A power climate risk assessment method, medium and program product oriented to the influence of high temperature and / or drought events on both supply and demand sides

Abstract

This invention discloses a method, medium, and program product for assessing the impact of high-temperature and / or drought events on both the supply and demand sides of the power system. It belongs to the interdisciplinary field of meteorological disaster risk assessment and energy system security. First, multi-source data from the target area is collected and preprocessed. Then, high-temperature or drought events are identified based on temperature percentile thresholds and the SPEI drought index. A bivariate distribution model is constructed using a joint probability density function to identify combined high-temperature and drought events. Next, response models for the power supply side and load side are constructed separately to quantitatively assess the output changes of different power sources and the load response characteristics of various users under high-temperature and / or drought scenarios. Furthermore, key indicators such as power shortages and load risk exposure intensity are calculated through supply-demand coupling offset analysis. Finally, the risk model is driven by climate model prediction data to output the risk evolution trend under future scenarios. This invention can provide support for improving the climate resilience of power systems and for dispatching decisions.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of meteorological disaster risk assessment and energy system security. It involves the identification and prediction of extreme weather and climate events and the power climate risk assessment of typical meteorological disasters. Specifically, it is a power climate risk assessment method, medium and program product for the impact of high temperature and / or drought events on both the supply and demand sides. It is used to improve the resilience assessment and risk prevention and control capabilities of power systems under extreme climate scenarios and provide accurate climate resilience decision support for the planning of new power systems. Background Technology

[0002] As global warming continues to intensify, the frequency, intensity, and duration of extreme weather and climate events are showing a significant upward trend, posing unprecedented challenges to the safe operation of power systems. In particular, extreme events such as high temperatures, droughts, and their combined superposition, due to their synchronous, persistent, and amplifying characteristics, have become one of the key meteorological factors affecting the stability and reliability of modern power systems.

[0003] The new power system widely adopts a high proportion of renewable energy, highly electronic equipment, and high-inter-regional transmission configuration, significantly enhancing its responsiveness to meteorological conditions. For example, under high-temperature conditions, the grid load increases significantly, especially the load on air conditioning and electric cooling systems. In drought conditions, reduced surface runoff severely affects the inflow and output of hydropower stations, leading to a sharp decline in power generation capacity. When high-temperature and drought events occur simultaneously, not only does it cause a surge in peak electricity demand, but it also simultaneously compresses hydropower supply capacity, forming a typical "peak load + low power supply" compound disaster characteristic.

[0004] Traditional power meteorological risk assessment methods often focus on the impact of single meteorological factors on the power system, failing to effectively construct a systematic assessment framework integrating meteorology, hydrology, and energy demand, and lacking dynamic scenario-based analysis paths for typical extreme events. Furthermore, existing methods generally lack collaborative identification mechanisms for complex disasters, making it difficult to accurately capture the significant temporal synchronicity and spatial coupling characteristics of high-temperature and drought events, thus hindering risk prediction, load assessment, and supply and demand regulation decisions. For example, Chinese patent CN117937431A discloses a power supply and demand forecasting method based on climate models and emission scenarios, but it relies on linear empirical formulas to quantify the impact of climate on power generation efficiency, fails to couple hydrological dynamic processes, and lacks a collaborative identification mechanism for high-temperature and drought complex events; CN116937574A proposes a power system operation risk assessment framework under extreme weather conditions, but only focuses on changes in transmission failure rates, failing to cover the dynamic responses of both power output attenuation and load demand surges.

[0005] At the level of disaster event identification, some studies have attempted to introduce univariate threshold models or composite indicators based on statistical distributions for preliminary classification, but the joint characterization of dry / wet processes and warm / thermal processes remains relatively crude. In terms of meteorological-hydrological-power response modeling, current methods generally suffer from weak data integration capabilities, simplified response relationship modeling, and poor regional adaptability. Furthermore, the assessment of the evolution trend of compound disaster risks under future climate scenarios still lacks a unified data generation mechanism and quantitative standards. In addition, the dual-side impacts of power supply and demand under compound extreme events exhibit nonlinearity, lag, and cross-domain coupling characteristics, necessitating the establishment of multivariate models that comprehensively consider meteorological input, hydrological response, and the dynamic characteristics of the power system, and the introduction of an assessment mechanism driven by high-frequency, multi-source observational data.

[0006] In summary, existing technologies have many shortcomings in assessing the risks of combined high-temperature and drought disasters that affect both the supply and demand sides of the power system. Therefore, how to construct a technology that can objectively identify combined high-temperature and drought disasters and comprehensively assess their coupled impact on both the supply and demand sides of the power system, thereby achieving accurate and quantitative assessment of power climate risks, is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0007] (a) Purpose of the invention

[0008] To address the aforementioned deficiencies and shortcomings of existing technologies, this invention aims to provide a power climate risk assessment method, medium, and program product for the dual impacts of high temperature and / or drought events on supply and demand. By constructing a composite disaster identification model that integrates run-length theory threshold parameters and joint probability density functions, it objectively identifies high temperature events, drought events, and their combined co-occurrence processes. Furthermore, by combining multi-source data from meteorology, hydrology, and power system operation, it constructs a dynamic modeling mechanism for power generation output and load response, assessing the power system's supply-demand imbalance and risk exposure level under extreme events. Finally, by incorporating multi-scenario climate simulation data, it simulates the evolution trend of future high temperature and drought composite events, enabling resilience identification and risk warning of regional power systems under the influence of composite disasters, thereby effectively improving the power system's comprehensive risk prevention and control capabilities in response to extreme climate events.

[0009] (II) Technical Solution

[0010] To achieve the objective of this invention and solve its technical problems, the present invention adopts the following technical solution:

[0011] The first objective of this invention is to provide a power climate risk assessment method for the impact of high temperature and / or drought events on both the supply and demand sides. This method identifies high temperature, drought, and combined high temperature and drought events and quantitatively assesses their risk impact on the power system's power source and load sides, providing decision support for the planning, operation, and management of the power system. It includes at least the following steps:

[0012] S100. Multi-source data acquisition and preprocessing: Collect multi-source data covering the target area, including at least historical meteorological observation data, hydrological and water resources data, and power system operation data, and preprocess the multi-source data to construct a fusion analysis dataset that is consistent in time and space, has a unified format, and is comparable and traceable.

[0013] S200. High Temperature and / or Drought Event Identification: Construct objective identification indicators for identifying high temperature events and drought events. High temperature events are judged by the criterion that the daily temperature exceeds a preset percentile threshold and reaches at least a preset minimum duration. Drought events are identified by the criterion that the Standardized Precipitation Evapotranspiration Index (SPEI) is below a preset drought threshold and reaches at least a preset minimum duration. Construct a joint distribution model of high temperature and drought based on the joint probability density function to identify simultaneous high temperature and drought composite events.

[0014] S300. Power System Source Side Response Modeling: Based on the identified high temperature and / or drought events, combined with the differentiated characteristics of different power source types in the power system, the relationship between high temperature and / or drought factors and hydropower, wind power and / or photovoltaic power output is fitted, and a multi-energy coordinated supply capacity response model of the power system source side is constructed to quantitatively evaluate the capacity change trend and output sensitivity of various power source units under high temperature and / or drought scenarios.

[0015] S400. Power System Load-Side Response Modeling: Based on historical electricity load data and combined with the sensitivity of different user types to meteorological elements, the multivariate relationship between high temperature and / or drought factors and daily electricity load is fitted. Based on typical day time period division and load characteristic cluster analysis, a power system load-side response model is constructed to quantitatively evaluate peak load increase, base load shift, load sensitivity and uncertainty under high temperature and / or drought scenarios.

[0016] S500. Supply and Demand Coupling Misalignment Analysis and Risk Indicator Calculation: Couple analysis is performed on the quantitative evaluation results of the power system source side response model and the power system load side response model to construct a misalignment relationship model between available power supply and real-time load demand in the target area, and output typical indicators characterizing the power system carrying capacity and risk exposure level under high temperature and / or drought scenarios.

[0017] S600. Future Scenario-Driven Risk Evolution Trend Simulation: Based on climate model output climate prediction data, extract high temperature and / or drought event prediction information for the target area in the future period, drive the construction of a deviation relationship model between available power supply and real-time load demand in the target area, and output the power climate risk level classification and trend prediction information of the target area in the future period.

[0018] The second objective of this invention is to provide a computer program product, including computer instructions for executing the above-mentioned power climate risk assessment method for the impact of high temperature and / or drought events on both the supply and demand sides.

[0019] The third objective of this invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned power climate risk assessment method for the impact of high temperature and / or drought events on both the supply and demand sides.

[0020] (III) Technical Effects

[0021] Compared with existing technologies, the power climate risk assessment method, medium, and program products of the present invention, which address the impact of high temperature and / or drought events on both the supply and demand sides, have the following beneficial and significant technical effects:

[0022] (1) This invention constructs a mechanism for identifying and quantifying high temperature, drought and compound events that integrate meteorological, hydrological and power operation data. By constructing a compound disaster identification model that integrates run theory threshold parameters and joint probability density function, it objectively identifies high temperature and / or drought events, significantly improving the ability to characterize the synchronicity and compound characteristics of extreme climate processes.

[0023] (2) This invention proposes a response mechanism for collaborative modeling of the power supply side and the load side of the power system. It is designed for various power types such as hydropower, wind power and photovoltaic power. It integrates their environmental sensitivity, power generation performance boundary and operation constraint characteristics to construct a multi-energy collaborative supply capacity response model. At the same time, it combines user-classified load and meteorological factor sensitivity analysis to establish a refined load-side response model, thereby systematically realizing the characterization of the dynamic evolution of power system supply and demand under high temperature and drought scenarios.

[0024] (3) This invention constructs a supply and demand offset risk analysis framework with a four-element coupling of "meteorology-hydrology-power supply-load", forming a complete technical system covering risk identification, response modeling, indicator evaluation and future trend prediction. In particular, the introduction of a multi-mode prediction driving mechanism based on future climate scenarios enables a forward-looking assessment of the evolution trend of high temperature and drought power risks, significantly enhancing the scientific support capability for improving the long-term climate resilience of the power system and formulating emergency management strategies. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating the implementation of the power climate risk assessment method of this invention, which addresses the impact of high temperature and / or drought events on both the supply and demand sides.

[0026] Figure 2 This is a technical architecture diagram of the power climate risk assessment method of the present invention, which addresses the impact of high temperature and / or drought events on both the supply and demand sides. Detailed Implementation

[0027] This invention aims to provide a method, medium, and program product for assessing the impact of high temperature and / or drought events on both the supply and demand sides of the power system. It is used to identify high temperature, drought, and combined high temperature and drought events and quantitatively assess their risk impact on the power source and load sides, providing decision support for the planning, operation, and management of the power system. To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. The described embodiments are only some embodiments of this invention and are intended to explain the invention, and should not be construed as limiting the invention.

[0028] As a specific example, such as Figure 1 , Figure 2 As shown in the embodiment of the present invention, the method for assessing the power climate risk on both the supply and demand sides of high temperature and / or drought events includes the following steps:

[0029] S100. Multi-source data acquisition and preprocessing:

[0030] Collect multi-source data covering the target area, including at least historical meteorological observation data, hydrological and water resources data, and power system operation data, and preprocess the multi-source data to construct a spatiotemporally consistent, formatted, comparable, and traceable fusion analysis dataset.

[0031] In this embodiment of the invention, the multi-source data covers at least 10 consecutive years of historical data for the target area. The meteorological observation data includes at least daily time series data of maximum temperature, minimum temperature, average temperature, precipitation, evapotranspiration, relative humidity, wind speed, and light intensity. The hydrological and water resources data includes at least daily runoff data of major river basin control stations and daily water level and inflow data of key reservoirs. The power system operation data includes at least daily power generation data classified by power source type (hydropower, photovoltaic, wind power, etc.) and hourly power load data classified by user category (residential, industrial, agricultural, commercial, etc.).

[0032] Preferably, preprocessing of multi-source data includes at least the following:

[0033] S101. Missing Value Handling and Anomaly Removal: Missing data items are repaired using sliding window interpolation, multivariate regression imputation, or empirical imputation methods based on historical distribution characteristics; abrupt changes or physically unreasonable values ​​are identified and removed using the Z-score method and time-series abrupt change detection methods based on moving averages.

[0034] S102. Data Standardization and Time Alignment: Standardize the time resolution of each data source, including summarizing hourly data into daily data and interpolating and reconstructing non-continuous data; at the same time, align all data according to a unified start and end time and a unified spatial partition.

[0035] S103. Spatial Mapping and Zonal Weighted Integration: Based on administrative divisions, meteorological station distribution, or power grid operation units, spatial mapping is performed on the data. The data is then aggregated regionally using area weighting, load weighting, or watershed weighting methods to output a representative sequence at the regional scale.

[0036] S104. Format Encapsulation and Data Management: Add metadata tags to the preprocessed data and encapsulate it in a structured manner according to a unified data standard to ensure that the data is traceable, verifiable, and reusable.

[0037] It should be noted that multi-source data acquisition and preprocessing are not only the fundamental data support for the power climate risk assessment system of this invention, but also prerequisites for ensuring the accuracy of subsequent high temperature and / or drought event identification and the effectiveness of power supply and load side modeling. Through systematic data quality control, consistent processing of time and spatial dimensions, and fusion and reconstruction of multi-dimensional information, the problems of inconsistent dimensions, resolution, and missing noise among heterogeneous data such as meteorological, hydrological, and power data are effectively solved.

[0038] S200. Identification of high temperature and / or drought events:

[0039] Objective identification indicators for high temperature events and drought events are constructed. High temperature events are judged by the fact that the daily temperature exceeds the preset percentile threshold for a certain number of days. Drought events are identified by the fact that the standardized precipitation evapotranspiration index (SPEI) is lower than the preset drought threshold and meets the requirement of persistence. A joint distribution model of high temperature and drought is constructed based on the joint probability density function to identify simultaneous high temperature and drought composite events.

[0040] In this embodiment of the invention, identifying high-temperature and / or drought events based on historical meteorological observation data mainly includes the following sub-steps:

[0041] S210. High Temperature Event Identification: Construct objective identification indicators for high temperature events, calculate the historical percentile value of the daily maximum temperature in the target area, select the 90th or 95th percentile value as the high temperature threshold, and determine a high temperature event when the daily temperature continuously exceeds the high temperature threshold for no less than 3 days.

[0042] S220. Drought Event Identification: Construct an objective identification index for drought events, using the SPEI index, which can comprehensively reflect the surface water balance, as the drought identification factor, and -1 or -1.5 as the criterion threshold. When the SPEI index is lower than the criterion threshold for three consecutive months or more, it is determined to be a drought event.

[0043] S230. Identification of high temperature and drought combined events: A bivariate joint probability distribution model between high temperature intensity indicators (such as daily temperature anomaly integral) and drought intensity indicators (such as the minimum SPEI value) is established using the Copula joint probability density function. The joint probability of different intensity combinations is obtained by fitting the marginal distribution and constructing the joint function. When the joint probability exceeds the set confidence level (such as 0.9) and there is a time overlap between the high temperature event and the drought event, it is determined to be a high temperature and drought combined event.

[0044] S240. Extraction of Quantitative Characteristic Indicators: For the identified high temperature and / or drought events, extract their quantitative characteristic indicators, including at least the event frequency D, average intensity I, and maximum intensity I. max Average duration L and longest duration L max Area A affected;

[0045] S250. Calculation of the comprehensive risk index for each event: For the identified high temperature and / or drought events, a linear weighted model is used. The comprehensive risk index R is calculated separately, where the weight coefficients a~f are determined according to the analytic hierarchy process or the entropy weight method. The R value is used to reflect the degree of catastrophicity of the event on the spatiotemporal scale and to support the classification and ranking of risk levels in different regions.

[0046] S260. Spatial Identification and Regional Aggregation Analysis: Spatial interpolation is performed on the identification results under a unified grid scale to determine the regional distribution patterns and high-risk cluster areas of high temperature, drought and compound events. Disaster statistics and aggregation at the administrative scale are carried out in combination with regional power grid division units, and a historical disaster event database containing single and compound events is formed.

[0047] Preferably, step S230 above, when constructing the bivariate joint probability distribution model between the high temperature intensity index and the drought intensity index, includes at least the following sub-steps:

[0048] S231. Construction of Marginal Distribution Functions: Let the high-temperature intensity index be a random variable X, and the drought intensity index be a random variable Y, corresponding to continuous observation data such as the daily temperature anomaly integral value and the minimum SPEI value, respectively. The kernel density estimation method is used to fit and construct the marginal distribution functions F for the high-temperature intensity index and the drought intensity index, respectively. X ( x) and F Y ( y ):

[0049]

[0050] in, x , y These are the actual observed values ​​corresponding to the high temperature intensity index and the drought intensity index. , The kernel density estimation functions for variables X and Y are constructed using the Gaussian kernel function. t , s All are integral variables.

[0051] S232. Copula Joint Function Construction and Parameter Fitting: Gumbel or Clayton functions are selected as the joint function configuration to characterize the tail dependence between high temperature and drought events. Maximum likelihood estimation is used to estimate and fit the Copula function parameters, constructing a two-dimensional joint distribution model C of bivariate indicators of high temperature and drought intensity. θ (F X ( x ),F Y ( y )):

[0052]

[0053] in, u =F X ( x ), v =F Y ( y ), where θ is a Copula parameter representing the correlation strength between variables. When using the Gumbel or Clayton function, the Copula function is defined as follows:

[0054]

[0055] or,

[0056] S233. Construct the Composite Dry Heat Index (SCEI): using the joint probability value P(X≤) x ,Y≤ y )=C θ (F X ( x ),F Y ( y As a probability indicator of the occurrence of high temperature and drought complex events, the complex dry heat index SCEI=Φ is introduced. -1 [C θ (F X ( x),F Y ( y The joint probability is transformed using a normal mapping, where Φ -1 As the inverse function of the standard normal distribution, SCEI achieves a standardized mapping of the joint probability, exhibiting comparability and linear interpretability.

[0057] S234. Criteria for determining a combined high-temperature and drought event: Set a joint probability confidence level threshold α∈(0,1), such as α=0.9. When a high-temperature event and a drought event overlap in time and their joint probability is greater than or equal to the set threshold α, or equivalently SCEI≥Φ, the event is considered a combined high-temperature event. -1 When (α) occurs, it is determined to be a combined high-temperature and drought event.

[0058] In this embodiment of the invention, step S200 establishes objective identification criteria for high temperature, drought, and their combined events, achieving unified classification and hierarchical identification of different meteorological disaster types in the spatiotemporal dimension. Based on this, the invention introduces the Copula joint probability modeling method. By fitting the marginal distribution functions of high temperature intensity and drought intensity and constructing a bivariate joint distribution model, it accurately quantifies the probability characteristics of the joint occurrence of the two types of disaster events. This overcomes the limitations of traditional empirical rules in handling nonlinear dependencies and extreme tail-related risks, significantly enhancing the accuracy and spatiotemporal representation of combined extreme events. Simultaneously, the proposed Composite Heat and Dryness Index (SCEI) provides a standardized normalized quantitative indicator of combined events, offering high-quality, quantifiable, and comparable input parameters for subsequent comprehensive risk index calculation (step S250) and regional cluster analysis (step S260).

[0059] S300. Power System Source Side Response Modeling:

[0060] Based on the identified high temperature and / or drought events, and combined with the differentiated characteristics of different power sources in the power system, the relationship between high temperature and / or drought factors and the output of hydropower, wind power and / or photovoltaic power generation is fitted. A multi-energy coordinated supply capacity response model of the power source side of the power system is constructed to quantitatively evaluate the production capacity change trend and output sensitivity of various power source units under high temperature and / or drought scenarios.

[0061] In this embodiment of the invention, based on the differentiated characteristics of different power types, the power generation change trend and output sensitivity of various power units under high temperature and / or drought scenarios are evaluated, including the following sub-steps:

[0062] S310. Power source type classification and parameter configuration: Classify the main power sources in the power system of the target area, including hydropower, wind power and / or photovoltaic power, and collect the basic parameters of each type of power source, including at least the installed capacity, conventional output, full-load capacity, peak-shaving performance and environmental sensitivity coefficient used to characterize the sensitivity of output to changes in meteorological conditions.

[0063] S320. Key meteorological factors and power generation performance correlation modeling: Identify key meteorological factors that affect the operating status of various power sources under high temperature and / or drought conditions. For example, hydropower is affected by key factors such as runoff and reservoir water level, wind power depends on wind speed stability, and photovoltaic power is affected by irradiance and photovoltaic panel temperature. Combine the collected historical operating data of various power sources and meteorological and hydrological data, and use multiple regression, multi-factor response surface function or data-driven methods (such as random forest, support vector regression, etc.) to establish the supply capacity response model of various power sources under high temperature and / or drought scenarios.

[0064] S330. Construct a multi-energy coordinated supply capacity response model for the power supply side: Integrate various power supply response models, establish a multi-energy coordinated supply capacity response model under a unified framework, and use the identified high temperature and / or drought event processes as input scenarios to quantitatively evaluate the power supply evolution trend and output degradation degree of the power system in the target area under the impact of high temperature and / or drought factors.

[0065] S340. Extraction of risk indicators on the power supply side: Based on the modeling results, typical risk assessment indicators are extracted, including the energy efficiency reduction caused by disasters, the total output loss of the system, the power supply safety margin of the system, the peak-shaving gap, and the frequency regulation and reserve insufficiency. The spatial distribution and type contribution analysis results of the power supply side response capability are output according to the spatial grid or power grid partition.

[0066] Preferably, in step S320, the construction of the hydropower supply capacity response model includes at least the following:

[0067] S321. Basin runoff simulation and drought driving mechanism identification: Integrating topographic data, land use type, soil parameters, groundwater parameters, and daily multi-source precipitation data, temperature and evapotranspiration meteorological driving factors, a distributed hydrological model is used to simulate the daily hydrological process of the target basin. High temperature and / or drought event information is introduced as external meteorological disturbance input to identify the inflow attenuation path and runoff sensitive area under high temperature and drought. The runoff evolution trend, adjustable water volume and dynamic water storage of each hydropower hub control section and reservoir area in the basin are output.

[0068] S322. Construct a meteorological-hydrological-hydropower supply response model: Using dynamic hydrological factors output by the hydrological model as input variables, and combining historical operating data of hydropower stations, including at least daily power generation records, scheduling rules, reservoir capacity curves and power output characteristic parameters, construct a meteorological-hydrological-hydropower chain supply response model through multiple regression analysis, response surface function modeling or machine learning methods to achieve prediction and fitting of hydropower capacity evolution under high temperature and drought scenarios.

[0069] S323. Reservoir Dispatch Simulation and Capacity Degradation Estimation: Combining the cascade dispatch structure within the basin, the reservoir capacity regulation process of each hydropower station is simulated. The minimum ecological discharge flow, peak power generation constraints, and power generation head range are set to construct a refined dispatch response model. The typical drought and high temperature process driven by the SCEI index is input to simulate the degradation of hydropower generation capacity under different intensity events. The reservoir head change curve, installed capacity output loss ratio, and reservoir capacity consumption rate index are output.

[0070] S324. Resilience Analysis and Spatial Heterogeneity Assessment: Based on gridded zoning, the power generation fluctuation rate, regulation capacity surplus ratio, and output stability coefficient of hydropower stations in various regions are statistically analyzed under multiple meteorological and hydrological scenarios to construct a spatiotemporal resilience distribution map of hydropower. Based on comparative analysis of typical high-temperature and drought years, sensitive power supply nodes affected by high temperature and drought are identified.

[0071] In this embodiment of the invention, step S300 establishes a multi-energy collaborative supply capacity assessment mechanism based on differentiated environmental response characteristics, using high temperature, drought, and their combined events as scenario-driven factors, and targeting various power sources such as hydropower, wind power, and solar power. Specifically, by introducing a distributed hydrological model and fusing historical power output data into the model, a high-precision simulation of the hydropower capacity degradation trend under high temperature and drought conditions is achieved. By constructing separate power output response models for hydropower and wind / solar power, the sensitivity and resilience characteristics of different power source outputs to high temperature and / or drought events are quantified.

[0072] S400. Power System Load-Side Response Modeling:

[0073] Based on historical electricity load data and the sensitivity of different user types to meteorological factors, a multivariate relationship between high temperature and / or drought factors and daily electricity load is fitted. Based on typical day time period division and load characteristic cluster analysis, a power system load-side response model is constructed to quantitatively assess peak load increase, base load shift, load sensitivity and uncertainty under high temperature and / or drought scenarios.

[0074] In this embodiment of the invention, the construction of the power system load-side response model includes at least:

[0075] S410. User Category Classification and Load Structure Identification: The power system load is divided into residential electricity consumption, industrial electricity consumption, agricultural electricity consumption, and commercial electricity consumption. Based on the historical electricity consumption data of each type of user, a load response database is constructed for each user type.

[0076] S420. Identification of meteorological driving factors and analysis of lag response: Select key meteorological variables that affect load changes and combine them with drought and / or high temperature event indicators. Use causal relationship tests or time-lag regression analysis to identify the lag time windows of various load responses to meteorological events.

[0077] S430. Construction of Multivariate Load Response Model: For different user types, a nonlinear response relationship between load and meteorological variables is established through a data-driven approach, and the model is corrected through residual analysis to improve prediction performance and robustness.

[0078] S440. Peak load and base load offset quantification: Based on the identified high temperature and / or drought event process, simulate the electricity consumption changes of various users in the same period, calculate the peak load increase, base load offset trend and load structure change ratio, and output load sensitivity and user type contribution data.

[0079] S450. Load Response Uncertainty and Vulnerability Assessment: Combining model predictions and historical event results, calculate the range of uncertainty in load response and identify sensitive areas where the load response rate exceeds a preset threshold; and assess the load-side climate vulnerability based on load growth rate, lag time, and magnitude, revealing the weak links in the power system's load impact balance under high temperature and drought conditions.

[0080] It should be noted that step S400, by separately modeling the power consumption structure of different user types and combining meteorological-driven hysteresis analysis and nonlinear response modeling, enables this invention to accurately reveal the load fluctuation mechanism and structural changes driven by high temperature, drought, and their combined events. Through peak offset simulation and systemic vulnerability quantification during typical event periods, it provides necessary load-side reinforcement for the assessment of the power system's resilience to extreme climates under dynamic supply-demand balance, thereby improving the accuracy and robustness of the overall risk assessment.

[0081] S500. Supply and Demand Coupling Shift Analysis and Risk Indicator Calculation:

[0082] The quantitative evaluation results of the power system source-side response model and load-side response model are coupled and analyzed to construct a model of the offset relationship between available power supply and real-time load demand in the target area, and output typical indicators characterizing power carrying capacity and risk exposure level under high temperature and / or drought scenarios.

[0083] As a preferred approach, the supply-demand coupling offset relationship model constructs a power shortage time series based on the difference sequence between daily time-period statistical power supply capacity and power load. By setting a supply-demand offset threshold, it calculates the regional load risk exposure intensity, cumulative offset duration, and annual average power shortage rate.

[0084] S600. Future Scenario-Driven Risk Evolution Trend Simulation:

[0085] Based on climate model output climate prediction data, the system extracts the prediction information of high temperature, drought and their combined events in the target area in the future period, drives the migration relationship model and outputs the classification and trend prediction information of power climate risk level in the target area in the future period.

[0086] As a preferred approach, the climate data input for future scenario simulations is output using the CMIP6 multi-model ensemble. Based on bias correction and statistical downscaling methods, a simulation sequence with consistent daily scale and geographical coverage is generated. Typical future high-temperature and / or drought events are extracted using high-temperature and / or drought event identification methods, and the offset relationship model is driven to output risk evolution trend curves under the corresponding scenarios. Furthermore, a risk level map of the power grid zoning in the target area and a disaster resilience level index system are constructed. The output resilience assessment results include at least the maximum load risk, the rate of decline in reserve capacity margin, and the frequency of imbalance in key load areas, which are used to assist in the formulation of power system emergency response strategies and energy structure optimization schemes under extreme weather conditions.

[0087] The power climate risk assessment method described in this embodiment constructs an identification system with high temperature and / or drought events as the core driving factors. Combined with multi-energy coordinated supply response modeling on the power source side and nonlinear response modeling mechanism on the load side, it achieves dynamic response simulation of both the power system's supply and demand sides under extreme climate conditions. Compared to existing methods that focus only on single meteorological factors or static output analysis, this invention introduces Copula joint modeling, distributed hydrological simulation, and a meteorological-hydropower linkage mechanism, significantly enhancing the ability to identify and quantify the response to disaster-causing processes of combined high temperature and drought events. This demonstrates the method's significant innovation in model systematicity, simulation accuracy, and climate adaptability. The above method not only possesses good engineering adaptability and promotion potential but also provides forward-looking technical support for enhancing the resilience and operational decision-making of power systems under the background of climate change.

[0088] The objectives of this invention have been fully and effectively achieved through the above embodiments. Those skilled in the art will understand that this invention includes, but is not limited to, the contents described in the accompanying drawings and the specific embodiments described above. Although the invention has been described with reference to what is currently considered the most practical and preferred embodiments, it should be understood that the invention is not limited to the disclosed embodiments, and any modifications that do not depart from the functional and structural principles of the invention will be included within the scope of the claims.

Claims

1. A power climate risk assessment method for high temperature, drought and compound events affecting both supply and demand sides, characterized in that, Comprising the following steps: S100. Collecting multi-source data of the target area, including historical meteorological observation data, hydrological and water resources data, and power system operation data, and preprocessing the multi-source data; S200. Constructing objective identification indicators for identifying high temperature events and drought events, with high temperature events being identified by daily temperature exceeding a preset percentile threshold and lasting for at least a preset minimum duration, and drought events being identified by SPEI index being lower than a preset drought threshold and lasting for at least a preset minimum duration; constructing a joint distribution model of high temperature and drought based on a joint probability density function to identify synchronous high temperature and drought compound events; S300. Based on the identified high temperature, drought, and compound events, and in combination with the differentiated characteristics of different power source types in the power system, fitting the relationship between high temperature, drought, and compound factors and the output of hydropower, wind power, and / or photovoltaic power generation to construct a power source side multi-energy collaborative supply capacity response model of the power system; S400. Based on historical electricity load data, in combination with the sensitivity of different user types to meteorological elements, fitting the multivariate relationship between high temperature, drought, and compound factors and daily electricity load, and based on typical day period division and load characteristic clustering analysis, constructing a load side response model of the power system; S500. Coupling analysis of the quantitative evaluation results of the power source side multi-energy collaborative supply capacity response model and the load side response model of the power system, constructing a deviation relationship model between available power supply and real-time load demand in the target area, and outputting typical indicators representing the carrying capacity and risk exposure level of the power system under high temperature, drought, and compound scenarios, wherein the deviation relationship model is based on the difference sequence between power supply capacity and electricity load at daily time period granularity, constructing a power gap time series, and through setting a supply-demand deviation critical value, calculating the regional load risk exposure intensity, cumulative deviation duration, and annual power shortage rate indicators; S600. Based on climate prediction data output by a climate model, extracting high temperature, drought, and compound event estimation information of the target area in the future period, driving the constructed deviation relationship model between available power supply and real-time load demand in the target area, and outputting target area power climate risk level division and trend prediction information in the future period, wherein the climate data input for future scenario simulation adopts CMIP6 multi-model ensemble output, and daily scale and geographically consistent simulation sequences are generated based on bias correction and statistical downscaling methods, typical high temperature, drought, and compound events in the future are extracted through high temperature, drought, and compound event identification methods, and the deviation relationship model is driven to output risk evolution trend curves under the corresponding scenarios, and a target area power grid partition risk level atlas and disaster resilience level index system is constructed. 2.The power climate risk assessment method for high temperature, drought and compound event oriented, double-sided influence on supply and demand, according to claim 1, wherein, In step S100, the multi-source data covers the target area for more than 10 years of historical period, and the meteorological observation data at least includes daily time series data of maximum temperature, minimum temperature, average temperature, precipitation, evapotranspiration, relative humidity, wind speed and light intensity, the hydrological and water resource data at least includes time series data of daily runoff of main basin control station and daily water level and inflow of key reservoir, and the power system operation data at least includes time series data of daily power generation according to power source type and hourly power load according to user category. 3.The power climate risk assessment method for high-temperature, drought, and compound event oriented, double-sided influence on supply and demand, according to claim 1 or 2, characterized in that, In step S100, the pre-processing of the multi-source data at least includes: S101. Missing value processing and abnormality elimination: using sliding window interpolation method, multivariate regression filling method or empirical interpolation method based on historical distribution characteristics to repair data missing items; using Z-score method and time series mutation detection method based on sliding mean to identify and eliminate mutation anomalies or physically unreasonable values; S102. Data standardization and time alignment processing: uniformly processing the time resolution of each data source, including aggregating hourly data into daily data and interpolating and reconstructing non-continuous data; meanwhile, aligning all data according to unified start time and unified spatial partition; S103. Spatial mapping and partition weighted integration: mapping data based on administrative division, meteorological station distribution or power grid operation unit, and regionally aggregating data by area weighting, load weighting or basin weighting to output regional scale representative sequence; S104. Format packaging and data management: adding metadata labels to the pre-processed data and structurally packaging according to unified data standards. 4.The power climate risk assessment method for high temperature, drought and compound event oriented, double-sided influence on supply and demand, according to claim 1, wherein, In step S200, high temperature, drought and compound events are identified based on historical meteorological observation data, including: S210. High temperature event identification: constructing objective identification index of high temperature event, calculating historical percentile value of daily maximum temperature in the target area, selecting 90% or 95% percentile value as high temperature threshold, and determining a high temperature event when daily temperature continuously exceeds the high temperature threshold for at least 3 consecutive days; S220. Drought event identification: constructing objective identification index of drought event, taking SPEI index which can comprehensively reflect the surface water budget as the drought identification factor, and taking -1 or -1.5 as the threshold value, and determining a drought event when SPEI index is below the threshold value for 3 consecutive months or more; S230. High temperature and drought compound event identification: using Copula joint probability density function to establish bivariate joint probability distribution model between high temperature intensity index and drought intensity index, and obtaining joint occurrence probability of different intensity combinations by edge distribution fitting and joint function construction, and determining a high temperature and drought compound event when the joint probability exceeds the set confidence level and the high temperature event and the drought event overlap in time; S240. Quantitative characteristic index parameter extraction: For the identified high temperature, drought and compound events, their quantitative characteristic indexes are extracted, at least including event frequency D, average intensity I, maximum intensity I max , average duration L and longest duration L max , affected area A; S250. Comprehensive risk index calculation: Linear weighted model is used to calculate the comprehensive risk index R for the identified high temperature, drought and compound events The weight coefficients a~f are determined according to the analytic hierarchy process or entropy weight method. S260. Spatial identification and regional aggregation analysis: spatially interpolating the identification results on the unified grid scale to determine the regional distribution pattern and high-risk aggregation area of high temperature, drought and compound events, and combining with regional power grid division unit to perform disaster statistics and aggregation at administrative area scale.

5. The method for power climate risk assessment for high temperature, drought and compound event oriented, double-sided influence on supply and demand, according to claim 4, characterized in that, In step S230, at least the following steps are included when constructing the bivariate joint probability distribution model between the high temperature intensity index and the drought intensity index: S231. The kernel density estimation method is used to fit the marginal distribution function F of the high temperature intensity index and the drought intensity index, respectively X ( x ) and F Y ( y ), where X and Y are the high temperature intensity index and the drought intensity index, respectively, x , y are the actual observed values of the high temperature intensity index and the drought intensity index, respectively. S232. Select Gumbel or Clayton structure function as the joint function configuration, estimate and fit the Copula function parameters by maximum likelihood estimation method, and construct the two-dimensional joint distribution model C of high temperature intensity index and drought intensity index θ (F X ( x ),F Y ( y )); S233. Take the joint probability value P(X≤ x ,Y≤ y ) as the probability index of the occurrence of high-temperature and drought compound events, and introduce the SCEI index SCEI=Φ -1 [C θ (F X ( x ),F Y ( y ))] to perform normal mapping conversion on the joint probability, where Φ -1 is the inverse function of the standard normal distribution; S234. Set a joint probability confidence level threshold. When a high temperature event and a drought event overlap in time and their joint probability is greater than or equal to the set threshold, it is determined as a high temperature and drought compound event.

6. The method for power climate risk assessment for high temperature, drought and compound event oriented, double-sided influence on supply and demand, according to claim 5, characterized in that, In step S300, based on the differentiated characteristics of different power supply types, the change trend of the output of each type of power supply unit under high temperature, drought and compound scenarios is evaluated, including the following sub-steps: S310. Power supply type classification and parameter configuration: classify the main power supply in the target regional power system according to the power supply type, and collect the basic parameters of each type of power supply, including at least installed capacity, conventional output, full-load capacity, peak shaving performance and environmental sensitivity coefficient for representing the sensitivity of output to changes in meteorological conditions; S320. Key meteorological factor and power generation performance correlation modeling: identify the key meteorological factors of each type of power supply under high temperature, drought and compound events, and combine historical operation data of each type of power supply and meteorological and hydrological data to establish a supply capacity response model of each type of power supply under high temperature, drought and compound scenarios; S330. Constructing power supply side multi-energy collaborative supply capacity response model: integrating the response models of each type of power supply, establishing a multi-energy collaborative supply capacity response model under a unified framework, and taking the identified high temperature, drought and compound event process as the input scenario, quantitatively evaluating the evolution trend of power supply side energy supply and the degree of output degradation of the target regional power system under the impact of high temperature, drought and compound factors; S340. Power supply side risk index extraction: based on the modeling results, extract typical risk evaluation indexes, including disaster enabling efficiency reduction, system total output loss, system power supply safety margin, peak shaving gap, frequency modulation and reserve deficiency, and output power supply side response capacity spatial distribution and type contribution analysis results according to spatial grid or power grid partition.

7. The method of claim 6, wherein the method is characterized by, In step S320, the construction of the hydropower supply capacity response model includes at least the following sub-steps: S321. Basin runoff simulation and drought driving mechanism identification: integrating terrain data, land use type, soil parameters, groundwater parameters, and daily multi-source precipitation data, air temperature and evapotranspiration meteorological driving factors, using a distributed hydrological model to simulate daily hydrological processes in the target basin, and introducing high temperature, drought and compound event information as external meteorological disturbance input, identifying the runoff attenuation path and sensitive area under the dominance of high temperature and drought, and outputting the runoff evolution trend, adjustable water quantity and dynamic storage of each hydropower hub control section and reservoir area in the basin; S322. Constructing meteorological-hydrological-hydropower supply response model: taking the dynamic hydrological factors output by the hydrological model as input variables, and combining historical operation data of hydropower stations, including at least daily output records, dispatching rules, reservoir capacity curves and output characteristic parameters, constructing a meteorological-hydrological-hydropower chain supply response model through multiple regression analysis, response surface function modeling or machine learning method, realizing the prediction and fitting of the evolution of hydropower output under the driving of high temperature and drought scenarios; S323. Reservoir operation simulation and capacity degradation estimation: Simulate the reservoir regulation process of each hydropower station combined with the cascade operation structure in the basin, set the minimum ecological discharge, peak shaving power generation constraints and power generation water head interval, build a refined dispatching response model, and input the typical drought and high temperature process driven by the SCEI index, simulate the degradation of the power generation capacity of the hydropower station under different intensity events, and output the reservoir water head change curve, installed power output loss ratio and reservoir capacity consumption rate index; S324. Resilience analysis and spatial heterogeneity evaluation: Based on the grid partition, the power generation fluctuation rate, regulation capacity remaining ratio and output stability coefficient of each regional hydropower station under various meteorological and hydrological scenarios are calculated, a spatial and temporal resilience distribution map of hydropower is constructed, and based on the comparative analysis of typical high temperature and drought years, the high temperature and drought impact sensitive power supply nodes are identified. 8.The power climate risk assessment method for high temperature, drought and compound event oriented, double-sided influence on supply and demand, according to claim 1, wherein, In step S400, the construction of the power system load side response model at least includes the following sub-steps: S410. User classification and load structure identification: The power system load is divided into residential electricity, industrial electricity, agricultural electricity and commercial electricity, and based on the historical electricity consumption data of each type of user, a load response database for each user type is constructed; S420. Meteorological driving factor identification and lag response analysis: Select the key meteorological variables that affect load change and combine the drought, high temperature and composite event index, and use causal relationship test or time lag regression analysis to identify the lag time window of the response of each type of load to meteorological events; S430. Multivariate load response model construction: For different user types, a nonlinear response relationship between load and meteorological variables under high temperature, drought and composite conditions is established through data-driven methods, and the model residual error is analyzed and corrected; S440. Peak load and base load offset amount quantification: Based on the identified high temperature, drought and composite event process, the electricity consumption change of each type of user in the same period is simulated, the peak load increase, base load offset trend and load structure change ratio are calculated, and the load sensitivity and user type contribution data are output; S450. Load response uncertainty and vulnerability assessment: Combined with model prediction and historical event results, the uncertainty range of load response is calculated, and sensitive blocks with load response rate greater than the preset threshold are identified; and based on the load growth rate, lag time and amplitude, the climate vulnerability of the load side is evaluated, and the weak link of the power system in supply and demand balance under high temperature and drought conditions is revealed.

9. A computer program product comprising computer instructions, characterized in that, The computer instructions are used to execute the power climate risk assessment method for the influence of high temperature, drought and composite events on both supply and demand sides according to any one of claims 1-8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the power climate risk assessment method for the influence of high temperature, drought and composite events on both supply and demand sides according to any one of claims 1-8.

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