Power grid resource optimal configuration method and system for high-temperature heat wave event

Abstract

The invention discloses a power grid resource optimal configuration method and system oriented to a high-temperature heat wave event, which is applied to the field of power system optimal configuration, and comprises the following steps: constructing a meteorological-power grid coupling correlation model based on meteorological and spatial thermal data of a target area, and establishing a power supply side output and load side demand model oriented to the high-temperature heat wave event; obtaining a power generation and load power sequence through region division and simulation, and performing space-time analysis to obtain a load complementation index and a power grid interaction index; determining a long-term flexibility demand based on the payload data; and finally, constructing a cross-regional resource allocation model by taking the indexes and the demands as constraints, solving by adopting an improved multivariate universe algorithm, and outputting an optimal resource allocation scheme. The method can effectively improve the scientificity and adaptability of the power grid resource configuration under the extremely high-temperature heat wave event through quantifying the cooperative influence of the high-temperature heat wave event and the urban environment, and remarkably improves the power supply reliability of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system optimization configuration, in particular to a power grid resource optimization configuration method and system for high-temperature heat wave events. BACKGROUND

[0002] With the intensification of global climate change and the rapid advancement of urbanization, the coupling of high-temperature heat wave weather and urban heat island effect has become an important environmental factor affecting the safe and stable operation of modern power systems. Under high-temperature heat wave weather, regional power grids face the dual pressures of a sharp increase in cooling demand on the load side and a decline in power generation efficiency on the power supply side, which puts higher requirements on the optimization configuration of flexible resources.

[0003] Currently, the power grid resource optimization configuration method for high-temperature heat wave events usually adopts linear correlation analysis of a single meteorological element, that is, the influence of high-temperature heat wave weather on load demand is analyzed independently, the effect of urban heat island effect on local microclimate is evaluated separately, and the results of the two are simply superimposed. This analysis strategy leads to a lack of coordination between meteorological influence mechanisms and power grid response characteristics, resulting in systematic deviations in the estimation of load peaks and power supply outputs under high-temperature heat wave events, which further causes mismatches between resource configuration schemes and actual demands, resulting in structural contradictions between regional power supply gaps and insufficient utilization of transmission channels, seriously affecting the safe and stable operation of power grids and resource utilization efficiency under high-temperature heat wave events. SUMMARY

[0004] The present application provides a power grid resource optimization configuration method and system for high-temperature heat wave events to solve the problem of insufficient accuracy of regional power grid resource configuration under the coordinated influence of high-temperature heat wave events and urban environment, to realize precise coordinated optimization configuration of power grid resources under high-temperature heat wave events.

[0005] To solve the above technical problems, the present application embodiment provides a power grid resource optimization configuration method for high-temperature heat wave events, comprising: Based on the historical meteorological data and spatial thermal data of the target region, the coordinated influence of high-temperature heat wave events and urban environment on the target power system is analyzed, and a meteorological-power grid coupling correlation model is constructed according to the analysis results; According to the meteorological-power grid coupling correlation model, a power supply side output model and a load side demand model of the target power system are established; The target region is divided, and based on the power supply side output model and the load side demand model, each sub-region is simulated and calculated to obtain a power generation power sequence and a load power sequence; According to the load power sequence, the time and space distribution of each sub-region is analyzed to obtain the load complementarity index and the power grid interaction index between each sub-region; calculating net load data of the target power system based on the power generation sequence and the load power sequence; determining a long-term flexibility requirement parameter of the target power system based on the net load data, and constructing a cross-regional resource allocation model considering the synergistic effect of the high-temperature heat wave event and the urban environment, with the load complementation index, the grid interaction index, and the long-term flexibility requirement parameter as constraint conditions; solving the cross-regional resource allocation model by using an improved multi-universe algorithm, and outputting a resource allocation scheme of the target power system.

[0006] As one of the preferred solutions, before the resource optimization and allocation of the target region, the method further comprises determining that the high-temperature heat wave event occurs in the target region based on historical meteorological data of the target region, comprising: extracting meteorological feature data under the high-temperature heat wave event from the historical meteorological data, and formulating a high-temperature heat wave intensity index and a high-temperature heat wave stress index; processing the historical meteorological data according to the high-temperature heat wave intensity index and the high-temperature heat wave stress index by using a generalized S transform method; based on the processing result, identifying and determining the high-temperature heat wave event in the target region.

[0007] As one of the preferred solutions, the power supply side output model and the load side demand model of the target power system are established according to the meteorological-grid coupling correlation model, comprising: determining key meteorological parameters affecting the power generation equipment output and power load demand in the target power system based on the meteorological-grid coupling correlation model; based on the key meteorological parameters, constructing the power supply side output model and the load side demand model.

[0008] As one of the preferred solutions, the time and space distribution analysis of each sub-region according to the load power sequence is performed to obtain the load complementation index and the grid interaction index between each sub-region, comprising: extracting daily load peak values between each sub-region according to the load power sequence; quantifying the synchronicity of the daily load peak values between each sub-region by using an improved weighted spatiotemporal Pearson correlation coefficient to obtain the load complementation index; analyzing the load transfer disturbance resistance between each sub-region by using a modified relative gain array based on the load power sequence and the grid topology parameters of the target power system to obtain the grid interaction index.

[0009] As one of the preferred solutions, the improvement of the multi-universe algorithm comprises: Based on the load complementary index, the information exchange process between individuals in the multi-universe algorithm is guided; And according to the synergistic effect of the high-temperature heat wave event and the urban environment, the global search parameters of the multi-universe algorithm are adaptively adjusted.

[0010] Another embodiment of the application provides a power grid resource optimization configuration system for high-temperature heat wave events, comprising: A synergistic analysis module is configured to analyze the synergistic effect of high-temperature heat wave events and urban environments on target power systems based on historical meteorological data and spatial thermal data of target regions, and to construct a meteorological-power grid coupling correlation model according to the analysis results; A coupling construction module is configured to establish a power source side output model and a load side demand model of the target power system according to the meteorological-power grid coupling correlation model; A simulation calculation module is configured to divide the target region, perform simulation calculation on each sub-region based on the power source side output model and the load side demand model, and obtain a power generation power sequence and a load power sequence; A space-time analysis module is configured to perform space-time distribution analysis on each sub-region according to the load power sequence, and obtain load complementary indexes and power grid interaction indexes between each sub-region; A calculation module is configured to calculate the net load data of the target power system based on the power generation power sequence and the load power sequence; A model configuration module is configured to determine long-term flexibility demand parameters of the target power system based on the net load data, and to construct a cross-regional resource configuration model considering the synergistic effect of the high-temperature heat wave event and the urban environment, with the load complementary indexes, the power grid interaction indexes, and the long-term flexibility demand parameters as constraint conditions; A resource configuration module is configured to solve the cross-regional resource configuration model using an improved multi-universe algorithm, and to output a resource configuration scheme of the target power system.

[0011] As one of the preferred solutions, before the resource optimization configuration of the target region, the method further comprises based on the historical meteorological data of the target region, the synergistic analysis module is further configured to: Extract meteorological feature data under the high-temperature heat wave event from the historical meteorological data, and develop high-temperature heat wave intensity indexes and high-temperature heat wave stress indexes; Using the generalized S transform method, the historical meteorological data is processed according to the high-temperature heat wave intensity indexes and the high-temperature heat wave stress indexes; Based on the processing results, the high-temperature heat wave event of the target region is identified and determined.

[0012] As one of the preferred solutions, the coupling construction module is further used for: determining key meteorological parameters affecting power generation equipment output and power load demand in the target power system based on the meteorological-grid coupling correlation model; constructing the power source side output model and the load side demand model based on the key meteorological parameters.

[0013] As one of the preferred solutions, the space-time analysis module is further used for: extracting daily load peaks between each of the sub-regions according to the load power sequence; quantifying the synchronism of the daily load peaks between each of the sub-regions by using an improved weighted space-time Pearson correlation coefficient to obtain the load complementation index; analyzing the anti-disturbance of load transfer between each of the sub-regions by using a modified relative gain array based on the load power sequence and the grid topology parameters of the target power system to obtain the grid interaction index.

[0014] As one of the preferred solutions, the resource configuration module is further used for: guiding the information exchange process between individuals in the multi-universe algorithm based on the load complementation index; and adaptively adjusting the global search parameters of the multi-universe algorithm according to the synergistic effect of the high-temperature heat wave event and the urban environment.

[0015] Compared with the prior art, the beneficial effects of the embodiments of the present application are at least one of the following: (1) The present application accurately quantifies the synergistic effect of high-temperature heat wave events and urban environments on power systems by constructing a meteorological-grid coupling correlation model; based on the coupling model, a power source side output model and a load side demand model are established to realize accurate simulation of power generation and power consumption characteristics under high-temperature heat wave events; through division and space-time distribution analysis of the target region, load complementation indexes and grid interaction indexes between sub-regions are obtained to provide a basis for cross-regional collaborative optimization; finally, a cross-regional resource configuration model is constructed and an improved multi-universe algorithm is used to solve it to output an optimal resource configuration scheme. The present application can effectively overcome the influence of high-temperature heat wave weather, realize stable and continuous power supply of the system, solve the power supply and demand contradiction caused by high-temperature heat wave events, and ensure the safety of regional power grids.

[0016] (2) The present application establishes a complete technical chain from meteorological environment analysis to power grid optimal configuration, effectively solves the resource configuration deviation problem caused by the mismatch between meteorological influence mechanism and power grid response characteristics under high temperature heat wave event, and significantly improves the power supply reliability and resource utilization efficiency of the power grid under high temperature heat wave event. In addition, the improved multiverse algorithm is adopted, the intelligent guidance and parameter dynamic adjustment of the optimization search process are realized by fusing the load complementary index and the climate risk self-adaptive mechanism, the limitations of traditional optimization algorithm in processing high-dimensional nonlinear problems are overcome, and an effective solving tool is provided for the power grid resource optimal configuration under high temperature heat wave event, which significantly improves the feasibility and practicality of the scheme while ensuring the calculation efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a flowchart of the power grid resource optimal configuration method for high temperature heat wave event in one embodiment of the present application; Figure 2 is a multi-source historical meteorological data set from 2014 to 2024 in one embodiment of the present application; Figure 3 is a schematic diagram of the power grid resource optimal configuration system for high temperature heat wave event in one embodiment of the present application.

[0018] Reference signs: Among them, 11, collaborative analysis module, 12, coupling construction module, 13, simulation calculation module, 14, time and space analysis module, 15, calculation module, 16, model configuration module, 17, resource configuration module. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. The purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0020] In the description of the present application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as understood by those skilled in the art. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments, and are not intended to limit the present application. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0021] An embodiment of the present application provides a power grid resource optimal configuration method for high-temperature heat wave events, and specifically, please refer to Figure 1 , Figure 1 A flowchart of the power grid resource optimal configuration method for high-temperature heat wave events in an embodiment of the present application is shown, which includes steps S1-S7: S1: Based on historical meteorological data and spatial thermal data of a target region, the synergistic effect of high-temperature heat wave events and urban environment on the target power system is analyzed, and a meteorological-power grid coupling correlation model is constructed according to the analysis result; Since the prior art usually considers climate conditions or urban local environment in isolation, the coupling amplification effect of the superposition of high-temperature heat wave events and urban environment (typically represented by urban heat island effect) on the power system cannot be accurately depicted. Therefore, an accurate model capable of quantitatively describing such synergistic effect is needed as the input basis and scientific premise of the entire optimal configuration method.

[0022] Before constructing the model, it is necessary to accurately identify and determine whether a high-temperature heat wave event occurs in the target region. This pre-step is an important prerequisite for the implementation of the method, and by establishing an objective high-temperature heat wave event identification mechanism, the method can effectively distinguish between regular high-temperature weather and high-temperature heat wave events, ensure that the resource configuration scheme is only started when necessary, and avoid resource waste.

[0023] Preferably, in an embodiment of the present application, before the resource optimal configuration of the target region, the method further includes determining that a high-temperature heat wave event occurs in the target region based on historical meteorological data of the target region, including: extracting meteorological feature data under high-temperature heat wave events from the historical meteorological data, and formulating high-temperature heat wave intensity indicators and high-temperature heat wave stress indicators; processing the historical meteorological data according to the high-temperature heat wave intensity indicators and the high-temperature heat wave stress indicators by using a generalized S transform method; based on the processing result, identifying and determining the high-temperature heat wave event of the target region.

[0024] According to the definition of high-temperature heat wave events by the meteorological department, a weather event with daily maximum temperature greater than or equal to 35℃ and continuous occurrence for 3 days or more is defined as a high-temperature heat wave event. To achieve accurate identification of high-temperature events and construct related indicators, the present embodiment selects historical meteorological data from 2014 to 2024 collected from a public meteorological data platform, including at least daily maximum temperature, relative humidity, wind speed and precipitation. To intuitively present the core features of the data, a time series variation graph of daily maximum temperature in a typical city in the past 11 years is plotted, as shown in Figure 2 , which clearly shows the fluctuation rule of temperature in the time dimension.

[0025] The meteorological feature data under the high-temperature heat wave event refers to historical meteorological data collected during the occurrence of the high-temperature heat wave event and capable of representing the environmental characteristics of the high-temperature heat wave. The high-temperature heat wave intensity index is a comprehensive index for quantifying the severity of the high-temperature heat wave event. The high-temperature heat wave stress index is a comprehensive index for evaluating the human comfort and equipment operation conditions under the heat wave environment, and reflects the actual heat environment stress degree. The generalized S transform is an improved time-frequency analysis method, which optimizes the time-frequency resolution of the traditional S transform by introducing adjustable parameters. The core idea is to use a variable Gaussian window function to realize adaptive time-frequency analysis of non-stationary signals.

[0026] In view of the fact that the core typical features of the high-temperature heat wave event are significant temperature rise and long high-temperature duration, the high-temperature heat wave intensity index and the high-temperature heat wave stress index are constructed based on historical meteorological data to quantify the high-temperature heat wave event. The calculation formula of the high-temperature heat wave intensity index is as follows: In the formula, is the number of days of the high-temperature heat wave event; T max,i is the maximum temperature of the i-th day of the high-temperature heat wave event; T avg is the daily average maximum temperature under normal circumstances, which is the daily basis temperature of the high-temperature heat wave event; is the time of the last arrival of the i-th day of the high-temperature heat wave event, calculated in hours; T avg is the time of the first arrival of the i-th day of the high-temperature heat wave event, calculated in hours. T avg

[0027] The calculation formula of the high-temperature heat wave stress index is as follows: In the formula, T is the environmental temperature, and RH is the relative humidity of the air.

[0028] Based on the constructed high-temperature heat wave intensity index and high-temperature heat wave stress index, the generalized S transform is used to accurately identify the high-temperature heat wave event, which specifically includes: ​​Firstly, the collected historical meteorological data is preprocessed by removing mean value and trend, so as to eliminate the influence of long-term climate change, accurately capture short-term high temperature data in high temperature heat wave event process, and improve data smoothness. Secondly, the optimized parameters a and b are used to balance the resolution of high temperature heat wave intensity index and high temperature heat wave stress index, and the high temperature heat wave event data is selected. The generalized S transform is carried out on each group of data, and through the transform, one-dimensional signal is converted into high-resolution time-frequency spectrogram, so as to simultaneously show the characteristics of signal in time and frequency domain. The expression of S is as follows: In the formula, a and b are optimization parameters, which are taken as values between 0 and 1, is a non-stationary signal.

[0029] Then, by calculating the video features of the spectral characteristics I HW and I TH , the parameters a and b are calculated, and the expression is as follows: Finally, the typical high temperature heat wave event data is calculated according to the following formula, and the high-resolution spectrogram of I HW and I TH after generalized S transform is obtained: After obtaining the high-resolution spectrogram, all the spectrograms are converted into gray images of the same size to eliminate the scale difference caused by different highest temperatures, and then the spectral Figure Two moment U is calculated, and the local variance and complexity index of each gray image are calculated: In the formula, m and n are the row and column numbers of the spectrogram respectively; and are the gray values of the spectrogram at (x, y) point, and u is the average value of 3*3 field pixels with (x, y) point as the center. According to the different situations of high temperature heat wave events in each region, the critical point of U value is set to judge whether it is a high temperature heat wave event.

[0030] On the basis of identifying the high-temperature heat wave event, a meteorology-power grid coupling correlation model is constructed by analyzing the synergistic effect of the high-temperature heat wave event and the urban environment (i.e. urban heat island effect). The spatial thermal data refers to the thermal environment data containing spatial position information for quantifying the urban heat island effect, and the core source thereof is satellite remote sensing image. The surface temperature is obtained by inversion, and the average temperature difference between the central city and the surrounding suburbs is further calculated. The meteorology-power grid coupling correlation model is a mathematical model for accurately quantifying the influence of the synergistic effect of the high-temperature heat wave event and the urban environment on the key meteorological parameters. The core purpose thereof is to capture the coupling amplification effect ignored in the traditional method, and to provide the corrected meteorological field input for the subsequent accurate modeling of the source and load of the power system.

[0031] Specifically, first, the historical meteorological data and the spatial thermal data of the target region are obtained, and the historical meteorological data set is preprocessed by removing the mean value and the trend to eliminate the interference of the long-term climate trend. The spatial thermal data is processed to obtain the heat island intensity time series data according to the average temperature difference between the central city and the surrounding suburbs. Then, based on the processed data, the meteorology-power grid coupling correlation model is constructed. In this embodiment, the model includes three sub-models, which are: The coupled city temperature model has the expression: In the formula, T is the temperature at time t without high-temperature heat wave event and without heat island effect; k is an empirical coefficient for correcting the synergistic effect of multiple factors; is the total time of high-temperature heat wave-urban heat island effect; is the average temperature of the central city under the high-temperature heat wave-urban heat island effect; is the average temperature of the surrounding city under the high-temperature heat wave-urban heat island effect; is the high-temperature heat wave-heat island effect intensity index.

[0032] The coupled city relative humidity model has the expression: In the formula, T HU,t represents the coupled temperature of the i-th sub-region under the synergistic effect, which is calculated by the coupled city temperature model in the meteorology-power grid coupling correlation model, and the value thereof comprehensively considers the background temperature and the temperature increasing effect of the high-temperature heat wave-urban heat island coupling effect; the constants 7.45 and 235 are empirical coefficients for calculating the saturated water vapor pressure, which are obtained by fitting the experimental data of the pure water surface at different temperatures.

[0033] The coupled city ground wind speed model has the expression: In the formula, T P0 is the atmospheric pressure; Pw is the wind pressure at time t; R is the dry air specific gas constant.

[0034] The weather-grid coupling correlation model takes the preprocessed historical weather data and spatial heat data as input, and finally outputs a set of key weather parameter time series data, i.e. coupled temperature, coupled humidity and coupled wind speed, by coupling the city temperature model, the city relative humidity model and the city ground wind speed model.

[0035] S2: According to the weather-grid coupling correlation model, the power source side output model and the load side demand model of the target power system are established; In the prior art, the power source and load model usually use standard weather data or simple temperature indicators, and the special microclimate formed under the coupling of high temperature heat wave event and urban environment is not fully considered, which leads to significant deviation in supply and demand prediction under extreme weather. Therefore, in step S2, the refined weather parameters obtained in step S1 are converted into power source output and load demand data that can be directly used for power grid operation simulation.

[0036] Preferably, in an embodiment of the present application, according to the weather-grid coupling correlation model, the power source side output model and the load side demand model of the target power system are established, including: Based on the weather-grid coupling correlation model, the key weather parameters affecting the power generation equipment output and power load demand in the target power system are determined; Based on the key weather parameters, the power source side output model and the load side demand model are constructed.

[0037] Among them, the key weather parameter refers to the coupled weather parameter output by the weather-grid coupling correlation model, which directly participates in the calculation of the power source side output and the load side demand, and is the bridge connecting the macro climate condition and the power system operation state; the power source side output model refers to the mathematical model used to calculate the active power output of wind power generation, photovoltaic power generation and hydroelectric power generation under certain weather and environmental conditions; the load side demand model refers to the mathematical model used to predict the total demand of active power in the power system.

[0038] Specifically, in the embodiment of the present application, the HSIC (Hilbert-Schmidt Independence Criterion) kernel correlation analysis method is used to analyze the influence degree of weather parameters on source side output, and the key weather parameters affecting source side output and load side demand are obtained. HSIC kernel correlation analysis is a non-parametric dependence measure based on kernel method, which can capture linear / nonlinear, monotonic / non-monotonic relationships. The process includes: Let X and Y be the sample matrices of weather variables and target variables (such as wind power output) respectively, and the sample size is n. The estimator of HSIC is defined as: where X and Y are kernel matrices, respectively, which are usually constructed using Gaussian kernel functions: where H is a centering matrix, denotes the trace of a matrix.

[0039] Calculate the HSIC value of all meteorological parameters and all source load variables. Set the screening rule: for any source load variable, arrange all its corresponding meteorological parameters in descending order of HSIC value, select the top two meteorological parameters, and determine them as the key meteorological parameters affecting the variable.

[0040] After determining the key meteorological parameters, build the power supply side output model and the load side demand model, wherein the power supply side output model includes the hydropower output model, the wind power output model and the photovoltaic output model.

[0041] Meteorological conditions have a significant impact on hydropower generation. In order to better quantify the impact of these conditions on hydropower generation, the following hydropower generation model is proposed, whose formula is as follows: where, is the hydropower generation at time t; is the turbine efficiency, reflecting the efficiency of the turbine in converting hydraulic energy into electrical energy; is the water density, usually 1000 kg / m³; is the gravitational acceleration; is the daily average flow; is the rainfall conversion coefficient, representing the proportion of rainfall converted into runoff. This parameter is dimensionless and its value is usually between 0.3 and 0.7, depending on the characteristics of the basin; S represents the area of the basin. is the precipitation at time t; H is the water head height.

[0042] The traditional wind power output model mainly determines the power output of the wind turbine based on wind speed and hub height. However, this output model ignores the influence of temperature on air density, which in turn affects the power generation. To solve this problem, this embodiment introduces an air density correction parameter, and the improved formula is as follows: where, is the wind power generation at time t; is the rated wind power generation; Tt is the temperature at time t; is the air density at time t; is the standard air density, equal to 1.225 kg / m³; is the corrected wind speed of the wind turbine at time t; F 0 is the rated wind speed of the wind turbine; T 0 is the reference temperature, generally taken as 25℃.

[0043] As a core component of renewable energy, the power generation of photovoltaic power generation is affected by multiple uncertain factors such as solar radiation intensity and environmental temperature, showing significant intermittency and volatility. Considering the influence of multiple factors, the height of the photovoltaic module is approximately calculated, and a photovoltaic output model that can effectively describe and predict the output characteristics of photovoltaic power is constructed, as shown in the following formula: In the formula, is the photovoltaic power generation at time t; is the rated photovoltaic power generation; is the downward shortwave radiation flux at time t; is the standard downward shortwave radiation flux; k is the temperature coefficient, reflecting the degree of influence of temperature change on photovoltaic power generation; is the surface temperature of the photovoltaic panel at time t.

[0044] The load side demand model considers the multiplication effect of high temperature heat wave-heat island coupling environment on electricity load, and takes coupling temperature and coupling humidity as the main driving factors, as shown in the following formula: In the formula, is the total load size at time t; p(·) represents a meteorological-related load, which is an abstract function related to climate and meteorological conditions, P base represents industrial load, P HU,t and the load related to climate and meteorological conditions.

[0045] By integrating key meteorological parameters into the load model, accurate mapping from meteorological conditions to power supply and demand is achieved. Especially in the high temperature heat wave-heat island coupling environment, this method can capture the dual pressure of "limited power output" and "surging load demand", providing a more realistic simulation basis for subsequent power grid optimization configuration.

[0046] S3: Divide the target area, and simulate and calculate each sub-region based on the power source side output model and the load side demand model to obtain the power generation sequence and the load power sequence; The existing technology usually calculates the entire target area as a homogeneous whole, ignoring the different response characteristics of different sub-regions to extreme weather due to differences in geographical location, urban form and energy structure, resulting in inaccurate assessment of the complementary potential and power transmission demand between regions.

[0047] wherein, sub-region refers to a number of spatial units divided according to the grid structure and climate characteristics for conducting fine power balance analysis. The power generation sequence refers to a sequence formed by arranging the active power output of all power generation devices in a sub-region in a certain time period in chronological order. The load power sequence refers to a sequence formed by arranging the total active power demand of a sub-region in a certain time period in chronological order.

[0048] Specifically, at the spatial division level, the target region is divided into a plurality of sub-regions based on the grid structure and climate characteristics, and the division principles of the sub-regions include: Grid topology principle: based on ensuring that each sub-region has high electrical connection strength inside and is connected to the outside through limited tie lines.

[0049] Climate consistency principle: try to ensure that the same sub-region has similar high temperature weather characteristics and urban heat island effect intensity inside.

[0050] For each high temperature heat wave event period identified in step S1, the corresponding coupled meteorological parameter time sequence of each sub-region is extracted or interpolated, including the representative coupled temperature, coupled humidity and coupled wind speed of the sub-region. Then the coupled meteorological parameter time sequence of each sub-region is input into the load side demand model of step S2, and the hourly power demand of the sub-region under the coupled meteorological condition is calculated, i.e. the load power sequence. The power output of the power supply side of the sub-region in the same period is calculated to obtain the power generation sequence of the sub-region.

[0051] Through spatial division and parallel simulation, the macro meteorological-grid coupling influence is decomposed and implemented to each specific sub-region, generating power generation sequences and load power sequences with high spatial and temporal resolution that can reflect spatial heterogeneity. This provides an indispensable and real data basis for step S4 to analyze the load complementarity and grid interaction between regions, making it possible to optimize the cross-regional resource allocation, thereby improving the overall resilience and synergy of regional power grids.

[0052] S4: According to the load power sequence, analyze the spatial and temporal distribution of each sub-region to obtain the load complementarity index and grid interaction index between each sub-region; Under high temperature heat wave events, the load of each sub-region does not change in isolation, but there is a complex spatio-temporal correlation. Simply adding up the loads of each region may mask the key distribution characteristics. For example, the load peaks of some regions may occur at the same time, exacerbating the power supply pressure of the entire system; while the load peaks of other regions may occur at different times, thereby having the potential for mutual support.

[0053] The load complementation index is a comprehensive measurement parameter for quantifying the difference and complementation potential of load curves (especially daily peak values) between different sub-regions in time and space dimensions. The index takes into account actual power grid connections and geographical and climatic factors, and the more negative the value, the better the complementarity. The grid interaction index is a safety evaluation parameter for quantifying the disturbance intensity of the entire power grid operation state (such as power flow and voltage) when transferring unit load power from one sub-region to another sub-region under a specific power grid structure. The larger the index value, the higher the risk of load transfer operation.

[0054] Preferably, in one embodiment of the present application, the time and space distribution of each sub-region is analyzed according to the load power sequence to obtain the load complementation index and the grid interaction index between each sub-region, including: According to the load power sequence, the daily load peak value between each sub-region is extracted; The improved weighted space-time Pearson correlation coefficient is used to quantify the synchronization of daily load peak values between each sub-region to obtain the load complementation index; According to the load power sequence and the grid topology parameters of the target power system, the disturbance resistance of load transfer between each sub-region is analyzed through the modified relative gain array to obtain the grid interaction index.

[0055] The daily load peak value refers to the maximum power value reached in the load power sequence within a natural day. The improved weighted space-time Pearson correlation coefficient is a statistical method for measuring the correlation between two time series. Based on the classic Pearson correlation coefficient, a spatial weight matrix is introduced, which takes into account geographical distance, electrical distance, climate zone consistency, and other factors, making the calculation results more reflect the true correlation under spatial correlation constraints. The grid topology parameters are a set of parameters that describe the physical connection relationship of the power network, usually including node connection relationship, line impedance, transformer ratio, line rated capacity, etc. The modified relative gain array is an analysis tool based on the concept of relative gain array in control theory. In this embodiment, it is "modified" and applied to the power system to analyze the coupling relationship between power generation adjustment and load change between different sub-regions, and especially considers the influence of thermal environment on the disturbance characteristics of the grid. The disturbance resistance of load transfer refers to the ability of the power system to maintain stable operation without producing severe fluctuations or instability when part of the load in one sub-region is transferred to another sub-region through the grid. Strong disturbance resistance means that load transfer has little impact on the system.

[0056] Specifically, first, according to the load power sequence, the daily load peak value of each sub-region during the high-temperature heat wave event is extracted to form the daily load peak value time sequence of each sub-region. This step is the basis of the analysis, aiming to capture the most critical load characteristics of each region under the high-temperature heat wave event.

[0057] Secondly, the improved weighted spatio-temporal Pearson correlation coefficient is adopted to quantify the synchronism of daily load peak values among each sub-region, so as to obtain the load complementarity index. The traditional Pearson correlation coefficient only measures the linear correlation of time series from the statistical point of view, but the improvement made in this embodiment is that a weight factor reflecting the spatial correlation characteristics between sub-regions is introduced. In specific implementation, the weight factor is determined by the electrical distance, geographical distance and climate zoning consistency between sub-regions. The weight coefficients of each factor are calculated by the entropy weight method, and then the comprehensive spatio-temporal coupling weight is constructed. In the calculation, the daily load peak value sequence of each sub-region and its mean value sequence are substituted into the formula of the improved correlation coefficient, and the result is the load complementarity index. The formula of the improved weighted Pearson correlation coefficient is as follows: In the formula, is the improved weighted correlation coefficient; and are the daily load peak value time series of sub-regions a and b on the tth day under the high temperature heat wave-heat island effect; and are the mean values thereof; is the spatio-temporal coupling weight between sub-regions a and b, which is determined by the following factors: In the formula, is the electrical distance between cities, reflecting the grid connection strength, and D takes the median of all sets; is the geographical distance between cities, G takes the median of all sets; is the climate zone consistency coefficient, which is 1 if the same climate zone and 0.5 otherwise; is the economic complementarity index, and α, β, γ, δ are the weight coefficients of each factor, which are calculated by the entropy weight method and satisfy α+β+γ+δ=1.

[0058] The closer the load complementarity index is to 1, the higher the synchronism of the load peak values of the two regions is, and the poorer the complementarity is. The closer the load complementarity index is to -1, the more obvious the peak-shifting characteristics of the load peak values of the two regions are, and the stronger the complementarity is. In this way, the region pairs with actual mutual aid potential can be more accurately identified, providing a key basis for subsequent cross-regional resource allocation.

[0059] Then, according to the load power sequence and the pre-acquired grid topology parameters of the target power system, the anti-disturbance of load transfer among each sub-region is analyzed through the modified relative gain array, and the grid interaction index is obtained.

[0060] Specifically, first, the topology parameters of the power grid need to be collected, including the node impedance matrix, the transmission line rated capacity and the current load rate, and the real-time operation data, including the voltage, frequency, and line loss rate of each node. At the same time, the baseline operation state of each sub-region is defined based on the historical operation data of the target region, including the baseline power generation power and the baseline load power, which reflect the typical operation level of the system without extreme weather disturbance. Then, the input vector is defined as the adjustment amount of the power generation power of each sub-region, and the output vector is defined as the actual change amount of the load of each sub-region. By solving the power flow equation of the power grid, an original correlation matrix representing the relationship between power generation adjustment and load response is constructed, which reflects the electrical coupling strength between nodes under standard working conditions. The key improvement of the embodiment is to introduce a thermal environment disturbance correction factor to optimize the correlation matrix , forming a corrected correlation matrix ′. The factor is specially used to quantify the influence of high-temperature heat wave and urban heat island effect on the operation characteristics of the power grid. Under the synergistic effect of high-temperature heat wave event and heat island effect, the thermal stability limit of the power grid elements decreases, the transmission capacity of the line decreases, and the sensitivity of the load to power change increases. The correction factor is calculated by the high-temperature heat wave intensity index, the high-temperature heat island effect intensity index, and the environmental temperature and humidity parameters. The numerical value is positively correlated with the degree of thermal environment stress. The calculation formula is as follows: In the formula, f is a sub-region that can export load; g is a sub-region that needs to import load from other sub-regions due to insufficient power generation; is a correction coefficient. The stronger the high-temperature heat wave-heat island effect, the larger the numerical value. When the numerical value is greater than 1, it has an amplification effect on the same power generation adjustment, the load response of the sub-region is more significant, and the anti-disturbance ability is increased.

[0061] The corrected optimization correlation matrix ′ is the product of the original correlation value and the thermal environment disturbance correction factor, and the formula is as follows: Calculate the disturbance coefficient: introduce RAG to measure the anti-disturbance ability of load transfer between sub-regions, and the formula is as follows: The numerical value of RAG is used as an interaction index of the power grid, and its physical meaning is clear: when the index value tends to 1, it means that when the load transfer is performed on this transmission path, the disturbance to other parts of the system is large, and the anti-disturbance ability of the system is poor; when the index value is far from 1, it means that the mutual coupling effect of load transfer is small, and the safety of the transmission path is high.

[0062] Through this complete analysis process, the safety boundary and risk level of load transfer between sub-regions under high-temperature heat wave events can be accurately evaluated. This analysis ensures that the cross-regional load support scheme can effectively balance the power demand while considering the stability and safety of the power grid operation, avoiding systemic risks caused by ignoring the impact of the thermal environment on the operation characteristics of the power grid.

[0063] S5: Calculate the net load data of the target power system based on the generated power sequence and the load power sequence; Under high-temperature heat wave events, the surge in load demand and the possible limitations on power generation capacity can exacerbate the fluctuation amplitude and change rate of net load. By accurately calculating the net load, the complex supply-demand balance problem can be transformed into the analysis and optimization of a single sequence, thereby clearly revealing the power shortage (net load is positive) or power surplus (net load is negative) of the system in the time and space dimensions, providing direct and quantitative basis for cross-regional resource optimization allocation.

[0064] wherein the net load data refers to the difference between the total load power and the total generated power in the power system at a specific time. The generated power sequence refers to a series of data points arranged in time sequence reflecting the total generated power of the power system or a certain region at different time points. The load power sequence refers to a series of data points arranged in time sequence reflecting the total power demand of the power system or a certain region at different time points.

[0065] Specifically, the generated power sequence and the load power sequence of each sub-region in the same time period generated by step S3 are obtained. These sequences are usually time series data with the same time resolution (such as 15 minutes or 1 hour), covering the period of the high-temperature heat wave event of interest.

[0066] Then, the net load calculation is performed. For each sub-region in the target power system, at each same time point, the total load power value of the sub-region at that time point is subtracted from the total generated power value of the sub-region at that time point, and the resulting difference is the net load of the sub-region at that time point. Repeating this calculation for all time points yields the net load time sequence of each sub-region. Adding the net load sequences of each sub-region by time point yields the net load time sequence of the entire target power system.

[0067] S6: Determine the long-term flexibility demand parameter of the target power system based on the net load data, and construct a cross-regional resource allocation model considering the coordinated influence of high-temperature heat wave events and urban environment, with the load complementation index, the grid interaction index, and the long-term flexible demand parameter as constraint conditions; Traditional resource configuration models usually only consider the physical limits of devices (such as line rated capacity), ignoring the spatio-temporal correlation characteristics of load behavior and the fact that the grid vulnerability increases under high-temperature heat wave events. This embodiment introduces load complementarity indicators and grid interaction indicators as constraints, deeply integrating "grid physical characteristics" with "load spatio-temporal behavior" and "environmental risk". This makes the optimization model no longer static and isolated, but can dynamically respond to the supply-demand relationship between regions and the safety risk under environmental stress, thereby developing an economic and robust configuration scheme.

[0068] In an embodiment of the present application, the long-term flexibility demand of the target power system needs to be determined based on the net load data calculated in step S5 before constructing the cross-regional resource configuration model. The long-term flexibility demand of the grid under the synergistic influence of high-temperature heat wave events and urban heat island effect is analyzed from two dimensions: net load fluctuation regulation demand and net load persistent imbalance response demand.

[0069] Net load fluctuation regulation demand refers to the ability of the system to respond to short-term fluctuations in net load. By analyzing the fluctuation characteristics of the net load time series of each sub-region, the upward regulation capacity (to respond to positive fluctuations in net load) and downward regulation capacity (to respond to negative fluctuations in net load) required by the system are determined. In specific implementation, the change extreme value of the net load time series within a rolling time window (such as 15 minutes, 1 hour) is calculated, and the fluctuation range at a certain confidence level (such as 95%) is taken as the regulation demand. At the same time, the rate requirement of net load fluctuation is considered to ensure that the flexibility resources configured by the system can track the changes in net load. The corresponding long-term flexibility demand expression is as follows: In the formula, is the average net load in the nth high-temperature heat wave-heat island effect existing period; is the average net load on the tth day in the nth high-temperature heat wave-heat island effect existing period; is the average system load output on the tth day in the nth high-temperature heat wave-heat island effect existing period; is the average photovoltaic output on the tth day in the nth high-temperature heat wave-heat island effect existing period; is the average wind power output on the tth day in the nth high-temperature heat wave-heat island effect existing period.

[0070] Net load persistent imbalance response demand refers to the ability of the system to respond to long-term persistent deviation of net load from the baseline value. By calculating the cumulative deviation of the net load time series during the high-temperature heat wave event, the energy buffer capacity required is determined. In specific implementation, the net load time series is integrated during the high-temperature period, the cumulative deviation of the net load from the baseline value is calculated, and the energy deficit or surplus under the most severe condition is considered. The corresponding long-term flexibility demand expression is as follows: wherein, is the adjustable energy flexibility demand of the nth high-temperature heat wave-heat island effect existing period; t E and t S is the start time and end time of the high-temperature heat wave-heat island effect existing period.

[0071] Then a cross-regional resource allocation model capable of effectively responding to the synergistic effect of high-temperature heat wave and heat island is constructed, which adopts a double-layer optimization structure, respectively optimizes from the aspects of system reliability and economic operation, and through innovative constraint conditions, the characteristics of the power grid under high-temperature heat wave event are taken into account.

[0072] Specifically, first, the overall architecture of the model is designed: a cross-regional resource allocation model is constructed, the upper model of which takes the optimization of system reliability as the core target, and the lower model takes the optimization of resource utilization economy as the core target, the two levels of models are coupled through decision variables, and together constitute a complete optimization framework.

[0073] Then, the upper reliability model is constructed, the upper model focuses on system reliability optimization, taking reliability as the objective function, mainly including the annual power supply shortage expectation of the regional power grid under the coupling effect of high-temperature heat wave and heat island and the system load shedding power, and the specific formula is: wherein, is the upper objective function; is the annual power supply shortage expectation of the regional power grid under the coupling effect of high-temperature heat wave and heat island; is the system load shedding power under the synergistic effect of high-temperature heat wave event and heat island effect; is the probability of failure outage at time t under the coupling effect of high-temperature heat wave and heat island; is the active power output of the hydrogen storage unit at time t under the coupling effect of high-temperature heat wave and heat island; is the load shedding power under the synergistic effect of high-temperature heat wave event and heat island effect within ; is the time interval under the synergistic effect of high-temperature heat wave event and heat island effect.

[0074] The constraint conditions of the upper model include: (1) Multi-regional energy storage collaborative capacity constraint: ensure that the energy storage configuration capacity of each sub-region is within the safe operation range after considering the regional complementary relationship, and its expression is as follows: wherein, θij is the complementary coefficient based on load synchronization analysis for the sub-region set with complementary relationship with sub-region i. is the safe minimum hydrogen storage capacity allowed by the energy storage system under normal circumstances; is the safe maximum hydrogen storage capacity allowed by the energy storage system under normal circumstances, is the hydrogen storage capacity of sub-region i at time t under the synergistic effect of high-temperature heat wave event and heat island effect.

[0075] (2) Cross-regional unit coordinated output constraint: limit the upper and lower limits of each region support output, ensure that the cross-regional power support is within the safe and controllable range, and its expression is as follows: In the formula, is the support city set of sub-region i, is the support coefficient based on electrical distance and channel capacity; is the minimum safe output of hydrogen storage unit under normal circumstances; is the maximum safe output of hydrogen storage unit under normal circumstances; is the output of hydrogen storage unit of sub-region i at time t under the synergistic effect of high-temperature heat wave event and heat island effect.

[0076] (3) Multi-region coordinated load shedding constraint: specifies the maximum load shedding ratio allowed by each region, and ensures the reasonable distribution of load control measures, and its expression is as follows: In the formula, is the load shedding amount of sub-region i at time t under the synergistic effect of high-temperature heat wave event and heat island effect; is the maximum load shedding ratio allowed by the regional power grid; is the active power of the point of common coupling of sub-region i at time t under the synergistic effect of high-temperature heat wave event and heat island effect.

[0077] Then, the lower economic model is constructed, and the lower model focuses on economic operation optimization, and the objective function is defined as maximizing resource utilization and minimizing energy storage operation loss, and the specific formula is: In the formula, is the lower target function; is the resource utilization under the coupling effect of high-temperature heat wave and heat island; is the total capacity of the system; is the hydrogen storage operation loss amount under the synergistic effect of high-temperature heat wave event and heat island effect; and is the hydrogen energy input and output amount under the synergistic effect of high-temperature heat wave event and heat island effect.

[0078] The constraint conditions of the lower layer model not only include traditional hydrogen storage operation constraints, power balance constraints, renewable energy output constraints, thermal power unit constraints and distribution line load flow constraints, but also innovatively introduce constraints based on load complementarity indexes and grid interaction indexes.

[0079] (1) Hydrogen storage operation constraints, the expression is as follows: In the formula, is the discharge power of hydrogen storage at t moment under the synergistic effect of high temperature heat wave event and heat island effect; is the maximum discharge power of hydrogen storage; is the charging power of hydrogen storage at t moment under the synergistic effect of high temperature heat wave event and heat island effect; is the maximum charging power of hydrogen storage.

[0080] (2) Multi-region power balance constraints, the expression is as follows: In the formula, , , are the conventional unit, hydrogen storage and new energy output of sub-region i at t moment under the synergistic effect of high temperature heat wave event and heat island effect; is the load demand power of sub-region i at t moment under the synergistic effect of high temperature heat wave event and heat island effect, is the load shedding power; is the power transmission power from sub-region i to j.

[0081] (3) Renewable energy output constraints, the expression is as follows: In the formula, , , are the minimum output of photovoltaic, wind and hydraulic units; , , are the maximum output of photovoltaic, wind and hydraulic units; , , are the output of photovoltaic, wind and hydraulic units at t moment under the synergistic effect of high temperature heat wave event and heat island effect.

[0082] (4) Thermal power unit constraints, the expression is as follows: In the formula, is the load flow of distribution line at t moment under the synergistic effect of high temperature heat wave event and heat island effect;N i N i is the number of hydrogen storage units connected to the i-th distribution line; U L V is the rated voltage of the distribution line; cosδ is the power factor.

[0083] (5) Long-term flexibility constraint, the expression is as follows: In the formula, is the maximum upward adjustment flexibility of the system; is the upward adjustment flexibility of the hydrogen storage at time t; is the upward adjustment flexibility of other energy storages at time t; is the minimum downward adjustment flexibility of the system; is the downward adjustment flexibility of the hydrogen storage at time t; is the downward adjustment flexibility of other energy storages at time t; is the maximum adjustable energy that the system can provide during the period of high-temperature heat wave-heat island effect.

[0084] (6) Cross-regional transmission constraint based on load complementarity index, the expression is as follows: In the formula, is the improved weighted correlation coefficient (i.e. load complementarity index) between sub-region i and sub-region j, which ensures that the transmission capacity between sub-regions with high load synchronization is limited, and the transmission channel between sub-regions with strong complementarity is fully utilized.

[0085] In this embodiment, the load complementarity index, grid interaction index and flexibility demand parameter are integrated into the optimization process, so that the final configuration scheme not only strictly follows the physical operation rules of the system, but also accurately meets the real operation environment and demand of the power grid under high-temperature heat wave events. The resulting scheme can significantly improve the overall economy of resource utilization while ensuring power supply reliability and safety, and avoid waste or insufficient investment.

[0086] S7: Solve the cross-regional resource configuration model by using the improved multi-universe algorithm, and output the resource configuration scheme of the target power system.

[0087] The traditional multi-universe algorithm has the defects of slow convergence speed and easy to fall into local optimum when dealing with high-dimensional, multi-constrained and nonlinear power grid optimization problems. By introducing load complementarity index guidance information exchange, the algorithm search direction is consistent with the actual power grid operation rules; through the climate risk adaptive mechanism, the algorithm parameters can dynamically respond to environmental changes, thereby significantly improving the optimization efficiency and solution quality.

[0088] Preferably, in one embodiment of the present application, the improvement of the multi-universe algorithm includes: Based on the load complementarity index, guide the information exchange process between individuals in the multi-universe algorithm; And according to the synergistic effect of high-temperature heat wave event and urban environment, the global search parameters of the multi-universe algorithm are adaptively adjusted. The multi-universe optimization algorithm is a swarm intelligence optimization algorithm inspired by the multi-universe theory, which searches for global optimization by simulating the mechanisms of white holes, black holes and wormholes in the universe. In one embodiment of the present application, in order to solve the problem that the traditional optimization algorithm is easy to fall into local optimum and slow in convergence speed when dealing with cross-regional resource allocation model, the multi-universe optimization algorithm is improved.

[0089] The specific improvement points of the multi-universe optimization algorithm are as follows: (1) Chaotic initial universe generation: In the starting stage of the algorithm, each universe individual corresponds to a complete set of configuration and operation scheme, the dimension of which is determined by all decision variables of the upper and lower layers in step S6, including the configuration capacity of hydrogen energy storage of each city, the unit output at each time, the load shedding power, etc. The initial universe group is generated by using Logistic chaotic mapping to ensure that the initial solution is uniformly distributed in the search space. In specific implementation, for each universe individual (i.e. a complete configuration scheme), its decision variables are initialized through chaotic sequence. After the initial universe is generated, the initial optimal solution is selected through fitness calculation to provide a benchmark for subsequent iteration. The fitness function F(X) is constructed by using a double-layer objective integration mechanism based on fuzzy analytic hierarchy process (FAHP), which includes three components: standardized upper-layer reliability objective weight, standardized lower-layer economic objective weight, and constraint violation penalty. The constraint violation penalty is designed with an adaptive penalty factor, which allows moderate constraint violation in the early iteration stage to expand the search range, and strictly punishes in the later iteration stage to ensure a feasible solution. The optimal initial fitness of each initial universe is calculated as follows: In the formula, is the solution in the nth universe that meets the optimal initial fitness.

[0090] (2) White hole-black hole exchange mechanism guided by load synchronization: The current population is sorted by fitness, with the top 50% being "white holes" (high-quality universes) and the bottom 50% being "black holes" (to-be-optimized universes). The traditional random matching method is improved, and each black hole is matched with the most complementary white hole according to the load synchronization analysis results of the sub-regions. Set the exchange dimension ratio, and preferentially select the dimensions with high load synchronization for information exchange to ensure that the characteristics of high-quality solutions are effectively transmitted between related sub-regions. During the exchange process, the satisfaction of each constraint condition is checked synchronously, and the dimensions that do not meet the constraint conditions are repaired to ensure that the final solution meets all constraint conditions.

[0091] (3) Climate risk adaptive inflation rate adjustment: the inflation rate of the basic multi-universe optimization algorithm is linearly decreasing, which cannot be dynamically adjusted according to population diversity. The application improves it to an adaptive nonlinear inflation rate that integrates climate risk indicators, and adjusts the inflation rate through universe diversity and real-time risk feedback. Specifically, first calculate the real-time universe diversity, whose formula is: In the formula, d is the problem dimension; is the average value of the jth dimension. After obtaining the universe diversity, an adaptive nonlinear inflation rate formula is constructed, and the high temperature heat wave risk index is introduced to dynamically adjust the inflation rate, whose formula is: In the formula, is the initial maximum inflation rate; is the minimum inflation rate; is the climate risk coefficient calculated according to real-time weather prediction data.

[0092] (4) Adaptive wormhole mechanism: the worse the fitness of the universe, the higher the probability of jumping out of the current area through the wormhole. The risk perception mechanism is introduced, and the universes in the high-risk climate area are given higher wormhole transition probability. The wormhole probability formula is as follows: In the formula, is the current global optimal fitness; is the current worst fitness; is the minimum value to avoid denominator 0; is the risk adjustment coefficient.

[0093] The steps for solving by using the improved multi-universe algorithm are as follows: firstly, algorithm parameters are initialized, and initial universe groups covering the search space are generated by using chaotic mapping, each universe represents a complete resource configuration candidate scheme, and the fitness is calculated to evaluate the advantages and disadvantages of the scheme. Subsequently, the algorithm enters the iterative optimization process, in each generation, the white hole-black hole exchange mechanism is executed based on the fitness ranking, which promotes the transfer of high-quality solution characteristics to poor-quality solutions; at the same time, the climate risk adaptive mechanism is introduced, and the expansion rate and other global search parameters are dynamically adjusted according to the real-time high temperature heat wave and heat island effect intensity, so that the exploration ability of the algorithm under adverse weather conditions is enhanced. In the iteration process, the fitness-driven wormhole transition mechanism is also used to help the population jump out of the local optimum. When the maximum iteration number or the convergence accuracy requirement is met, the algorithm terminates and the global optimal universe is decoded into the final resource configuration scheme. The scheme clearly gives the optimal configuration capacity of hydrogen energy storage in each sub-region, the optimal unit output plan at each time in the dispatching period and the optimal transmission power of the inter-regional tie line, forming an optimized operation strategy that can coordinate the high temperature heat wave event and the influence of urban environment.

[0094] Another embodiment of the present application provides a power grid resource optimization configuration system for high temperature heat wave events, specifically, please refer to Figure 3 , Figure 3 The figure shows a schematic diagram of the power grid resource optimization configuration system for high temperature heat wave events in one embodiment of the present application, which includes: The collaborative analysis module 11 is used for analyzing the collaborative influence of high temperature heat wave events and urban environment on the target power system based on the historical meteorological data and spatial thermal data of the target region, and constructing a meteorological-grid coupling correlation model according to the analysis result; The coupling construction module 12 is used for establishing the power source side output model and the load side demand model of the target power system according to the meteorological-grid coupling correlation model; The simulation calculation module 13 is used for dividing the target region, and performing simulation calculation on each sub-region based on the power source side output model and the load side demand model to obtain the power generation power sequence and the load power sequence; The space-time analysis module 14 is used for performing space-time distribution analysis on each sub-region according to the load power sequence to obtain the load complementarity index and the grid interaction index between each sub-region; The calculation module 15 is used for calculating the net load data of the target power system based on the power generation power sequence and the load power sequence; The model configuration module 16 is used for determining the long-term flexibility demand parameter of the target power system based on the net load data, and constructing a cross-regional resource configuration model considering the collaborative influence of high temperature heat wave events and urban environment by taking the load complementarity index, the grid interaction index and the long-term flexibility demand parameter as constraint conditions; The resource configuration module 17 is configured to solve the cross-regional resource configuration model by using the improved multi-universe algorithm, and output a resource configuration scheme of the target power system.

[0095] Preferably, in one embodiment of the present application, before the resource optimization configuration of the target region, the method further comprises, based on the historical meteorological data of the target region, the cooperative analysis module is further configured to: extract meteorological feature data under a high-temperature heat wave event from the historical meteorological data, and formulate a high-temperature heat wave intensity index and a high-temperature heat wave stress index; process the historical meteorological data according to the high-temperature heat wave intensity index and the high-temperature heat wave stress index by using a generalized S transform method; based on the processing result, identify and determine the high-temperature heat wave event of the target region.

[0096] Preferably, in one embodiment of the present application, the coupling construction module is further configured to: determine key meteorological parameters affecting the power output of the power generation equipment and the power load demand in the target power system based on the meteorological-grid coupling correlation model; construct a power source side output model and a load side demand model based on the key meteorological parameters.

[0097] Preferably, in one embodiment of the present application, the space-time analysis module is further configured to: extract daily load peak values between each sub-region according to the load power sequence; quantify the synchronism of the daily load peak values between each sub-region by using an improved weighted space-time Pearson correlation coefficient, and obtain a load complementarity index; analyze the anti-disturbance of the load transfer between each sub-region by using a modified relative gain array according to the load power sequence and the grid topology parameters of the target power system, and obtain a grid interaction index.

[0098] Preferably, in one embodiment of the present application, the resource configuration module is further configured to: guide the information exchange process between individuals in the multi-universe algorithm based on the load complementarity index; and adaptively adjust the global search parameters of the multi-universe algorithm according to the cooperative influence of the high-temperature heat wave event and the urban environment.

[0099] Compared with the prior art, the embodiment of the present application has at least one of the following advantages: (1) The present application accurately quantifies the synergistic effect of high temperature heat wave events and urban environment on the power system by constructing a meteorology-power grid coupling correlation model; based on the coupling model, a power source output model and a load demand model are established to accurately simulate the characteristics of power generation and electricity consumption under high temperature heat wave events; through the division of the target area and the analysis of the space-time distribution, the load complementary index and the power grid interaction index between each sub-region are obtained, which provides the basis for cross-regional collaborative optimization; finally, through the construction of a cross-regional resource allocation model and the use of an improved multiverse algorithm for solution, the optimal resource allocation scheme is output. The present application can effectively overcome the influence of high temperature heat wave weather, realize stable and continuous power supply of the system, solve the power supply and demand contradiction caused by high temperature heat wave events, and ensure the safety of regional power grid.

[0100] (2) The present application establishes a complete technical chain from meteorological environment analysis to power grid optimization configuration, effectively solves the resource allocation deviation problem caused by the mismatch between meteorological influence mechanism and power grid response characteristics under high temperature heat wave events, significantly improves the power supply reliability and resource utilization efficiency of the power grid under high temperature heat wave events. In addition, the improved multiverse algorithm is adopted, the intelligent guidance and parameter dynamic adjustment of the optimization search process are realized by fusing the load complementary index and the climate risk adaptive mechanism, the limitations of traditional optimization algorithms in dealing with high-dimensional nonlinear problems are overcome, and an effective solving tool is provided for the power grid resource optimization configuration under high temperature heat wave events. The feasibility and practicality of the scheme are significantly improved while ensuring the calculation efficiency.

[0101] The above-described embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as limiting the scope of the present patent. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the protection scope of the present patent should be subject to the appended claims.

Claims

1. A method for optimizing power grid resource allocation in response to high-temperature heat wave events, characterized in that, include: Based on historical meteorological and spatial thermodynamic data of the target area, the combined impact of high-temperature heat wave events and urban environment on the target power system is analyzed, and a meteorological-power grid coupling correlation model is constructed based on the analysis results. Based on the meteorological-power grid coupling correlation model, establish the power supply side output model and load side demand model of the target power system; The target area is divided into sub-regions, and simulation calculations are performed on each sub-region based on the power output model and the load demand model to obtain the power generation sequence and the load power sequence. Based on the load power sequence, a spatiotemporal distribution analysis is performed on each of the sub-regions to obtain load complementarity indicators and power grid interaction indicators among the sub-regions. Based on the power generation sequence and the load sequence, calculate the net load data of the target power system; Based on the net load data, the long-term flexibility demand parameters of the target power system are determined. With the load complementarity index, the grid interaction index, and the long-term flexibility demand parameters as constraints, a cross-regional resource allocation model considering the synergistic impact of the high-temperature heat wave event and the urban environment is constructed. An improved multiverse algorithm is used to solve the cross-regional resource allocation model and output the resource allocation scheme of the target power system.

2. The method for optimizing power grid resource allocation in response to high-temperature heat wave events as described in claim 1, characterized in that, Before optimizing resource allocation in the target area, the method further includes determining, based on historical meteorological data of the target area, that the high-temperature heat wave event has occurred in the target area, including: Meteorological characteristic data under the high-temperature heat wave event are extracted from the historical meteorological data to formulate high-temperature heat wave intensity index and high-temperature heat wave stress index. The generalized S-transform method is used to process the historical meteorological data based on the high-temperature heat wave intensity index and the high-temperature heat wave stress index; Based on the processing results, the high-temperature heat wave event in the target area is identified and determined.

3. The method for optimizing power grid resource allocation in response to high-temperature heat wave events as described in claim 1, characterized in that, The step of establishing the power supply-side output model and load-side demand model of the target power system based on the meteorological-power grid coupling correlation model includes: Based on the meteorological-power grid coupling correlation model, the key meteorological parameters affecting the power output of generating equipment and the power load demand in the target power system are determined. Based on the key meteorological parameters, the power output model on the power supply side and the demand model on the load side are constructed.

4. The method for optimizing power grid resource allocation in response to high-temperature heat wave events as described in claim 1, characterized in that, The step of performing spatiotemporal distribution analysis on each of the sub-regions based on the load power sequence to obtain load complementarity indicators and grid interaction indicators among the sub-regions includes: Based on the load power sequence, the daily load peak values ​​between each of the sub-regions are extracted; An improved weighted spatiotemporal Pearson correlation coefficient is used to quantify the synchronicity of the daily load peaks among the sub-regions, thereby obtaining the load complementarity index. Based on the load power sequence and the grid topology parameters of the target power system, the disturbance resistance of load transfer between each sub-region is analyzed through a modified relative gain array to obtain the grid interaction index.

5. A method for optimizing power grid resource allocation in response to high-temperature heat wave events as described in claim 1, characterized in that, Improvements to the multiverse algorithm include: Based on the aforementioned load complementarity index, the information exchange process between individuals in the multiverse algorithm is guided. Based on the synergistic effect between the heat wave event and the urban environment, the global search parameters of the multiverse algorithm are adaptively adjusted.

6. A power grid resource optimization allocation system for high-temperature heat wave events, characterized in that, include: The collaborative analysis module is used to analyze the collaborative impact of high-temperature heat wave events and urban environment on the target power system based on historical meteorological data and spatial thermodynamic data of the target area, and to construct a meteorological-power grid coupling correlation model based on the analysis results; The coupling construction module is used to establish the power output model and load demand model of the target power system based on the meteorological-power grid coupling correlation model. The simulation calculation module is used to divide the target area, perform simulation calculations on each sub-area based on the power output model and the load demand model, and obtain the power generation sequence and the load power sequence. The spatiotemporal analysis module is used to perform spatiotemporal distribution analysis on each of the sub-regions based on the load power sequence, and to obtain load complementarity index and power grid interaction index between the sub-regions. A calculation module is used to calculate the net load data of the target power system based on the power generation sequence and the load power sequence. The model configuration module is used to determine the long-term flexibility demand parameters of the target power system based on the net load data, and to construct a cross-regional resource allocation model that considers the synergistic impact of the high-temperature heat wave event and the urban environment, using the load complementarity index, the grid interaction index and the long-term flexibility demand parameters as constraints. The resource allocation module is used to solve the cross-regional resource allocation model using an improved multiverse algorithm and output the resource allocation scheme of the target power system.

7. A power grid resource optimization and allocation system for high-temperature heat wave events as described in claim 6, characterized in that, Before optimizing resource allocation in the target area, the method further includes using historical meteorological data of the target area, and the collaborative analysis module is also used for: Meteorological characteristic data under the high-temperature heat wave event are extracted from the historical meteorological data to formulate high-temperature heat wave intensity index and high-temperature heat wave stress index. The generalized S-transform method is used to process the historical meteorological data based on the high-temperature heat wave intensity index and the high-temperature heat wave stress index; Based on the processing results, the high-temperature heat wave event in the target area is identified and determined.

8. A power grid resource optimization and allocation system for high-temperature heat wave events as described in claim 6, characterized in that, The coupling construction module is also used for: Based on the meteorological-power grid coupling correlation model, the key meteorological parameters affecting the power output of generating equipment and the power load demand in the target power system are determined. Based on the key meteorological parameters, the power output model on the power supply side and the demand model on the load side are constructed.

9. A power grid resource optimization and allocation system for high-temperature heat wave events as described in claim 6, characterized in that, The spatiotemporal analysis module is also used for: Based on the load power sequence, the daily load peak values ​​between each of the sub-regions are extracted; An improved weighted spatiotemporal Pearson correlation coefficient is used to quantify the synchronicity of the daily load peaks among the sub-regions, thereby obtaining the load complementarity index. Based on the load power sequence and the grid topology parameters of the target power system, the disturbance resistance of load transfer between each sub-region is analyzed through a modified relative gain array to obtain the grid interaction index.

10. A power grid resource optimization and allocation system for high-temperature heat wave events as described in claim 6, characterized in that, The resource configuration module is also used for: Based on the aforementioned load complementarity index, the information exchange process between individuals in the multiverse algorithm is guided. Based on the synergistic effect between the heat wave event and the urban environment, the global search parameters of the multiverse algorithm are adaptively adjusted.

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