High-power electric hydrogen energy hub grid-connected peak-shaving control method and system

Abstract

This invention belongs to the field of power grid peak-shaving control technology, and particularly relates to a method and system for grid-connected peak-shaving control of high-power electric hydrogen energy hubs. The method includes: fusing heterogeneous data from multi-source market and compliant data of the electric hydrogen energy hub, deconstructing the interaction relationships of the fusion results to obtain soft-connection interconnection variables; heterogeneously coupling the soft-connection interconnection variables with the internal operating constraints of the electric hydrogen energy hub to obtain a decision architecture; enhancing and reconstructing the resilience of the decision architecture based on the real-time operating status of the electric hydrogen energy hub to obtain a resilient operating framework; adaptively evolving strategies within the resilient operating framework based on the real-time market state of the electric hydrogen energy hub to obtain robust strategies; mapping the robust strategies to a strategy space to obtain a strategy graph; and parameterizing and adapting the strategies within the strategy graph based on the real-time grid state of the electric hydrogen energy hub, encoding the adaptation results into real-time optimized control commands. This invention can improve the regulation efficiency of grid-connected peak-shaving for electric hydrogen energy hubs.

Description

Technical Field

[0001] This invention belongs to the field of power grid peak shaving control technology, and particularly relates to a method and system for grid-connected peak shaving control of high-power electric hydrogen energy hubs. Background Technology

[0002] The electric-hydrogen energy hub is a crucial platform for achieving coordinated operation between the power system and the hydrogen energy system. Through the coupling of processes such as water electrolysis for hydrogen production, fuel cell power generation, and energy storage, it can play a key role in grid peak shaving and renewable energy consumption. With the deepening of energy transition, the operation of the electric-hydrogen energy hub must simultaneously respond to electricity market trading signals, comply with industry regulations, and meet the operational safety constraints of its internal physical equipment. Its regulation process becomes a systemic decision-making problem involving multi-source heterogeneous data and multiple complex constraints.

[0003] Existing methods typically treat external factors such as electricity market clearing data and compliance constraints as static or isolated boundary conditions, failing to deeply analyze the complex interaction mechanisms and coupling paths between these factors and dynamic operational constraints within the hub, such as capacity regulation and energy storage. This results in decision-making models that struggle to accurately respond to dynamic changes in market and compliance requirements in real-world environments. Furthermore, most control strategies rely on pre-defined typical scenarios, lacking a resilient framework capable of dynamic and forward-looking strategy evolution based on real-time grid conditions and market dynamics. The strategies lack adaptability to fluctuations and disturbances, hindering the robustness and continuous optimization capabilities of grid-connected peak-shaving control. Therefore, improving the control efficiency of grid-connected peak-shaving in electric-hydrogen energy hubs has become an urgent problem to be solved. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a method and system for controlling grid-connected peak shaving of a high-power electric hydrogen energy hub, which can improve the regulation efficiency of grid-connected peak shaving of the electric hydrogen energy hub.

[0005] To achieve the above objectives, the present invention provides a high-power electric hydrogen energy hub grid-connected peak-shaving control method, comprising:

[0006] S1. Perform heterogeneous data fusion on multi-source market and compliance data related to the electric hydrogen energy hub, deconstruct the interaction relationship of the fusion results, and obtain the soft connection interconnection variables of the electric hydrogen energy hub.

[0007] S2. Heterogeneously couple the soft-connection interconnection variables with the internal operating constraints of the electric hydrogen energy hub to obtain the decision architecture of the electric hydrogen energy hub.

[0008] S3. Based on the real-time operational status of the electric hydrogen energy hub, the decision-making architecture is reconstructed to enhance resilience, resulting in a resilient operational framework for the electric hydrogen energy hub.

[0009] S4. Based on the real-time market status of the electric hydrogen energy hub, an adaptive strategy evolution is performed within a resilient operation framework to obtain a robust strategy for the electric hydrogen energy hub.

[0010] S5. Perform policy space mapping on the robust policy to obtain the policy map of the hydrogen energy hub;

[0011] S6. Based on the real-time grid status of the electric hydrogen energy hub, perform parameterized adaptation in the strategy graph, and encode the adaptation results into real-time optimized control commands for the electric hydrogen energy hub.

[0012] Preferably, in S1, the process of obtaining the soft interconnection variables of the electric hydrogen energy hub includes:

[0013] Extract electricity market clearing data, compliance constraint data released by industry compliance agencies, and load forecast data related to the electric hydrogen energy hub to generate a multi-source data feature set for the electric hydrogen energy hub;

[0014] By reducing and integrating the feature sets of multi-source data, standardized data for the electric hydrogen energy hub is obtained.

[0015] The interaction relationship matrix of the electric hydrogen energy hub is obtained by interactively constructing the inherent correlation attributes of standardized data.

[0016] The interaction path is extracted from the interaction matrix to obtain the external coupling path of the hydrogen energy hub;

[0017] The external coupling path is quantized and mapped to obtain the soft connection interconnection variables of the electric hydrogen energy hub.

[0018] Preferably, in S2, the process of obtaining the decision-making architecture for the hydrogen energy hub includes:

[0019] The operational limits of the electric hydrogen energy hub in the processes of capacity regulation, energy storage and power generation are aggregated to obtain the internal operational constraint set of the electric hydrogen energy hub;

[0020] By analyzing the dynamic interaction between soft-connection interconnection variables and internal operational constraint sets, the constraint variable mapping relationship of the electric hydrogen energy hub is obtained.

[0021] Based on the mapping relationship of constraint variables, the interaction space of soft connection interconnection variables and internal operation constraint set is constructed to obtain the coupled decision space of the electric hydrogen energy hub.

[0022] The decision logic within the coupled decision space is hierarchically reconstructed to obtain the decision architecture of the hydrogen energy hub.

[0023] Preferably, in S3, the process of obtaining the resilient operating framework of the electric hydrogen energy hub includes:

[0024] The operational characteristics of the electric hydrogen energy hub are extracted by analyzing its real-time operational status.

[0025] Based on situational characteristics, resilience and vulnerability are identified in the decision-making architecture, resulting in a list of resilience defects for the electric hydrogen energy hub.

[0026] Based on the resilience defect list, the decision-making architecture is reconstructed to obtain a resilient operation framework for the electric hydrogen energy hub.

[0027] Preferably, in S4, the process of obtaining a robust strategy for the electro-hydrogen energy hub includes:

[0028] The real-time market status of the electric hydrogen energy hub is extracted to obtain the market status parameters of the electric hydrogen energy hub.

[0029] Based on market state parameters, feasible strategies are enumerated within the constraints of the resilient operation framework to obtain candidate strategies for the electric hydrogen energy hub.

[0030] Based on the optimization objectives within the resilient operation framework, candidate strategies are evaluated and trade-offs are carried out to obtain the strategic effectiveness of the electric hydrogen energy hub.

[0031] Based on the effectiveness of the strategy, the candidate strategies are selected for their robustness to obtain the robust strategy for the electric hydrogen energy hub.

[0032] Preferably, the process for obtaining candidate strategies for an electric hydrogen energy hub includes:

[0033] Modal analysis of market state parameters yields the potential operating modes of the electric-hydrogen energy hub.

[0034] By deconstructing the potential operating modes, the basic operations of the electric hydrogen energy hub are obtained;

[0035] Based on the combinatorial logic of the resilient operation framework, the basic operations are logically arranged to obtain the preliminary strategy for the electric hydrogen energy hub;

[0036] Based on the constraint boundary, the feasibility of the preliminary strategy is verified, and candidate strategies for the electric hydrogen energy hub are obtained. Each candidate strategy constitutes a candidate strategy set.

[0037] The preferred formula for calculating strategy effectiveness is as follows:

[0038] ;

[0039] ;

[0040] ;

[0041] In the formula, E represents the strategy effectiveness. is the adaptive optimization factor of the resilient operating framework, and k is the global satisfaction of the constraint boundaries in the resilient operating framework. This is the normalized comprehensive return quantification value. The parameter volatility of the candidate strategy, The volatility threshold allowed by the resilient operating framework. This is the normalized real-time operational status prediction time window. Let C represent the matching degree between the candidate strategy and the potential operating mode, C be the cost coefficient of the operational complexity of the initial strategy, and S be the comprehensive benefit quantification value of the optimization objective within the resilient operating framework. The benchmark revenue for the electric hydrogen energy hub, For predicting the real-time operational status, This serves as the benchmark time window for the hydrogen energy hub.

[0042] Preferably, in S5, the process of obtaining the strategy map of the electric hydrogen energy hub includes:

[0043] By identifying the dimensional attributes of the robust strategy, the strategy feature dimensions of the electric hydrogen energy hub are obtained.

[0044] By logically constructing the strategic feature dimensions, the strategic space basis of the electric hydrogen energy hub is obtained;

[0045] Within the policy space basis, the robust policies are analyzed by correlation topology to obtain the policy correlation relationships of the electric hydrogen energy hub;

[0046] By performing topological mapping on the strategic relationships, a strategic map of the hydrogen energy hub is obtained.

[0047] Preferably, in S6, the process of performing parameterized adaptation in the strategy graph and encoding the adaptation results into real-time optimized control commands for the electric hydrogen energy hub includes:

[0048] The real-time status of the power grid of the hydrogen energy hub is read in real time to obtain the power grid status information of the hydrogen energy hub.

[0049] Based on grid status information, hierarchical dimensional matching of the strategy map is performed to obtain the map matching results of the electric hydrogen energy hub.

[0050] The matching results of the spectrum are used to calibrate the adaptation parameters to obtain the parameter adaptation benchmark for the electric hydrogen energy hub;

[0051] Based on the parameter adaptation benchmark, the strategy map is dynamically tuned to obtain the grid adaptation strategy for the electric hydrogen energy hub.

[0052] By enhancing the anti-interference capabilities of the grid adaptation strategy, an enhanced execution strategy for the electric hydrogen energy hub is obtained.

[0053] The enhanced execution strategy is structured and encoded to obtain real-time optimized control commands for the electric hydrogen energy hub.

[0054] A high-power electric-hydrogen energy hub grid-connected peak-shaving control system, used to implement the above methods, includes:

[0055] The Data Fusion Deconstruction Module is used to perform heterogeneous data fusion on multi-source market and compliance data related to the electric hydrogen energy hub, and to deconstruct the interaction relationship of the fusion results to obtain the soft connection interconnection variables of the electric hydrogen energy hub.

[0056] The coupling decision module is used to heterogeneously couple the soft-connection interconnection variables with the internal operating constraints of the electric hydrogen energy hub to obtain the decision architecture of the electric hydrogen energy hub.

[0057] The resilience module is used to enhance and reconstruct the decision-making architecture based on the real-time operational status of the electric hydrogen energy hub, thereby obtaining a resilient operational framework for the electric hydrogen energy hub.

[0058] The market-driven strategy module is used to perform adaptive strategy evolution within a resilient operation framework based on the real-time market status of the electric hydrogen energy hub, thereby obtaining a robust strategy for the electric hydrogen energy hub.

[0059] The strategy mapping module is used to perform strategy space mapping on robust strategies to obtain the strategy map of the electric hydrogen energy hub.

[0060] The grid adaptation code module is used to perform parameterized adaptation in the strategy graph based on the real-time grid status of the electric hydrogen energy hub, and encode the adaptation results into real-time optimized control commands for the electric hydrogen energy hub.

[0061] The present invention has the following beneficial effects:

[0062] This invention extracts electricity market clearing data, compliance constraints, and load forecasting data, performs heterogeneous data fusion and interaction relationship deconstruction to generate soft-connection interconnect variables, and then dynamically couples these variables with operational constraints such as capacity regulation and energy storage within the electric-hydrogen energy hub to construct a decision-making architecture. This dynamically transforms external market and compliance requirements into decision inputs, overcoming the shortcomings of treating external factors as static boundaries in traditional methods, improving the response accuracy to dynamic changes in the real environment, and enhancing the precision of grid-connected peak-shaving control.

[0063] This invention reconstructs the decision-making architecture based on the real-time operational status of the electric hydrogen energy hub, forming a resilient operation framework. Within this framework, adaptive strategy evolution is performed based on the immediate market conditions, including potential operation mode identification, primitive operation deconstruction, and disturbance resistance selection. Robust strategies are generated and mapped to a strategy graph, realizing online dynamic generation and optimization of strategies. This enables the system to autonomously adapt to grid fluctuations and market disturbances, improving the adaptability and robustness of the grid-connected peak-shaving system. Attached Figure Description

[0064] Figure 1 This is a schematic flowchart of the method of the present invention;

[0065] Figure 2 This is a functional block diagram of the system of the present invention. Detailed Implementation

[0066] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0067] Example 1: As Figure 1 As shown, the steps of the grid-connected peak-shaving control method for high-power electric hydrogen energy hubs include:

[0068] S1. Perform heterogeneous data fusion on multi-source market and compliance data related to the electric hydrogen energy hub, deconstruct the interaction relationship of the fusion results, and obtain the soft connection interconnection variables of the electric hydrogen energy hub.

[0069] S2. Heterogeneously couple the soft-connection interconnection variables with the internal operating constraints of the electric hydrogen energy hub to obtain the decision architecture of the electric hydrogen energy hub.

[0070] S3. Based on the real-time operational status of the electric hydrogen energy hub, the decision-making architecture is reconstructed to enhance resilience, resulting in a resilient operational framework for the electric hydrogen energy hub.

[0071] S4. Based on the real-time market status of the electric hydrogen energy hub, an adaptive strategy evolution is performed within a resilient operation framework to obtain a robust strategy for the electric hydrogen energy hub.

[0072] S5. Perform policy space mapping on the robust policy to obtain the policy map of the hydrogen energy hub;

[0073] S6. Based on the real-time grid status of the electric hydrogen energy hub, perform parameterized adaptation in the strategy graph, and encode the adaptation results into real-time optimized control commands for the electric hydrogen energy hub.

[0074] In S1, the soft-connection variables of the electric hydrogen energy hub are obtained, including:

[0075] Extract electricity market clearing data, compliance constraint data released by industry compliance agencies, and load forecast data related to the electric hydrogen energy hub to generate a multi-source data feature set for the electric hydrogen energy hub;

[0076] By reducing and integrating the feature sets of multi-source data, standardized data for the electric hydrogen energy hub is obtained.

[0077] The interaction relationship matrix of the electric hydrogen energy hub is obtained by interactively constructing the inherent correlation attributes of standardized data.

[0078] The interaction path is extracted from the interaction matrix to obtain the external coupling path of the hydrogen energy hub;

[0079] The external coupling path is quantized and mapped to obtain the soft connection interconnection variables of the electric hydrogen energy hub.

[0080] Electricity market clearing data is collected from electricity trading records. This data represents the actual transaction results, such as the volume and price, determined after the electricity transaction is completed. Compliance constraint data is extracted from relevant compliance management documents. This data represents the compliance requirements and restrictions formulated for the operation of the electric-hydrogen energy hub. Load forecast data is compiled by combining historical electricity consumption data, current commercial levels, and the operating status of electrical equipment within the coverage area of ​​the electric-hydrogen energy hub. This data is the result of estimating electricity demand for a specific future period based on historical electricity consumption data and current electricity consumption trends. These three types of data from different channels and with related uses are integrated to form a multi-source data feature set for the electric-hydrogen energy hub. This data feature set is a comprehensive data collection that integrates electricity market transaction information, compliance requirements, and electricity demand forecasts.

[0081] First, check whether there are format differences, missing data, or duplicates in the multi-source data feature set. For data with different formats, adjust it according to the preset unified format standard, unify the data with different time formats into a fixed year, month, day, hour, and minute record format, and convert the numerical data with different units into units of the same magnitude. For missing data, supplement it based on the historical average data of the data category or reasonable data in similar scenarios. For duplicate data, retain one valid record and delete the rest of the duplicates. After the above processing, the standardized data of the electric hydrogen energy hub is obtained. This data is uniform in format, complete and without redundancy, and can be directly used for subsequent analysis.

[0082] First, we systematically analyze the attributes corresponding to each data item in the standardized data, including the transaction price and transaction volume attributes in the electricity market clearing data, the operating limit and compliance threshold attributes in the compliance constraint data, and the predicted electricity consumption and prediction period attributes in the load forecast data. Then, we analyze the interaction between each pair of attributes. For example, an increase in transaction price will lead to an adjustment in predicted electricity consumption, and the operating limit attribute will limit the maximum value of transaction volume. Next, we arrange these attributes in a row and column format, with each row and column corresponding to a different attribute. Each position in the matrix is ​​marked with a specific association identifier based on the degree and direction of influence between the corresponding attributes. This yields the interaction matrix of the electric hydrogen energy hub, which is a structured data set that clearly presents the relationships between the various attributes of the standardized data.

[0083] A comprehensive analysis of the association markers among all attributes in the interaction matrix was conducted to identify relationships that span the internal systems and external environment of the electric-hydrogen energy hub. Examples include the relationship between electricity market transaction prices and predicted electricity consumption, and the relationship between compliance thresholds and transaction electricity volume within compliance constraints. The impact paths formed by these relationships constitute the external coupling paths. By systematically examining and classifying all relationships in the matrix, paths involving only internal hub attributes were eliminated, retaining those related to external data attributes. Ultimately, the external coupling paths of the electric-hydrogen energy hub were obtained, representing the specific channels through which external factors influence its operation.

[0084] First, determine the actual intensity of each influencing factor in each external coupling path. For example, for every fixed change in the transaction price, the specific value of the corresponding change in the predicted electricity consumption; for every fixed increase in the compliance threshold, the corresponding adjustment range of the transaction electricity volume. Then, convert these intensities according to a unified quantitative standard so that each external coupling path corresponds to a specific quantitative value. This quantitative value is the soft connection interconnection variable of the electric hydrogen energy hub. This variable is a quantifiable indicator that can intuitively reflect the degree of influence of external factors on the operation of the electric hydrogen energy hub.

[0085] The above process ensures that the data sources are comprehensive and targeted, providing a complete and effective foundation for subsequent data processing, improving the reliability of data support for grid-connected peak-shaving regulation efficiency, eliminating data format, missing, and redundant issues, reducing the difficulty of subsequent processing, improving the accuracy of data processing, and providing high-quality data support for the precision of regulation strategies; it also structures and visualizes data attribute relationships, clearly presenting the correlation, avoiding omissions or misjudgments of key correlations, improving the scientific nature of subsequent decision-making, accurately screening key channels of external factors affecting hub operation, eliminating irrelevant interference, and improving the efficiency and targeting of data processing; and it transforms abstract influence relationships into specific and operable quantitative indicators, providing feasible support for subsequent variable coupling and decision-making architecture construction, and enhancing the effectiveness of grid-connected peak-shaving regulation.

[0086] In S2, the decision-making framework for the hydrogen energy hub is obtained, including:

[0087] The operational limits of the electric hydrogen energy hub in the processes of capacity regulation, energy storage and power generation are aggregated to obtain the internal operational constraint set of the electric hydrogen energy hub;

[0088] By analyzing the dynamic interaction between soft-connection interconnection variables and internal operational constraint sets, the constraint variable mapping relationship of the electric hydrogen energy hub is obtained.

[0089] Based on the mapping relationship of constraint variables, the interaction space of soft connection interconnection variables and internal operation constraint set is constructed to obtain the coupled decision space of the electric hydrogen energy hub.

[0090] The decision logic within the coupled decision space is hierarchically reconstructed to obtain the decision architecture of the hydrogen energy hub.

[0091] The operational boundaries and safety thresholds of the electric-hydrogen energy hub in the processes of capacity regulation, energy storage, and power generation are aggregated to obtain the internal operational constraint set of the electric-hydrogen energy hub. Capacity regulation refers to the range within which the electric-hydrogen energy hub adjusts its hydrogen production capacity; energy storage refers to the capacity range within the hub's energy storage equipment for storing electrical or hydrogen energy; and power generation refers to the power output range of the hub's power generation equipment. The operational boundaries are the maximum and minimum operational limits for each stage during normal operation, and the safety thresholds are the critical values ​​that ensure equipment does not break down and that no safety accidents occur during operation.

[0092] In practice, the technical parameters of all equipment involved in capacity regulation in the hub are first collected to determine the upper and lower limits of the stable operating capacity of each piece of equipment under different operating conditions. Then, the storage capacity parameters of energy storage equipment are collected to clarify the safety requirements for its maximum storage capacity and minimum remaining storage capacity. Next, the rated power, maximum output power, and minimum stable output power of the power generation-related equipment are collected. Finally, the operating boundaries and safety thresholds corresponding to capacity regulation, energy storage, and power generation scattered on various equipment are sorted and organized by function and integrated into a unified constraint standard. This set containing all relevant constraints is the internal operating constraint set.

[0093] By analyzing the dynamic interaction between soft-connection variables and the internal operational constraint set, the constraint variable mapping relationship of the electric-hydrogen energy hub is obtained. The soft-connection variables were previously obtained through the fusion and deconstruction of multi-source market and compliance data, reflecting the influencing factors of the external market and compliance management environment on the electric-hydrogen energy hub.

[0094] Dynamic interaction relationships refer to the phenomenon where changes in soft-connection interconnect variables cause adjustments to related constraints in the internal runtime constraint set, or where the current state of the internal runtime constraint set limits the scope of influence of the soft-connection interconnect variables. In practice, each soft-connection interconnect variable and each constraint item in the internal runtime constraint set is listed one by one. Then, changes in the soft-connection interconnect variables are monitored in real time to observe whether the corresponding internal constraint items change. Simultaneously, the changes in the value range of the soft-connection interconnect variables when the internal constraint items are in different states are recorded. Then, from these monitoring records, the explicit interaction relationships between each soft-connection interconnect variable and the specific internal constraint item are extracted. Combinations with no or insignificant relationships are eliminated. The extracted explicit relationships are the constraint variable mapping relationships.

[0095] Based on the constraint variable mapping relationship, an interaction space is constructed between the soft-connection interconnection variables and the internal operational constraint set to obtain the coupled decision space of the electric-hydrogen energy hub. The interaction space is the decision reference range jointly formed by the soft-connection interconnection variables and the internal operational constraint set under their interaction; all decisions must be made within this range to ensure feasibility. In practice, based on the constraint variable mapping relationship, the effective value range of each soft-connection interconnection variable and the allowable range of each internal constraint term are first determined, and then the value range of the soft-connection interconnection variable is correlated with the allowable range of the corresponding internal constraint term.

[0096] Next, all the ranges corresponding to these associations are integrated to form a set containing the feasible ranges after the interaction of all soft-connected variables and internal constraints. The decision reference range covered by this set is the coupled decision space, and all subsequent decision-making must be carried out within this space.

[0097] The decision logic within the coupled decision space is hierarchically reconstructed to obtain the decision architecture of the electric hydrogen energy hub. Decision logic refers to the sequence, dependencies, and execution rules followed when making decisions within the coupled decision space. Hierarchical reconstruction divides these decision logics into different levels according to their importance, execution order, or scope of influence, forming a structured architecture.

[0098] In practice, the process begins by identifying all possible decision-making items within the coupled decision space, clarifying the triggering conditions, required soft-connection variables, internal constraints, and the impact of each decision on other decision-making items. Next, decision-making items are categorized into different levels based on their core importance: the core level corresponds to decisions directly affecting the safe and stable operation of the hub, the intermediate level to decisions affecting operational efficiency, and the basic level to supporting decisions. Then, the decision transmission paths and feedback mechanisms between different levels are defined to ensure that lower-level decisions provide a basis for higher-level decisions. Finally, these hierarchically categorized decision-making items, triggering conditions, dependencies, and transmission mechanisms are structurally integrated to form a structured decision-making system, which is the decision architecture.

[0099] Step S2 ensures the completeness and accuracy of the internal operating constraint set, provides a reliable basis for heterogeneous coupling, improves the basic reliability of decision architecture construction, clarifies the mapping relationship between soft connection interconnection variables and internal operating constraints, provides a clear basis for the construction of the interaction space, improves the accuracy of the integration of the two, constructs a coupled decision space covering external requirements and internal constraints, delineates the feasible scope of decisions, improves the feasibility and effectiveness of decisions, forms a structured decision architecture, clarifies decision priorities and transmission paths, and improves the systematicness and execution efficiency of grid connection and peak shaving decisions.

[0100] In S3, a resilient operating framework for the electric hydrogen energy hub is obtained, including:

[0101] The operational characteristics of the electric hydrogen energy hub are extracted by analyzing its real-time operational status.

[0102] Based on situational characteristics, resilience and vulnerability are identified in the decision-making architecture, resulting in a list of resilience defects for the electric hydrogen energy hub.

[0103] Based on the resilience defect list, the decision-making architecture is reconstructed to obtain a resilient operation framework for the electric hydrogen energy hub.

[0104] When extracting operational characteristics from the real-time operational status of the hydrogen-electric energy hub, it is first clarified that the real-time operational status is the actual set of operational data of the hub in core aspects such as capacity adjustment, energy storage, and power generation, covering the operating parameters, energy conversion efficiency, and load carrying capacity of each device. This real-time data is continuously collected through the data acquisition devices built into the equipment to ensure the continuity and integrity of the data. The collected raw data is classified and organized according to the three core aspects of capacity adjustment, energy storage, and power generation, and each aspect is further subdivided into data items for the corresponding equipment.

[0105] For each data category, key indicators such as average level, maximum fluctuation range, and rate of change over a certain period of time are calculated. These are compared with the preset normal operation data range to select data features that can reflect the core attributes of the current operating status. These core data features are then integrated to form situation features, which are a set of concrete descriptions covering the key states of each operating link.

[0106] When identifying the resilience and vulnerability of the decision-making architecture based on situational characteristics, it is first clarified that the decision-making architecture is a hierarchical decision-making system formed by the heterogeneous coupling of variables and internal operational constraints through soft connections. It includes the logical relationships and execution rules of the core decision-making layer, intermediate decision-making layer, and basic decision-making layer. Pre-set operational adaptation standards are then established, which are formulated based on the requirements for the safe and stable operation of the hub, clarifying the response capabilities that each decision-making level should possess under different operational states. The situational characteristics are then compared one by one with the pre-set operational adaptation standards of each level of the decision-making architecture to analyze the differences between the current operational state and the adaptation standards of each decision-making level. Finally, it is verified whether these differences will lead to the decision-making architecture's inability to respond effectively in the current and potential operational scenarios, i.e., whether the differences will cause problems such as decision lag and control failure.

[0107] All identified problems of this type are categorized and recorded according to decision-making level, scope of impact, and severity to form a resilience deficiency list. The resilience deficiency list is a detailed list that clearly identifies problems such as insufficient response capabilities, logical gaps, and delayed responses at each level of the decision-making framework.

[0108] When reconstructing the operational logic of the decision-making architecture based on the resilience defect list, corresponding optimization solutions are formulated for each defect item in the list. For example, for the defect of lacking energy storage limit upper limit response logic in the core decision-making layer, the logic of automatically triggering the capacity adjustment equipment to reduce load operation when the storage amount reaches the preset warning value is designed; the connection points between the optimization solution and the original decision-making logic are sorted out to ensure that the new logic is consistent with the dependency relationship of the original decision-making level and does not destroy the stability of the original architecture.

[0109] Adjust the execution process at each decision-making level, supplement missing decision-making links, and optimize lagging response steps. For example, add a feedback verification link for the optimized logic of the core decision-making level at the intermediate decision-making level to ensure that the operation status after the execution of control instructions meets expectations. Reintegrate all optimized decision logics by level, clarify the decision boundaries, interaction paths and emergency response mechanisms of each level, and form a resilient operation framework. The resilient operation framework is a structured decision-making system with the ability to resist interference and quickly adapt to changes in real-time operation status.

[0110] By collecting and integrating real-time data from various operational stages and accurately extracting core features, reliable evidence can be provided for vulnerability identification, ensuring the targeted nature of resilience enhancement, avoiding optimization failures, and accurately comparing with preset adaptation standards to clarify the location and impact of resilience shortcomings in the decision-making architecture. This provides clear optimization targets for reconstruction, ensures the implementation of enhancement measures, improves the ability to cope with complex operational situations, optimizes decision-making logic according to the defect list, improves response mechanisms and execution processes, builds a resilient operational framework, rapidly adapts to changes in the situation, resists operational disturbances, ensures stable and reliable grid connection and peak shaving, and improves control efficiency.

[0111] In S4, robust strategies for the electric hydrogen energy hub are obtained, including:

[0112] Key parameters are extracted from the real-time market status of the electric hydrogen energy hub to obtain the market status parameters of the electric hydrogen energy hub.

[0113] Based on market state parameters, feasible strategies are enumerated within the constraints of the resilient operation framework to obtain candidate strategies for the electric hydrogen energy hub, including:

[0114] Modal analysis of market state parameters yields the potential operating modes of the electric-hydrogen energy hub.

[0115] By deconstructing the potential operating modes, the basic operations of the electric hydrogen energy hub are obtained;

[0116] Based on the combinatorial logic of the resilient operation framework, the basic operations are logically arranged to obtain the preliminary strategy for the electric hydrogen energy hub;

[0117] Based on the constraint boundary, the feasibility of the preliminary strategy is verified, and candidate strategies for the electric hydrogen energy hub are obtained. Each candidate strategy constitutes a candidate strategy set.

[0118] Based on the optimization objectives within the resilient operation framework, candidate strategies are evaluated and trade-offs are carried out to obtain the strategic effectiveness of the electric hydrogen energy hub.

[0119] Based on the effectiveness of the strategy, the candidate strategies are selected for their robustness to obtain the robust strategy for the electric hydrogen energy hub.

[0120] Real-time market status refers to the collection of actual operational data related to the current market transactions, price fluctuations, and energy supply and demand relationships of the electric hydrogen energy hub. This data is collected through the market transaction data platform and supply and demand monitoring system connected to the electric hydrogen energy hub. It includes basic data such as real-time transaction prices, transaction volumes, real-time purchase volumes from energy demanders, and real-time supply volumes from suppliers within the current time period. Then, based on whether data changes will lead to adjustments in hub capacity regulation, power output, or energy storage strategies, data items that directly impact the operational decisions of the electric hydrogen energy hub are selected, while irrelevant and redundant market information is eliminated. Subsequently, the selected basic data is standardized in terms of time granularity and statistical units. For example, price data collected at different frequencies is standardized to records every minute, and transaction volume data with different units of measurement are converted to a unified energy unit. Finally, these filtered and standardized data are integrated to form a dataset that accurately reflects the core operational status of the current market; this dataset is the market status parameter.

[0121] Market status parameters are extracted datasets reflecting the current core operating status of the market. Based on the historical operating data and market operation patterns of the electric hydrogen energy hub, a preset market status classification standard is formulated to clarify the characteristic range corresponding to different market statuses. For example, when the real-time transaction price is below 30% of the historical price range and the supply is much greater than the demand, it is a low electricity price and oversupply state; when the price is above 70% of the historical range and the demand is much greater than the supply, it is a high electricity price and undersupply state; when the price is in the middle range and the supply and demand are basically balanced, it is a stable operating state.

[0122] Then, compare each data in the market status parameters with the preset classification standards one by one. For example, compare the current real-time transaction price with the historical price range, and compare the current supply and demand difference with the preset supply and demand balance threshold. Then, determine the current market status category based on the comparison results. Each status category corresponds to a possible operating mode of the hub.

[0123] Finally, all the operating modes corresponding to the determined state categories are summarized to form a set of operating directions that the hub can choose under the current market conditions. This set is the potential operating mode, which specifically includes types such as high-load power generation mode, energy storage priority mode, and capacity adjustment mode.

[0124] Potential operating modes are the set of operating directions that a hub can choose. First, for each potential operating mode, the core operating links required for its implementation are identified. The core operating links include equipment start-up and shutdown and power adjustment in the capacity regulation link, increase or decrease of storage volume and switching of storage mode in the energy temporary storage link, and adjustment of unit operating status and output power in the power production link. Then, the complete operation process of each core operating link is broken down into independent operating steps that cannot be further divided. The criterion for judging an independent operating step is that the step cannot be broken down into smaller operating units with clear functions. For example, the power generation link in the high-load power generation mode is broken down into steps such as starting the standby generator set, increasing the unit output power to the rated value, and stabilizing the unit operating parameters.

[0125] Next, the specific execution requirements for each independent operation step are clarified, including the order of operations, key actions in the execution process, and the target state to be achieved. For example, starting a standby generator set requires first checking the status of the unit, then starting the power system, and finally confirming that the unit has entered the standby state. Finally, all the independent operation steps after the breakdown of all potential operating modes are integrated to form a set of the smallest operation units with clear functions and execution requirements. This set is the primitive operation.

[0126] The combinatorial logic of the resilient operation framework refers to the rules for matching primitive operations, execution order constraints, and functional coordination requirements specified in the framework. These rules are formulated based on the bottom line of safe operation and optimization goals of the hub. According to the core goal of each potential operation mode, the corresponding primitive operation is selected from the set of primitive operations. For example, the core goal of the energy storage priority mode is to maximize energy storage. Primitive operations such as increasing the storage capacity of energy storage devices and switching energy storage devices to high-efficiency storage modes are selected. Then, the execution order of the selected primitive operations is determined according to the combinatorial logic of the resilient operation framework to ensure that the execution result of the previous primitive operation can provide the necessary conditions for the next primitive operation.

[0127] For example, first switch the energy storage device to a high-efficiency storage mode, then perform the operation to increase the storage capacity, then supplement the collaborative control logic between the basic operations, and clarify the parameter coordination relationship in the execution process of different basic operations. For example, while increasing the power generation, simultaneously adjust the input power of the energy storage device to avoid energy supply and demand imbalance. Finally, combine these basic operations arranged in sequence and containing collaborative logic to form a complete operation plan for a specific potential operating mode. This plan is the preliminary strategy.

[0128] Constraint boundaries are the clearly defined operational limitations of the hub within the resilient operation framework. These include specific requirements such as equipment operation safety thresholds, capacity adjustment ranges, and upper and lower limits of energy storage capacity. First, each operational step in the preliminary strategy is compared with the constraint boundaries one by one to check whether the execution of each operational step exceeds the limits of the constraint boundaries. For example, it is checked whether the operation of increasing power generation exceeds the maximum rated power of the unit, and whether the operation of increasing energy storage exceeds the maximum storage capacity of the energy storage device. Then, the complete execution process of the preliminary strategy is simulated, and the changes in various operating parameters of the hub during the execution process are monitored to confirm that the parameter changes are always within the constraint boundary range and will not cause equipment failure or operational safety issues.

[0129] For preliminary strategies that do not exceed the constraint boundaries and have no safety issues during simulation, they are directly included in the candidate strategy pool. For preliminary strategies where some operation steps exceed the constraint boundaries, if the constraint requirements can be met by adjusting the execution parameters of the operation steps, they are included in the candidate strategy pool after adjustment. If the constraint requirements cannot be met by adjusting the parameters, they are directly eliminated. Finally, all preliminary strategies that meet the constraint boundary requirements are integrated to form a set of strategies that can be actually executed. This set is the candidate strategy set.

[0130] The optimization objectives within the resilient operation framework include clearly defined goals such as maximizing operational efficiency, minimizing operational costs, and optimizing energy utilization efficiency. Each objective has a corresponding quantitative evaluation standard. Based on the core operational needs of the hub, the weight of each optimization objective in the evaluation is set. For example, when the hub's core requirement is efficient operation, the weight of maximizing operational efficiency is higher than that of other objectives. Then, according to the quantitative evaluation standard of each optimization objective, the performance value of each candidate strategy under each objective is calculated.

[0131] For example, when calculating operational efficiency, the ratio of actual output energy to energy consumed after strategy execution is used as the performance value. When calculating operational costs, the sum of various costs such as equipment wear and energy consumption during strategy execution is used as the performance value. Then, the performance values ​​of each candidate strategy under each objective are multiplied by the corresponding weight and summed to obtain the comprehensive evaluation result of each candidate strategy. Finally, the comprehensive evaluation result is defined as strategy effectiveness. Strategy effectiveness is a quantitative indicator that can comprehensively reflect the overall performance of candidate strategies under all optimization objectives.

[0132] Strategy effectiveness is a comprehensive evaluation of candidate strategies. First, the strategy effectiveness of all candidate strategies is ranked from highest to lowest. Then, referring to the common amplitude of historical market fluctuations, a scenario of small market fluctuations is simulated, such as price fluctuations not exceeding 10% and supply and demand fluctuations not exceeding 15%. Under the simulated fluctuation scenario, the strategy effectiveness of each candidate strategy is recalculated. The difference between the effectiveness after the fluctuation and the original effectiveness is divided by the original effectiveness to obtain the magnitude of the effectiveness change. Then, based on the hub's tolerance to market fluctuations, a disturbance resistance threshold is set. For example, the magnitude of the effectiveness change does not exceed 5% to meet the disturbance resistance requirement. Subsequently, candidate strategies with high strategy effectiveness rankings and whose effectiveness change magnitude does not exceed the disturbance resistance threshold under the simulated fluctuation scenario are selected. Finally, the candidate strategy with the highest strategy effectiveness and the best disturbance resistance performance is selected. This strategy is the robust strategy. A robust strategy is the optimal strategy that can achieve the optimization goal under the current market conditions and still maintain stable operation when facing small market fluctuations.

[0133] The above process ensures that market status parameters are accurate and targeted, providing reliable support for determining potential operating modes, improving the accuracy and adaptability of strategy formulation, clarifying feasible market operating directions, avoiding blind strategy formulation, improving strategy evolution efficiency, breaking down complex operating modes into the smallest operating units, reducing the difficulty of strategy arrangement, providing clear basic units, ensuring that initial strategies follow safety constraints and optimization objectives, guaranteeing functionality and synergy, improving feasibility and effectiveness, eliminating strategies that do not meet operating restrictions, screening executable candidate strategy sets, ensuring execution safety and stability, comprehensively considering the overall performance of candidate strategies, avoiding one-sidedness, achieving optimal balance of multiple objectives, improving the overall efficiency of hub operation, screening the optimal strategy with strong anti-disturbance capabilities, ensuring stable operation and optimization efficiency under market fluctuations, and improving the reliability and robustness of grid connection peak shaving control.

[0134] The specific formula for calculating strategy effectiveness is as follows:

[0135] ;

[0136] ;

[0137] ;

[0138] In the formula, E represents the strategy effectiveness. is the adaptive optimization factor of the resilient operating framework, and k is the global satisfaction of the constraint boundaries in the resilient operating framework. This is the normalized comprehensive return quantification value. The parameter volatility of the candidate strategy, The volatility threshold allowed by the resilient operating framework. This is the normalized real-time operational status prediction time window. Let C represent the matching degree between the candidate strategy and the potential operating mode, C be the cost coefficient of the operational complexity of the initial strategy, and S be the comprehensive benefit quantification value of the optimization objective within the resilient operating framework. The benchmark revenue for the electric hydrogen energy hub, For predicting the real-time operational status, This serves as the benchmark time window for the hydrogen energy hub.

[0139] In the formula for calculating policy effectiveness, E is a quantitative evaluation result of the overall performance of candidate policies, which is used for subsequent anti-disturbance selection to determine robust policies. The result is the resilience enhancement and reconstruction result from the resilience operation framework. It is a quantitative representation of the framework's adaptive optimization capability and reflects the level of adaptation and optimization to candidate strategies. k comes from the statistics of the candidate strategies' satisfaction of the constraint boundaries. It is obtained by calculating the proportion of strategies that satisfy all constraints and measures whether the strategies fully comply with the operation constraints.

[0140] Is S and The normalized calculation eliminates dimensional differences, allowing the return index to be calculated in conjunction with other dimensionless parameters; S is the weighted sum of the returns of optimization objectives such as maximizing operating efficiency and minimizing operating costs, and is the core basic data reflecting the level of strategy returns. The optimal comprehensive benefit quantification value from the normal operation of the electric hydrogen energy hub over the past 3 to 6 months is used as a reference benchmark for S normalization to ensure the consistency of benefit assessment under different scenarios.

[0141] The statistics of changes in core parameters during strategy simulation execution are obtained by calculating the average value of parameter changes per unit time, reflecting the stability of strategy parameters. The upper limit of parameter fluctuation is defined by the constraint boundaries determined during framework reconstruction, combined with equipment safety thresholds and historical data settings.

[0142] yes and The result is obtained through normalization calculation, which eliminates the influence of time dimension and adapts to the overall dimensionless calculation requirements of the formula. The forecast span is determined based on the data update frequency and market change cycle of the electric hydrogen energy hub, reflecting the range of predictions for future operating trends; This is a preset standard prediction time window for the hydrogen energy hub, which can be set to 1 hour. A normalized reference benchmark to unify the evaluation scale for forecast time;

[0143] C comes from the operational evaluation of the initial strategy, which is derived by statistically analyzing the number of primitive operations and the complexity of the steps, reflecting the additional cost of strategy execution.

[0144] The overall formula should be passed first. Calculate the degree of compliance with volatility, and Multiply k and S to get the core score, then use and The denominator is adjusted to a score, which is then multiplied by C to account for the complexity cost, ultimately yielding a comprehensive evaluation result that provides a basis for robust strategy selection.

[0145] In S5, a strategy map of the electric hydrogen energy hub is obtained, including:

[0146] By identifying the dimensional attributes of the robust strategy, the strategy feature dimensions of the electric hydrogen energy hub are obtained.

[0147] By logically constructing the strategic feature dimensions, the strategic space basis of the electric hydrogen energy hub is obtained;

[0148] Within the policy space basis, the robust policies are analyzed by correlation topology to obtain the policy correlation relationships of the electric hydrogen energy hub;

[0149] By performing topological mapping on the strategic relationships, a strategic map of the hydrogen energy hub is obtained.

[0150] When identifying the dimensional attributes of robust strategies, we first clarify that robust strategies are the optimal strategies selected earlier that are highly resistant to disturbances and can achieve the optimization objectives. These strategies include related operations such as capacity regulation, energy storage, and power generation. Then, we sort out the key attributes corresponding to these operations. Market response scenario refers to the specific situations such as electricity price fluctuations and load changes that the operation targets. Equipment type refers to the specific equipment categories involved in the operation, such as capacity regulation equipment, energy storage equipment, and power generation equipment. Time period refers to the fixed duration interval from the start to the completion of the operation. Energy change range refers to the specific range of energy increase or decrease within the hub after the operation is executed. Safety requirements refer to the equipment operating limits and system stability standards that must be followed during the operation. Then, we classify and integrate these attributes to ensure that each attribute can accurately reflect a core feature of the robust strategy. The final set of classified key attributes is the strategy feature dimension.

[0151] When constructing the strategy feature dimensions logically, first clarify the core connotation of each strategy feature dimension, and then analyze the relationship logic between each dimension. For example, different market response scenarios correspond to different energy adjustment needs. In scenarios with high electricity prices, the energy change range corresponding to the power output needs to be increased. In scenarios with high load, the energy storage range of energy storage devices needs to be adjusted. Then, according to the relationship logic, all strategy feature dimensions are hierarchically divided. Market response scenarios and safety requirements that directly affect the core objectives of strategy execution are taken as first-level dimensions. Equipment types and time cycles that support the realization of core objectives are taken as second-level dimensions. Energy change range that refines the execution details is taken as third-level dimensions.

[0152] Then, the priority order of dimensions within each level is determined, with first-level dimensions having higher priority than second-level dimensions, and second-level dimensions having higher priority than third-level dimensions. At the same time, the triggering relationship between different levels of dimensions is clarified. After the market response scenario of the first-level dimension is determined, the selection of the device type of the second-level dimension is triggered. After the device type of the second-level dimension is determined, the setting of the energy change range of the third-level dimension is triggered. Through such hierarchical division and clear relationship, a structured dimensional framework is constructed. This framework is the strategy space base.

[0153] When performing correlation topology analysis on robust strategies within the policy space base, the execution flow of each robust strategy is broken down one by one based on the hierarchical structure and dimensional relationships of the policy space base. The specific values ​​of the strategy in the first, second, and third dimensions are clarified. For example, a certain robust strategy is high electricity price in the first-level market response scenario, power generation equipment in the second-level equipment type, and an increase of 20%-30% in the third-level energy change range. Then, the values ​​of different robust strategies in each dimension are compared to find the dimension combinations with the same values ​​or causal relationships.

[0154] For example, when multiple robust strategies respond to a market scenario of high electricity prices, they all select power generation equipment as the equipment type and the energy change magnitude increases. This forms a set of related dimensions. Then, we can sort out the transmission paths between these related dimensions: market response scenario: high electricity prices → equipment type: power generation equipment → energy change magnitude increases. At the same time, we can record the correlation of the robust strategy execution effect corresponding to each related combination. For example, the execution effect is to increase revenue and meet safety requirements. By systematically organizing these dimension combinations, transmission paths, and execution effect correlations, a clear and structured set of correlations is formed, which is the strategy correlation relationship.

[0155] When performing topological mapping on the strategy relationships, the presentation rules of the topological mapping are first determined. The first, second, and third-level dimensions of the strategy space base are used as the node levels of the graph. The first-level dimension nodes are located at the top layer of the graph, the second-level dimension nodes are located in the middle layer, and the third-level dimension nodes are located in the lower layer. The specific value of each dimension is used as the specific node under the corresponding level. Then, according to the transmission path in the strategy relationship, the relevant nodes are connected with lines. The node with the market response scenario of high electricity price is connected with the node with the equipment type of power generation equipment. The node with the equipment type of power generation equipment is connected with the node with the energy change amplitude increased by 20%-30%. The thickness of the lines is set according to the degree of correlation. The lines corresponding to the transmission path where multiple robust strategies exist are thicker than the lines corresponding to the path where only a single robust strategy exists.

[0156] At the same time, the number of robust strategies and the core execution effect of each node are marked next to it. For example, the node of power generation equipment is marked with 5 robust strategies and the execution effect is to improve the revenue by 15%-20%. Through such node setting, line connection and information labeling, the relationship between strategies is presented in a visual structured graphic form, which is the strategy map.

[0157] The S5 steps can form clear policy feature dimensions, providing a clear basis for the construction of the policy space base, improving the pertinence and accuracy of policy space mapping, building a structured policy space base, clarifying dimension priorities and triggering relationships, providing a clear framework for relational topology parsing, improving parsing efficiency and orderliness, accurately identifying policy association combinations and transmission paths, forming complete policy association relationships, providing comprehensive and accurate data for topology mapping, ensuring that the policy graph truly reflects the internal connections of policies, transforming abstract association relationships into a visualized policy graph, clearly presenting core information, improving the intuitiveness and efficiency of subsequent parameterization adaptation, and providing support for the rapid generation of real-time optimization control instructions.

[0158] In S6, parameterized adaptation is performed in the strategy graph, and the adaptation results are encoded into real-time optimization control commands for the electric hydrogen energy hub, including:

[0159] The real-time status of the power grid of the hydrogen energy hub is read in real time to obtain the power grid status information of the hydrogen energy hub.

[0160] Based on grid status information, hierarchical dimensional matching of the strategy map is performed to obtain the map matching results of the electric hydrogen energy hub.

[0161] The matching results of the spectrum are used to calibrate the adaptation parameters to obtain the parameter adaptation benchmark for the electric hydrogen energy hub;

[0162] Based on the parameter adaptation benchmark, the strategy map is dynamically tuned to obtain the grid adaptation strategy for the electric hydrogen energy hub.

[0163] By enhancing the anti-interference capabilities of the grid adaptation strategy, an enhanced execution strategy for the electric hydrogen energy hub is obtained.

[0164] The enhanced execution strategy is structured and encoded to obtain real-time optimized control commands for the electric hydrogen energy hub.

[0165] The electric-hydrogen energy hub has been connected to grid status monitoring equipment such as voltage sensors, current sensors, and frequency monitors distributed at key nodes connected to the grid and on internal power transmission lines. These devices continuously collect data at fixed time intervals based on the sensitivity to changes in grid status. The collected data includes voltage and current values, power transmission frequency, line load rate, power values ​​of energy storage devices interacting with the grid, and power values ​​of power generation equipment outputting to the grid at the nodes connected to the hub. After removing abnormal data that deviates from the historical normal operating data range of the monitoring point by more than a fixed proportion due to equipment failure or signal interference, the remaining valid data is classified and organized according to data type to form grid status information containing various key grid operation indicators. This information can comprehensively reflect the actual operating status of the current interaction between the grid and the electric-hydrogen energy hub.

[0166] The strategy graph is a visualized, structured graph previously generated through robust strategy space mapping. It includes a primary market response scenario, a secondary equipment type, a tertiary energy change magnitude node hierarchy, and associated paths. Core indicators such as line load rate, voltage stability data, and power interaction values ​​are extracted from grid status information. These core indicators are mapped to the market response scenario nodes in the primary dimension of the strategy graph. Based on the matching results in the primary dimension, the corresponding types are selected from the equipment type nodes in the secondary dimension of the strategy graph. Based on the matching results in the secondary dimension, the specific range is determined in the energy change magnitude nodes in the tertiary dimension. Finally, the matching nodes and associated paths of all levels are compiled and summarized to form a graph matching result that clearly defines the matching nodes, associated paths, and corresponding robust strategies for each dimension.

[0167] Extract the range of grid state parameters and execution effect data, including grid stability and hub operating efficiency, from the robust strategy in the historical execution process of the graph matching results. Compare the various indicators in the current grid state information with the historical data to determine the differences in grid state parameters corresponding to the current grid state and the historical best execution effect. Adjust the equipment operation parameters and energy interaction parameters involved in the robust strategy according to the differences to ensure that the adjusted parameters enable the strategy to achieve a similar historical best execution effect under the current grid state. Fix the adjusted parameters, clarify the specific value range and standard value of each parameter, and form a parameter adaptation benchmark that provides a clear reference standard for subsequent strategy tuning.

[0168] Based on the standard values ​​in the parameter adaptation benchmark, adjust the execution details of the robust strategy corresponding to the matching node in the strategy graph, including the start-up sequence of power generation equipment, the rate of increase in output power, and the energy release rhythm of energy storage equipment. At the same time, check the compatibility between the tuned strategy and other related nodes in the strategy graph to avoid conflicts with the execution logic of other potential strategies. Then, simulate the execution process of the tuned strategy, monitor the changes in grid status information and the response of hub operation parameters during the execution process, and confirm that all parameters are within the safe operating range and can achieve the peak shaving optimization target. The strategy after adjustment, compatibility check and simulation verification is determined as the grid adaptation strategy that is fully adapted to the current real-time grid status.

[0169] The analysis includes external disturbances that may affect the grid status, such as sudden changes in grid load and fluctuations in external energy supply. Based on historical operational data of the hub, the potential range of grid status changes caused by each disturbance is determined. For each disturbance and its corresponding range of change, a response mechanism is added to the grid adaptation strategy. When a sudden increase in grid load is predicted, the strategy automatically increases the reserve power of generating equipment in advance. When external energy supply fluctuates, the strategy automatically adjusts the charging and discharging modes of energy storage devices to compensate for the fluctuations. These response mechanisms are integrated with the execution logic of the original grid adaptation strategy. Triggering conditions for the response mechanisms are set based on the change thresholds of relevant indicators in the grid status information. When an indicator reaches the threshold, the corresponding response mechanism is automatically activated. The execution steps of the response mechanisms are clearly defined, forming a strengthened execution strategy capable of responding to external disturbances and maintaining stable execution performance.

[0170] The execution steps of the enhanced execution strategy are broken down into a series of continuous specific actions with clear execution subjects and operational requirements according to time sequence and logical relationship. Each action corresponds to an operation of a specific device within the hub. According to the instruction format determined based on the device control protocol and recognizable by the power grid and hub equipment, each specific action is converted into a standardized code language. The converted code is then structured and organized to clarify the execution order, correlation, and triggering logic between codes, avoiding chaotic instruction execution. The standardized code set after organization is the real-time optimization control instruction, which can be directly sent to the corresponding device within the hub to guide the device to accurately perform peak shaving operations.

[0171] The above process ensures the reliability of power grid status information, provides support for subsequent matching, improves the effectiveness of peak-shaving control commands, matches according to the strategy map hierarchy, accurately locates robust strategies, improves strategy adaptation efficiency, combines historical and current status to adjust parameters, avoids blind adjustments, improves strategy adaptation accuracy, adjusts strategies and verifies compatibility and security, ensures stable and reliable execution, analyzes disturbances and adds response mechanisms, enhances strategy anti-disturbance, stabilizes peak-shaving targets, standardizes structured coding commands, reduces transmission and execution errors, and improves peak-shaving control response speed and accuracy.

[0172] Example 2: Figure 2 As shown, this embodiment is a high-power electric hydrogen energy hub grid-connected peak-shaving control system for implementing the method in Embodiment 1. This system can be installed in electronic equipment and includes:

[0173] The Data Fusion Deconstruction Module is used to perform heterogeneous data fusion on multi-source market and compliance data related to the electric hydrogen energy hub, and to deconstruct the interaction relationship of the fusion results to obtain the soft connection interconnection variables of the electric hydrogen energy hub.

[0174] The coupling decision module is used to heterogeneously couple the soft-connection interconnection variables with the internal operating constraints of the electric hydrogen energy hub to obtain the decision architecture of the electric hydrogen energy hub.

[0175] The resilience module is used to enhance and reconstruct the decision-making architecture based on the real-time operational status of the electric hydrogen energy hub, thereby obtaining a resilient operational framework for the electric hydrogen energy hub.

[0176] The market-driven strategy module is used to perform adaptive strategy evolution within a resilient operation framework based on the real-time market status of the electric hydrogen energy hub, thereby obtaining a robust strategy for the electric hydrogen energy hub.

[0177] The strategy mapping module is used to perform strategy space mapping on robust strategies to obtain the strategy map of the electric hydrogen energy hub.

[0178] The grid adaptation code module is used to perform parameterized adaptation in the strategy graph based on the real-time grid status of the electric hydrogen energy hub, and encode the adaptation results into real-time optimized control commands for the electric hydrogen energy hub.

Claims

1. A method for grid-connected peak-shaving control of a high-power electric hydrogen energy hub, characterized in that, Includes the following steps: S1. Perform heterogeneous data fusion on multi-source market and compliance data related to the electric hydrogen energy hub, deconstruct the interaction relationship of the fusion results, and obtain the soft connection interconnection variables of the electric hydrogen energy hub. S2. Heterogeneously couple the soft-connection interconnection variables with the internal operating constraints of the electric hydrogen energy hub to obtain the decision architecture of the electric hydrogen energy hub. S3. Based on the real-time operational status of the electric hydrogen energy hub, the decision-making architecture is reconstructed to enhance resilience, resulting in a resilient operational framework for the electric hydrogen energy hub. S4. Based on the real-time market state of the electric hydrogen energy hub, adaptive strategy evolution is performed within a resilient operation framework to obtain a robust strategy for the electric hydrogen energy hub. The process includes: The real-time market status of the electric hydrogen energy hub is extracted to obtain the market status parameters of the electric hydrogen energy hub. Based on market state parameters, feasible strategies are enumerated within the constraints of the resilient operation framework to obtain candidate strategies for the electric hydrogen energy hub. Based on the optimization objectives within the resilient operation framework, candidate strategies are evaluated and trade-offs are carried out to obtain the strategic effectiveness of the electric hydrogen energy hub. Based on the effectiveness of the strategy, the candidate strategies are selected for their robustness to obtain the robust strategy for the electric hydrogen energy hub. The specific formula for calculating strategy effectiveness is as follows: ； ； ； In the formula, E represents the strategy effectiveness. is the adaptive optimization factor of the resilient operating framework, and k is the global satisfaction of the constraint boundaries in the resilient operating framework. This is the normalized comprehensive return quantification value. The parameter volatility of the candidate strategy, The volatility threshold allowed by the resilient operating framework. This is the normalized real-time operational status prediction time window. Let C represent the matching degree between the candidate strategy and the potential operating mode, C be the cost coefficient of the operational complexity of the initial strategy, and S be the comprehensive benefit quantification value of the optimization objective within the resilient operating framework. The benchmark revenue for the electric hydrogen energy hub, For predicting the real-time operational status, This serves as the benchmark time window for the hydrogen energy hub. S5. Perform policy space mapping on the robust policy to obtain the policy map of the hydrogen energy hub; S6. Based on the real-time grid status of the electric-hydrogen energy hub, perform parameterized adaptation in the strategy graph, and encode the adaptation results into real-time optimized control commands for the electric-hydrogen energy hub. The process includes: The real-time status of the power grid of the hydrogen energy hub is read in real time to obtain the power grid status information of the hydrogen energy hub. Based on grid status information, hierarchical dimensional matching of the strategy map is performed to obtain the map matching results of the electric hydrogen energy hub. The matching results of the spectrum are used to calibrate the adaptation parameters to obtain the parameter adaptation benchmark for the electric hydrogen energy hub; Based on the parameter adaptation benchmark, the strategy map is dynamically tuned to obtain the grid adaptation strategy for the electric hydrogen energy hub. By enhancing the anti-interference capabilities of the grid adaptation strategy, an enhanced execution strategy for the electric hydrogen energy hub is obtained. The enhanced execution strategy is structured and encoded to obtain real-time optimized control commands for the hydrogen energy hub.

2. The high-power electric hydrogen energy hub grid-connected peak-shaving control method as described in claim 1, characterized in that, In S1, the process of obtaining the soft-connection variables of the hydrogen energy hub includes: Extract electricity market clearing data, compliance constraint data released by industry compliance agencies, and load forecast data related to the electric hydrogen energy hub to generate a multi-source data feature set for the electric hydrogen energy hub; By reducing and integrating the feature sets of multi-source data, standardized data for the electric hydrogen energy hub is obtained. The interaction relationship matrix of the electric hydrogen energy hub is obtained by interactively constructing the inherent correlation attributes of standardized data. The interaction path is extracted from the interaction matrix to obtain the external coupling path of the hydrogen energy hub; The external coupling path is quantized and mapped to obtain the soft connection interconnection variables of the electric hydrogen energy hub.

3. The high-power electric hydrogen energy hub grid-connected peak-shaving control method as described in claim 1, characterized in that, In S2, the process of obtaining the decision-making framework for the hydrogen energy hub includes: The operational limits of the electric hydrogen energy hub in the processes of capacity regulation, energy storage and power generation are aggregated to obtain the internal operational constraint set of the electric hydrogen energy hub; By analyzing the dynamic interaction between soft-connection interconnection variables and internal operational constraint sets, the constraint variable mapping relationship of the electric hydrogen energy hub is obtained. Based on the mapping relationship of constraint variables, the interaction space of soft connection interconnection variables and internal operation constraint set is constructed to obtain the coupled decision space of the electric hydrogen energy hub. The decision logic within the coupled decision space is hierarchically reconstructed to obtain the decision architecture of the hydrogen energy hub.

4. The high-power electric hydrogen energy hub grid-connected peak-shaving control method as described in claim 1, characterized in that, In S3, the process of obtaining the resilient operating framework for the electric hydrogen energy hub includes: The operational characteristics of the electric hydrogen energy hub are extracted by analyzing its real-time operational status. Based on situational characteristics, resilience and vulnerability are identified in the decision-making architecture, resulting in a list of resilience defects for the electric hydrogen energy hub. Based on the resilience defect list, the decision-making architecture is reconstructed to obtain a resilient operation framework for the electric hydrogen energy hub.

5. The high-power electric hydrogen energy hub grid-connected peak-shaving control method as described in claim 1, characterized in that, The process of obtaining candidate strategies for the electric hydrogen energy hub includes: Modal analysis of market state parameters yields the potential operating modes of the electric-hydrogen energy hub. By deconstructing the potential operating modes, the basic operations of the electric hydrogen energy hub are obtained; Based on the combinatorial logic of the resilient operation framework, the basic operations are logically arranged to obtain the preliminary strategy for the electric hydrogen energy hub; Based on the constraint boundary, the feasibility of the preliminary strategy is verified to obtain candidate strategies for the electric hydrogen energy hub. Each candidate strategy constitutes a candidate strategy set.

6. The high-power electric hydrogen energy hub grid-connected peak-shaving control method as described in claim 1, characterized in that, In S5, the process of obtaining the strategy map of the hydrogen energy hub includes: By identifying the dimensional attributes of the robust strategy, the strategy feature dimensions of the electric hydrogen energy hub are obtained. By logically constructing the strategic feature dimensions, the strategic space basis of the electric hydrogen energy hub is obtained; Within the policy space basis, the robust policies are analyzed by correlation topology to obtain the policy correlation relationships of the electric hydrogen energy hub; By performing topological mapping on the strategic relationships, a strategic map of the hydrogen energy hub is obtained.

7. A high-power electric-hydrogen energy hub grid-connected peak-shaving control system, characterized in that, The method for implementing the high-power electric hydrogen energy hub grid-connected peak-shaving control according to any one of claims 1-6 includes: The Data Fusion Deconstruction Module is used to perform heterogeneous data fusion on multi-source market and compliance data related to the electric hydrogen energy hub, and to deconstruct the interaction relationship of the fusion results to obtain the soft connection interconnection variables of the electric hydrogen energy hub. The coupling decision module is used to heterogeneously couple the soft-connection interconnection variables with the internal operating constraints of the electric hydrogen energy hub to obtain the decision architecture of the electric hydrogen energy hub. The resilience module is used to enhance and reconstruct the decision-making architecture based on the real-time operational status of the electric hydrogen energy hub, thereby obtaining a resilient operational framework for the electric hydrogen energy hub. The market-driven strategy module is used to perform adaptive strategy evolution within a resilient operation framework based on the real-time market status of the electric hydrogen energy hub, thereby obtaining a robust strategy for the electric hydrogen energy hub. The strategy mapping module is used to perform strategy space mapping on robust strategies to obtain the strategy map of the electric hydrogen energy hub. The grid adaptation code module is used to perform parameterized adaptation in the strategy graph based on the real-time grid status of the electric hydrogen energy hub, and encode the adaptation results into real-time optimized control commands for the electric hydrogen energy hub.

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