Method, system, device and medium for optimizing energy system capacity and synergistically scheduling spatio-temporal decoupling of mobile resources under extreme disaster disturbances

Abstract

The present invention relates to the field of energy scheduling technology, and in particular to a method, system, device and medium for coordinated scheduling of energy system capacity optimization and mobile resource spatiotemporal decoupling under extreme disaster disturbances, the method comprising: according to extreme disaster event data, linking equipment failure probability and line failure probability, and coupling load change parameters to form a disaster response database; converting renewable energy output fluctuations and user load demand deviations into fuzzy variables, and establishing a capacity configuration optimization model in combination with the disaster response database to generate multi-energy complementary capacity configuration; introducing mobile resources to establish an elasticity enhancement scheduling model, and generating an elasticity enhancement plan by implementing damaged road network topology reconstruction, spatiotemporal transfer chain modeling and action state coordinated regulation of mobile resources. The present invention drives the coordinated decision-making of equipment capacity optimization and dynamic scheduling of mobile resources through the disaster response database, and achieves dual elastic gains of fixed resource optimization and rapid post-disaster response of mobile resources under extreme disaster events.

Description

Technical Field

[0001] The present invention relates to the technical field of energy dispatch, and particularly to a method, system, device and medium for optimizing the capacity of an energy system and decoupling and coordinating the spatio-temporal scheduling of mobile resources under extreme disaster disturbances. Background Art

[0002] To enhance the resilience of the energy system, resistance and recovery are two key aspects, that is, a resilient energy system should be able to resist the impact of destructive events, especially catastrophic events, and recover quickly. On the one hand, the energy system needs to provide safer and more reliable power services for users under normal weather conditions; on the other hand, it also needs to overcome various disturbances during extreme weather events to ensure the continuity and stability of energy supply. Therefore, it is particularly urgent to develop new energy system planning methods and mobile resource scheduling schemes. By optimizing equipment capacity and using energy conversion equipment to achieve multi-energy complementarity, the economy and sustainability of the energy system can be effectively improved. Especially when extreme weather or disasters occur, the flexible scheduling ability of mobile resources will become the key to meeting user needs and enhancing system resilience.

[0003] However, in the existing technical research on the optimization of the energy system configuration in extreme weather, attention is often only paid to the impact on renewable energy, ignoring the dynamic changes in the demand-side load and the collaborative scheduling potential of multiple types of mobile resources (such as emergency generators and in-vehicle energy storage), resulting in insufficient system resilience and difficulty in effectively coping with the complex disturbances on both the supply and demand sides in disaster scenarios. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] In view of the above-mentioned shortcomings and deficiencies of the existing technology, the present invention provides a method, system, device and medium for optimizing the capacity of an energy system and decoupling and coordinating the spatio-temporal scheduling of mobile resources under extreme disaster disturbances, which solves the technical problems that the existing energy system planning and mobile resource scheduling methods do not fully consider the dynamic change characteristics of user loads under extreme weather and insufficiently explore the collaborative scheduling potential of multiple types of power sources, resulting in limited optimization of the energy system.

[0006] (II) Technical Solutions

[0007] To achieve the above object, the main technical solutions adopted by the present invention include:

[0008] In a first aspect, an embodiment of the present invention provides a method for optimizing the capacity of an energy system and coordinately scheduling spatio-temporal decoupling of mobile resources under extreme disaster disturbances, including: According to the obtained extreme disaster event data, linking the equipment failure probability and line failure probability obtained through the dual-state resilience adaptive Markov and Monte Carlo processing, and coupling the load change parameters driven by extreme disaster events to form a disaster response database; Converting the fluctuations in the output of renewable energy and the deviation of user load demand into fuzzy variables through fuzzy chance constraints, and establishing a capacity configuration optimization model in combination with the disaster response database, with the minimization of the levelized cost of electricity as the optimization goal to generate a multi-energy complementary capacity configuration; Based on the multi-energy complementary capacity configuration, introducing mobile resources to establish an elastic improvement scheduling architecture, and generating an elastic improvement plan adaptive to extreme disaster events by implementing damaged road network topology reconstruction, spatio-temporal transfer chain modeling, and action state coordinated control of mobile resources.

[0009] Optionally, according to the obtained extreme disaster event data, linking the equipment failure probability and line failure probability obtained through the dual-state resilience adaptive Markov and Monte Carlo processing, and coupling the load change parameters driven by extreme disaster events to form a disaster response database includes:

[0010] According to the obtained extreme disaster event data of multiple observation points, a multi-dimensional extreme disaster event database is formed; among them, the extreme disaster event data is a set of disaster events that exceed the preset extreme threshold and include earthquakes, storms, extremely high temperature droughts, and extremely low temperature ice disasters;

[0011] Using a dual-state resilience adaptive Markov switching model to analyze the state transition chain of equipment, generating the equipment failure probability through the dynamic game of the failure rate and repair rate, and performing multi-scenario sampling simulation through the Monte Carlo method to generate a time-series equipment failure state set;

[0012] According to the spatial distribution intensity and time accumulation parameters under the current extreme disaster event, simulating the dynamic diffusion process of the extreme disaster event through parameter space gradient mapping and time accumulation effect analysis to generate a set of line failure probabilities including energy network lines and pipe networks;

[0013] Constructing a dynamic load growth evolution model, and quantifying the load dynamic increment of extreme low temperature events or extreme high temperature events by introducing a humidity correction term, a wind speed correction term, and an irradiance correction term to form a time-series load dynamic parameter set;

[0014] Aligning the equipment failure state set, the line failure probability set, and the load dynamic parameter set in space and time, and combining with the multi-dimensional extreme disaster event database to generate a disaster response database.

[0015] Optionally, the dual-state resilience adaptive Markov switching model is: ;

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] In the formula, is the probability of the equipment j operating normally, is the probability of the equipment j failing. 1 indicates that the equipment j is operating normally, and 0 indicates that the equipment j has a malfunction during operation, , is the time-varying repair rate; for the power grid and gas network, , is the number of emergency repair teams, is the baseline repair rate without additional emergency repair resources, is the baseline number of emergency repairs; for the energy system, , and are the time-varying normal operation time of the equipment and the time-varying repair time of the equipment respectively, is a number randomly generated from a uniform distribution [0, 1], is a number randomly generated from a uniform distribution [0, 1], is the state of the equipment j at t time, is the failure rate, , K ={earthquake, storm, extreme high temperature and drought, extreme low temperature and ice disaster}, K is the set of disaster types, is the real-time intensity index of the disaster K , is the weight coefficient of the disaster K , satisfying w k = 1, is the non-linear influence function of the disaster K used to standardize the physical dimensions of different disasters, is the amount of maintenance resource input disaster intensity the maintenance and disaster game intensity that suppresses the ability of, R ( t ) ≥ D ( t ) when, γ( t ) ≥ 0.5 indicates maintenance dominance and increases the probability of the equipment being in a normal state; R ( t ) < D ( t ) When γ ( t ) < 0.5, it indicates disaster suppression and increases the risk of failures, θ being the resource disaster ratio benchmark threshold;

[0021] The dynamic load growth evolution model is: ;

[0022] Wherein, is the predicted load at time t when extreme weather occurs, is t the predicted load at time when extreme weather does not occur, t is the load increment at time after extreme weather occurs, which is related to the perceived temperature is the neural network load prediction model, represents t the actual temperature at time represents t the air relative humidity correction term at time represents the wind speed correction term, represents the solar radiation correction term.

[0023] Optionally, by using fuzzy chance constraints, the fluctuations in the output of renewable energy and the deviation of user load demand are converted into fuzzy variables, and a capacity configuration optimization model is established in combination with the disaster response database, with the minimum power levelized cost as the optimization goal, and the multi - energy complementary capacity configuration generated includes:

[0024] Use fuzzy chance constraints to construct membership functions, and convert the random fluctuations in wind - solar power generation and the deviation of user load demand into a set of fuzzy variables under credible probability constraints;

[0025] In response to regional energy demand, based on the disaster response database, construct a system topology including energy supply equipment, energy storage equipment, and energy conversion units, and configure capacity configuration elements including equipment output sets, equipment status sets, and efficiency parameter sets;

[0026] Establish a capacity configuration optimization model with the minimum power levelized cost as the goal, generate model constraint conditions based on the capacity configuration elements, and embed the set of fuzzy variables into the model constraint conditions to generate a fuzzy feasible solution space that integrates uncertainties;

[0027] Iteratively solve the capacity configuration optimization model in the fuzzy feasible solution space, and output the multi - energy complementary capacity configuration adapted to the system topology.

[0028] Optionally, the device output set is: ;

[0029] In the formula, is t the electricity price at time and i is the power generation of device t at time ; i is the cooling capacity of device t at time ; i is the heating capacity of device t at time ; i is the hydrogen production of the electro - hydrogen conversion device t at time ; t are the building's electricity load demand, heat load demand, and cooling load demand at time respectively; t is the capacity of the energy storage device i at time

[0030] The device status set is expressed as: ;

[0031] In the formula, represents the operating status of device i at time t . When is 1, it means that device i is in normal operation. When is 0, it means that device i is in a malfunction state. are the charging state and discharging state of the energy storage device i at time t respectively. When the energy storage device i is in a malfunction state, or is 0. When it is in normal operation, or is 1. ;

[0032] The capacity configuration optimization model is:

[0033] ;

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] In the formula, TNPC is the total net present value, CRF is the capital recovery factor, is t the electric load at time is t the charging load of the electric vehicle at time is t the reduced electric load at time is the annual investment cost of the system, is the discount rate, is the equipment life, is the equipment i unit investment cost, is the equipment i unit investment cost, is the annual operation and maintenance cost of the system, is the equipment i unit operation and maintenance cost, is the equipment i operating power.

[0039] Optionally, based on the multi - energy complementary capacity configuration, introduce mobile resources to establish an elastic enhancement scheduling architecture. By implementing the damaged road network topology reconstruction, spatio - temporal transfer chain modeling and action - state collaborative regulation of mobile resources, generate an elastic enhancement scheme adaptable to extreme disaster events, including:

[0040] On the basis of the multi - energy complementary capacity configuration, introduce mobile resources including mobile emergency power vehicles, electric vehicles and diesel generators as elastic supplementary energy supply units, and establish an elastic enhancement scheduling architecture that coordinates fixed equipment and mobile resources;

[0041] According to the obtained disaster damage information, remove the damaged road network nodes and edge sets, reconstruct the road network topology, and generate a set of feasible scheduling paths that minimize the distance of mobile resources from the starting point to the target energy system;

[0042] Analyze the departure timestamp, estimated arrival timestamp, driving duration interval and charging stop time period of mobile resources to generate time - chain features, and extract the starting position coordinates, target energy supply station coordinates and path node coordinate sets of mobile resources to generate space - chain features;

[0043] Verify the time-chain features and space-chain features, and calculate the maximum service radius, shortest response time, and continuous power supply duration threshold of mobile resources based on the spatio-temporal feature combinations that pass the verification, so as to form a spatio-temporal schedulable boundary;

[0044] Obtain the action set representing the energy supply behavior parameters of the mobile resources according to the energy supply characteristics of the obtained mobile resources, and construct the state set representing the operating state of the mobile resources according to the mutual exclusion constraints of device operation;

[0045] In the elastic enhancement scheduling architecture, with the goal of minimizing the total cost of mobile resource scheduling, the lowest load reduction, and the minimum path energy consumption, based on the spatio-temporal schedulable boundary, dynamically solve the optimal solutions of the parameter of the action set of the mobile resources, the flag sequence of the state set, and the priority of the feasible scheduling path set through a multi-layer collaborative optimization algorithm, and generate an elastic enhancement plan adaptable to extreme disaster events.

[0046] Optionally, in the elastic enhancement scheduling architecture, with the goal of minimizing the total cost of mobile resource scheduling, the lowest load reduction, and the minimum path energy consumption, based on the spatio-temporal schedulable boundary, dynamically solve the optimal solutions of the parameter of the action set of the mobile resources, the flag sequence of the state set, and the priority of the feasible scheduling path set through a multi-layer collaborative optimization algorithm, and the generation of an elastic enhancement plan adaptable to extreme disaster events includes:

[0047] Encode the parameter of the action set and the flag sequence of the state set of the mobile resources into the position vector of the honey badger individual, and generate an initial honey badger population that meets the spatio-temporal schedulable boundary;

[0048] Encode the priority of the feasible scheduling path set into the migration path chain of the bird flock individual, generate an initial bird flock population that meets the spatio-temporal schedulable boundary and load it into the path memory bank;

[0049] Construct an inner and outer layer collaborative optimization framework. In the inner layer architecture, the upper layer honey badger population performs a two-objective optimization of the global scheduling cost and load reduction, and the lower layer bird flock population optimizes the priority of the feasible scheduling path set with the goal of minimizing the path energy consumption through the path memory bank sharing mechanism. In the outer layer architecture, use the simulated annealing algorithm model to receive the optimal solution output by the inner layer architecture as the initial solution and perform perturbation optimization;

[0050] For each honey badger individual, match the lowest energy consumption sequence of the corresponding path chain from the initial bird flock population, calculate the total cost of mobile resource scheduling, load reduction, and path energy consumption indicators, and aggregate them into a comprehensive performance indicator through normalized weights to generate a prey odor concentration-migration gravity double drive scale;

[0051] Sort the double populations according to the double drive scale, select the current global optimal solution, and combine the introduced dynamic density factor to control the honey badger and bird flock individuals to perform two-stage optimization in the neighborhood of the current global optimal solution;

[0052] In the wide - area mining stage, expand the search step length of the upper - layer honey badger population and prey odor concentration, traverse the combinations of action - set parameters and state - set flag sequences, and in the lower - layer bird flock population, update the topological weights in the path memory bank according to the path requirements of individual honey badgers to generate a wide - area feasible path set;

[0053] In the fine - mining stage, shrink the search step length of the upper - layer honey badger population, lock the local optimum of the combinations of action - set parameters and state - set flag sequences, and in the lower - layer bird flock population, perform priority sorting on the wide - area feasible path set driven by migration gravity;

[0054] Perform constraint verification on the updated honey badger and bird flock individuals. If the updated honey badger individuals and bird flock individuals violate the spatio - temporal schedulable boundary, perform topological re - routing repair, adjust the node order and passing weights of the path chain, and apply bird flock gravity calibration to re - distribute priorities based on the path memory bank, and feedback the repaired individuals to the corresponding populations to update the distribution of honey badgers and bird flocks;

[0055] After the iteration of the inner - layer architecture, input the current optimal scheduling scheme into the outer - layer simulated annealing algorithm model, generate new solutions through Gaussian perturbation and calculate the fitness difference, jump out of the local optimum with a probabilistic acceptance strategy, and output a scheme that satisfies the spatio - temporal schedulable boundary conditions and is jointly optimal for multiple objectives, including the action - set parameters of mobile resources, the state - set flag sequences, and the priorities of the feasible scheduling path set.

[0056] In a second aspect, an energy - system capacity optimization and mobile - resource spatio - temporal decoupling collaborative scheduling system under extreme - disaster perturbations provided by an embodiment of the present invention includes: a database construction module, which is used to link the device failure probability and line failure probability obtained through two - state resilience - adaptive Markov and Monte Carlo processing according to the acquired extreme - disaster event data, and couple the load change parameters driven by extreme - disaster events to construct a disaster - response database; a configuration generation module, which is used to convert the fluctuations of renewable - energy output and the deviation of user load demand into fuzzy variables through fuzzy chance - constrained programming, establish a capacity - configuration optimization model in combination with the disaster - response database, and generate a multi - energy complementary capacity configuration with the minimum power - levelized cost as the optimization goal; a configuration improvement module, which is used to introduce mobile resources based on the multi - energy complementary capacity configuration to establish an elastic - improvement scheduling architecture, and generate an extreme - disaster - event - adaptive elastic - improvement scheme by implementing damaged - road - network topological reconstruction, spatio - temporal transfer - chain modeling, and action - state collaborative regulation of mobile resources.

[0057] In a third aspect, an embodiment of the present invention provides an automatic annotation device for high-quality drainage pipeline image data based on a deep learning model with multi-feature fusion, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for optimizing the energy system capacity and decoupling the spatio-temporal coordination of mobile resources under extreme disaster disturbances as described above.

[0058] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which computer-executable instructions are stored, and when the executable instructions are executed by a processor, the method for optimizing the energy system capacity and decoupling the spatio-temporal coordination of mobile resources under extreme disaster disturbances as described above is implemented.

[0059] (III) Beneficial effects

[0060] The beneficial effects of the present invention are as follows: First, by integrating multi-source extreme disaster event observation data, a two-state resilience adaptive Markov process is used to dynamically simulate the equipment failure probability and line failure risk, and a dynamic incremental model of user cooling / heating / electricity load driven by meteorological parameters is coupled to construct a disaster response database. This database breaks through the limitations of the decoupling of meteorological data from the equipment failure model and user load characteristics in traditional methods. This database not only covers multi-dimensional failure mechanisms on the equipment side, but also couples the time-varying incremental model of cooling / heating / electricity load driven by meteorological factors, breaking through the limitations of one-sided disaster risk assessment or static load prediction in traditional planning, and deeply exploring the impacts of extreme weather and natural disasters on the supply side and demand side.

[0061] On this basis, based on fuzzy chance constraints, the fluctuations in the output of renewable energy and the range of user load fluctuations are converted into credibility fuzzy variables, and combined with the set of fault scenarios generated by the disaster response database, a capacity configuration optimization model with the goal of minimizing the levelized cost of electricity is constructed. This model realizes the coordinated planning of the capacities of energy supply equipment, energy storage devices, and energy conversion equipment under extreme weather through multi-energy coupling constraints, embedding of equipment failure probability, and definition of fuzzy chance constraint boundaries. This method reduces the total system cost while ensuring the energy supply reliability in the scenario of high-penetration renewable energy, and solves the problem of insufficient resilience optimization caused by the simplification of meteorological disaster risk factors to fixed thresholds in existing planning. Through this comprehensive optimization, the adaptability and resilience of the system in the face of extreme climate conditions or natural disasters are significantly improved, and while pursuing system economy, the ability of the system to cope with sudden extreme events is enhanced.

[0062] Furthermore, on the basis of optimizing the multi-energy complementary capacity, three types of mobile resources, namely mobile emergency power supply vehicles, electric vehicles, and diesel generators, are innovatively introduced. Elasticity enhancement is achieved through the implementation of damaged road network topology reconstruction, spatio-temporal transfer chain modeling, and action state coordination mechanisms for mobile resources.

[0063] Thus, through the collaborative decision-making of disaster response database-driven equipment capacity optimization and dynamic scheduling of mobile resources, the present invention realizes the dual elastic gains of strengthening the disaster resistance of fixed equipment and rapid post-disaster response of mobile resources under extreme weather conditions, providing an innovative solution for the design and operation of highly elastic energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 Schematic diagram of the composition of the energy system of the method provided by the embodiment of the present invention;

[0065] Figure 2 Schematic diagram of the process of the method provided by the embodiment of the present invention;

[0066] Figure 3 Specific process schematic diagram of step S1 of the method provided by the embodiment of the present invention;

[0067] Figure 4 Schematic diagram of equipment failure transfer of the dual-state resilience adaptive Markov of the method provided by the embodiment of the present invention;

[0068] Figure 5 Schematic diagram of the process of extracting system equipment failures based on Monte Carlo of the method provided by the embodiment of the present invention;

[0069] Figure 6 Specific process schematic diagram of step S2 of the method provided by the embodiment of the present invention;

[0070] Figure 7 Specific process schematic diagram of step S3 of the method provided by the embodiment of the present invention;

[0071] Figure 8 Road network path diagram of the method provided by the embodiment of the present invention;

[0072] Figure 9 Specific process schematic diagram of step S35 of the method provided by the embodiment of the present invention;

[0073] Figure 10 Schematic diagram of the elastic effect of the energy system of the method provided by the example of the present invention;

[0074] Figure 11 Overall process schematic diagram of the method provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] To better explain the present invention for easy understanding, the present invention will be described in detail below with reference to the accompanying drawings and through specific embodiments.

[0076] Prior to this, to facilitate understanding of the technical solution provided by the present invention, the concept of the energy system will be introduced first.

[0077] It should be clear that, as shown in Figure 1 the energy system in the embodiment of the present invention includes three parts: an input end, a system end, and a demand end; among them, the input end includes a power grid, solar energy, wind energy, geothermal energy, and natural gas, and combines energy conversion devices in the system end (electrolyzers, photovoltaic panels, wind turbines, ground-source heat pumps, gas turbines, electric chillers, fuel cells, waste heat boilers, etc.) to realize the conversion between energies to meet the cooling, heating, power, and hydrogen loads of each building at the demand end. At the same time, relying on energy storage devices such as hydrogen storage tanks, batteries, chilled water storage tanks, and heat storage tanks to achieve dynamic balance between supply and demand; using mobile resources such as electric vehicles, mobile emergency vehicles, and mobile diesel generators to ensure the load supply when the power supply is insufficient due to extreme weather and other reasons, and improve the system flexibility.

[0078] The energy system equipment has a strong coupling relationship. The operation strategy of the established energy system is proposed, which adopts cooling-determined heating, heating-determined hydrogen, and hydrogen-determined power; electricity, as the bottom-layer energy, gives priority to the consumption of renewable energy, and then is powered by a gas turbine. The excess electricity is stored in the battery, and the insufficient electricity is purchased from the power grid; the building cooling load is mainly supplied by an electric chiller and a ground-source heat pump. When the rated power of the ground-source heat pump is insufficient, the electric chiller is started, and the excess cooling capacity is stored in the chilled water storage tank; the building heating load is mainly supplied by the waste heat recovery of the gas turbine and the ground-source heat pump. When the waste heat of the gas turbine cannot meet the demand, the ground-source heat pump is started to supply heat, and the excess heat is stored in the heat storage tank; when the renewable energy and the gas turbine cannot meet the electricity load, the electricity consumed by the electrolyzer for hydrogen production is purchased from the power grid.

[0079] On this basis, as shown in Figure 2 the energy system capacity optimization and mobile resource spatio-temporal decoupling collaborative scheduling method proposed in the embodiment of the present invention includes: according to the obtained extreme disaster event data, linking the equipment failure probability and line failure probability obtained by processing with dual-state resilience adaptive Markov and Monte Carlo, and coupling the load change parameters driven by extreme disaster events to form a disaster response database; converting the renewable energy output fluctuation and user load demand deviation into fuzzy variables through fuzzy chance constraints, and establishing a capacity configuration optimization model in combination with the disaster response database, with the minimum power levelized cost as the optimization goal to generate a multi-energy complementary capacity configuration; based on the multi-energy complementary capacity configuration, introducing mobile resources to establish an elasticity improvement scheduling framework, and generating an elasticity improvement plan adaptive to extreme disaster events through implementing the damaged road network topology reconstruction, spatio-temporal transfer chain modeling, and action state collaborative regulation of mobile resources.

[0080] First, by integrating multi-source observation data of extreme disaster events, a two-state resilience adaptive Markov process is used to dynamically simulate the equipment failure probability and line failure risk, and a dynamic incremental model of user cooling / heating / electricity load driven by meteorological parameters is coupled to construct a disaster response database. This database breaks through the limitation of the decoupling of meteorological data from the equipment failure model and user load characteristics in traditional methods. It not only covers multi-dimensional failure mechanisms on the equipment side but also couples the time-varying incremental model of cooling / heating / electricity load driven by meteorological factors, breaking through the limitations of one-sided disaster risk assessment or static load forecasting in traditional planning, and deeply exploring the impacts of extreme weather and natural disasters on the supply side and demand side.

[0081] On this basis, based on fuzzy chance constraints, the fluctuations in renewable energy output and user load range are converted into credibility fuzzy variables. Combining with the set of fault scenarios generated by the disaster response database, a capacity configuration optimization model with the goal of minimizing the levelized cost of electricity is constructed. Through multi-energy coupling constraints, embedding of equipment failure probability, and definition of fuzzy chance constraint boundaries, this model realizes the coordinated planning of the capacities of energy supply equipment, energy storage devices, and energy conversion equipment under extreme weather. This method reduces the total system cost while ensuring the energy supply reliability in scenarios of high penetration of renewable energy, and solves the problem of insufficient resilience optimization caused by the simplification of meteorological disaster risk factors to fixed thresholds in existing planning. Through this comprehensive optimization, the adaptability and resilience of the system in the face of extreme climate conditions or natural disasters are significantly improved, and while pursuing system economy, the system's ability to respond to sudden extreme events is enhanced.

[0082] Furthermore, on the basis of multi-energy complementary capacity optimization, three types of mobile resources, namely mobile emergency power vehicles, electric vehicles, and diesel generators, are innovatively introduced. Elasticity enhancement is achieved through the implementation of damaged road network topology reconstruction, spatio-temporal transfer chain modeling, and action state coordination mechanism of mobile resources.

[0083] Thus, through the collaborative decision-making of equipment capacity optimization driven by the disaster response database and dynamic scheduling of mobile resources, the present invention realizes a dual elastic gain of strengthening the disaster resistance of fixed equipment and rapid post-disaster response of mobile resources under extreme weather, providing an innovative solution for the design and operation of highly resilient energy systems.

[0084] To better understand the above technical solution, the exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings.

[0085] Specifically, an embodiment of the present invention provides a method for capacity optimization and spatio-temporal decoupling collaborative scheduling of an energy system under extreme disaster disturbances, which includes:

[0086] S1. Based on the multi-source extreme disaster event data, link the equipment failure probability and line failure probability under extreme disaster events obtained through the two-state resilience adaptive Markov and Monte Carlo processing, and couple the meteorological-driven load change parameters to form a disaster response database.

[0087] Furthermore, as Figure 3 shown, step S1 includes:

[0088] S11. Based on the collected extreme disaster event data of multiple observation points, form a multi-dimensional extreme disaster event database; among them, the extreme disaster event data is a set of meteorological events that exceed the corresponding extreme thresholds and include earthquakes, storms, extremely high temperature droughts, and extremely low temperature ice disasters.

[0089] In this step, select the historical extreme weather data of multiple meteorological observation stations in different regions. The collected extreme weather includes earthquakes, typhoons, droughts caused by extremely high temperatures, and ice disasters caused by extremely low temperatures.

[0090] Specifically, for earthquake disasters, relevant parameters such as earthquake time, magnitude, and intensity in the epicenter area need to be collected. The collected earthquake data is mainly medium-level earthquakes above magnitude 5; for typhoon disasters, relevant parameters such as solar radiation intensity and real-time wind speed need to be collected; for high-temperature drought weather, relevant meteorological parameters such as the temperature and solar irradiance intensity on the day when the daily maximum temperature exceeds the extreme threshold need to be collected; for low-temperature ice disasters, relevant meteorological parameters such as the temperature of the freezing weather, real-time wind speed, freezing rain time, and freezing rain rate need to be collected.

[0091] Furthermore, analyze the impacts of extreme weather on the supply side and load side of the energy system: when an earthquake disaster occurs, the lines are damaged and there is no external power grid supply, the ground-source heat pumps are damaged and cannot provide heating and cooling, the photovoltaic and wind turbines are damaged and cannot supply power, some gas pipelines fail, and some roads are damaged or congested, resulting in changes in the dispatching routes of mobile emergency vehicles and affecting the restoration of load supply; typhoon weather mainly affects overhead power lines, wind turbines, and photovoltaics; extremely high-temperature weather often causes drought, resulting in a decrease in external power grid supply, limited output of photovoltaics and wind turbines, and when the temperature is higher than 15°C, the efficiency of gas turbines decreases by 0.7% - 1% for every 1°C increase in temperature, and the cooling load of users will increase when high-temperature weather occurs; extremely low-temperature weather will lead to ice disasters, at this time some overhead lines fail, the output of photovoltaics and wind turbines is limited, and the heating load of users will increase when low-temperature weather occurs.

[0092] S12. Use the two-state resilience adaptive Markov process to analyze the state transition chain of the equipment, generate the equipment failure probability through the dynamic game of failure rate and repair rate, and perform multi-scenario sampling simulation through the Monte Carlo method to generate a time-series set of equipment failure states.

[0093] In a specific example, refer to Figure 4, to improve the resilience and reliability of the energy system, a method combining a two-state resilient adaptive Markov process and Monte Carlo simulation is used to generate device failure time series scenarios. As Figure 4 shown in the two-state resilient adaptive Markov state transition model, the core lies in constructing a time-varying transition mechanism for the device operation state (normal / fault) through the dynamic interaction of the failure rate and repair rate, and generalizing it to the external power grid circuit and natural gas underground pipeline. The two-state resilient adaptive Markov switching model is specifically as follows: ;

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] In the formula, is the probability of the device j operating normally, is the probability of the device j failing. 1 indicates that the device j is in a normal operating state, and 0 indicates that the device j has a fault in operation. , is the time-varying repair rate; for the power grid and gas network, , is the number of repair teams, is the benchmark repair rate without additional repair resources, is the benchmark number of repairs; for the energy system, , or are the time-varying normal operating time of the device and the time-varying repair time of the device respectively, is a number randomly generated from the uniform distribution [0, 1], is a number randomly generated from the uniform distribution [0, 1], is the state of the device j at t time, is the failure rate, , K ={earthquake, storm, extremely high temperature and drought, extremely low temperature and ice disaster}, K is the set of disaster types, is the real-time intensity index of the disaster K , is the weight coefficient of the disaster K , satisfying wk = 1, is the non - linear influence function of disasters, used to standardize the physical dimensions of different disasters, K and is the maintenance - disaster game intensity that quantifies the suppression ability of the input amount of maintenance resources on the disaster intensity. When R ( t ) ≥ D ( t ), γ ( t ) ≥ 0.5, indicating that maintenance is dominant and the probability of the equipment being in the normal state is increased; R ( t ) < D ( t ), γ ( t ) < 0.5, indicating that disasters suppress and the failure risk is increased. is the reference threshold of the resource - disaster ratio.

[0099] Furthermore, referring to Figure 5 , the steps of fault extraction based on Monte Carlo are as follows:

[0100] (1) Define model parameters: First, define the states. State 0 indicates that the equipment is in the normal working state, and state 1 indicates that the equipment is in the running - fault state. Second, calculate the probability

[0101] j of the equipment being in the normal working state and the probability j of the equipment being in the running - fault state. Determine the time interval. The time step in this embodiment is set to 1 hour. Usually, under normal circumstances, the time step of the operation cycle is shorter than the maintenance time of the equipment. Therefore, within this time step, the equipment is considered irreparable and in the fault state. Finally, obtain the interruption model j of the equipment.

[0102] (2) Initialize the simulation: First, set the initial state of the equipment. Usually, it is considered that the initial state of the equipment is in the working state. Second, determine the number of Monte Carlo simulations. Initialize 100 simulations to obtain sufficient data for analysis.

[0103] (3) Conduct the simulation: Each simulation starts from the initial state. At each time step, randomly determine whether the equipment undergoes a state transition according to the probability of the current equipment and the transition state, and record the state of the equipment and the time of the transition.

[0104] (3)Data collection: Collect the time when the device enters the failure state in each simulation, and calculate key indicators such as the failure rate and the mean time to failure.

[0105] In this embodiment, the traditional static reliability model limitations are broken through by the dynamic failure rate - repair rate game mechanism, and the high-fidelity simulation of complex scenarios such as the cascading failure of gas pipelines caused by earthquakes and the hourly breakage of lines caused by typhoons is realized by combining Monte Carlo iteration under the constraint of the disaster duration, providing high-confidence input boundary conditions for the subsequent capacity configuration optimization.

[0106] S13. According to the spatial distribution intensity and time accumulation parameters under the current extreme disaster event, simulate the dynamic diffusion process of the extreme disaster event through parameter space gradient mapping and time accumulation effect analysis, and generate a set of line failure probabilities including the energy network lines and pipe networks.

[0107] Specifically, for different types of extreme disaster events, a set of failure probabilities of the energy network lines and pipe networks is dynamically generated through the following steps:

[0108] (1) In the earthquake scenario, based on the spatial gradient distribution of the intensity in the epicenter area and combined with the correlation parameters fitted from historical earthquake data, analyze the seismic damage risk gradient of different sections along the gas pipeline as follows: ;

[0109] In the formula, is the earthquake occurrence frequency, is the earthquake magnitude, and its size depends on the intensity of the earthquake epicenter area ;

[0110] a and b are fitting parameters, and the fitting parameters in different regions need to be obtained by fitting the local historical earthquake data.

[0111] Judge the earthquake hazard rate according to the intensity of the earthquake epicenter area, and thus calculate the failure probability of the gas pipeline:

[0112] ;

[0113] ;

[0114] In the formula, is the intensity of the earthquake epicenter area, is the earthquake hazard rate, is the failure probability of the gas pipeline, is the pipeline length.

[0115] In the earthquake scenario, the energy supply system has suffered severe damage, presenting a complex and severe energy supply-demand situation. The characteristics of the energy supply in the energy system are as follows: 1) Interruption and limitation of energy supply: The external power grid lines and natural gas pipelines are damaged, resulting in a complete interruption of the external power grid supply. The underground natural gas pipelines are significantly reduced, directly restricting the power generation of gas turbines. At the same time, renewable energy sources such as photovoltaic and wind turbines are damaged by the earthquake and cannot supply power normally, and the energy supply methods have been severely hit. 2) Associated impact on the energy supply system: The damage to the power supply system not only leads to insufficient power supply but also has a chain reaction on building heating and cooling. The ground-source heat pump is damaged and cannot supply heating and cooling normally. The electric chiller still needs to consume electricity to operate under the condition of tight cooling supply, further exacerbating the contradiction between power supply and demand. 3) Adjustment of the energy supply strategy: In the extremely tight power supply situation, in order to give priority to meeting the energy demand of buildings, the electrolyzers originally used for hydrogen production stop working, and the limited resources are centrally allocated. The electrical load on the demand side is mainly met by the discharge of batteries and mobile resources. At the same time, some non-critical electrical loads are restricted or reduced.

[0116] (2) In the typhoon scenario, extract the ratio of the real-time wind speed to the designed wind speed under the typhoon movement path, and identify the designed wind speed threshold and the duration of the typhoon weather. The probability of wire failure under the typhoon is: ;

[0117] ;

[0118] In the formula, is the wind speed at the time of event t ; is the designed wind speed; is the maximum wind speed that the line can withstand; is the length of the line; is the duration of the typhoon weather.

[0119] In the typhoon scenario, the characteristics of energy supply between energy system devices are as follows: 1) Fault simulation and detection: Using the Batts typhoon model, integrating the typhoon wind field model and the typhoon movement attenuation model, obtaining typhoon data such as wind speed and wind direction, and dynamically simulating and characterizing the transmission line fault problems to provide a basis for the subsequent response of power supply equipment. 2) Energy supply interruption: When the typhoon passes through the external lines of the energy system and causes damage, it leads to a complete interruption of the external power grid supply. At the same time, renewable energy sources such as photovoltaic and wind turbines are damaged due to excessive wind speed and cannot supply power normally. At this time, the gas turbine undertakes the main power supply task, and the battery and mobile resources cooperate as emergency power supply equipment to jointly ensure the power supply under typhoon conditions. 3) Adjustment of energy supply strategy: The main force of power supply changes to the gas turbine, which undertakes the task of meeting the main power load. The battery and mobile resources cooperate as emergency power supply equipment to jointly ensure the power supply under typhoon conditions and alleviate the contradiction between power supply and demand.

[0120] (3) In the ice disaster scenario, according to the spatial heterogeneity of the freezing rain rate and the persistence of the freezing rain time, calculate the spatio-temporal evolution trajectory of the ice thickness on the transmission line as follows:

[0121] ;

[0122] ;

[0123] Where, is the thickness of the ice; is the freezing rain time; is the freezing rain rate; is the density of the ice; is the density of the freezing rain; is the liquid water content, calculated by the formula ; is the ice load per unit wire line; is the conductor diameter; is the wind load; is a constant coefficient, in the unit of ; is the cross-boundary factor; is the combined ice ball load, calculated from the ice load and the wind load.

[0124] Based on the spatio-temporal evolution trajectory of the ice thickness, establish the following probability model of the transmission line under ice disaster: ;

[0125] ;

[0126] Where, is the fault probability per unit line; and They are the first threshold and the second threshold of the puck load respectively.

[0127] In the extreme weather scenario of ice disaster, the heating load increases, and the energy supply operation and linkage in the equipment room present the following specific states: 1) Energy supply is blocked: The ice disaster causes the photovoltaic and wind turbines to be affected by the ice thickness, and their power generation capacity is severely limited. At the same time, the external lines are overloaded due to icing, resulting in limited power supply capacity of the power grid. The normal power supply system is impacted, and various original energy supply channels are ineffective or weakened to varying degrees, and the overall energy supply is in trouble; 2) Energy supply dominance and cooperation: The gas turbine becomes the main power supply equipment during the ice disaster. When the power generation of the gas turbine still cannot meet the power demand, mobile resources are quickly put into use to supplement the power.

[0128] S14. Construct a temperature-driven load growth model. By introducing humidity correction terms, wind speed correction terms, and irradiance correction terms, quantify the load dynamic increment of extreme low temperature events or extreme low temperature events, and form a time-series load dynamic parameter set.

[0129] Specifically, the dynamic load growth evolution models for drought weather caused by high temperature and ice disaster weather caused by low temperature are as follows: ;

[0130] In the formula, is the t predicted load at the time of extreme weather occurrence, is the t predicted load at the time when extreme weather does not occur, is the load increment at the t time after the occurrence of extreme weather, which is related to the perceived temperature ; is the neural network load prediction model, and the predicted load is related to the perceived temperature. represents t the actual air temperature at the time, t represents the air relative humidity correction term at the time, represents the wind speed correction term, and

[0131] represents the solar radiation correction term. Among them, the neural network load prediction model is a data-driven mathematical function, and its core function is to predict the load under specific meteorological conditions by learning the hidden rules between historical meteorological data and power load. The predicted load output by the model reflects the quantitative impact of the combined action of temperature, humidity, wind speed, and radiation under normal meteorological conditions on electricity consumption behavior. The neural network load prediction model can adopt network structures such as LSTM (Long Short-Term Memory Network), CNN (Convolutional Neural Network), or Transformer.In extreme high-temperature scenarios, all types of power generation equipment are significantly affected, as follows: 1) Restricted renewable energy power generation: Excessively high temperatures reduce the conversion efficiency of photovoltaic modules, resulting in reduced power generation. At the same time, high temperatures may affect the normal operation of mechanical components and electronic elements inside wind turbines, thereby restricting the power generation capacity of wind turbines. As a result, photovoltaic and wind power, which are originally important sources of renewable energy power generation, are difficult to fully exert their efficiency in extreme high temperatures. 2) Restricted traditional energy power generation: In extreme high temperatures, gas turbine power generation is also restricted. Excessively high temperatures reduce the air density entering the gas turbine, leading to a decrease in combustion efficiency, thus restricting the power generation of gas turbines and making it difficult to maintain normal power generation levels.

[0132] S15. Align the equipment failure state set, line failure probability set, and load dynamic parameter set in space and time, and combine with the multi-dimensional extreme disaster event database to generate a disaster response database. Through space-time grid alignment technology, multi-dimensionally couple the equipment failure state set, line failure probability set, and load dynamic parameter set to generate a disaster response database.

[0133] S2. Convert the fluctuations in renewable energy output and the deviation of user load demand into fuzzy variables through fuzzy chance constraints, and establish a capacity configuration optimization model in combination with the disaster response database. With cost minimization as the optimization goal, generate a multi-energy complementary capacity configuration.

[0134] Furthermore, as Figure 6 shown, step S2 includes:

[0135] S21. In response to regional energy demand, construct a system topology including energy supply equipment, energy storage equipment, and energy conversion units based on the disaster response database, and configure capacity configuration elements including equipment output sets, equipment state sets, and efficiency parameter sets.

[0136] Specifically, the equipment output set is used to characterize the dynamic balance state of energy production and demand in the energy system at time t. It includes the energy supply efficiency of energy supply equipment such as wind turbines and photovoltaics in the energy system, building electrical load demand, heating load demand, cooling load demand, hydrogen for hydrogen fuel vehicles, load demand, SOC of energy storage equipment, etc., to comprehensively reflect the output information of the energy system at the current moment. Therefore, the output of each equipment in the energy system at time t is expressed as:

[0137] ;

[0138] In the formula, is the t time electricity price, is the power generation of equipment i at t time; is the equipment i at tCooling capacity at a certain moment; For the equipment i At t Heating capacity at a certain moment; For the electro-hydrogen conversion equipment i At t Hydrogen production at a certain moment; Are respectively the building's electrical load demand, heat load demand, and cooling load demand at time t; Is the capacity of energy storage equipment i at time t.

[0139] The equipment status set is used to describe the operation reliability of system equipment and the charging and discharging dynamics of energy storage. It includes the operation status (normal operation or fault status) of all equipment in the energy system and the charging and discharging status of energy storage equipment, etc., to comprehensively reflect the operation status information of each equipment in the energy system at the current moment. Therefore t The status of each equipment in the energy system at a certain moment is expressed as: ;

[0140] In the formula, Indicates the operation status of equipment i At t A certain moment, obtained from formula (14). When it is 1, it means that equipment i is in a normal operation state. When Is 1, it means that equipment i is in a normal operation state. When Is 0, it means that equipment i Is in a running fault state; Are respectively the charging state and discharging state of energy storage equipment i At t A certain moment. When the energy storage equipment i Is in a fault state, Or Is 0. In a normal operation state, Or Is 1, and .

[0141] The energy efficiency parameter set is used to quantify the energy conversion and transmission losses of equipment, and it includes: energy supply equipment efficiency, energy storage loss coefficient, and energy conversion efficiency.

[0142] S22. Establish a capacity configuration optimization model with the goal of minimizing the levelized cost of electricity, generate model constraint conditions based on capacity configuration elements, and embed the fuzzy variable set into the model constraint conditions to generate a fuzzy feasible solution space that integrates uncertainty.

[0143] In a specific embodiment, collect the unit investment cost, discount rate, and investment period of each device in the energy system, collect the unit operation and maintenance cost of each device, determine the power supply quantity and the power load reduction quantity at each moment, and construct a capacity configuration optimization model for enhancing the resilience of the energy system considering extreme weather and natural disasters; this capacity configuration optimization model aims to minimize the Levelized Cost of Electricity (LCOE), which represents the average electricity price per kilowatt-hour and is calculated from the Total net present cost (TNPC), the Capital recovery factor (CRF), and the total electricity quantity, and evaluate the annual total cost of the system over its 20-year service life, including the initial investment, operation, and maintenance costs, as follows:

[0144] ;

[0145] ;

[0146] ;

[0147] ;

[0148] ;

[0149] In the formula, TNPC is the total net present cost, CRF is the capital recovery factor, is t the power load at time is t the charging load of the electric vehicle at time is t the reduced power load at time is the annual investment cost of the system, is the discount rate, is the device life, is the device i unit investment cost, is the device i installed capacity, is the annual operation and maintenance cost of the system, is the device i unit operation and maintenance cost, is the device i operating power.

[0150] S23. Iteratively solve the capacity configuration optimization model in the fuzzy feasible solution space, and output the multi-energy complementary capacity configuration adapted to the system topology.

[0151] S3. Based on the multi - energy complementary capacity configuration, introduce mobile resources to establish an elastic improvement scheduling framework. By implementing the damaged road network topology reconstruction, spatio - temporal transfer chain modeling, and action - state collaborative regulation of mobile resources, generate an elastic improvement plan adaptable to extreme disaster events.

[0152] Further, as Figure 7 shown, step S3 includes:

[0153] S31. On the basis of the multi - energy complementary capacity configuration, introduce mobile resources including mobile emergency power vehicles, electric vehicles, and diesel generators as elastic supplementary energy supply units, and establish an elastic improvement scheduling framework for the coordination of fixed equipment and mobile resources.

[0154] S32. According to the obtained disaster damage information, remove the damaged road network nodes and edge sets, reconstruct the road network topology, and generate a set of feasible scheduling paths that minimize the distance of mobile resources from the starting point to the target energy system.

[0155] In this embodiment, as Figure 8 shown, the road topology network includes nodes A - M and edge sets, where the edge weights represent the distances between adjacent nodes. When road damage is caused by an earthquake disaster, the scheduling path of mobile emergency resources is blocked, resulting in a delay in load restoration. To address this problem, the following steps are executed: First, based on the road damage information collected after the earthquake (such as bridge collapse, road surface cracking), identify the damaged nodes and edge sets (such as the road interruption between nodes D - E). Second, remove the damaged edge sets and update the road network adjacency matrix to generate a sparse road network structure under the post - disaster reachability constraint; furthermore, use the Dijkstra algorithm to search for the optimal scheduling path of the mobile emergency power vehicle from the starting point (such as node A) to the target energy system (such as node M) in the reconstructed road network, search for the shortest driving route of the mobile resources using the Dijkstra algorithm, and output a set of feasible paths that minimize the distance (such as A→B→F→L→M, A→B→G→K→M, A→C→H→J→M, etc.).

[0156] S33. Analyze the departure timestamp, estimated arrival timestamp, driving duration interval, and charging stop times of mobile resources to generate time - chain features including time availability constraints, and extract the starting position coordinates, target energy supply site coordinates, and path node coordinate sets of mobile resources to generate space - chain features including space connectivity constraints.

[0157] In this step, the travel chain of electric vehicles is divided into two parts: the time chain and the space chain; the time chain mainly describes the time distribution of electric vehicle travel, including: departure time ( ), arrival time ( ), driving time ( ), and parking time ( ); The spatial chain mainly describes the spatial transfer of electric vehicle trips, including: the starting point ( ), the ending point ( ), and the driving path ( ); The time chain and the spatial chain are expressed as: ;

[0158] ;

[0159] S34. Verify the time chain characteristics and the spatial chain characteristics. Based on the spatio-temporal feature combinations that pass the verification, calculate the maximum service radius, the shortest response time, and the continuous power supply duration threshold of the mobile resources to form the spatio-temporal schedulable boundary. Specifically, establish the association rules between the time chain and the spatial chain, and implement the spatio-temporal consistency verification rules and spatio-temporal conflict detection to screen out the scheduling schemes with time window overlap conflicts, path node failures, or unavailable charging periods at stops.

[0160] S35. Obtain the action set representing the energy supply behavior parameters of the mobile resources according to the energy supply characteristics of the obtained mobile resources, and construct the state set representing the operating states of the mobile resources according to the mutual exclusion constraints of the device operations.

[0161] In this step, the mobile resource action set mainly includes the charging and discharging power of electric vehicles ( ), the electric power consumed during driving ( ), the power supply power of mobile emergency power vehicles ( ), and the power generation power of mobile diesel generators ( ), so as to comprehensively represent the situation of scheduling mobile resources at a certain moment after extreme weather or natural disasters to restore the building energy load. The action sets of mobile resources such as electric vehicles (Electric vehicles, EVs), mobile emergency vehicles (Mobile energystorage system, MESSs), and mobile diesel generators (Diesel Generator, DGs) are expressed as: ;

[0162] The mobile resource state set mainly includes the charging state of electric vehicles ( ), the discharging state ( ), the start-stop state of mobile emergency vehicles ( ), and the start-stop state of mobile diesel generators ( ), so as to comprehensively represent the scheduling states of mobile resources such as electric vehicles; the mobile resource state set is expressed as: ;

[0163] In the formula, when the i th electric vehicle is in the charging state the value is 1, and when the electric vehicle is in the discharging state The value is 1, and it cannot be charged and discharged simultaneously. ; When the mobile emergency power vehicle arrives at the energy system and discharges to supply the building's energy demand The value is 1, otherwise when there is no need for the mobile emergency vehicle to discharge or the mobile emergency power vehicle has not arrived at the energy system The value is 0; When the mobile diesel generator is in the power supply state The value is 1, otherwise it is 0.

[0164] S36. In the elastic improvement scheduling framework, aiming at minimizing the total cost of mobile resource scheduling, minimizing the load shedding amount, and minimizing the path energy consumption, based on the spatio-temporal schedulable boundary, through a multi-layer collaborative optimization algorithm, dynamically solve the parameters of the action set of mobile resources, the flag sequence of the state set, and the optimal solution of the priority of the feasible scheduling path set, and generate an elastic improvement plan adaptive to extreme disaster events.

[0165] To quickly restore the load supply and further improve the system elasticity, with the core optimization goals of minimizing the operating cost of mobile resources, maximizing the load restoration amount, and minimizing the path energy consumption within the scheduling period, construct the following multi-objective function system: ;

[0166] ;

[0167] ;

[0168] ;

[0169] ;

[0170] In the formula, represents the number of consecutive days of extreme weather or natural disasters; , , and are the scheduling costs of EV, DG, and MESS and the amount of unsupplied load respectively; is the path energy consumption during the scheduling of electric vehicles; , and are the unit energy supply prices respectively; and are the discharge amounts of EV and MESS respectively; is the amount of diesel consumed by DG for power supply; , , are the amounts of unsupplied electric load, unsupplied cooling load, and unsupplied heating load respectively.

[0171] Furthermore, asFigure 9 As shown in the figure, step S35 includes:

[0172] S351. Encode the action set parameters and state set flag sequences of mobile resources into the position vectors of honey badger individuals to generate an initial honey badger population that satisfies the spatio-temporal schedulable boundary. And set the algorithm parameters of each layer and the maximum number of iterations.

[0173] S352. Encode the priority of the feasible scheduling path set into the migration path chain of flock individuals to generate an initial flock population that satisfies the spatio-temporal schedulable boundary and load it into the path memory bank.

[0174] S353. Construct an inner and outer layer collaborative optimization framework. In the inner layer architecture, the upper layer honey badger population performs a two-objective optimization of the global scheduling cost and load reduction. The lower layer flock population optimizes the priority of the feasible scheduling path set with the goal of minimizing path energy consumption through the path memory bank sharing mechanism. In the outer layer architecture, the simulated annealing algorithm model receives the optimal solution output by the inner layer architecture as the initial solution and performs perturbation optimization.

[0175] S354. For each honey badger individual, match the lowest energy consumption sequence of the corresponding path chain from the initial flock population, calculate the total cost of mobile resource scheduling, load reduction, and path energy consumption indicators, and aggregate them into a comprehensive performance indicator through normalized weights to generate a dual-driven scale of prey odor concentration - migration attraction.

[0176] S355. Sort the dual populations according to the dual-driven scale, select the current global optimal solution, and combine the introduced dynamic density factor to control the honey badger and flock individuals to perform two-stage optimization within the neighborhood of the current global optimal solution.

[0177] S356. In the wide-area mining stage, expand the search step of the upper layer honey badger population and the prey odor concentration, traverse the combinations of action set parameters and state set flag sequences. In the lower layer flock population, update the topological weights in the path memory bank according to the path requirements of honey badger individuals to generate a wide-area feasible path set.

[0178] S357. In the fine-grained mining stage, contract the search step of the upper layer honey badger population to lock the local optimum of the combinations of action set parameters and state set flag sequences. In the lower layer flock population, perform a priority sorting of the wide-area feasible path set driven by migration attraction.

[0179] S358. Perform constraint verification on the updated honey badger and flock individuals. If the updated honey badger individuals and flock individuals violate the spatio-temporal schedulable boundary, perform topological re-routing repair, adjust the node order and passing weights of the path chain, and apply flock gravity calibration to reassign priorities based on the path memory bank. Feed the repaired individuals back to the corresponding populations to update the honey badger and flock distributions.

[0180] S359. After the inner-layer architecture iteration is completed, the current optimal scheduling plan is input into the outer-layer simulated annealing algorithm model. New solutions are generated through Gaussian perturbation and the fitness differences are calculated. A probabilistic acceptance strategy is used to jump out of the local optimum, and a solution that satisfies the spatio-temporal schedulable boundary conditions and is jointly optimal in terms of dual objectives is output, including the parameter set of mobile resource actions, the state set flag sequence, and the priority of the feasible scheduling path set. Specifically, after the inner-layer dual-model algorithm completes the iteration, the currently found optimal scheduling plan is obtained, output, and used as the initial solution for the outer-layer simulated annealing optimization stage to further optimize and adjust the scheduling plan. In the simulated annealing optimization stage, new solutions with dual-objective joint optimality for the parameter set of mobile resource actions, the state set flag sequence, and the priority of the feasible scheduling path set are generated by perturbing the initial solution, and the fitness differences are calculated. If the difference is less than 0, the new solution is accepted. If the difference is greater than 0, the acceptance probability of the new solution is calculated, and the new solution is selectively accepted according to the acceptance probability. When the simulated annealing algorithm layer reaches the maximum number of iterations at different temperatures according to the cooling coefficient, the multi-layer collaborative optimization is terminated.

[0181] Reference Figure 10 , which shows the trend of the system performance changing with time after adopting the above-mentioned solution, as well as the impact of different strategies adopted in different stages on the system performance. Under ideal conditions, the energy supply level of the system remains at , when an extreme weather event or natural disaster occurs, the equipment of the energy system is affected, and the system energy supply level begins to decline until the end time of the extreme weather event or natural disaster, the system energy supply level drops to . And at the end time of the extreme weather event or natural disaster, the system recovery strategy begins to be implemented. At time , the system energy supply begins to recover until time when the system performance is fully restored. After considering the energy system capacity configuration optimization model provided by the present invention example, the ability of the energy system to overcome extreme weather and natural disasters is improved. When an extreme weather event or natural disaster occurs, the system energy supply level drops to , and the system performance is improved by compared with that before the capacity configuration optimization. After considering the mobile resource scheduling model provided by the present invention example, such as considering electric vehicles, the recovery time can be advanced from to , quickly restoring the load supply.

[0182] Additionally, an energy system resilience improvement system considering mobile resource load recovery under extreme weather conditions provided by an embodiment of the present invention includes: a database construction module, configured to link the equipment failure probability and line failure probability under extreme disaster events obtained through dual-effect Markov and Monte Carlo processing based on multi-source extreme disaster event data, and couple the meteorological-driven load change parameters to form a disaster response database. A configuration generation module, configured to convert the fluctuations in renewable energy output and the deviation of user load demand into fuzzy variables through fuzzy chance constraints, establish a capacity configuration optimization model in combination with the disaster response database, and generate a multi-energy complementary capacity configuration with the minimization of the levelized cost of electricity as the optimization objective. A configuration improvement module, configured to introduce mobile resources based on the multi-energy complementary capacity configuration to establish a resilience improvement scheduling architecture, and generate a resilience improvement plan adaptable to extreme disaster events by implementing the damaged road network topology reconstruction, spatio-temporal transfer chain modeling, and action state coordinated control of mobile resources.

[0183] Moreover, an automatic annotation device for high-quality drainage pipeline image data based on a deep learning model with multi-feature fusion provided by an embodiment of the present invention includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the method for optimizing the capacity of the energy system and decoupling and coordinating the spatio-temporal scheduling of mobile resources under extreme disaster disturbances as described above.

[0184] Furthermore, an embodiment of the present invention provides a computer-readable storage medium, on which computer-executable instructions are stored, and when the executable instructions are executed by a processor, the method for optimizing the capacity of the energy system and decoupling and coordinating the spatio-temporal scheduling of mobile resources under extreme disaster disturbances as described above is implemented.

[0185] In summary, an embodiment of the present invention provides a method, system, device, and medium for optimizing the capacity of the energy system and decoupling and coordinating the spatio-temporal scheduling of mobile resources under extreme disaster disturbances, aiming to achieve a three-level linkage architecture of the energy system in a disaster scenario, including disaster data-driven modeling, multi-energy capacity collaborative optimization, and dynamic scheduling of mobile resources. Refer to Figure 11 , and its core logic is divided into the following steps:

[0186] Step 1: Obtain the historical extreme weather data of multiple different meteorological station observation points. The extreme weather and natural disasters collected mainly include earthquakes, typhoons, extreme high-temperature weather, and extreme low-temperature weather. And establish an extreme weather database based on the historical extreme weather data; Step 2: Simulate the device state transition probability through a two-state resilience adaptive Markov process, combine Monte Carlo random sampling to generate device cascading failure scenarios, construct a weather disaster energy system response database including device output limitation, load dynamic disturbance, and energy supply-demand imbalance, and generate a disaster response database based on the extreme weather database; Step 3: Define the uncertainty of wind-solar power generation as a fuzzy variable using fuzzy chance constraints and establish a fuzzy chance constraint model, and generate an energy system configuration optimization model under extreme weather through the disaster response database. With the goal of minimizing the levelized cost of electricity, call solvers such as Gurobi to optimize the device capacity of the energy system, determine the optimal energy system capacity configuration, and transfer the optimized results to the dispatching layer. The optimized energy system can effectively mitigate the interruption of energy supply under extreme events and improve the resilience and reliability of the system; Step 4: To further enhance the resilience of the energy system, on the basis of capacity configuration, couple mobile resources such as mobile emergency power vehicles, mobile diesel generators, and electric vehicles. With the goal of jointly minimizing the total dispatching cost, the load shedding amount, and the path energy consumption, adopt a multi-layer collaborative optimization framework to dynamically solve the power distribution, charge-discharge timing, and path priority, and generate an adaptive resilience plan for extreme disaster events, thereby dispatching mobile resources to supply power to restore load supply at the fastest speed.

[0187] The present invention realizes accurate risk quantification through the drive of a disaster database, and the fuzzy constraint model (step) ensures the economic-resilience balance of multi-energy capacity coordination, and finally breaks through the upper limit of the response ability of fixed devices with the help of dynamic scheduling of mobile resources.

[0188] Since the system / device described in the above embodiments of the present invention is the system / device adopted for implementing the method in the above embodiments of the present invention, based on the method described in the above embodiments of the present invention, those skilled in the art can understand the specific structure and deformation of the system / device, so it will not be elaborated here. Any system / device adopted by the method in the above embodiments of the present invention belongs to the scope to be protected by the present invention.

[0189] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, as well as the combination of processes and / or blocks in the flowcharts and / or block diagrams.

[0190] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications after learning the basic creative concept. Therefore, the technical solution should be interpreted to include the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0191] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the technical solution of the present invention and its equivalent technologies, the present invention should also include these modifications and variations.

Claims

1. An energy system capacity optimization and spatio-temporal decoupling collaborative scheduling method of mobile resources under extreme disaster disturbances, characterized in that, Including: Based on the obtained extreme disaster event data, link the equipment failure probability and line failure probability obtained through the dual-state resilience adaptive Markov and Monte Carlo processing, and couple the load change parameters driven by extreme disaster events to form a disaster response database; Convert the renewable energy output fluctuations and user load demand deviations into fuzzy variables through fuzzy chance constraints, and establish a capacity configuration optimization model in combination with the disaster response database. With the minimization of the levelized cost of electricity as the optimization goal, generate a multi-energy complementary capacity configuration; Based on the multi-energy complementary capacity configuration, introduce mobile resources to establish an elastic improvement scheduling framework. By implementing the damaged road network topology reconstruction, spatio-temporal transfer chain modeling and action state collaborative regulation of mobile resources, generate an extreme disaster event adaptive elastic improvement plan, including: on the basis of the multi-energy complementary capacity configuration, introduce mobile resources including mobile emergency power vehicles, electric vehicles and diesel generators as elastic supplementary energy supply units, and establish an elastic improvement scheduling framework for the coordination of fixed equipment and mobile resources; Remove the damaged road network nodes and edge sets according to the obtained disaster damage information, reconstruct the road network topology, and generate a set of feasible scheduling paths that minimize the distance from the starting point to the target energy system for mobile resources; Analyze the departure timestamp, expected arrival timestamp, driving duration interval and charging stop time period of mobile resources to generate time chain characteristics, and extract the starting position coordinates, target energy supply site coordinates and path node coordinate sets of mobile resources to generate space chain characteristics; Verify the time chain characteristics and space chain characteristics, and based on the spatio-temporal feature combinations that pass the verification, calculate the maximum service radius, shortest response time and continuous power supply duration threshold of mobile resources to form a spatio-temporal schedulable boundary; Obtain the action set representing the energy supply behavior parameters of mobile resources according to the energy supply characteristics of mobile resources, and construct a state set representing the operating state of mobile resources according to the mutual exclusion constraints of equipment operation; In the elastic improvement scheduling framework, with the goals of minimizing the total cost of mobile resource scheduling, the lowest load curtailment and the minimum path energy consumption, based on the spatio-temporal schedulable boundary, dynamically solve the optimal solutions of the parameters of the action set, the flag sequence of the state set and the priority of the set of feasible scheduling paths of mobile resources through a multi-layer collaborative optimization algorithm, and generate an extreme disaster event adaptive elastic improvement plan.

2. The method for optimizing the capacity of an energy system and coordinately scheduling spatio-temporal decoupling of mobile resources under extreme disaster disturbances as claimed in claim 1, wherein Based on the obtained extreme disaster event data, link the equipment failure probability and line failure probability obtained through the dual-state resilience adaptive Markov and Monte Carlo processing, and couple the load change parameters driven by extreme disaster events, the disaster response database includes: Based on the obtained extreme disaster event data of multiple observation points, form a multi-dimensional extreme disaster event database; among them, the extreme disaster event data is a set of disaster events including earthquakes, storms, extreme high temperature and drought, and extreme low temperature and ice disasters that exceed the preset extreme thresholds; Adopt a dual-state resilience adaptive Markov switching model to analyze the state transition chain of equipment, generate the equipment failure probability through the dynamic game of the failure rate and the repair rate, and perform multi-scenario sampling simulation through the Monte Carlo method to generate a time-series equipment failure state set; According to the spatial distribution intensity and time accumulation parameters under current extreme disaster events, simulate the dynamic diffusion process of extreme disaster events through parameter space gradient mapping and time accumulation effect analysis, and generate a set of line failure probabilities including energy network lines and pipe networks; Construct a dynamic load growth evolution model, and quantify the dynamic load increment of extreme low temperature events or extreme high temperature events by introducing humidity correction terms, wind speed correction terms and irradiation correction terms to form a time-series load dynamic parameter set; Align the equipment failure state set, line failure probability set and load dynamic parameter set in space and time, and combine with a multi-dimensional extreme disaster event database to generate a disaster response database.

3. The method for optimizing the energy system capacity and spatio-temporal decoupling collaborative scheduling of mobile resources under extreme disaster disturbances as claimed in claim 2, wherein, The two-state resilient adaptive Markov switching model is as follows: ; ; ; ; ; Wherein, is the probability of normal operation of the device j , is the probability of device j failure. 1 indicates that the device j is in normal operation state, and 0 indicates that the device j has a failure during operation, , is the time-varying repair rate; for the power grid and gas network, , is the number of emergency repair teams, is the baseline repair rate without additional emergency repair resources, is the emergency repair baseline quantity; for the energy system, , and are the time-varying normal operation time of the device and the time-varying repair time of the device respectively, is a number randomly generated from a uniform distribution [0, 1], is a number randomly generated from a uniform distribution [0, 1], is the state of the device j at t moment, is the failure rate, , K ={earthquake, storm, extreme high temperature and drought, extreme low temperature and ice disaster}, K is the set of disaster types, is the real-time intensity index of the disaster K , is the weight coefficient of the disaster K , satisfying w k = 1, is the non-linear influence function of the disaster K , used to standardize the physical dimensions of different disasters, is the quantified maintenance resource input against the suppression ability of the disaster intensity maintenance and disaster game intensity, R ( t ) ≥ D ( t ) when, γ ( t ) ≥ 0.5, indicating that maintenance is dominant and the probability of the normal state of the device is increased; R ( t ) < D ( t ) when, γ ( t ) < 0.5, indicating that the disaster suppresses and increases the failure risk, resource-disaster ratio benchmark threshold; The dynamic load growth evolution model is as follows: ; In the formula, is the predicted load at time t when extreme weather occurs, is t the predicted load at time when extreme weather does not occur, t is the load increment at time after extreme weather occurs, which is related to the perceived temperature is the neural network load prediction model, represents t the actual temperature at time represents t the air relative humidity correction term at time represents the wind speed correction term, represents the solar radiation correction term.

4. The method for optimizing the capacity of the energy system and coordinately scheduling the spatio-temporal decoupling of mobile resources under extreme disaster disturbances according to claim 1, wherein Convert the fluctuations of renewable energy output and the deviation of user load demand into fuzzy variables through fuzzy chance constraints, and establish a capacity configuration optimization model in combination with the disaster response database, with the minimum levelized cost of electricity as the optimization goal, and generate a multi-energy complementary capacity configuration including: Construct a membership function using fuzzy chance constraints to convert the random fluctuations of wind and solar power generation and the deviation of user load demand into a set of fuzzy variables under credible probability constraints; In response to regional energy demands, construct a system topology including energy supply equipment, energy storage equipment and energy conversion units based on the disaster response database, and configure capacity configuration elements including equipment output sets, equipment state sets and efficiency parameter sets; Establish a capacity configuration optimization model with the minimum levelized cost of electricity as the goal, generate model constraint conditions based on the capacity configuration elements, and embed the set of fuzzy variables into the model constraint conditions to generate a fuzzy feasible solution space that integrates uncertainties; Iteratively solve the capacity configuration optimization model in the fuzzy feasible solution space, and output a multi-energy complementary capacity configuration adapted to the system topology.

5. The method for optimizing the energy system capacity and spatio-temporal decoupling collaborative scheduling of mobile resources under extreme disaster disturbances as claimed in claim 4, wherein, The device output set is as follows: ; Wherein, is t the time-of-use electricity price, is the power generation of the device i at t time; is the cooling capacity of the device i at t time; is the heating capacity of the device i at t time; is the hydrogen production of the electro-hydrogen conversion device i at t time; are respectively t the building's electrical load demand, heat load demand and cooling load demand at is t the capacity of the energy storage device i at ； In the formula, represents the operating state of the device i at t moment. When is 1, it means the device i is in a normal operating state. When is 0, it means the device i is in a running fault state. are respectively the charging state and discharging state of the energy storage device i at t moment. When the energy storage device i is in a fault state, or is 0. When in a normal operating state, or is 1, and ; The capacity configuration optimization model is as follows: ; ； ； ； ； In the formula, TNPC is the total net present value, CRF is the capital recovery factor, is t the electricity load at time is t the charging load of the electric vehicle at time is t the reduced electricity load at time is the annual investment cost of the system, is the discount rate, is the equipment life, is the i unit investment cost of the equipment, the i installed capacity of the equipment, is the annual operation and maintenance cost of the system, is the i unit operation and maintenance cost of the equipment, is the i operating power of the equipment.

6. The method for optimizing the capacity of the energy system and the collaborative scheduling of mobile resources with spatio-temporal decoupling under extreme disaster disturbances according to claim 1, wherein, In the elastic improvement scheduling architecture, with the goals of minimizing the total cost of mobile resource scheduling, minimizing the load curtailment amount and minimizing the path energy consumption, based on the spatio-temporal schedulable boundary, dynamically solve the optimal solutions of the parameter set of the action set of mobile resources, the flag sequence of the state set and the priority of the feasible scheduling path set through a multi-layer collaborative optimization algorithm, and generate an elastic improvement plan adaptive to extreme disaster events including: Encode the parameter set of the action set and the flag sequence of the state set of mobile resources into the position vector of honey badger individuals to generate an initial honey badger population that meets the spatio-temporal schedulable boundary; Encode the priority of the feasible scheduling path set into the migration path chain of bird individuals, generate an initial bird population that meets the spatio-temporal schedulable boundary and load it into the path memory bank; An inner-outer layer collaborative optimization framework is constructed. In the inner layer, the upper honey badger population performs dual-objective optimization of global scheduling cost and load reduction. The lower bird population optimizes the priority of feasible scheduling path sets through the path memory library sharing mechanism with the goal of minimizing path energy consumption. In the outer layer, the simulated annealing algorithm model is used to receive the optimal solution output by the inner layer as the initial solution to perform disturbance optimization. For each honey badger individual, the minimum energy consumption sequence of the corresponding path chain is matched from the initial bird population, and the total cost of mobile resource scheduling, load reduction and path energy consumption index are calculated. The normalized weights are aggregated into comprehensive performance indicators to generate a dual-driven scale of prey odor concentration-migration gravity; The dual populations are sorted according to the dual-drive scale, the current global optimal solution is selected, and combined with the introduced dynamic density factor, the honey badgers and bird individuals are controlled to perform two-stage optimization in the neighborhood of the current global optimal solution; In the wide-area mining stage, the search step length is expanded in the upper honey badger population and the concentration of prey odor, and the combination of action set parameters and state set flag sequences is traversed. In the lower bird population, the topological weights in the path memory are updated according to the path requirements of individual honey badgers to generate a wide-area feasible path set; In the fine mining stage, the search step size is shrunk in the upper honey badger population to lock the local optimum of the combination of action set parameters and state set flag sequence, and the migration gravity-driven priority sorting is performed on the wide-area feasible path set in the lower bird population; Constraint checks are performed on the updated honey badgers and bird flocks. If the updated honey badgers and bird flocks violate the spatiotemporal schedulable boundaries, topological rerouting repair is performed, the node order and pass weight of the path chain are adjusted, and the bird flock gravity calibration is applied. Priorities are reallocated based on the path memory library, and the repaired individuals are fed back to the corresponding populations to update the distribution of honey badgers and bird flocks. After the iteration of the inner architecture is completed, the current optimal scheduling plan is input into the outer simulated annealing algorithm model, and a new solution is generated through Gaussian perturbation and the fitness difference is calculated. The local optimum is jumped out with a probabilistic acceptance strategy, and the mobile resource action set parameters, state set flag sequence and feasible scheduling path set priority plan that meet the spatiotemporal schedulable boundary conditions and are jointly optimal for multiple objectives are output.

7. An energy system capacity optimization and mobile resource spatio-temporal decoupling collaborative scheduling system under extreme disaster disturbances, characterized in that, include: A database construction module is used to link the equipment failure probability and line failure probability obtained through two-state resilience adaptive Markov and Monte Carlo processing based on the acquired extreme disaster event data, and couple the load change parameters driven by extreme disaster events to form a disaster response database; The configuration generation module is used to convert the output fluctuation of renewable energy and the deviation of user load demand into fuzzy variables through fuzzy opportunity constraints, establish a capacity configuration optimization model in combination with the disaster response database, and generate multi-energy complementary capacity configuration with the minimization of the levelized cost of electricity as the optimization goal; Configure an enhancement module, which is used to introduce mobile resources based on the multi-energy complementary capacity configuration to establish an elastic enhancement scheduling architecture. By implementing the damaged road network topology reconstruction, spatio-temporal transfer chain modeling and action-state collaborative regulation of mobile resources, an elastic enhancement plan adaptable to extreme disaster events is generated, including: on the basis of the multi-energy complementary capacity configuration, introducing mobile resources including mobile emergency power supply vehicles, electric vehicles and diesel generators as elastic supplementary energy supply units, and establishing an elastic enhancement scheduling architecture that coordinates fixed equipment and mobile resources; removing damaged road network nodes and edge sets according to the obtained disaster damage information, reconstructing the road network topology, and generating a set of feasible scheduling paths that minimize the distance from the starting point of the mobile resource to the target energy system; parsing the departure timestamp, expected arrival timestamp, driving duration interval and charging stop time period of the mobile resource to generate time chain features, and extracting the starting position coordinates, target energy supply station coordinates and path node coordinate sets of the mobile resource to generate space chain features; verifying the time chain features and space chain features, and calculating the maximum service radius, shortest response time and continuous power supply duration threshold of the mobile resource based on the spatio-temporal feature combination that passes the verification to form a spatio-temporal schedulable boundary; obtaining the action set representing the energy supply behavior parameters of the mobile resource according to the energy supply characteristics of the obtained mobile resource, and constructing a state set representing the operating state of the mobile resource according to the mutual exclusion constraints of equipment operation. In the elastic enhancement scheduling architecture, with the goal of minimizing the total cost of mobile resource scheduling, the lowest load reduction and the minimum path energy consumption, based on the spatio-temporal schedulable boundary, the optimal solutions of the parameters of the action set, the flag sequence of the state set and the priority of the feasible scheduling path set of the mobile resource are dynamically solved through a multi-layer collaborative optimization algorithm, and an elastic enhancement plan adaptable to extreme disaster events is generated.

8. An automatic annotation device for high-quality drainage pipeline image data of a deep learning model based on multi-feature fusion, characterized in that, Including: At least one processor; And a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for optimizing the energy system capacity and spatio-temporal decoupling and collaborative scheduling of mobile resources under extreme disaster disturbances as described in any one of claims 1-6.

9. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that, When the executable instructions are executed by the processor, the method for optimizing the energy system capacity and spatio-temporal decoupling and collaborative scheduling of mobile resources under extreme disaster disturbances as described in any one of claims 1-6 is implemented.

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