A method and device for quantitatively analyzing output characteristics of a water, wind, light, and storage multi-energy complementary system, and a storage medium

Abstract

The present application relates to the technical field of new energy power generation, and discloses a water, wind, light and storage multi-energy complementary system output characteristic quantitative analysis method and device and storage medium, the present application adopts multi-layer nested wavelet decomposition to realize multi-scale output synchronous analysis, makes up for the limitation of single scale analysis; through multi-energy coupling constraint correction, it is in line with the actual operation condition, and the analysis deviation is greatly reduced. A three-dimensional quantitative index system is constructed, which is complementary, stable and adjustable, and which gets rid of the defects of traditional qualitative evaluation; with the aid of shapley value method, the contribution proportion of each power source is quantified, and the adjustment positioning of each type of power source is clear. The whole set of analysis logic is in line with the actual operation constraint of the basin water, wind, light and storage, the quantitative result is objective and complete, which can provide accurate quantitative support for system capacity configuration, hierarchical and time-optimized scheduling, and effectively improve the new energy consumption and system stable operation level.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, specifically to a method, device, and storage medium for quantitative analysis of the output characteristics of a multi-energy complementary system of hydro, wind, solar, and energy storage. Background Technology

[0002] A multi-energy complementary system, with hydropower as its core and coupled with wind power, photovoltaics, and energy storage, is a key solution for overcoming the bottleneck of new energy consumption and building a new type of power system. Quantitative analysis of output characteristics is the underlying foundation for the planning, scheduling, and operation control of multi-energy systems. Accurately characterizing the output patterns and complementary synergistic relationships of various power sources can effectively improve the system's power supply stability and operational efficiency, possessing extremely high engineering application value.

[0003] Current mainstream analytical methods only perform single-source statistical calculations for hydropower, wind power, and photovoltaic power, relying on basic parameters such as mean and variance combined with output curves to qualitatively determine the complementarity relationship, and often using the Pearson correlation coefficient to assess complementarity. However, the above methods have obvious shortcomings. They mainly rely on linear correlation coefficients for qualitative assessment and lack multi-dimensional quantitative indicators, making it difficult to support capacity allocation and scheduling optimization. Summary of the Invention

[0004] This invention provides a method, device, and storage medium for quantitative analysis of the output characteristics of a multi-energy complementary system of water, wind, solar, and storage, in order to solve the problem that existing analysis techniques rely on qualitative evaluation based on linear correlation coefficients, lack multi-dimensional quantitative indicators, and are difficult to support capacity configuration and scheduling optimization.

[0005] In a first aspect, the present invention provides a method for quantitative analysis of the output characteristics of a multi-energy complementary system of hydro, wind, solar, and storage, the method comprising: Operational data of a multi-energy complementary system of hydropower, wind power, solar power, and energy storage are collected and subjected to discrete wavelet decomposition according to a multi-layered nested time scale; wherein, the operational data includes hydropower data, wind power data, photovoltaic data, energy storage data, and grid data; Based on the coupled constraint model of the hydropower, wind power, photovoltaic power and energy storage multi-energy complementary system, the hydropower output sequence, wind power output sequence, photovoltaic power output sequence and energy storage output sequence obtained after decomposition are corrected respectively. At each time scale, the power output characteristics of the corrected hydropower output sequence, wind power output sequence, photovoltaic power output sequence, energy storage power output sequence, and the total system output sequence obtained by combining the power output sequences of each power source are analyzed, and corresponding multidimensional quantitative statistical data are generated. Multidimensional quantitative indicators are calculated for the multidimensional quantitative statistical data at each of the aforementioned time scales to generate a multidimensional quantitative indicator matrix; wherein, the multidimensionality includes at least a complementarity dimension, a stability dimension, and a regulatory capability dimension. Based on the multi-dimensional quantitative index matrix, the contribution of each power source to the system complementarity and system stability is calculated respectively.

[0006] This invention employs multi-layer nested wavelet decomposition to achieve synchronous analysis of power output across multiple scales, overcoming the limitations of single-scale analysis. Multi-energy coupling constraint correction aligns with actual operating conditions, significantly reducing analytical bias. A complementary, stable, and regulating three-dimensional quantitative index system is constructed, overcoming the shortcomings of traditional qualitative assessments. The Shapley value method is used to quantify the contribution ratio of each power source, clarifying the regulatory positioning of various power sources. The entire analytical logic aligns with the actual operational constraints of watershed hydropower, wind power, solar power, and energy storage, providing objective and complete quantitative results. This offers precise quantitative support for system capacity configuration and hierarchical, time-based optimized scheduling, effectively improving the absorption of new energy and the stable operation of the system.

[0007] In one optional implementation, the operational data of the multi-energy complementary system (hydropower, wind power, solar power, and energy storage) is collected and subjected to discrete wavelet decomposition according to a multi-layered nested time scale; wherein, the operational data includes hydropower data, wind power data, photovoltaic data, energy storage data, and grid data, including: Collect operational data from a multi-energy complementary system of hydropower, wind power, solar power, and energy storage; wherein, the operational data includes hydropower data, wind power data, solar power data, energy storage data, and power grid data; Outlier processing and time alignment are performed on the hydropower data, wind power data, photovoltaic data, energy storage data, and power grid data. Discrete wavelet decomposition is performed on the processed hydropower data, wind power data, photovoltaic data, and energy storage data according to multi-level nested time scales; wherein, the multi-level nested time scales include second-level time scales, minute-level time scales, hour-level time scales, day-level time scales, month-level time scales, and grade-level time scales.

[0008] This invention first completes data anomaly removal and time series alignment to eliminate analysis errors caused by data noise and time series misalignment, ensuring the reliability of the data source. Through discrete wavelet transform, the power output sequence is divided into six nested scales from seconds to grades, simultaneously capturing differentiated power output patterns such as short-term power fluctuations, intraday peak shaving, and seasonal complementarity. This overcomes the one-sidedness of traditional single-scale analysis, completely separates power output components of different frequencies, and provides a hierarchical and accurate time series foundation for subsequent multi-dimensional quantitative evaluation and coupling constraint correction, significantly improving the comprehensiveness of multi-energy complementarity pattern analysis.

[0009] In one optional implementation, the coupled constraint model based on the hydropower output sequence, wind power output sequence, photovoltaic output sequence, and energy storage output sequence obtained after decomposition are corrected respectively, including: A coupled constraint model for the hydro-wind-solar-storage multi-energy complementary system is established; wherein the coupled constraint model includes at least a hydropower constraint model, an energy storage constraint model, and a power grid constraint model. The hydropower output sequence, wind power output sequence, photovoltaic power output sequence and energy storage output sequence obtained after decomposition at each time scale are sequentially input into the coupled constraint model; The hydropower constraint model is used to perform output-flow conversion and basic limit correction, reservoir capacity-flow dynamic balance correction and cascade upstream and downstream hydraulic connection correction on the hydropower output sequence at each time scale. The energy storage constraint model is used to perform initial verification of charging and discharging power and closed-loop correction of SOC dynamic balance at each time scale. Based on the corrected hydropower output sequence and energy storage output sequence at each time scale, combined with the decomposed wind power output sequence and photovoltaic output sequence at each time scale, the total system output sequence is generated. The power grid constraint model is used to correct the global power output constraint of the system's total power output sequence at each time scale. The corrected hydropower output series, wind power output series, photovoltaic output series and energy storage output series at each time scale are subjected to multi-scale consistency verification.

[0010] This invention constructs a coupled constraint system comprising three sub-models: hydropower, energy storage, and power grid. It sequentially completes hierarchical corrections for cascade hydraulic constraints, closed-loop constraints on energy storage state of charge, and global power grid output constraints. This fully considers the complex operational boundary conditions of multi-energy systems, overcoming the shortcomings of traditional analyses that neglect operational constraints and rely on ideal output data. Furthermore, multi-scale consistency verification ensures the coordination and self-consistency of output components at each scale, effectively reducing the deviation between analysis results and actual operating conditions. This yields an output sequence with engineering feasibility, providing a data foundation that closely matches real-world operating scenarios for subsequent multi-dimensional quantitative evaluation of output characteristics.

[0011] In one optional implementation, the power output characteristic analysis includes statistical characteristic analysis, probability distribution analysis, fluctuation characteristic analysis, and correlation analysis; the power output characteristic analysis is performed on the corrected hydropower output sequence, wind power output sequence, photovoltaic power output sequence, energy storage power output sequence, and the total system output sequence obtained by combining the power output sequences of each power source at each time scale, generating corresponding multidimensional quantitative statistical data, including: Statistical characteristic analysis was performed on the corrected hydropower output series, wind power output series, photovoltaic power output series, energy storage power output series and total system output series according to each time scale; According to each time scale, probability distribution analysis is performed on the corrected wind power output sequence, photovoltaic power output sequence, hydropower output sequence and total system output sequence to generate the corresponding probability density function and cumulative distribution function. Based on the dimensions of fluctuation amplitude, rate of change, and duration, the fluctuation characteristic indicators of the corrected hydropower output sequence, wind power output sequence, photovoltaic output sequence, energy storage output sequence, and total system output sequence at each time scale are calculated. According to each time scale, the corrected hydropower output series, wind power output series, photovoltaic power output series, and energy storage output series are combined in pairs, and the Pearson correlation coefficient and Spearman correlation coefficient of each combination are calculated to generate a cross-power source correlation matrix. By combining statistical characteristic analysis, probability density function, cumulative distribution function, fluctuation characteristic index and cross-source correlation matrix at various time scales, corresponding multidimensional quantitative statistical data are generated.

[0012] This invention simultaneously conducts four types of power output analysis: statistical characteristics, probability distribution, fluctuation characteristics, and correlation, to characterize the operational patterns of each power source and the total system output from multiple dimensions. It comprehensively employs dual correlation coefficients to analyze power source synergy, and combines distribution fitting and multi-dimensional fluctuation indicators to fully uncover time-series characteristics, abandoning the qualitative analysis method based on a single indicator. By integrating the results of various analyses, it constructs multi-dimensional quantitative statistical data to comprehensively characterize the random characteristics, fluctuation features, and complementary correlations of power output, providing complete and multi-level data support for subsequent construction of a three-dimensional quantitative indicator matrix and calculation of power source contribution.

[0013] In one optional implementation, the step of performing probability distribution analysis on the corrected wind power output sequence, photovoltaic power output sequence, hydropower output sequence, and total system output sequence according to each time scale includes: According to each time scale, the corrected wind power output sequence is fitted with a Weibull distribution to generate the corresponding probability density function and cumulative distribution function; The modified photovoltaic power output sequence is fitted with beta distribution according to each time scale to generate the corresponding probability distribution function and cumulative distribution function. Log-normal distributions were fitted to the corrected hydropower output sequence at each time scale to generate the corresponding probability distribution function and cumulative distribution function. Nonparametric fitting of kernel density estimation is performed on the corrected total system output sequence at each time scale to generate the corresponding probability distribution function and cumulative distribution function.

[0014] This invention selects fitting models based on the different stochastic characteristics of power output from various sources. Wind power uses a Weibull distribution, photovoltaics uses a beta distribution, and hydropower uses a log-normal distribution for parameter fitting. The total system output is fitted using kernel density estimation nonparametric fitting, accurately adapting to the data distribution characteristics of various power outputs. By obtaining the probability density function and cumulative distribution function, the distribution law of the output interval and the output guarantee rate are quantified, effectively overcoming the limitations of single statistical indicators and achieving a refined characterization of the stochastic characteristics of output. This provides a reliable distribution basis for the quantitative calculation of complementarity and stability indicators.

[0015] In one optional implementation, the step of calculating multidimensional quantitative indicators for the multidimensional quantitative statistical data at each of the aforementioned time scales to generate a multidimensional quantitative indicator matrix; wherein the multidimensionality includes at least a complementary dimension, a stability dimension, and a regulatory capability dimension, including: Based on the complementarity dimension, the complementarity of multidimensional quantitative statistical data under each time scale is calculated, and the corresponding complementarity matrix is ​​generated. Based on the stability dimension, the smoothness of the multidimensional quantized statistical data at each time scale is calculated, and the corresponding smoothness matrix is ​​generated. Based on the regulation capacity dimension, the smoothness of the multidimensional quantitative statistical data under each time scale is calculated, and the corresponding regulation margin matrix is ​​generated. By combining the complementarity matrix, smoothness matrix, and adjustment margin matrix at each of the aforementioned time scales, a multi-dimensional quantitative index matrix is ​​generated.

[0016] This invention calculates complementarity, smoothness, and regulation margin from three dimensions: complementarity, stability, and regulation capability, forming various index matrices, and then integrates them to construct a multi-dimensional quantitative index matrix. Breaking through the limitations of traditional methods that rely solely on correlation coefficients to assess complementarity, this invention establishes a standardized quantitative evaluation system. This system can comprehensively characterize the system's synergistic complementarity level, output fluctuation stability, and power source adjustability potential across multiple nested time scales, providing a standardized, multi-dimensional quantitative calculation basis for subsequent calculations of each power source's contribution.

[0017] In one optional implementation, the step of calculating the contribution of each power source to system complementarity and system stability based on the multi-dimensional quantification index matrix includes: Using water electron collectors, wind electron collectors, photovoltaic subsets and energy storage subsets as the main game subjects, complementary degree characteristic functions and smoothness characteristic functions are constructed respectively; Based on the multi-dimensional quantitative index matrix at each time scale, the Shapley value method is used to traverse all power source subsets and solve the marginal gain of each power source to the system complementarity using the complementarity characteristic function to generate the corresponding Shapley value. Based on the multi-dimensional quantitative index matrix at each time scale, the Shapley value method is used to traverse all power source subsets and solve the marginal gain of each power source on system stability using the smoothness characteristic function to generate the corresponding Shapley value. The Shapley values ​​of the system complementarity corresponding to each power source are normalized to obtain the contribution of each power source to the system complementarity. The Shapley values ​​of the system stability corresponding to each power source are normalized to obtain the contribution of each power source to the system stability.

[0018] This invention constructs two independent characteristic functions: complementarity and smoothness. It introduces the Shapley value method to traverse all power supply consortium subsets, calculating the marginal gain of each power supply for system complementarity and stability, and then normalizing to obtain a quantitative contribution. This overcomes the limitation of traditional methods that can only analyze pairwise correlations between power supplies, enabling quantitative identification of individual contributions in a multi-power supply collaborative system. It clearly distinguishes the magnitude of the roles of various power supplies in system complementarity and output stability, providing intuitive data support for power capacity planning and multi-energy collaborative scheduling strategy formulation.

[0019] In one optional implementation, the method further includes: The multidimensional quantitative statistical data, multidimensional quantitative index matrix, and the contribution of each power source to the system complementarity and system stability at each time scale are visualized.

[0020] This invention visualizes multidimensional quantitative statistical data, multidimensional quantitative index matrices, and the contributions of each power source to the system's complementarity and stability, resulting in output statistics tables, probability distribution curves, index comparison tables, and power source contribution pie charts. This intuitively displays the complex multidimensional numerical calculation results, facilitating the rapid identification of system output patterns, complementary characteristics, and the contribution levels of each power source at different time scales. It lowers the threshold for data analysis and supports engineers in efficiently conducting system evaluations and making solution decisions.

[0021] Secondly, the present invention provides a device for quantitative analysis of the output characteristics of a multi-energy complementary system of hydro, wind, solar, and energy storage, the device comprising: A multi-source data acquisition module is used to collect operational data of a multi-energy complementary system of hydropower, wind power, photovoltaic power, and energy storage, and to perform discrete wavelet decomposition according to a multi-layer nested time scale; wherein, the operational data includes hydropower data, wind power data, photovoltaic data, energy storage data, and power grid data; The coupling constraint processing module is used to correct the hydropower output sequence, wind power output sequence, photovoltaic power output sequence and energy storage output sequence obtained after decomposition based on the coupling constraint model of the hydropower-wind-solar-storage multi-energy complementary system. The multi-dimensional quantitative analysis module is used to perform output characteristic analysis on the corrected hydropower output sequence, wind power output sequence, photovoltaic output sequence, energy storage output sequence, and the total system output sequence obtained by combining the output sequences of each power source at each time scale, and generate corresponding multi-dimensional quantitative statistical data. The multidimensional quantitative indicator calculation module is used to perform multidimensional quantitative indicator calculation on multidimensional quantitative statistical data under each time scale and generate a multidimensional quantitative indicator matrix; wherein, the multidimensionality includes at least a complementarity dimension, a stability dimension, and a regulatory capability dimension. The contribution decomposition module is used to calculate the contribution of each power source to the system complementarity and system stability, respectively, based on the multi-dimensional quantitative index matrix.

[0022] Thirdly, the present invention provides a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the method for quantitative analysis of the output characteristics of a multi-energy complementary system of water, wind, solar and storage as described in the first aspect or any of the corresponding embodiments. Attached Figure Description

[0023] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the first step in the quantitative analysis method of the output characteristics of a multi-energy complementary system of water, wind, solar and storage according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the second process of the quantitative analysis method for the output characteristics of a multi-energy complementary system of water, wind, solar and storage according to an embodiment of the present invention. Figure 3 This is a pie chart showing the contribution of each power source's output characteristics on an hourly scale according to an embodiment of the present invention. Figure 4 This is a probability density (PDF) distribution map of power output of each power source on an hourly scale according to an embodiment of the present invention; Figure 5 This is a comparison chart of the cumulative distribution (CDF) of total system output on an hourly scale according to an embodiment of the present invention; Figure 6 This is a pie chart showing the complementary contribution of each power source on an hourly scale according to an embodiment of the present invention. Figure 7 This is a pie chart showing the contribution of each power source to stability on an hourly scale according to an embodiment of the present invention; Figure 8 This is a comparison chart of the output of each power source and the total output of the system on a typical day (hourly level) according to an embodiment of the present invention; Figure 9 This is a bar chart comparing complementary characteristics across time scales according to an embodiment of the present invention; Figure 10 This is a structural block diagram of a device for quantitative analysis of the output characteristics of a multi-energy complementary system of water, wind, solar and energy storage according to an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0026] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0027] 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. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0028] This invention provides a quantitative analysis method for the output characteristics of a multi-energy complementary system integrating hydropower, wind power, solar power, and energy storage. It employs multi-layer nested wavelet decomposition to achieve simultaneous multi-scale output analysis, overcoming the limitations of single-scale analysis. Multi-energy coupling constraint correction aligns with actual operating conditions, significantly reducing analytical bias. A three-dimensional quantitative index system of complementarity, stability, and regulation is constructed, overcoming the shortcomings of traditional qualitative assessments. The Shapley value method is used to quantify the contribution ratio of each power source, clarifying the regulation role of each type of power source. The entire analytical logic aligns with the actual operating constraints of the watershed's hydropower, wind power, solar power, and energy storage systems, resulting in objective and complete quantitative results. This provides precise quantitative support for system capacity configuration and hierarchical time-sharing optimization scheduling, effectively improving the absorption of new energy and the stable operation of the system.

[0029] This embodiment provides a method for quantitative analysis of the output characteristics of a multi-energy complementary system of water, wind, solar, and storage. Figure 1 This is a flowchart of a method for quantitative analysis of the output characteristics of a multi-energy complementary system based on water, wind, solar, and energy storage, according to an embodiment of the present invention. Figure 1As shown, the process includes the following steps: Step S101: Collect the operation data of the hydropower, wind power, photovoltaic, and energy storage multi-energy complementary system and perform discrete wavelet decomposition according to a multi-layer nested time scale; wherein, the operation data includes hydropower data, wind power data, photovoltaic data, energy storage data, and grid data.

[0030] It should be noted that the hydro-wind-solar-storage multi-energy complementary system refers to a combined power generation system with cascade hydropower as the regulation core, integrating wind power and solar clean energy, and equipped with energy storage devices; the operation data refers to the system's measured time series dataset, which includes hydropower, wind power, solar power, energy storage output information, as well as relevant parameters related to grid operation constraints; the multi-layer nested time scale refers to a hierarchical time series system including second-level, minute-level, hour-level, day-level, month-level, and grade-level; and the discrete wavelet decomposition refers to a multi-resolution time series analysis method that decomposes the original output sequence into different frequency components to separate the output trend and fluctuation information at different time scales.

[0031] In this embodiment of the invention, operational data of a multi-energy complementary system including hydropower, wind power, photovoltaic, energy storage, and power grid data are collected. After time series alignment and outlier data removal, a suitable wavelet basis function is selected, and discrete wavelet transform is used to perform hierarchical decomposition of the power output time series of hydropower, wind power, photovoltaic, and energy storage. The corresponding power output component sequences at multiple nested time scales of seconds, minutes, hours, days, months, and years are obtained so that power output correction can be carried out in combination with the coupled constraint model.

[0032] Step S102: Based on the coupled constraint model of the multi-energy complementary system of hydropower, wind power, photovoltaic power and energy storage, the hydropower output sequence, wind power output sequence, photovoltaic power output sequence and energy storage output sequence obtained after decomposition are corrected respectively.

[0033] It should be noted that the coupled constraint model refers to a mathematical model that integrates the boundary conditions of hydropower, energy storage, and power grid operation, including operating rules such as hydraulic constraints, energy storage state of charge constraints, and power grid power limits; the output sequence refers to the real-time active power time series data of the power source arranged in chronological order.

[0034] In this embodiment of the invention, a pre-constructed coupled constraint model containing a hydropower constraint sub-model, an energy storage constraint sub-model, and a power grid constraint sub-model is invoked. The hydropower output sequence, wind power output sequence, photovoltaic output sequence, and energy storage output sequence obtained from decomposition at each nested time scale are imported sequentially. The hydraulic boundary correction, energy storage charge state closed-loop correction, and system total output power grid constraint verification are completed step by step to obtain the effective output sequence of each power source that meets the constraints of the actual operating conditions, supporting subsequent multi-scale output characteristic analysis.

[0035] Step S103: At each time scale, the output characteristics of the corrected hydropower output sequence, wind power output sequence, photovoltaic power output sequence, energy storage power output sequence, and the total system output sequence obtained by combining the power output sequences of each power source are analyzed to generate corresponding multidimensional quantitative statistical data.

[0036] It should be noted that the total system output sequence refers to the combined total power time series obtained by superimposing the outputs of hydropower, wind power, photovoltaic power, and energy storage at the same time. Output characteristic analysis refers to the quantitative analysis work carried out on time-series output data, including four types of analysis: statistical characteristics, probability distribution, fluctuation characteristics, and correlation. Multidimensional quantitative statistical data refers to the collection of all analysis results, including statistical indicators, distribution functions, fluctuation indicators, and power source correlation coefficient matrices, at various time scales.

[0037] In this embodiment of the invention, the power output sequences of each power source after coupling constraint correction are superimposed to obtain the total power output sequence of the system. At each nested time scale, four types of power output characteristics analysis are carried out simultaneously: statistical characteristics, probability distribution, fluctuation characteristics, and correlation. Various statistical indicators, distribution functions, fluctuation parameters, and cross-power source correlation coefficient matrices are obtained. All analysis results are integrated to form multidimensional quantitative statistical data, providing complete basic data for the subsequent construction of a three-dimensional quantitative indicator matrix.

[0038] Step S104: Calculate multidimensional quantitative indicators for multidimensional quantitative statistical data at each time scale to generate a multidimensional quantitative indicator matrix; wherein, the multidimensionality includes at least the complementarity dimension, the stability dimension, and the adjustment capability dimension.

[0039] It should be noted that the multi-dimensional quantitative index matrix refers to a structured numerical matrix formed by integrating complementarity, smoothness, and adjustment margin at various time scales; the three dimensions respectively represent the level of power supply synergy and complementarity, the degree of output stability, and the system's adjustable potential.

[0040] In this embodiment of the invention, based on the multidimensional quantitative statistical data obtained at each time scale, quantitative index calculations are carried out from three dimensions: complementarity, stability, and regulation capability. The system complementarity, output smoothness, and power supply regulation margin corresponding to each time scale are solved one by one to generate the index matrix corresponding to each dimension. Then, the quantitative results of all time scales and all evaluation dimensions are integrated to construct a complete multidimensional quantitative index matrix, providing a standardized quantitative basis for the subsequent Shapley value power supply contribution measurement.

[0041] Step S105: Calculate the contribution of each power source to the system complementarity and system stability based on the multi-dimensional quantitative index matrix.

[0042] It should be noted that the contribution rate refers to the percentage of a single type of power source that contributes quantitatively to the overall complementarity and stability of the system, calculated using the Shapley value method.

[0043] In this embodiment of the invention, based on a multi-dimensional quantitative index matrix, hydropower, wind power, photovoltaic power, and energy storage are used as the main players in the game. The complementarity feature function for adapting system complementarity and the smoothness feature function for adapting system stability are constructed respectively. The Shapley value method is used to traverse all power source subsets, solve the marginal gain corresponding to each power source, and generate Shapley values ​​corresponding to complementarity and stability respectively. Finally, the Shapley values ​​corresponding to complementarity and stability are normalized respectively to accurately obtain the contribution of each power source to system complementarity and system stability.

[0044] This embodiment provides a method for quantitative analysis of the output characteristics of a multi-energy complementary system of water, wind, solar, and storage. Figure 2 This is a flowchart of a method for quantitative analysis of the output characteristics of a multi-energy complementary system based on water, wind, solar, and energy storage, according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps: Step S201: Collect the operation data of the hydropower, wind power, photovoltaic, and energy storage multi-energy complementary system and perform discrete wavelet decomposition according to the multi-layer nested time scale; wherein, the operation data includes hydropower data, wind power data, photovoltaic data, energy storage data, and grid data.

[0045] Specifically, step S201 includes: Step S2011: Collect operational data of the hydropower, wind power, solar power, and energy storage multi-energy complementary system; wherein, the operational data includes hydropower data, wind power data, photovoltaic data, energy storage data, and grid data.

[0046] In this embodiment of the invention, the multi-source data of the hydro-wind-solar-storage multi-energy complementary system are: Hydropower data: power output, reservoir water level, inflow, and outflow of each cascade hydropower station; Wind power data: Total active power output, wind speed, and wind direction for each wind farm; Photovoltaic data: Total active power output, solar irradiance, and ambient temperature of each photovoltaic power station; Energy storage data: the charging and discharging power and state of charge (SOC) of the energy storage system; Power grid data: upper limit of total system output, dispatching plan curve.

[0047] Step S2012 involves outlier processing and time alignment of hydropower data, wind power data, photovoltaic data, energy storage data, and power grid data.

[0048] In this embodiment of the invention, outliers are removed from hydropower data, wind power data, photovoltaic data, energy storage data, and power grid data using the 3σ criterion, short-term missing data is filled using linear interpolation, and long-term missing data is filled using the historical average value method.

[0049] All data are aligned to the same time base to generate a time series dataset.

[0050] Step S2013: Perform discrete wavelet decomposition on the processed hydropower data, wind power data, photovoltaic data, and energy storage data according to multi-level nested time scales; wherein, the multi-level nested time scales include second-level time scales, minute-level time scales, hour-level time scales, day-level time scales, month-level time scales, and grade-level time scales.

[0051] In this embodiment of the invention, Discrete Wavelet Transform (DWT) is used to decompose the original power output time series of each power source (i.e., the processed hydropower data, wind power data, photovoltaic data, and energy storage data) into six time scales: Scale 1: Seconds (1-60 seconds) Scale 2: Minute-level (1-60 minutes) Scale 3: Hourly (1-24 hours) Scale 4: Daily (1-30 days) Scale 5: Monthly (1-12 months) Scale 6: Grade (1 year and above) The decomposition process uses the db4 wavelet basis function, and the number of decomposition layers is determined according to the data length to ensure that the time series length at each scale is no less than 1000 sample points.

[0052] Step S202: Based on the coupled constraint model of the multi-energy complementary system of hydropower, wind power, photovoltaic power and energy storage, the hydropower output sequence, wind power output sequence, photovoltaic power output sequence and energy storage output sequence obtained after decomposition are corrected respectively.

[0053] Specifically, step S202 includes: Step S2021: Establish a coupled constraint model for a multi-energy complementary system of hydropower, wind power, solar power, and energy storage; wherein the coupled constraint model includes at least a hydropower constraint model, an energy storage constraint model, and a power grid constraint model.

[0054] In this embodiment of the invention, a coupling constraint model of the multi-energy system is established to generate feasible output ranges for each power source: Hydroelectric constraint model:

[0055] In the formula, Power output for hydroelectricity, For the reservoir capacity, The data represents the outbound flow rate, with the subscripts min and max indicating the minimum and maximum values, respectively.

[0056] Energy storage constraint model:

[0057] In the formula, This refers to the charging and discharging power of energy storage (negative for charging, positive for discharging). and These represent the maximum charging and discharging power, respectively, and SOC(t) represents the state of charge.

[0058] Power grid constraint model:

[0059] In the formula, To provide power for wind power, Contribute to photovoltaic power This is the maximum total output allowed by the power grid.

[0060] Based on the aforementioned constraint model, the output data at each time scale are corrected to generate a feasible output dataset that conforms to actual operating conditions.

[0061] Step S2022: Input the hydropower output sequence, wind power output sequence, photovoltaic power output sequence and energy storage output sequence obtained after decomposition at each time scale into the coupled constraint model in sequence.

[0062] In this embodiment of the invention, the overall correction process is as follows: 1) Input the original output component sequences of hydropower, wind power, photovoltaic, and energy storage at each time scale into the coupled constraint model; 2) Single-power source independent constraint verification and initial correction (output limit, ramp rate, etc.); 3) Watershed hydropower coupling constraint linkage correction (reservoir capacity-discharge balance, cascade hydraulic connection); 4) Closed-loop constraint correction for energy storage system (charge and discharge power, SOC operating range); 5) Coordination and correction of global power grid output constraints (upper and lower limits of total output, matching of dispatching plans); 6) Multi-scale temporal consistency verification and deviation callback; 7) Output feasible output datasets for each scale with correction labels.

[0063] Step S2023 involves using a hydropower constraint model to sequentially perform output-flow conversion and basic limit correction, reservoir capacity-flow dynamic balance correction, and upstream-downstream hydraulic connection correction for the hydropower output sequence at each time scale.

[0064] In this embodiment of the invention, the coupling constraint correction of the hydropower system is as follows: For the power output sequence of cascade hydropower stations in a river basin (i.e., the hydropower output sequence at various time scales), the dynamic balance of reservoir capacity and water volume is the core, and a linkage correction is carried out in combination with power output limits and cascade hydraulic connections. The specific execution logic is as follows: 1) Output-flow conversion and basic limit correction Based on the power output formula of a hydropower station (ρ is the density of water, g is the acceleration due to gravity, H is the operating head, and η is the power generation efficiency), converting the hydropower output series at each time scale into the corresponding outflow series. First, verify the upper and lower limits of output and flow rate. or If the value is not specified, the outbound flow will be directly truncated to the corresponding boundary value and updated synchronously.

[0065] 2) Dynamic balance correction between storage capacity and flow rate With the current time scale and time step Calculate the temporal changes in reservoir capacity point by point, using units as the unit: ,in, Q in (t) represents the inflow sequence at the corresponding time scale. If the calculated... Exceeding the permitted storage capacity range Then the outbound flow rate is corrected in the reverse direction. This ensures that the reservoir capacity just reaches the allowable limit, and then the corrected flow rate is converted back to the hydropower output value, ensuring that the reservoir water level / capacity always operates between the dead water level and the normal storage level throughout the entire period.

[0066] 3) Correction of hydraulic connection between upstream and downstream of the cascade For a cascade hydropower station group in a river basin, a water flow propagation lag time τ is introduced to establish a coupling relationship between upstream and downstream water volumes: the inflow to the downstream power station equals the outflow to the upstream power station plus the inter-regional inflow, i.e. When adjusting the output of the upstream power station, the reservoir capacity constraints of the downstream power station are checked simultaneously. If the upstream water release causes the downstream reservoir capacity to exceed the limit, the upstream outflow is adjusted back to avoid downstream overflow or water level falling below the dead water level, thus realizing a closed loop of hydraulic coupling constraints for all cascade power stations.

[0067] Step S2024: The energy storage output sequence at each time scale is initially verified for charge and discharge power and the SOC dynamic balance closed-loop correction is performed sequentially using the energy storage constraint model.

[0068] In this embodiment of the invention, the energy storage system constraint correction is as follows: For energy storage charge and discharge power sequences, with the core objective of ensuring the state of charge (SOC) does not exceed limits throughout the entire process, a two-step correction method is adopted: "initial power limit correction → SOC closed-loop back calculation → secondary power correction". Step 1: Initial Verification of Charge and Discharge Power. First, the power output sequence of the energy storage at each time scale is truncated by power limits: In the charging state (output is negative), if... Then it is corrected to In the discharge state (output is positive), if Then it is corrected to .

[0069] Step 2: SOC Dynamic Balancing Closed-Loop Correction Calculate the SOC time series changes point by point according to the time step: ,in, For the rated capacity of energy storage, during charging A negative SOC indicates an increase in SOC; a positive SOC indicates a decrease in SOC during discharge. If the calculated value is... Exceeding the allowed operating range Then, the maximum allowable charging and discharging power at the current moment is calculated in reverse. A second correction is made to ensure that the SOC does not overcharge or over-discharge throughout the entire period, and to comply with the actual operation rules of energy storage.

[0070] Step S2025: Based on the corrected hydropower output sequence and energy storage output sequence at each time scale, and combined with the decomposed wind power output sequence and photovoltaic output sequence at each time scale, generate the total system output sequence.

[0071] In this embodiment of the invention, the hydropower and energy storage output sequences at each time scale after coupling constraint correction are combined with the wind power and photovoltaic output sequences that have been decomposed by wavelet at the corresponding time scale, and the power is superimposed and integrated at each time node to generate a multi-scale system total output sequence that fits the actual operating constraints of the system.

[0072] Step S2026: Correct the global output constraint of the system's total output sequence at each time scale using the grid constraint model.

[0073] In this embodiment of the invention, the global power output constraint correction on the grid side is as follows: After completing the corrections for single power source and coupling constraints, the total system output is verified and coordinated using grid constraints. The formula for calculating the total system output is as follows: in, To provide power for wind power, Contribute to photovoltaic power.

[0074] like (Maximum total output allowed by the power grid) Output adjustment will be carried out according to the priority of "prioritizing the consumption of new energy and orderly reducing the power supply": priority will be given to reducing the output of photovoltaic and wind power (simulating curtailment of solar and wind power), followed by reducing the output of hydropower, and finally adjusting the energy storage to reduce the discharge power / convert to charging, until the total output meets the upper limit requirement; If there are constraints on the dispatch plan curve, the dispatch plan value is used as the target, and deviations are smoothed out first through energy storage charging and discharging and hydropower regulation. Wind and solar power output is used as the base power and is not forcibly increased, so as to ensure that the correction result conforms to the actual dispatch logic of the power grid.

[0075] Step S2027: Perform multi-scale consistency verification on the corrected hydropower output sequence, wind power output sequence, photovoltaic power output sequence and energy storage output sequence at each time scale.

[0076] In this embodiment of the invention, the specific multi-timescale differential correction strategy is as follows: To address the physical meaning and analytical objectives of the six time scales, different constraint weights and correction priorities are matched to avoid distortion of high-frequency components or deviation of low-frequency components from long-term operating patterns caused by using the same correction rule across all scales. 1. Second-level scale (1-60 seconds): Focuses on correcting output ramp-up rate constraints and instantaneous power limits of equipment. Only verifies whether the output change rate of hydropower and energy storage exceeds the equipment response limit, without introducing slow variable constraints such as reservoir capacity and SOC, preserving the true physical characteristics of high-frequency fluctuations, and serving the analysis of second-level power fluctuation smoothing.

[0077] 2. Minute-level scale (1-60 minutes): Focuses on correcting short-term output upper and lower limits, short-term fluctuation constraints of energy storage SOC, and minute-level total output limits. Verifies the SOC fluctuation range and total output exceeding limits within short time scales, serving minute-level power balance and primary frequency regulation characteristic analysis.

[0078] 3. Hourly Scale (1-24 hours): Focuses on correcting the daily outflow constraints of hydropower, the daily reservoir capacity variation range, the daily operating range of energy storage SOC, and the hourly grid dispatch output limit. Fully incorporates water balance and SOC closed-loop constraints to serve the analysis of daily peak-shaving characteristics and hourly complementary patterns.

[0079] 4. Daily Scale (1-30 days): Focuses on correcting daily total water balance, daily reservoir capacity variation constraints, daily energy storage circulation depth constraints, and daily total power generation limits. Verifies the total constraints of water and electricity generation on a daily basis, serving the analysis of inter-day complementarity characteristics and daily-level dispatching operations.

[0080] 5. Monthly Scale (1-12 months): Focuses on correcting monthly total inflow constraints, monthly reservoir capacity adjustment range, and monthly total power generation constraints. It verifies the seasonal variations in total basin runoff and reservoir capacity on a monthly basis, serving the analysis of seasonal complementarity patterns and monthly scheduling plans.

[0081] 6. Grade-level (1 year or more): Focuses on revising annual runoff constraints, multi-year regulation reservoir capacity limits, and annual power generation constraints. It focuses on long-term water balance and annual reservoir capacity cycle changes, serving the analysis of annual complementary characteristics and long-term system planning.

[0082] After all time scales have completed a single round of correction, a multi-scale consistency check is performed: the corrected six-level scale components are reconstructed using wavelets to obtain the corrected original resolution output sequence, and a full constraint check is performed again; if there are points exceeding the limits in the reconstructed sequence, the output components of the corresponding low-frequency scale are adjusted in reverse until all coupling constraints are met across all scales and time periods.

[0083] Step S203: At each time scale, the output characteristics of the corrected hydropower output sequence, wind power output sequence, photovoltaic power output sequence, energy storage power output sequence, and the total system output sequence obtained by combining the power output sequences of each power source are analyzed to generate corresponding multidimensional quantitative statistical data.

[0084] It should be noted that this step follows the feasible output dataset generated in step S202 and serves as a foundational step for multi-dimensional quantitative evaluation. The core objective is to perform a refined statistical characterization of hydropower, wind power, photovoltaic power, energy storage, and total system output across six time scales. This involves quantifying the inherent output patterns, fluctuations, and correlations of individual power sources and total system output at their respective scales. Furthermore, it provides all the underlying statistical parameters for the three-dimensional quantitative index calculation in step S204 and the contribution decomposition in step S205. Simultaneously, cross-scale comparisons preliminarily identify the complementary characteristics across different time dimensions, making it a core data support step for achieving "multi-scale nested analysis."

[0085] Unlike existing technologies that are only used to simply describe force characteristics, all analyses in this step are calculated independently and output in layers according to six time scales. This not only characterizes the force behavior within a single scale, but also provides a comparable unified statistical benchmark for revealing the complementary laws across scales.

[0086] Specifically, step S203 includes: Step S2031: Perform statistical characteristic analysis on the corrected hydropower output sequence, wind power output sequence, photovoltaic power output sequence, energy storage power output sequence and total system output sequence according to each time scale.

[0087] In this embodiment of the invention, statistical characteristic analysis is performed: Analysis content: For the power output sequences of hydropower, wind power, photovoltaic, and energy storage, as well as the total power output sequence of the system at various time scales, seven basic statistical measures were calculated, including mean, variance, standard deviation, maximum value, minimum value, kurtosis, and skewness.

[0088] 1) The mean, maximum, and minimum values ​​depict the concentration trend and range of power output, reflecting the average power output level and ultimate power output capability of the power source; 2) Variance and standard deviation characterize the dispersion of force and are the core basic parameters for quantifying the level of force fluctuation; 3) Kurtosis and skewness characterize the morphological features of the force probability distribution. Kurtosis reflects the probability of extreme force events, while skewness reflects the degree of symmetry and the direction of deviation of the force distribution.

[0089] Technical purpose and function: 1) Quantify the basic output attributes of each power source at the corresponding time scale, and clarify the inherent differences in output characteristics of different power sources; 2) It provides direct calculation parameters for core quantitative indicators such as complementarity, smoothness, and volatility suppression rate in step S204, and is the calculation cornerstone of the entire quantitative indicator system; 3) By comparing the statistical differences between a single power source and the total output of the system, we can make a preliminary and intuitive judgment on the improvement effect of multi-energy complementarity on the output dispersion and extreme value level.

[0090] Step S2032: According to each time scale, perform probability distribution analysis on the corrected wind power output sequence, photovoltaic power output sequence, hydropower output sequence and total system output sequence respectively, and generate the corresponding probability density function and cumulative distribution function.

[0091] In some optional implementations, step S2032 above includes: Step a1: Fit the corrected wind power output sequence to a Weibull distribution according to each time scale to generate the corresponding probability density function and cumulative distribution function.

[0092] In this embodiment of the invention, based on the wind power output sequence with constraint corrections completed at each time scale, the parameters are fitted using the Weibull distribution model adapted to the random fluctuation characteristics of wind power output, and the optimal distribution parameters are obtained. Based on this, the wind power output probability density function and cumulative distribution function corresponding to each time scale are constructed to accurately characterize the random distribution law of wind power at different time scales.

[0093] Step a2: Fit the beta distribution of the corrected photovoltaic power output sequence according to each time scale to generate the corresponding probability distribution function and cumulative distribution function.

[0094] In this embodiment of the invention, based on the constrained photovoltaic power output sequence at each time scale, and combined with the distribution characteristics of photovoltaic power output limited to the range of 0 to rated power, a beta distribution model adapted to bounded random variables is used to perform parameter fitting, solve for the optimal shape parameters, and sequentially generate the probability density function and cumulative distribution function corresponding to photovoltaic power output at each time scale, accurately characterizing the randomness and interval distribution law of photovoltaic power output, and improving the system output probability characteristic data system.

[0095] Step a3: Fit the corrected hydropower output sequence to a log-normal distribution according to each time scale to generate the corresponding probability distribution function and cumulative distribution function.

[0096] In this embodiment of the invention, based on the constrained and corrected hydropower output sequence at each time scale, and considering the time-series distribution characteristics of hydropower output being always positive and generally right-skewed, a log-normal distribution model is used to perform parameter fitting, solve for the optimal distribution parameters, and sequentially generate the probability density function and cumulative distribution function corresponding to hydropower output at each time scale, accurately characterizing the temporal random distribution law of hydropower output and improving the multi-source output probability feature system.

[0097] Step a4: Perform nonparametric fitting of kernel density estimation on the corrected total system output sequence according to each time scale to generate the corresponding probability distribution function and cumulative distribution function.

[0098] In this embodiment of the invention, based on the system total output sequence of the single power source distribution fitted at each time scale, the fixed parameter distribution model is abandoned. Instead, the kernel density estimation nonparametric fitting method is used to adaptively match the complex and irregular distribution characteristics of the total output, determine the optimal kernel function and bandwidth parameter, and generate the probability density function and cumulative distribution function corresponding to the total output of the system on a scale-by-scale. This accurately characterizes the random distribution characteristics of the overall output of the multi-energy coupled system and improves the complete output probability analysis system.

[0099] It is worth mentioning that probability distribution fitting was performed on the time series of each power source and the total system output to generate the corresponding probability density function (PDF) and cumulative distribution function (CDF). Adaptive fitting methods were adopted for the output characteristics of different power sources: Weibull distribution was used for wind power output, Beta distribution for photovoltaic power output, log-normal distribution for hydropower output, and kernel density estimation was used for nonparametric fitting of the total system output to ensure the accuracy of the distribution fitting.

[0100] Technical purpose and function: 1) Reveal the probability and distribution patterns of different output levels, and clarify the output probability distribution pattern of the system at various time scales; 2) Provides distribution function support for the quantitative calculation of indicators such as output guarantee rate and adjustment margin in step S204, and can directly calculate the probability of meeting any output threshold through CDF; 3) Compare the differences in the distribution patterns before and after complementarity (single power source vs. total system output) to intuitively demonstrate the "peak shaving and valley filling" reshaping effect of multi-energy complementarity on the output distribution.

[0101] Step S2033: Based on the dimensions of fluctuation amplitude, rate of change, and duration, calculate the fluctuation characteristic indicators of the corrected hydropower output sequence, wind power output sequence, photovoltaic power output sequence, energy storage output sequence, and total system output sequence at each time scale.

[0102] In this embodiment of the invention, the fluctuation characteristics are analyzed as follows: Analysis content: Volatility characteristic indicators are calculated from three dimensions: fluctuation amplitude, rate of change, and duration. Specifically, these include: 1) Output fluctuation rate: The ratio of the absolute value of the output difference between adjacent sampling times to the rated capacity of the power supply, reflecting the output fluctuation amplitude per unit time; 2) Ramp rate: The maximum change in output within a set time window, reflecting the extreme rate of change of force and impact intensity; 3) Duration of fluctuation: The cumulative duration for which the output is continuously higher or lower than the set fluctuation threshold, reflecting the sustained characteristics of large fluctuations.

[0103] Technical purpose and function: 1) Matching the fluctuation analysis needs of different time scales: second / minute level focuses on instantaneous upswing impact, hour level focuses on intraday fluctuation amplitude, and month / year level focuses on seasonal fluctuation cycle; 2) Provide basic calculation data for indicators such as fluctuation suppression rate and complementary duration in step S204, and quantify the effect of complementary system on fluctuation suppression; 3) Identify the fluctuation weaknesses and fluctuation patterns of each power source to provide directional reference for subsequent system optimization scheduling and power source structure adjustment.

[0104] Step S2034: According to each time scale, the corrected hydropower output sequence, wind power output sequence, photovoltaic power output sequence, and energy storage output sequence are combined in pairs, and the Pearson correlation coefficient and Spearman correlation coefficient of each combination are calculated to generate a cross-power source correlation matrix.

[0105] In this embodiment of the invention, correlation analysis is performed as follows: Analysis Content: At each time scale, hydropower, wind power, photovoltaic power, and energy storage are paired, and Pearson correlation coefficients and Spearman correlation coefficients are calculated separately to form a correlation coefficient matrix (i.e., a cross-power source correlation matrix). The Pearson correlation coefficient measures the linear correlation between power source outputs, while the Spearman correlation coefficient measures the monotonic correlation, adapting to the non-normal distribution of power output data and ensuring the comprehensiveness of the correlation assessment.

[0106] Technical purpose and function: 1) The positive / negative relationship between power sources is initially quantified on a single time scale. The higher the negative correlation, the stronger the natural complementary potential, thus achieving a preliminary qualitative judgment on complementarity. 2) By comparing the differences in correlation coefficients across six time scales, the scaling effect of "complementarity changing with time scale" is revealed, confirming the necessity of multi-scale analysis. 3) It provides a correlation reference benchmark for the contribution decomposition based on Shapley values ​​in step S205, and can verify the rationality and consistency of the contribution decomposition results.

[0107] Step S2035: Combine statistical characteristic analysis, probability density function, cumulative distribution function, fluctuation characteristic index and cross-source correlation matrix at each time scale to generate corresponding multidimensional quantitative statistical data.

[0108] In this embodiment of the invention, the statistical characteristic table of statistical characteristics analysis of each power source and total system output at each time scale, the distribution fitting parameters, fluctuation characteristic parameters and correlation coefficient matrix corresponding to the probability density function and cumulative distribution function respectively, and other data are combined to generate multidimensional quantitative statistical data, which are used as the standardized input for the calculation of multidimensional quantitative indicators in step S204.

[0109] Step S204: Calculate multidimensional quantitative indicators for multidimensional quantitative statistical data at each time scale to generate a multidimensional quantitative indicator matrix; wherein, the multidimensionality includes at least the complementarity dimension, the stability dimension, and the adjustment capability dimension.

[0110] It should be noted that this step, based on the statistical characteristics of each time scale output in step S203, constructs a complete quantitative indicator system from three independent dimensions: complementarity, stability, and adjustment capability, to comprehensively quantitatively evaluate the system output characteristics at each time scale. The positioning, calculation logic, and engineering value of the three types of indicators are as follows: steps S2041-S2044.

[0111] Specifically, step S204 includes: Step S2041: Calculate the complementarity of multidimensional quantitative statistical data at each time scale according to the complementarity dimension, and generate the corresponding complementarity matrix.

[0112] In this embodiment of the invention, the complementarity dimension is used to quantify the synergistic complementary effect of multiple power sources.

[0113] Core positioning: Measuring the strength of the synergistic effect of the output fluctuations of different power sources canceling each other out and smoothing out peaks and valleys is a dimension that reflects the core value of a multi-energy complementary system. This dimension exhibits significant nonlinear synergistic characteristics—the overall complementary effect of the system is not equal to the linear superposition of the individual characteristics of each power source, and therefore it is included in the contribution decomposition scope of step S205.

[0114] Core metrics and calculation logic: Complementarity: ,in, The variance of the total system output. For the first i The output variance of a power source For the first i Capacity weighting of each power supply; Complementary duration: The proportion of the cumulative duration during which the total output fluctuation of the system is below a set threshold within a statistical period, reflecting the time-duration capability of the complementary effect; Complementary coverage rate: The proportion of time intervals in which the complementary effect is significant (the total output fluctuation in a single period is less than 50% of the sum of the weighted fluctuations of each power source) to the total time, reflecting the time coverage of the complementary effect.

[0115] Step S2042: Calculate the smoothness of multidimensional quantified statistical data at each time scale according to the stability dimension, and generate the corresponding smoothness matrix.

[0116] In this embodiment of the invention, the stability dimension is defined as the level of stability of the total output of the quantified system.

[0117] Core positioning: Measuring the fluctuation of the system's total output and the reliability of power supply, it is a direct reflection of the operating performance of a multi-energy complementary system. Its index value is also affected by the synergistic effect of multiple power sources, exhibiting a nonlinear superposition effect, and therefore is included in the contribution decomposition scope of step S205.

[0118] Core metrics and calculation logic: Smoothness: ,in, This represents the average total output of the system. Volatility suppression rate: ,in, Standard deviation of total power output for wind and solar power; Output guarantee rate: The percentage of time during which the total output of the system is greater than or equal to the set output threshold, reflecting the power supply reliability level of the system.

[0119] Step S2043: Calculate the smoothness of multidimensional quantitative statistical data at each time scale according to the adjustment capability dimension, and generate the corresponding adjustment margin matrix.

[0120] In this embodiment of the invention, the adjustment capability dimension is: Core positioning: It measures the upper limit of the system's ability to adjust output and its response performance through controllable power sources (hydropower, energy storage), and is a core supporting parameter for the system to participate in grid peak shaving and frequency regulation.

[0121] Technical reasons for not including contribution decomposition: This dimension of the index is mainly determined by the equipment parameters and operating constraints of each power source. The overall regulation capability is a linear superposition of the regulation resources of each power source (the total regulation margin of the system is equal to the sum of the adjustable capacities of each power source). There is no significant nonlinear synergistic effect. The regulation contribution of each power source can be directly determined by its own parameters without the need for decomposition through game theory.

[0122] Core metrics and calculation logic: Adjustment margin: ,in, and These are the system's maximum and minimum adjustable output forces, respectively. This is the system's rated capacity; Response speed: The adjustment time required for the system output to be adjusted from the current value to the target value, which is divided into second-level fast response (primary frequency modulation) and minute-level slow response (secondary frequency modulation / AGC), respectively corresponding to the analysis needs of different time scales; Adjustment accuracy: The relative deviation rate between the actual output and the target output under steady-state operation, reflecting the accuracy of system adjustment.

[0123] Step S2044: Combine the complementarity matrix, smoothness matrix, and adjustment margin matrix at each time scale to generate a multi-dimensional quantitative index matrix.

[0124] In this embodiment of the invention, based on the complementarity matrix, smoothness matrix and adjustment margin matrix calculated at each time scale, the quantitative results of the three major evaluation dimensions of complementarity, stability and adjustment capability are integrated and fused and standardized according to the unified time series dimension and evaluation standard to construct a standardized multi-dimensional quantitative index matrix that covers multiple nested time scales and includes multi-dimensional evaluation indicators, providing a standardized and unified quantitative analysis basis for the subsequent accurate solution of the contribution of each power source.

[0125] Step S205: Based on the multi-dimensional quantitative index matrix, calculate the contribution of each power source to the system complementarity and system stability, respectively.

[0126] It should be noted that this step addresses the complementarity and stability dimensions in step S204, which involve nonlinear synergistic effects. It employs the Shapley value method from cooperative game theory to quantify the marginal contribution of each power source to the overall complementary effect and output stability of the system, thus resolving the problem that existing technologies cannot clearly define the weight of each power source's role. Since the regulation capability dimension is primarily characterized by linear superposition, the regulation contribution of each power source can be directly determined through its own parameters, without requiring decomposition through game theory methods; therefore, it is not included in the decomposition scope of this step.

[0127] Method principle: The Shapley value method is a classic approach in cooperative game theory for fairly distributing the total payoff of an alliance. Its core idea is to calculate the marginal payoff of each individual member joining the alliance, following all possible alliance formation orders, and then take a weighted average of all marginal payoffs to obtain the member's fair contribution to the alliance's total payoff. In this application, hydropower, wind power, solar power, and energy storage are considered as participants in the cooperative game, and the system's complementarity / smoothness is considered as the alliance's total payoff. By calculating the marginal gain of each power source when joining subsets of different sizes, the fair contribution of each power source to the overall system characteristics is finally obtained.

[0128] Specifically, step S205 includes: Step S2051: Using water electron collectors, wind electron collectors, photovoltaic subsets and energy storage subsets as the main game subjects, construct complementarity feature functions and smoothness feature functions respectively.

[0129] In this embodiment of the invention, the Shapley value method is used to calculate the contribution of each power source to the overall complementarity and stability of the system: Define the characteristic function v(S) as the complementarity or smoothness of the system composed of the power source subset S.

[0130] Specifically, the constructed multi-dimensional quantitative index matrix takes four power source subsets—hydropower, wind power, photovoltaic power, and energy storage—as the main participants in the game. Combining the system complementarity quantitative rules and output stability evaluation standards at various time scales, complementary degree characteristic functions and smoothness characteristic functions adapted to the calculation of alliance revenue are constructed respectively, laying the model foundation for subsequent quantification of the marginal contribution of each power source using the Shapley value method.

[0131] Step S2052: Based on the multi-dimensional quantitative index matrix at each time scale, the Shapley value method is used to traverse all power source subsets respectively, and the marginal gain of each power source to the system complementarity is solved using the complementarity characteristic function to generate the corresponding Shapley value.

[0132] In this embodiment of the invention, relying on the constructed multi-dimensional quantitative index matrix and complementarity feature function, the entire power cooperation subset consisting of hydropower, wind power, photovoltaic, and energy storage is traversed at each time scale. The system complementarity difference before and after each power source joins the alliance is calculated one by one to obtain the marginal gain of complementarity of a single type of power source. Combined with the Shapley value weighted average algorithm, the iterative solution is completed, and finally the Shapley value of the system complementarity corresponding to each power source is obtained, providing raw data for subsequent contribution normalization calculation.

[0133] Step S2053: Based on the multi-dimensional quantitative index matrix at each time scale, the Shapley value method is used to traverse all power source subsets and solve the marginal gain of each power source on system stability using the smoothness characteristic function to generate the corresponding Shapley value.

[0134] In this embodiment of the invention, by combining the multi-dimensional quantitative index matrix at various time scales with the pre-constructed smoothness feature function, and relying on the Shapley value method to traverse all power cooperation subsets consisting of hydropower, wind power, photovoltaic, and energy storage, the difference in system smoothness before and after each power source joins the alliance is calculated to obtain the marginal gain of a single type of power source on system stability. The Shapley value of the system stability corresponding to each power source is obtained by weighted average calculation, providing benchmark data for subsequent normalization calculation of stability contribution.

[0135] Specifically, calculate the first i Shapley values ​​for each power supply:

[0136] in: It is the complete set consisting of all the power sources in the system. Represents the total number of power supplies. Let the contribution of a power source be the one whose value is to be determined. The entire series does not include power supplies. any subset of power sources, The number of power sources contained in subset S; It is the complementary characteristic function of the system. Output subset The corresponding system output complementarity / smoothness index, It is a power supply Merge into subsets The resulting marginal increase in complementarity, that is, the subset of the power supply pair The marginal complementary contribution; and For subset The weighting, in its physical sense, refers to the weighting of all power sources when they randomly form an alliance. Just happened to join the alliance The probability of.

[0137] Step S2054: Normalize the Shapley values ​​of the system complementarity corresponding to each power source to obtain the contribution of each power source to the system complementarity.

[0138] In this embodiment of the invention, the Shapley values ​​of all power sources are normalized to obtain the contribution of each power source to the complementary characteristics of the system:

[0139] in, For the first i The complementary contributions of each power source satisfy the normalization constraint. .

[0140] Step S2055: Normalize the Shapley values ​​of the system stability corresponding to each power supply to obtain the contribution of each power supply to the system stability.

[0141] In this embodiment of the invention, the Shapley values ​​of all power supplies are normalized to obtain the contribution of each power supply to the system stability characteristics:

[0142] in, For the first The stable contribution of each power source satisfies the normalization constraint. .

[0143] Step S206 involves visualizing the multidimensional quantitative statistical data, multidimensional quantitative index matrix, and the contribution of each power source to the system complementarity and system stability at each time scale.

[0144] In this embodiment of the invention, the results output and visualization output are as follows: 1) Statistical table of output characteristics at various time scales; Table 1. Comparison of Statistical Characteristics of Total Output of Systems at Multiple Time Scales (Corresponding to the statistical characteristic analysis output in step S203, covering six time scales)

[0145] Note: The statistics are calculated based on the feasible output dataset after coupling constraint correction; as the time scale increases, the output standard deviation gradually decreases and the distribution tends to flatten, which intuitively reflects the complementary smoothing effect under multiple scales.

[0146] 2) Comparison Table of Multi-Dimensional Quantitative Indicators Table 2. Comparison of Quantitative Indicators Across Multiple Time Scales and Dimensions (The output of the multi-dimensional quantitative analysis in step S204 is the core comparative result.)

[0147] Note: The power output guarantee rate is calculated with 120MW as the threshold; the regulation capacity gradually increases with the time scale, which is due to the gradual release of the hydropower reservoir capacity regulation capacity; complementarity and stability reach their peak on the monthly scale, reflecting the core value of seasonal complementarity.

[0148] 3) Pie chart showing the contribution of each power source to the overall system characteristics Table 3. Comparison of the contribution of each power source's output characteristics on an hourly scale. (Corresponding to the contribution decomposition output in step S205, in conjunction with...) Figure 3 (Contribution pie chart used)

[0149] 4) Comparison of output curves and complementary characteristics at different time scales like Figure 4 The probability distribution diagram shown is a probability density (PDF) distribution of power output for each power source on an hourly scale. like Figure 5 The diagram shows a comparison of the cumulative distribution (CDF) of the total system output at the hourly scale. like Figure 6 The pie chart showing the complementary contributions of each power source at an hourly scale is shown. like Figure 7 The pie chart showing the contribution of each power source to stability at the hourly scale is shown. Comparison of output curves and complementary characteristics: (e.g.) Figure 8 The graph shows a comparison of the output of each power source and the total output of the system on a typical day (hourly level). like Figure 9 The bar chart showing the comparison of complementary properties across time scales; Output a complete quantitative analysis report of output characteristics based on the above content.

[0150] In specific application examples: 1. General Principles and Overview of the Analytical Object 1.1 Basis for Compilation and Analytical Objectives: This report is compiled based on the quantitative analysis method for the output characteristics of a watershed hydro-wind-solar-storage multi-energy complementary system proposed in this application. The core objectives are: to comprehensively reveal the complementary laws across multiple time scales, quantitatively assess the system's output characteristics, and clarify the functional positioning and contribution weight of each power source, providing a quantitative basis for system planning and operation. Furthermore, this report highlights its core improvements over traditional analysis reports: coverage of six time scales, inclusion of three-dimensional quantitative indicators, consideration of actual operational coupling constraints, and the ability to attribute power source contributions.

[0151] 1.2 System Configuration and Basic Parameters: List the basic engineering parameters of the analysis object, including the total installed capacity of the cascade hydropower stations in the basin, the type of regulating reservoir, the total installed capacity of wind farms and photovoltaic power stations, the rated power and rated capacity of the energy storage system, the upper limit of the grid connection point output, etc., corresponding to the system boundary of multi-source data acquisition in step S201.

[0152] 1.3 The data foundation description clearly states the time span (no less than one complete hydrological year) and sampling frequency (no less than 1Hz) of the raw data used in the analysis, covering six major categories of data: hydropower, wind power, photovoltaic, energy storage, meteorology, and power grid, which is completely consistent with the data collection requirements of step S201.

[0153] 2. Explanation of Basic Data Processing and Constraint Correction 2.1 Detailed description of the data preprocessing process: The preprocessing rules in step S201 are as follows: abnormal output data are removed by using the 3σ criterion, short-term missing data are filled by linear interpolation, and long-term missing data are filled by the historical average value method. Finally, the time base of the full data is aligned to form a standardized time series dataset.

[0154] 2.2 Multi-timescale decomposition description: Discrete wavelet transform (DWT) and db4 wavelet basis function are used to decompose the original output sequence into six time scales: second, minute, hour, day, month and grade. The number of decomposition levels meets the requirement that the sample size of each scale is not less than 1000 points, which corresponds to the decomposition method and scale division rules in step S201.

[0155] 2.3. Detailed description of the multi-energy coupling constraint correction results: The constraint model and correction effect of step S202 are described, listing the specific parameters of hydropower reservoir capacity constraints, upstream and downstream hydraulic connection constraints, energy storage charging and discharging and SOC constraints, and the upper limit constraint of total power grid output; the correction effect is quantitatively explained, such as the proportion of out-of-limit output points in the original sequence, the proportion of feasible output after constraint correction, and the reduction in the deviation between the analysis results and actual operating data after correction, corresponding to the beneficial effect of "the deviation between the analysis results and the actual data is reduced by more than 50%" of this invention, clearly distinguishing it from the limitations of the unconstrained ideal analysis of the prior art.

[0156] 3. Analysis of the characteristics of the power output foundation at multiple time scales Corresponding to the four types of basic analysis in step S203, statistical tables and probability distribution diagrams of output characteristics at each time scale are embedded and expanded layer by layer according to scale: 3.1 Statistical Characteristics Cross-Scale Comparative Analysis: By comparing the mean, variance, standard deviation, extreme values, kurtosis, skewness, and other statistical measures of hydropower, wind power, photovoltaic, energy storage, and total system output across six time scales, the changes in output concentration trend, dispersion, and distribution pattern over time scales are summarized.

[0157] 3.2 Output Probability Distribution Fitting Analysis: List the probability distribution fitting results of each power source under typical time scales (Weibull distribution for wind power, Beta distribution for photovoltaic power, log-normal distribution for hydropower, and kernel density estimation of total system output). Combine PDF and CDF curves to analyze the probability of occurrence of different output levels, as well as the peak shaving and valley filling effect of multi-energy complementarity on the output distribution.

[0158] 3.3 Multi-scale analysis of fluctuation characteristics: From the three dimensions of volatility, ramp rate and fluctuation duration, the output fluctuation characteristics at different time scales are compared to clarify the scale differences: the second / minute level is mainly characterized by high-frequency random fluctuations, the hour level reflects intraday fluctuation patterns, and the month / year level reflects seasonal fluctuation characteristics.

[0159] 3.4. Power source correlation scale effect analysis: The Pearson correlation coefficient and Spearman correlation coefficient matrix between power sources at various time scales is presented. The variation law of positive and negative correlation between power sources with time scale is analyzed, revealing the scale effect that "the natural complementarity is strongest at the monthly scale", which confirms the necessity of multi-time scale analysis and corresponds to the technical problem of "one-sided analysis at a single time scale" in this application.

[0160] 4. Quantitative evaluation of multi-dimensional output characteristics Corresponding to the three-dimensional quantitative indicator system in step S204, a multi-dimensional quantitative indicator comparison table is embedded to comprehensively present the quantitative level of the system's output characteristics: 4.1 Quantitative evaluation of complementarity: Based on three indicators—complementarity degree, complementarity duration, and complementarity coverage—the intensity of power supply synergy complementarity is quantitatively evaluated at different time scales. This clarifies the time scale at which the core complementary value of the system lies, and contrasts with the shortcomings of existing technologies that only use correlation coefficients for qualitative judgment, thus achieving accurate quantification of the complementary effect.

[0161] 4.2 Quantitative evaluation of stability dimension is based on three indicators: smoothness, fluctuation suppression rate, and output guarantee rate. It quantitatively evaluates the stability level of the total output of the system and the reliability of power supply, clarifies the effect of hydropower and energy storage on the fluctuation of new energy sources, and the power supply guarantee capability under different output thresholds.

[0162] 4.3 Quantitative evaluation of regulation capacity: Based on three indicators, namely regulation margin, response speed and regulation accuracy, the system’s adjustable resource potential and regulation performance are quantitatively evaluated, and the upper limit of regulation capacity under different time scales is clarified, so as to provide parameter support for the system to participate in power grid peak shaving and frequency regulation.

[0163] 4.4 Comprehensive evaluation conclusions: Summarize the overall level of the system in three dimensions: complementarity, stability, and regulation capability. Compare the differences in indicators among three scenarios: wind-solar combined operation, water-wind-solar combined operation, and water-wind-solar-storage combined operation. Quantify the comprehensive improvement effect of multi-energy complementarity, corresponding to the beneficial effect of this invention that "improves the accuracy and objectivity of evaluation by more than 60%".

[0164] 5. Attribution analysis of the contribution of system output characteristics Corresponding to the Shapley value contribution decomposition method in step S205, a pie chart of each power source's contribution and a numerical comparison table are embedded to solve the technical problem that existing technologies cannot quantify the contribution of a single power source. 5.1. Brief Description of Contribution Decomposition Method: Based on the contribution decomposition principle of Shapley value in cooperative game theory, this paper clarifies the definition of characteristic function, the logic of marginal contribution calculation and normalization rules, and explains that this invention can fairly quantify the marginal contribution of each power source to the overall characteristics of the system.

[0165] 5.2 The results of the complementarity contribution decomposition list the contribution ratio of each power source to the system complementarity, and clarify the functional positioning of the core complementary power source and the auxiliary complementary power source; compare the contribution differences at different time scales, for example, the energy storage complementarity contribution ratio is higher at the second scale, and the hydropower complementarity contribution is dominant at the monthly scale, revealing the role pattern of different power sources at each scale.

[0166] 5.3 The stability contribution decomposition results list the contribution ratio of each power source to the system output stability, compare the differences in complementary contributions, highlight the core role of energy storage in smoothing short-term fluctuations, and verify the rationality of the functional positioning of each power source.

[0167] 6. Core Conclusions and Engineering Application Recommendations 6.1 Core Analysis Conclusions: The core conclusions of the entire report are summarized as follows: First, the complementary laws at multiple time scales are clarified, defining the complementary characteristics and core value at different scales; second, the overall level of the system's output characteristics is quantified, defining the three types of performance: complementarity, stability, and regulation; and third, the functional positioning of each power source is clarified, defining the role and contribution weight of each power source in the system.

[0168] 6.2 System Planning and Capacity Configuration Recommendations Based on the contribution analysis results and complementary characteristics, we propose directions for optimizing the power structure and recommendations for capacity allocation. For example, we recommend matching the optimal wind and solar power capacity with the watershed's hydropower regulation capacity and configuring energy storage capacity according to short-term fluctuations to mitigate demand.

[0169] 6.3 Optimization of Scheduling Operation Recommendations Based on the characteristics of multiple time scales, a multi-scale scheduling strategy is proposed: at the second / minute level, energy storage is mainly used to smooth fluctuations; at the hour level, hydropower is mainly used for intraday peak regulation; and at the month / year level, reservoir capacity is mainly used for seasonal adjustment, providing direct guidance for the hierarchical scheduling of the system.

[0170] 6.4 Recommendations for Future Expansion and Upgrades: Based on the current shortcomings of the system, propose directions and priorities for future expansion and upgrades to provide a reference for the long-term planning of the system.

[0171] The beneficial effects of this invention are as follows: 1. Comprehensive revelation of complementary laws across multiple time scales: Because this invention constructs a nested analysis architecture with six time scales, it can simultaneously analyze the system's output characteristics and complementary laws at different time scales, from seconds to grades. Compared to existing single-time-scale analysis methods, the comprehensiveness of revealing complementary laws is improved by more than 80%, providing targeted guidance for optimized scheduling at different time scales.

[0172] 2. Comprehensive quantitative evaluation of output characteristics: This invention establishes a quantitative index system encompassing three dimensions—complementarity, stability, and regulation capability—and proposes 12 core quantitative indicators. This enables a comprehensive and accurate evaluation of the output characteristics of multi-energy complementary systems from multiple perspectives. Compared with existing qualitative analysis methods, the accuracy and objectivity of the evaluation results are improved by more than 60%, and can be directly used for comparing and selecting system capacity configuration and optimal scheduling schemes.

[0173] 3. Significantly improved practicality of analysis results: Because this invention introduces a multi-energy system coupling constraint preprocessing module, it fully considers actual operating conditions such as hydropower reservoir capacity constraints, energy storage charging and discharging constraints, and grid output constraints during the analysis process. The generated feasible output dataset is more consistent with the actual system operation. Compared with existing unconstrained analysis methods, the deviation between the analysis results and actual operation is reduced by more than 50%, significantly enhancing practicality.

[0174] 4. The role and contribution of each power source are clarified: Because this invention proposes a contribution decomposition method based on Shapley values, it can quantify the contribution of each power source to the overall complementarity and stability of the system. This allows system operators to clearly understand the positioning and role of each power source in the system, providing clear guidance for system optimization, power source structure adjustment, and capacity expansion planning.

[0175] This embodiment also provides a device for quantitative analysis of the output characteristics of a multi-energy complementary system of hydro, wind, solar, and energy storage. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0176] This embodiment provides a device for quantitative analysis of the output characteristics of a multi-energy complementary system of water, wind, solar, and storage, such as... Figure 10 As shown, it includes: The multi-source data acquisition module 301 is used to acquire the operation data of the hydropower, wind power, photovoltaic, energy storage and multi-energy complementary system and perform discrete wavelet decomposition according to the multi-layer nested time scale; the operation data includes hydropower data, wind power data, photovoltaic data, energy storage data and power grid data; The coupling constraint processing module 302 is used to correct the hydropower output sequence, wind power output sequence, photovoltaic power output sequence and energy storage output sequence obtained after decomposition based on the coupling constraint model of the multi-energy complementary system of hydropower, wind power, photovoltaic power and energy storage. The multi-dimensional quantitative analysis module 303 is used to perform output characteristic analysis on the corrected hydropower output sequence, wind power output sequence, photovoltaic output sequence, energy storage output sequence and the total system output sequence obtained by combining the output sequences of each power source at each time scale, and generate corresponding multi-dimensional quantitative statistical data. The multidimensional quantitative indicator calculation module 304 is used to calculate multidimensional quantitative indicators for multidimensional quantitative statistical data at various time scales and generate a multidimensional quantitative indicator matrix; wherein, the multidimensionality includes at least the complementary dimension, the stability dimension and the adjustment capability dimension. The contribution decomposition module 305 is used to calculate the contribution of each power source to the system complementarity and system stability based on the multi-dimensional quantitative index matrix.

[0177] The power output characteristic quantitative analysis device for a multi-energy complementary system (hydro-wind-solar-storage) provided in this invention can execute the power output characteristic quantitative analysis method for a multi-energy complementary system (hydro-wind-solar-storage) provided in any embodiment of this invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0178] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the quantitative analysis method for the output characteristics of a multi-energy complementary system of hydro, wind, solar, and storage shown in the above embodiments is implemented.

[0179] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for quantitative analysis of the output characteristics of a multi-energy complementary system of hydropower, wind power, solar power, and energy storage, characterized in that, The method includes: Operational data of a multi-energy complementary system of hydropower, wind power, solar power, and energy storage are collected and subjected to discrete wavelet decomposition according to a multi-layered nested time scale; wherein, the operational data includes hydropower data, wind power data, photovoltaic data, energy storage data, and grid data; Based on the coupled constraint model of the hydropower, wind power, photovoltaic power and energy storage multi-energy complementary system, the hydropower output sequence, wind power output sequence, photovoltaic power output sequence and energy storage output sequence obtained after decomposition are corrected respectively. At each time scale, the power output characteristics of the corrected hydropower output sequence, wind power output sequence, photovoltaic power output sequence, energy storage power output sequence, and the total system output sequence obtained by combining the power output sequences of each power source are analyzed, and corresponding multidimensional quantitative statistical data are generated. Multidimensional quantitative indicators are calculated for the multidimensional quantitative statistical data at each of the aforementioned time scales to generate a multidimensional quantitative indicator matrix; wherein, the multidimensionality includes at least a complementarity dimension, a stability dimension, and a regulatory capability dimension. Based on the multi-dimensional quantitative index matrix, the contribution of each power source to the system complementarity and system stability is calculated.

2. The method according to claim 1, characterized in that, The system collects operational data from a multi-energy complementary system of hydropower, wind power, solar power, and energy storage, and performs discrete wavelet decomposition according to a multi-layered nested time scale. The operational data includes hydropower data, wind power data, solar power data, energy storage data, and grid data, including: Collect operational data from a multi-energy complementary system of hydropower, wind power, solar power, and energy storage; wherein, the operational data includes hydropower data, wind power data, solar power data, energy storage data, and power grid data; Outlier processing and time alignment are performed on the hydropower data, wind power data, photovoltaic data, energy storage data, and power grid data. Discrete wavelet decomposition is performed on the processed hydropower data, wind power data, photovoltaic data, and energy storage data according to multi-level nested time scales; wherein, the multi-level nested time scales include second-level time scales, minute-level time scales, hour-level time scales, day-level time scales, month-level time scales, and grade-level time scales.

3. The method according to claim 1, characterized in that, The coupled constraint model based on the hydropower, wind power, solar power, and energy storage multi-energy complementary system performs correction processing on the decomposed hydropower output sequence, wind power output sequence, solar power output sequence, and energy storage output sequence, respectively, including: A coupled constraint model for the hydro-wind-solar-storage multi-energy complementary system is established; wherein the coupled constraint model includes at least a hydropower constraint model, an energy storage constraint model, and a power grid constraint model. The hydropower output sequence, wind power output sequence, photovoltaic power output sequence and energy storage output sequence obtained after decomposition at each time scale are sequentially input into the coupled constraint model; The hydropower constraint model is used to perform output-flow conversion and basic limit correction, reservoir capacity-flow dynamic balance correction and cascade upstream and downstream hydraulic connection correction on the hydropower output sequence at each time scale. The energy storage constraint model is used to perform initial verification of charging and discharging power and closed-loop correction of SOC dynamic balance at each time scale. Based on the corrected hydropower output sequence and energy storage output sequence at each time scale, combined with the decomposed wind power output sequence and photovoltaic output sequence at each time scale, the total system output sequence is generated. The power grid constraint model is used to correct the global power output constraint of the system's total power output sequence at each time scale. The corrected hydropower output series, wind power output series, photovoltaic output series and energy storage output series at each time scale are subjected to multi-scale consistency verification.

4. The method according to claim 1, characterized in that, The power output characteristic analysis includes statistical characteristic analysis, probability distribution analysis, fluctuation characteristic analysis, and correlation analysis. At each time scale, the power output characteristics of the corrected hydropower output sequence, wind power output sequence, photovoltaic power output sequence, energy storage power output sequence, and the total system output sequence obtained by combining the power output sequences of each power source are analyzed to generate corresponding multi-dimensional quantitative statistical data, including: Statistical characteristic analysis was performed on the corrected hydropower output series, wind power output series, photovoltaic power output series, energy storage power output series and total system output series according to each time scale; According to each time scale, probability distribution analysis is performed on the corrected wind power output sequence, photovoltaic power output sequence, hydropower output sequence and total system output sequence to generate the corresponding probability density function and cumulative distribution function. Based on the dimensions of fluctuation amplitude, rate of change, and duration, the fluctuation characteristic indicators of the corrected hydropower output sequence, wind power output sequence, photovoltaic output sequence, energy storage output sequence, and total system output sequence at each time scale are calculated. According to each time scale, the corrected hydropower output series, wind power output series, photovoltaic power output series, and energy storage output series are combined in pairs, and the Pearson correlation coefficient and Spearman correlation coefficient of each combination are calculated to generate a cross-power source correlation matrix. By combining statistical characteristic analysis, probability density function, cumulative distribution function, fluctuation characteristic index and cross-source correlation matrix at various time scales, corresponding multidimensional quantitative statistical data are generated.

5. The method according to claim 4, characterized in that, The probability distribution analysis of the corrected wind power output sequence, photovoltaic power output sequence, hydropower output sequence, and total system output sequence according to each time scale includes: According to each time scale, the corrected wind power output sequence is fitted with a Weibull distribution to generate the corresponding probability density function and cumulative distribution function; The modified photovoltaic power output sequence is fitted with beta distribution according to each time scale to generate the corresponding probability distribution function and cumulative distribution function. Log-normal distributions were fitted to the corrected hydropower output sequence at each time scale to generate the corresponding probability distribution function and cumulative distribution function. Nonparametric fitting of kernel density estimation is performed on the corrected total system output sequence at each time scale to generate the corresponding probability distribution function and cumulative distribution function.

6. The method according to claim 1, characterized in that, The process involves calculating multidimensional quantitative indicators for the multidimensional quantitative statistical data at each of the aforementioned time scales to generate a multidimensional quantitative indicator matrix; wherein the multidimensionality includes at least a complementary dimension, a stability dimension, and a regulatory capability dimension, including: Based on the complementarity dimension, the complementarity of multidimensional quantitative statistical data under each time scale is calculated, and the corresponding complementarity matrix is ​​generated. Based on the stability dimension, the smoothness of the multidimensional quantized statistical data at each time scale is calculated, and the corresponding smoothness matrix is ​​generated. Based on the regulation capacity dimension, the smoothness of the multidimensional quantitative statistical data under each time scale is calculated, and the corresponding regulation margin matrix is ​​generated. By combining the complementarity matrix, smoothness matrix, and adjustment margin matrix at each of the aforementioned time scales, a multi-dimensional quantitative index matrix is ​​generated.

7. The method according to claim 1, characterized in that, The step of calculating the contribution of each power source to system complementarity and system stability based on the multi-dimensional quantitative index matrix includes: Using water electron collectors, wind electron collectors, photovoltaic subsets and energy storage subsets as the main game subjects, complementary degree characteristic functions and smoothness characteristic functions are constructed respectively; Based on the multi-dimensional quantitative index matrix at each time scale, the Shapley value method is used to traverse all power source subsets and solve the marginal gain of each power source to the system complementarity using the complementarity characteristic function to generate the corresponding Shapley value. Based on the multi-dimensional quantitative index matrix at each time scale, the Shapley value method is used to traverse all power source subsets and solve the marginal gain of each power source on system stability using the smoothness characteristic function to generate the corresponding Shapley value. The Shapley values ​​of the system complementarity corresponding to each power source are normalized to obtain the contribution of each power source to the system complementarity. The Shapley values ​​of the system stability corresponding to each power source are normalized to obtain the contribution of each power source to the system stability.

8. The method according to claim 7, characterized in that, The method further includes: The multidimensional quantitative statistical data, multidimensional quantitative index matrix, and the contribution of each power source to the system complementarity and system stability at each time scale are visualized.

9. A device for quantitative analysis of the output characteristics of a multi-energy complementary system of water, wind, solar, and energy storage, characterized in that, The device includes: A multi-source data acquisition module is used to collect operational data of a multi-energy complementary system of hydropower, wind power, photovoltaic power, and energy storage, and to perform discrete wavelet decomposition according to a multi-layer nested time scale; wherein, the operational data includes hydropower data, wind power data, photovoltaic data, energy storage data, and power grid data; The coupling constraint processing module is used to correct the hydropower output sequence, wind power output sequence, photovoltaic power output sequence and energy storage output sequence obtained after decomposition based on the coupling constraint model of the hydropower-wind-solar-storage multi-energy complementary system. The multi-dimensional quantitative analysis module is used to perform output characteristic analysis on the corrected hydropower output sequence, wind power output sequence, photovoltaic output sequence, energy storage output sequence, and the total system output sequence obtained by combining the output sequences of each power source at each time scale, and generate corresponding multi-dimensional quantitative statistical data. The multidimensional quantitative indicator calculation module is used to perform multidimensional quantitative indicator calculation on multidimensional quantitative statistical data under each time scale and generate a multidimensional quantitative indicator matrix; wherein, the multidimensionality includes at least a complementarity dimension, a stability dimension, and a regulatory capability dimension. The contribution decomposition module is used to calculate the contribution of each power source to the system complementarity and system stability, respectively, based on the multi-dimensional quantitative index matrix.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the quantitative analysis method for the output characteristics of the hydro-wind-solar-storage multi-energy complementary system as described in any one of claims 1 to 8.

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