A method and device for multi-energy flow failure modeling and supply-demand evaluation of a multi-pole weather-adapted marine electric-hydrogen-alcohol system

Abstract

The present disclosure relates to a kind of multi-extreme weather adapted offshore electric-hydrogen-alcohol system multi-energy flow failure modeling and supply-demand evaluation method and device.The method includes: constructing system success flow model based on GO method, identifying key equipment failure transmission chain;GO chart is converted into fault tree, combined with the minimum cut set method and sequential Monte Carlo method Quantification system failure probability and equipment contribution;Build wind and light hydrogen alcohol power model, introduce operation control strategy, dynamically evaluate power and fuel supply and demand reliability index;Establish fault-supply coupling mechanism, under the extreme weather such as typhoon, cold wave, strong wind and rain, the output of wind and light and equipment failure rate are doubly corrected, and a three-dimensional comprehensive reliability index system is constructed.The present application realizes the complete causal chain modeling from equipment failure to supply shortage, improves the accuracy and comprehensiveness of system comprehensive reliability evaluation under multi-extreme weather scenario, and can be used for system optimization design, operation decision and risk prevention and control.

Description

Technical Field

[0001] This disclosure relates to the field of electric-hydrogen-alcohol systems, and more specifically, to a method and apparatus for multi-energy flow failure modeling and supply-demand assessment of marine electric-hydrogen-alcohol systems adapted to multiple extreme weather conditions. Background Technology

[0002] Driven by the "dual carbon" goals, offshore wind power and photovoltaics have become core components of energy transformation due to their resource advantages. my country's cumulative installed capacity of offshore wind power has exceeded 30 million kilowatts and is expected to surpass 50 million kilowatts by 2025, while offshore photovoltaics has entered the stage of large-scale demonstration. The electricity-hydrogen-ethanol coupling system, through the "electricity-to-hydrogen, hydrogen-to-methanol" pathway, provides an effective solution to the problem of renewable energy consumption and has broad application prospects in scenarios such as deep-sea power supply and ship refueling. However, the marine environment is characterized by high salt spray, high humidity, strong winds and turbulence, and frequent extreme weather events such as typhoons, cold waves, and severe storms, resulting in a significantly higher equipment failure rate than onshore environments. Furthermore, the system involves deep coupling of three energy flows: electricity, hydrogen, and alcohol. Core equipment forms a complex failure chain, with intertwined failure transmission mechanisms. It also needs to balance the dual demands of power supply and hydrogen / methanol fuel production, making supply and demand balancing extremely difficult. Current reliability assessments face three core challenges: the lack of accurate modeling for the quantitative impact of extreme weather on equipment failure rates; the unclear coupling mechanism of multi-energy flow failures, making it difficult for single-energy flow analysis to reflect cascading effects; and the assessment system's overemphasis on the power side while neglecting fuel supply indicators, resulting in biased assessment results. Therefore, developing assessment technologies that are adaptable to multiple extreme weather conditions, cover multi-energy flow failure couplings, and consider both supply and demand dimensions has become a key bottleneck for the large-scale application of such systems.

[0003] The closest existing technologies to this invention mainly focus on three categories of research. The first category is onshore multi-energy flow integrated energy reliability assessment methods, focusing on coupled systems such as electricity-gas and electricity-heat. These methods use single algorithms such as fault tree analysis or Monte Carlo simulations to construct assessment models, with a core focus on power supply reliability. Fault tree analysis is used to analyze the impact of equipment failures on power supply, or Monte Carlo simulations are used to measure indicators such as power outage probability and power availability. These methods do not address deep coupling of multi-energy flows or adaptation to offshore scenarios. While some solutions incorporate natural gas supply factors, the processing methods are relatively simplified. The second category is energy system adaptability assessment technology under single extreme weather conditions. This involves analyzing the impact of specific weather events such as typhoons and cold waves on the output or failure rate changes of individual devices such as wind turbines and photovoltaic modules. It uses measured data to statistically analyze equipment parameter attenuation coefficients, thereby correcting power supply reliability indicators. These methods do not yet consider comprehensive adaptation to multiple extreme weather conditions, nor do they involve the analysis of the cascading effects of multi-energy flow fault propagation. The third category is reliability assessment schemes for single-energy systems at sea, applicable to single systems such as offshore wind power and offshore hydrogen production. By analyzing failure modes of core equipment such as wind turbine gearbox failure and electrolyzer membrane electrode degradation, a single-energy-flow fault tree model is constructed to assess system reliability under normal marine conditions. This type of research has not yet been extended to multi-energy-flow scenarios such as hydrogen-to-methanol production, nor has it integrated the joint assessment of extreme weather and fuel supply reliability.

[0004] In summary, while existing technologies have made some progress, they still have significant shortcomings in meeting the specific needs of offshore electricity-hydrogen-alcohol systems: First, they only perform local parameter corrections for single extreme weather events and have not yet established a quantitative correlation model between "weather conditions and equipment failure rates," making it difficult to adapt to the combined effects of multiple extreme weather events at sea. Furthermore, they do not consider the cascading failures of multiple energy flows caused by extreme weather, resulting in significant discrepancies between the assessment results and actual offshore operating scenarios. Second, they either only analyze single energy flow failure modes or simply superimpose the independent failure probabilities of different energy flows, without clearly defining the failure propagation of the three energy flows: electricity, hydrogen, and alcohol. First, the linkage effect of core failure chains, such as those from PEM electrolyzers to hydrogen storage tanks and reactors to distillation columns, has not been modeled, making it difficult to accurately identify weak links in the system. Second, the system generally focuses on power-side reliability indicators while neglecting the key dimension of hydrogen / methanol fuel supply, failing to meet the system's dual requirements of "power supply security and fuel production." Third, the system often uses single algorithms such as pure fault tree or pure Monte Carlo methods, without forming a complementary logic of "fault chain location + time-series simulation verification." Fault tree methods are difficult to quantify indicator fluctuations under multiple scenarios, and Monte Carlo methods lack precise location of weak equipment, so the evaluation accuracy needs to be improved.

[0005] Therefore, one or more methods are needed to solve the above problems.

[0006] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this disclosure is to provide a method and apparatus for multi-energy flow failure modeling and supply-demand assessment of marine electric-hydrogen-alcohol systems adapted to multiple extreme weather conditions, thereby overcoming, to at least some extent, one or more problems caused by the limitations and defects of related technologies.

[0008] According to one aspect of this disclosure, a method for multi-energy flow failure modeling and supply-demand assessment of marine electric-hydrogen-alcohol systems adapted to multiple extreme weather conditions is provided, including: A success flow model of an offshore electricity-hydrogen-alcohol system is constructed based on the GO method. The core units in the system are mapped to GO operators, a GO signal flow graph of the system is constructed, and the failure propagation chain of key equipment is identified. The GO signal flow graph is transformed into a fault tree model, and a dual strategy combining the minimum cut set method and the sequential Monte Carlo method is used to quantify the system failure probability and the reliability contribution of key equipment. A power model for wind-solar-storage-hydrogen-ethanol was constructed, and an operation control strategy for the hydrogen-ethanol system was introduced. Based on the sequential Monte Carlo method, the reliability of power and fuel supply and demand was dynamically evaluated, and the probability of power shortage, the expected power shortage, the probability of hydrogen shortage, and the probability of methanol shortage were statistically analyzed. A fault-supply-demand coupling correlation mechanism is established, and the equipment fault state sequence is used as a dynamic input constraint for supply and demand simulation. Under extreme weather conditions, the wind and solar power output and equipment failure rate are double-corrected. A three-dimensional comprehensive reliability index system covering system status, power supply and fuel supply is constructed, and the comprehensive reliability assessment results of the system are output.

[0009] In one exemplary embodiment of this disclosure, the mapping rules for the GO operator in the method include: The two-state unit uses type 1 operators, and the logic control relationship uses type 5 operators. The type 1 operators represent two states: normal operation or fault. The type 5 operators simulate the control logic's allocation of energy flow. The failure propagation chain includes at least the hydrogen path failure chain, which leads to the shutdown of methanol synthesis due to the failure of the PEM electrolyzer, and the power path failure chain, which leads to the disconnection of electrical load due to the failure of the wind turbine or photovoltaic system.

[0010] In one exemplary embodiment of this disclosure, the specific rules for converting the GO signal flow graph into a fault tree model in the method are as follows: The top event of the system is defined as the inability of the electro-hydrogen-alcohol system to reliably supply electricity / hydrogen / alcohol, the failure of the core functional module is defined as the intermediate event, and the basic failure of the components is defined as the bottom event. The AND gates and OR gates in the fault tree are used to connect them according to the logical relationship in the GO signal flow.

[0011] In one exemplary embodiment of this disclosure, the method uses the minimum cut set method to qualitatively analyze the weak links of the system, and the system failure probability is expressed as the probability of the union of all minimum cut set events; The sequential Monte Carlo method is used to dynamically simulate the "run-fail-repair" state sequence of equipment and calculate the Fussell-Vesely importance to quantify the contribution of each device to the system's unreliability.

[0012] In one exemplary embodiment of this disclosure, the method includes a wind-solar-storage-ethanol power model comprising: Wind power output model, photovoltaic power output model, flexible load model for PEM hydrogen production and methanol synthesis, and battery energy storage model; The hydrogen-alcohol system operation control strategy includes three strategies: Prioritize ensuring full operation of methanol synthesis, prioritize absorbing abandoned power to increase hydrogen production capacity, and minimize energy storage pressure by operating under dual-load fluctuations.

[0013] In one exemplary embodiment of this disclosure, the extreme weather conditions in the method include typhoons, cold waves with rain and snow, and strong winds and heavy rain; The dual correction includes: introducing an attenuation coefficient k to the wind power output. we Introducing a degradation coefficient k to photovoltaic power output pve Introduce a multiplication factor k to the equipment failure rate λ Establish a causal chain model of extreme weather → increased equipment failure rate → decreased system availability → exacerbated supply and demand imbalance.

[0014] In one exemplary embodiment of this disclosure, the specific rules of the fault-supply-demand coupling correlation mechanism in the method include: When the PEM hydrogen production system fails, the hydrogen production power and hydrogen output are set to zero; when the methanol synthesis system fails, the synthesis power and methanol output are set to zero; when the power supply equipment fails, its available output is corrected; when the energy storage fails, the charging and discharging capacity is set to zero. The fault condition will persist until the equipment is repaired.

[0015] In one exemplary embodiment of this disclosure, the method includes a three-dimensional comprehensive reliability index system comprising: The system's overall availability, probability of power shortage, expected power shortage, redundancy ratio of wind, solar and energy storage, probability of hydrogen shortage, probability of methanol shortage, and contribution of key equipment failures.

[0016] In one exemplary embodiment of this disclosure, the simulation process for comprehensive system reliability assessment includes: The outer simulation layer simulates the operation-fault-repair sequence of each device during the simulation cycle; The inner simulation performs power allocation and supply-demand balance verification based on equipment availability and weather conditions at each time step. Statistical output of all reliability metrics.

[0017] In one aspect of this disclosure, a multi-energy flow failure modeling and supply-demand assessment device for a marine electric-hydrogen-alcohol system adapted to multiple extreme weather conditions is provided, comprising: The system modeling module is used to construct a success flow model of the marine electric-hydrogen-alcohol system based on the GO method, map the core units in the system to GO operators, construct the system GO signal flow graph, and identify the failure propagation chain of key equipment. The fault quantification analysis module is used to transform the GO signal flow graph into a fault tree model. It employs a dual strategy combining the minimum cut set method and the sequential Monte Carlo method to quantify the system failure probability and the reliability contribution of key equipment. The supply and demand dynamic assessment module is used to construct a power model for wind, solar, storage, hydrogen and methanol, introduce a hydrogen-methanol system operation control strategy, and dynamically assess the reliability of power and fuel supply and demand based on the sequential Monte Carlo method, and statistically analyze the probability of power shortage, the expected power shortage, the probability of hydrogen shortage and the probability of methanol shortage. The comprehensive evaluation module is used to establish a fault-supply-demand coupling mechanism, using the equipment fault state sequence as a dynamic input constraint for supply and demand simulation. Under extreme weather conditions, it performs dual correction on wind and solar power output and equipment failure rate, constructs a three-dimensional comprehensive reliability index system covering system status, power supply and fuel supply, and outputs the comprehensive reliability evaluation results of the system.

[0018] This disclosure discloses an exemplary method for multi-energy flow failure modeling and supply-demand assessment of a marine electricity-hydrogen-alcohol system adapted to extreme weather conditions. The method includes: constructing a system success flow model based on the Go graph method to identify the failure propagation chain of key equipment; converting the Go graph into a fault tree, and quantifying the system failure probability and equipment contribution by combining the minimum cut set method and the sequential Monte Carlo method; constructing a wind-solar-hydrogen-alcohol power model, introducing an operation control strategy, and dynamically evaluating the reliability indicators of electricity and fuel supply and demand; establishing a fault-supply-demand coupling mechanism, and performing dual corrections on wind and solar power output and equipment failure rate under extreme weather conditions such as typhoons, cold waves, and severe storms, thus constructing a three-dimensional comprehensive reliability index system. This invention achieves complete causal chain modeling from equipment failure to supply shortage, improving the accuracy and comprehensiveness of the system's comprehensive reliability assessment under multiple extreme weather scenarios, and can be used for system optimization design, operation and maintenance decisions, and risk prevention and control.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0020] The above and other features and advantages of this disclosure will become more apparent from the detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0021] Figure 1 A flowchart is shown below illustrating a multi-energy flow failure modeling and supply-demand assessment method for a marine electric-hydrogen-alcohol system adapted to multiple extreme weather conditions, according to an exemplary embodiment of this disclosure. Figure 2 A schematic diagram of the standard GO operator is shown for a multi-energy flow failure modeling and supply-demand assessment method for a marine electric-hydrogen-alcohol system adapted to multiple extreme weather conditions, according to an exemplary embodiment of the present disclosure. Figure 3 The PEM electrolysis hydrogen production GO signal flow diagram is shown for a multi-energy flow failure modeling and supply-demand assessment method for a marine electro-hydrogen-alcohol system adapted to multiple extreme weather conditions, according to an exemplary embodiment of the present disclosure. Figure 4 A time-series simulation flowchart of power / fuel reliability assessment is shown for a multi-energy flow failure modeling and supply-demand assessment method for a marine electric-hydrogen-alcohol system adapted to multiple extreme weather conditions, according to an exemplary embodiment of this disclosure. Figure 5 A flowchart illustrating the system integrated reliability assessment of a multi-energy flow failure modeling and supply-demand assessment method for a marine electric-hydrogen-alcohol system adapted to multiple extreme weather conditions, according to an exemplary embodiment of the present disclosure, is shown. Figure 6 A schematic block diagram of a multi-energy flow failure modeling and supply-demand assessment device for a marine electric-hydrogen-alcohol system adapted to multiple extreme weather conditions is shown in accordance with an exemplary embodiment of the present disclosure. Detailed Implementation

[0022] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0023] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details described, or other methods, components, materials, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, materials, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0024] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0025] In this example embodiment, a multi-energy flow failure modeling and supply-demand assessment method for marine electric-hydrogen-alcohol systems adapted to multiple extreme weather conditions is first provided; (Refer to...) Figure 1 As shown, the multi-energy flow failure modeling and supply-demand assessment method for a marine electric-hydrogen-alcohol system adapted to multiple extreme weather conditions may include the following steps: Step S110: Construct a success flow model of the marine electric-hydrogen-alcohol system based on the GO method, map the core units in the system to GO operators, construct the system GO signal flow graph, and identify the failure propagation chain of key equipment.

[0026] Step S120: The GO signal flow graph is transformed into a fault tree model. A dual strategy combining the minimum cut set method and the sequential Monte Carlo method is used to quantify the system failure probability and the reliability contribution of key equipment.

[0027] Step S130: Construct a power model for wind-solar-storage-hydrogen-ethanol system, introduce a hydrogen-ethanol system operation control strategy, dynamically evaluate the reliability of power and fuel supply and demand based on the sequential Monte Carlo method, and statistically analyze the probability of power shortage, the expected power shortage, the probability of hydrogen shortage, and the probability of methanol shortage. Step S140: Establish a fault-supply-demand coupling correlation mechanism, use the equipment fault state sequence as a dynamic input constraint for supply and demand simulation, perform dual correction on wind and solar power output and equipment failure rate under extreme weather conditions, construct a three-dimensional comprehensive reliability index system covering system status, power supply and fuel supply, and output the comprehensive reliability assessment results of the system.

[0028] This disclosure discloses an exemplary method for multi-energy flow failure modeling and supply-demand assessment of a marine electricity-hydrogen-alcohol system adapted to extreme weather conditions. The method includes: constructing a system success flow model based on the Go graph method to identify the failure propagation chain of key equipment; converting the Go graph into a fault tree, and quantifying the system failure probability and equipment contribution by combining the minimum cut set method and the sequential Monte Carlo method; constructing a wind-solar-hydrogen-alcohol power model, introducing an operation control strategy, and dynamically evaluating the reliability indicators of electricity and fuel supply and demand; establishing a fault-supply-demand coupling mechanism, and performing dual corrections on wind and solar power output and equipment failure rate under extreme weather conditions such as typhoons, cold waves, and severe storms, thus constructing a three-dimensional comprehensive reliability index system. This invention achieves complete causal chain modeling from equipment failure to supply shortage, improving the accuracy and comprehensiveness of the system's comprehensive reliability assessment under multiple extreme weather scenarios, and can be used for system optimization design, operation and maintenance decisions, and risk prevention and control.

[0029] The following will further explain a multi-energy flow failure modeling and supply-demand assessment method for a marine electric-hydrogen-alcohol system adapted to multiple extreme weather conditions in this example embodiment.

[0030] Example 1: To address the need for multi-energy flow reliability assessment of offshore electricity-hydrogen-methanol systems under extreme weather conditions, existing technologies suffer from the following key shortcomings: In terms of adapting to multiple extreme weather conditions, a quantitative mapping mechanism between weather conditions and equipment failure rates has not been established, making it impossible to comprehensively assess the combined impact of various extreme weather events such as typhoons, cold waves, and severe storms on system reliability. Regarding the analysis of multi-energy flow failure mechanisms, the fault propagation logic of the three energy flows (electricity, hydrogen, and methanol) has not been clarified, and there is a lack of modeling capabilities for the core failure chain linkage effects from the PEM electrolyzer to the hydrogen storage tank and from the reactor to the distillation column, making it difficult to accurately characterize the complex failure characteristics of multi-energy flow coupling. In terms of the completeness of the assessment system, there is an overemphasis on power-side reliability indicators, failing to cover the hydrogen / methanol fuel supply dimension, and thus failing to support the system's dual requirements of "power supply assurance and fuel production." Regarding the effectiveness of assessment algorithms, many rely on single assessment algorithms, lacking complementary verification through mechanism analysis and scenario simulation, making it difficult to balance assessment depth and breadth. To address the aforementioned issues, this invention proposes a multi-energy flow failure modeling and supply-demand assessment method for marine electric-hydrogen-alcohol systems adapted to multiple extreme weather conditions. This method aims to overcome the shortcomings of existing technologies in terms of scenario adaptability, failure mechanism analysis, comprehensiveness of assessment dimensions, and algorithm accuracy. It achieves accurate capture of the multi-energy flow failure characteristics of the system under multiple extreme weather scenarios, as well as a comprehensive and high-precision assessment of the reliability of power and fuel supply and demand in both dimensions. This provides technical support for the optimized design, precise operation and maintenance, and safe and stable operation of marine electric-hydrogen-alcohol systems.

[0031] In step S110, a success flow model of the marine electric-hydrogen-alcohol system can be constructed based on the GO method, mapping the core units in the system to GO operators, constructing the system GO signal flow graph, and identifying the failure propagation chain of key equipment.

[0032] In this example embodiment, the mapping rules for the GO operator in the method include: The two-state unit uses type 1 operators, and the logic control relationship uses type 5 operators. The type 1 operators represent two states: normal operation or fault. The type 5 operators simulate the control logic's allocation of energy flow. The failure propagation chain includes at least the hydrogen path failure chain, which leads to the shutdown of methanol synthesis due to the failure of the PEM electrolyzer, and the power path failure chain, which leads to the disconnection of electrical load due to the failure of the wind turbine or photovoltaic system.

[0033] In step S120, the GO signal flow graph can be transformed into a fault tree model. A dual strategy combining the minimum cut set method and the sequential Monte Carlo method is used to quantify the system failure probability and the reliability contribution of key equipment.

[0034] In this example embodiment, the specific rules for converting the GO signal flow graph into a fault tree model are as follows: The top event of the system is defined as the inability of the electro-hydrogen-alcohol system to reliably supply electricity / hydrogen / alcohol, the failure of the core functional module is defined as the intermediate event, and the basic failure of the components is defined as the bottom event. The AND gates and OR gates in the fault tree are used to connect them according to the logical relationship in the GO signal flow.

[0035] In this example embodiment, the method uses the minimum cut set method to qualitatively analyze the weak points of the system, and the system failure probability is expressed as the probability of the union of all minimum cut set events; The sequential Monte Carlo method is used to dynamically simulate the "run-fail-repair" state sequence of equipment and calculate the Fussell-Vesely importance to quantify the contribution of each device to the system's unreliability.

[0036] In step S130, a power model for wind-solar-storage-hydrogen-ethanol can be constructed, a hydrogen-ethanol system operation control strategy can be introduced, the reliability of power and fuel supply and demand can be dynamically evaluated based on the sequential Monte Carlo method, and the probability of power shortage, the expected power shortage, the probability of hydrogen shortage and the probability of methanol shortage can be statistically analyzed. In this example embodiment, the wind-solar-storage-ethanol power model includes: Wind power output model, photovoltaic power output model, flexible load model for PEM hydrogen production and methanol synthesis, and battery energy storage model; The hydrogen-alcohol system operation control strategy includes three strategies: Prioritize ensuring full operation of methanol synthesis, prioritize absorbing abandoned power to increase hydrogen production capacity, and minimize energy storage pressure by operating under dual-load fluctuations.

[0037] In this example embodiment, the extreme weather conditions in the method include typhoons, cold waves with rain and snow, and strong winds and heavy rain; The dual correction includes: introducing an attenuation coefficient k to the wind power output. we Introducing a degradation coefficient k to photovoltaic power output pve Introduce a multiplication factor k to the equipment failure rate λ Establish a causal chain model of extreme weather → increased equipment failure rate → decreased system availability → exacerbated supply and demand imbalance.

[0038] In step S140, a fault-supply-demand coupling correlation mechanism can be established, and the equipment fault state sequence can be used as a dynamic input constraint for supply and demand simulation. Under extreme weather conditions, the wind and solar power output and equipment failure rate can be double-corrected, and a three-dimensional comprehensive reliability index system covering system status, power supply and fuel supply can be constructed to output the comprehensive reliability assessment results of the system.

[0039] In this example embodiment, the specific rules of the fault-supply-demand coupling correlation mechanism in the method include: When the PEM hydrogen production system fails, the hydrogen production power and hydrogen output are set to zero; when the methanol synthesis system fails, the synthesis power and methanol output are set to zero; when the power supply equipment fails, its available output is corrected; when the energy storage fails, the charging and discharging capacity is set to zero. The fault condition will persist until the equipment is repaired.

[0040] In this example embodiment, the three-dimensional comprehensive reliability index system in the method includes: The system's overall availability, probability of power shortage, expected power shortage, redundancy ratio of wind, solar and energy storage, probability of hydrogen shortage, probability of methanol shortage, and contribution of key equipment failures.

[0041] In this example embodiment, the simulation process for comprehensive system reliability assessment includes: The outer simulation layer simulates the operation-fault-repair sequence of each device during the simulation cycle; The inner simulation performs power allocation and supply-demand balance verification based on equipment availability and weather conditions at each time step. Statistical output of all reliability metrics.

[0042] Example 2: Step 1: System structure modeling and GO signal flow construction This step first constructs a success flow model of the offshore electricity-hydrogen-alcohol system using the Goal-Oriented Method (GO), visually representing the physical connections and logical relationships between subsystems, and analyzing the failure propagation paths of key equipment. Each node is characterized by 17 standard operators from the GO method to represent its operational status, such as... Figure 2 As shown.

[0043] Step 1.1: Constructing the GO signal flow for the electro-hydrogen-ethanol system. The core units in the offshore electro-hydrogen-ethanol coupled system, such as wind turbines, photovoltaics, energy storage batteries, PEM electrolyzers, hydrogen storage tanks, methanol synthesis units, and platform electrical loads, are mapped to "operators" in the GO method. The energy flow and control signal flow between each unit are connected by "signal flows" to form a complete system GO signal flow diagram. Taking water electrolysis for hydrogen production as an example... Figure 3 As shown.

[0044] The choice of operator depends on the device characteristics: (1) Two-state units use type 1 operators to represent their normal working state or fault state.

[0045] (2) The logic relationship can use the fifth type of operator to simulate the distribution of energy flow by the control logic.

[0046] (3) Signal flow represents actual energy or logical connection and runs through the entire system.

[0047] Step 1.2: Identify the failure propagation chain of critical equipment. Based on the constructed GO signal flow graph, identify the critical path from the source to the load, and analyze the impact of critical equipment failure on downstream equipment and the overall system function.

[0048] Taking hydrogen production and supply as an example, the core failure transmission chain is as follows: PEM electrolyzer failure → hydrogen production is 0 → no input to hydrogen separator → no input to dryer → hydrogen storage tank cannot be filled with hydrogen → methanol synthesis stops due to lack of hydrogen → system methanol fuel supply fails.

[0049] Similarly, in the power supply path, the failure propagation chain can be expressed as: wind turbine / photovoltaic failure → reduction in total available power of the system → deep discharge of energy storage until depletion → electrical load is cut off → system power supply failure.

[0050] Through this step, the present invention provides a unified formal description of the system's physical structure, energy flow, and failure propagation logic, laying the foundation for subsequent fault tree modeling and quantitative analysis.

[0051] Step 2: Fault tree construction and failure quantification analysis based on the GO model This step transforms the GO model established in step 1 into a fault tree, and combines the minimum cut set method and the sequential Monte Carlo method into a dual evaluation strategy to quantitatively analyze the system failure probability and the contribution of key modules.

[0052] Step 2.1: Transform the GO graph into a fault tree. Following the dual logic of "success" and "failure," map the operators and signal flows in the GO graph to logic gates and bottom events in the fault tree. Specifically: (1) The top event is defined as "the electro-hydrogen-alcohol system cannot reliably supply electricity / hydrogen / alcohol"; (2) Intermediate events correspond to the failure of core functional modules, such as "PEM hydrogen production system failure" and "methanol synthesis system failure"; (3) The basic events are the basic failures of each component, such as "PEM electrolyzer membrane electrode decay" and "methanol reactor failure". (4) Based on the AND and OR logic relationships of the signal flow in the GO diagram, use the corresponding AND gates and OR gates to connect them in the fault tree. Finally, a fault tree is formed with the top event as the root, the middle events as branches, and the bottom events as leaves.

[0053] Step 2.2: Dual Reliability Quantitative Assessment. A dual assessment strategy combining the minimum cut set method and the sequential Monte Carlo method is adopted.

[0054] Minimal Cut-Set Method: First, perform a qualitative analysis of the fault tree to find all minimal cut sets that lead to the top event. Assume the system has... k The smallest cut set, the th smallest cut set j The minimum cut set is denoted as C j Its probability of occurrence is P ( C j Under the condition that the minimal cut sets are independent of each other, the overall failure probability of the system. F sys It can be approximated as:

[0055] in, kj=1C j This represents the union of all minimal cut sets; the occurrence of any minimal cut set indicates a system failure. P ( C j ) is the first j The probability of occurrence of a minimum cut set is calculated by multiplying the failure rates of each basic event in that cut set. This method can quickly locate the weak points of a system.

[0056] Sequential Monte Carlo method: In order to overcome the limitations of the minimal cut set method in handling dynamic timing and complex maintenance strategies, the sequential Monte Carlo method is introduced for complementary verification.

[0057] (1) Initialization: Set the total simulation duration T Time step t .

[0058] (2) State sampling: For each device i Based on its failure rate λ i and repair rate μ i The "run-failure-repair" state sequence on the time axis is generated by sampling.

[0059] (3) System state analysis: at each time step t Based on the combination of states of all devices, determine the overall state of the system (normal / faulty), and determine whether the system function has been lost according to the logic in the GO diagram.

[0060] (4) Indicator statistics: repeated simulation N Next, the average failure probability of the statistical system F MC And the contribution of each device to the system's unreliability, i.e., the Fussell-Vesely importance. I i FV :

[0061] This importance can be quantified for each device, providing a precise basis for subsequent design optimization.

[0062] Step 3, Reliability assessment of electricity / fuel supply and demand This step focuses on the dynamic balance of supply and demand in the system. By constructing key equipment models and introducing various operation control strategies, the sequential Monte Carlo method is used to quantify the reliability of power and fuel supply under different scenarios.

[0063] Step 3.1: Construct power models for key system equipment. To achieve timing simulation, mathematical models of each power generation and consumption unit need to be established. The main models include: Wind power output model:

[0064] in, v ( t (This refers to real-time wind speed.) v ci , v r, v co These are the cut-in, rated, and cut-out wind velocities, respectively. P rated This is the rated power.

[0065] Photovoltaic power output model:

[0066] in, G ( t () represents the actual light intensity. P STC The power is given under standard test conditions, and α is the temperature coefficient. T cell ( t ( ) represents the temperature of the solar panel.

[0067] PEM hydrogen production and methanol synthesis models: Both are considered as flexible loads with power regulation capabilities, and their operating power is constrained by upper and lower limits.

[0068]

[0069]

[0070] in, L PEMmin , L PEMmax Minimum and maximum loading rates for PEM hydrogen production L MeOHmin , L MeOHmax These represent the minimum and maximum loading rates for methanol synthesis.

[0071] Battery energy storage model:

[0072] in, SOC ( t ( ) is in a charged state. P ch ( t )and P dis ( t () represents the charging and discharging power. η ch and η dis For charging and discharging efficiency, E bat This refers to the battery capacity.

[0073] Step 3.2: Introduce the operation control strategy for the hydrogen-alcohol system. Define the power balance equation of the system:

[0074] in, P load ( t This represents the platform's basic electrical load. According to... P total ( t Given the sign and magnitude of , design and evaluate three operational control strategies: (1) Strategy 1: Prioritize ensuring that methanol synthesis and PEM hydrogen production operate at rated power. If P total ( t If the remaining charge is greater than 0, the remaining charge will charge the battery; if... P total ( t If the value is less than 0, the PEM hydrogen production power will be reduced first to maintain full methanol operation, and the insufficient part will be discharged by the battery.

[0075] (2) Strategy 2: Methanol synthesis always runs at full capacity, while PEM hydrogen production power fluctuates within the rated range. P total ( t When the power output is greater than 0, priority should be given to increasing the PEM hydrogen production capacity to the maximum to absorb the wasted electricity; P total ( t When ) < 0, the PEM hydrogen production power is reduced to the minimum first, and the insufficient part is supplemented by battery discharge.

[0076] (3) Strategy 3: The power of PEM hydrogen production and methanol synthesis both fluctuate. P total ( t When )>0, prioritize increasing PEM hydrogen production, then increase methanol synthesis; P total ( t When ) < 0, prioritize reducing PEM hydrogen production to the minimum, then reduce methanol synthesis to the minimum, in order to minimize the charging and discharging pressure of the energy storage system.

[0077] Step 3.3: Calculation of supply and demand reliability indicators based on the sequential Monte Carlo method. Within each Monte Carlo simulation step, wind speed and solar irradiance are sampled, wind and solar power output is calculated, and power allocation and supply-demand balance verification are performed according to the selected operating strategy. The following key indicators are statistically analyzed: Probability of Power Outage (LOLP):

[0078] in, I loss ( m , t ) is the power shortage indication function. If the first... m In this simulation tIf there is a power shortage at any time, the value is 1; otherwise, it is 0.

[0079] Low Battery Expectations (EENS):

[0080] Wind-Solar-Storage Redundancy Ratio (PRC): Measures the system's backup capacity to cope with power fluctuations.

[0081]

[0082] in: E bat SOC ( t ):exist t The actual capacity of the instantaneous energy storage system that can be used to supply power.

[0083] Hydrogen / methanol shortage probability: defined as the percentage of time during the simulation period when the hydrogen / methanol storage is below the safety threshold.

[0084]

[0085] in, I fuel_loss ( m , t If at time t Fuel supply is insufficient; otherwise, it is 0.

[0086] Step 3.4: As Figure 4 As shown, this is a time-series analysis under extreme weather conditions. For three typical marine extreme weather events—typhoons, cold waves with rain and snow, and strong winds and torrential rains—the attenuation coefficient of wind and solar power output under extreme weather conditions is quantified. k we , k pve The corrected wind and solar power output model is as follows:

[0087] in: k we , k pve The power output attenuation coefficient for wind and solar power is determined by the type and duration of extreme weather events.

[0088] Substitute the revised wind and solar power output model into the calculation process of step 3.3 to calculate the changes Δ of each reliability index under extreme weather conditions. I It satisfies the formula:

[0089] in: I eThese are index values ​​under extreme weather conditions; I n Index values ​​under normal weather conditions.

[0090] Step 4, Comprehensive System Reliability Assessment Step 4 deeply integrates equipment failure assessment with supply and demand assessment to construct a complete causal chain from equipment failure to insufficient energy supply, thereby achieving comprehensive reliability quantification of the system under multidimensional uncertainty.

[0091] Step 4.1: Establish a fault-supply-demand coupling mechanism. The "operation-fault-repair" state sequence of each device generated in Step 2.2 will be used as the dynamic input constraint for the supply-demand simulation in Step 3.3. The specific rules are as follows: (1) PEM hydrogen production system fault coupling: If a "PEM electrolyzer fault" state is sampled at a certain moment in step 2 simulation, then in the supply and demand simulation in step 3, the hydrogen production power at that moment will be... P PEM ( t =0, hydrogen production QH2 ( t The hydrogen storage tank has a capacity of 0, and the hydrogen storage capacity cannot be increased. This state will persist until the equipment is fully repaired. t repair .

[0092] (2) Methanol synthesis system fault coupling: If a key piece of equipment in the methanol synthesis unit (such as the methanol reactor, separator, etc.) fails, the synthesis function will be lost due to the fault. In this state, the methanol synthesis system will be set to complete. P MeOH ( t )=0, methanol production Q MeOH ( t )=0.

[0093] (3) Power supply equipment fault coupling: If the power generation unit (wind turbine, photovoltaic, or energy storage unit) fails, its available output will be adjusted according to the fault type in the simulation of step 3. For example, when the energy storage system fails, its charging and discharging capabilities are lost, i.e., the discharge power is reduced. P dis ( t ) and charging power P ch ( t When all ) are 0, energy storage cannot participate in the system's power balance regulation.

[0094] (4) The superimposed coupling of failures and extreme weather: In extreme weather scenarios, increased environmental stress will lead to a higher failure rate of equipment foundations. i Equivalent failure rate under extreme weather conditions λ i,extreme It can be represented as:

[0095] in, k λ (weather) is the failure rate multiplication factor under extreme weather conditions. This corrected failure rate will be substituted into the state sampling process in step 2.2 to achieve a complete causal chain modeling of "extreme weather → increased equipment failure rate → decreased system availability → exacerbated supply and demand imbalance".

[0096] Step 4.2: As Figure 5 To comprehensively quantify the system's overall performance under multiple uncertainties, this invention integrates the evaluation results from steps 2 and 3 to construct a comprehensive reliability index system covering three dimensions: "system state, power supply, and fuel supply." (1) System fault status indicators: System overall availability A sys It is defined as the proportion of time during which the system can simultaneously meet the basic requirements of electrical load, hydrogen load, and methanol load.

[0097]

[0098] in, T outage,m For the first m In this simulation, the cumulative time of any energy supply interruption caused by equipment failure or supply-demand imbalance is recorded.

[0099] Contribution of critical equipment failures I i FV The Fussell-Vesely importance calculated in step 2.2 is used to quantify the contribution of each device to the overall unavailability of the system, providing a basis for device redundancy design and spare parts strategy.

[0100] (2) Power supply dimension indicators: The probability of power shortage (LOLP) and expected power shortage (EENS) from step 3.3 are used, but at this time... P supply ( t Consider the impact of equipment failure status.

[0101] (3) Fuel supply dimension indicators: using the hydrogen shortage probability from step 3.3. P loss,H2 Methanol supply shortage probability P loss,MeOH Indicators such as these should be considered to limit the production of hydrogen / alcohol due to equipment failure.

[0102] Step 4.3: Comprehensive Reliability Assessment Process and Simulation Implementation. Based on the coupled simulation model in Step 4.1 and the indicator system in Step 4.2, a complete comprehensive reliability assessment is performed. The simulation process is as follows: (1) Initialization: Set the total simulation duration, step size, equipment failure rate, repair rate, historical wind and solar data, and meteorological event calendar.

[0103] (2) Outer layer simulation (fault state simulation): For each device, simulate its “run-fault-repair” timing state during the simulation cycle.

[0104] (3) Inner layer simulation (supply and demand balance simulation): In each time step, based on the available status of the equipment determined by the outer layer and combined with the current meteorological conditions, calculate the wind and solar power output, perform power allocation (strategy 3), simulate energy storage and hydrogen / alcohol dynamics, and determine whether there is a shortage of electricity / hydrogen / alcohol load.

[0105] (4) Indicator Statistics: After the simulation, all reliability indicators are statistically analyzed, and quantitative results such as the overall system availability, power outage probability, and methanol supply shortage probability are output. These results can be directly used to identify cascading failure risks, guide redundant design and optimize operation and maintenance strategies, and formulate differentiated preventive maintenance plans based on equipment importance and failure rate.

[0106] In the embodiments of this example, the beneficial effects of the technical solution of the present invention include: by combining the GO method with fault tree analysis, visualization and quantitative analysis of multi-energy flow failure propagation are achieved, which can identify the failure contribution of key equipment such as PEM electrolyzers and methanol reactors, providing a basis for locating weak links in the system and optimizing redundancy design. A dual evaluation strategy of minimum cut set method and sequential Monte Carlo method is adopted, taking into account both static mechanism analysis and dynamic time-series simulation, improving the comprehensiveness and accuracy of the evaluation. By constructing a wind-solar-storage-ethanol power model and introducing three ethanol operation strategies, the reliability of power and fuel supply and demand can be dynamically evaluated, and indicators such as the probability of power shortage, hydrogen shortage, and methanol shortage can be quantified, meeting the dual needs of power supply and fuel production. For three types of extreme weather—typhoons, cold waves, and severe storms—a meteorological-failure rate quantitative mapping is established, and through dual correction of wind and solar power output attenuation and failure rate doubling, the adaptability of the evaluation results to actual marine scenarios is improved. By using a fault-supply-demand coupling mechanism, a complete causal chain from equipment failure to insufficient supply is constructed, and indicators such as overall system availability and contribution of key equipment are output, which can be directly used for system design optimization, operation and maintenance strategy formulation and risk prevention and control.

[0107] In this example embodiment, this disclosure presents a method for establishing a multi-energy flow failure model of a marine electricity-hydrogen-ethanol system based on GO signal flow. This involves constructing a GO graph of the system, analyzing the failure propagation chain of key equipment, and establishing specific rules for converting the GO graph into a fault tree. A dual strategy combining the minimum cut set method and the sequential Monte Carlo method is employed to calculate the system failure probability and quantify the reliability contribution of key equipment. Based on the sequential Monte Carlo method, a method for dynamically evaluating the reliability of electricity and fuel supply and demand is developed by constructing a wind-solar-storage-ethanol power model and introducing multiple operating strategies to calculate indicators such as the probability of power shortage and the probability of hydrogen / methanol shortage. For extreme weather conditions, a method for time-series simulation analysis of system reliability is implemented by introducing wind and solar power output attenuation coefficients and doubling corrections for equipment failure rates. Finally, a comprehensive reliability assessment method that couples fault state sequences with supply and demand simulation is achieved by establishing a dynamic coupling mechanism between equipment failure state sequences and supply and demand simulation, and by constructing a comprehensive index system covering system state, electricity, and fuel dimensions.

[0108] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0109] Furthermore, this example embodiment also provides a multi-energy flow failure modeling and supply-demand assessment device for a marine electric-hydrogen-alcohol system adapted to multiple extreme weather conditions. (Refer to...) Figure 6 As shown, the device 200 for multi-energy flow failure modeling and supply-demand assessment of a marine electric-hydrogen-alcohol system adapted to multiple extreme weather conditions may include: a system modeling module 210, a fault quantification analysis module 220, a supply-demand dynamic assessment module 230, and a comprehensive assessment module 240. Wherein: System modeling module 210 is used to construct a success flow model of the marine electric-hydrogen-alcohol system based on the GO method, map the core units in the system to GO operators, construct the system GO signal flow graph, and identify the failure propagation chain of key equipment. The fault quantification analysis module 220 is used to convert the GO signal flow graph into a fault tree model and adopt a dual strategy combining the minimum cut set method and the sequential Monte Carlo method to quantify the system failure probability and the reliability contribution of key equipment. The supply and demand dynamic assessment module 230 is used to construct a power model for wind, solar, storage, hydrogen and methanol, introduce a hydrogen-methanol system operation control strategy, dynamically assess the reliability of power and fuel supply and demand based on the sequential Monte Carlo method, and statistically analyze the probability of power shortage, the expected power shortage, the probability of hydrogen shortage and the probability of methanol shortage. The comprehensive evaluation module 240 is used to establish a fault-supply-demand coupling mechanism, using the equipment fault state sequence as a dynamic input constraint for supply and demand simulation. Under extreme weather conditions, it performs dual correction on wind and solar power output and equipment failure rate, constructs a three-dimensional comprehensive reliability index system covering system status, power supply and fuel supply, and outputs the comprehensive reliability evaluation results of the system.

[0110] The specific details of the multi-energy flow failure modeling and supply-demand assessment device modules for marine electric-hydrogen-alcohol systems adapted to various extreme weather conditions mentioned above have been described in detail in the corresponding multi-energy flow failure modeling and supply-demand assessment method for marine electric-hydrogen-alcohol systems adapted to various extreme weather conditions, so they will not be repeated here.

[0111] It should be noted that although several modules or units of a multi-energy flow failure modeling and supply-demand assessment device 200 for a marine electric-hydrogen-alcohol system adapted to multiple extreme weather conditions are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0112] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0113] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0114] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for multi-energy flow failure modeling and supply-demand assessment of marine electric-hydrogen-alcohol systems adapted to multiple extreme weather conditions, characterized in that, The method includes: A success flow model of an offshore electricity-hydrogen-alcohol system is constructed based on the GO method. The core units in the system are mapped to GO operators, a GO signal flow graph of the system is constructed, and the failure propagation chain of key equipment is identified. The GO signal flow graph is transformed into a fault tree model, and a dual strategy combining the minimum cut set method and the sequential Monte Carlo method is used to quantify the system failure probability and the reliability contribution of key equipment. A power model for wind-solar-storage-hydrogen-ethanol was constructed, and an operation control strategy for the hydrogen-ethanol system was introduced. Based on the sequential Monte Carlo method, the reliability of power and fuel supply and demand was dynamically evaluated, and the probability of power shortage, the expected power shortage, the probability of hydrogen shortage, and the probability of methanol shortage were statistically analyzed. A fault-supply-demand coupling correlation mechanism is established, and the equipment fault state sequence is used as a dynamic input constraint for supply and demand simulation. Under extreme weather conditions, the wind and solar power output and equipment failure rate are double-corrected. A three-dimensional comprehensive reliability index system covering system status, power supply and fuel supply is constructed, and the comprehensive reliability assessment results of the system are output.

2. The method as described in claim 1, characterized in that, The mapping rules for the GO operator in the method include: The two-state unit uses type 1 operators, and the logic control relationship uses type 5 operators. The type 1 operators represent two states: normal operation or fault. The type 5 operators simulate the control logic's allocation of energy flow. The failure propagation chain includes at least the hydrogen path failure chain, which leads to the shutdown of methanol synthesis due to the failure of the PEM electrolyzer, and the power path failure chain, which leads to the disconnection of electrical load due to the failure of the wind turbine or photovoltaic system.

3. The method as described in claim 1, characterized in that, The specific rules for converting the GO signal flow graph into a fault tree model in the method are as follows: The top event of the system is defined as the inability of the electro-hydrogen-alcohol system to reliably supply electricity / hydrogen / alcohol, the failure of the core functional module is defined as the intermediate event, and the basic failure of the components is defined as the bottom event. The AND gates and OR gates in the fault tree are used to connect them according to the logical relationship in the GO signal flow.

4. The method as described in claim 1, characterized in that, In the method, the minimum cut set method is used to qualitatively analyze the weak links of the system, and the system failure probability is expressed as the probability of the union of all minimum cut sets. The sequential Monte Carlo method is used to dynamically simulate the "operation-failure-repair" state sequence of equipment and calculate the Fussell-Vesely importance to quantify the contribution of each device to the system's unreliability.

5. The method as described in claim 1, characterized in that, The method includes a wind-solar-hydrogen-storage-ethanol power model comprising: Wind power output model, photovoltaic power output model, flexible load model for PEM hydrogen production and methanol synthesis, and battery energy storage model; The hydrogen-alcohol system operation control strategy includes three strategies: Prioritize ensuring full operation of methanol synthesis, prioritize absorbing abandoned power to increase hydrogen production capacity, and minimize energy storage pressure by operating under dual-load fluctuations.

6. The method as described in claim 1, characterized in that, The extreme weather conditions mentioned in the method include typhoons, cold waves, rain and snow, and strong winds and heavy rain. The dual correction includes: introducing an attenuation coefficient k to the wind power output. we Introducing a degradation coefficient k to photovoltaic power output pve Introduce a multiplication factor k to the equipment failure rate λ Establish a causal chain model of extreme weather → increased equipment failure rate → decreased system availability → exacerbated supply and demand imbalance.

7. The method as described in claim 1, characterized in that, The specific rules of the fault-supply-demand coupling correlation mechanism in the method include: When the PEM hydrogen production system fails, the hydrogen production power and hydrogen output are set to zero; when the methanol synthesis system fails, the synthesis power and methanol output are set to zero; when the power supply equipment fails, its available output is corrected; when the energy storage fails, the charging and discharging capacity is set to zero. The fault condition will persist until the equipment is repaired.

8. The method as described in claim 1, characterized in that, The method described includes a three-dimensional comprehensive reliability index system, which comprises: The system's overall availability, probability of power shortage, expected power shortage, redundancy ratio of wind, solar and energy storage, probability of hydrogen shortage, probability of methanol shortage, and contribution of key equipment failures.

9. The method as described in claim 1, characterized in that, The simulation process for comprehensive system reliability assessment in the method includes: The outer simulation layer simulates the operation-fault-repair sequence of each device during the simulation cycle; The inner simulation performs power allocation and supply-demand balance verification based on equipment availability and weather conditions at each time step. Statistical output of all reliability metrics.

10. A multi-energy flow failure modeling and supply-demand assessment device for a marine electric-hydrogen-alcohol system adapted to multiple extreme weather conditions, characterized in that, The device includes: The system modeling module is used to construct a success flow model of the marine electric-hydrogen-alcohol system based on the GO method, map the core units in the system to GO operators, construct the system GO signal flow graph, and identify the failure propagation chain of key equipment. The fault quantification analysis module is used to transform the GO signal flow graph into a fault tree model. It employs a dual strategy combining the minimum cut set method and the sequential Monte Carlo method to quantify the system failure probability and the reliability contribution of key equipment. The supply and demand dynamic assessment module is used to construct a power model for wind, solar, storage, hydrogen and methanol, introduce a hydrogen-methanol system operation control strategy, and dynamically assess the reliability of power and fuel supply and demand based on the sequential Monte Carlo method, and statistically analyze the probability of power shortage, the expected power shortage, the probability of hydrogen shortage and the probability of methanol shortage. The comprehensive evaluation module is used to establish a fault-supply-demand coupling mechanism, using the equipment fault state sequence as a dynamic input constraint for supply and demand simulation. Under extreme weather conditions, it performs dual correction on wind and solar power output and equipment failure rate, constructs a three-dimensional comprehensive reliability index system covering system status, power supply and fuel supply, and outputs the comprehensive reliability evaluation results of the system.

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