A multi-local microgrid distribution robust elasticity planning method and system considering mobile hydrogen energy storage coordination

Abstract

The application discloses a kind of considering mobile hydrogen energy storage coordination's multi-local microgrid distribution robust elasticity planning method and system, wherein the method comprises: constructing the operation model of electric-hydrogen coupling system considering mobile hydrogen carrier, to characterize the electric energy transmission capability of physical line and the hydrogen energy transfer capability of mobile hydrogen carrier between local microgrid;Disaster uncertainty set for describing extreme disaster scenario is constructed;Based on the operation model of electric-hydrogen coupling system and disaster uncertainty set, the distribution robust elasticity planning model including pre-disaster defense planning model, worst-case scenario attack model and post-disaster emergency dispatching model is constructed and the target elasticity planning scheme is obtained by joint iteration solution.The method can form cross-local alternative energy supply capacity by using mobile hydrogen carrier when physical line is damaged, realize trade-off between reducing line reinforcement investment and post-disaster emergency dispatching cost, improve the energy supply elasticity and operation economy of multi-local microgrid under extreme disaster.

Description

Technical Field

[0001] This invention belongs to the field of power grid planning and dispatching technology, specifically relating to a multi-local microgrid distributed bar elastic planning method and system that considers mobile hydrogen energy storage coordination. Background Technology

[0002] With the accelerated transition to a high proportion of renewable energy in the power system, geographically dispersed multi-local microgrid systems are widely used in island clusters, remote mountainous areas, and other regions. However, when these multi-local microgrids encounter extreme natural disasters such as typhoons and earthquakes, they not only face severe fluctuations in wind and solar power output, but their interconnecting cables and critical transmission lines within the local area network are also highly susceptible to damage and breakage, leading to serious power outages. Existing resilience planning and recovery mechanisms typically rely on reinforcing critical physical lines before a disaster and deploying conventional mobile energy storage devices after a disaster to maintain power supply.

[0003] However, in practical applications, for multi-regional microgrids with long spatial distances, relying solely on laying or reinforcing long-distance physical cables faces extremely high investment costs, and the disaster resistance capacity of the physical network has an upper limit. Once an extreme disaster causes multiple failures in the main line, the physical energy transmission channels between multiple local microgrids will be completely cut off, inevitably forming multiple isolated operating areas. At the same time, conventional mobile energy storage devices are limited by the physical characteristics of low battery energy density, making them unsuitable for long-distance, large-capacity cross-regional energy transmission tasks, and unable to provide continuous power supply support under harsh operating conditions or long-term isolated operation scenarios.

[0004] Consequently, the existing system faces a severe supply-demand imbalance under extreme disasters: on the one hand, critical physical lines are highly susceptible to being severed by extreme disasters, and attempting to prevent network outages through indiscriminate line reinforcement inevitably leads to a huge waste of conservative investment; on the other hand, long-distance cross-regional hydrogen energy dispatch faces dual constraints of economics and transportation capacity, resulting in the inability to effectively send hydrogen energy from resource-rich areas, while the cost of obtaining hydrogen energy for relief in remote disaster-stricken areas is too high. This contradiction between the excessively high cost of physical defense and the limited economics of mobile relief makes it impossible for the existing system to achieve energy sharing between isolated areas, ultimately leading to high load shedding penalties in areas with power shortages, while resource-rich areas suffer from severe power waste.

[0005] Therefore, existing technologies urgently need a method to break through the dual dilemma of excessively high physical defense costs and limited mobile rescue capabilities. This method should be based on a multi-local microgrid distributed elastic planning approach that coordinates pre-disaster fixed physical defense lines with post-disaster cross-regional flexible resources to form a closed-loop configuration for pre-disaster defense and post-disaster recovery. Summary of the Invention

[0006] One of the objectives of this invention is to at least solve one or more of the aforementioned problems in the prior art. In other words, one of the objectives of this invention is to provide a multi-local microgrid distributed bar elastic planning method and system that considers mobile hydrogen energy storage coordination to meet one or more of the aforementioned requirements.

[0007] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a multi-local microgrid distributed bar elastic planning method considering mobile hydrogen energy storage coordination, comprising the following steps: The physical network parameters of multiple local microgrids are obtained. Based on the physical network parameters, an operation model of the electric-hydrogen coupling system considering mobile hydrogen carriers is constructed. The electric-hydrogen coupling system operation model is used to characterize the power transmission capacity of the physical lines and the hydrogen energy transfer capacity of mobile hydrogen carriers between local microgrids. Construct a disaster uncertainty set to describe extreme disaster scenarios; Based on the operation model of the electric-hydrogen coupling system and the disaster uncertainty set, a sub-Bru bar elastic programming model is constructed. The sub-Bru bar elastic programming model includes a pre-disaster defense planning model, a worst-case attack model, and a post-disaster emergency dispatch model. Among them, the pre-disaster defense planning model takes line reinforcement decision and mobile hydrogen carrier spatiotemporal pre-scheduling decision as pre-disaster defense decision variables, and aims to minimize the sum of pre-disaster defense cost and post-disaster emergency scheduling cost under the worst disaster scenario. It determines the configuration relationship between line reinforcement planning that provides fixed power transmission capacity and mobile hydrogen carrier spatiotemporal pre-scheduling planning that provides cross-regional alternative energy supply capacity. The worst-case scenario attack model is used to identify the worst-case disaster scenario that maximizes the cost of post-disaster emergency response within the disaster uncertainty set. The post-disaster emergency dispatch model is used to dispatch equipment and mobile hydrogen transport vehicles in multiple local microgrids under the most severe disaster scenarios, so as to minimize the cost of solving the post-disaster emergency dispatch problem. The pre-disaster defense planning model, the worst-case attack model, and the post-disaster emergency dispatch model are jointly iteratively solved to obtain the target elastic planning scheme, which includes the route reinforcement planning and the spatiotemporal pre-dispatch planning of mobile hydrogen transport carriers.

[0008] As a preferred implementation method, the post-disaster emergency dispatch model schedules equipment and mobile hydrogen transport vehicles within multiple local area microgrids, including: When a disaster causes physical lines to become unavailable, resulting in a decrease in the power transmission capacity between local microgrids, mobile hydrogen carriers are dispatched to construct cross-regional virtual energy channels to replace the damaged physical lines. By coordinating hydrogen loading, transportation, unloading, or power generation operations between resource-rich local microgrids and load-deficient local microgrids, hydrogen energy is transferred across regions to compensate for the power transmission capacity gap.

[0009] As a preferred implementation, a disaster uncertainty set is constructed to describe extreme disaster scenarios, including: Historical operational data of multiple local area microgrids are obtained, and based on this historical operational data, a disaster uncertainty set is constructed to describe extreme disaster scenarios.

[0010] As a preferred implementation, a nested column and constraint generation algorithm is used to jointly iteratively solve the pre-disaster defense planning model, the worst-case attack model, and the post-disaster emergency dispatch model, including: Solve the pre-disaster defense planning model to obtain candidate route reinforcement plans and candidate spatiotemporal pre-scheduling plans for mobile hydrogen transport carriers; Under the planning of candidate route reinforcement and candidate mobile hydrogen transport carrier spatiotemporal pre-scheduling, the worst-case attack model is solved to determine the candidate worst-case disaster scenarios from the disaster uncertainty set; Under the candidate worst-case disaster scenario, solve the post-disaster emergency dispatch model to obtain the corresponding minimum post-disaster emergency dispatch cost; The candidate worst-case disaster scenario and the corresponding minimum post-disaster emergency dispatch cost are fed back into the pre-disaster defense planning model; Repeat the above steps until the preset convergence condition is met, and output the target elastic planning scheme.

[0011] As a further preferred implementation, the preset convergence conditions include: The lower bound is updated based on the target value of the pre-disaster defense planning model, and the upper bound is updated based on the candidate route reinforcement plan, the candidate mobile hydrogen carrier spatiotemporal pre-scheduling plan, and the corresponding minimum post-disaster emergency scheduling cost. When the relative error between the upper and lower bounds is not greater than the preset convergence threshold, the preset convergence condition is determined to be met.

[0012] As a preferred implementation, pre-disaster defense costs include line reinforcement costs and spatiotemporal pre-scheduling costs for mobile hydrogen transport vehicles, while post-disaster emergency scheduling costs include the operating costs of mobile hydrogen transport vehicles, the operating costs of electricity-hydrogen coupling equipment, power curtailment penalty costs, and load shedding penalty costs.

[0013] As a preferred implementation method, the pre-disaster defense planning model determines whether candidate physical lines should be reinforced based on the reduced post-disaster emergency dispatch costs and the increased line reinforcement costs after reinforcement.

[0014] As a preferred implementation, when candidate physical lines are not reinforced or are unavailable under the worst disaster scenario, the post-disaster emergency dispatch model determines whether to activate mobile hydrogen carriers to perform cross-regional alternative energy supply based on the load deficit of the corresponding local microgrid, the surplus of renewable energy, and the availability of mobile hydrogen carriers.

[0015] As a preferred implementation, the spatiotemporal pre-scheduling decision of the mobile hydrogen transport vehicle is used to determine the initial response location and pre-scheduling path of the mobile hydrogen transport vehicle when a disaster occurs, including at least one of the following: the initial local microgrid where the mobile hydrogen transport vehicle resides before the disaster occurs, the initial hydrogen storage state, the pre-scheduling path, and the pre-scheduling period.

[0016] On the other hand, the present invention also provides a multi-local microgrid distributed bar elastic planning system considering mobile hydrogen energy storage coordination, comprising: The parameter acquisition module is used to acquire the physical network parameters of multiple local area micronets; The operational model construction module is used to construct an operational model of the electric-hydrogen coupling system considering mobile hydrogen carriers based on physical network parameters. The electric-hydrogen coupling system operational model is used to characterize the power transmission capacity of the physical lines and the hydrogen energy transfer capacity of the mobile hydrogen carriers between local microgrids. The uncertainty set construction module is used to construct disaster uncertainty sets to describe extreme disaster scenarios; The elastic planning model construction module is used to construct a sub-Bruker elastic planning model based on the electric-hydrogen coupling system operation model and disaster uncertainty set. The sub-Bruker elastic planning model includes a pre-disaster defense planning model, a worst-case scenario attack model, and a post-disaster emergency dispatch model. Among them, the pre-disaster defense planning model takes line reinforcement decision and mobile hydrogen carrier spatiotemporal pre-scheduling decision as pre-disaster defense decision variables, and aims to minimize the sum of pre-disaster defense cost and post-disaster emergency scheduling cost under the worst disaster scenario. It determines the configuration relationship between line reinforcement planning that provides fixed power transmission capacity and mobile hydrogen carrier spatiotemporal pre-scheduling planning that provides cross-regional alternative energy supply capacity. The worst-case scenario attack model is used to identify the worst-case disaster scenario that maximizes the cost of post-disaster emergency response within the disaster uncertainty set. The post-disaster emergency dispatch model is used to dispatch equipment and mobile hydrogen transport vehicles in multiple local microgrids under the most severe disaster scenarios, so as to minimize the cost of solving the post-disaster emergency dispatch problem. The joint iterative solution module is used to jointly iteratively solve the pre-disaster defense planning model, the worst-case attack model, and the post-disaster emergency dispatch model to obtain the target elastic planning scheme, which includes the route reinforcement planning and the spatiotemporal pre-schedule planning of mobile hydrogen transport carriers.

[0017] Compared with existing technologies, the multi-local microgrid distributed bar elastic planning method and system considering mobile hydrogen energy storage collaboration provided by this invention has the following beneficial effects: This invention utilizes mobile hydrogen transport carriers as cross-regional virtual energy channels that can replace and complement fixed power lines. Its post-disaster availability is embedded in the joint decision-making process of pre-disaster line reinforcement and spatiotemporal pre-deployment. This effectively allows post-disaster system recovery capabilities to be constrained and guided in the allocation ratio of rigid physical network reinforcement and flexible mobile resources before a disaster. It breaks the dual dilemma of excessively high physical defense costs and limited mobile rescue capabilities. Ultimately, in isolated scenarios where physical lines break due to extreme disasters, it avoids the conservative investment waste caused by indiscriminate line reinforcement and overcomes the distance and economic bottlenecks of conventional post-disaster rescue through pre-deployed high-energy-density mobile hydrogen transport carriers. This significantly reduces system load shedding losses and power waste, and significantly improves the power supply resilience and overall economy of multi-regional microgrids under uncertain disasters. Attached Figure Description

[0018] Figure 1 This is a flowchart of the multi-local microgrid distributed bar elastic planning method considering mobile hydrogen energy storage coordination according to the present invention; Figure 2 This is a load island topology diagram for a verification example of the present invention; Figure 3 This is a schematic diagram illustrating the specific scheduling of hydrogen transport vessels as a verification example of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0020] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of the invention. Various processes or components may be appropriately omitted, substituted, or added to the various examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0021] To facilitate understanding of the present invention, the multi-local area network used in the present invention will be introduced before specifically describing the embodiments of the present invention.

[0022] Multi-local microgrids are commonly found in power systems composed of geographically dispersed microgrids, such as island clusters, remote mountainous areas, and distributed industrial parks. Power transmission paths can be established between multiple local microgrids via physical lines. Each local microgrid can be equipped with wind power generation units, photovoltaic power generation units, loads, electrolysis devices, hydrogen storage devices, fuel cells, battery systems, and corresponding control equipment. Mobile hydrogen transport vehicles can be hydrogen transport vehicles, hydrogen transport ships, or other mobile carriers capable of transferring hydrogen energy between different local microgrids.

[0023] In island scenarios, mobile hydrogen transport vehicles can be hydrogen transport ships. Resource islands and load islands are equipped with docking port facilities to cooperate with hydrogen transport ships, enabling them to perform hydrogen loading, transportation, unloading, or power generation operations between resource-rich microgrids and load-deficient microgrids.

[0024] Embodiments of the present invention provide a multi-local microgrid distributed bar elastic planning method considering mobile hydrogen energy storage collaboration, the flowchart of which is shown below. Figure 1 As shown, steps S100-S400 are included: S100 acquires the physical network parameters of multiple local area micronets and constructs an operation model of the electric-hydrogen coupling system, which includes a mobile hydrogen transport carrier, based on the physical network parameters.

[0025] In one specific embodiment of the present invention, physical network parameters may include local microgrid sets, node sets, physical line sets, line connection relationships, line operating status, line reinforcement information, line power flow related parameters, upper and lower limits of line transmission capacity, renewable energy output, load demand, fuel cell parameters, electrolysis device parameters, hydrogen storage device parameters, battery system parameters, mobile hydrogen transport carrier parameters, and docking port facilities or docking point information.

[0026] The acquired physical network parameters are organized into a multi-local microgrid physical network parameter set and a device parameter set, and used as inputs for the mobile hydrogen transport carrier model, the stationary electric-hydrogen equipment model, the electric-hydrogen supply and demand balance model, and the line power flow model.

[0027] As a feasible example, the mobile hydrogen transport vehicle model takes parameters such as the local microgrid set, scheduling period, initial location of the mobile vehicle, and travel time as inputs to construct operational constraints that describe the transfer of hydrogen energy and collaborative power generation between local microgrids by the mobile hydrogen transport vehicle.

[0028] Specifically, it may include the following constraints: ; ; ; ; ; ; in: , and Let this be a Boolean variable, representing the mobile carrier's location in local micronet i at time t. When the mobile carrier travels at a speed k at time t When the mobile carrier generates electricity in region i ; This indicates the rated hourly hydrogen consumption of the mobile carrier at position k. This represents the amount of hydrogen consumed by the moving vehicle traveling at speed k. This indicates the amount of hydrogen consumed by the mobile power generation system. This represents the power generation of the mobile carrier in region i; This indicates the hydrogen storage state of the mobile carrier at time t; Indicates the rated hydrogen storage capacity of the mobile carrier; These represent the minimum and maximum hydrogen storage states of the mobile carrier, respectively; and This represents the power generation coefficient of the mobile carrier.

[0029] The stationary electric-hydrogen equipment model includes a fuel cell model, a hydrogen storage device model, an electrolysis device model, and a battery system model, which constructs the operational constraints for the conversion, storage, and release of electrical and hydrogen energy within a local microgrid.

[0030] The fuel cell model describes the relationship between fuel cell power generation, power generation indicators, rated power generation, and hydrogen consumption coefficient, enabling hydrogen energy to be converted into electrical energy when there is a load shortage. The hydrogen storage device model describes the rated hydrogen storage power, actual hydrogen storage power, hydrogen storage state, rated hydrogen storage capacity, upper and lower limits of hydrogen storage state, and initial hydrogen storage state. The electrolysis device model describes the water electrolysis power, operating indicators, rated water electrolysis power, hydrogen production power, and electrolysis device operating efficiency, enabling surplus renewable energy to be converted into hydrogen energy. The battery system model describes the battery charging indicators, discharging indicators, charging power, discharging power, maximum charge and discharge power, state of charge, rated capacity, and upper and lower limits of state of charge.

[0031] Specifically, the fuel cell model may include the following constraints: ; ; In the formula: This indicates the power generation capacity of the hydrogen fuel cell; A symbol indicating the power generation of a hydrogen fuel cell; This indicates the rated power output of the hydrogen fuel cell; and This represents the hydrogen consumption coefficient of the fuel cell.

[0032] Hydrogen storage device models may include the following constraints: ; ; ; In the formula: Indicates the rated hydrogen storage capacity of the hydrogen storage device; This indicates the actual hydrogen storage power of the hydrogen storage device at time t; This indicates the hydrogen storage state of the hydrogen storage device at time t; Indicates the rated hydrogen storage capacity of the hydrogen storage device; These represent the minimum and maximum hydrogen storage states of the hydrogen storage device, respectively. This indicates the initial hydrogen storage state of the hydrogen storage device.

[0033] The electrolysis device model may include the following constraints: ; ; In the formula: This indicates the actual water electrolysis power of the electrolysis device at time t; This is a Boolean variable; its value is 1 when the electrolysis device is operating. Indicates the rated water electrolysis power of the electrolysis unit; Indicates the hydrogen production capacity of the electrolysis unit; This indicates the rated operating efficiency of the electrolysis unit.

[0034] The battery system model may include the following constraints: ; ; ; ; ; ; In the formula: and This is a Boolean variable representing the time when the battery system is charging. When the battery system discharges ; and This indicates the charging and discharging power of the battery system; and These represent the maximum charging and discharging power of the battery system. Indicates the load status of the battery system; Indicates the rated capacity of the battery system; These represent the minimum and maximum values ​​of the battery system's state of charge, respectively.

[0035] In the electricity-hydrogen balance and line power flow model, the output of the mobile hydrogen transport carrier model, the output of the stationary electricity-hydrogen equipment model, the node load, the output of renewable energy, the line connection relationship, the line reinforcement status and the line damage status are used as inputs to establish electricity-hydrogen supply and demand balance constraints and physical line power flow constraints.

[0036] Among them, the hydrogen supply and demand balance constraint is used to ensure that renewable energy power generation, fuel cell power generation, battery charging and discharging, hydrogen production by electrolysis devices, hydrogen storage and release by hydrogen storage devices, hydrogen loading and unloading by mobile hydrogen carriers, and load demand meet the energy balance within the corresponding time period; the line power flow constraint is used to characterize the power transmission capacity of normal lines, damaged lines, reinforced lines, and unreinforced lines under disaster scenarios.

[0037] Specifically, the electricity-hydrogen balance and power flow model can include the following constraints: ; ; ; ; In the formula: M is a relatively large positive number; Indicates the line The reinforcement situation, when This indicates that the line is being reinforced. Indicates the line Whether it is damaged, when When the signal is clear, it indicates that the line is not damaged and can operate normally. Indicates the line at time t The power flowing through; Indicates the line susceptance; Indicates time line Phase angle difference of voltages at both ends of the node; and They represent the lines respectively. The upper and lower limits of transmission capacity.

[0038] In the above-mentioned electric-hydrogen coupling system operation model that takes into account the mobile hydrogen carrier, the physical circuit forms a fixed power transmission path, the electrolysis device converts surplus wind and solar power into hydrogen energy, the hydrogen storage device and the mobile hydrogen carrier carry the hydrogen energy, and the fuel cell converts the hydrogen energy into electrical energy to compensate for the load deficit. This operation model characterizes both the power transmission capacity of the physical circuit and the hydrogen energy transfer capacity of the mobile hydrogen carrier between local microgrids.

[0039] S200, constructing a disaster uncertainty set to describe extreme disaster scenarios.

[0040] In one specific embodiment of the present invention, the disaster uncertainty set includes a line fault uncertainty set and a net load uncertainty set. Extreme disasters can be typhoons, earthquakes, ice storms, floods, or other disasters that may cause physical line failures, fluctuations in renewable energy output, and abnormal changes in load demand.

[0041] In some preferred embodiments, before constructing the disaster uncertainty set to describe extreme disaster scenarios, historical operating data of multiple local area microgrids are first acquired. The disaster uncertainty set is constructed based on the historical operating data to provide a fuzzy set of possible values ​​and probability distributions of line fault status and net load for the worst-case scenario attack model in the absence of precise probability distributions of extreme disasters.

[0042] In the process of constructing the uncertainty set of line faults, the information entropy theory is used to describe the uncertainty of line faults. Information entropy usually refers to the Shannon information entropy that quantifies the uncertainty of random variables. The uncertainty set of line faults can be constructed based on the line fault rate and fault state.

[0043] Specifically, the relational expression for the uncertainty set of line faults is as follows: ; in, Indicates the line The failure rate; Represents the budget of the uncertain set of line fault entropy; This represents the collection of lines in a load island microgrid.

[0044] In the process of constructing the net load uncertainty set, a relational expression is used to describe the relationship between historical data carriers, discrete empirical distributions, Wasserstein distance, and fuzzy set radius, so as to output the net load or new energy output fuzzy set, which together with the line fault uncertainty set forms the search space of the worst-case attack model.

[0045] A single historical data entry is represented as follows: ; in, It is a single historical data carrier that contains hourly renewable energy output and load demand.

[0046] The relational expression for the net load uncertainty set is: ; ; in, It is a discrete distribution constructed based on historical data, where each historical data point is treated as a support point for a random variable, and the probability corresponding to each data point is 1. .

[0047] Since the empirical distribution is not the same as the true distribution, step S200 further uses the Wasserstein distance as a metric. Distance: ; ; ; To ensure that the fuzzy set can both contain a reasonable true probability distribution and avoid an overly conservative model due to an excessively wide range, the radius of the fuzzy set is constrained: ; in, This represents the empirical sample size. The diameter of the sample space; This represents the number of historical samples. This represents the confidence level of the sample data.

[0048] By utilizing the disaster uncertainty set composed of the line fault uncertainty set and the net load uncertainty set, the method of this application can simultaneously consider the impact of physical line unavailability, wind and solar power output fluctuations, and load demand changes on the resilient planning of multi-local microgrids.

[0049] The aforementioned uncertainty set does not require a completely accurate probability distribution under extreme disasters in advance. Instead, it forms a set of scenarios and probability distribution constraints that can be used by the sub-Bruker model based on historical data and distribution distance constraints, thereby providing input for the search of the worst-case scenario in S300.

[0050] S300, based on the operating model of the electric-hydrogen coupling system and the disaster uncertainty set, constructs a sub-Bruker elastic programming model.

[0051] The Bruker elastic programming model includes a pre-disaster defense planning model, a worst-case scenario attack model, and a post-disaster emergency dispatch model. The pre-disaster defense planning model generates the line reinforcement set and the initial response configuration of mobile vehicles; the worst-case scenario attack model generates candidate worst-case line fault states, net load / new energy output scenarios, and probability distributions; and the post-disaster emergency dispatch model generates the minimum post-disaster emergency dispatch cost and equipment and mobile vehicle dispatch schemes.

[0052] The decision variables of the pre-disaster defense planning model include decisions on line reinforcement and spatiotemporal pre-scheduling of mobile hydrogen transport carriers.

[0053] Among them, the line reinforcement decision is used to determine whether a specific physical line should be included in the line reinforcement plan; the spatiotemporal pre-scheduling decision of the mobile hydrogen transport vehicle is used to determine the initial response location and pre-scheduling path of the mobile hydrogen transport vehicle when a disaster occurs.

[0054] The output line reinforcement set and the initial response configuration of mobile vehicles complement each other in the pre-disaster defense planning model. The line reinforcement set is used to enhance the fixed power transmission capacity of some physical lines under disaster conditions; the initial response configuration of mobile vehicles is used to reduce the risk of post-disaster temporary dispatch being limited by travel time, journey time and initial available hydrogen.

[0055] When the lines are still available or the transmission capacity is sufficient to meet the power exchange needs between local microgrids, the fixed lines undertake the main power transmission tasks; when the lines are unavailable or the transmission capacity is reduced, the mobile hydrogen carriers compensate for the cross-regional energy gap by loading, transporting, unloading hydrogen or generating electricity.

[0056] Furthermore, the spatiotemporal pre-scheduling decision for mobile hydrogen transport carriers may also include at least one of the following: initial deployment location, initial hydrogen storage status, pre-scheduling route, and pre-scheduling period.

[0057] When determining whether candidate physical lines should be reinforced, the pre-disaster defense planning model uses an objective function to comprehensively weigh the reduction in post-disaster emergency dispatch costs under the worst-case scenario and the increased line reinforcement costs after reinforcing the candidate physical lines, while also meeting budget constraints.

[0058] Specifically, if a certain reinforcement combination minimizes the sum of pre-disaster defense costs and post-disaster emergency dispatch costs under the worst-case scenario within budget constraints, then that reinforcement combination can be considered as a candidate for the target line reinforcement set.

[0059] As a feasible example, the pre-disaster preparedness planning model uses the following objective function: ; in, This indicates that the investment cost for the first phase consists of the cost of reinforcing the route and the cost of pre-deploying hydrogen transport vessels. The function passed to the lower-level model represents the operating cost of the system under the most severe disaster scenario under the optimal planning scheme.

[0060] .

[0061] in, This represents the equivalent cost of line reinforcement. Indicates reinforcement of the line The cost; This indicates the price of hydrogen.

[0062] The calculation method for recovery costs is as follows:

[0063] in, Indicates the discount rate; Indicates the number of years the reinforced line has been in operation; This indicates the number of days affected by disasters each year.

[0064] It is understandable that the set of line reinforcement and the initial response configuration of mobile carriers are not determined independently, but are jointly constrained by the objective function in the same pre-disaster defense planning model.

[0065] Using the costs of line reinforcement and the pre-deployment costs of hydrogen transport vessels to form the investment cost allows the pre-disaster defense planning model to include any candidate physical line in the line reinforcement set if reinforcement can significantly reduce the operating costs, power curtailment penalties, or load shedding penalties of mobile hydrogen transport vehicles under the worst-case disaster scenario. If, given the initial response location, initial pre-scheduled path, and travel time constraints, mobile hydrogen transport vehicles can compensate for the reduction in power transmission capacity caused by the unavailability of the line with a lower post-disaster emergency dispatch cost, the pre-disaster defense planning model can reduce its reliance on the reinforcement of that line.

[0066] The cost of line reinforcement also needs to meet the budget constraints for line reinforcement: ; in, This indicates the budget for line reinforcement, to prevent the need to reinforce all lines and thus avoid economic waste.

[0067] Mobile vehicle pre-scheduling needs to meet the following constraints: ; ; ; ; ; The constraints of the mobile vehicle pre-scheduling model ensure the continuity of mobile vehicles in both time and space. Among these constraints, This indicates that the mobile carrier moves from the regional microgrid at time t. Heading to the regional microgrid ; This indicates that hydrogen transport vessels originate from the regional microgrid. To regional microgrid Travel time.

[0068] The worst-case scenario attack model takes the line reinforcement set and the initial pre-scheduled route of the mobile vehicle output by the pre-disaster defense planning model, and the line fault uncertainty set and net load uncertainty set output by S200 as inputs. Its goal is to identify the candidate worst-case disaster scenario that maximizes the post-disaster emergency dispatch cost or expected operating cost within the scope of the disaster uncertainty set, and to jointly transfer the dual uncertainties of line faults and net load to the post-disaster emergency dispatch model.

[0069] The goal of the post-disaster emergency dispatch model is to minimize the cost of post-disaster emergency dispatch by optimizing the operation of the microgrid power-hydrogen coupling system, the operation of mobile carriers, and the load shedding amount, based on the current line reinforcement plan and the spatiotemporal pre-dispatch plan of mobile hydrogen carriers, under the condition that the worst disaster scenario is known.

[0070] When candidate physical lines are not reinforced or are unavailable under the worst-case disaster scenario, the post-disaster emergency dispatch model determines whether to activate mobile hydrogen carriers to perform cross-regional alternative energy supply based on the load deficit of the corresponding local microgrid, the surplus of renewable energy, and the availability of mobile hydrogen carriers. If the resource-rich local microgrid has a surplus of renewable energy, the electrolysis unit converts the surplus electricity into hydrogen energy; under the conditions of meeting the constraints of availability, hydrogen storage capacity, and spatiotemporal continuity, the mobile hydrogen carrier loads hydrogen in the resource-rich local microgrid and transports it to the load-deficient local microgrid; after arriving at the load-deficient local microgrid, the mobile hydrogen carrier can unload hydrogen to a local hydrogen storage device or generate electricity through fuel cells, converting hydrogen energy into electricity to compensate for the power transmission capacity gap.

[0071] As a feasible example, the objective function of the post-disaster emergency dispatch model is expressed as: ; ; ; ; ; ; ; in, This indicates the operating and maintenance costs of hydrogen transport vessels; This indicates the maintenance cost of hydrogen transport vessels; This represents the operating and maintenance cost of the fuel cell at time t; This indicates the maintenance cost of the fuel cell; This indicates the operating and maintenance costs of the battery system; This indicates the maintenance cost of the battery system; This represents the maintenance cost of renewable energy generation at time t. This indicates the maintenance cost of renewable energy generators; Indicates the cost of load shedding; This represents the cost per unit of load shedding loss. Represents a node The weight.

[0072] The output of the post-disaster emergency dispatch model is used to verify the effectiveness of the pre-disaster defense planning model in the worst disaster scenario, and is fed back to the pre-disaster defense planning model during the iteration process.

[0073] Through S300, the line reinforcement system provides fixed power transmission capacity, the initial response configuration of mobile hydrogen carriers provides cross-regional alternative energy supply capacity, and the worst-case scenario attack model transmits the uncertainties of line failures and net load to post-disaster dispatch. The post-disaster emergency dispatch model then feeds back the minimum operating costs of equipment and mobile carriers, as well as the penalties for power curtailment and load shedding, to pre-disaster defense. This model chain enables the planning scheme to simultaneously address physical line interruptions, renewable energy output fluctuations, and load demand changes, while achieving a balance between pre-disaster investment, post-disaster energy supply resilience, and operational economics.

[0074] S400 performs joint iterative solutions on the pre-disaster defense planning model, the worst-case attack model, and the post-disaster emergency dispatch model to obtain the target elastic planning scheme.

[0075] Because the sub-Bruker resilient programming model constructed in step S300 has a three-level nested structure of "min-max-min" and includes discrete line state variables and continuous operational variables, it is difficult to solve directly. In a specific embodiment of the present invention, step S400 uses a nested column and constraint generation algorithm (Nested C&CG) to jointly iteratively solve the three-level sub-Bruker resilient programming model, that is, decomposing the model into a main problem and sub-problems to handle the coupling relationship between the pre-disaster defense planning model, the worst-case scenario attack model, and the post-disaster emergency dispatch model.

[0076] Specifically, the joint iterative solution process for step S400 is as follows: S401. Initialize the outer loop, setting the upper bound of the outer loop. The lower realm Set the external circulation convergence gap Number of outer loop iterations .

[0077] S402. Solve the outer loop master problem, that is, solve the pre-disaster defense planning model to obtain the candidate route reinforcement plan and the candidate mobile hydrogen transport carrier spatiotemporal pre-scheduling plan.

[0078] Solving the main problem yields the optimal planning scheme for the g-th iteration. and optimal value And update the lower bound according to the following formula.

[0079]

[0080] S403. Initialize the inner loop, and set the upper bound of the inner loop. The lower realm Set the inner loop convergence gap Number of outer loop iterations .

[0081] S404. Input the planning scheme into the inner loop, that is, solve the worst-case attack model and the post-disaster emergency dispatch model. First, solve the main problem of the inner loop, find the line fault state and wind and solar power output scenario that maximize the operating cost, and update the lower bound of the inner loop. .

[0082] Under the planning of candidate route reinforcement and candidate mobile hydrogen transport carrier spatiotemporal pre-scheduling, the worst-case attack model is solved to determine the candidate worst-case disaster scenarios from the disaster uncertainty set.

[0083] S405. Based on the line fault state and wind and solar power output scenario that maximize operating cost obtained from the inner loop master problem, solve the post-disaster emergency dispatch model to obtain the corresponding minimum post-disaster emergency dispatch cost, and update the upper bound of the inner loop. .

[0084] In step S405, if the relative error between the upper and lower bounds of the inner loop satisfies If the inner loop converges, it will output the actual operating cost in the current scenario. Otherwise, return to step S404 and set .

[0085] S406. Feed the worst-case disaster scenario output by the inner loop and the corresponding minimum post-disaster emergency dispatch cost back to the pre-disaster defense planning model, so that the subsequent pre-disaster defense planning considers the worst-case disaster scenario when generating the route reinforcement plan and the spatiotemporal pre-schedule plan for candidate mobile hydrogen transport carriers, and updates the upper bound of the outer loop according to the following formula: .

[0086] S407. Repeat the above steps until the preset convergence condition is met, and output the target elastic planning scheme.

[0087] Specifically, according to Determine if the outer loop has converged. If it has converged, terminate the algorithm; otherwise, return to step S402.

[0088] The target resilience planning scheme includes route reinforcement planning and spatiotemporal pre-scheduling planning for mobile hydrogen transport vehicles, and may further include equipment and mobile hydrogen transport vehicle scheduling schemes under the most severe disaster scenarios.

[0089] In one feasible example, the relative errors of both the outer and inner loops of the Nested C&CG algorithm are set to 0.01%, and the constructed model is solved using gurobi in MATLAB.

[0090] To verify the effectiveness and feasibility of the multi-local microgrid distributed bar elastic planning method considering mobile hydrogen storage coordination proposed in this application, a verification example is also provided. Taking an island microgrid as a specific application scenario, a hydrogen transport vessel is used as the mobile hydrogen transport carrier. This verification example adopts an improved IEEE 18-node system, which includes 18 nodes and 17 power distribution lines. The system integrates various types of distributed power sources, such as wind turbines, photovoltaic power generation units, and hydrogen fuel cells, while adding three additional isolated nodes representing three resource islands. The system is configured with a hydrogen transport vessel with a hydrogen storage capacity of 500 kg and its docking port facilities, allowing the hydrogen transport vessel to transfer hydrogen energy between load islands and resource islands by navigation. Figure 2 This is a topology diagram of the load islands.

[0091] The relative error of the outer and inner loop frameworks of the NC&CG algorithm was set to 0.01%. The constructed model was solved using gurobi in MATLAB.

[0092] By solving the bibliometric elastic programming model, the system determined that lines 4-5, 5-6, 6-7, 7-8, and 8-9 would need reinforcement in the pre-disaster phase, with a reinforcement investment cost of 7451.47 yuan. Simultaneously, the system completed the initial pre-scheduling of hydrogen transport vessels, with a pre-scheduling cost of 2940.00 yuan. The above-mentioned line reinforcement plan provides a fixed power transmission guarantee for the load islands, and the initial pre-scheduling of hydrogen transport vessels ensures that they have a responsive location and available hydrogen supply in the event of a disaster.

[0093] In the second phase of attack-defense calculations, the worst-case scenario was a simultaneous line failure on lines 15-16 and 17-18. This failure would cut off the direct power supply to some end-point loads, putting a localized area at risk of becoming isolated. To address this worst-case scenario, hydrogen transport vessels implemented a coordinated time-space scheduling system for emergency resource requisition, cross-regional support, and isolated power supply.

[0094] The specific operational status of the hydrogen transport vessels under scheduling is as follows: Figure 3As shown. During the early morning hours, the power demand on the load island is relatively low, while the wind speed on the resource island is high. The local electrolysis unit converts the surplus wind power that cannot be transmitted to other areas into hydrogen on-site, and the hydrogen transport ship absorbs the hydrogen on-site, absorbing the surplus wind energy. Subsequently, the hydrogen transport ship goes to the load island and supplies 143.6 kg of hydrogen at t=4, filling the buffer space of the local hydrogen storage device and providing sufficient backup fuel for the fuel cell to cope with the daytime load ramp-up on the load island, realizing the pre-allocation of cross-regional energy. During the daytime hours, the photovoltaic output on the resource island gradually increases, and the electrolysis unit on the resource island absorbs the limited zero-carbon wind and solar power and produces hydrogen on a large scale. To collect these clean energy sources scattered across the islands, the hydrogen transport ship goes to resource island 2 and resource island 1 at t=7 and t=10 respectively, transferring 90.4 kg and 90.8 kg of hydrogen generated by electrolysis on the island to the ship, efficiently completing the daytime resource collection over a wide area. Faced with a severe power shortage on the load island caused by line breaks and high load, the hydrogen-carrying vessel returned to the load island at t=12 to perform an emergency power supply mission. During this phase, the hydrogen-carrying vessel provided direct power support to the core disaster area. Simultaneously, the hydrogen-carrying vessel exhibited dynamic start-stop characteristics that precisely tracked source-load fluctuations: at t=13 and t=17, due to a short-term surge in local renewable energy that filled the power gap, the hydrogen-carrying vessel entered standby mode, avoiding the ineffective loss of onboard hydrogen energy. Ultimately, the hydrogen-carrying vessel released 393.6 kg of surplus hydrogen to the load island, laying the foundation for the long-term recovery of the microgrid the following day.

[0095] Table 1 details the planning and operational cost structure of the strategy proposed in this invention, as well as a comparison of different strategies. If only pre-disaster line reinforcement is implemented without utilizing hydrogen-carrying vessels, the emergency power demand of the load island cannot be met after a disaster causes localized power outages and creates isolated areas. The surplus zero-carbon power of the resource island also cannot be absorbed, resulting in high load shedding penalties and power curtailment penalties. If only hydrogen-carrying vessels are relied upon without pre-disaster reinforcement, the main grid will be largely paralyzed. Limited by the time and space constraints and capacity limits of vessel navigation, the system cannot promptly fill energy gaps at multiple points, leading to severe load shedding losses. As shown in the table, thanks to line reinforcement and optimized scheduling of hydrogen-carrying vessels, the method proposed in this invention successfully achieves zero load shedding. This invention utilizes the time and space flexibility of hydrogen-carrying vessels to successfully transfer surplus energy from the resource island to the faulty island area, significantly reducing power curtailment losses and greatly improving the resilience of the island microgrid under extreme disasters without requiring additional investment in power generation facilities.

[0096] Table 1 Comparison of different strategies

[0097] This application also provides a multi-local microgrid distributed bar elastic planning system considering mobile hydrogen energy storage collaboration. This system includes a parameter acquisition module, an operational model construction module, an uncertainty set construction module, an elastic planning model construction module, and a joint iterative solution module. Each module can be deployed on the same server, energy management platform, scheduling and control terminal, or implemented collaboratively by multiple computing nodes.

[0098] The parameter acquisition module is used to acquire the physical network parameters of multiple local area micronets and output them to the runtime model construction module.

[0099] The operational model building module is used to construct an operational model of the electro-hydrogen coupling system, taking into account the mobile hydrogen carrier, based on the physical network parameters.

[0100] The electric-hydrogen coupling system operation model is used to characterize the power transmission capability of the physical circuit and the hydrogen energy transfer capability of the mobile hydrogen carrier between local microgrids.

[0101] Specifically, the operation model construction module can establish operation models for mobile hydrogen transport carriers, stationary electric-hydrogen equipment, electric-hydrogen supply and demand balance models, and line power flow models.

[0102] The uncertainty set building module is used to construct disaster uncertainty sets to describe extreme disaster scenarios.

[0103] This module can generate uncertain sets of line faults and net load based on parameters such as line fault rate, line fault status, information entropy budget, historical operating data, renewable energy output, load demand, empirical distribution, Wasserstein distance, and fuzzy set radius, and output them to the flexible planning model construction module.

[0104] The resilient planning model construction module is used to construct a sub-Bruker resilient planning model based on the electric-hydrogen coupling system operation model and the disaster uncertainty set. The sub-Bruker resilient planning model includes a pre-disaster defense planning model, a worst-case scenario attack model, and a post-disaster emergency dispatch model.

[0105] Among them, the pre-disaster defense planning model outputs the set of line reinforcement and the initial response configuration of mobile carriers; the worst-case scenario attack model outputs the candidate worst-case line fault status, net load / new energy output scenarios and probability distribution; and the post-disaster emergency dispatch model outputs the minimum post-disaster emergency dispatch cost and the equipment and mobile carrier dispatch scheme.

[0106] The joint iterative solution module is used to jointly iteratively solve the pre-disaster defense planning model, the worst-case attack model, and the post-disaster emergency dispatch model to obtain the target elastic planning scheme. The target elastic planning scheme includes route reinforcement planning and spatiotemporal pre-schedule planning for mobile hydrogen transport vehicles.

[0107] The joint iterative solution module can use nested column and constraint generation algorithms to iterate in the following order: candidate planning generation, candidate worst-case disaster scenario search, minimum post-disaster emergency dispatch cost calculation, scenario and cost feedback, and upper and lower bound relative error convergence output.

[0108] The process of implementing the steps in the above embodiments for each module will not be described again here.

[0109] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0110] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This invention is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A multi-local microgrid distributed bar elastic planning method considering mobile hydrogen energy storage collaboration, characterized in that, Includes the following steps: The physical network parameters of multiple local microgrids are obtained. Based on the physical network parameters, an operation model of an electric-hydrogen coupling system considering mobile hydrogen carriers is constructed. The electric-hydrogen coupling system operation model is used to characterize the power transmission capability of physical lines and the hydrogen energy transfer capability of mobile hydrogen carriers between local microgrids. Construct a disaster uncertainty set to describe extreme disaster scenarios; Based on the electric-hydrogen coupling system operation model and the disaster uncertainty set, a sub-Bruker elastic planning model is constructed, which includes a pre-disaster defense planning model, a worst-case scenario attack model, and a post-disaster emergency dispatch model. The pre-disaster defense planning model uses line reinforcement decision and mobile hydrogen carrier spatiotemporal pre-scheduling decision as pre-disaster defense decision variables, and aims to minimize the sum of pre-disaster defense cost and post-disaster emergency scheduling cost under the worst disaster scenario. It determines the configuration relationship between line reinforcement planning that provides fixed power transmission capacity and mobile hydrogen carrier spatiotemporal pre-scheduling planning that provides cross-regional alternative energy supply capacity. The worst-case scenario attack model is used to identify the worst-case disaster scenario that maximizes the cost of post-disaster emergency dispatch in the disaster uncertainty set. The post-disaster emergency dispatch model is used to dispatch the equipment and the mobile hydrogen transport carrier within the multi-local micronetwork under the most severe disaster scenario, so as to minimize the cost of solving the post-disaster emergency dispatch. The pre-disaster defense planning model, the worst-case attack model, and the post-disaster emergency dispatch model are jointly iteratively solved to obtain the target elastic planning scheme, which includes the route reinforcement planning and the spatiotemporal pre-schedule planning of the mobile hydrogen transport carrier.

2. The multi-local microgrid distributed bar elastic planning method considering mobile hydrogen energy storage coordination as described in claim 1, characterized in that, The post-disaster emergency dispatch model schedules equipment within the multi-local micronetwork and the mobile hydrogen transport vehicle, including: When a disaster causes physical lines to become unavailable, resulting in a decrease in the power transmission capacity between local microgrids, the mobile hydrogen transport carrier is dispatched to construct a cross-regional virtual energy channel to replace the damaged physical lines. By coordinating hydrogen loading, transportation, unloading, or power generation operations between resource-rich local microgrids and load-deficient local microgrids, hydrogen energy is transferred across regions to compensate for the power transmission capacity gap.

3. The multi-local microgrid distributed bar elastic planning method considering mobile hydrogen energy storage coordination as described in claim 1, characterized in that, Construct a disaster uncertainty set to describe extreme disaster scenarios, including: Historical operational data of multiple local area microgrids are obtained, and based on the historical operational data, a disaster uncertainty set is constructed to describe extreme disaster scenarios.

4. The multi-local microgrid distributed bar elastic planning method considering mobile hydrogen energy storage coordination as described in claim 1, characterized in that, The joint iterative solution of the pre-disaster defense planning model, the worst-case attack model, and the post-disaster emergency dispatch model employs a nested column and constraint generation algorithm, including: Solving the pre-disaster defense planning model yields candidate route reinforcement plans and candidate spatiotemporal pre-scheduling plans for mobile hydrogen transport carriers; Under the candidate route reinforcement plan and the candidate mobile hydrogen carrier spatiotemporal pre-scheduling plan, the worst-case scenario attack model is solved to determine the candidate worst-case disaster scenarios from the disaster uncertainty set; Under the candidate worst-case disaster scenario, solve the post-disaster emergency dispatch model to obtain the corresponding minimum post-disaster emergency dispatch cost; The candidate worst-case disaster scenario and the corresponding minimum post-disaster emergency dispatch cost are fed back into the pre-disaster defense planning model; Repeat the above steps until the preset convergence condition is met, and output the target elastic planning scheme.

5. The multi-local microgrid distributed bar elastic planning method considering mobile hydrogen energy storage coordination as described in claim 4, characterized in that, The preset convergence conditions include: The lower bound is updated based on the target value of the pre-disaster defense planning model, and the upper bound is updated based on the candidate route reinforcement plan, the candidate mobile hydrogen carrier spatiotemporal pre-scheduling plan, and the corresponding minimum post-disaster emergency scheduling cost. When the relative error between the upper bound and the lower bound is not greater than the preset convergence threshold, it is determined that the preset convergence condition is met.

6. The multi-local microgrid distributed bar elastic planning method considering mobile hydrogen energy storage coordination as described in claim 1, characterized in that, The pre-disaster defense costs include the cost of reinforcing the power lines and the cost of pre-scheduling the mobile hydrogen transport carrier in time and space. The post-disaster emergency dispatch costs include the operating costs of the mobile hydrogen transport carrier, the operating costs of the electric-hydrogen coupling equipment, the cost of power curtailment penalties, and the cost of load shedding penalties.

7. The multi-local microgrid distributed bar elastic planning method considering mobile hydrogen energy storage collaboration as described in claim 1, characterized in that, The pre-disaster defense planning model determines whether to reinforce the candidate physical lines based on the reduced post-disaster emergency dispatch costs and the increased line reinforcement costs resulting from the reinforcement of the candidate physical lines.

8. The multi-local microgrid distributed bar elastic planning method considering mobile hydrogen energy storage collaboration as described in claim 1, characterized in that, When the candidate physical lines are not reinforced or are unavailable under the worst-case disaster scenario, the post-disaster emergency dispatch model determines whether to activate the mobile hydrogen transport carrier to perform cross-regional alternative energy supply based on the load deficit of the corresponding local microgrid, the surplus of renewable energy, and the availability of the mobile hydrogen transport carrier.

9. The multi-local microgrid distributed bar elastic planning method considering mobile hydrogen energy storage coordination as described in claim 1, characterized in that, The spatiotemporal pre-scheduling decision of the mobile hydrogen transport vehicle is used to determine the initial response location and pre-scheduling path of the mobile hydrogen transport vehicle when a disaster occurs, including at least one of the following: the initial local microgrid where the mobile hydrogen transport vehicle resides before the disaster occurs, the initial hydrogen storage state, the pre-scheduling path, and the pre-scheduling period.

10. A multi-local microgrid distributed bar elastic planning system considering mobile hydrogen energy storage coordination, characterized in that, include: The parameter acquisition module is used to acquire the physical network parameters of multiple local area micronets; The operational model construction module is used to construct an operational model of the electric-hydrogen coupling system considering the mobile hydrogen carrier based on the physical network parameters. The electric-hydrogen coupling system operational model is used to characterize the power transmission capability of the physical lines and the hydrogen energy transfer capability of the mobile hydrogen carrier between local microgrids. The uncertainty set construction module is used to construct disaster uncertainty sets to describe extreme disaster scenarios; The elastic planning model construction module is used to construct a sub-Brussels elastic planning model based on the electric-hydrogen coupling system operation model and the disaster uncertainty set. The sub-Brussels elastic planning model includes a pre-disaster defense planning model, a worst-case scenario attack model, and a post-disaster emergency dispatch model. The pre-disaster defense planning model uses line reinforcement decision and mobile hydrogen carrier spatiotemporal pre-scheduling decision as pre-disaster defense decision variables, and aims to minimize the sum of pre-disaster defense cost and post-disaster emergency scheduling cost under the worst disaster scenario. It determines the configuration relationship between line reinforcement planning that provides fixed power transmission capacity and mobile hydrogen carrier spatiotemporal pre-scheduling planning that provides cross-regional alternative energy supply capacity. The worst-case scenario attack model is used to identify the worst-case disaster scenario that maximizes the cost of post-disaster emergency dispatch in the disaster uncertainty set. The post-disaster emergency dispatch model is used to dispatch the equipment and the mobile hydrogen transport carrier within the multi-local micronetwork under the most severe disaster scenario, so as to minimize the cost of solving the post-disaster emergency dispatch. The joint iterative solution module is used to jointly iteratively solve the pre-disaster defense planning model, the worst-case attack model, and the post-disaster emergency dispatch model to obtain the target elastic planning scheme, which includes the line reinforcement planning and the spatiotemporal pre-schedule planning of the mobile hydrogen transport carrier.

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