A method for analyzing extreme climate under cold wave and dependence of extreme value of heterogeneous energy in a park

Abstract

The application discloses a kind of cold wave extreme climate under park heterogeneity energy extreme value dependence analysis method, comprising: to the fluctuation of multiple-source heterogeneity energy data is filtered, and the risk polar coordinate transformation is carried out to extreme disturbance residual, and based on the radial component obtained by transformation selects extreme value threshold, and extreme supply-demand imbalance sample is screened using extreme value threshold;According to the deviation degree of extreme supply-demand imbalance sample relative to risk synchronous area, estimate angle support interval;And extreme supply-demand imbalance sample is non-parametric Bootstrap resampling, and the statistical quantity experience distribution for extreme value dependence structure determination is constructed;Based on the statistical quantity experience distribution and angle support interval, supply-side risk and demand-side risk are tested, and the extreme value dependence category is determined according to the verification result, and the dispatching strategy corresponding to the extreme value dependence category is generated.The application can identify the synergistic risk mode of supply-side sudden drop and demand-side surge before cold wave arrives, and improve the safety and dispatching foresight of park energy system.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, specifically to a method for analyzing the extreme dependence of heterogeneous energy in a park under extreme cold wave climate. Background Technology

[0002] With the increasing penetration rate of distributed renewable energy in the park's energy management system (PEMS) and the increase in diverse loads such as data center cooling loads and electric vehicle charging piles, the mutual influence between heterogeneous energy sources is becoming more and more significant. Under cold weather conditions, due to the combined effects of low temperature and reduced sunlight, the park's energy system often experiences a simultaneous phenomenon of a significant decrease in photovoltaic output and a rapid increase in electric heating load, which in turn leads to supply and demand imbalances and increased system operation risks.

[0003] Currently, analyses of the energy dependence of industrial parks have the following limitations:

[0004] Failure of linear correlation: Existing methods such as Pearson correlation coefficient can only reflect the average correlation under normal operation, and cannot characterize the tail synchronization characteristics of photovoltaic power output decline and electric heating load increase under extreme cold wave conditions, leading to risk assessment bias.

[0005] Parametric models are too rigid: Copula-based modeling methods require pre-defined distribution patterns, making it difficult to accurately describe the asymmetric and heavy-tailed distribution characteristics of supply contraction and demand expansion under cold wave conditions.

[0006] Insufficient granularity of dependency structure identification: Existing methods have difficulty distinguishing between different extreme coupling modes such as full dependency, strong dependency, weak dependency and independence, and cannot provide support for differentiated scheduling strategies in cold wave scenarios.

[0007] Therefore, there is an urgent need for an analytical method that can automatically identify the extreme value dependence structure of heterogeneous energy sources in typical cold wave impact scenarios. Summary of the Invention

[0008] This invention provides a method for analyzing the extreme dependence of heterogeneous energy in a park under extreme cold wave conditions, in order to identify the coupling relationship between a sharp drop in photovoltaic power output and a surge in electric heating load under cold wave conditions.

[0009] This invention provides a method for extreme value dependence analysis of heterogeneous energy in a park under extreme cold wave climate, the method comprising:

[0010] Acquire multi-source heterogeneous energy data of the park under cold wave scenarios, and filter out fluctuations in the multi-source heterogeneous energy data to obtain extreme disturbance residuals;

[0011] Risk polar coordinate transformation is performed on the extreme disturbance residuals, and extreme value thresholds are selected based on the radial components obtained by the transformation. Extreme value thresholds are used to screen extreme supply and demand imbalance samples under the cold wave scenario.

[0012] Based on the degree of deviation of the extreme supply and demand imbalance sample from the risk synchronization region, the angular support interval is estimated; and nonparametric Bootstrap resampling is performed on the extreme supply and demand imbalance sample to construct a statistical empirical distribution for determining the extreme value dependence structure.

[0013] Based on the empirical distribution of statistics and the angle support interval, supply-side risks and demand-side risks are tested, and the extreme value dependence category between supply-side risks and demand-side risks is determined according to the test results.

[0014] Based on the extreme value dependency category, generate the risk warning level and corresponding dispatch strategy for the park's energy system under cold wave conditions.

[0015] In some embodiments of the present invention, the extreme value dependency categories include full dependency, strong dependency, weak dependency, and asymptotic independence;

[0016] Based on empirical distributions of statistical measures and angular support intervals, supply-side and demand-side risks are tested. The extreme value dependence categories between supply-side and demand-side risks are determined based on the test results, including:

[0017] Based on the support set test statistic in the empirical distribution of the statistic, verify whether the supply-side risk and the demand-side risk fall into the same support interval.

[0018] If supply-side risks and demand-side risks are in the same angle support range, then based on the variance characteristics of the angle variation statistics in the empirical distribution of statistics, the extreme value dependence category between supply-side risks and demand-side risks can be determined as strong dependence or full dependence.

[0019] If supply-side risks and demand-side risks are not in the same support range, a one-sided extreme test is performed based on the transformation function of the angle-related variable. Based on the results of the one-sided extreme test, the extreme value dependence category between supply-side risks and demand-side risks is determined to be either weak dependence or asymptotic independence.

[0020] In some embodiments of the present invention, determining the extreme value dependency category between supply-side risk and demand-side risk as strong dependency or total dependency includes:

[0021] If the variance of the angle variation statistic is greater than a preset variance threshold, the extreme value dependency category is determined to be strong dependency; if the variance of the angle variation statistic is less than or equal to the preset variance threshold, the extreme value dependency category is determined to be full dependency.

[0022] Based on the results of the one-sided extreme value test, the extreme value dependence category between supply-side risk and demand-side risk is determined to be either weak dependence or asymptotic independence, including:

[0023] When the results of the one-sided extreme test indicate the existence of risk spillover, the extreme value dependence category is determined to be weak dependence; when the results of the one-sided extreme test indicate that the supply-side risk and the demand-side risk are independent of each other, the extreme value dependence category is determined to be asymptotic independence.

[0024] In some embodiments of the present invention, the angular support interval is estimated based on the degree of deviation of the extreme supply-demand imbalance sample from the risk synchronization region, including:

[0025] The angular support range can be estimated using the following formula:

[0026] ;

[0027] In the formula, λ is the tuning parameter, a and b are the two endpoints of the angular support interval, i.e., the parameters to be estimated, and D... n To support the set test statistic, Estimated by Hill estimator.

[0028] In some embodiments of the present invention, nonparametric bootstrap resampling is performed on samples of extreme supply-demand imbalances to construct an empirical distribution of statistics for determining extreme value dependence structures, including:

[0029] According to the preset number of resampling times, samples with replacement are sampled for extreme supply and demand imbalances to obtain multiple Bootstrap resampling samples.

[0030] The support set test statistic is determined based on the degree of deviation of the Bootstrap resampled sample from the risk synchronization region corresponding to the angle support interval.

[0031] Based on the distribution characteristics of the angle-related variables in the Bootstrap resampled samples, determine the angle variation statistic;

[0032] Based on the support set test statistic and the angle variation statistic, an empirical distribution of the obtained statistic is constructed.

[0033] In some embodiments of the present invention, the support set test statistic D n and angle variation statistic T n They are respectively characterized as:

[0034]

[0035] In the formula, k(n) is an extreme value threshold parameter used to screen samples of the most severe supply-demand imbalance under cold wave conditions; R (i) The radial components are arranged in descending order. and Let i be the accompanying variable corresponding to the i-th sample; This is a risk synchronization cone; a and b are the two endpoints of the corresponding angle support interval; This is a distance measure in polar coordinates.

[0036] In some embodiments of the present invention, the extreme perturbation residual is a two-dimensional absolute residual vector;

[0037] A risk polar coordinate transformation is performed on the extreme disturbance residuals, and an extreme value threshold is selected based on the radial component obtained from the transformation. This extreme value threshold is then used to screen samples of extreme supply-demand imbalances under cold wave scenarios, including:

[0038] The two-dimensional absolute residual vector is subjected to L1 polar coordinate transformation to obtain radial and angular components. The radial component represents the overall intensity of the supply-demand imbalance in the park under the cold wave conditions, and the angular component represents the relative contribution ratio of supply-side risk and demand-side risk.

[0039] Multiple two-dimensional residual vectors are sorted according to the magnitude of their radial components, and extreme value thresholds are determined based on the sorted radial components.

[0040] Two-dimensional residual vectors whose radial components satisfy the extreme value threshold are identified as samples of extreme supply and demand imbalance.

[0041] In some embodiments of the present invention, fluctuation filtering is performed on multi-source heterogeneous energy data to obtain extreme disturbance residuals, including:

[0042] Extract supply-side energy output sequences and demand-side load sequences from multi-source heterogeneous energy data;

[0043] Fluctuation sequences were constructed from the energy output sequence on the supply side and the load sequence on the demand side to obtain the supply-side fluctuation sequence and the demand-side fluctuation sequence.

[0044] The supply-side volatility series and demand-side volatility series were fitted using an exponential generalized autoregressive conditional heteroscedasticity model, respectively.

[0045] Based on the fitting results, the supply-side filtering residual and the demand-side filtering residual are extracted respectively, and the extreme disturbance residual is obtained based on the supply-side filtering residual and the demand-side filtering residual.

[0046] In some embodiments of the present invention, the risk warning level and corresponding scheduling strategy of the park's energy system under cold wave conditions are generated according to the extreme value dependence category, including:

[0047] In cases where the extreme value dependency category is strong dependency or full dependency, a high-level risk warning is generated, and a collaborative defense scheduling strategy is generated. The collaborative defense scheduling strategy includes at least one of the following: early activation of the energy storage system, adjustment of backup power capacity configuration, and implementation of load reduction strategy.

[0048] When the extreme value dependency category is weak dependency, a local risk warning is generated, and a local adjustment strategy is generated. The local adjustment strategy is used to make local adjustments to the risk objects on the supply side and / or the risk objects on the demand side.

[0049] When the extreme value dependency category is asymptotically independent, an independent scheduling prompt is generated, and a supply-side optimized scheduling strategy and a demand-side optimized scheduling strategy are generated respectively to perform independent optimized scheduling on the supply side and the demand side.

[0050] In the extreme value dependency analysis method for heterogeneous energy in industrial parks under extreme cold wave climate provided by this invention, the extreme disturbance residuals are transformed by risk polar coordinates, and extreme value thresholds are selected based on the radial components obtained from the transformation. These extreme value thresholds are then used to screen extreme supply-demand imbalance samples under cold wave scenarios. An angular support interval is estimated based on the deviation of the extreme supply-demand imbalance samples from the risk synchronization region. Nonparametric bootstrap resampling is then performed on the extreme supply-demand imbalance samples to construct a statistical empirical distribution for determining the extreme value dependency structure. Based on the statistical empirical distribution and the angular support interval, supply-side and demand-side risks are tested, and the extreme value dependency category between supply-side and demand-side risks is determined based on the test results. Based on the extreme value dependency category, a risk warning level and corresponding scheduling strategy for the industrial park's energy system under cold wave conditions are generated. This enables the identification of a coordinated risk pattern of a sudden drop in supply and a surge in demand before the arrival of a cold wave, improving the safety and scheduling foresight of the industrial park's energy system. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart illustrating the method for analyzing the extreme value dependence of heterogeneous energy in a park under extreme cold wave climate, as provided in this embodiment of the invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0055] "A and / or B" includes the following three combinations: A only, B only, and a combination of A and B.

[0056] The use of "applies to" or "configured to" in this invention implies an open and inclusive language, which does not exclude the applicability to or configuration to devices performing additional tasks or steps. Additionally, the use of "based on" implies openness and inclusivity, because processes, steps, calculations, or other actions "based on" one or more conditions or values ​​may in practice be based on additional conditions or values ​​beyond those conditions.

[0057] In this invention, the term "exemplary" is used to mean "serving as an example, illustration, or description." Any embodiment described as "exemplary" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0058] The following describes, with reference to the accompanying drawings, the method for analyzing the extreme value dependence of heterogeneous energy in a park under extreme cold wave climate provided by the present invention.

[0059] like Figure 1 As shown in the figure, this invention provides a method for extreme value dependence analysis of heterogeneous energy in a park under extreme cold wave climate. The method includes the following steps:

[0060] S101 acquires multi-source heterogeneous energy data of the park under the cold wave scenario, and performs fluctuation filtering on the multi-source heterogeneous energy data to obtain extreme disturbance residuals.

[0061] S102, perform risk polar coordinate transformation on the extreme disturbance residuals, and select the extreme value threshold based on the radial component obtained by the transformation. Use the extreme value threshold to screen extreme supply and demand imbalance samples under the cold wave scenario.

[0062] S103. Estimate the angular support interval based on the degree of deviation of the extreme supply and demand imbalance sample from the risk synchronization region; and perform nonparametric Bootstrap resampling on the extreme supply and demand imbalance sample to construct a statistical empirical distribution for determining the extreme value dependence structure.

[0063] S104, based on the empirical distribution of statistics and the angle support interval, examines supply-side risks and demand-side risks, and determines the extreme value dependence category between supply-side risks and demand-side risks based on the verification results.

[0064] S105, generate risk warning levels and corresponding dispatch strategies for the park's energy system under cold wave conditions based on extreme value dependence categories.

[0065] The present invention provides a method for analyzing the extreme value dependence of heterogeneous energy in a park under extreme cold wave conditions. This method performs a risk polar coordinate transformation on the extreme disturbance residuals and selects an extreme value threshold based on the radial component obtained from the transformation. The extreme value threshold is then used to screen extreme supply-demand imbalance samples under cold wave scenarios. An angular support interval is estimated based on the deviation of the extreme supply-demand imbalance samples from the risk synchronization region. Non-parametric bootstrap resampling is then performed on the extreme supply-demand imbalance samples to construct a statistical empirical distribution for determining the extreme value dependence structure. Based on the statistical empirical distribution and the angular support interval, supply-side and demand-side risks are tested, and the extreme value dependence category between supply-side and demand-side risks is determined based on the test results. Based on the extreme value dependence category, a risk warning level and corresponding scheduling strategy for the park's energy system under cold wave conditions are generated. This enables the identification of a coordinated risk pattern of a sudden drop in supply and a surge in demand before the arrival of a cold wave, improving the safety and scheduling foresight of the park's energy system.

[0066] In some embodiments of the present invention, the extreme value dependency categories include full dependency, strong dependency, weak dependency, and asymptotic independence.

[0067] Based on empirical distributions of statistical measures and angular support intervals, supply-side and demand-side risks are tested. The extreme value dependence categories between supply-side and demand-side risks are determined based on the test results, including:

[0068] Based on the support set test statistic in the empirical distribution of the statistic, we can verify whether supply-side risks and demand-side risks fall into the same support interval.

[0069] If supply-side risks and demand-side risks are in the same angle support range, then based on the variance characteristics of the angle variation statistics in the empirical distribution of statistics, the extreme value dependence category between supply-side risks and demand-side risks can be determined as strong dependence or full dependence.

[0070] If supply-side risks and demand-side risks are not in the same support range, a one-sided extreme test is performed based on the transformation function of the angle-related variable. Based on the results of the one-sided extreme test, the extreme value dependence category between supply-side risks and demand-side risks is determined to be either weak dependence or asymptotic independence.

[0071] In some embodiments of the present invention, determining the extreme value dependency category between supply-side risk and demand-side risk as strong dependency or total dependency includes:

[0072] When the variance of the angle variation statistic is greater than the preset variance threshold, the extreme value dependence category is determined to be strong dependence, indicating that the two types of risks change synchronously within a certain proportion range; when the variance of the angle variation statistic is less than or equal to the preset variance threshold, the extreme value dependence category is determined to be full dependence, indicating that the two types of risks occur synchronously at an approximately fixed proportion under cold wave conditions.

[0073] In some examples, if not rejected : Further testing The support set of S is a single point, that is, whether the two types of risks occur simultaneously in a fixed proportion; if If the variance is extremely low, it is considered fully dependent. This is understandable, as it is determined through... If you do the test, <1 indicates The interval length is less than 1, which means we reject the answer. .

[0074] Based on the results of the one-sided extreme value test, the extreme value dependence category between supply-side risk and demand-side risk is determined to be either weak dependence or asymptotic independence, including:

[0075] When the one-sided extreme test results indicate the existence of risk spillover, the extreme value dependency category is determined to be weak dependency; when the one-sided extreme test results indicate that supply-side risks and demand-side risks are independent, the extreme value dependency category is determined to be asymptotic independence. g(Θ)

[0076] In some demonstration In the example, if If rejected, the angle component Θ is converted to g(Θ) by the transformation function g(Θ). Test: The support set of S is 0 or 1.

[0077] ;

[0078] If rejected, it is determined to be a weak dependency (with the risk of spillover); otherwise, it is determined to be asymptotic independence.

[0079] In some embodiments of the present invention, the angular support interval is estimated based on the degree of deviation of the extreme supply-demand imbalance sample from the risk synchronization region, including:

[0080] The angle support range can be estimated using the following formula. :

[0081] ;

[0082] In the formula, λ is the tuning parameter, a and b are the two endpoints of the angular support interval, i.e., the parameters to be estimated, and D... n To support the set test statistic, Estimated by Hill estimator.

[0083] Understandably, the above-mentioned angle supports the range. This is used to characterize the main coupling range between a sharp drop in photovoltaic output and a surge in electric heating load under cold wave conditions.

[0084] The extreme value dependence analysis method for heterogeneous energy in industrial parks under extreme cold wave climate provided in this embodiment of the invention obtains the angular support interval based on nonparametric bootstrap resampling and penalized optimization estimation, and realizes the asymptotic classification of extreme value dependence structure automatically identifying it from limited historical energy data.

[0085] In some embodiments of the present invention, nonparametric bootstrap resampling is performed on samples of extreme supply-demand imbalances to construct an empirical distribution of statistics for determining extreme value dependence structures, including:

[0086] Based on the preset number of resampling operations, samples with replacement are sampled from extreme supply and demand imbalance samples to obtain B Bootstrap resampling samples of size n.

[0087]

[0088] In some examples, B is 200, m = ⌈6n / k(n)⌉, where n is the total number of resampled samples, and in some examples n is set to 20000.

[0089] The support set test statistic is determined based on the degree of deviation of the Bootstrap resampled sample from the risk synchronization region corresponding to the angle support interval.

[0090] Based on the distribution characteristics of the angle covariance of the Bootstrap resampled sample, determine the angle variation statistic.

[0091] In some examples, the support set test statistic D n and angle variation statistic T n They are respectively characterized as:

[0092]

[0093] In the formula, k(n) is an extreme value threshold parameter used to screen samples of the most severe supply-demand imbalance under cold wave conditions; R (i) The radial components are arranged in descending order. and Let i be the accompanying variable corresponding to the i-th sample; This is a risk synchronization cone; a and b are the two endpoints of the corresponding angle support interval; This is a distance measure in polar coordinates.

[0094] Based on the support set test statistic and the angle variation statistic, an empirical distribution of the obtained statistic is constructed.

[0095] In some embodiments of the present invention, the extreme perturbation residual is a two-dimensional absolute residual vector Z=(X,Y)∈ X represents the decrease in photovoltaic power output under extreme cold weather conditions, used to characterize the intensity of supply-side risk; Y represents the increase in electric heating load under cold weather conditions, used to characterize the intensity of demand-side risk.

[0096] A risk polar coordinate transformation is performed on the extreme disturbance residuals, and an extreme value threshold is selected based on the radial component obtained from the transformation. This extreme value threshold is then used to screen samples of extreme supply-demand imbalances under cold wave scenarios, including:

[0097] The two-dimensional absolute residual vector is transformed by L1 polar coordinates to obtain the radial component R = X + Y and the angular component Θ = X / (X + Y). The radial component represents the overall intensity of the supply and demand imbalance in the park under the cold wave conditions, and the angular component represents the relative contribution ratio of supply-side risk (i.e., the decline in photovoltaic output) and demand-side risk (i.e., the increase in electric heating load).

[0098] It is understandable that the supply-demand imbalance process of a sudden drop in photovoltaic output and a surge in electric heating load is uniformly described by the same random vector. Assuming that Z follows a multivariate canonical distribution, its tail behavior under extreme risk conditions is characterized by the extreme value measure η:

[0099]

[0100] In the formula, t is the sample size. This indicates weak convergence, and b(t) is the scaling function.

[0101] The above modeling is used to describe the evolution of the combined extreme behavior of declining photovoltaic output and rising electric heating load as the intensity of cold waves increases. The risk index is transformed into polar coordinates using the L1 norm, and defined as: (1) radial component R = X + Y; (2) angular component Θ = X / (X + Y). Under this characterization, the extreme value measure can be decomposed into:

[0102]

[0103] Where r is the radial component, α is the power-law parameter, and S is the angular probability measure defined on the simplex ∆1={θ∈[0,1]}. The support set of the angular measure S is used to characterize the coupling ratio between the sharp drop in photovoltaic power output and the surge in electric heating load under extreme cold wave conditions.

[0104] Multiple two-dimensional residual vectors are sorted according to the magnitude of their radial components, and an extreme value threshold is determined based on the sorted radial components. Extreme value threshold The criteria used to define the impact of extreme cold waves can be automatically selected using a modified minimum distance method.

[0105] The k two-dimensional residual vectors whose radial components satisfy the extreme value threshold are identified as extreme supply and demand imbalance samples (i.e., representative samples of the most severe supply and demand imbalance).

[0106] The extreme value dependence analysis method for heterogeneous energy in industrial parks under extreme cold wave climate provided in this embodiment of the invention uses L1 polar coordinate transformation to characterize the angular distribution characteristics of photovoltaic, wind power and multiple loads in the park under extreme risk conditions.

[0107] In some embodiments of the present invention, fluctuation filtering is performed on multi-source heterogeneous energy data to obtain extreme disturbance residuals, including:

[0108] Extract supply-side energy output sequences and demand-side load sequences from multi-source heterogeneous energy data.

[0109] In some examples, multi-source heterogeneous energy data includes photovoltaic power, wind power, natural gas flow, and combined load. In cold wave scenarios, the focus is on the supply-demand shock caused by the decrease in photovoltaic output and the increase in electric heating load.

[0110] Fluctuation sequences were constructed for the energy output sequence on the supply side and the load sequence on the demand side, respectively, to obtain the supply-side fluctuation sequence and the demand-side fluctuation sequence.

[0111] The supply-side volatility series and demand-side volatility series were fitted using an exponential generalized autoregressive conditional heteroscedasticity model.

[0112] Understandably, given the significant fluctuation and clustering characteristics of the aforementioned multi-source heterogeneous energy data, in order to avoid interference from daily operational fluctuations in the identification of extreme events, an exponential generalized autoregressive conditional heteroscedasticity (de-GARCH) model is used to fit the original logarithmic fluctuation sequence, extract the absolute filtered residuals, and construct a sample sequence that satisfies the independent and identically distributed assumption.

[0113] Based on the fitting results, the supply-side filtering residual and the demand-side filtering residual are extracted respectively, and the extreme disturbance residual is obtained based on the supply-side filtering residual and the demand-side filtering residual.

[0114] The extreme value dependence analysis method for heterogeneous energy in industrial parks under extreme cold weather provided in this embodiment of the invention uses an exponential generalized autoregressive conditional heteroscedasticity model to dynamically de-fluctuate the energy series, thereby achieving accurate capture of pure impact samples under extreme weather conditions. This ensures that subsequent analysis targets abnormal fluctuations caused by a sudden drop in photovoltaic output and a surge in electric heating load, rather than conventional operating noise.

[0115] In some embodiments of the present invention, the risk warning level and corresponding scheduling strategy of the park's energy system under cold wave conditions are generated according to the extreme value dependence category, including:

[0116] When the extreme value dependency category is strong dependency or full dependency, a high-level risk warning is generated, and a collaborative defense scheduling strategy is generated. The collaborative defense scheduling strategy includes at least one of the following: early activation of the energy storage system, adjustment of backup power capacity configuration, and implementation of load reduction strategy.

[0117] When the extreme value dependency category is weak dependency, a local risk warning is generated, and a local adjustment strategy is generated. The local adjustment strategy is used to make local adjustments to the risk objects on the supply side and / or the risk objects on the demand side.

[0118] When the extreme value dependency category is asymptotically independent, an independent scheduling prompt is generated, and a supply-side optimized scheduling strategy and a demand-side optimized scheduling strategy are generated respectively to perform independent optimized scheduling on the supply side and the demand side.

[0119] The extreme value dependence analysis method for heterogeneous energy in industrial parks under extreme cold wave climate provided in this embodiment of the invention guides the differentiated formulation of energy storage scheduling, reserve capacity configuration and load control strategies for different coupling modes such as full dependence, strong dependence, weak dependence and asymptotic independence, so that the system can change from post-event response to pre-event prevention and improve the overall operational safety margin.

[0120] In summary, the method provided in this invention, by performing a risk polar coordinate transformation on the extreme disturbance residuals, characterizes the synchronous evolution of photovoltaic power output decline and electric heating load increase under extreme tail conditions. This overcomes the limitation of traditional linear correlation methods that only reflect average relationships, achieving accurate identification of the coordinated risk of "supply-side plunge—demand-side surge," thereby effectively avoiding underestimation of the intensity of cold wave impacts. It does not require pre-setting the probability distribution form, directly extracting extreme value dependence features from historical data through Bootstrap resampling. Combined with an exponential generalized autoregressive conditional heteroscedasticity model, it effectively eliminates interference from normal operational fluctuations, ensuring high stability and reliability of the analysis results obtained under extreme conditions such as cold waves. The method provided by this invention can assess the probability of a sudden drop in distributed energy output and a surge in electricity load occurring simultaneously in an industrial park under extreme cold waves, high temperatures, or severe convective weather, and its impact on the frequency and voltage support of microgrids; analyze the linkage risks of regional electricity-gas-heat systems during extreme load peaks or upstream supply interruptions, identify extreme vulnerabilities and cascading failure paths between heterogeneous energy systems; and assess the dependence strength between extreme values ​​of large-scale electric vehicle charging loads and extreme values ​​of regional distribution network transformer overloads during extreme charging peak periods on holidays, providing decision support for mobile energy storage dispatch.

[0121] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0122] The above provides a detailed description of the method for analyzing the extreme value dependence of heterogeneous energy in a park under extreme cold wave climate provided by the embodiments of the present invention. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for analyzing the extreme value dependence of heterogeneous energy in a park under extreme cold wave climate, characterized in that, The method includes: Acquire multi-source heterogeneous energy data of the park under cold wave scenario, and perform fluctuation filtering on the multi-source heterogeneous energy data to obtain extreme disturbance residuals; The extreme perturbation residuals are subjected to risk polar coordinate transformation, and extreme value thresholds are selected based on the radial components obtained by the transformation. The extreme value thresholds are used to screen extreme supply and demand imbalance samples under the cold wave scenario. Based on the degree of deviation of the extreme supply and demand imbalance sample from the risk synchronization region, the angular support interval is estimated; and nonparametric Bootstrap resampling is performed on the extreme supply and demand imbalance sample to construct a statistical empirical distribution for determining the extreme value dependence structure. Based on the empirical distribution of the statistics and the angle support interval, the supply-side risk and demand-side risk are tested, and the extreme value dependence category between the supply-side risk and demand-side risk is determined according to the test results. Based on the extreme value dependency category, generate the risk warning level and corresponding dispatch strategy for the park's energy system under cold wave conditions.

2. The method for analyzing the extreme value dependence of heterogeneous energy in a park under extreme cold wave climate as described in claim 1, characterized in that, The extreme value dependency categories include full dependency, strong dependency, weak dependency, and asymptotic independence; The method of testing supply-side and demand-side risks based on the empirical distribution of the statistical quantities and the angular support interval, and determining the extreme value dependence category between the supply-side and demand-side risks based on the verification results, includes: Based on the support set test statistic in the empirical distribution of the statistic, verify whether the supply-side risk and the demand-side risk fall into the same angle support interval. If the supply-side risk and the demand-side risk are in the same angle support interval, then based on the variance characteristics of the angle variation statistics in the empirical distribution of the statistics, the extreme value dependence category between the supply-side risk and the demand-side risk is determined to be strong dependence or full dependence. If the supply-side risk and the demand-side risk are not in the same angle support interval, a one-sided extreme test is performed based on the transformation function of the angle accompanying variable. Based on the result of the one-sided extreme test, the extreme value dependence category between the supply-side risk and the demand-side risk is determined to be either weak dependence or asymptotic independence.

3. The method for analyzing the extreme value dependence of heterogeneous energy in a park under extreme cold wave climate as described in claim 2, characterized in that, The determination of whether the extreme value dependency category between the supply-side risk and the demand-side risk is strong dependency or total dependency includes: If the variance of the angle variation statistic is greater than a preset variance threshold, the extreme value dependency category is determined to be strong dependency; if the variance of the angle variation statistic is less than or equal to the preset variance threshold, the extreme value dependency category is determined to be full dependency. The determination of whether the extreme value dependency category between the supply-side risk and the demand-side risk is weak dependency or asymptotically independent based on the results of a one-sided extreme value test includes: If the one-sided extreme test results indicate the existence of risk spillover, the extreme value dependency category is determined to be weak dependency; if the one-sided extreme test results indicate that the supply-side risk and the demand-side risk are independent of each other, the extreme value dependency category is determined to be asymptotically independent.

4. The method for analyzing the extreme value dependence of heterogeneous energy in a park under extreme cold wave climate as described in claim 1, characterized in that, The estimation of the angular support interval based on the degree of deviation of the extreme supply-demand imbalance sample from the risk synchronization region includes: The angular support range can be estimated using the following formula: ； In the formula, λ is the tuning parameter, a and b are the two endpoints of the angular support interval, i.e., the parameters to be estimated, and D... n To support the set test statistic, Estimated by Hill estimator.

5. The method for analyzing the extreme value dependence of heterogeneous energy in a park under extreme cold wave climate as described in claim 1, characterized in that, The step of performing nonparametric bootstrap resampling on the extreme supply-demand imbalance samples to construct a statistical empirical distribution for determining extreme value dependence structures includes: According to the preset number of resampling times, the extreme supply and demand imbalance samples are sampled with replacement to obtain multiple Bootstrap resampling samples. The support set test statistic is determined based on the degree of deviation of the Bootstrap resampled sample from the risk synchronization region corresponding to the angle support interval. Based on the distribution characteristics of the angle-related variables of the Bootstrap resampled samples, determine the angle variation statistic; Based on the support set test statistic and the angle variation statistic, an empirical distribution of the statistic is constructed.

6. The method for analyzing the extreme value dependence of heterogeneous energy in a park under extreme cold wave climate as described in claim 5, characterized in that, The support set test statistic D n and the angle variation statistic T n They are respectively characterized as: In the formula, k(n) is an extreme value threshold parameter used to screen samples of the most severe supply-demand imbalance under cold wave conditions; R (i) The radial components are arranged in descending order. and Let i be the accompanying variable corresponding to the i-th sample; This is a risk synchronization cone; a and b are the two endpoints of the corresponding angle support interval; This is a distance measure in polar coordinates.

7. The method for analyzing the extreme value dependence of heterogeneous energy in a park under extreme cold wave climate as described in claim 1, characterized in that, The extreme perturbation residual is a two-dimensional absolute residual vector; The step of performing a risk polar coordinate transformation on the extreme disturbance residuals, selecting an extreme value threshold based on the radial component obtained from the transformation, and using the extreme value threshold to screen extreme supply and demand imbalance samples under the cold wave scenario includes: The two-dimensional absolute residual vector is subjected to L1 polar coordinate transformation to obtain radial and angular components. The radial component represents the overall intensity of the supply-demand imbalance in the park under cold wave conditions, and the angular component represents the relative contribution ratio of supply-side risk and demand-side risk. The multiple two-dimensional residual vectors are sorted according to the magnitude of the radial components, and the extreme value threshold is determined based on the sorted radial components. The two-dimensional residual vector whose radial component satisfies the extreme value threshold is determined as the extreme supply and demand imbalance sample.

8. The method for analyzing the extreme value dependence of heterogeneous energy in a park under extreme cold wave climate as described in claim 1, characterized in that, The process of filtering out fluctuations in the multi-source heterogeneous energy data to obtain extreme disturbance residuals includes: Extract the supply-side energy output sequence and the demand-side load sequence from the multi-source heterogeneous energy data; Fluctuation sequences are constructed from the energy output sequence on the supply side and the load sequence on the demand side to obtain the supply-side fluctuation sequence and the demand-side fluctuation sequence, respectively. The supply-side fluctuation series and the demand-side fluctuation series were fitted using an exponential generalized autoregressive conditional heteroscedasticity model, respectively. Based on the fitting results, the supply-side filtering residual and the demand-side filtering residual are extracted respectively, and the extreme disturbance residual is obtained based on the supply-side filtering residual and the demand-side filtering residual.

9. The method for analyzing the extreme value dependence of heterogeneous energy in a park under extreme cold wave climate as described in claim 1, characterized in that, The step of generating the risk warning level and corresponding dispatch strategy for the park's energy system under cold wave conditions based on the extreme value dependence category includes: When the extreme value dependency category is strong dependency or full dependency, a high-level risk warning is generated, and a collaborative defense scheduling strategy is generated. The collaborative defense scheduling strategy includes at least one of the following: early activation of the energy storage system, adjustment of backup power capacity configuration, and implementation of load reduction strategy. When the extreme value dependency category is weakly dependent, a local risk warning is generated, and a local adjustment strategy is generated. The local adjustment strategy is used to locally adjust the risk objects on the supply side and / or the risk objects on the demand side. When the extreme value dependency category is asymptotically independent, an independent scheduling prompt is generated, and a supply-side optimized scheduling strategy and a demand-side optimized scheduling strategy are generated respectively, so as to perform independent optimized scheduling on the supply side and the demand side respectively.

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