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

Abstract

The invention discloses an electric power climate risk assessment method for the influence of high temperature and / or drought events on both sides of supply and demand, a medium and a program product, and belongs to the technical field of meteorological disaster risk assessment and energy system safety crossing. Then identifying a high-temperature or drought event based on a temperature percentile threshold value and an SPEI drought index, and constructing a bivariate distribution model by adopting a joint probability density function to identify a high-temperature drought composite event; respectively constructing a power supply side response model and a load side response model, and quantitatively evaluating output changes of different power supplies and load response characteristics of various users in a high-temperature and / or drought scene; key indexes such as a power gap and load risk exposure intensity are calculated through supply-demand coupling offset analysis; and finally, based on the climate mode prediction data, driving the risk model, and outputting a risk evolution trend in a future scene. The method can provide support for climate toughness improvement and scheduling decision making of the power system.

Description

TECHNICAL FIELD

[0001] The application belongs to the cross technical field of meteorological disaster risk assessment and energy system safety, relates to extreme weather and climate event identification and estimation and power climate risk assessment of typical meteorological disasters, and in particular to a power climate risk assessment method for the influence of high temperature and / or drought events on both supply and demand sides, a medium and a program product, which is used to improve the resilience assessment and risk prevention and control capability of power systems under extreme climate conditions and provide accurate climate resilience decision support for new power system planning. BACKGROUND

[0002] With the continuous intensification of global climate warming trend, the frequency, intensity and duration of extreme weather and climate events are showing a significant upward trend, which brings unprecedented challenges to the safe operation of power systems. Especially the extreme events of high temperature, drought and their composite superposition, because of their synchronicity, persistence and linkage amplification characteristics, have become one of the key meteorological factors affecting the stability and reliability of modern power systems.

[0003] New power systems widely use high proportion of renewable energy, high electronic equipment and high cross-region power transmission configuration, which significantly enhances the responsiveness to meteorological conditions. For example, under the background of high temperature, the power grid load increases significantly, especially the load of air conditioner, electric refrigeration and the like increases sharply. Under the condition of drought, the surface runoff decreases, which seriously affects the inflow and output of hydropower stations, resulting in a significant decrease in power generation capacity. When high temperature and drought events occur simultaneously, not only does it cause the peak load of power demand to rise sharply, but also it simultaneously compresses the supply capacity of hydropower, forming the typical composite disaster characteristics of "peak load + low valley of power supply".

[0004] Traditional power meteorological risk assessment methods mainly focus on the influence of a single meteorological factor on the power system, and fail to effectively build a systematic assessment framework integrating meteorological-hydrological-energy demand processes, and also fail to establish a dynamic scenario analysis path for typical extreme events. In addition, the existing methods generally lack a collaborative identification mechanism for composite disasters, making it difficult to accurately capture the significant characteristics of high temperature and drought events in time synchronization and spatial coupling, thereby restricting risk prediction, load assessment and supply and demand regulation decisions. For example, Chinese patent CN117937431A discloses a power supply and demand prediction method based on climate model and emission scenario, but it relies on linear empirical formula to quantify the influence of climate on power generation efficiency, does not couple hydrological dynamic process, and lacks a collaborative identification mechanism for high temperature and drought composite events; CN116937574A proposes a power system operation risk assessment framework under extreme weather conditions, but only focuses on the change of power transmission failure rate, without covering the dynamic response of both power supply output attenuation and load demand surge.

[0005] In the aspect of disaster event identification, some studies attempt to introduce univariate threshold models or composite indicators based on statistical distribution for preliminary division, but the joint representation of dry and wet processes and warm and hot processes is still relatively rough. In the aspect of meteorological-hydrological-power response modeling, current methods generally have weak data integration capability, simplified response relationship modeling, and poor regional adaptability. At the same time, the evaluation of the evolution trend of compound disaster risk under future climate scenarios also lacks a unified data generation mechanism and quantitative standard. In addition, the power supply and demand under the influence of compound extreme events have nonlinear, lagging and cross-domain coupling characteristics, and it is necessary to establish a multivariate model that comprehensively considers meteorological input, hydrological response and dynamic characteristics of power system, and introduce a high-frequency, multi-source observation data-driven evaluation mechanism.

[0006] In summary, the existing technology has many deficiencies in the evaluation of high-temperature and drought compound disaster risk facing the supply and demand sides of the power system. Therefore, how to construct a method that can objectively identify high-temperature and drought compound disasters and comprehensively evaluate their coupled influence on the supply and demand sides of the power system, so as to realize accurate and quantitative evaluation of power climate risk, is a technical problem that needs to be solved in the field. SUMMARY

[0007] (I) Invention purposes In view of the above defects and deficiencies of the prior art, the present application aims to provide a power climate risk assessment method, medium and program product facing the influence of high temperature and / or drought events on the supply and demand sides, by constructing a compound disaster identification model that integrates run-length theory threshold parameters and joint probability density functions, objectively identifying high temperature events, drought events and their compound co-occurrence processes; further combining meteorological, hydrological and power system operation and other multi-source data, constructing a dynamic modeling mechanism of power supply side output and load side response, evaluating the supply and demand deviation and risk exposure level of the power system under extreme events; and combining multi-scenario climate simulation data, simulating the evolution trend of future high-temperature and drought compound events, realizing the resilience identification and risk warning of regional power systems under the influence of compound disasters, thereby effectively improving the comprehensive risk prevention and control capability of the power system in response to extreme climate events.

[0008] (II) Technical solutions To achieve the purposes of the application and solve its technical problems, the application adopts the following technical solutions: The first purpose of the present application is to provide a power climate risk assessment method facing the influence of high temperature and / or drought events on the supply and demand sides, for identifying high temperature, drought and high temperature and drought compound events and quantitatively evaluating their risk influence on the power supply side and load side of the power system, and providing decision support for the planning, operation and management of the power system, at least including the following steps: S100. Multi-source data collection and preprocessing: Collect multi-source data covering the target area, including at least historical meteorological observation data, hydrological and water resource data, and power system operation data, and preprocess the multi-source data to construct a fused analysis dataset with consistent time and space, unified format, comparability, and traceability; S200. High temperature and / or drought event identification: Construct objective identification indicators for identifying high temperature events and drought events, where a high temperature event is identified by daily temperature exceeding a pre-set percentile threshold and lasting for at least a pre-set minimum duration, and a drought event is identified by a Standardized Precipitation Evapotranspiration Index (SPEI) being lower than a pre-set drought threshold and lasting for at least a pre-set minimum duration; construct 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. Power system power supply side response modeling: Based on the identified high temperature and / or drought events, and considering the differentiated characteristics of different power source types in the power system, fit the relationship between high temperature and / or drought factors and the output of hydropower, wind power, and / or photovoltaic power generation, and construct a power system power supply side multi-energy collaborative supply capability response model to quantitatively evaluate the capacity change trend and output sensitivity of each type of power source under high temperature and / or drought scenarios; S400. Power system load side response modeling: Based on historical electricity load data, and considering the sensitivity of different user types to meteorological elements, fit the multivariate relationship between high temperature and / or drought factors and daily electricity load, and based on typical day period division and load characteristic clustering analysis, construct a power system load side response model to quantitatively evaluate the peak load increase, basic load transfer, load sensitivity, and uncertainty under high temperature and / or drought scenarios; S500. Supply-demand coupling deviation analysis and risk indicator calculation: Perform coupling analysis on the quantitative evaluation results of the power system power supply side response model and the power system load side response model, construct a deviation relationship model between available power supply and real-time load demand in the target area, and output typical indicators representing the carrying capacity and risk exposure level of the power system under high temperature and / or drought scenarios; 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 future periods, drive the constructed deviation relationship model between available power supply and real-time load demand in the target area, and output information on the classification and trend prediction of power climate risks in the target area in future periods.

[0009] The second inventive objective of the present application is to provide a computer program product comprising computer instructions for executing the above-mentioned power climate risk assessment method facing the influence of high temperature and / or drought events on both supply and demand sides.

[0010] The third inventive objective of the present application is to provide a computer-readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the above-mentioned power climate risk assessment method facing the influence of high temperature and / or drought events on both supply and demand sides.

[0011] (Three) Technical effects Compared with the prior art, the power climate risk assessment method facing the influence of high temperature and / or drought events on both supply and demand sides, the medium and the program product of the present application have the following beneficial and significant technical effects: (1) The present application constructs a high temperature, drought and composite event identification and quantitative evaluation mechanism integrating meteorological, hydrological and power operation data. By constructing a composite disaster identification model integrating run-length theory threshold parameters and joint probability density functions, the high temperature and / or drought events are objectively identified, and the ability to describe the synchronicity and complexity of extreme climate processes is significantly improved.

[0012] (2) The present application proposes a response mechanism for coordinated modeling of the power supply side and the load side of the power system. For various power sources such as hydropower, wind power and photovoltaic power, the environmental sensitivity, power generation performance boundary and operation constraint characteristics are integrated to construct a multi-energy collaborative supply capability response model. At the same time, combined with user classification load and meteorological factor sensitivity analysis, a refined load side response model is established, thereby systematically realizing the description of the power supply and demand dynamic evolution process under high temperature and drought scenarios.

[0013] (3) The present application constructs a four-element coupling supply and demand deviation risk analysis framework of "meteorology-hydrology-power load", forming a full-process technical system covering risk identification, response modeling, index evaluation and future trend prediction. In particular, a multi-mode prediction driving mechanism based on future climate scenarios is introduced, which can realize the forward-looking assessment of the evolution trend of high temperature and drought power risks, and significantly enhance the scientific support capability of long-term climate resilience improvement and emergency management strategy formulation of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is the implementation flowchart of the power climate risk assessment method facing the influence of high temperature and / or drought events on both supply and demand sides of the present application; Figure 2 is the technical architecture block diagram of the power climate risk assessment method facing the influence of high temperature and / or drought events on both supply and demand sides of the present application. DETAILED DESCRIPTION

[0015] The present application aims to provide a power climate risk assessment method, medium and program product facing the influence of high temperature and / or drought events on both supply and demand sides, for identifying high temperature, drought and high temperature and drought combined events and quantitatively assessing the risk influence on the power source side and load side of the power system, and providing decision support for the planning, operation and management of the power system. In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme in the embodiments of the present application will be described in more detail below in combination with the drawings in the embodiments of the present application. The described embodiments are only some of the embodiments of the present application, and are intended to explain the present application, and cannot be understood as limiting the present application.

[0016] As a specific example, as shown in Figure 1 、 Figure 2 The power climate risk assessment method facing the influence of high temperature and / or drought events on both supply and demand sides provided by the embodiments of the present application comprises the following steps: S100. Multi-source data acquisition and preprocessing: Collect multi-source data covering the target area, at least including historical meteorological observation data, hydrological and water resource data and power system operation data, and preprocess the multi-source data to construct a fusion analysis data set with consistent time and space, unified format, comparability and traceability.

[0017] In the embodiments of the present application, the multi-source data at least covers a historical period of more than 10 years in the target area, wherein 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 daily runoff time series data of main basin control sites and daily water level and inflow time series data of key reservoirs, and the power system operation data at least includes daily power generation data divided by power source type (hydroelectric power, photovoltaic power, wind power, etc.) and hourly power load data divided by user category (residential, industrial, agricultural, commercial, etc.).

[0018] As a preferred, when preprocessing 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, interpolating and reconstructing non-continuous data; at the same time, aligning all data according to unified end time and unified spatial partition; S103. Spatial mapping and zoning weighting integration: based on administrative division, meteorological station distribution or power grid operation unit, the data is spatially mapped, and the data is regionally aggregated through area weighting, load weighting or watershed weighting, and a representative sequence at the regional scale is output; S104. Format packaging and data management: add metadata labels to the preprocessed data and structure the packaging according to the unified data standard to ensure traceability, verifiability and reusability of the data.

[0019] It should be noted that multi-source data acquisition and preprocessing is not only the basic data support link of the power climate risk assessment system of the present application, but also the prerequisite for ensuring the accuracy of subsequent high temperature and / or drought event identification and the effectiveness of power supply side and load side modeling. Through systematic data quality control, consistent processing in time and space dimensions, and fusion and reconstruction of multi-dimensional information, the problems of non-uniform dimension, inconsistent resolution and missing noise between meteorological, hydrological and power heterogeneous data are effectively solved.

[0020] S200. High temperature and / or drought event identification: Objective identification indicators of high temperature events and drought events are constructed. The high temperature event is determined by daily temperature exceeding the preset percentile threshold for several days, and the drought event is determined by the standardized precipitation evapotranspiration index SPEI being lower than the preset drought threshold and meeting the continuity. A joint distribution model of high temperature and drought is constructed based on the joint probability density function to identify the synchronous high temperature and drought composite event.

[0021] In the embodiment of the present application, the high temperature and / or drought event is identified based on historical meteorological observation data, which mainly includes the following sub-steps: S210. High temperature event identification: construct an objective identification indicator of high temperature events, calculate the historical percentile value of the daily maximum temperature in the target area, select 90% or 95% percentile value as the high temperature threshold, and determine a high temperature event when the daily temperature continuously exceeds the high temperature threshold for not less than 3 days; S220. Drought event identification: construct an objective identification indicator of drought events, use the SPEI index which can comprehensively reflect the surface water budget as the drought identification factor, and use -1 or -1.5 as the criterion threshold, and determine a drought event when the SPEI index is lower than the criterion threshold for 3 months or more; S230. High temperature and drought compound event identification: a bivariate joint probability distribution model between high temperature intensity index (such as daily temperature anomaly integral) and drought intensity index (such as SPEI minimum value) is established by using Copula joint probability density function, and the joint occurrence probability of different intensity combinations is obtained by fitting the edge distribution and constructing the joint function. When the joint probability exceeds the set confidence level (such as 0.9) and the high temperature event and the drought event overlap in time, it is determined as a high temperature and drought compound event; S240. Quantitative characteristic index parameter extraction: for the identified high temperature and / or drought events, their quantitative characteristic indexes are extracted, including at least event frequency D, average intensity I, maximum intensity I max , average duration L and maximum duration L max , and influence area A; S250. Comprehensive risk index calculation of each event: the linear weighted model is used to calculate the comprehensive risk index R of the identified high temperature and / or drought events, respectively, wherein the weight coefficients a~f are determined according to the analytic hierarchy process or entropy weight method, and the R value is used to reflect the strong disaster-causing degree of the event in the time and space scale, and support the risk level division and ordering evaluation of different regions; S260. Spatial identification and regional aggregation analysis: the spatial interpolation is performed on the identification results under the unified grid scale to determine the regional distribution pattern and high-risk aggregation area of high temperature, drought and compound events. The disaster statistics and aggregation are performed at the administrative scale in combination with the regional power grid division unit, and the historical disaster event library containing single and compound events is formed.

[0022] As preferred, in the step S230, when the bivariate joint probability distribution model between high temperature intensity index and drought intensity index is constructed, at least the following sub-steps are included: S231. Construction of edge distribution function: let the high temperature intensity index be random variable X and the drought intensity index be random variable Y, which correspond to the continuous observation data such as daily temperature anomaly integral value and SPEI minimum value, respectively. The edge distribution functions F X ( x ) and F Y ( y ) of the high temperature intensity index and the drought intensity index are fitted and constructed by using kernel density estimation method: wherein, x 、 y are the actual observation values corresponding to the high temperature intensity index and the drought intensity index, 、 are the kernel density estimation functions of variables X and Y, which are constructed by using Gaussian kernel function, t 、 sare integral variables.

[0023] S232. Copula joint function construction and parameter fitting: Gumbel or Clayton function is selected as the joint function configuration to depict the tail dependence characteristics between high temperature and drought events. The maximum likelihood estimation method is used to estimate and fit the Copula function parameters, and the two-dimensional joint distribution model C of high temperature and drought intensity index is constructed. θ X x Y y where, u X x v Y y and θ is the Copula parameter and represents the correlation strength between variables. When Gumbel or Clayton function is selected, the Copula function is defined as: or,

[0024] S233. Construction of composite dry heat index SCEI: The joint probability value P(X≤ x ,Y≤ y )=C θ (F X x ),F Y y ) is used as the probability index of high temperature and drought composite event, and the composite dry heat index SCEI=Φ -1 [C θ (F X x ),F Y y )] is introduced to map and convert the joint probability to normal, where Φ -1 is the inverse function of the standard normal distribution. SCEI realizes the standardized mapping of joint probability, has comparability and linear interpretability.

[0025] S234. High temperature and drought composite event determination standard: Set the joint probability confidence level threshold α∈(0,1), such as α=0.9, when the high temperature event and the drought event overlap in time and their joint probability is greater than or equal to the set threshold α or equivalently SCEI≥Φ -1 (α), it is determined as a high temperature and drought composite event.

[0026] ​​​​​​​​​​​​​​In the embodiment of the present application, step S200 realizes the unified classification and hierarchical identification of different meteorological disaster types in the time and space dimensions by constructing objective identification criteria for high temperature, drought and their composite events. On this basis, the present application introduces the Copula joint probability modeling method, accurately quantifies the probability characteristics of the joint occurrence of the two types of disaster events by fitting the marginal distribution functions of high temperature intensity and drought intensity and constructing a bivariate joint distribution model, thereby breaking through the limitations of traditional empirical rules in dealing with nonlinear dependence relationships and extreme tail coordination risks, and significantly enhancing the identification accuracy and spatiotemporal expression ability of composite extreme events. At the same time, the proposed composite dry heat index SCEI provides a standard normalized joint event quantitative index, which provides high-quality, quantifiable and comparable input parameters for subsequent comprehensive risk index calculation (step S250) and regional clustering analysis (step S260).

[0027] S300. Power system power side response modeling: Based on the identified high temperature and / or drought events, the differences in different power source types in the power system are combined, the relationship between high temperature and / or drought factors and the output of hydropower, wind power and / or photovoltaic power generation is fitted, and a power system power side multi-energy collaborative supply capability response model is constructed to quantitatively evaluate the power generation change trend and output sensitivity of each type of power unit under high temperature and / or drought scenarios.

[0028] In the embodiment of the present application, based on the differences in different power source types, the power generation change trend and output sensitivity of each type of power unit under high temperature and / or drought scenarios are evaluated, including the following sub-steps: S310. Power source type classification and parameter configuration: classify the main power sources in the target regional power system, including hydropower, wind power and / or photovoltaic power, and collect the basic parameters of each type of power source, 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 affecting the operating state changes of each type of power source under high temperature and / or drought conditions, such as the influence of key factors such as runoff and reservoir water level on hydropower, the dependence of wind power on wind speed stability, and the influence of irradiance intensity and photovoltaic panel temperature on photovoltaic power, etc., and combine the collected historical operation data of each type of power source and meteorological and hydrological data to establish a supply capability response model of each type of power under high temperature and / or drought scenarios using multivariate regression, multifactor response surface function or data-driven methods (such as random forest, support vector regression, etc.); S330. Constructing a power supply side multi-energy collaborative supply capability response model: fuse various power supply response models, establish a multi-energy collaborative supply capability response model under a unified framework, and input the identified high temperature and / or drought event process as the input scenario to quantitatively evaluate the evolution trend of the power supply side of the target area power system under the impact of high temperature and / or drought factors and the degradation degree of the output; S340. Power supply side risk indicator extraction: based on the modeling results, extract typical risk evaluation indicators, including disaster enabling efficiency drop, system total output loss, system power supply safety margin, peak regulation gap, frequency modulation and reserve deficiency, and output the power supply side response capability spatial distribution and type contribution degree analysis results according to the spatial grid or power grid partition.

[0029] As preferred, in step S320, the construction of the water and electricity supply capability response model at least includes: S321. Basin runoff simulation and drought driving mechanism identification: fuse terrain data, land use type, soil parameters, groundwater parameters, and daily multi-source precipitation data, air temperature and evapotranspiration meteorological driving factors, use a distributed hydrological model to simulate daily hydrological processes in the target basin, and input high temperature and / or drought event information as external meteorological disturbance input to identify the runoff attenuation path and runoff sensitive area under the dominance of high temperature and / or drought, and output the runoff evolution trend, adjustable water quantity and dynamic storage of each water and electricity hub control section and reservoir area in the basin; S322. Constructing a meteorological-hydrological-water power supply response model: taking the dynamic hydrological factors output by the hydrological model as input variables, and combining historical operation data of the hydropower station, at least including daily output record, dispatching rule, reservoir capacity curve and output characteristic parameter, constructing a meteorological-hydrological-water chain supply response model through multiple regression analysis, response surface function modeling or machine learning method, realizing the prediction and fitting of the water and electricity production evolution under the driving of high temperature and drought scenario; S323. Reservoir dispatching simulation and capacity degradation estimation: combine the cascade dispatching structure in the basin to simulate the reservoir capacity regulation process of each hydropower station, set the minimum ecological discharge flow, peak regulation power generation constraints and power generation water head interval, construct a refined dispatching response model, and input the typical drought and high temperature process driven by the SCEI index, simulate the degradation of water and electricity generation capacity under different intensity events, and output the reservoir water head change curve, installed output loss ratio and reservoir capacity consumption rate index; S324. Resilience analysis and spatial heterogeneity evaluation: based on the grid partition, statistics the capacity fluctuation rate, regulation capacity remaining ratio and output stability coefficient of the water and electricity station under various meteorological and hydrological scenarios, construct a water and electricity spatio-temporal resilience distribution map, and based on the comparative analysis of typical high temperature and drought years, identify the high temperature and drought impact sensitive power supply nodes.

[0030] 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.

[0031] S400. Power System Load-Side Response Modeling: 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.

[0032] In this embodiment of the invention, the construction of the power system load-side response model includes at least: 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. 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. 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. 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. 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.

[0033] 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.

[0034] S500. Supply and Demand Coupling Shift Analysis and Risk Indicator Calculation: 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.

[0035] 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.

[0036] S600. Future Scenario-Driven Risk Evolution Trend Simulation: 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.

[0037] 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.

[0038] 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.

[0039] 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 method for assessing the climate risk of electricity supply and demand in light of the impacts of high temperature and / or drought events, characterized in that, Includes the following steps: S100. Collect multi-source data of the target area, including historical meteorological observation data, hydrological and water resources data, and power system operation data, and preprocess the multi-source data; S200. Construct objective identification indicators for identifying high-temperature events and drought events. High-temperature events are judged by the daily temperature exceeding a preset percentile threshold and reaching at least a preset minimum duration. Drought events are identified by the SPEI index being lower than a preset drought threshold and reaching 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. S300. Based on the identified high temperature and / or drought events, and combined with the differentiated characteristics of different power sources in the power system, fit the relationship between high temperature and / or drought factors and hydropower, wind power and / or photovoltaic power output, and construct a multi-energy coordinated supply capacity response model for the power source side of the power system. S400. Based on historical electricity load data and combined with the sensitivity of different user types to meteorological elements, fit the multivariate relationship between high temperature and / or drought factors and daily electricity load, and construct a power system load-side response model based on typical day time period division and load characteristic cluster analysis. S500. Couple analysis of the quantitative evaluation results of the power system power source side multi-energy coordinated supply capacity response model and the power system load side response model, construct the offset 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. S600. Based on climate model outputs climate prediction data, extracts high temperature and / or drought event prediction information for the target area in the future, drives the construction of a deviation relationship model between available power supply and real-time load demand in the target area, and outputs power climate risk level classification and trend prediction information for the target area in the future.

2. The power climate risk assessment method according to claim 1, which addresses the dual impacts of high temperature and / or drought events on both supply and demand sides, is characterized in that... In step S100, the multi-source data covers a continuous historical period of more than 10 years in 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 of major river basin control stations and daily water level and inflow of key reservoirs. The power system operation data includes at least daily power generation classified by power source type and hourly power load classified by user category.

3. The power climate risk assessment method according to claim 1 or 2, addressing the dual impacts of high temperature and / or drought events on both supply and demand sides, is characterized in that... In step S100, the preprocessing of multi-source data includes at least the following: 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. 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. 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. 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.

4. The power climate risk assessment method according to claim 1, which addresses the dual impacts of high temperature and / or drought events on both supply and demand sides, is characterized in that... In step S200, high-temperature and / or drought events are identified based on historical meteorological observation data, including: 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. S220. Drought Event Identification: Construct an objective indicator for identifying 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. S230. Identification of high temperature and drought combined events: A bivariate joint probability distribution model between high temperature intensity index and drought intensity index 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 and the high temperature event and the drought event overlap in time, it is determined to be a high temperature and drought combined event. 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; S250. Comprehensive Risk Index Calculation: A linear weighted model is used for identified high temperature and / or drought events. The comprehensive risk index R is calculated, and the weighting coefficients a~f are determined based on the analytic hierarchy process or the entropy weight method. 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.

5. The power climate risk assessment method according to claim 4, which addresses the dual impacts of high temperature and / or drought events on both supply and demand sides, is characterized in that... In step S230, constructing a bivariate joint probability distribution model between the high-temperature intensity index and the drought intensity index includes at least the following: S231. The marginal distribution functions F of the high temperature intensity index and the drought intensity index are fitted and constructed using the kernel density estimation method, respectively. X ( x ) and F Y ( y ), where X and Y are the high temperature intensity index and drought intensity index, respectively. x , y These are the actual observed values ​​corresponding to the high temperature intensity index and the drought intensity index, respectively. S232. Using Gumbel or Clayton structure functions as the joint function configuration, the parameters of the Copula function are estimated and fitted using the maximum likelihood estimation method to construct a two-dimensional joint distribution model C of bivariate high temperature intensity index and drought intensity index. θ (F X ( x ),F Y ( y )); S233. With the joint probability value P(X≤ x ,Y≤ y The SCEI index is introduced as a probability indicator of the occurrence of high temperature and drought complex events, with SCEI=Φ. -1 [C θ (F X ( x ),F Y ( y The joint probability is transformed using a normal mapping, where Φ -1 It 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 to be a high-temperature and drought composite event.

6. The power climate risk assessment method according to claim 5, which addresses the dual impacts of high temperature and / or drought events on both supply and demand sides, is characterized in that... In step S300, based on the differentiated characteristics of different power sources, the power generation change trends and output sensitivity of various power sources under high temperature and / or drought scenarios are evaluated, including the following sub-steps: S310. Power source type classification and parameter configuration: Classify the main power sources in the power system of the target area according to the power source type, 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. S320. Correlation Modeling of Key Meteorological Factors and Power Generation Performance: Identify key meteorological factors that affect the operating status of various power sources under high temperature and / or drought events, and combine historical operating data and meteorological and hydrological data of various power sources to establish a supply capacity response model for various power sources under high temperature and / or drought scenarios. 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. 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.

7. The power climate risk assessment method according to claim 6, addressing the dual impacts of high temperature and / or drought events on supply and demand, is characterized in that... 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 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. 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. 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. 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.

8. The power climate risk assessment method according to claim 1, which addresses the dual impacts of high temperature and / or drought events on both supply and demand sides, is characterized in that... In step S400, the construction of the power system load-side response model includes at least the following sub-steps: 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. 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. S430. Construction of Multivariate Load Response Model: For different user types, establish the nonlinear response relationship between load and meteorological variables under high temperature and / or drought background using a data-driven method, and correct it through model residual analysis; S440. Quantification of Peak Load and Base Load Deviation: Based on the identified high temperature and / or drought events, simulate the electricity consumption changes of various users in the same period, calculate the peak load increase, base load deviation trend and load structure change ratio, and output load sensitivity and user type contribution data. 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.

9. The power climate risk assessment method according to claim 1, which addresses the dual impacts of high temperature and / or drought events on both supply and demand sides, is characterized in that... In step S500, the 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 the supply and demand offset threshold, it calculates the regional load risk exposure intensity, cumulative offset duration, and annual average power shortage rate indicators.

10. The power climate risk assessment method according to claim 1, which addresses the dual impacts of high temperature and / or drought events on both supply and demand sides, is characterized in that... In step S600, the climate data input for future scenario simulation 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 through high temperature and / or drought event identification methods. The offset relationship model is driven to output the risk evolution trend curve under the corresponding scenario. 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.

11. A computer program product comprising computer instructions, characterized in that, The computer instructions are used to execute the power climate risk assessment method according to any one of claims 1 to 10, which addresses the impact of high temperature and / or drought events on both the supply and demand sides.

12. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by the processor, it implements the power climate risk assessment method according to any one of claims 1 to 10, which addresses the impact of high temperature and / or drought events on both the supply and demand sides.

Images