An evaluation method for multi-scenario power grid interaction capability of an electric-hydrogen coupling system cluster

Abstract

The application provides an evaluation method for multi-scene power grid interaction capability of an electric-hydrogen coupling system cluster, which comprises obtaining a scene membership set of each electric-hydrogen coupling system according to interaction scene attributes and an operation data set, obtaining a scene sample time period set and a scene weight set; obtaining a scene evaluation index set based on an evaluation index set divided according to evaluation index attributes, obtaining an evaluation index value set and an index weight set in combination with the operation data set and the scene sample time period set, and further obtaining a basic interaction capability value set and a synergistic effect correction item set; obtaining an interaction capability index set according to the basic interaction capability value set, the synergistic effect correction item set and the scene weight set to evaluate the power grid interaction capability of each electric-hydrogen coupling system. The evaluation method can realize quantitative, scene-based and interpretable evaluation of the power grid interaction capability of the electric-hydrogen coupling system cluster in multiple interaction scenes, and can provide technical support for configuration optimization of the electric-hydrogen coupling system cluster in a new energy-rich area participating in multiple interaction scenes.

Description

Technical Field

[0001] This application relates to the field of electro-hydrogen coupling system technology, specifically to a method for evaluating the multi-scenario power grid interaction capability of an electro-hydrogen coupling system cluster. Background Technology

[0002] The evaluation of the grid interaction capability of electro-hydrogen coupling system clusters in multiple scenarios refers to the process of identifying, quantifying, and evaluating the support capability, adaptability, and bottlenecks of electro-hydrogen coupling system clusters under different grid interaction scenarios, based on grid-side, electro-hydrogen-side, and load-side operating parameters, for applications such as renewable energy-rich areas, electro-hydrogen coupling bases, and integrated source-grid-load-storage hydrogen systems. Its purpose is to provide quantitative basis for electro-hydrogen coupling system clusters to participate in renewable energy consumption, regulation of power transmission constraints, power supply support, and grid operation auxiliary decision-making. It is widely used in renewable energy base planning, electro-hydrogen coupling system configuration, dispatch operation evaluation, and grid interaction capability analysis.

[0003] With the rapid growth of installed capacity of new energy sources such as wind power and photovoltaics, new energy power systems are gradually exhibiting characteristics such as a high proportion of new energy, high fluctuation in power output, high dependence on external transmission, and high regulation demand. In areas rich in new energy sources, wind and solar power output is significantly affected by meteorological conditions, exhibiting obvious randomness, intermittency, and volatility. Simultaneously, new energy bases often face problems such as limited local load absorption capacity, significant constraints on external transmission channels, and increased pressure to ensure supply during peak load periods. Against this backdrop, electro-hydrogen coupling systems, through methods such as hydrogen production via electrolysis, hydrogen storage, hydrogen energy utilization, and hydrogen-to-electricity feedback, can absorb electricity to produce hydrogen when there is a surplus of new energy, and provide support through hydrogen storage release or hydrogen power generation when power is scarce. This makes them an important technical means to improve the capacity for new energy absorption, enhance system flexibility, and improve the operating status of the power grid.

[0004] However, the interaction between the electro-hydrogen coupling system cluster and the power grid is not a simple energy conversion process under a single operating state, but is influenced by multiple factors, including the output level of new energy sources, local load demand, external transmission capacity, hydrogen storage status, electrolyzer operating status, fuel cell or hydrogen power generation capacity, and flexible load response capability. For the power grid, the functions of the electro-hydrogen coupling system vary under different operating scenarios: in scenarios where external transmission is limited, its focus is on absorbing surplus power that cannot be consumed locally or transmitted externally, reducing the level of wind and solar curtailment; in scenarios where power supply is guaranteed, its focus is on compensating for power supply gaps through hydrogen storage release and fuel cell or hydrogen power generation units, improving the reliability of system power supply; in scenarios where new energy consumption is coordinated, its focus is on improving the effective utilization rate of new energy sources, increasing the proportion of green hydrogen production, and smoothing out new energy fluctuations. Therefore, accurately identifying the power grid interaction scenarios in which the electro-hydrogen coupling system cluster operates and quantitatively evaluating its multi-scenario interaction capabilities has become an important issue in the engineering application and scheduling decision-making of electro-hydrogen coupling systems.

[0005] Currently, much research in existing technologies focuses on the optimal scheduling, capacity configuration, or economic operation of electro-hydrogen coupling systems. For example, common approaches typically establish mathematical models based on factors such as wind power, photovoltaics, electrolyzers, hydrogen storage tanks, fuel cells, and load demand. The goal is to optimize electrolyzer power, hydrogen storage status, fuel cell output, and grid power purchase and sale, aiming for the lowest operating cost, minimum wasted electricity, maximum hydrogen production revenue, or highest power supply reliability. While these approaches can achieve relatively optimal operating strategies given specific objectives, their core focus is on "how to schedule" or "how to optimize configuration," rather than "how to evaluate the adaptability of electro-hydrogen coupling system clusters under different grid interaction scenarios." Therefore, when the grid dispatching side needs to determine whether a particular electro-hydrogen coupling system cluster is better suited for tasks such as renewable energy consumption, regulation of restricted power transmission, or power supply support, traditional optimal scheduling schemes struggle to directly provide clear capability levels, scenario adaptability, and bottleneck indicators. Summary of the Invention

[0006] This application aims to overcome the problems existing in the evaluation technology of the grid interaction capability of electric-hydrogen coupled system clusters, such as rough scene identification, lack of scene-specific evaluation indicators, static weight settings, insufficient characterization of collaborative relationships, and difficulty in supporting scheduling decisions. It provides a method for evaluating the grid interaction capability of electric-hydrogen coupled system clusters in multiple scenarios.

[0007] To achieve the above objectives, the technical solution adopted in this application is as follows: a method for evaluating the multi-scenario grid interaction capability of an electric-hydrogen coupling system cluster, comprising: acquiring an evaluation index set, an evaluation level classification, and an operational dataset for each electric-hydrogen coupling system; The scene membership set of each electro-hydrogen coupling system is obtained based on the interactive scene attributes and the running dataset. The scene sample time period set and scene weight set are obtained based on the scene membership set; The evaluation index set for a scenario is obtained by dividing the evaluation index set based on the evaluation index attributes; the first value set is obtained based on the running dataset, the scenario sample time period set, and the scenario evaluation index set; the evaluation index value set is obtained based on the first value set and the evaluation level division; and the index weight set is obtained based on the first value set and the scenario correction coefficient set. The basic interaction capability value set and the synergistic effect correction term set are obtained based on the evaluation index value set and the index weight set, respectively. An interaction capability index set is obtained based on the basic interaction capability value set, the synergy effect correction term set, and the scenario weight set; the grid interaction capability of each electro-hydrogen coupling system is evaluated based on the interaction capability index set.

[0008] In some embodiments, the evaluation index set is obtained by dividing the evaluation index set based on the evaluation index attributes, including: dividing the evaluation index set based on the evaluation index attributes to obtain a first evaluation index set and at least one second evaluation index set, wherein the second evaluation index set corresponds one-to-one with the interactive scenario, the first evaluation index set represents a set of evaluation indicators that are common to the electro-hydrogen coupling system in each interactive scenario, and the second evaluation index set represents a set of evaluation indicators that are specific to the electro-hydrogen coupling system in the corresponding interactive scenario. By integrating the first evaluation index set and the second evaluation index set, a scenario evaluation index set for the electro-hydrogen coupling system in the corresponding interactive scenario is obtained.

[0009] In some embodiments, the first value set represents the value set of evaluation indicators in the interactive scenario obtained by physical modeling methods, and the evaluation level division represents the correspondence between the evaluation level and the range of interactive ability index values. The evaluation index value set is obtained based on the first value set and the evaluation level classification, including: Standardize the first value set to obtain the second value set; Based on the evaluation level classification, obtain the level score set and the cloud model parameter set of evaluation indicators for the evaluation level; The evaluation level membership degree of each evaluation indicator is obtained based on the cloud model parameter set and the second value set; The evaluation index value set is obtained based on the grade score set and the membership degree of the evaluation grade.

[0010] In some embodiments, the cloud model parameter set represents a set of cloud model parameters, including a first cloud model parameter and a second cloud model parameter. The first cloud model parameter represents the cloud model expectation of the evaluation level corresponding to the evaluation index in the corresponding interactive scenario, and the second cloud model parameter represents the cloud model entropy of the evaluation level corresponding to the evaluation index in the corresponding interactive scenario.

[0011] In some embodiments, the evaluation index value set represents the set of cloud model evaluation values ​​of the evaluation index in the corresponding interactive scenario.

[0012] In some embodiments, obtaining the index weight set based on the first value set includes: Obtain the scene correction coefficient set, which represents the set of scene correction coefficients for the evaluation index in the corresponding interactive scene; Using the analytic hierarchy process, a first weight set is obtained based on the scenario evaluation index set. The first weight set represents the set of first weights, and the first weights represent the subjective weights of the evaluation indexes in the corresponding interactive scenario. The entropy weight method is adopted to obtain the second weight set based on the second value set. The second weight set represents the set of second weights, and the second weights represent the objective weights of the evaluation indicators in the corresponding interactive scenarios. The third weight set is obtained based on the first weight set and the second weight set. The third weight set represents the set of third weights. The third weights are obtained by weighted summation of the first weights and the corresponding second weights. The indicator weight set is obtained based on the third weight set and the scenario correction coefficient set. The indicator weight set represents the set of weight values ​​of the evaluation indicators in the corresponding interactive scenario.

[0013] In some embodiments, a synergy effect correction term set is obtained based on the evaluation index value set and the index weight set, including: Based on the evaluation index value set, the response evaluation value set of each electro-hydrogen coupling system on each side of the interaction is obtained. The response evaluation value set represents the set of response evaluation values ​​of the electro-hydrogen coupling system on the corresponding side in the corresponding interaction scenario. The geometric mean method is used to obtain the basic cooperative itemset of the corresponding electro-hydrogen coupling system based on the response evaluation value set of each one side; The scenario characteristic capability value set of the corresponding electro-hydrogen coupling system is obtained based on the evaluation index value set and index weight set; The corresponding electro-hydrogen coupling system's synergistic effect correction itemset is obtained based on the basic synergistic itemset and the scenario feature capability value set.

[0014] In some embodiments, a single side includes a grid side, an electro-hydrogen side, and a load side.

[0015] In some embodiments, the interactive capability index set includes a first interactive capability index set and a second interactive capability index set. The first interactive capability index set represents a set of first interactive capability indices, and the first interactive capability index represents the interactive capability index of the electro-hydrogen coupling system in the corresponding interactive scenario. The second interactive capability index set represents a set of second interactive indices, and the second interactive index represents the interactive capability index of the electro-hydrogen coupling system in all interactive scenarios. The interaction capability index set is obtained based on the basic interaction capability value set, the synergy effect correction term set, and the scenario weight set, including: The first set of interaction capability indices for the corresponding electro-hydrogen coupling system is obtained based on the basic interaction capability value set and the synergistic effect correction term set. The second interactive capability index set is obtained by weighted summation of the first interactive capability index set and the scenario weight set.

[0016] In some embodiments, the first interactivity index is: ; in, For the first The first electro-hydrogen coupling system in the... The first interactive capability index in each interactive scenario Number the electro-hydrogen coupling systems in the electro-hydrogen coupling system cluster. Number the interactive scenes. To minimize the function, To maximize the function, For the first The first electro-hydrogen coupling system in the... Basic interactive capability values ​​in each interactive scenario For the first The first electro-hydrogen coupling system in the... Synergy effect correction term in interactive scenarios.

[0017] Compared with the prior art, this application has the following advantages: 1. This application obtains an interaction capability index by using scenario membership, scenario sample time period sets, scenario evaluation index sets, and introducing multiple synergistic effect correction terms between one side on the basis of basic interaction capability values. This enables the quantitative, scenario-based, and interpretable evaluation of the grid interaction capability of electric-hydrogen coupling system clusters in multiple interactive scenarios. It can provide technical support for the configuration optimization of electric-hydrogen coupling system clusters participating in multiple interactive scenarios in new energy-rich areas.

[0018] 2. This application transforms the traditional fixed-threshold hard-classification of different interactive scenarios into continuous quantization recognition by using scene membership. This avoids the problem of simply dividing complex operating states into a single interactive scenario, and effectively preserves the features of multiple interactive scenarios alternating or overlapping within the same scheduling cycle. Compared to interactive scenario discrimination methods that rely solely on human experience or fixed thresholds, this approach quantifies the interactive scenario recognition results into membership values ​​of 0-1, providing a clear data foundation for the subsequent calculation of evaluation index values ​​for different interactive scenarios.

[0019] 3. This application calculates the evaluation indicators for each interactive scenario based on the scenario sample time period set, which can avoid the dilution of the evaluation results of each interactive scenario by non-scenario sample time periods, and make the evaluation results more accurately reflect the real support capability of the electric-hydrogen coupling system cluster under specific grid demand.

[0020] 4. This application constructs a set of scenario evaluation indicators to systematically characterize the multi-dimensional interaction capabilities of an electro-hydrogen coupling system cluster. This avoids the problem of one-sided evaluation results caused by using only a single indicator.

[0021] 5. This application introduces multiple synergistic effect correction terms between one side based on the basic interaction capability value. These synergistic effect correction terms reflect the degree of synergy between different one-sided responses. With this correction method, when any one-sided response is weak, the synergistic correction effect will decrease accordingly, thus avoiding the problem of a single advantageous indicator masking the overall synergistic shortcomings of the system, making the interaction capability index more consistent with the actual operating mechanism of the electro-hydrogen coupling system cluster. Attached Figure Description

[0022] Figure 1 This is a schematic diagram illustrating the principle of the multi-scenario power grid interaction capability evaluation method for the electro-hydrogen coupling system cluster in this application embodiment; Detailed Implementation To address the issues raised in the background section, existing methods for scene identification often rely on manual experience or fixed thresholds. For example, when renewable energy output exceeds a certain proportion, it is classified as a renewable energy surplus scenario; when the load exceeds a certain threshold, it is classified as a supply guarantee scenario; and when the utilization rate of transmission channels is high, it is classified as a transmission restriction scenario. While these methods are simple and intuitive, they struggle to handle the overlapping of multiple scenarios in actual power grid operation. In reality, a single scheduling period may simultaneously exhibit multiple characteristics, including high renewable energy output, restricted transmission channels, and increased net load fluctuations. Using a single hard threshold to classify it as a unique scenario can easily lead to misjudgment and further affect the statistical range of subsequent evaluation indicators and the accuracy of the evaluation results.

[0023] Regarding evaluation indicators, existing technologies typically use metrics such as renewable energy absorption rate, curtailment rate, hydrogen production efficiency, energy storage utilization rate, power supply reliability, and operating costs to evaluate the operational performance of electro-hydrogen coupling systems. While these indicators can reflect system performance from a certain perspective, most evaluation methods still rely on full-cycle statistics and fail to select corresponding sample periods based on specific scenarios. For example, combining curtailment reduction effects during periods of restricted power transmission with those during normal operation may dilute the true regulation capacity of the electro-hydrogen coupling system during critical restricted periods; similarly, uniformly evaluating supply guarantee scenarios and renewable energy surplus scenarios makes it difficult to distinguish the differences in the grid support roles of hydrogen storage release capacity and hydrogen production absorption capacity. Therefore, the existing evaluation indicator system suffers from insufficient scenario specificity and fails to accurately reflect the functional differences of electro-hydrogen coupling system clusters under different grid demands.

[0024] In terms of comprehensive evaluation methods, existing technologies often employ methods such as the Analytic Hierarchy Process (AHP), entropy weight method, fuzzy comprehensive evaluation, TOPSIS, grey relational analysis, and cloud models to comprehensively score multi-indicator evaluation objects. These methods can, to some extent, address issues such as different dimensions of multiple indicators, different importance of indicators, and comprehensive ranking of evaluation results. However, traditional comprehensive evaluation methods typically use fixed weights or static indicator systems, rarely considering the dynamic changes in the importance of indicators in different power grid interaction scenarios for the electric-hydrogen coupling system. For example, in scenarios where power transmission is constrained, the conversion and utilization rate of constrained power and the rate of reduction in curtailment should have higher weights; in scenarios where renewable energy consumption is coordinated, the renewable energy consumption rate, green hydrogen production rate, and fluctuation absorption capacity are more critical; and in scenarios where power supply is guaranteed, the supply satisfaction rate and energy supply reliability should be the focus of evaluation. If the same set of fixed weights is used for different scenarios, the evaluation results will be difficult to reflect the differences between the actual needs of the power grid and the functional positioning of the electric-hydrogen coupling system.

[0025] Furthermore, existing evaluation methods for electro-hydrogen coupling systems often separately statistically analyze indicators from the power side, hydrogen energy side, and load side, lacking a characterization of the synergistic relationship between electricity, hydrogen, and load. In reality, the grid interaction capability of an electro-hydrogen coupling system cluster depends not only on the performance of individual components but also on the coordination between grid regulation response, hydrogen production conversion, hydrogen storage regulation, hydrogen-electricity feedback, and flexible load response. If the electrolyzer has high hydrogen production efficiency but insufficient load response capability, or if the fuel cell has a certain output capacity but poor regulation and tracking accuracy, the overall system interaction capability may still be limited. Existing evaluation methods, which only weight and sum individual indicators, are prone to having a single advantageous indicator masking the system's synergistic shortcomings, making it difficult to accurately reveal the comprehensive support capability of the electro-hydrogen coupling system cluster when participating in grid interaction.

[0026] In summary, existing technologies still have the following shortcomings in evaluating the grid interaction capabilities of multi-scenario electric-hydrogen coupled system clusters: First, existing research mainly focuses on system optimization scheduling or capacity configuration, lacking specialized evaluation methods for grid interaction capabilities; second, existing scenario classifications mostly rely on fixed thresholds or manual experience, making it difficult to reflect the overlapping and continuous changes of multiple scenarios; third, existing indicators mostly adopt full-cycle statistical methods, lacking a scenario-specific evaluation mechanism based on scenario sample time periods; at the same time, existing weighting methods are mostly fixed weights, making it difficult to reflect the differences in the importance of core indicators under different scenarios; finally, existing evaluation methods do not adequately consider the synergistic relationship between grid response, hydrogen production conversion, and load response, making it difficult to accurately identify system capability shortcomings.

[0027] Therefore, there is an urgent need for a multi-scenario power grid interaction capability evaluation method and device for electric-hydrogen coupled system clusters. This device should be able to identify typical scenarios such as power transmission constraint regulation, new energy consumption coordination, and power supply support based on the operating parameters of the power grid side, electric-hydrogen side, and load side. It should construct an evaluation index system that combines general interaction capability indicators with scenario characteristic indicators, and calculate the scenario-specific interaction capability index and comprehensive interaction capability index through scenario adaptive combination weighting, cloud model evaluation, and electric-hydrogen-load coordinated correction. This will output the capability level, scenario adaptation status, and ranking results of the bottleneck indicators, providing technical support for the planning, configuration, operation optimization, and power grid dispatch auxiliary decision-making of electric-hydrogen coupled system clusters.

[0028] To clearly illustrate the technical features of this solution, the implementation methods of this application will be described in detail below with reference to the accompanying drawings and embodiments. This will allow for a full understanding and implementation of how this application uses technical means to solve technical problems and achieve corresponding technical effects. The embodiments of this application and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this application.

[0029] See Figure 1In a first aspect, this application proposes a method for evaluating the multi-scenario grid interaction capability of an electro-hydrogen coupling system cluster. The electro-hydrogen coupling system cluster includes at least one electro-hydrogen coupling system, i.e., the cluster includes one electro-hydrogen coupling system or at least two electro-hydrogen coupling systems. This method is applicable to evaluating the multi-scenario grid interaction capability of a single electro-hydrogen coupling system, and also applicable to evaluating the multi-scenario grid interaction capability of an electro-hydrogen coupling system cluster including at least two systems. The method includes: acquiring an evaluation index set, an evaluation level classification, and an operational dataset for each electro-hydrogen coupling system. The operational dataset represents the set of time-series operational data for each side within a scheduling cycle. Optionally, the operational dataset originates from the scheduling automation system, energy management system, new energy power prediction system, electrolyzer control system, hydrogen storage monitoring system, fuel cell control system, flexible load management platform, historical operational database, or scheduling simulation model output results of the electro-hydrogen coupling system cluster. By unifying the time scale, correcting outliers, and filling in missing values ​​for multi-source data, a unified and reliable data input is provided for subsequent calculations. The operational dataset of an electro-hydrogen coupling system can be represented as follows: ; in, For the first Operational dataset of an electro-hydrogen coupling system Number the electro-hydrogen coupling systems in the electro-hydrogen coupling system cluster. For the first The first electro-hydrogen coupling system in the... The scheduling period is the first The running dataset on the side, This refers to the sequence number of the scheduling period within the scheduling cycle. Numbering is done on one side only. This represents the total number of scheduling periods within the scheduling cycle.

[0030] In some embodiments, a single side includes a grid side, an electric-hydrogen side, and a load side. The operation dataset includes a grid-side operation dataset, an electric-hydrogen side operation dataset, and a load-side operation dataset. The grid-side operation dataset represents the set of time-series operation data of the grid side within the scheduling period. The electric-hydrogen side operation dataset represents the set of time-series operation data of the electric-hydrogen side within the scheduling period. The load-side operation dataset represents the set of time-series operation data of the load side within the scheduling period. Optionally: The power grid side operation dataset is as follows: ; in, For the first The first electro-hydrogen coupling system in the... Data set of grid-side operation for each scheduling period. For the first The first electro-hydrogen coupling system in the... The output power of new energy sources during each dispatch period For the first The first electro-hydrogen coupling system in the... Power grid load during each scheduling period For the first The first electro-hydrogen coupling system in the... The amount of conventional or other stable power available for power supply support during each scheduling period. For the first The first electro-hydrogen coupling system in the... The non-new energy rigid output power during the dispatch period, i.e., the first... The first electro-hydrogen coupling system in the... Non-new energy output that is difficult to reduce within a certain scheduling period due to reasons such as minimum technical output, heating constraints, safety constraints, or planning constraints. For the first The first electro-hydrogen coupling system in the... The actual power transmitted during each scheduling period For the first The first electro-hydrogen coupling system in the... Maximum allowable power of the external transmission channel during each scheduling period For the first The first electro-hydrogen coupling system in the... Maximum external support power for each scheduling period In the first The first scheduling period did not consider the first The power wasted when an electro-hydrogen coupling system participates in the interaction In the first Consideration of the first scheduling period The power wasted after the interaction of an electro-hydrogen coupling system For the power grid in the first The scheduling period is directed to the first The equivalent regulation power required by the electro-hydrogen coupling system cluster to which each electro-hydrogen coupling system is located.

[0031] The data set for the hydrogen-electricity side operation is as follows: ; in, For the first The first electro-hydrogen coupling system in the... Data set of the hydrogen-electricity side operation during each scheduling period. In the first The scheduling period is the first The operating power of the electrolyzer in the electro-hydrogen coupling system For the first The scheduling period is the first New energy power of an electro-hydrogen coupling system In the first The scheduling period is the first The power generation capacity of a fuel cell in an electro-hydrogen coupling system For the first The electro-hydrogen coupling system cluster where the [number] electro-hydrogen coupling system is located is in the [number]th [year]. The equivalent regulating power actually provided to the power grid during each scheduling period For the first The first electro-hydrogen coupling system in the... Hydrogen production during each scheduling period, For the first The first electro-hydrogen coupling system in the... Hydrogen storage capacity for each scheduling period, For the first The first electro-hydrogen coupling system in the... Maximum hydrogen storage capacity for each scheduling period; The load-side operational dataset is as follows: ; in, For the first The first electro-hydrogen coupling system in the... Load-side operational dataset for each scheduling period For the first The first electro-hydrogen coupling system in the... Flexible load demand response command power for each scheduling period For the first The first electro-hydrogen coupling system in the... The actual response power of flexible load during each scheduling period. For the first The first electro-hydrogen coupling system in the... Energy demand during each scheduling period For the first The first electro-hydrogen coupling system in the... Unsupplied energy during each scheduling period; After obtaining the above-mentioned operational dataset, time scale unification and outlier processing are performed on data from different sources. If the data sampling intervals of different electro-hydrogen coupling systems are different, they are converted into time-series data sequences under a unified scheduling period. For missing data, interpolation between adjacent time periods, historical averages of the same type of day, or scheduling plan values ​​can be used to fill in the gaps. For obviously abnormal data, corrections or removals can be made based on the rated capacity of the equipment, the upper and lower limits of operation, or the historical percentile range.

[0032] In summary, the first The operational dataset of an electro-hydrogen coupling system can be represented as follows: .

[0033] The scene membership set for each electro-hydrogen coupling system is obtained based on the interactive scene attributes and the running dataset; specifically, the interactive scenes are classified according to their attributes to obtain the scene category set. The scene membership set for each electro-hydrogen coupling system is obtained based on the scene category set and the running dataset.

[0034] Optionally, interactive scenarios can be categorized based on their attributes to obtain scenarios such as transmission restriction regulation, power supply support, and new energy consumption coordination. The scenario membership set includes the membership set of transmission restriction regulation scenarios, power supply support scenarios, and new energy consumption coordination scenarios.

[0035] The interactive scenario attributes of the restricted external transmission adjustment scenario, namely the restricted external transmission characteristics, are as follows: the output of new energy is relatively high, the output of non-new energy has a certain rigidity, the local load absorption capacity is limited, the external transmission channel reaches or approaches the upper limit of the transmission capacity, and the system has surplus power that cannot be completely absorbed by local consumption and external transmission channels.

[0036] The membership set for calculating the restricted outbound delivery adjustment scenario includes: The constrained surplus power set of the electro-hydrogen coupling system is calculated based on the operational dataset. This set represents the collection of constrained surplus power for each scheduling period within the scheduling cycle of the corresponding electro-hydrogen coupling system. The constrained surplus power is: ; in, For the first The first electro-hydrogen coupling system in the... Constrained surplus power during each scheduling period Indicates taking The maximum value among all terms.

[0037] The membership set of the restricted power supply regulation scenarios for the electro-hydrogen coupling system is calculated based on the constrained surplus power set and the operational dataset. This membership set represents the set of membership degrees for the restricted power supply regulation scenarios of the corresponding electro-hydrogen coupling system during each scheduling period within the scheduling cycle. The membership degrees for the restricted power supply regulation scenarios are: ; in, For the first The first electro-hydrogen coupling system in the... Membership degree of delivery restriction adjustment scenarios during a scheduling period Indicates taking The minimum value among all terms. It is a very small positive number, used to avoid the denominator being zero. Indicates the first The first electro-hydrogen coupling system in the... The rigid available output for each scheduling period, of which... , The larger the value, the higher the value. The first electro-hydrogen coupling system in the... The more pronounced the delivery restrictions during a given scheduling period, the better. When, it indicates the first The first electro-hydrogen coupling system in the... There is no significant confined surplus power in the scheduling period, which means that the first scheduling period has no significant confined surplus power. The first electro-hydrogen coupling system in the... There are no obvious restrictions on external transmission during the scheduling period.

[0038] The interactive scenario attributes, or power supply characteristics, of the power supply support scenarios are: insufficient output of new energy sources or high load demand, resulting in a power supply gap in the system, requiring the electric-hydrogen coupling system cluster to provide supplementary power support through hydrogen storage and release, fuel cell power generation, or hydrogen power generation units.

[0039] The membership set for calculating power supply support scenarios includes: The power supply gap set of the electro-hydrogen coupling system is calculated based on the operational dataset. This power supply gap set represents the set of power supply gaps for each scheduling period within the scheduling cycle of the corresponding electro-hydrogen coupling system. The power supply gap power is: ; in, For the first The first electro-hydrogen coupling system in the... Power shortage during each scheduling period; The membership set of power supply support scenarios for the electric-hydrogen coupling system is calculated based on the power supply gap power set and the operation data set. The membership set of power supply support scenarios represents the set of membership degrees of the power supply support scenarios for the corresponding electric-hydrogen coupling system in each scheduling period within the scheduling cycle. The membership degree of the power supply support scenarios is: ; in, For the first The first electro-hydrogen coupling system in the... The membership degree of power supply support scenarios for each scheduling period, among which... , The larger the value, the higher the value. The first electro-hydrogen coupling system in the... The more pronounced the power supply characteristics during the dispatching period, that is, the more pronounced the power supply characteristics during the dispatching period. The first electro-hydrogen coupling system in the... The higher the power supply shortage during a specific scheduling period, When, it indicates the first The first electro-hydrogen coupling system in the... There is no significant power shortage during the scheduling period, which means that the first scheduling period does not have a significant power shortage. The first electro-hydrogen coupling system in the... There are no obvious power supply guarantee characteristics during the scheduling period.

[0040] The interactive scenario attributes, namely the collaborative consumption characteristics, of the new energy consumption collaborative scenario are: the output of new energy is at a high level, the external transmission channels are not severely restricted, the system does not have a significant power supply gap, and new energy can be collaboratively consumed through local loads, external transmission channels, and the electric-hydrogen coupling system cluster.

[0041] The membership set for calculating the coordinated consumption of new energy sources includes: Based on the operational dataset, the set of relative renewable energy output levels and the set of effective renewable energy absorption levels for the electric-hydrogen coupling system are calculated. The set of relative renewable energy output levels represents the set of relative renewable energy output levels for each scheduling period within the scheduling cycle of the corresponding electric-hydrogen coupling system. The relative renewable energy output levels are: ; in, For the first The first electro-hydrogen coupling system in the... The relative output level of new energy sources during each dispatch period; The effective renewable energy absorption rate set represents the set of effective renewable energy absorption rates for each scheduling period within the scheduling cycle of the corresponding electric-hydrogen coupling system. The effective renewable energy absorption rate is: ; in, For the first The first electro-hydrogen coupling system in the... The effective absorption rate of new energy during each dispatch period; The membership set of new energy consumption coordination scenarios is obtained based on the set of relative output levels of new energy, the set of effective consumption levels of new energy, the membership set of external transmission restriction regulation scenarios, and the membership set of power supply guarantee support scenarios. The membership set of new energy consumption coordination scenarios represents the set of membership degrees of new energy consumption coordination scenarios for each scheduling period of the corresponding electric-hydrogen coupling system within the scheduling cycle. The membership degree of new energy consumption coordination scenarios is: ; in, For the first The first electro-hydrogen coupling system in the... When the relative output level and effective consumption degree of new energy are relatively high, and the membership degree of the external transmission restriction regulation scenario and the power supply support scenario are low, the electric-hydrogen coupling system is closer to the new energy consumption coordination scenario in the corresponding scheduling period.

[0042] By using scene membership, different interactive scenes are transformed from traditional fixed-threshold hard-classification to continuous quantization recognition. This avoids the problem of simply dividing complex operating states into single interactive scenes, and effectively preserves the characteristics of multiple interactive scenes alternating or overlapping within the same scheduling cycle. Compared to interactive scene discrimination methods that rely solely on human experience or fixed thresholds, this method quantifies the interactive scene recognition results into membership values ​​of 0-1, providing a clear data foundation for the subsequent calculation of evaluation index values ​​for different interactive scenes.

[0043] The scene sample time period set and scene weight set are obtained based on the scene membership set; The scenario sample time period set represents the set of scheduling time periods of the electric-hydrogen coupling system in the corresponding interactive scenario within the scheduling cycle; ; in, For the first The first electro-hydrogen coupling system in the... A collection of scheduling periods in various interactive scenarios. Number the interactive scenes. For the first The first electro-hydrogen coupling system in the... The first scheduling period Membership degree of each interactive scene For the first Category thresholds for each interactive scenario The category threshold is used to determine whether a given scheduling period belongs to a corresponding interaction scenario. To avoid relying solely on manual experience to set the threshold, this application can determine the category threshold based on historical operation samples and quantile statistics. .

[0044] Specifically, first obtain the first The operational dataset of the first electro-hydrogen coupling system within the historical scheduling cycle is used to obtain the first scenario membership degree calculation method. The membership sequence of each interactive scenario in the historical samples:

[0045] in, For the first The first electro-hydrogen coupling system corresponds to the [number]th [system] in the historical samples. The set of membership degrees of each interactive scene; For the first The first electro-hydrogen coupling system in the... The historical scheduling period belongs to the first Membership degree of each interactive scene.

[0046] Then, for the membership set Sort the values ​​in ascending order to get:

[0047] in, , … The membership set sorted by numerical value from smallest to largest The ordinal number of the membership degree; Let the first The quantile level of each interactive scenario is Then the category threshold for this scenario is:

[0048] in, It is a quantile function; Indicates rounding up; For the sorted position at the th The scene membership value of the position.

[0049] In one implementation, the quantile levels for scenarios with restricted power transmission, coordinated renewable energy consumption, and power supply support can be determined based on the frequency of occurrence and the degree of scheduling importance, respectively. For example, for scenarios with low occurrence frequency but high scheduling importance, the quantile level can be appropriately lowered. To avoid missed identification; for scenarios with high frequency of occurrence and the need to select typical samples, the quantile level can be appropriately increased. To enhance the representativeness of the sample.

[0050] When historical samples are insufficient or complete operational data is lacking, the category threshold... Initial values ​​can also be given based on scheduling procedures, expert experience, or statistical results of similar historical systems, and updated based on new samples during subsequent operation.

[0051] To address the issue that existing evaluation methods often employ unified statistics across the entire lifecycle, making it difficult to reflect the actual support capabilities under key interactive scenarios, this application calculates evaluation indicators for each interactive scenario based on a set of scenario sample time periods. In other words, the evaluation indicator values ​​in the second evaluation indicator set are calculated only within the corresponding scenario sample time periods. For example, the restricted power conversion utilization rate and curtailment reduction rate are calculated only within the scenario sample time period corresponding to the external transmission restricted regulation scenario; the new energy absorption rate, green hydrogen production rate, and fluctuation absorption capacity index are calculated only within the scenario sample time period corresponding to the new energy absorption coordination scenario; and the supply satisfaction rate and energy supply reliability are calculated only within the scenario sample time period corresponding to the power supply guarantee support scenario. This approach avoids the dilution of evaluation results for each interactive scenario by non-scenario sample time periods, enabling the evaluation results to more accurately reflect the true support capabilities of the electric-hydrogen coupling system cluster under specific grid demands.

[0052] The scene weight set is obtained based on the scene membership set, including: Based on the scene membership set, the cumulative scene intensity of each electro-hydrogen coupling system in each interactive scene within the scheduling period is calculated. The cumulative scene intensity is: ; in, For the first The first electro-hydrogen coupling system in the... The cumulative scene intensity in each interactive scenario The duration of a single scheduling period; The scene weights of the electro-hydrogen coupling system in the corresponding interaction scenarios within the scheduling cycle are obtained based on the cumulative scene intensity. The scene weights are a set of scene weights, and the scene weights are: ; in, For the first The first electro-hydrogen coupling system in the... Scene weights in each interactive scenario This represents the total number of interactive scenarios.

[0053] The larger the scene weight, the higher the value of the scene. The more frequently an electro-hydrogen coupling system exhibits characteristics of this type of interaction scenario within a scheduling period, the more dominant the scenario becomes. When it is necessary to determine the dominant scenario, the interaction scenario with the highest membership degree can be taken as the dominant interaction scenario for that scheduling period. Furthermore, when the first... When the time set of scene samples for the first electro-hydrogen coupling system in an interactive scenario is empty, it indicates that the first... No such interaction scenario occurred in the current scheduling cycle for the first electro-hydrogen coupling system. The scene weight of the electro-hydrogen coupling system in this interactive scenario is 0.

[0054] The evaluation index set is obtained by dividing the evaluation index set based on the evaluation index attributes. In some embodiments, the evaluation index set is obtained by dividing the evaluation index set based on the evaluation index attributes, including: obtaining a first evaluation index set and at least one second evaluation index set based on the evaluation index attributes, wherein the second evaluation index set corresponds one-to-one with the interactive scenario.

[0055] The first evaluation index set represents a set of evaluation indexes that are common to the electro-hydrogen coupling system in each interaction scenario. The evaluation indexes in the first evaluation index set are used to reflect the basic interaction capabilities that the electro-hydrogen coupling system cluster must have in different scenarios. The second evaluation index set represents a set of evaluation indicators specifically for the electro-hydrogen coupling system in the corresponding interactive scenarios. The evaluation indicators in the second evaluation index set are used to reflect the differentiated support capabilities of the electro-hydrogen coupling system cluster in different interactive scenarios. By integrating the first and second evaluation index sets, a scenario evaluation index set for the electro-hydrogen coupling system in the corresponding interactive scenario is obtained: ; in, For the first The first electro-hydrogen coupling system in the... A set of scene evaluation indicators for interactive scenarios, namely For the first The first electro-hydrogen coupling system in the... A set of evaluation metrics for interactive scenarios. For the first The first electro-hydrogen coupling system in the... The first set of evaluation indicators in each interactive scenario For the first The first electro-hydrogen coupling system in the... The second set of evaluation indicators for interactive scenarios.

[0056] By constructing a set of evaluation indicators to characterize the general interactive capabilities across multiple interactive scenarios (the first set of evaluation indicators) and a set of evaluation indicators to characterize the specific interactive capabilities of individual interactive scenarios (the second set of evaluation indicators), a systematic characterization of the multidimensional interactive capabilities of an electro-hydrogen coupling system cluster is achieved. This avoids the problem of one-sided evaluation results caused by using only a single indicator.

[0057] Optionally, when the interaction scenario is divided into three scenarios—external power transmission constraint adjustment, power supply support, and new energy consumption coordination—the number of second evaluation index sets is three. The first evaluation index set and the three second evaluation index sets are shown in Table 1. Table 1. First set of evaluation indicators and three sets of second evaluation indicators.

[0058] The evaluation indicators in Table 1 are all benefit-type evaluation indicators, that is, the larger the evaluation indicator value, the stronger the corresponding capability. If cost-type indicators such as operating cost, regulation cost, power curtailment loss or carbon emission intensity are introduced in other implementation methods, they will be standardized according to cost-type indicators.

[0059] The evaluation indicators in the first set of evaluation indicators are used to evaluate the basic energy conversion capability, load response capability, and regulation tracking capability of the electro-hydrogen coupling system cluster under different interaction scenarios. The evaluation indicators in the first set of evaluation indicators are not independent interaction scenarios, but rather basic evaluation indicators that can be calculated under all interaction scenarios. Within the first set of evaluation indicators: Hydrogen production efficiency is used to characterize the efficiency of converting electrical energy into hydrogen energy during the electrolysis process. A higher hydrogen production efficiency indicates a stronger ability of the electro-hydrogen coupling system cluster to produce hydrogen per unit of electrical energy. The hydrogen production efficiency is: ; in, For the first The first electro-hydrogen coupling system in the... Hydrogen production efficiency in interactive scenarios This is the lower heating value of hydrogen. Load response capability characterizes the degree to which flexible loads in an electro-hydrogen coupling system cluster respond to grid demand. A load response capability closer to 1 indicates a closer approximation between the actual and demanded power of the flexible loads, signifying a stronger load response capability; conversely, a load response capability closer to 0 indicates a greater deviation in the flexible load response. Load response capability is defined as follows: ; in, For the first The first electro-hydrogen coupling system in the... Load response capability in interactive scenarios; The regulation response satisfaction rate characterizes the degree to which the actual equivalent regulation power provided by the electro-hydrogen coupling system cluster meets the power demand for grid regulation. A higher regulation response satisfaction rate indicates that the actual regulation capacity provided by the electro-hydrogen coupling system cluster better meets the grid regulation demand. To avoid miscounting a response as effective when the actual response direction is opposite to the regulation demand direction, an effective regulation power with the same direction is first defined. The effective regulation power is: ; in, For the first The first electro-hydrogen coupling system in the... Effective regulation power for each scheduling period; Based on effective power regulation, the regulation response satisfaction rate is: ; in, For the first The first electro-hydrogen coupling system in the... Adjustment response satisfaction rate in interactive scenarios.

[0060] Regulation accuracy characterizes the deviation between the actual regulated power of the electro-hydrogen coupling system cluster and the power demand of the power grid. A regulation accuracy closer to 1 indicates a smaller tracking error between the actual regulated power and the power demand of the power grid; a regulation accuracy closer to 0 indicates a larger tracking deviation. Regulation accuracy is: ; in, For the first The first electro-hydrogen coupling system in the... Adjustment precision in each interactive scenario.

[0061] The evaluation focus of the transmission-constrained regulation scenario is whether the electro-hydrogen coupling system cluster can absorb the constrained surplus power and reduce the curtailment of wind and solar power when transmission channels are limited. A dedicated set of second evaluation indicators is provided for the transmission-constrained regulation scenario: The confined power conversion efficiency is used to characterize the proportion of confined surplus power absorbed and converted by the electrolysis hydrogen production process. A higher confined power conversion efficiency indicates a stronger ability of the electro-hydrogen coupling system cluster to absorb and convert confined surplus power. The confined power conversion efficiency is: ; in, For the first Limited power conversion efficiency of an electro-hydrogen coupling system For the first The set of scene sample time periods for an electro-hydrogen coupling system in the first interaction scenario, i.e. For the first A set of scenario time periods for an electro-hydrogen coupling system under a restricted external transmission regulation scenario.

[0062] The curtailment reduction rate characterizes the degree of improvement in wind and solar curtailment levels after the interaction of the electric-hydrogen coupling system cluster. A higher curtailment reduction rate indicates a more significant mitigation effect of the electric-hydrogen coupling system cluster on the curtailment problem. The curtailment reduction rate is: ; in, For the first The rate of power waste reduction in an electro-hydrogen coupling system.

[0063] The evaluation of power supply support scenarios focuses on whether the electro-hydrogen coupling system cluster can provide power support through hydrogen storage release, fuel cell power generation, or hydrogen power generation units when renewable energy output is insufficient or load demand is high. A dedicated set of secondary evaluation indicators is provided for power supply support scenarios: The supply satisfaction rate characterizes the extent to which an electro-hydrogen coupling system cluster compensates for power supply gaps. A higher supply satisfaction rate indicates a stronger ability of the electro-hydrogen coupling system cluster to compensate for power supply gaps. The supply satisfaction rate is: ; in, For the first The supply satisfaction rate of an individual electro-hydrogen coupling system For the first The scene sample time set of the electro-hydrogen coupling system in the second interaction scenario, that is For the first A sample time period of an electro-hydrogen coupling system in a power supply support scenario; Energy supply reliability characterizes the degree to which the energy demand of an electro-hydrogen coupling system cluster is met after interaction. The closer the energy supply reliability is to 1, the smaller the unsupplied energy and the higher the reliability. Energy supply reliability is: ; in, For the first Power supply reliability of the electro-hydrogen coupling system.

[0064] The evaluation focus of renewable energy consumption coordination scenarios lies in whether the electric-hydrogen coupling system cluster can coordinate with local loads and external transmission channels to improve renewable energy utilization, increase the proportion of green hydrogen production, and reduce the impact of renewable energy fluctuations on the power grid. A dedicated set of secondary evaluation indicators is provided for renewable energy consumption coordination scenarios: The renewable energy absorption rate is used to characterize the proportion of renewable energy electricity that is effectively utilized. A higher renewable energy absorption rate indicates a higher level of effective utilization of renewable energy. The renewable energy absorption rate is: ; in, For the first The renewable energy absorption rate of an electro-hydrogen coupling system For the first The scene sample time set of the electro-hydrogen coupling system in the third interaction scenario, that is For the first A set of scenario samples for an electro-hydrogen coupling system in a new energy consumption and synergistic application scenario; Green hydrogen production rate is used to characterize the proportion of hydrogen produced from renewable energy sources to the total hydrogen production. A higher green hydrogen production rate indicates a higher proportion of hydrogen production generated from renewable energy sources within the electro-hydrogen coupling system cluster. First, calculate the amount of hydrogen produced from renewable energy sources: ; in, For the first The first electro-hydrogen coupling system in the... The amount of hydrogen produced from new energy power during each scheduling period; Based on this, the green hydrogen production rate is calculated as follows: ; in, For the first Green hydrogen production rate of an electro-hydrogen coupling system; The fluctuation absorption capacity index is used to characterize the ability of an electric-hydrogen coupling system cluster to mitigate fluctuations in new energy sources and system net power. A higher fluctuation absorption capacity index indicates a stronger ability of the electric-hydrogen coupling system cluster to absorb fluctuations in either new energy sources or system net power. First, calculate the grid-connected net power before the electric-hydrogen coupling system cluster participates: ; in, For the first time to participate in the electro-hydrogen coupling system cluster The first electro-hydrogen coupling system in the... Net grid-connected power during each scheduling period; Next, calculate the net grid-connected power after the participation of the electro-hydrogen coupling system cluster: ; in, For the participation of the electro-hydrogen coupling system cluster after the first The first electro-hydrogen coupling system in the... Net grid-connected power during each scheduling period; Finally, calculate the fluctuation absorption capacity index: ; in, For the first The fluctuation absorption capacity index of an electro-hydrogen coupling system.

[0065] A first value set is obtained based on the running dataset, the scene sample time period set, and the scene evaluation index set; in some embodiments, the first value set represents the value set of evaluation indexes in the interactive scene calculated by the physical modeling method, the first value represents the value of the evaluation index in the interactive scene calculated by the physical modeling method, and the first value set is a set of first values; Optionally, to facilitate a one-to-one correspondence between evaluation index values ​​and weights, the first value set is in matrix form; the first value vector corresponding to each electro-hydrogen coupling system is: ; in, For the first The first electro-hydrogen coupling system in the... The first value vector in each interactive scenario For the first The first electro-hydrogen coupling system in the... The first value of the first evaluation indicator in each interactive scenario. For the first The first electro-hydrogen coupling system in the... The first value of the second evaluation metric in each interactive scenario. For the first The first electro-hydrogen coupling system in the... In the first interactive scenario The first value of each evaluation indicator For the first The total number of evaluation indicators in each interactive scenario; The first value set corresponding to the electro-hydrogen coupling system cluster is: ; in, For the electro-hydrogen coupling system cluster in the first The first-value matrix in each interactive scenario For the first Evaluation indicator numbers for each interactive scenario; This first value matrix serves as the input for subsequent calculations. If a certain evaluation indicator cannot be calculated due to missing data, it can be supplemented using the historical average of the same scenario, the average of similar evaluation objects, or the expert-set value, and the data source of that evaluation indicator should be marked in the evaluation results.

[0066] The evaluation index value set is obtained based on the first value set and the evaluation level classification, including: The first set of values ​​is standardized to obtain the second set of values. The second set of values ​​represents the collection of second values, and the second values ​​represent the standardized values ​​obtained by processing the first set of values ​​using the standardization method. Since different evaluation indicators have different dimensions and numerical ranges, directly performing weighted calculations would cause the evaluation results to be affected by the dimensions. Therefore, the values ​​in the first set of values ​​are first standardized.

[0067] Optionally, for evaluation indicators where a larger value indicates stronger interactivity, such as benefit-type evaluation indicators, the following standardization method shall be adopted: ; For evaluation indicators where smaller values ​​indicate stronger interactivity, such as cost-based evaluation indicators, the following standardization method is adopted: ; in, For the first The first electro-hydrogen coupling system in the... The first interactive scenario Standardized values ​​of each evaluation indicator; Similarly, the standardized second value set can be represented in matrix form as follows: ; in, For the electro-hydrogen coupling system cluster in the first The second-value matrix in an interactive scenario; Based on the evaluation level classification, a set of level scores and a set of cloud model parameters for the evaluation indicators are obtained. The evaluation level classification represents the correspondence between the evaluation level and the range of interaction capability index values. The evaluation level classification is as follows: ; in, The total number of evaluation levels, As the evaluation level number, For the first The lower limit of the range for each evaluation level. For the first The upper limit of the evaluation level range. For example, if interaction ability is divided into five evaluation levels, the evaluation level division is as follows: the first evaluation level represents weak interaction ability, and the corresponding interaction ability index value range is... The second evaluation level indicates weaker interaction ability, and the corresponding interaction ability index value range is [range missing]. The third evaluation level represents general interaction ability, and the corresponding interaction ability index value range is: The fourth evaluation level represents strong interactivity, and the corresponding interactivity index value range is [range missing]. The fifth evaluation level represents strong interactivity, and the corresponding interactivity index value range is: .

[0068] In some embodiments, the cloud model parameter set represents a set of cloud model parameters, including a first cloud model parameter and a second cloud model parameter. The first cloud model parameter represents the cloud model expectation of the evaluation level corresponding to the evaluation index in the corresponding interactive scenario, and the second cloud model parameter represents the cloud model entropy of the evaluation level corresponding to the evaluation index in the corresponding interactive scenario.

[0069] The parameters for the first cloud model are: ; in, For the first In the first interactive scenario The corresponding evaluation index is the first The expected value of a cloud model with a rating level; The parameters for the second cloud model are: ; in, For the first In the first interactive scenario The corresponding evaluation index is the first Cloud model entropy at each rating level This refers to the cloud model entropy adjustment coefficient. In practical applications, the cloud model entropy adjustment coefficient... Adjustments can be made based on historical sample distribution, expert experience, or the degree of ambiguity in the evaluation level boundaries; typically, a value of 100% is used. Optionally, when This corresponds to the Three Sigma principle and is a commonly used value, which allows the evaluation level range to approximately correspond to the main coverage area of ​​the cloud droplet distribution.

[0070] The evaluation level membership degree of each evaluation indicator is obtained based on the cloud model parameter set and the second value set; the evaluation level membership degree is: ; in, For the first The first electro-hydrogen coupling system in the... The first interactive scenario The evaluation index is for the first The membership degree of each evaluation level; The evaluation index value set is obtained based on the grade score set and the membership degree of the evaluation grade. The grade score set represents the set of grade scores for each evaluation grade, and the grade scores are: ; in, For the first The rating score for each evaluation level. The adjustment coefficient is the representative value of the grade, which satisfies... Optionally, take here , indicating that the midpoint of the evaluation level range is taken; In some embodiments, the evaluation index value set represents the set of cloud model evaluation values ​​for the evaluation indexes in the corresponding interactive scenario. The cloud model evaluation values ​​are: ; in, For the first The first electro-hydrogen coupling system in the... The first interactive scenario The cloud model evaluation value of each evaluation indicator. The higher the value of the cloud model evaluation, the better the performance of the corresponding evaluation indicator.

[0071] By introducing a cloud model evaluation method, standardized evaluation index values ​​are transformed into cloud model evaluation values ​​for single evaluation indicators, thus addressing the issue of fuzzy boundaries between interactive capability levels. Based on the classification of evaluation levels, the membership degree of each evaluation index to different evaluation levels is calculated using cloud model expectation and entropy parameters. Compared to direct linear weighted scoring, this method reflects the transitional relationship between evaluation levels, ensuring that the evaluation object near the evaluation level boundary—the electro-hydrogen coupling system—does not experience abrupt level determinations due to minor numerical fluctuations. This improves the adaptability of the evaluation results to complex operating states and uncertain operating data.

[0072] The index weight set is obtained based on the first value set; specifically: Using the Analytic Hierarchy Process (AHP), a first weight set is obtained based on the scenario evaluation index set. This first weight set represents the set of first weights, which in turn represent the subjective weights of the evaluation indicators in the corresponding interactive scenario. These subjective weights reflect expert experience, scheduling objectives, and scenario operational requirements. Specifically: Construct an evaluation index judgment matrix based on the scenario evaluation index set: ; in, For the first Evaluation index judgment matrix for each interactive scenario For the first The first interactive scenario The evaluation index is relative to the first The importance scale of each evaluation indicator For the first Evaluation indicator numbers for each interactive scenario; The evaluation index judgment matrix satisfies: ; in, For the first The first interactive scenario The evaluation index is relative to the first The importance scale of each evaluation indicator For the first The first interactive scenario Each evaluation indicator is a scale of importance relative to itself. The geometric mean of each row element in the evaluation index judgment matrix is ​​calculated based on the following: ; in, The evaluation index judgment matrix is ​​the first one. The first interactive scenario The geometric mean of the row elements corresponding to each evaluation indicator; The first weight set is obtained by calculating the geometric mean, and the first weight is: ; in, For the first The first interactive scenario The first weight corresponding to each evaluation indicator.

[0073] The first weight set can be represented in matrix form as follows: ; in, For the first The first weight set in each interactive scenario; To ensure the rationality of the evaluation index judgment matrix, a consistency check is performed on the evaluation index judgment matrix. First, the approximate value of the largest eigenvalue of the evaluation index judgment matrix is ​​calculated for each interaction scenario: ; in, For the first Approximate value of the largest eigenvalue of the evaluation index judgment matrix in an interactive scenario; The consistency index of the evaluation matrix is ​​calculated based on the approximate value of the largest eigenvalue. ; in, For the first Consistency index of evaluation index judgment matrix in interactive scenarios; Calculate the consistency ratio based on the consistency index: ; in, For the first Consistency ratio of evaluation index judgment matrix in each interactive scenario The order of the matrix used to evaluate the criteria The corresponding average random consistency index, It is the order of the matrix. The function can be obtained by looking up a table, each It corresponds to a unique value.

[0074] when If the evaluation index judgment matrix meets the consistency requirement, then the importance scale in the evaluation index judgment matrix is ​​considered to meet the consistency requirement; otherwise, the importance scale in the evaluation index judgment matrix is ​​adjusted until the consistency requirement is met.

[0075] The entropy weight method is adopted to obtain a second weight set based on the second value set. The second weight set represents the set of second weights, and the second weights represent the objective weights of the evaluation indicators in the corresponding interaction scenario. The objective weights are used to reflect the degree of difference and information contribution of each evaluation indicator among the evaluation objects, i.e., the electro-hydrogen coupling system. Specifically: The percentage of each evaluation index for each electro-hydrogen coupling system in the corresponding interactive scenario is calculated based on the second value set: ; in, For the first The first electro-hydrogen coupling system in the... The first interactive scenario The percentage of scenarios for each evaluation indicator; Based on the scene proportion, the information entropy of each evaluation indicator in the corresponding interactive scene is calculated. A higher information entropy value indicates a smaller difference in the evaluation indicator across different electro-hydrogen coupling systems, and a weaker distinguishing ability; conversely, a lower information entropy value indicates a larger difference in the evaluation indicator across different electro-hydrogen coupling systems, and a stronger distinguishing ability. The information entropy is: ; in, For the first In the first interactive scenario Information entropy of each evaluation indicator; The difference coefficient of each evaluation index in the corresponding interaction scenario is calculated based on information entropy: ; in, For the first In the first interactive scenario The coefficient of difference among the evaluation indicators; The second weight is calculated based on the difference coefficient: ; in, For the first The first interactive scenario The second weight corresponding to each evaluation indicator; The third weight set is obtained based on the first and second weight sets. The third weight set represents the set of third weights, and is obtained by weighted summation of the first weights and their corresponding second weights. The third weights are: ; in, For the first The first interactive scenario The third weight corresponding to each evaluation indicator For the first The subjective weighting ratio in each interactive scenario satisfies Subjective weighting ratio The weighting can be determined based on the attributes of the interactive scenario. When the interactive scenario places greater emphasis on power grid security and dispatch objectives, the proportion of subjective weighting can be increased. When interactive scenarios place greater emphasis on differences in actual operational data, the weighting of subjective factors can be reduced. ; Obtain the scene correction coefficient set, which represents the set of scene correction coefficients for evaluation indicators in the corresponding interactive scene. The importance of evaluation indicators varies across different interactive scenes; for example, in each interactive scene, scene-specific evaluation indicators are given more attention than general evaluation indicators. Obtain the indicator weight set based on the third weight set and the scene correction coefficient set, which represents the set of weight values ​​for evaluation indicators in the corresponding interactive scene. The scene correction coefficients satisfy: When the The evaluation index is the first one. When considering core metrics in interactive scenarios , For the first The first interactive scenario The scenario correction coefficients corresponding to each evaluation indicator; When the The evaluation index is the first one. When considering general metrics in an interactive scenario, ; When the The evaluation index is the first one. When weak indicators are present in an interactive scenario ; Optionally, in scenarios where power transmission is constrained, the correction coefficients for the constrained power conversion utilization rate and the curtailment reduction rate range from 1.1 to 1.5; in scenarios where renewable energy consumption is coordinated, the correction coefficients for the renewable energy consumption rate, green hydrogen production rate, and fluctuation absorption capacity index range from 1.1 to 1.5; and in scenarios where power supply is guaranteed, the correction coefficients for the supply satisfaction rate and energy supply reliability range from 1.1 to 1.5. In each interactive scenario, the correction coefficients for the general evaluation indicators range from 0.8 to 1.2.

[0076] The weight values ​​of the evaluation indicators in the corresponding interactive scenarios are: ; in, For the first The first interactive scenario The weight values ​​of each evaluation indicator.

[0077] After correction by the scenario correction coefficient, the evaluation indicators that serve as core indicators in different interaction scenarios receive higher weight values, thus making the evaluation results more consistent with the operational requirements of the corresponding interaction scenarios. In addition, when the difference of a certain evaluation indicator is small in all evaluation objects, i.e., the electro-hydrogen coupling system, causing the objective weight of the evaluation indicator to be close to zero, a lower limit of the weight value of the final evaluation indicator can be set to avoid important evaluation indicators being completely ignored.

[0078] To address the issue that fixed weights in existing comprehensive evaluation methods are difficult to adapt to different interactive scenarios, this application obtains indicator weights by combining scenario correction coefficients with subjective and objective weights. In this way, the evaluation weights are no longer static fixed values, but can be adjusted according to the functional requirements of the interactive scenario, thereby improving the relevance and rationality of the evaluation results under different interactive scenarios.

[0079] The set of indicator weights is represented in vector form as follows: ; in, For the first A set of indicator weights for each interactive scenario For the first The weight value of the first evaluation indicator in each interactive scenario. For the first The weight value of the second evaluation indicator in each interactive scenario. For the first In the first interactive scenario The weight values ​​of each evaluation indicator; And the set of indicator weights satisfies: .

[0080] The basic interaction capability value set and the synergistic effect correction term set are obtained based on the evaluation index value set and the index weight set, respectively. The basic interaction capability value set is obtained by weighted summation of the evaluation index value set and the index weight set. This value set characterizes the set of basic interaction capabilities of the electro-hydrogen coupling system in each interaction scenario. The basic interaction capabilities are: ; in, For the first The first electro-hydrogen coupling system in the... Basic interactive capabilities in various interactive scenarios.

[0081] The grid interaction capability of an electro-hydrogen coupling system cluster depends not only on the evaluation index level of each side but also on the degree of coordination between the sides. When each side includes the grid side, the electro-hydrogen side, and the load side, if the hydrogen production efficiency is high, the flexible load response is good, and the electro-hydrogen coupling system can effectively track the grid regulation demand, it indicates that the electro-hydrogen coupling system has a strong electro-hydrogen-load coordinated interaction capability. Therefore, it is necessary to construct a coordination effect correction term between the sides to correct the value of the basic interaction capability. In some embodiments, the coordination effect correction term set is obtained based on the evaluation index value set and the index weight set, including: Based on the evaluation index value set, the response evaluation value set of each electro-hydrogen coupling system in each single side participating in the interaction is obtained. The response evaluation value set represents the set of response evaluation values ​​of the electro-hydrogen coupling system in the corresponding single side under the corresponding interaction scenario. The response evaluation value represents the arithmetic mean of the cloud model evaluation values ​​of the evaluation indexes related to the response of the corresponding single side in the first evaluation index set. Optionally, the response evaluation values ​​include grid-side response evaluation values, hydrogen-electricity side response evaluation values, and load-side response evaluation values. The grid-side response evaluation value represents the arithmetic mean of the cloud model evaluation values ​​of the evaluation indicators related to the grid-side response in the first evaluation index set, wherein the regulation response satisfaction rate and regulation accuracy are evaluation indicators related to the grid-side response in the first evaluation index set. The hydrogen-electricity side response evaluation value represents the arithmetic mean of the cloud model evaluation values ​​of the evaluation indicators related to the hydrogen-electricity side response in the first evaluation index set, wherein hydrogen production efficiency is an evaluation indicator related to the hydrogen-electricity side response in the first evaluation index set. The load-side response evaluation value represents the arithmetic mean of the cloud model evaluation values ​​of the evaluation indicators related to the load-side response in the first evaluation index set, wherein load response capability is an evaluation indicator related to the load-side response in the first evaluation index set.

[0082] The grid-side response evaluation value is: ; in, For the first The first electro-hydrogen coupling system in the... Evaluation values ​​of power grid-side response in interactive scenarios For the first The first electro-hydrogen coupling system in the... The cloud model evaluation value of the adjustment response satisfaction rate in an interactive scenario. For the first The first electro-hydrogen coupling system in the... Evaluation value of cloud model for adjusting accuracy in interactive scenarios; The evaluation value of the hydrogen-electric side response is: ; in, For the first The first electro-hydrogen coupling system in the... Evaluation values ​​of the hydrogen-electric response in interactive scenarios For the first The first electro-hydrogen coupling system in the... The cloud model evaluation value of hydrogen production efficiency in an interactive scenario; The load-side response evaluation value is: ; in, For the first The first electro-hydrogen coupling system in the... Load-side response evaluation values ​​in interactive scenarios For the first The first electro-hydrogen coupling system in the... The cloud model evaluation value of load response capability in interactive scenarios.

[0083] Using the geometric mean method, the basic cooperative itemset of the corresponding electro-hydrogen coupling system is obtained based on the response evaluation value set of each side. The basic cooperative itemset represents the set of basic cooperative terms of the electro-hydrogen coupling system in each interaction scenario. When the response of any one side is weak, the basic cooperative terms are suppressed, thus reflecting the impact of the cooperative shortcomings among multiple sides on the overall interaction capability. The basic cooperative terms are: ; in, For the first The first electro-hydrogen coupling system in the... Basic collaborative items between one side in an interactive scenario; The scenario characteristic capability value set of the corresponding electro-hydrogen coupling system is obtained based on the evaluation index value set and the index weight set; specifically: Based on the evaluation index value set, the scenario feature index set for each interactive scenario is obtained. The scenario feature index set represents the set of evaluation indicators in the second evaluation index set under the corresponding interactive scenario. For the external transmission restriction regulation scenario, the scenario feature capability value set includes the restricted power conversion utilization rate and the curtailment reduction rate. For the power supply guarantee support scenario, the scenario feature capability value set includes the supply guarantee satisfaction rate and the energy supply reliability. For the new energy consumption coordination scenario, the scenario feature capability value set includes the new energy consumption rate, the green hydrogen production rate, and the fluctuation absorption capacity index.

[0084] Based on the scene feature capability value set and the index weight set, the scene feature capability value set of the corresponding electro-hydrogen coupling system is obtained. The scene feature capability value set represents the set of scene feature capability values ​​of the electro-hydrogen coupling system in each interaction scenario. The scene feature capability values ​​are: ; in, For the first The first electro-hydrogen coupling system in the... Scene feature capability values ​​in each interactive scenario. For the first A set of scene feature indicators for interactive scenarios.

[0085] Based on the basic synergistic itemset and the scenario feature capability value set, the corresponding synergistic effect correction itemset of the electro-hydrogen coupling system is obtained. The synergistic effect correction itemset represents the set of synergistic effect correction items for the electro-hydrogen coupling system in each interaction scenario. The synergistic effect correction itemset is as follows: ; in, For the first The first electro-hydrogen coupling system in the... The synergistic effect correction term between one side in an interactive scenario For the first The collaborative correction coefficient in each interactive scenario satisfies Cooperative correction coefficient The adjustment can be made according to the importance of the scenario, historical operating data or expert experience. Optionally, in the embodiments of this application, the collaborative correction coefficient is 0.10 in the scenario of restricted power transmission, 0.12 in the scenario of power supply guarantee and support, and 0.15 in the scenario of coordinated new energy consumption.

[0086] To address the issue that existing evaluation methods rarely consider the synergistic relationship between responses from different sides, this application introduces multiple synergistic effect correction terms based on the basic interaction capability value. These synergistic effect correction terms reflect the degree of synergy between different one-sided responses. With this correction method, when any one-sided response is weak, the synergistic correction effect will decrease accordingly, thus avoiding the problem of a single superior indicator masking the overall system's synergistic shortcomings, and making the interaction capability index more consistent with the actual operating mechanism of the electro-hydrogen coupling system cluster.

[0087] An interaction capability index set is obtained based on the basic interaction capability value set, the synergy effect correction term set, and the scenario weight set; the grid interaction capability of each electro-hydrogen coupling system is evaluated based on the interaction capability index set.

[0088] In some embodiments, the interactive capability index set includes a first interactive capability index set and a second interactive capability index set. The first interactive capability index set represents a set of first interactive capability indices, and the first interactive capability index represents the interactive capability index of the electro-hydrogen coupling system in the corresponding interactive scenario. The second interactive capability index set represents a set of second interactive indices, and the second interactive index represents the interactive capability index of the electro-hydrogen coupling system in all interactive scenarios. The interaction capability index set is obtained based on the basic interaction capability value set, the synergy effect correction term set, and the scenario weight set, including: The first interaction capability index set of the corresponding electro-hydrogen coupling system is obtained based on the basic interaction capability value set and the synergistic effect correction term set; in some embodiments, the first interaction capability index is: ; in, For the first The first electro-hydrogen coupling system in the... The first interactive capability index in each interactive scenario , The larger the value, the stronger the interaction capability between the corresponding electro-hydrogen coupling system and the power grid in the corresponding interaction scenario. Number the interactive scenes. To minimize the function, To maximize the function, For the first The first electro-hydrogen coupling system in the... Basic interactive capability values ​​in each interactive scenario For the first The first electro-hydrogen coupling system in the... A synergy adjustment term is used in interactive scenarios. This term moderately adjusts the basic interaction capability value, rather than replacing it. Through this synergy adjustment term, the coupling relationship between each side can be reflected, preventing high-level indicators on one side from masking insufficient overall system synergy.

[0089] The interaction capability of the electro-hydrogen coupling system in a single interaction scenario is evaluated based on the evaluation level classification and the first interaction capability index, as shown in Table 2.

[0090] Table 2 Evaluation of the Interaction Capability of the Electro-Hydrogen Coupling System in a Single Interaction Scenario

[0091] The second set of interactive capability indices is obtained by weighted summation of the first set of interactive capability indices and the scene weight set. The second set of interactive capability indices is: ; in, For the first The second interaction capability index of an electro-hydrogen coupling system The larger the value, the stronger the comprehensive interaction capability of the corresponding electro-hydrogen coupling system across multiple interactive scenarios throughout the entire scheduling cycle.

[0092] The interaction capability of the electro-hydrogen coupling system in all interaction scenarios is evaluated based on the evaluation level classification and the second interaction capability index, as shown in Table 3.

[0093] Table 3 Evaluation of the Interaction Capabilities of the Electro-Hydrogen Coupling System in All Interaction Scenarios

[0094] The final interactive capability index output by this application includes a first interactive capability index that corresponds one-to-one with the interactive scenario and is used to characterize the interactive capability of the electro-hydrogen coupling system in the corresponding interactive scenario, and a second interactive capability index that characterizes the interactive capability of the electro-hydrogen coupling system in all interactive scenarios. It can not only give the interactive capability score and the corresponding interactive capability index in a single interactive scenario and in all interactive scenarios, but also clarify the capability level and adaptation status of the electro-hydrogen coupling system cluster in different power grid interactive scenarios, providing a quantitative basis for the power grid dispatching side to judge the adaptability of the electro-hydrogen coupling system to different interactive scenarios.

[0095] In some embodiments, the method for evaluating the multi-scenario grid interaction capability of an electro-hydrogen coupling system cluster further includes obtaining the weak indicators of the electro-hydrogen coupling system in the corresponding scenario based on the evaluation index value set and the index weight set. Specifically: Based on the evaluation index value set and index weight set, the shortcoming contribution set of each electro-hydrogen coupling system in each interaction scenario is obtained. The shortcoming contribution set is the set of shortcoming contributions of the evaluation index of the electro-hydrogen coupling system in the corresponding interaction scenario; the shortcoming contribution is: ; in, For the first The first electro-hydrogen coupling system in the... In the first interactive scenario The contribution of each evaluation indicator to its shortcomings. For the first Evaluation index numbers for each interactive scenario; Obtain the threshold set of the short-board contribution of each electro-hydrogen coupling system. The threshold set of the short-board contribution is the set of the thresholds of the short-board contribution of the electro-hydrogen coupling system in the corresponding interaction scenario. Will The corresponding evaluation index is used as the first The first electro-hydrogen coupling system in the... Weak indicators in interactive scenarios For the first The first electro-hydrogen coupling system in the... Threshold for the contribution of shortcomings in each interactive scenario.

[0096] By calculating the contribution of bottlenecks, key evaluation indicators affecting the improvement of interactive capabilities in corresponding interactive scenarios can be identified. For example, when the bottleneck contribution of the power conversion utilization rate or curtailment reduction rate is large in the scenario of restricted power transmission, it indicates that the system's ability to absorb restricted surplus power is insufficient. When the bottleneck contribution of the green hydrogen production rate or fluctuation absorption capacity index is large in the scenario of coordinated new energy consumption, it indicates that the system's level of new energy hydrogen production utilization or fluctuation smoothing ability is insufficient. When the bottleneck contribution of the power supply guarantee rate or energy supply reliability is large in the scenario of power supply guarantee support, it indicates that the system's hydrogen storage release or hydrogen power generation support capacity is insufficient. The identification results can directly serve the adjustment of electrolyzer capacity configuration, hydrogen storage capacity optimization, fuel cell configuration, and flexible load response strategies in the electro-hydrogen coupling system.

[0097] Secondly, this application proposes an evaluation device for the multi-scenario grid interaction capability of an electric-hydrogen coupling system cluster, which is used to implement a method for evaluating the multi-scenario grid interaction capability of an electric-hydrogen coupling system cluster. The evaluation device for the multi-scenario grid interaction capability of an electric-hydrogen coupling system cluster includes a data acquisition module, a scenario identification module, an index calculation module, an index weight calculation module, and an interaction capability evaluation module. The data acquisition module is used to acquire the scene correction coefficient set, evaluation index set, evaluation level classification, and the operation dataset of each electro-hydrogen coupling system. The scene recognition module is used to obtain the scene membership set of each electro-hydrogen coupling system based on the interactive scene attributes and the running dataset, and to obtain the scene sample time period set and scene weight set based on the scene membership set. The indicator calculation module is used to obtain the scene evaluation indicator set by dividing the evaluation indicator set based on the evaluation indicator attributes, to obtain the first value set based on the running dataset, the scene sample time period set and the scene evaluation indicator set, and to obtain the evaluation indicator value set based on the first value set and the evaluation level division. The indicator weight calculation module is used to obtain the indicator weight set based on the first value set and the scenario correction coefficient set; The interaction capability evaluation module is used to obtain the basic interaction capability value set and the synergistic effect correction term set based on the evaluation index value set and the index weight set, respectively; to obtain the interaction capability index set based on the basic interaction capability value set, the synergistic effect correction term set and the scenario weight set; and to evaluate the grid interaction capability of each electric-hydrogen coupling system based on the interaction capability index set.

[0098] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus (systems), or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0099] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce an instruction that executes via the processor of the computer or other programmable data processing apparatus to create an instruction for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] Finally, it should be noted that the above content is only used to illustrate the technical solution of this application, and is not intended to limit the scope of protection of this application. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of this application shall not depart from the substance and scope of the technical solution of this application.

Claims

1. A method for evaluating the multi-scenario grid interaction capability of an electro-hydrogen coupling system cluster, characterized in that, include: The evaluation index set, evaluation level classification, and operational dataset for each electro-hydrogen coupling system were obtained respectively. Based on the interactive scenario attributes and the running dataset, obtain the scenario membership set for each of the electro-hydrogen coupling systems; Based on the scene membership set, obtain the scene sample time period set and the scene weight set respectively; The scenario evaluation index set is obtained by dividing the evaluation index set based on the evaluation index attributes; A first value set is obtained based on the running dataset, the scene sample time period set, and the scene evaluation index set; an evaluation index value set is obtained based on the first value set and the evaluation level division; and an index weight set is obtained based on the first value set. Based on the evaluation index value set and index weight set, the basic interaction capability value set and the synergistic effect correction term set are obtained respectively; The interaction capability index set is obtained based on the basic interaction capability value set, the synergy effect correction term set, and the scenario weight set; The grid interaction capability of each electro-hydrogen coupling system is evaluated based on the set of interaction capability indices.

2. The method for evaluating the multi-scenario grid interaction capability of an electro-hydrogen coupling system cluster according to claim 1, characterized in that, The evaluation index set is divided based on the evaluation index attributes to obtain a scenario evaluation index set, including: dividing the evaluation index set based on the evaluation index attributes to obtain a first evaluation index set and at least one second evaluation index set, wherein the second evaluation index set corresponds one-to-one with the interactive scenario, the first evaluation index set represents a set of evaluation indicators that are common to the electro-hydrogen coupling system in each interactive scenario, and the second evaluation index set represents a set of evaluation indicators that are specific to the electro-hydrogen coupling system in the corresponding interactive scenario. By integrating the first evaluation index set and the second evaluation index set, a scenario evaluation index set for the electro-hydrogen coupling system in the corresponding interactive scenario is obtained.

3. The method for evaluating the multi-scenario grid interaction capability of an electro-hydrogen coupling system cluster according to claim 2, characterized in that, The first value set represents the value set of evaluation indicators in the interactive scenario obtained by physical modeling methods, and the evaluation level division represents the correspondence between the evaluation level and the range of interactive ability index values. The evaluation index value set is obtained based on the first value set and the evaluation level classification, including: The first value set is standardized to obtain the second value set; Based on the evaluation level classification, a set of level scores and a set of cloud model parameters for the evaluation indicators for the evaluation level are obtained. The evaluation level membership degree of each evaluation index is obtained based on the cloud model parameter set and the second value set; The evaluation index value set is obtained based on the grade score set and the evaluation grade membership degree.

4. The method for evaluating the multi-scenario grid interaction capability of an electro-hydrogen coupling system cluster according to claim 3, characterized in that, The cloud model parameter set represents a collection of cloud model parameters, which include a first cloud model parameter and a second cloud model parameter. The first cloud model parameter represents the cloud model expectation of the evaluation level corresponding to the evaluation index in the corresponding interactive scenario, and the second cloud model parameter represents the cloud model entropy of the evaluation level corresponding to the evaluation index in the corresponding interactive scenario.

5. The method for evaluating the multi-scenario grid interaction capability of an electro-hydrogen coupling system cluster according to claim 4, characterized in that, The evaluation index value set represents the set of cloud model evaluation values ​​of the evaluation index in the corresponding interactive scenario.

6. The method for evaluating the multi-scenario grid interaction capability of an electro-hydrogen coupling system cluster according to claim 3, characterized in that, The indicator weight set is obtained based on the first value set, including: Obtain a scene correction coefficient set, wherein the scene correction coefficient set represents the set of scene correction coefficients of the evaluation index in the corresponding interactive scene; Using the analytic hierarchy process, a first weight set is obtained based on the set of scene evaluation indicators. The first weight set represents the set of first weights, and the first weights represent the subjective weights of the evaluation indicators in the corresponding interactive scene. The entropy weight method is used to obtain a second weight set based on the second value set. The second weight set represents the set of second weights, and the second weight represents the objective weight of the evaluation index in the corresponding interactive scenario. A third weight set is obtained based on the first weight set and the second weight set. The third weight set represents a set of third weights. The third weights are obtained by weighted summation of the first weights and the corresponding second weights. The indicator weight set is obtained based on the third weight set and the scenario correction coefficient set. The indicator weight set represents the set of weight values ​​of the evaluation indicator in the corresponding interactive scenario.

7. The method for evaluating the multi-scenario grid interaction capability of an electro-hydrogen coupling system cluster according to claim 2, characterized in that, The synergy effect correction term set is obtained based on the evaluation index value set and index weight set, including: Based on the evaluation index value set, the response evaluation value set of each of the electro-hydrogen coupling systems is obtained on each side of the interaction. The response evaluation value set represents the set of response evaluation values ​​of the electro-hydrogen coupling system on the corresponding side in the corresponding interaction scenario. Using the geometric mean method, the basic cooperative itemset corresponding to the electro-hydrogen coupling system is obtained based on the response evaluation value set of each side; Based on the evaluation index value set and index weight set, obtain the scene characteristic capability value set corresponding to the electro-hydrogen coupling system; The synergistic effect correction set corresponding to the electro-hydrogen coupling system is obtained based on the basic synergistic itemset and the scene feature capability value set.

8. The method for evaluating the multi-scenario grid interaction capability of an electro-hydrogen coupling system cluster according to claim 7, characterized in that, The single side includes the grid side, the hydrogen-electric side, and the load side.

9. The method for evaluating the multi-scenario grid interaction capability of an electro-hydrogen coupling system cluster according to claim 7, characterized in that, The set of interactive capability indices includes a first set of interactive capability indices and a second set of interactive capability indices. The first set of interactive capability indices represents the set of first interactive capability indices, and the first interactive capability index represents the interactive capability index of the electro-hydrogen coupling system in the corresponding interactive scenario. The second set of interactive capability indices represents the set of second interactive capability indices, and the second interactive index represents the interactive capability index of the electro-hydrogen coupling system in all the interactive scenarios. An interaction capability index set is obtained based on the basic interaction capability value set, the synergy effect correction term set, and the scene weight set, including: Based on the basic interaction capability value set and the synergistic effect correction term set, a first interaction capability index set corresponding to the electro-hydrogen coupling system is obtained; The second interactive capability index set is obtained by weighted summation of the first interactive capability index set and the scene weight set.

10. The method for evaluating the multi-scenario grid interaction capability of an electro-hydrogen coupling system cluster according to claim 9, characterized in that, The first interactive capability index is: ； in, For the first The first electro-hydrogen coupling system in the... The first interactive capability index in each interactive scenario Number the electro-hydrogen coupling systems in the electro-hydrogen coupling system cluster. Number the interactive scenes. To minimize the function, To maximize the function, For the first The first electro-hydrogen coupling system in the... Basic interactive capability values ​​in each interactive scenario For the first The first electro-hydrogen coupling system in the... Synergy effect correction term in interactive scenarios.

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