Power distribution system fault recovery method and system considering mobile emergency energy scheduling

Abstract

The application provides a power distribution system fault recovery method and system considering mobile emergency energy scheduling, and belongs to the field of power system automation. The method comprises the following steps: a double-layer collaborative optimization model is constructed, the double-layer collaborative optimization model comprises an upper-layer self-healing reconstruction model and a lower-layer mobile emergency energy scheduling model; an improved whale optimization algorithm is used to iteratively solve the double-layer collaborative optimization model. A collaborative optimization switch state and a mobile emergency energy scheduling scheme are obtained; according to the solving result, an integrated self-healing scheme containing a switch operation sequence and a mobile emergency energy scheduling instruction is output and is executed. The application realizes the cooperation of the three domains of power topology, mobile energy and traffic network, effectively improves the power supply recovery efficiency, reliability and economy, and enhances the disaster resistance of the power distribution network.

Description

Technical Field

[0001] This invention belongs to the field of power system automation technology, and particularly relates to a method and system for fault recovery of power distribution systems that takes into account mobile emergency energy dispatch. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the advancement of new power system construction, distribution systems are rapidly evolving towards active distribution networks with a high proportion of distributed power sources and frequent source-load interactions. Extreme natural disasters and emergencies can easily trigger multi-segment faults and equipment damage in the distribution network, causing widespread and prolonged power outages, significantly increasing the requirements for the system's fault self-healing capabilities. Self-healing technology can achieve autonomous fault identification, isolation, and rapid power restoration, and is a core direction for enhancing grid resilience.

[0004] Traditional fault recovery primarily relies on network reconfiguration and isolated operation of local distributed power sources, which has significant limitations. Distributed power output is highly susceptible to environmental influences and exhibits strong fluctuations; in disaster scenarios, its generating capacity is insufficient to continuously support critical loads. Simply relying on static topology adjustments cannot effectively compensate for power deficits, limiting the scope and effectiveness of recovery. To supplement emergency capabilities, existing technologies mostly employ fixed-location emergency power generation vehicles and diesel generators; however, their fixed locations and poor response flexibility prevent dynamic allocation based on fault location and load importance.

[0005] Mobile Emergency Energy (MEER) possesses advantages such as mobility, dispatchability, and plug-and-play capability, making it an important resource for enhancing emergency resilience. However, existing dispatch strategies and distribution network self-healing decisions operate independently, lacking deep collaboration with network reconfiguration, resulting in low resource utilization efficiency. Furthermore, most recovery models fail to consider actual road network conditions such as post-disaster road damage and traffic congestion, planning routes based on ideal road conditions. This leads to significant errors in the estimated arrival time of mobile energy, resulting in poor engineering feasibility. In addition, optimization objectives are singular, focusing only on minimizing power loss load or the fewest switching operations, without comprehensively considering dispatch costs, compensation benefits, network losses, and outage losses, making it difficult to balance reliability and economy.

[0006] From a solution perspective, this problem belongs to the category of mixed-integer nonlinear programming, with both discrete and continuous variables, resulting in high solution complexity. Traditional intelligent optimization algorithms are prone to getting trapped in local optima and have slow convergence speeds, failing to meet the real-time requirements of fault recovery. Summary of the Invention

[0007] To overcome the shortcomings of the prior art, this invention provides a method and system for power distribution system fault recovery that considers mobile emergency energy dispatch. By constructing a two-layer collaborative optimization model and combining it with the improved Whale Optimization Algorithm (IWOA) for solving, the integrated collaboration between power distribution network self-healing reconfiguration and dynamic dispatch of mobile emergency energy is achieved.

[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides a method for fault recovery of a power distribution system that takes into account mobile emergency energy dispatch; Methods for power distribution system fault recovery considering mobile emergency energy dispatch include: Step S1: Construct a two-layer collaborative optimization model, which includes an upper-layer self-healing reconfiguration model and a lower-layer mobile emergency energy dispatch model. The upper-layer self-healing reconfiguration model aims to minimize the number of switching actions and network active power loss, optimizes the state of segmented switches and tie switches, generates a radial island scheme, and uses candidate access nodes of mobile emergency energy as decision variables to calculate the net power deficit of each island and pass it to the lower layer. The lower-level mobile emergency energy dispatch model aims to maximize the recovery of critical loads and the comprehensive dispatch benefits of mobile emergency energy. It introduces a traffic congestion coefficient to correct travel time, plans the travel path, access node and charging and discharging strategy of mobile emergency energy based on the net power deficit, and feeds back the available power to the upper-level model to correct the load distribution and topology within the island. Step S2: The improved whale optimization algorithm is used to iteratively solve the two-layer collaborative optimization model. The improved whale optimization algorithm, based on the feedback information of power deficit and available power between the upper and lower layers, uses nonlinear adaptive convergence factor, optimal individual local disturbance mechanism and dynamic spiral parameter adjustment to collaboratively optimize the switching state and mobile emergency energy dispatch scheme. Step S3: Based on the solution results, output an integrated self-healing solution that includes a switch operation sequence and a mobile emergency energy dispatch command, and issue it for execution.

[0009] As a further technical solution, the objective function of the upper-layer self-healing reconstruction model is:

[0010] In the formula, The overall optimization objective function of the upper-level self-healing reconstruction model; This represents the total number of switch operations. For the total active power loss of the network, , It is a positive weighting coefficient, and ; This refers to the number of a single branch road; It is the set of all branches in the distribution network; This is the set of states of all switches in the system. This is a set of candidate access nodes for mobile emergency power.

[0011] As a further technical solution, the objective function of the lower-level mobile emergency energy dispatch model is:

[0012] in, The overall optimization objective function for the lower-level MEER scheduling is to maximize it. To restore the total critical load, The power grid compensation cost per unit load restored. The unit time scheduling cost of MEER Let k be the passage time of the k-th MEER. The set of MEER units participating in the scheduling; , The positive weighting coefficients of the lower-level mobile emergency energy dispatch model and .

[0013] As a further technical solution, the introduction of a traffic congestion coefficient to correct travel time specifically involves:

[0014] in, Let k be the passage time of the kth MEER. This represents the distance traveled. Where is the rated vehicle speed, and c is the traffic coefficient representing the degree of road congestion after a natural disaster. The value of c is related to the road conditions and traffic flow after the disaster.

[0015] As a further technical solution, the two-layer collaborative optimization model also satisfies power balance constraints, node voltage constraints, dynamic constraints on the state of charge of mobile emergency energy sources, mutual exclusion and power limit constraints on the charging and discharging of mobile emergency energy sources, and radial topology constraints of the distribution network.

[0016] As a further technical solution, the nonlinear adaptive convergence factor is:

[0017] In the formula, This represents the number of iterations. This represents the maximum number of iterations. The probability of an exponentially decreasing convergence factor; It is a random number. The value of implies that the convergence factor decreases in different ways; The optimal local perturbation mechanism for individuals is as follows:

[0018] like If the result is positive, then update the optimal individual; otherwise, retain the individual. For the disturbance intensity, These are random numbers that follow a standard normal distribution.

[0019] The dynamic spiral parameter is adjusted as follows:

[0020] In the formula, , which is the distance vector between the current individual and the best individual; The globally optimal position found in the t-th iteration; for[ A random number between [1, 1]; For adaptive spinor parameters; This represents the maximum number of iterations.

[0021] A second aspect of the present invention provides a power distribution system fault recovery system that takes into account mobile emergency energy dispatch.

[0022] A power distribution system fault recovery system considering mobile emergency energy dispatch includes: The dual-layer optimization model construction module is configured to: construct a dual-layer collaborative optimization model, which includes an upper-layer self-healing reconfiguration model and a lower-layer mobile emergency energy dispatch model. The upper-layer self-healing reconfiguration model aims to minimize the number of switching actions and network active power loss, optimizes the state of segmented switches and tie switches, generates a radial island scheme, and uses candidate access nodes of mobile emergency energy as decision variables to calculate the net power deficit of each island and pass it to the lower layer. The lower-level mobile emergency energy dispatch model aims to maximize the recovery of critical loads and the comprehensive dispatch benefits of mobile emergency energy. It introduces a traffic congestion coefficient to correct travel time, plans the travel path, access node and charging and discharging strategy of mobile emergency energy based on the net power deficit, and feeds back the available power to the upper-level model to correct the load distribution and topology within the island. The optimization algorithm solution module is configured to: use an improved whale optimization algorithm to iteratively solve the two-layer collaborative optimization model; the improved whale optimization algorithm, based on the feedback information of power deficit and available power between the upper and lower layers, uses a nonlinear adaptive convergence factor, an optimal individual local disturbance mechanism, and dynamic spiral parameter adjustment to collaboratively optimize the switching state and mobile emergency energy dispatch scheme. The instruction issuance and execution module is configured to: output an integrated self-healing solution containing a switch operation sequence and a mobile emergency energy dispatch instruction based on the solution results, and issue it for execution.

[0023] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of a power distribution system fault recovery method considering mobile emergency energy dispatch as described in the first aspect of the present invention.

[0024] A fourth aspect of the present invention provides an electronic device including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of a power distribution system fault recovery method considering mobile emergency energy dispatch as described in the first aspect of the present invention.

[0025] The fifth aspect of the present invention provides a computer program product, including a computer program / instruction that, when executed by a processor, implements the steps of the fault recovery method for a power distribution system considering mobile emergency energy dispatch as described in the first aspect of the present invention.

[0026] The above one or more technical solutions have the following beneficial effects: (1) This invention deeply couples mobile emergency energy with the self-healing reconfiguration of the distribution network, breaking through the limitations of traditional methods that rely solely on network reconfiguration and local distributed power sources. It uses mobile emergency energy as a flexibly deployable virtual power source to participate in topology optimization, significantly improving the ability to fill power gaps and support critical load power supply under extreme disasters. Compared with fixed emergency power sources, resources can be dynamically allocated according to the fault location and load importance, greatly expanding the power supply restoration range and improving the restoration speed, fundamentally enhancing the disaster resistance resilience and power supply reliability of the distribution network.

[0027] (2) This invention introduces a post-disaster traffic congestion coefficient to accurately correct the travel time and route planning of mobile emergency energy, making the dispatching scheme conform to the actual road network conditions at the disaster site. This effectively solves the problem of recovery delays caused by road condition prediction deviations in traditional methods and significantly improves the feasibility of the scheme. At the same time, it adopts multi-objective collaborative optimization to comprehensively balance the load recovery amount, operating costs, grid revenue and network losses, thereby achieving a balance between power supply reliability and operational economy, and improving the efficiency of emergency resource utilization and overall benefits.

[0028] (3) The present invention adopts the improved whale optimization algorithm to solve the two-layer collaborative model. Through three major improvements, namely nonlinear convergence factor, optimal individual perturbation and dynamic spiral parameter, the global search capability and convergence accuracy are greatly improved. It overcomes the defects of traditional algorithm that are prone to premature convergence and slow convergence, meets the requirements of fault recovery real-time and scheme accuracy, forms an integrated self-healing decision, and significantly improves the rapid autonomous recovery capability of distribution network under complex faults.

[0029] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0030] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0031] Figure 1 This is a flowchart of the method in the first embodiment.

[0032] Figure 2 This is a flowchart of the fault self-healing recovery method of the two-layer collaborative optimization model in the first embodiment.

[0033] Figure 3 This is a flowchart of the improved whale optimization algorithm in the first embodiment.

[0034] Figure 4 This is a system structure diagram of the second embodiment. Detailed Implementation

[0035] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0036] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0037] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0038] The overall approach of this invention addresses the challenge of power distribution system fault recovery under extreme disasters. It constructs a two-layer collaborative optimization model that deeply couples the self-healing reconfiguration of the power distribution network with the dynamic scheduling of mobile emergency energy. The upper layer optimizes switch states and islanding, and calculates power deficits. The lower layer, considering post-disaster traffic conditions, completes resource scheduling and path planning, introducing traffic congestion coefficients to improve the feasibility of the solution. Multi-objective optimization is employed to balance reliability and economy. Furthermore, an improved whale optimization algorithm is proposed to address the premature convergence and slow convergence issues of mixed-integer nonlinear programming. Ultimately, this achieves rapid self-healing across the three domains of electrical topology, mobile energy, and transportation networks, comprehensively enhancing the disaster resilience of the power distribution network.

[0039] Example 1 When power distribution systems encounter extreme natural disasters such as typhoons, earthquakes, and snowstorms, complex fault scenarios such as multi-section line failures, equipment damage, and large-scale load power loss are prone to occur. In such situations, traditional recovery methods relying on network reconstruction and local distributed power sources are insufficient to address issues such as insufficient power output, difficulty in filling power gaps, and inflexible allocation of emergency resources. Furthermore, post-disaster road congestion and traffic restrictions further exacerbate the disconnect between emergency dispatch plans and the actual situation on the ground, resulting in low efficiency and poor reliability of power restoration.

[0040] In response to the aforementioned complex faults and extreme operating scenarios, and to achieve rapid power restoration in non-faulty areas after fault isolation, this embodiment discloses a power distribution system fault recovery method that considers mobile emergency energy dispatch, taking into account reliability, economy, and on-site feasibility, and comprehensively optimizing distribution network topology reconfiguration and dynamic dispatch of mobile emergency energy.

[0041] Specifically, such as Figure 1 As shown, a power distribution system fault recovery method considering mobile emergency energy dispatch includes: Step S1: Construct a two-layer collaborative optimization model, which includes an upper-layer self-healing reconfiguration model and a lower-layer mobile emergency energy dispatch model.

[0042] like Figure 2 As shown, when a power distribution system experiences multi-section line faults, equipment damage, and widespread load outages, the distribution automation terminal collects fault information, switch status, distributed power output, load power, and importance levels to identify permanent fault sections and preliminarily delineate non-fault-related power loss areas that can form isolated operation zones. Simultaneously, it acquires the location, capacity, and power parameters of mobile emergency power sources (MEERs), as well as post-disaster road traffic, congestion, and damage information, providing input data for the construction of a two-layer collaborative optimization model.

[0043] Based on the acquired data, a two-layer collaborative optimization model is constructed, which includes an upper-layer self-healing reconfiguration model and a lower-layer mobile emergency energy dispatch model. The upper-layer self-healing reconfiguration model is oriented towards distribution network self-healing reconfiguration decision-making. Its core task is to form several safe and stable islanded operation units by optimizing the switching operation strategy after isolating the fault area.

[0044] Unlike traditional fault recovery methods that rely solely on internal distributed generation (DG), this model treats the Mobile Access Node (MEER) as a virtual power injection point with variable location and controllable power. The MEER's access location not only determines its power supply path to the power-loss load but also directly affects the network topology, power flow distribution, and line losses. Therefore, this model explicitly introduces the set of candidate MEER access nodes, nmeer, as a key decision variable into the optimization framework, constructing an extended collaborative optimization space encompassing "mobile resource deployment—on / off state adjustment—power flow redistribution."

[0045] Specifically, the upper-layer self-healing reconfiguration model has two optimization objectives: minimizing the total number of switching actions and minimizing the total active power loss of the network. This ensures both power restoration speed and system operational efficiency. The number of switching actions reflects operational convenience and equipment wear costs, while network active power loss reflects power transmission efficiency and the economic efficiency of MEER energy utilization. These two are balanced through weighting coefficients and can be dynamically adjusted based on disaster level, load importance, or MEER endurance status. The objective function is shown in the following equation:

[0046] In the formula, The overall optimization objective function of the upper-level self-healing reconstruction model; This represents the total number of switch operations. For the total active power loss of the network, , It is a positive weighting coefficient, and ; This refers to the number of a single branch road; It is the set of all branches in the distribution network; This is the set of states of all switches in the system. This is a set of candidate access nodes for mobile emergency power.

[0047] The lower-level mobile emergency energy dispatch model is geared towards dynamic dispatch decision-making for MEERs. Its core task is to rationally allocate multiple MEER resources based on the power deficit information provided by the upper level, thereby maximizing the recovery of critical loads. The modeling considers both the power grid's reliability requirements and the economic benefits for MEER operators, with the optimization objectives being to maximize the total load recovery and the overall MEER dispatch revenue (i.e., grid compensation revenue minus dispatch costs). Specifically, the model incorporates a post-disaster traffic congestion coefficient to accurately characterize MEER travel time, ensuring the dispatch scheme's engineering feasibility. The objective function is shown below:

[0048] in, The overall optimization objective function for the lower-level MEER scheduling is to maximize it. To restore the total critical load, The power grid compensation cost per unit load restored. The unit time scheduling cost of MEER Let k be the passage time of the k-th MEER. The set of MEER units participating in the scheduling; , The positive weighting coefficients of the lower-level mobile emergency energy dispatch model and .

[0049] To accurately reflect the impact of road traffic conditions on the delivery time of Mobile Emergency Energy (MEER) after a natural disaster, this embodiment also introduces a traffic congestion coefficient into the lower-level scheduling model to dynamically correct the actual travel speed of the MEER. Specifically, the travel time required for the MEER to travel from its initial standby position to the target access point depends not only on the path distance and the vehicle's rated speed, but also on the degree of congestion in the post-disaster road network: the more congested the roads, the lower the effective travel speed and the longer the travel time. As shown in the following formula:

[0050] in, Let k be the passage time of the kth MEER. This represents the distance traveled. Where is the rated vehicle speed, and c is the traffic coefficient representing the degree of road congestion after a natural disaster. The value of c is related to the road conditions and traffic flow after the disaster.

[0051] Compared with traditional methods that rely solely on fixed distributed power sources and separate network reconfiguration from emergency resource scheduling, this embodiment explicitly couples MEER access location decisions in the upper-layer reconfiguration model and drives precise MEER delivery with lower-layer power deficit as a hard constraint. For the first time, it achieves closed-loop self-healing through the collaboration of three domains: "electrical topology optimization, mobile energy deployment, and traffic route planning," significantly improving the recovery efficiency, resource utilization economy, and engineering feasibility of the distribution network under extreme disasters.

[0052] Furthermore, during the optimization process, the two-layer collaborative optimization model must simultaneously satisfy five constraints: power balance constraint, node voltage constraint, dynamic constraint of the charge state of mobile emergency energy source, mutual exclusion and power limit constraint of mobile emergency energy source charging and discharging, and radial topology constraint of distribution network, in order to ensure that the fault recovery scheme is safe, feasible, and engineering-executable.

[0053] (1) Power balance constraint means that the sum of the active power output of all distributed power sources and the active power output of mobile emergency power sources within the island is equal to the sum of the total load power within the island and the network active power loss, ensuring the balance of active power supply and demand in the system and avoiding power shortage or excess that could lead to system instability. Specifically:

[0054] In the formula, n is the number of distributed power sources in the island; Z is the number of mobile emergency power sources (MEERs) connected to the island; The active power output of the i-th DG during time period t; Let be the discharge power of the k-th MEER; Let be the load power of node j; Let N be the network active power loss of the island during time period t; N is the set of load nodes within the island.

[0055] (2) Node voltage constraints refer to the requirement that, after power supply is restored, the voltage amplitude of all nodes in the system must be maintained within the specified safe upper and lower limits to prevent equipment insulation damage and load malfunction due to voltage exceeding limits, thus ensuring the stable operation of the distribution network. Specifically: After recovery, the voltage at each node must be maintained within safe limits:

[0056] In the formula, For nodes The voltage amplitude; , These correspond to nodes respectively. The minimum and maximum point voltages; V is the set of all nodes in the system.

[0057] (3) MEER (Mechanical Energy Storage and Emergency Energy) state of charge dynamic constraint refers to the dynamic change of the state of charge of the mobile emergency energy source during the charging and discharging process. The SOC value at each moment must be maintained between the minimum and maximum limits to ensure that the energy storage unit does not overcharge or over-discharge, extend the service life of the equipment, and ensure the continuity of power supply. Specifically: The state of charge of MEER evolves dynamically during the charging and discharging process and is constrained by capacity limits:

[0058]

[0059] In the formula, Let the state of charge of the k-th MEER be defined in time period t. Its rated capacity; , These are the charging and discharging power, respectively. , For charge and discharge efficiency; The scheduling time step; For the MEER set.

[0060] (4) MEER charge / discharge mutual exclusion and power limit constraint means that mobile emergency energy cannot be charged and discharged simultaneously at the same time, and the charging and discharging power shall not exceed the rated maximum limit of the equipment, so as to meet the physical operating characteristics of the equipment and avoid overload damage. Specifically: MEERs cannot be charged and discharged simultaneously at the same time, and the power must not exceed the equipment limit.

[0061]

[0062]

[0063] In the formula, , These are the maximum charging power and the maximum discharging power, respectively. , It is a binary variable representing the charging and discharging status (where 1 is enabled and 0 is disabled).

[0064] (5) Distribution network topology constraints refer to the requirement that the power supply network formed after fault recovery must remain connected and have a radial structure, meeting the requirements of distribution network operation specifications and relay protection coordination, and avoiding problems such as ring networks and isolated networks. Specifically: The restored network must be a connected radial structure:

[0065]

[0066] In the formula, A set of power supply nodes; It is a set of closed branches; Indicates a branch Whether it is in operation.

[0067] Step S2: The improved whale optimization algorithm is used to iteratively solve the two-layer collaborative optimization model and output the collaborative optimization switch state and mobile emergency energy dispatch scheme.

[0068] When solving mixed-integer nonlinear optimization problems such as power distribution system fault recovery, the original whale algorithm suffers from several drawbacks. Firstly, its convergence factor employs a linearly decreasing strategy, making it difficult to dynamically balance the early-stage global search with the later-stage local exploitation capabilities. This can easily lead to over-searching or under-exploitation. Secondly, the algorithm lacks a local perturbation mechanism, making it prone to getting trapped in local optima during iteration, resulting in premature convergence. Furthermore, because the spiral parameters are fixed, the early-stage search range is limited, and the later-stage optimization accuracy is insufficient, leading to slow convergence speed and low solution accuracy. Consequently, it fails to meet the real-time and optimal solution requirements of fault recovery.

[0069] Therefore, to efficiently solve the self-healing optimization model of the two-layer distribution network fault constructed in step S2, this embodiment proposes an improved whale optimization algorithm (IWOA). By designing a nonlinear adaptive convergence factor, introducing an optimal individual local perturbation mechanism, and dynamically adjusting the spiral parameters, the algorithm's global search capability, convergence accuracy, and robustness are significantly improved. This effectively overcomes the premature convergence problem and meets the stringent real-time requirements of fault recovery. The flowchart of the improved whale optimization algorithm is shown below. Figure 3 As shown.

[0070] To overcome the imbalance in search capability caused by the linear decrease of the convergence factor 'a' in the traditional whale optimization algorithm, this embodiment introduces a nonlinear decay strategy. This allows the convergence factor to be dynamically adjusted during the iteration process, achieving a balance between global exploration and local development. The update method for the convergence factor 'a' is as follows:

[0071] In the formula, This represents the number of iterations. This represents the maximum number of iterations. The probability of an exponentially decreasing convergence factor; It is a random number. The value of implies that the convergence factor decreases in different ways; Secondly, the idea of ​​optimal local perturbation is introduced into the optimization algorithm. This involves performing a small-scale random search around the optimal individual (i.e., local perturbation) to find individuals with higher fitness, thereby accelerating the global convergence of the algorithm and preventing it from getting trapped in local optima early on. The local perturbation generated by the optimal individual is shown in the following equation:

[0072] like If the result is positive, then update the optimal individual; otherwise, retain the individual. For the disturbance intensity, These are random numbers that follow a standard normal distribution.

[0073] To further improve the search space coverage, an adaptive screw parameter b is introduced, which varies with the number of iterations to control the range and density of the spiral search. The improved spiral update formula is as follows:

[0074] In the formula, , which is the distance vector between the current individual and the best individual; The globally optimal position found in the t-th iteration; for[ A random number between [1, 1]; For adaptive spinor parameters; This represents the maximum number of iterations.

[0075] The design principle of parameter b is based on the dynamic iterative change of the spiral equation. As the number of iterations increases, the shape of the spiral adjusts accordingly. In the early stages of the algorithm's iteration, due to the larger radius of the spiral, individuals can explore a wider search area, effectively covering more individuals in the population. This strategy enhances the algorithm's ability to search for the optimal solution globally. In the later stages of iteration, the radius of the spiral gradually decreases, allowing individuals to perform a refined search near the optimal solution, thereby improving the algorithm's convergence speed and optimization accuracy.

[0076] Step S3: Based on the solution results, output an integrated self-healing solution that includes a switch operation sequence and a mobile emergency energy dispatch command, and issue it for execution.

[0077] The optimal switch states output by the upper-level model are organized into a sequence of opening and closing operations for sectional switches and tie switches, clearly defining the operation order, timing, and execution nodes. Simultaneously, information such as mobile emergency energy paths, access nodes, departure times, and charging / discharging power output by the lower-level model is encapsulated into standardized scheduling instructions.

[0078] Subsequently, through the power distribution automation system and the mobile emergency energy management platform, the integrated self-healing solution is simultaneously distributed to the field terminals and execution units to complete fault isolation, network reconstruction, and plug-and-play mobile emergency energy supply, thereby achieving rapid recovery of power loss loads.

[0079] Example 2 This embodiment discloses a power distribution system fault recovery system that takes into account mobile emergency energy dispatch; like Figure 4 As shown, a power distribution system fault recovery system considering mobile emergency energy dispatch includes: The dual-layer optimization model construction module is configured to: construct a dual-layer collaborative optimization model, which includes an upper-layer self-healing reconfiguration model and a lower-layer mobile emergency energy dispatch model. The upper-layer self-healing reconfiguration model aims to minimize the number of switching actions and network active power loss, optimizes the state of segmented switches and tie switches, generates a radial island scheme, and uses candidate access nodes of mobile emergency energy as decision variables to calculate the net power deficit of each island and pass it to the lower layer. The lower-level mobile emergency energy dispatch model aims to maximize the recovery of critical loads and the comprehensive dispatch benefits of mobile emergency energy. It introduces a traffic congestion coefficient to correct travel time, plans the travel path, access node and charging and discharging strategy of mobile emergency energy based on the net power deficit, and feeds back the available power to the upper-level model to correct the load distribution and topology within the island. The optimization algorithm solution module is configured to: use an improved whale optimization algorithm to iteratively solve the two-layer collaborative optimization model; the improved whale optimization algorithm, based on the feedback information of power deficit and available power between the upper and lower layers, uses a nonlinear adaptive convergence factor, an optimal individual local disturbance mechanism, and dynamic spiral parameter adjustment to collaboratively optimize the switching state and mobile emergency energy dispatch scheme. The instruction issuance and execution module is configured to: output an integrated self-healing solution containing a switch operation sequence and a mobile emergency energy dispatch instruction based on the solution results, and issue it for execution.

[0080] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0081] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in a power distribution system fault recovery method considering mobile emergency energy dispatch as described in Embodiment 1.

[0082] Example 4 The purpose of this embodiment is to provide an electronic device.

[0083] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in a power distribution system fault recovery method considering mobile emergency energy dispatch as described in Embodiment 1.

[0084] Example 5 Embodiment 5 of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps in a power distribution system fault recovery method considering mobile emergency energy dispatch as described in Embodiment 1.

[0085] The steps and methods involved in the apparatuses of Embodiments 2, 3, 4, and 5 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0086] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0087] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A power distribution system fault recovery method considering mobile emergency energy dispatch, characterized in that, include: Step S1: Construct a two-layer collaborative optimization model, which includes an upper-layer self-healing reconfiguration model and a lower-layer mobile emergency energy dispatch model. The upper-layer self-healing reconfiguration model aims to minimize the number of switching actions and network active power loss, optimizes the state of segmented switches and tie switches, generates a radial island scheme, and uses candidate access nodes of mobile emergency energy as decision variables to calculate the net power deficit of each island and pass it to the lower layer. The lower-level mobile emergency energy dispatch model aims to maximize the recovery of critical loads and the comprehensive dispatch benefits of mobile emergency energy. It introduces a traffic congestion coefficient to correct travel time, plans the travel path, access node and charging and discharging strategy of mobile emergency energy based on the net power deficit, and feeds back the available power to the upper-level model to correct the load distribution and topology within the island. Step S2: The improved whale optimization algorithm is used to iteratively solve the two-layer collaborative optimization model. The improved whale optimization algorithm, based on the feedback information of power deficit and available power between the upper and lower layers, uses nonlinear adaptive convergence factor, optimal individual local disturbance mechanism and dynamic spiral parameter adjustment to collaboratively optimize the switching state and mobile emergency energy dispatch scheme. Step S3: Based on the solution results, output an integrated self-healing solution that includes a switch operation sequence and a mobile emergency energy dispatch command, and issue it for execution.

2. The power distribution system fault recovery method considering mobile emergency energy dispatch as described in claim 1, characterized in that, The objective function of the upper-layer self-healing reconstruction model is: In the formula, The overall optimization objective function of the upper-level self-healing reconstruction model; This represents the total number of switch operations. For the total active power loss of the network, , It is a positive weighting coefficient, and ; This refers to the number of a single branch road; It is the set of all branches in the distribution network; This is the set of states of all switches in the system. This is a set of candidate access nodes for mobile emergency power.

3. The power distribution system fault recovery method considering mobile emergency energy dispatch as described in claim 1, characterized in that, The objective function of the lower-level mobile emergency energy dispatch model is: in, The overall optimization objective function for the lower-level MEER scheduling is to maximize it. To restore the total critical load, The power grid compensation cost per unit load restored. The unit time scheduling cost of MEER Let k be the passage time of the k-th MEER. The set of MEER units participating in the scheduling; , The positive weighting coefficients of the lower-level mobile emergency energy dispatch model and .

4. The power distribution system fault recovery method considering mobile emergency energy dispatch as described in claim 1, characterized in that, The introduction of a traffic congestion factor to correct travel time is specifically as follows: in, Let k be the passage time of the kth MEER. This represents the distance traveled. Where is the rated vehicle speed, and c is the traffic coefficient representing the degree of road congestion after a natural disaster. The value of c is related to the road conditions and traffic flow after the disaster.

5. The power distribution system fault recovery method considering mobile emergency energy dispatch as described in claim 1, characterized in that, The two-layer collaborative optimization model also satisfies power balance constraints, node voltage constraints, dynamic constraints on the state of charge of mobile emergency energy sources, mutual exclusion and power limit constraints on the charging and discharging of mobile emergency energy sources, and radial topology constraints of the distribution network.

6. The power distribution system fault recovery method considering mobile emergency energy dispatch as described in claim 1, characterized in that, The nonlinear adaptive convergence factor is: In the formula, This represents the number of iterations. This represents the maximum number of iterations. The probability of an exponentially decreasing convergence factor; It is a random number. The value of implies that the convergence factor decreases in different ways; The optimal local perturbation mechanism for individuals is as follows: like If the result is positive, then update the optimal individual; otherwise, retain the individual. For the disturbance intensity, These are random numbers distributed according to a standard normal distribution. The dynamic spiral parameter is adjusted as follows: In the formula, , which is the distance vector between the current individual and the best individual; The globally optimal position found in the t-th iteration; for[ A random number between [1, 1]; For adaptive spinor parameters; This represents the maximum number of iterations.

7. A power distribution system fault recovery system considering mobile emergency energy dispatch, characterized in that, include: The dual-layer optimization model construction module is configured to: construct a dual-layer collaborative optimization model, which includes an upper-layer self-healing reconfiguration model and a lower-layer mobile emergency energy dispatch model. The upper-layer self-healing reconfiguration model aims to minimize the number of switching actions and network active power loss, optimizes the state of segmented switches and tie switches, generates a radial island scheme, and uses candidate access nodes of mobile emergency energy as decision variables to calculate the net power deficit of each island and pass it to the lower layer. The lower-level mobile emergency energy dispatch model aims to maximize the recovery of critical loads and the comprehensive dispatch benefits of mobile emergency energy. It introduces a traffic congestion coefficient to correct travel time, plans the travel path, access node and charging and discharging strategy of mobile emergency energy based on the net power deficit, and feeds back the available power to the upper-level model to correct the load distribution and topology within the island. The optimization algorithm solution module is configured to: use an improved whale optimization algorithm to iteratively solve the two-layer collaborative optimization model; the improved whale optimization algorithm, based on the feedback information of power deficit and available power between the upper and lower layers, uses a nonlinear adaptive convergence factor, an optimal individual local disturbance mechanism, and dynamic spiral parameter adjustment to collaboratively optimize the switching state and mobile emergency energy dispatch scheme. The instruction issuance and execution module is configured to: output an integrated self-healing solution containing a switch operation sequence and a mobile emergency energy dispatch instruction based on the solution results, and issue it for execution.

8. A computer-readable storage medium having a program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the power distribution system fault recovery method considering mobile emergency energy dispatch as described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the power distribution system fault recovery method considering mobile emergency energy dispatch as described in any one of claims 1-6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps in the power distribution system fault recovery method considering mobile emergency energy dispatch as described in any one of claims 1-6.

Images