynamic screening of available energy nodes;On this basis, energy-task collaborative double-layer optimization model is constructed, the upper optimization virtual power plant output allocation, the lower optimization repair path and task scheduling;Improved multi-objective evolutionary algorithm is used to solve, to minimize repair time, energy gap and traffic cost as the goal, output optimal repair path and virtual power plant coordination scheme.The application realizes rapid energy recovery and key load guarantee under the assistance of virtual power plant, improves the post-disaster resilience and resource utilization efficiency of power system.

Description

Technical Field

[0001] This invention belongs to the field of power system emergency dispatch and virtual power plant operation control technology. Specifically, it relates to a method and device for collaborative repair and energy optimization dispatch of virtual power plants that integrates electric vehicle mobile energy storage resources for post-disaster scenarios. It involves technologies such as bidirectional charging and discharging control of electric vehicles, distributed energy aggregation, post-disaster power grid restoration dispatch, and transportation-energy collaborative optimization. Background Technology

[0002] In recent years, with the large-scale integration of flexible and controllable resources such as distributed power sources, energy storage devices, and electric vehicles, the operation mode of the power system has gradually developed towards diversification, distribution, and flexibility. Electric vehicles, with their onboard energy storage capabilities and inherent mobility, mainly participate in grid dispatch as adjustable loads or demand response resources during normal operation. However, in post-disaster emergency scenarios, their potential emergency energy supply and mobile support capabilities have not yet been fully explored.

[0003] Virtual Power Plants (VPPs), as an important technology for aggregating distributed energy resources and flexible loads, can achieve coordinated scheduling of multiple resources through unified modeling and coordinated control. However, existing VPP research and applications are mainly geared towards routine operation or market-based trading scenarios. In situations where natural disasters, extreme weather, or sudden accidents cause localized damage to the distribution network, they often lack targeted design for the post-disaster emergency phase, particularly failing to effectively integrate mobile energy storage resources such as electric vehicles for post-disaster power supply and repair support. Existing post-disaster repair scheduling methods for distribution networks primarily focus on repair task priority and traffic accessibility, achieving power restoration by optimizing repair paths and resource allocation. However, they typically consider energy supply and repair scheduling processes separately, neglecting the temporary power supply and emergency support capabilities of distributed power sources, energy storage devices, and electric vehicles in the post-disaster phase. In areas operating in isolation or with limited communication, relying solely on traditional repair mechanisms can easily lead to insufficient energy supply, unreasonable repair sequences, and inefficient resource allocation, thus delaying the overall recovery progress.

[0004] Meanwhile, post-disaster power system load demand exhibits significant spatiotemporal variability and uncertainty. Without unified modeling and coordinated scheduling of mobile energy storage resources, including electric vehicles, localized energy redundancy and insufficient power supply to critical loads can easily coexist, making it difficult to balance system recovery timeliness and energy utilization efficiency. Furthermore, existing scheduling methods often employ single-objective or static optimization strategies, lacking multi-objective collaborative scheduling mechanisms for complex post-disaster constraints, hindering the coordinated optimization of energy, transportation, and emergency repair tasks. Therefore, there is an urgent need for an energy coordination and scheduling method that can integrate electric vehicle mobile energy storage resources into a unified virtual power plant scheduling framework and deeply couple them with post-disaster emergency repair tasks. By constructing an energy accessibility model and a multi-layered optimization mechanism, this method can achieve rapid repair of the post-disaster power system and ensure critical load coverage, thereby enhancing the resilience and self-healing capabilities of the power grid under extreme events. Summary of the Invention

[0005] To address the problems existing in the post-disaster power distribution network repair process, such as the separation of emergency repair path planning and energy coordination, insufficient distributed resource support, and low utilization rate of virtual power plant dispatching capabilities, this invention proposes a method and device for rapid repair and energy coordination of post-disaster power systems based on virtual power plant support, thereby realizing dynamic coordination of multi-source energy and optimized allocation of emergency repair tasks after a disaster.

[0006] A method for collaborative emergency repair and energy optimization scheduling of virtual power plants based on electric vehicle mobile energy storage for post-disaster scenarios includes the following steps:

[0007] Step S1: Construct an energy accessibility model and a virtual power plant node model for the post-disaster power system; the virtual power plant nodes include at least fixed distributed power generation nodes, energy storage device nodes, and electric vehicle nodes with bidirectional charging and discharging capabilities;

[0008] Step S2: Based on post-disaster traffic topology and communication constraints, establish a priority matrix for emergency repair tasks;

[0009] Step S3: Based on the virtual power plant node model constructed in step S1 and the emergency repair task priority matrix established in step S2, construct the virtual power plant energy-task matching matrix to realize the dynamic selection of available power nodes.

[0010] Step S4: Based on the energy reachability model constructed in Step S1 and the virtual power plant energy-task matching matrix constructed in Step S3, the support relationships between virtual power plant nodes and task nodes are pre-screened and sorted to form a candidate set of available energy nodes and a priority sequence for task matching. This set is used as the initial basis for upper-level virtual power plant output allocation and lower-level emergency repair task scheduling, thus constructing a two-layer optimization model for post-disaster emergency repair and energy supply. The two-layer optimization model includes an upper-level virtual power plant energy coordination model and a lower-level emergency repair scheduling model.

[0011] Step S5: The improved multi-objective evolutionary algorithm is used to solve the two-layer optimization model constructed in step S4. The optimization objectives are repair time, energy gap and economic loss. The multi-objective optimal emergency repair path and virtual power plant coordination scheme are output.

[0012] Furthermore, the energy accessibility model in step S1 is implemented by establishing an energy transfer matrix between the virtual power plant node and the load node to be repaired, which is defined as:

[0013] ;

[0014] in, Represents virtual power plant nodes For load nodes The achievable energy transmission capability; For virtual power plant nodes Available power; For virtual power plant nodes With load nodes Accessible distance between them by transportation; For energy transfer efficiency; , This is the attenuation coefficient.

[0015] Furthermore, the emergency repair task priority matrix in step S2 comprehensively considers node importance indicators. Node load loss Communication availability and VPP's maximum energy supply potential The calculation formula is as follows:

[0016] ;

[0017] In the formula, in the formula, , , , There are four weighting coefficients.

[0018] Furthermore, the virtual power plant energy-task matching matrix in step S3 comprehensively considers energy supply potential, task response delay, and emergency repair task priority, and its calculation formula is as follows:

[0019] ;

[0020] In the formula, Represents virtual power plant nodes For load nodes The achievable energy transmission capability; Maximum transmittance; To provide emergency repair teams with support from VPP nodes To the task node Travel time; The maximum allowable delay; coefficient , , The impacts of energy supply potential, traffic delays, and repair task priority on overall coordination efficiency were measured separately; among them... This represents the energy-task matching degree between virtual power plant node i and the node j to be repaired, used to comprehensively characterize the degree of support and adaptability of the virtual power plant node for a specific emergency repair task. This serves as the basis for matching evaluation results and scheduling inputs before constructing the two-level optimization model.

[0021] Furthermore, based on the initial criteria for upper-level virtual power plant output allocation and lower-level emergency repair task scheduling, the upper-level virtual power plant energy coordination model in step S4 aims to maximize the utilization of local energy after a disaster, and its optimization function is:

[0022] ;

[0023] In the formula, To maximize the output of the node, As a penalty factor, For virtual power plant nodes Available power; For virtual power plant nodes With load nodes Accessible distance between them by transportation;

[0024] The lower-level emergency repair scheduling model has multiple objectives: minimizing repair time, minimizing energy deficit, and minimizing traffic costs. The optimization function is:

[0025] ;

[0026] In the formula, The total time required to complete the emergency repairs; This refers to load loss caused by insufficient energy. This represents the total distance traveled by the repair team. , , These are the multi-objective weighting coefficients.

[0027] Furthermore, in step S5, the improved non-dominated sorting genetic algorithm NSGA-II is used for multi-objective solution, and the fitness function is:

[0028] ;

[0029] In the formula, The energy requirement for node i; This is the energy penalty coefficient;

[0030] The scheduling process must also satisfy energy conservation and time constraints:

[0031] ;

[0032] ;

[0033] In the formula, For nodes At any moment Energy requirements; This refers to the completion time of the task node's repair. This is the maximum emergency repair time allowed by the system.

[0034] A virtual power plant collaborative repair and energy optimization scheduling device based on electric vehicle mobile energy storage for post-disaster scenarios, used to implement the method described above, includes:

[0035] The model building module is used to build an energy accessibility model and a virtual power plant node model for the post-disaster power system; the virtual power plant node includes at least a fixed distributed power generation node, an energy storage device node, and an electric vehicle node with bidirectional charging and discharging capabilities;

[0036] The emergency repair task priority matrix establishment module is used to establish an emergency repair task priority matrix based on post-disaster traffic topology and communication constraints.

[0037] The emergency repair task priority matrix construction module is used to construct a virtual power plant energy-task matching matrix based on the virtual power plant node model and the emergency repair task priority matrix, so as to realize the dynamic selection of available power nodes.

[0038] A two-layer optimization model construction module is used to pre-screen and sort the support relationships between virtual power plant nodes and task nodes based on the energy accessibility model and the virtual power plant energy-task matching matrix, forming a candidate set of available energy nodes and a priority sequence of task matching, which serves as the initial basis for upper-layer virtual power plant output allocation and lower-layer emergency repair task scheduling, thus constructing a post-disaster emergency repair-energy supply two-layer optimization model; the two-layer optimization model includes an upper-layer virtual power plant energy coordination model and a lower-layer emergency repair scheduling model;

[0039] The model solving and scheduling module is used to solve the two-layer optimization model using an improved multi-objective evolutionary algorithm, with repair time, energy gap and economic loss as optimization objectives, and outputs the multi-objective optimal emergency repair path and virtual power plant coordination scheme.

[0040] Furthermore, the energy reachability model constructed by the model building module is achieved by establishing an energy transfer matrix between the virtual power plant node and the load node to be repaired, which is defined as:

[0041] .

[0042] in, Represents virtual power plant nodes For load nodes The achievable energy transmission capability; For virtual power plant nodes Available power; For virtual power plant nodes With load nodes Accessible distance between them by transportation; For energy transfer efficiency; , This is the attenuation coefficient.

[0043] Furthermore, the upper-level virtual power plant energy coordination model aims to maximize the utilization of local energy resources after a disaster, and its optimization function is:

[0044] ;

[0045] In the formula, To maximize the output of the node, As a penalty factor, For virtual power plant nodes Available power; For virtual power plant nodes With load nodes Accessible distance between them by transportation;

[0046] The lower-level emergency repair scheduling model has multiple objectives: minimizing repair time, minimizing energy deficit, and minimizing traffic costs. The optimization function is:

[0047] ;

[0048] In the formula, The total time required to complete the emergency repairs; This refers to load loss caused by insufficient energy. This represents the total distance traveled by the repair team. , , These are the multi-objective weighting coefficients.

[0049] Furthermore, the model solving and scheduling module employs the improved non-dominated sorting genetic algorithm NSGA-II for multi-objective solving, with the fitness function being:

[0050] ;

[0051] In the formula, The energy requirement for node i; This is the energy penalty coefficient;

[0052] The scheduling process must also satisfy energy conservation and time constraints:

[0053] ;

[0054] ;

[0055] In the formula, For nodes At any moment Energy requirements; This refers to the completion time of the task node's repair. This is the maximum emergency repair time allowed by the system.

[0056] This invention introduces the aggregation, energy storage, and distributed power output capabilities of virtual power plants into the post-disaster emergency repair and dispatch system, forming a multi-objective optimization mechanism for energy-task coordination, and realizing the transformation from "task-driven repair" to "energy-coordinated repair." This method can effectively improve the recovery speed and power supply continuity of the power grid after a disaster, enhance system resilience and emergency response capabilities, and has high engineering application and promotion value. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the coupled framework of the power distribution network and transportation network after the disaster.

[0058] Figure 2 This is a flowchart illustrating a method for collaborative emergency repair and energy optimization scheduling of virtual power plants based on electric vehicle mobile energy storage, as described in an embodiment of the present invention, for post-disaster scenarios.

[0059] Figure 3 This is a schematic diagram of the energy-task collaborative two-layer optimization model structure based on electric vehicle mobile energy storage support in an embodiment of the present invention.

[0060] Figure 4 This is a flowchart illustrating the multi-objective optimization solution based on mobile energy storage support for electric vehicles, as described in an embodiment of the present invention.

[0061] Figure 5 This is a Pareto front distribution diagram of the three objectives of emergency repair completion time, travel distance, and supply loss in an embodiment of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0063] Please see Figure 2This invention provides a method for collaborative emergency repair and energy optimization scheduling of virtual power plants based on electric vehicle mobile energy storage in post-disaster scenarios. This method aims at restoring the power grid after a disaster, comprehensively considering the energy supply capacity of the virtual power plant, the importance of the repair task, traffic accessibility, and time-varying constraints. By establishing an energy-task matching matrix and designing a two-layer optimization model, it achieves integrated coordination of "energy support—task scheduling—path planning" in the post-disaster stage. The method includes the following steps:

[0064] Step S1: Construct an energy accessibility model and a virtual power plant node model for the post-disaster power system. The virtual power plant nodes include at least fixed distributed power generation nodes, energy storage device nodes, and electric vehicle nodes with bidirectional charging and discharging capabilities. In the post-disaster power system, virtual power plant nodes (VPPs) are widely distributed, and their output capacity is affected by energy storage capacity, communication accessibility, and transportation conditions. To quantify the energy supply potential between different nodes, an energy accessibility model is established:

[0065]

[0066] in, For virtual power plant nodes Available power; For virtual power plant nodes With load nodes Accessible distance between them by transportation; For energy transfer efficiency; , The attenuation coefficient is represented by this matrix, which reflects the degree of energy transferability between different energy supply units after a disaster.

[0067] To address post-disaster road blockages and the time-varying nature of traffic, the system incorporates time-varying weights for road segments during the path search process. Dynamic updates via GIS traffic module The energy reachability matrix is ​​automatically refreshed every scheduling cycle to ensure that the model reflects the latest feasible paths and energy storage capacities.

[0068] Step S2: Based on post-disaster traffic topology and communication constraints, establish a priority matrix for emergency repair tasks. For each load node, considering factors such as power outage losses, node importance, communication restoration requirements, and the support potential of virtual power plants, construct a task priority matrix:

[0069]

[0070] in, As an indicator of node importance, This refers to the load loss at the node. Indicates communication availability. Represented by the maximum energy supply potential available to the node. Weighting coefficients. – Determined according to the emergency plan. Matrix The output reflects the relative priority of the emergency repair tasks, providing a ranking basis for the scheduling model.

[0071] Step S3: Based on the virtual power plant node model constructed in Step S1 and the emergency repair task priority matrix established in Step S2, construct the virtual power plant energy-task matching matrix to achieve dynamic screening of available energy nodes. To achieve coordinated matching of virtual power plant energy resources and emergency repair tasks, the energy-task matching matrix is ​​defined as follows:

[0072]

[0073] in, Represents virtual power plant nodes For load nodes The achievable energy transmission capability; For the repair team from VPP node To the task node Travel time, For maximum transmission capacity, This represents the maximum tolerable latency. (Coefficient) , , The impact of energy supply potential, traffic delay, and emergency repair task priority on overall coordination efficiency is measured separately. This is achieved through a matrix... Solving this problem can determine "which virtual power plant prioritizes supporting which task node" and optimize task allocation during the scheduling phase.

[0074] in, This represents the energy-task matching degree between virtual power plant node i and the task node j to be repaired, and is used to comprehensively characterize the degree of support and adaptation of the virtual power plant node to a specific emergency repair task.

[0075] The larger the value, the more suitable virtual power plant node i is in terms of energy supply capacity, traffic response timeliness, and task priority matching to support task node j. In the subsequent disaster recovery-energy supply two-layer optimization model, It does not appear directly as an independent decision variable in the objective function or constraints, but rather serves as the matching evaluation result and scheduling input basis before constructing the two-level optimization model. Specifically, the system... The size of the support relationship between virtual power plant nodes and task nodes is pre-screened and sorted to form a candidate set of available power nodes and a priority sequence for task matching. This set is used as the initial basis for the power output allocation of the upper-level virtual power plant and the scheduling of the lower-level emergency repair tasks, thereby narrowing the optimization search range and improving the solution efficiency and rationality of the emergency repair-power supply coordinated scheduling.

[0076] Step S4: Based on the energy reachability model constructed in Step S1 and the virtual power plant energy-task matching matrix constructed in Step S3, the support relationships between virtual power plant nodes and task nodes are pre-screened and sorted to form a candidate set of available energy nodes and a priority sequence for task matching. This is used as the initial basis for upper-level virtual power plant output allocation and lower-level emergency repair task scheduling, thus constructing a post-disaster emergency repair-energy supply dual-layer optimization model. The dual-layer optimization model includes an upper-level virtual power plant energy coordination model and a lower-level emergency repair scheduling model. Figure 3 As shown, the system adopts a two-layer structure: an upper-layer "energy coordination optimization" and a lower-layer "emergency repair scheduling optimization". The upper layer uses the output allocation of virtual power plant nodes as the decision variable, with the goal of maximizing available energy and reducing energy redundancy; the lower layer uses emergency repair tasks and paths as decision variables, with the goal of minimizing emergency repair time and economic losses while meeting energy constraints.

[0077] The upper-level objective function is defined as:

[0078]

[0079] in, To maximize the output of the node, As a penalty factor, For virtual power plant nodes Available power; For virtual power plant nodes With load nodes The reachable distance between them by transportation. The lower-level objective function is:

[0080]

[0081] in, Total repair time, This refers to load loss caused by insufficient energy. This represents the total distance traveled by the repair team. , , These are the multi-objective weighting coefficients.

[0082] Step S5: Establish the constraints and energy balance relationship. The two-layer model is subject to both energy and time constraints:

[0083]

[0084]

[0085] in, This represents the node's energy demand. The virtual power plant node status is dynamically updated over time.

[0086]

[0087] In the formula, Let represent the available power of the i-th node in the virtual power plant at time t. This indicates that after a time step... Then, the node's updated available power. Represents the rated energy storage capacity or maximum available energy capacity of the i-th node. Indicates the first Each virtual power plant node at time The energy output is determined by the above relationship. During the rolling scheduling process, the system dynamically adjusts the output and repair plan to ensure continuous energy supply and optimal resource allocation.

[0088] Step S6: An improved multi-objective evolutionary algorithm is used to solve the two-layer optimization model constructed in step S4. The optimization objectives are repair time, energy gap, and economic loss. The algorithm outputs the multi-objective optimal emergency repair path and virtual power plant coordination scheme.

[0089] The flowchart of the two-layer model solution algorithm is as follows: Figure 4 As shown. Figure 4 As shown, this embodiment proposes a solution method for energy coordination and emergency repair scheduling in post-disaster power systems based on a multi-objective optimization algorithm, building upon the aforementioned energy-task collaborative modeling. This method introduces a non-dominated sorting genetic algorithm (NSGA-II) to solve the two-level optimization model, achieving collaborative optimization of energy coordination, task allocation, and path planning, and outputting a repair plan and energy recovery curve that can be used for command and dispatch. The specific implementation steps are as follows:

[0090] The first step is to establish a multi-objective optimization and solution mechanism. The model employs an improved Non-Dominated Sorting Genetic Algorithm (NSGA-II) for solution. By encoding the upper-level output and the lower-level task paths, joint evolution of "energy-task-path" is achieved. The algorithm's fitness function is set as follows:

[0091]

[0092] in, This is the energy penalty coefficient, used to constrain energy shortage situations. After each iteration, the algorithm filters and retains the Pareto front to ensure that the optimization result achieves a balance between repair time, energy gap, and economic cost.

[0093] The second step is to output the scheduling results and performance indicators. After optimization, the system outputs scheduling results including the virtual power plant output curve, emergency repair path sequence, load recovery schedule, and energy recovery rate curve. The energy recovery rate is defined as:

[0094]

[0095] In the formula, To indicate time The energy of nodes that have been restored to power supply by virtual power plants or emergency repairs; This represents the total energy required by the node.

[0096] This indicator reflects the progress of power supply during grid restoration. The system also calculates the restoration rate. Repair ratio with critical load This enables dynamic evaluation of the effectiveness of multi-objective scheduling strategies. In the formula, This module represents the number of nodes that have been restored. It enables visualized decision-making and dynamic tracking of energy recovery during the post-disaster repair process, providing data support for subsequent optimization and decision review.

[0097] Through the above steps, this embodiment realizes the integrated energy-task scheduling of the power grid after a disaster, taking into account both the output coordination of virtual power plants and the time and space constraints of emergency repair tasks, making the post-disaster recovery process more efficient, continuous and economical.

[0098] To verify the effectiveness of the proposed method for collaborative emergency repair and energy optimization scheduling of virtual power plants based on electric vehicle mobile energy storage, the following simulation example is constructed:

[0099] An improved IEEE 34-node distribution system was selected as the test object, and the node locations were spatially mapped using actual geographical information to construct a coupled distribution network-transportation network model (e.g., Figure 1 (As shown). The system is configured with 6 faulty nodes, each with different load levels, economic value, and key user attributes; two emergency repair teams are also configured, each located at a pre-set location.

[0100] On the virtual power plant side, several electric vehicle nodes with bidirectional charging and discharging capabilities are introduced as mobile energy storage units, whose initial state of charge (SOC) and maximum discharge power satisfy the operational constraints. Based on the traffic network structure, the travel time matrix between each virtual power plant node and the faulty node is calculated, and an energy-task matching matrix is ​​constructed in conjunction with the energy accessibility model.

[0101] Based on this, the two-layer optimization model described in this invention is used for solution. The upper-layer model determines task priorities and virtual power plant output allocation, while the lower-layer model optimizes repair paths and task scheduling order. An improved multi-objective evolutionary algorithm is used to jointly optimize "repair time, traffic costs, and energy gap." Simulation results are as follows: Figure 5 As shown.

[0102] Simulation results show that the method of the present invention can achieve coordinated optimization of repair path and energy supply in complex post-disaster environments. Compared with only considering the shortest path or a single priority strategy, it can effectively reduce the overall system repair time and power outage losses, and improve the recovery efficiency of critical load nodes, thus verifying the effectiveness and engineering application value of the proposed method.

[0103] This invention also provides a virtual power plant collaborative repair and energy optimization scheduling device based on electric vehicle mobile energy storage for post-disaster scenarios, used to implement the method described above, including:

[0104] The model building module is used to build an energy accessibility model and a virtual power plant node model for the post-disaster power system; the virtual power plant node includes at least a fixed distributed power generation node, an energy storage device node, and an electric vehicle node with bidirectional charging and discharging capabilities;

[0105] The emergency repair task priority matrix establishment module is used to establish an emergency repair task priority matrix based on post-disaster traffic topology and communication constraints.

[0106] The emergency repair task priority matrix construction module is used to construct a virtual power plant energy-task matching matrix based on the virtual power plant node model and the emergency repair task priority matrix, so as to realize the dynamic selection of available power nodes.

[0107] A two-layer optimization model construction module is used to construct a two-layer optimization model for post-disaster repair and energy supply based on the energy accessibility model and the virtual power plant energy-task matching matrix; the two-layer optimization model includes an upper-layer virtual power plant energy coordination model and a lower-layer repair scheduling model;

[0108] The model solving and scheduling module is used to solve the two-layer optimization model using an improved multi-objective evolutionary algorithm, with repair time, energy gap and economic loss as optimization objectives, and outputs the multi-objective optimal emergency repair path and virtual power plant coordination scheme.

[0109] Compared with the prior art, the present invention has the following characteristics and effects:

[0110] This invention pioneers a deep post-disaster integration of electric vehicle mobile energy storage and virtual power plants: Breaking away from the limitations of traditional virtual power plants that only aggregate fixed resources, it incorporates electric vehicle-mounted energy storage systems into the virtual power plant node set, serving as a mobile and dispatchable emergency energy supply unit. While ensuring vehicle safety and remaining power constraints, electric vehicles can provide temporary energy support for critical load nodes and emergency repair tasks, achieving an innovative post-disaster recovery model that shifts from "single-repair-driven" to "energy-task synergy."

[0111] This invention constructs a two-layer optimization architecture for energy-task coordination to achieve multi-dimensional collaborative scheduling: A two-layer optimization model is designed, coupling upper-layer virtual power plant energy coordination with lower-layer emergency repair scheduling. The upper layer optimizes the output allocation of the virtual power plant with the goal of maximizing the utilization of locally available energy; the lower layer optimizes the emergency repair path and task sequence with multiple objectives, including minimizing repair time, energy gap, and transportation costs. The two-layer model, through the tight coupling of energy conservation and time constraints, solves the problem of the separation between energy supply and emergency repair scheduling in traditional methods, achieving integrated coordination of "energy support—task allocation—path planning".

[0112] This invention innovatively introduces an energy accessibility model and dynamic matching mechanism to adapt to complex post-disaster environments: It constructs an energy transfer matrix to quantify the energy transmission attenuation relationship between virtual power plant nodes and load nodes, taking into account traffic accessibility, energy transmission efficiency, and time-varying road conditions. Simultaneously, it establishes a virtual power plant energy-task matching matrix to comprehensively evaluate energy supply potential, task response delay, and repair priority, enabling dynamic selection and priority adjustment of available energy nodes, effectively addressing complex constraints such as road blockages and communication restrictions after disasters.

[0113] This invention employs a multi-objective evolutionary algorithm to solve the problem, balancing recovery efficiency and economy: An improved non-dominated sorting genetic algorithm (NSGA-II) is used to perform Pareto optimization on three conflicting objectives—repair time, energy gap, and economic loss—and an energy penalty coefficient is introduced to ensure energy supply feasibility. Compared to single-objective or static optimization methods, this method achieves the best balance between recovery speed, energy supply continuity, and economic cost, outputting diverse optimal scheduling schemes for decision-makers to choose from.

[0114] Provides visual decision support and dynamic evaluation capabilities: This invention includes a result visualization module that can generate energy recovery curves and repair time sequence diagrams, and calculate energy recovery rate and load recovery rate, enabling dynamic tracking and performance evaluation of the post-disaster recovery process, and providing data support for emergency command and subsequent optimization review.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for collaborative emergency repair and energy optimization scheduling of virtual power plants based on electric vehicle mobile energy storage in post-disaster scenarios, characterized in that, Includes the following steps: Step S1: Construct an energy accessibility model and a virtual power plant node model for the post-disaster power system; the virtual power plant nodes include at least fixed distributed power generation nodes, energy storage device nodes, and electric vehicle nodes with bidirectional charging and discharging capabilities; Step S2: Based on post-disaster traffic topology and communication constraints, establish a priority matrix for emergency repair tasks; Step S3: Based on the virtual power plant node model constructed in step S1 and the emergency repair task priority matrix established in step S2, construct the virtual power plant energy-task matching matrix to realize the dynamic selection of available power nodes. Step S4: Based on the energy reachability model constructed in Step S1 and the virtual power plant energy-task matching matrix constructed in Step S3, the support relationships between virtual power plant nodes and task nodes are pre-screened and sorted to form a candidate set of available energy nodes and a priority sequence for task matching. This set is used as the initial basis for upper-level virtual power plant output allocation and lower-level emergency repair task scheduling, thus constructing a two-layer optimization model for post-disaster emergency repair and energy supply. The two-layer optimization model includes an upper-level virtual power plant energy coordination model and a lower-level emergency repair scheduling model. Step S5: The improved multi-objective evolutionary algorithm is used to solve the two-layer optimization model constructed in step S4. The optimization objectives are repair time, energy gap and economic loss. The multi-objective optimal emergency repair path and virtual power plant coordination scheme are output.

2. The method according to claim 1, characterized in that, The energy accessibility model in step S1 is achieved by establishing an energy transfer matrix between the virtual power plant node and the load node to be repaired, which is defined as follows: ； in, Represents virtual power plant nodes For load nodes The achievable energy transmission capability; For virtual power plant nodes Available power; For virtual power plant nodes With load nodes Accessible distance between them by transportation; For energy transfer efficiency; , This is the attenuation coefficient.

3. The method according to claim 2, characterized in that, The emergency repair task priority matrix in step S2 comprehensively considers the importance index of nodes. Node load loss Communication availability and VPP's maximum energy supply potential The calculation formula is as follows: ； In the formula, in the formula, , , , There are four weighting coefficients.

4. The method according to claim 3, characterized in that, The virtual power plant energy-task matching matrix in step S3 comprehensively considers energy supply potential, task response delay, and emergency repair task priority. Its calculation formula is as follows: ； In the formula, Represents virtual power plant nodes For load nodes The achievable energy transmission capability; Maximum transmittance; To provide emergency repair teams with support from VPP nodes To the task node Travel time; The maximum allowable delay; coefficient , , The impacts of energy supply potential, traffic delays, and repair task priority on overall coordination efficiency were measured separately; among them... This represents the energy-task matching degree between virtual power plant node i and the node j to be repaired, used to comprehensively characterize the degree of support and adaptability of the virtual power plant node for a specific emergency repair task. This serves as the basis for matching evaluation results and scheduling inputs before constructing the two-level optimization model.

5. The method according to claim 4, characterized in that, Based on the initial criteria for upper-level virtual power plant output allocation and lower-level emergency repair task scheduling, the upper-level virtual power plant energy coordination model in step S4 aims to maximize the utilization of local energy after a disaster, and its optimization function is: ； In the formula, To maximize the output of the node, As a penalty factor, For virtual power plant nodes Available power; For virtual power plant nodes With load nodes Accessible distance between them by transportation; The lower-level emergency repair scheduling model has multiple objectives: minimizing repair time, minimizing energy deficit, and minimizing traffic costs. The optimization function is: ； In the formula, The total time required to complete the emergency repairs; This refers to load loss caused by insufficient energy. This represents the total distance traveled by the repair team. , , These are the multi-objective weighting coefficients.

6. The method according to claim 1, characterized in that, In step S5, the improved non-dominated sorting genetic algorithm NSGA-II is used for multi-objective solution, and the fitness function is: ； In the formula, The energy requirement for node i; This is the energy penalty coefficient; The scheduling process must also satisfy energy conservation and time constraints: ； ； In the formula, For nodes At any moment Energy requirements; This refers to the completion time of the task node's repair. This is the maximum emergency repair time allowed by the system.

7. A virtual power plant collaborative repair and energy optimization scheduling device based on electric vehicle mobile energy storage for post-disaster scenarios, used to implement the method of any one of claims 1 to 6, characterized in that, include: The model building module is used to build an energy accessibility model and a virtual power plant node model for the post-disaster power system; the virtual power plant node includes at least a fixed distributed power generation node, an energy storage device node, and an electric vehicle node with bidirectional charging and discharging capabilities; The emergency repair task priority matrix establishment module is used to establish an emergency repair task priority matrix based on post-disaster traffic topology and communication constraints. The emergency repair task priority matrix construction module is used to construct a virtual power plant energy-task matching matrix based on the virtual power plant node model and the emergency repair task priority matrix, so as to realize the dynamic selection of available power nodes. A two-layer optimization model construction module is used to pre-screen and sort the support relationships between virtual power plant nodes and task nodes based on the energy accessibility model and the virtual power plant energy-task matching matrix, forming a candidate set of available energy nodes and a priority sequence of task matching, which serves as the initial basis for upper-layer virtual power plant output allocation and lower-layer emergency repair task scheduling, thus constructing a post-disaster emergency repair-energy supply two-layer optimization model; the two-layer optimization model includes an upper-layer virtual power plant energy coordination model and a lower-layer emergency repair scheduling model; The model solving and scheduling module is used to solve the two-layer optimization model using an improved multi-objective evolutionary algorithm, with repair time, energy gap and economic loss as optimization objectives, and outputs the multi-objective optimal emergency repair path and virtual power plant coordination scheme.

8. The apparatus according to claim 7, characterized in that, The energy reachability model constructed by the model building module is achieved by establishing an energy transfer matrix between virtual power plant nodes and load nodes to be repaired, which is defined as follows: ； in, Represents virtual power plant nodes For load nodes The achievable energy transmission capability; For virtual power plant nodes Available power; For virtual power plant nodes With load nodes Accessible distance between them by transportation; For energy transfer efficiency; , This is the attenuation coefficient.

9. The apparatus according to claim 7, characterized in that, The upper-level virtual power plant energy coordination model aims to maximize the utilization of local energy resources after a disaster, and its optimization function is: ； In the formula, To maximize the output of the node, As a penalty factor, For virtual power plant nodes Available power; For virtual power plant nodes With load nodes Accessible distance between them by transportation; The lower-level emergency repair scheduling model has multiple objectives: minimizing repair time, minimizing energy deficit, and minimizing traffic costs. The optimization function is: ； In the formula, The total time required to complete the emergency repairs; This refers to load loss caused by insufficient energy. This represents the total distance traveled by the repair team. , , These are the multi-objective weighting coefficients.

10. The apparatus according to claim 7, characterized in that, The model solving and scheduling module uses the improved non-dominated sorting genetic algorithm NSGA-II for multi-objective solving, with the fitness function as follows: ； In the formula, The energy requirement for node i; This is the energy penalty coefficient; The scheduling process must also satisfy energy conservation and time constraints: ； ； In the formula, For nodes At any moment Energy requirements; This refers to the completion time of the task node's repair. This is the maximum emergency repair time allowed by the system.

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