Distributed energy operation state sensing method and system based on climate toughness

Abstract

The invention discloses a distributed energy operation state sensing method and system based on climate toughness, and relates to the technical field of distributed energy. Comprising the following steps: calculating a regional risk index according to meteorological partitions, and combining a climate disaster intensity index and a climate toughness index; screening key nodes based on the node exposure index and the node importance index; evaluating the credibility of the data through the model deviation index and the neighborhood consistency index, and correcting the deviation; judging the operation state according to the climate pressure index and the toughness margin index; the system comprises a climate disaster evaluation module, a node exposure evaluation module, a data anomaly evaluation module and an operation state judgment module which are in signal connection. According to the invention, the climate disaster risk can be effectively evaluated, key nodes are preferentially monitored, the data accuracy is improved, and the climate toughness and operation stability of the distributed energy system are enhanced.

Description

Technical Field

[0001] This invention relates to the field of distributed energy technology, and more specifically, to a method and system for sensing the operational status of distributed energy based on climate resilience. Background Technology

[0002] With the rapid development of distributed energy, its operational status perception is crucial for ensuring energy supply security. However, intensified global climate change has led to frequent extreme weather events such as torrential rains, typhoons, and high temperatures, posing a serious threat to distributed energy systems. Existing technologies for distributed energy operational status perception primarily focus on monitoring conventional operating parameters, lacking in-depth integrated analysis of climate disaster factors. For example, traditional methods fail to effectively combine climate model prediction data with regional resilience assessments, resulting in delayed risk identification and inaccurate assessments. In node monitoring, methods often rely on a single dimension such as capacity or geographical location, neglecting the topological importance and exposure of nodes within the network, leading to unreasonable allocation of monitoring resources. Furthermore, data collection is susceptible to weather-related anomalies, but existing correction mechanisms are inadequate, affecting the reliability of status assessments. Therefore, there is an urgent need for a method and system that integrates climate resilience and achieves accurate status perception to enhance the adaptability and operational resilience of distributed energy resources under extreme weather conditions.

[0003] To address the above problems, this invention proposes a solution. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for sensing the operational status of distributed energy based on climate resilience, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A climate-resilient-based method for sensing the operational status of distributed energy resources, including the following steps; Step S1: Collect extreme precipitation, extreme wind speed and extreme temperature as climate disaster indicators. Based on the ratio of the climate model prediction value to the baseline value, select the maximum value as the disaster intensity index. Combine the climate resilience index with the adaptability index, transformation capacity index, exposure index and sensitivity index to calculate the climate resilience index and assess the regional climate resilience level. Step S2: Calculate the node exposure index and assess node exposure based on the time ratio of climate disaster impact; calculate the node importance index through load contribution ratio and topological influence factor; obtain the node comprehensive score based on the node exposure index and node importance index, and compare the node comprehensive score with the screening threshold to screen key nodes; Step S3: Calculate the model bias index and neighborhood consistency index to evaluate the reliability of node data, and set a threshold to determine whether the measurement data is abnormal. If abnormal, the average of the model prediction value and the neighborhood average measurement value is used to correct the data. Step S4: Determine the node's operational status based on the climate pressure index and resilience margin index, and classify it into three categories: normal state, early warning state, and emergency state based on the node's pressure and resilience margin.

[0006] In a preferred embodiment, step S1 includes the following: The disaster intensity index is calculated using the following formula: Where the subscript 'r' represents the area code, These are extreme precipitation forecast values. It is the baseline value for precipitation. These are extreme wind speed forecasts. This is the baseline wind speed value. These are predicted values ​​for extreme temperature deviations. It is a temperature reference value; The climate resilience index is calculated using the following formula: ;in, It is an adaptability index. It is a transformation capability index. It is an exposure index. It is a sensitivity index; The adaptability index is calculated using the following formula: ;in, It is the per capita investment in power resilience. It is the ratio of energy storage to backup capacity. It refers to the emergency response duration level; The transformation capability index is calculated using the following formula: ;in, It is the penetration rate of renewable energy. It is an index of the level of infrastructure modernization; The exposure index is calculated using the following formula: ;in, It refers to the area within the high-risk zone of the region. It is the total area of ​​the region; The sensitivity index is calculated using the following formula: ;in, It is population density. It refers to the degree of aging of the infrastructure; The regional risk index is calculated using the following formula: ;in, It is a disaster intensity index. It is a climate resilience index; and based on the regional risk index, risk thresholds are set to divide high-risk, medium-risk, and low-risk areas.

[0007] In a preferred embodiment, step S2 includes the following: The node exposure index is calculated using the following formula: ;in, It is the total time that node i is affected by climate disasters during the planning period. This is the total duration of the planning period; The node importance index is calculated using the following formula: ;in, It is the load contribution ratio of node i. It is the topological influence factor of node i; The load contribution ratio is calculated using the following formula: ;in, It is the average load or installed capacity of node i. It is the total load or total installed capacity of all distributed energy nodes; The topological impact factor is calculated using the following formula: ;in, It is the betweenness centrality of node i. It is the maximum betweenness centrality; The node exposure assessment module also calculates the node composite score by multiplying the node exposure index and the node importance index. The node composite score is calculated using the following formula: ;in, It is a node exposure index. It is a node importance index; and a screening threshold is set based on the node's comprehensive score to screen key nodes.

[0008] In a preferred embodiment, step S3 includes the following: The model bias index is calculated using the following formula: ;in, It is the measurement value of node i at time t. It is the model prediction value of node i at time t; The neighborhood consistency index is calculated using the following formula: ;in, It is the average measurement value of the neighboring nodes of node i at time t; Set model bias thresholds and neighborhood consistency thresholds to determine whether the measurement data is abnormal. If abnormal, use the mean of the model prediction value and the average measurement value of the neighborhood to correct the data. The data correction is calculated using the following formula: ;in, These are model predictions. It is the average measurement value of the neighborhood.

[0009] In a preferred embodiment, step S4 includes the following: The climate stress index is calculated using the following formula: ;in, It is the predicted load or pressure value of node i at time t. It is the design tolerance of node i; The toughness margin index is calculated using the following formula: ;in, It is the resilience of node i at time t; Based on the relationship between the climate pressure index and the resilience margin index, the operational status is divided into normal state, warning state, and emergency state. and This is the normal state, when and The current state is an emergency; the rest are warning states.

[0010] The climate-resilient distributed energy operation status sensing system includes: a climate disaster assessment module, a node exposure assessment module, a data anomaly assessment module, and an operation status determination module, with signal connections between the modules. Climate disaster assessment module: Collects extreme precipitation, extreme wind speed and extreme temperature as climate disaster indicators. Based on the ratio of climate model prediction value to baseline value, selects the maximum value as disaster intensity index and combines it with adaptability index, transition capacity index, exposure index and sensitivity index to calculate climate resilience index and assess the regional climate resilience level. Node Exposure Assessment Module: Calculates the node exposure index and assesses node exposure based on the time proportion of climate disaster impact; calculates the node importance index through load contribution ratio and topological influence factor; obtains the node comprehensive score based on the node exposure index and node importance index, and compares the node comprehensive score with the screening threshold to screen key nodes; Data anomaly assessment module: Calculates model bias index and neighborhood consistency index to assess the reliability of node data, and sets thresholds to determine whether the measurement data is abnormal. If abnormal, the average of the model prediction value and the neighborhood average measurement value is used to correct the data. Operational status determination module: Determines the operational status of nodes based on the climate pressure index and resilience margin index, and classifies them into three categories: normal status, early warning status, and emergency status according to node pressure and resilience margin.

[0011] The technical effects and advantages of the climate-resilient distributed energy operation status sensing method of this invention are as follows: By combining the climate disaster intensity index and the climate resilience index, including adaptability, transformation capacity, exposure and sensitivity, a regional risk index is calculated to achieve accurate classification of risk levels in different regions, providing a scientific basis for prioritizing the allocation of monitoring resources and emergency measures. By introducing node exposure and node importance indices, including comprehensive load contribution and topology impact factors, high-exposure and high-importance key nodes are screened out, monitoring resource allocation is optimized, and key links of the system are ensured to receive key protection. Furthermore, model bias and neighborhood consistency indices are used to assess and correct data credibility, effectively identifying and correcting abnormal data, and improving the accuracy and reliability of state-aware data. By determining the operating status (normal, early warning, emergency) using the climate stress index and resilience margin index, this invention enables tiered responses and timely implementation of measures such as load reduction and energy storage support. This enhances the system's adaptability and resilience under climate disasters, comprehensively improving the climate resilience and operational stability of distributed energy systems, reducing disaster losses, and has significant practical value. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the operation status sensing method for distributed energy based on climate resilience according to the present invention.

[0013] Figure 2 This is a schematic diagram of the distributed energy operation status sensing system module based on climate resilience of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Example: Please refer to Figure 1 As shown, this invention discloses a method for sensing the operational status of distributed energy resources based on climate resilience, including the following steps: Step S1: Collect extreme precipitation, extreme wind speed and extreme temperature as climate disaster indicators. Based on the ratio of the climate model prediction value to the baseline value, select the maximum value as the disaster intensity index. Combine the climate resilience index with the adaptability index, transformation capacity index, exposure index and sensitivity index to calculate the climate resilience index and assess the regional climate resilience level. Step S2: Calculate the node exposure index and assess node exposure based on the time ratio of climate disaster impact; calculate the node importance index through load contribution ratio and topological influence factor; obtain the node comprehensive score based on the node exposure index and node importance index, and compare the node comprehensive score with the screening threshold to screen key nodes; Step S3: Calculate the model bias index and neighborhood consistency index to evaluate the reliability of node data, and set a threshold to determine whether the measurement data is abnormal. If abnormal, the average of the model prediction value and the neighborhood average measurement value is used to correct the data. Step S4: Determine the node's operational status based on the climate pressure index and resilience margin index, and classify it into three categories: normal state, early warning state, and emergency state based on the node's pressure and resilience margin.

[0016] In step S1, extreme precipitation, extreme wind speed, and extreme temperature are collected as climate hazard indicators. Based on the ratio of climate model predictions to baseline values, the maximum value is selected as the hazard intensity index. This index, combined with the adaptability index, transition capacity index, exposure index, and sensitivity index, is used to calculate the climate resilience index, assessing the regional climate resilience level. Specific details include: The topology of the distribution network covered by the distributed energy system and the geographical coordinates of each distributed energy node are obtained. The switching, transformer or feeder segmentation points in the network are used as topology segmentation boundaries to divide the distribution network into multiple subnets. The set of nodes contained in each subnet is defined as a meteorological zone, and a region number is assigned to each meteorological zone. When the geographical range of a subnet exceeds the preset range, the subnet is further subdivided into multiple meteorological zones according to the nearest neighbor principle of the node's geographical coordinates to ensure that the meteorological conditions of the nodes in the same meteorological zone are consistent. The climate model adopts a scenario-based regional climate model. The scenario parameters include at least emission path parameters, land surface process parameters, and boundary condition parameters. The climate model output variables include at least precipitation, near-surface wind speed, and near-surface temperature. For each meteorological zone, the coordinates of the representative point of the zone are determined based on the geographical envelope of the zone. The grid point closest to the representative point is selected from the climate model grid data to output the sequence, or bilinear interpolation is performed on the output of adjacent grid points to obtain the zone-level meteorological time series. When node-level data is needed, the same spatial mapping is performed on the zone-level sequence based on the node coordinates. The temporal resolution uses the raw time step output by the climate model, preferably on an hourly or daily scale. The spatial resolution uses the grid resolution of the climate model. After spatial mapping, data is generated at the zonal or node level. Within a predetermined prediction window, the sliding cumulative value of the precipitation series at the zonal or node level is calculated and its maximum value is taken as the extreme precipitation prediction value. The maximum value or the maximum value of the moving average of the wind speed series is taken as the extreme wind speed prediction value. The maximum deviation of the temperature series relative to the average temperature of the baseline period is calculated as the extreme temperature deviation prediction value. The baseline thresholds include precipitation baseline, wind speed baseline, and temperature baseline, all of which are obtained through the statistical analysis of historical meteorological series of the same meteorological zone. Specifically, the upper quantile value of the corresponding statistical quantity is calculated as the baseline value to ensure that the calculation of the ratio between the predicted value and the baseline value has an implementable data basis. Based on meteorological zones, climate model output data is collected in each region. Since this invention focuses on the impact of climate disasters on distributed energy systems, disaster-related meteorological variables are selected. Extreme precipitation, extreme wind speed, and extreme temperature are chosen as climate disaster indicators. The prediction results are derived from scenario-based climate models, obtaining the predicted extreme precipitation values ​​for the study period. Extreme wind speed forecast value Extreme temperature deviation is The subscript r represents the region number; Based on preset benchmark thresholds, including precipitation benchmarks Wind speed benchmark and temperature reference The climate disaster intensity index is calculated by comparing the predicted value with the baseline value. The maximum value of the ratio of three meteorological indicators is used as the regional climate disaster intensity index. Extreme climate events are diverse and are usually constrained by the greatest risk factor. The Climate Resilience Index (CRI) is introduced to describe a region's comprehensive ability to withstand climate disasters. It is calculated by combining four aspects: adaptability, transition capacity, exposure, and sensitivity, and adopts the following definition: Adaptability Index This reflects the region's capacity for infrastructure development, management improvement, and resource reserves in response to climate change. Per capita power resilience investment is calculated based on the ratio of disaster-resistant investment in power facilities to population within the region. The ratio of energy storage capacity to maximum load within a given area is denoted as the energy storage to reserve capacity ratio. The ratio of the average time to restore power after a disaster to the target time is recorded as the emergency response time level. .

[0017] The adaptability index reflects a region's ability to address climate change through infrastructure development, improved management, and resource reserves. It is based on a comprehensive assessment of indicators such as GDP per capita, grid intelligence, energy storage capacity, and the completeness of disaster preparedness plans, and uses a geometric average of several key indicators. Per capita electricity resilience investment Energy storage to backup capacity ratio and emergency response duration level ; Adaptability Index Comprehensive per capita power resilience investment Energy storage to backup capacity ratio and emergency response duration level The calculation formula is expressed as: Among them, by classifying the emergency response duration levels Take the reciprocal This allows for a shorter response time and higher toughness; Transformation Capability Index Based on data on the proportion of renewable energy and the level of infrastructure modernization in a region, a transformation capacity index is calculated. The transformation capacity index reflects the region's ability to address long-term climate change through technological and institutional innovation. The proportion of distributed photovoltaic, wind power, and other renewable energy installed capacity in a region to the total installed capacity of the region is denoted as the renewable energy penetration rate. The infrastructure modernization index is obtained based on the level of infrastructure construction and intelligence within the region. ; Within each meteorological zone, the coverage rates of distribution automation, smart metering, observable and measurable distributed energy, and remote control are obtained. Distribution automation coverage is the ratio of the number of switches or feeder segments with telemetry, remote signaling, and remote control functions to the total number of switches or feeder segments in that zone. Smart metering coverage is the ratio of the number of metering points with remote meter reading capabilities to the total number of metering points. Observable and measurable distributed energy is the ratio of the number of distributed power sources and energy storage nodes capable of uploading measurement data in real time to the total number of distributed power sources and energy storage nodes in that zone. Remote control coverage is the ratio of the number of devices supporting remote start / stop, power setting, or charge / discharge control to the total number of devices. The geometric mean of these ratios is then calculated after normalization. Thus It reflects both the level of infrastructure construction and the level of intelligent access. When a certain ratio is missing, it is filled by the historical average value of the same region and recalculated in the next statistical period. Transformation Capability Index The calculation formula is expressed as follows: ; Exposure Index Reflecting the extent of a region's exposure to climate hazards, it is characterized by the proportion of the population or assets located in geological disaster zones, flood-prone areas, and flammable zones. The exposure level is calculated in the following ways: ;in, This indicates the area within the high-risk zone. The area represents the total regional area; the higher the exposure, the greater the extent to which the region is affected by extreme weather. Sensitivity Index Population density is used to measure a region's sensitivity to climate disasters. That is, the population per unit area and the degree of infrastructure aging. The two indicators, namely the proportion of old facilities capacity, are calculated using a geometric mean: ; Combining the above four sub-indices, the climate resilience index Defined as: It objectively reflects the comprehensive effect of adaptability and transformation capabilities against exposure and sensitivity. The larger the value, the higher the resilience of the region; By combining the intensity of climate disasters with resilience levels, a regional risk index can be constructed. , defined as the ratio of climate disaster intensity to the regional resilience index: , like The relatively large magnitude indicates a high level of resilience to climate disasters, but insufficient regional risk resistance, based on the overall survey. Data distribution allows setting risk thresholds. and For example, through statistical historical data The distribution is used, with the upper quartile as the high-risk threshold and the lower quartile as the low-risk threshold: when When an area is identified as high-risk, it should be given priority attention. when At that time, it was judged to be a medium-risk area; when At that time, it was judged to be a low-risk area; based on Different regions are prioritized, with high-risk areas receiving priority in monitoring resources and emergency measures, providing a basis for subsequent node selection.

[0018] In step S2, the node exposure index is calculated, and node exposure is assessed based on the time proportion of climate disaster impact; the node importance index is calculated using the load contribution ratio and topological influence factor; a comprehensive node score is obtained based on the node exposure index and the node importance index, and the comprehensive node score is compared with a screening threshold to screen key nodes. Specific details include: The Node Exposure Index (NED) is introduced to describe the degree to which equipment or facilities are exposed to extreme weather events. In this embodiment, exposure is defined as the proportion of time a node is affected by climate disasters during a future planning period, the planning period being [duration missing]. The total time during which a node i is predicted to be affected by extreme precipitation, extreme wind speed, or extreme temperature is given by the given information. Then the node exposure index is: Where i is the node index, representing the i-th distributed energy node, such as an energy storage device, photovoltaic power generation facility, or wind turbine. It is the node exposure index of the i-th node. Based on the time series output of the climate model, the exposure time is directly calculated. When the value is close to 1, it indicates that the node is under the influence of climate disasters for most of the time, and its exposure is very high; when When the value is close to 0, it indicates that the node exposure is very low; A Node Importance Index (NID) is introduced to reflect the degree of influence of nodes on the operation of the entire distributed energy system. Unlike traditional importance assessments based on a single dimension such as capacity size or geographical location, this index comprehensively considers node load contribution and network topology, specifically including two sub-indicators: load contribution ratio. and topological influence factor : Load contribution ratio The proportion of the load supplied by node i to the total system load is expressed by the formula: ;in, Let be the average load or installed capacity of node i, representing the node's contribution to the total system load. It is the total load or total installed capacity of all distributed energy nodes. This represents the load or installed capacity of the j-th node. Represents the set of all distributed energy nodes; Topological Influence Factor Used to characterize the structural importance of nodes in a power distribution network, betweenness centrality is used as a topological index. A network graph is constructed with distributed energy nodes, power supply nodes, and load nodes as the vertex set V and line or switch connection relationships as the edge set E. Each edge is assigned a weight, which can be the line electrical distance or equivalent impedance to reflect the transmission cost. For any pair of power nodes s and load nodes t, calculate the set of shortest paths in the network graph, and denote the total number of shortest paths as . The number of shortest paths passing through node i is Then the betweenness centrality of node i Defined as the summation over all (s,t) ; Dijkstra's shortest path algorithm is used to find the set of shortest paths for each (s,t) node in the weighted network graph, and calculations are performed for all nodes. Take the maximum value later The maximum betweenness centrality is denoted as The normalized topological impact factor is defined as follows: This ensures that the topology influence factor falls within a preset range; a larger value indicates that the node is located on more shortest transmission paths from power sources to loads. Node Importance Index Defined as the product of the load contribution ratio and the topology influence factor: ,once or Zero, A value of zero indicates that the node's importance is very low; only nodes with both high values ​​will exhibit high importance. ; Identifying the nodes that most need monitoring requires combining exposure and importance. This embodiment calculates a comprehensive node score. Node comprehensive score When the value is large, the node is designated as a critical node and prioritized for subsequent data reliability assessment and operational status determination. A threshold is set using statistical methods. This embodiment employs the quantile method, based on the values ​​of all nodes... Value distribution, taking the value corresponding to the upper quartile as the screening threshold. The specific process is as follows: Collect all nodes Values, sorted from smallest to largest, are selected from those at the 75th percentile. value as threshold , that is, quartiles; when When node i is identified as a critical node, it needs to be closely monitored in subsequent monitoring. when At this time, the node is at a lower priority and can be monitored using conventional methods.

[0019] In step S3, the model bias index and neighborhood consistency index are calculated to evaluate the reliability of node data. A threshold is set to determine whether the measurement data is abnormal. If abnormal, the average of the model prediction value and the average measurement value of the neighborhood is used for data correction. Specific details include: The reliability of the data is assessed by the Model Bias Index (MDI) and the Neighborhood Consistency Index (NCI), and outlier data is corrected. The measurement data sequence of the node during the historical normal operating period is extracted from the monitoring system as the output quantity, and the input quantities affecting the operation of the node are extracted simultaneously, including the time series of precipitation, wind speed and temperature of the meteorological zone, as well as the node's own historical load, output set value, energy storage charge status, switch status and other operating status parameters. The input quantities and output quantities are aligned with the same time resolution to form a sample set indexed by the time step. The mechanism model establishes corresponding power or load balance relationships based on node type. For example, it establishes a meteorological variable-driven output estimation relationship for power generation nodes, a temperature-related load estimation relationship for load nodes, and a state of charge update relationship for energy storage nodes, and obtains the expected power by combining charging and discharging commands. The data-driven model trains the above sample set, preferably using a sliding window regression model or an autoregressive model, to output the model prediction value of the node under normal operating conditions. Historical normal operating condition samples are divided into training and validation sets. Mechanism models and data-driven models are trained separately. Prediction error indices are calculated on the validation set. The error indices can be the mean absolute error or the mean squared error. When the prediction error of a model is less than a preset error threshold and the error fluctuation does not exceed a preset fluctuation threshold in multiple consecutive validation windows, the model is determined to be a suitable operating model for the node. The preset error threshold can be determined by the upper quantile of the fluctuation of the measured values ​​under historical normal operating conditions, and the preset fluctuation threshold can be determined by the standard deviation of the error sequence. Establish a suitable operating model for the node, and use a mechanistic model or data-driven model to predict the ideal operating value of the node under normal operating conditions. Let the measured value of node i at time t be... The model predicts the value. The model bias index is defined as the absolute value of the relative deviation between the measured value and the model value: ,when The larger the value, the greater the deviation between the measured value and the model prediction. To maintain a consistent judgment standard across all nodes, a model deviation threshold is set. Based on historical data, for example, the 90th percentile of MDI over a certain period can be used as a threshold, ensuring that most normal measurements fall within this threshold. This threshold is denoted as [threshold value]. ,when At that time, it was initially determined that the measurement was abnormal; Further considering the consistency with neighboring node data, let the neighborhood set of node i be . Including nodes connected to it or geographically proximate it, the neighborhood average measurement is defined as: ; The neighborhood consistency index is defined as the relative difference between a node's measured value and the average measured value of its neighborhood. ; By comparing the deviation between a node's measured value and the average measured value of its neighborhood, a neighborhood consistency index is used to determine whether the node's measured data meets the neighborhood consistency criterion. When the neighborhood consistency index exceeds the neighborhood consistency threshold, the node's measured data is determined to be a neighborhood consistency anomaly. The neighborhood consistency threshold is determined based on the distribution of historical normal operating condition data. Specifically, the neighborhood consistency threshold can be determined by the ninth percentile value of the neighborhood consistency index sequence in the historical normal operating condition dataset to ensure that the threshold setting has statistical basis and reproducibility. Exceeding the threshold This indicates that the node data differs significantly from that of surrounding nodes, and the threshold... Similarly, the settings can be based on the distribution of historical normal operating data, such as using the 90th percentile value; When a deviation occurs in the measurement data at a certain node, it could be due to either a genuine anomaly or a sensor malfunction. Therefore, a comprehensive judgment is made by combining MDI and NCI data. like and If the measurement value is found to be reliable, then no correction is needed. like or If so, it is determined that the measured value may be abnormal and further correction is needed; For outlier data, a mean correction is applied using the model's predicted values ​​and the average measurements from its neighborhood, i.e.: ;in, For the corrected data, when the model bias and the neighborhood bias are inconsistent, mean correction can reduce the impact of unilateral bias on the results. If the neighborhood data is also abnormal, it can be further identified by expanding the neighborhood range or using other filtering methods. The corrected data will be used as the input for the next step of running status determination to improve the accuracy of status perception.

[0020] In step S4, the node's operational status is determined based on the climate pressure index and resilience margin index. According to the node's pressure and resilience margin, it is categorized into three states: normal, warning, and emergency. Specific details include: Climate stress index The simplified calculation formula used to assess the impact of external climate on nodes is as follows: ;in, The load or pressure forecast value for node i at time t is a simplified output of the meteorological early warning system based on the current climate conditions and a climate model. The design tolerance for node i represents the maximum load or pressure that the node can withstand, provided by equipment design parameter standards or calculated based on historical load data. Resilience Margin Index The formula for calculating the remaining capacity of a node after it has withstood current climate stress is as follows: ;in, Let represent the resilience of node i at time t, and let represent the product of the node's equipment redundancy coefficient and intelligence coefficient. The equipment redundancy coefficient is the node's equipment redundancy rate, i.e., the ratio of standby equipment capacity to primary equipment capacity, reflecting the node's equipment redundancy capability. This coefficient is obtained through the node's equipment specifications or on-site testing data. The intelligence coefficient represents the node's equipment intelligence level, obtained through standardized indicators such as whether the equipment supports remote control and real-time monitoring. The predicted load or pressure value for node i at time t; According to the nodal climate pressure index and resilience margin index Based on the relationship, the operating status is divided into three categories: normal status, warning status, and emergency status. The specific judgment rules are as follows: Normal state: When and When the stress on the node is within the design range, the toughness margin is non-negative, and the system operates stably; Emergency: When and At that time, the stress at the nodes exceeded the design capacity by a large margin, and the toughness margin was clearly insufficient, requiring immediate action. Warning status: In other cases, the system is in a warning status, where the node pressure slightly exceeds the design value, but operation can still be maintained through resilience adjustments. threshold The settings are related to the system's prevention strategy and can be determined based on historical failure statistics or safety margin requirements. For example, It can be set to 10% of the design pressure or according to the node type. When a node is determined to be in an early warning or emergency state, the dispatch center can be notified to take measures such as load reduction, energy storage support or disconnection.

[0021] Please see Figure 2 As shown, this invention discloses a distributed energy operation status sensing system based on climate resilience, including: a climate disaster assessment module, a node exposure assessment module, a data anomaly assessment module, and an operation status determination module, with signal connections between the modules; Climate disaster assessment module: Collects extreme precipitation, extreme wind speed and extreme temperature as climate disaster indicators. Based on the ratio of climate model prediction value to baseline value, selects the maximum value as disaster intensity index and combines it with adaptability index, transition capacity index, exposure index and sensitivity index to calculate climate resilience index and assess the regional climate resilience level. Node Exposure Assessment Module: Calculates the node exposure index and assesses node exposure based on the time proportion of climate disaster impact; calculates the node importance index through load contribution ratio and topological influence factor; obtains the node comprehensive score based on the node exposure index and node importance index, and compares the node comprehensive score with the screening threshold to screen key nodes; Data anomaly assessment module: Calculates model bias index and neighborhood consistency index to assess the reliability of node data, and sets thresholds to determine whether the measurement data is abnormal. If abnormal, the average of the model prediction value and the neighborhood average measurement value is used to correct the data. Operational status determination module: Determines the operational status of nodes based on the climate pressure index and resilience margin index, and classifies them into three categories: normal status, early warning status, and emergency status according to node pressure and resilience margin.

[0022] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0023] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0024] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0025] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0026] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0027] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for sensing the operational status of distributed energy resources based on climate resilience, characterized in that, Includes steps; Step S1: Collect extreme precipitation, extreme wind speed and extreme temperature as climate disaster indicators. Based on the ratio of the climate model prediction value to the baseline value, select the maximum value as the disaster intensity index. Combine the climate resilience index with the adaptability index, transformation capacity index, exposure index and sensitivity index to calculate the climate resilience index and assess the regional climate resilience level. Step S2: Calculate the node exposure index and assess node exposure based on the time proportion of climate disaster impact; calculate the node importance index using the load contribution ratio and topological influence factor. A comprehensive node score is obtained based on the node exposure index and the node importance index. The comprehensive node score is then compared with a screening threshold to select key nodes. Step S3: Calculate the model bias index and neighborhood consistency index to evaluate the reliability of node data, and set a threshold to determine whether the measurement data is abnormal. If abnormal, the average of the model prediction value and the neighborhood average measurement value is used to correct the data. Step S4: Determine the node's operational status based on the climate pressure index and resilience margin index, and classify it into three categories: normal state, early warning state, and emergency state based on the node's pressure and resilience margin.

2. The distributed energy operation status sensing method based on climate resilience according to claim 1, characterized in that, The disaster intensity index is calculated using the following formula: Where the subscript 'r' represents the area code, These are extreme precipitation forecast values. It is the baseline value for precipitation. These are extreme wind speed forecasts. This is the baseline wind speed value. These are predicted values ​​for extreme temperature deviations. It is the temperature reference value.

3. The distributed energy operation status sensing method based on climate resilience according to claim 2, characterized in that, The climate resilience index is calculated using the following formula: ;in, It is an adaptability index. It is a transformation capability index. It is an exposure index. It is a sensitivity index; The adaptability index is calculated using the following formula: ;in, It is the per capita investment in power resilience. It is the ratio of energy storage to backup capacity. It refers to the emergency response duration level; The transformation capability index is calculated using the following formula: ;in, It is the penetration rate of renewable energy. It is an index of the level of infrastructure modernization; The exposure index is calculated using the following formula: ;in, It refers to the area within the high-risk zone of the region. It is the total area of ​​the region; The sensitivity index is calculated using the following formula: ;in, It is population density. It refers to the degree of aging of the infrastructure.

4. The distributed energy operation status sensing method based on climate resilience according to claim 2, characterized in that, The regional risk index is calculated using the following formula: ;in, It is a disaster intensity index. It is a climate resilience index; and based on the regional risk index, risk thresholds are set to divide high-risk, medium-risk, and low-risk areas.

5. The distributed energy operation status sensing method based on climate resilience according to claim 1, characterized in that, The node exposure index is calculated using the following formula: ;in, It is the total time that node i is affected by climate disasters during the planning period. It is the total duration of the planning period.

6. The distributed energy operation status sensing method based on climate resilience according to claim 5, characterized in that, The node importance index is calculated using the following formula: ;in, It is the load contribution ratio of node i. It is the topological influence factor of node i; The load contribution ratio is calculated using the following formula: ;in, It is the average load or installed capacity of node i. It is the total load or total installed capacity of all distributed energy nodes; The topological impact factor is calculated using the following formula: ;in, It is the betweenness centrality of node i. It is the maximum betweenness centrality; The node exposure assessment module also calculates the node composite score by multiplying the node exposure index and the node importance index. The node composite score is calculated using the following formula: ;in, It is a node exposure index. It is a node importance index; and a screening threshold is set based on the node's comprehensive score to screen key nodes.

7. The distributed energy operation status sensing method based on climate resilience according to claim 1, characterized in that, The model bias index is calculated using the following formula: ;in, It is the measurement value of node i at time t. It is the model prediction value of node i at time t; The neighborhood consistency index is calculated using the following formula: ;in, It is the average measurement value of the neighboring nodes of node i at time t.

8. The distributed energy operation status sensing method based on climate resilience according to claim 7, characterized in that, Set model bias thresholds and neighborhood consistency thresholds to determine whether the measurement data is abnormal. If abnormal, use the mean of the model prediction value and the average measurement value of the neighborhood to correct the data. The data correction is calculated using the following formula: ;in, These are model predictions. It is the average measurement value of the neighborhood.

9. The distributed energy operation status sensing method based on climate resilience according to claim 1, characterized in that, The climate stress index is calculated using the following formula: ;in, It is the predicted load or pressure value of node i at time t. It is the design tolerance of node i; The toughness margin index is calculated using the following formula: ;in, It is the resilience of node i at time t; Based on the relationship between the climate pressure index and the resilience margin index, the operational status is divided into normal state, warning state, and emergency state. and This is the normal state, when and The current state is an emergency; the rest are warning states.

10. A climate-resilient distributed energy operation status sensing system, used to implement the climate-resilient distributed energy operation status sensing method according to any one of claims 1-9, characterized in that... ； Climate disaster assessment module: Collects extreme precipitation, extreme wind speed and extreme temperature as climate disaster indicators. Based on the ratio of climate model prediction value to baseline value, selects the maximum value as disaster intensity index and combines it with adaptability index, transition capacity index, exposure index and sensitivity index to calculate climate resilience index and assess the regional climate resilience level. Node exposure assessment module: Calculates the node exposure index, assesses node exposure based on the time proportion of climate disaster impact, and calculates the node importance index through load contribution ratio and topological influence factor; A comprehensive node score is obtained based on the node exposure index and the node importance index. The comprehensive node score is then compared with a screening threshold to select key nodes. Data anomaly assessment module: Calculates model bias index and neighborhood consistency index to assess the reliability of node data, and sets thresholds to determine whether the measurement data is abnormal. If abnormal, the average of the model prediction value and the neighborhood average measurement value is used to correct the data. Operational status determination module: Determines the operational status of nodes based on the climate pressure index and resilience margin index, and classifies them into three categories: normal status, early warning status, and emergency status according to node pressure and resilience margin.

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