High-capacity energy storage power station new energy output characteristic quantification method

Abstract

The invention discloses a new energy output characteristic quantification method for a high-capacity energy storage power station, and belongs to the technical field of power systems, and the method comprises the steps: constructing a new energy output characteristic index system through the time characteristic analysis and output distribution characteristic analysis of the historical output data of new energy power generation, and carrying out the quantitative calculation of each index in the index system, the fluctuation and randomness characteristic parameters of new energy output are obtained based on the calculation result, the grid-connected scheduling strategy of the energy storage power station is adjusted based on the numerical values of the characteristic parameters, and the fluctuation and randomness of new energy power generation can be quantitatively analyzed from multiple time scales and multiple statistical dimensions. More comprehensive data support can be provided for energy storage scheduling, the change rule of new energy output in different operation scenes can be accurately represented, and a reliable basis is provided for power grid planning and energy storage configuration.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for quantifying the output characteristics of new energy sources in a large-capacity energy storage power station. Background Art

[0002] With the development of renewable energy power generation technologies, the proportion of wind power and photovoltaic power generation in the power system has gradually increased. In order to cope with the intermittent and volatile nature of renewable energy power generation, conventional frequency regulation units (such as thermal power units or hydropower units) are usually used in existing technologies to achieve frequency stability and supply and demand balance of the power grid by adjusting the output. In addition, some studies have proposed the use of energy storage systems to participate in grid frequency regulation, and to smooth out the fluctuations in renewable energy output through the charging and discharging operations of energy storage devices. For example, existing technologies have attempted to use electrochemical energy storage batteries or pumped storage devices to adjust power according to the grid frequency deviation signal to improve the system stability after the new energy is connected to the grid.

[0003] However, existing technologies have certain limitations. Traditional frequency regulation units rely on the physical properties of the rotating shaft to provide inertia and damping support, but with the increase in the penetration rate of new energy, their regulation capabilities are gradually insufficient, and frequent operations lead to increased wear and tear on the units, shortening their operating life. At the same time, existing energy storage frequency regulation schemes are mostly based on simple frequency deviation responses or fixed power scheduling, lacking systematic quantitative analysis of the output characteristics of new energy (such as fluctuation amplitude, probability distribution, and timing patterns). As a result, the configuration and scheduling strategies of the energy storage system are difficult to accurately match with the actual operating characteristics of new energy, limiting the frequency regulation efficiency and new energy absorption capacity.

[0004] Chinese patent, publication number: CN115967106A, publication date: April 14, 2023, discloses a quantitative relationship analysis method and system between energy storage scale and effective output of new energy sites. By constructing a wind power historical output characteristic evaluation system, the data in the wind power historical output characteristic evaluation system is corrected by using a multiple data detection method; the data in the corrected wind power historical output characteristic evaluation system is subjected to dimensionality reduction and clustering to generate typical output characteristic operation data; according to the typical output characteristic operation data and energy storage scale, the smoothing fluctuation rate, the new energy output peak-to-valley difference reduction rate and the system peak-to-valley difference reduction rate are calculated, and the optimal weight coefficient method is used to determine the weights of each change rate, and the relationship between energy storage scale and the effective output of new energy sites is quantitatively evaluated. However, only the static and dynamic characteristics of new energy are evaluated, and no comprehensive quantitative analysis of the output characteristics of new energy such as fluctuation amplitude, probability distribution, and timing law is performed, resulting in the analysis results being still relatively vague and abstract. Summary of the Invention

[0005] The present invention aims to address the problems of poor frequency regulation efficiency and new energy absorption capacity caused by the fact that existing energy storage frequency regulation schemes are mostly based on simple frequency deviation response or fixed power scheduling and lack systematic quantitative analysis of new energy output characteristics. The present invention provides a method for quantifying the new energy output characteristics of a large-capacity energy storage power station. By conducting time characteristic analysis and output distribution characteristic analysis on historical output data of new energy power generation, a new energy output characteristic index system is constructed. Each indicator in the index system is quantitatively calculated respectively, and the volatility and randomness characteristic parameters of the new energy output are obtained based on the calculation results. The grid-connected scheduling strategy of the energy storage power station is adjusted based on the numerical values of the characteristic parameters. The method can quantitatively analyze the volatility and randomness of new energy power generation from multiple time scales and multiple statistical dimensions, provide more comprehensive data support for energy storage scheduling, accurately characterize the changing patterns of new energy output in different operating scenarios, and provide a reliable basis for power grid planning and energy storage configuration.

[0006] In a first aspect, a technical solution provided in an embodiment of the present invention is: a method for quantifying the output characteristics of new energy sources in a large-capacity energy storage power station, comprising the following steps: S1. Collect historical output data of renewable energy power generation from energy storage power stations based on data collection principles; S2. Analyze the time characteristics and output distribution characteristics of historical output data to build a new energy output characteristic index system; S3. Construct a quantitative model for new energy output characteristic indicators to quantitatively calculate each indicator in the new energy output characteristic indicator system to obtain the volatility and randomness characteristic parameters of the new energy output; S4. Adjust the grid-connected dispatching strategy of the energy storage power station based on the volatility and randomness characteristic parameters.

[0007] In this proposal, by analyzing the temporal characteristics and output characteristic distribution of historical output data of renewable energy power generation in energy storage power stations, the volatility and randomness of renewable energy power generation are quantitatively analyzed from multiple time scales and statistical dimensions. Compared with the traditional single-indicator analysis method, this can provide more comprehensive data support for the scheduling of energy in energy storage power stations, thereby accurately characterizing the changing patterns of renewable energy output in different operating scenarios. By quantifying the characteristic parameters of the volatility and randomness of renewable energy output, it can help guide the design of scientific charging and discharging cycles and capacity configuration plans for energy storage power stations, thereby improving the renewable energy absorption capacity of energy storage power stations.

[0008] Preferably, in S1, collecting historical output data of renewable energy power generation of the energy storage power station based on the data collection principle includes the following steps: The historical output data of wind power, photovoltaic power generation, geothermal energy and biomass power generation within the historical time period are collected from the energy storage power station monitoring system, and the historical output data are preprocessed and normalized based on the environmental characteristics within the historical time period.

[0009] In this solution, historical output data of renewable energy power generation from energy storage power stations is collected, and data cleaning methods such as interpolation or deletion are used to pre-process the data. Furthermore, since renewable energy power generation output includes wind power, photovoltaic power generation, etc., the above output data is normalized for the convenience of subsequent analysis to improve analysis efficiency.

[0010] Preferably, in S2, performing time characteristic analysis and output distribution characteristic analysis on historical output data to construct a new energy output characteristic index system includes the following steps: The seasonal and daily characteristics of new energy output are obtained by analyzing the time characteristics of historical output data. The probability distribution, cumulative power distribution and guaranteed rate distribution of new energy hourly output are obtained by analyzing the output distribution characteristics of historical output data. A new energy output characteristic index system is constructed with the seasonal characteristics, daily characteristics, probability distribution, cumulative power distribution and guaranteed rate distribution of new energy hourly output as the first-level indicators and the influencing factors of the first-level indicators as the second-level indicators.

[0011] In this plan, to facilitate quantitative analysis of the renewable energy output of energy storage power stations at multiple time scales and statistical dimensions, the renewable energy output characteristics are divided into five primary indicators from the perspectives of time characteristics and output distribution characteristics. Under these five primary indicators, secondary indicators can also be divided according to different time periods. This detailed division of renewable energy output characteristics provides a sufficient data foundation for the subsequent quantification of renewable energy output characteristics through volatility and randomness characteristic parameters, thereby helping to optimize the energy scheduling of energy storage power stations in a targeted manner.

[0012] Preferably, the secondary indicators of the seasonal characteristics include the annual utilization hours of new energy and the monthly average output; the secondary indicators of the daily characteristics include the probability distribution of the daily maximum output of new energy and the daily average output; the secondary indicators of the probability distribution of the new energy output include the probability distribution of the output of new energy at all times and the probability distribution of the output at different time periods; the secondary indicators of the cumulative power distribution of the new energy output include the cumulative power distribution of the output of new energy at all times and the cumulative power distribution of the output at different time periods.

[0013] In this proposal, a new energy output characteristic index system is constructed based on the time scale and multiple statistical dimensions of the output characteristics of new energy in energy storage power stations. In order to have a more comprehensive analysis of the output characteristics of new energy, the factors affecting the primary indicators are also considered in this index system. They are associated with the primary indicators based on their relationship with the primary indicators. The average output, maximum output, cumulative power, time period output of new energy, and the time period and probability distribution corresponding to each output are analyzed separately, thereby increasing the dimension of the new energy output characteristic analysis to obtain more comprehensive quantitative results.

[0014] Preferably, the annual utilization hours of new energy are the ratio of the annual power generation of new energy to the corresponding power installed capacity, and the monthly average output of new energy is the ratio of the monthly power generation of new energy to the number of days in the corresponding month.

[0015] In this scheme, the average output and annual utilization hours of renewable energy are obtained by analyzing the power generation of renewable energy and the corresponding power generation days, thereby reflecting the quarterly output of renewable energy and thus reflecting the seasonal characteristics of renewable energy.

[0016] Preferably, the probability distribution of the daily maximum output of new energy is the probability that the daily maximum output is within a preset range within a sampling period of historical output data, and the formula is as follows: c max,i =max{P ij ,j=1,2,…,24} c max,i ∈C max Among them, C max is the set of daily maximum outputs within the sampling period of historical output data, c max,i For set C max The elements in p max (m,n) represents the probability that the daily maximum output is within the interval (m,n); The daily average output of new energy is the ratio of the total power generation of new energy in a certain hour to the total number of power generation equipment in the corresponding hour.

[0017] In this scheme, the daily output of renewable energy is reflected by the daily maximum output of renewable energy and the probability of the occurrence of the daily maximum output, thereby reflecting the daily characteristics of renewable energy.

[0018] Preferably, the probability distribution formula of the new energy output at all times is expressed as follows: Among them, if m <P ij ≤n,N ij =1, otherwise N ij=0; p(m,n) represents the probability that the output of new energy is within the interval (m,n) during the sampling period of historical output data; N ij If the value of the new energy output sampling point at time j on day i is within the interval (m,n], it will be included in the statistical analysis, where m and n are customized based on demand; The probability distribution formula of the renewable energy output in different time periods is as follows: Among them, if m <P ij ≤n,N ij =1, otherwise N ij =0, p j (m,n) represents the probability that the output of renewable energy in period j is within the interval (m,n].

[0019] In this scheme, by analyzing the probability distribution of the output of renewable energy in different time periods, the output characteristics of renewable energy under different environmental conditions and at different time periods can be reflected, which can be used to evaluate its potential impact on the stability of the power grid.

[0020] Preferably, the cumulative power distribution formula of the new energy output during all periods is expressed as follows: Among them, F n represents the cumulative probability that the renewable energy power generation is within the interval [0,n] during the sampling period of historical output data; T represents the annual utilization hours of renewable energy; P ij [if(0≤P ij ≤n)] means that if the new energy output sampling point value at time j on day i is in the interval [0,n], the output value will be accumulated and analyzed, and the accumulated integral power value that meets the requirements will be obtained.

[0021] In this scheme, by analyzing the cumulative power distribution of renewable energy output at different time periods, it can be used to evaluate the power generation efficiency and economy of renewable energy power generation at different output levels. The analysis results can be used to guide the capacity planning of energy storage power stations or energy storage systems, and provide a certain basis for the charging and discharging strategies of energy storage power stations.

[0022] Preferably, in S3, a new energy output characteristic index quantification model is constructed to quantify each index in the new energy output characteristic index system to obtain the volatility and randomness characteristic parameters of the new energy output, including the following steps: A quantitative model for new energy output characteristic indicators was constructed to calculate the mean and variance of the average output, maximum output, time period output, and cumulative power in the secondary indicators of the new energy output characteristic indicator system. The variance was normalized to obtain the fluctuation coefficient. The skewness and kurtosis of the corresponding output and cumulative power were calculated based on the variance to obtain the offset coefficient. The fluctuation coefficient and offset coefficient were used as volatility characteristic parameters. Based on the mean and variance, normal distribution fitting is performed on the average output, maximum output, time period output and cumulative power respectively. The fitting results are compared with the probability distribution of the corresponding output and cumulative power respectively, and the random characteristic parameters are obtained based on the comparison results.

[0023] In this scheme, by accurately calculating and evaluating each secondary indicator of the renewable energy output characteristic index system, the output characteristics of renewable energy in different dimensions are obtained. By classifying and organizing these output characteristics, they are divided into two major characteristics: volatility and randomness. The volatility of renewable energy output is comprehensively evaluated by analyzing the degree of deviation of each output from the average value, the difference ratio of maximum / minimum output, and the rate of change of each output. By comparing the ideal distribution probability of each output with the actual distribution probability, the randomness of renewable energy output is evaluated based on the degree of deviation between the two, thereby more comprehensively quantifying the renewable energy output characteristics of energy storage power stations and providing a comprehensive data foundation for evaluating the operation of energy storage power stations and energy scheduling.

[0024] Preferably, in S4, adjusting the grid-connected dispatching strategy of the energy storage power station based on the volatility and randomness characteristic parameters includes the following steps: If the volatility characteristic parameter exceeds the volatility threshold, the frequency regulation sensitivity and charge and discharge cycle of the energy storage power station will be adjusted. If the random characteristic parameter exceeds the random threshold, the predicted configuration parameters of the predicted output of the energy storage power station and the capacity configuration of the energy storage power station will be adjusted until the volatility characteristic parameter and the random characteristic parameter are within the preset safety value range.

[0025] In this solution, since each indicator in the renewable energy output characteristic index system can provide data guidance for a certain aspect of the energy storage power station's operation, and the grid-connected scheduling of the energy storage power station mainly focuses on the charging and discharging cycle and capacity configuration of the energy storage station, other monitoring and control methods such as frequency modulation and renewable energy output forecasting serve as scheduling aids. By analyzing the volatility and randomness characteristic parameters, the fault can be accurately located, allowing targeted adjustments to be made, so that the configuration and scheduling strategy of the energy storage system are accurately matched with the actual operating characteristics of renewable energy, thereby improving control efficiency and renewable energy absorption capacity.

[0026] The beneficial effects of the present invention are as follows: (1) The present invention analyzes the temporal characteristics and output characteristic distribution of the historical output data of renewable energy power generation in energy storage power stations, and quantitatively analyzes the volatility and randomness of renewable energy power generation from multiple time scales and multiple statistical dimensions. Compared with the traditional single-index analysis method, it can provide more comprehensive data support for the scheduling of energy in energy storage power stations, thereby accurately characterizing the changing patterns of renewable energy output in different operating scenarios; (2) By quantifying the volatility and random characteristic parameters of renewable energy output, the present invention can help guide the design of scientific charging and discharging cycles and capacity configuration plans for energy storage power stations, thereby improving the renewable energy absorption capacity of energy storage power stations.

[0027] The above content of the invention is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Other features, objects, and advantages of the present invention will become more apparent upon reading the detailed description of the non-limiting embodiments made with reference to the following drawings. The drawings are for the purpose of illustrating preferred embodiments only and are not to be construed as limiting the present invention. Like reference characters are used throughout the drawings to designate like parts.

[0029] Figure 1 This is a flow chart of a method for quantifying the output characteristics of new energy in a large-capacity energy storage power station according to the present invention; Figure 2 Schematic diagram of the new energy output characteristic index system of the present invention. DETAILED DESCRIPTION

[0030] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be performed in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but can also have additional steps not included in the figures; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0032] Example: Figure 1 As shown, in order to solve the problem that existing energy storage frequency regulation solutions are mostly based on simple frequency deviation response or fixed power scheduling, lacking systematic quantitative analysis of renewable energy output characteristics, resulting in poor frequency regulation efficiency and renewable energy absorption capacity, this embodiment provides a method for quantifying renewable energy output characteristics of a large-capacity energy storage power station, including the following steps: S1: Based on the data collection principle, the historical output data of renewable energy power generation of energy storage power stations is collected.

[0033] In this embodiment, based on the data collection principle, the historical output data of the energy storage power station's new energy power generation is collected, including the following steps: The historical output data of wind power, photovoltaic power generation, geothermal energy and biomass power generation within the historical time period are collected from the energy storage power station monitoring system, and the historical output data are preprocessed and normalized based on the environmental characteristics within the historical time period.

[0034] Specifically, data sources include the real-time monitoring system of new energy sites, wind speed and light data from meteorological stations, and output records from power grid dispatching centers. The sampling frequency can be set to minutes, hours, or days as needed to ensure the integrity and representativeness of the data.

[0035] This embodiment collects historical output data of renewable energy power generation from energy storage power stations, and pre-processes the data through data cleaning methods such as interpolation or deletion of some abnormal data in the historical output data caused by equipment failures, etc. In addition, since renewable energy power generation output includes wind power, photovoltaic power generation, etc., for the convenience of subsequent analysis, the above output data is normalized to improve analysis efficiency.

[0036] S2: Analyze the time characteristics and output distribution characteristics of historical output data to build a new energy output characteristic index system.

[0037] In this embodiment, the time characteristic analysis and output distribution characteristic analysis of historical output data are performed to construct a new energy output characteristic index system, including the following steps: The seasonal and daily characteristics of new energy output are obtained by analyzing the time characteristics of historical output data. The probability distribution, cumulative power distribution and guaranteed rate distribution of new energy hourly output are obtained by analyzing the output distribution characteristics of historical output data. A new energy output characteristic index system is constructed with the seasonal characteristics, daily characteristics, probability distribution, cumulative power distribution and guaranteed rate distribution of new energy hourly output as the first-level indicators and the influencing factors of the first-level indicators as the second-level indicators.

[0038] Specifically, when establishing an index system for renewable energy output characteristics, it is necessary to comprehensively consider the operating status of renewable energy power generation equipment, the climate characteristics of the geographical location, and the load demand of the grid access point. The design of the index system must also be compatible with the analysis needs of different time scales (such as short-term fluctuations and long-term trends).

[0039] To facilitate quantitative analysis of the renewable energy output of energy storage power stations at multiple time scales and statistical dimensions, this embodiment divides renewable energy output characteristics into five primary indicators from the perspectives of time characteristics and output distribution characteristics. Within these five primary indicators, secondary indicators can be further divided according to different time periods. This detailed division of renewable energy output characteristics provides a sufficient data foundation for subsequent quantification of renewable energy output characteristics through volatility and randomness characteristic parameters, thereby facilitating targeted optimization of energy scheduling in energy storage power stations.

[0040] In this embodiment, Figure 2 As shown, the secondary indicators of the seasonal characteristics include the annual utilization hours of new energy and the monthly average output; the secondary indicators of the daily characteristics include the probability distribution of the daily maximum output of new energy and the daily average output; the secondary indicators of the probability distribution of the new energy output include the probability distribution of the output of new energy at all times and the probability distribution of the output at different time periods; the secondary indicators of the cumulative power distribution of the new energy output include the cumulative power distribution of the output of new energy at all times and the cumulative power distribution of the output at different time periods.

[0041] In this embodiment, a new energy output characteristic index system is constructed based on the time scale and multiple statistical dimensions of the output characteristics of new energy in the energy storage power station. In order to provide a more comprehensive analysis of the output characteristics of new energy, the index system also considers the factors affecting the primary indicators and associates them with the primary indicators based on their relationships. As a result, the average output, maximum output, cumulative power, time period output of the new energy output, and the time period and probability distribution corresponding to each output are analyzed separately, thereby increasing the dimension of the new energy output characteristic analysis to obtain more comprehensive quantitative results.

[0042] In this embodiment, the annual utilization hours of new energy are the ratio of the annual power generation of the new energy to the corresponding installed power capacity, and the monthly average output of new energy is the ratio of the monthly power generation of the new energy to the number of days in the corresponding month. The specific formula is as follows: Wherein, T is the annual utilization hours; W year is the power generation of new energy; P corresponds to the installed capacity of power supply; P ij is the per-unit value of renewable energy output at hour j on day i; in, is the monthly average output of k new energy, k=1,2,…,12; W k is the total power generation of renewable energy in month k; T k is the total number of days in month k.

[0043] Specifically, seasonal analysis also includes an assessment of the correlation between renewable energy output and environmental factors (such as temperature, humidity, and wind direction) in different seasons to reveal the underlying patterns of output changes. The calculation of annual utilization hours requires consideration of the impact of equipment maintenance downtime and force majeure factors (such as extreme weather) on power generation. Correction coefficients can also be introduced to calibrate the data to reflect actual operating conditions. The statistical process for monthly average output can be further broken down to the ten-day (every 10 days) or weekly level to capture subtle trends in intra-month output changes. Furthermore, independent monthly average output models can be established for different renewable energy types (such as wind power and photovoltaics) to reflect their seasonal differences, such as wind power having higher output in winter and photovoltaics having a dominant output in summer. The variables in the calculation formula must also specify the temporal resolution of data acquisition (such as hourly or minute data), and pre-process abnormal data (such as zero output due to equipment failure) to ensure the reliability of the results.

[0044] This embodiment obtains the average output and annual utilization hours of the new energy by analyzing the power generation of the new energy and the corresponding power generation days, thereby reflecting the quarterly output of the new energy and thus reflecting the seasonal characteristics of the new energy.

[0045] In this embodiment, the probability distribution of the daily maximum output of new energy is the probability that the daily maximum output is within a preset range within the sampling period of historical output data, and the formula is as follows: c max,i =max{P ij ,j=1,2,…,24} c max,i ∈C max Among them, C maxis the set of daily maximum outputs within the sampling period of historical output data, c max,i For set C max The elements in p max (m,n) represents the probability that the daily maximum output is within the interval (m,n); The daily average output of renewable energy is the ratio of the total power generation of renewable energy in a certain hour to the total number of power generation equipment in the corresponding hour. The specific formula is as follows: in, is the average output of new energy at time j; is the total power generation of new energy at time j; P ij is the per-unit value of renewable energy output at hour j on day i; is the total number of samples when .

[0046] Specifically, the utilization hours of different time periods are statistically analyzed based on 24 hours of new energy utilization hours, namely: The relationship between annual utilization hours and time-based utilization hours is: Among them, T j is the number of hours of renewable energy utilization from period j to j+1 in the whole year, j=1,2,…,24; W j It is the renewable energy power generation from period j to j+1 throughout the year.

[0047] Specifically, in the daily characteristic analysis, the calculation of the probability distribution of the daily maximum output also needs to consider the impact of the installed layout of the new energy station (such as the spacing of wind turbines and the orientation of photovoltaic arrays) on the output peak. max The construction can be extracted from historical data through the sliding window method or peak detection algorithm, and verified in combination with the wind speed-power curve or the light-power curve. When calculating the daily maximum output, a time weighting factor can be introduced to highlight the output contribution of high-load periods during the day (such as the photovoltaic peak at noon or the wind speed peak at night). The interval division of probability statistics can be adaptively adjusted according to the output characteristics of the new energy type. For example, wind power can use a finer interval to reflect its rapid fluctuation characteristics, while photovoltaic power can use a looser interval to adapt to its sunshine pattern. The calculation of daily average output also needs to consider the smoothness of the intraday output curve. The standard deviation or coefficient of variation can be introduced as a supplementary indicator to quantify the degree of intraday fluctuation of the output. At the same time, the output curves of typical days (such as sunny days, cloudy days, and windy days) can be combined for comparative analysis to provide a more accurate reference for grid peak regulation and energy storage configuration.

[0048] This embodiment reflects the daily output of the new energy source by analyzing the daily maximum output of the new energy source and the probability of the occurrence of the daily maximum output, thereby reflecting the daily characteristics of the new energy source.

[0049] In this embodiment, the probability distribution formula of the new energy output at all times is expressed as follows: Among them, if m <P ij ≤n,N ij =1, otherwise N ij =0; p(m,n) represents the probability that the output of new energy is within the interval (m,n) during the sampling period of historical output data; N ij If the value of the new energy output sampling point at time j on day i is within the interval (m,n], it will be included in the statistical analysis; The probability distribution formula of the renewable energy output in different time periods is as follows: Among them, if m <P ij ≤n,N ij =1, otherwise N ij =0, p j (m,n) represents the probability that the output of renewable energy in period j is within the interval (m,n].

[0050] Specifically, the analysis of the probability distribution of output in each time period also includes studying the probability distribution characteristics of renewable energy output under extreme conditions (such as strong winds or strong sunlight) to assess its potential impact on grid stability. In the calculation of the probability distribution of output over all time periods, a multidimensional probability density function (such as a combined wind speed-light distribution) can be introduced to simultaneously characterize the interactive effects of wind power and photovoltaic output. In addition, the selection of the interval (m,n] needs to be dynamically adjusted according to the output range of renewable energy. For example, it can be divided based on the quantiles of historical data (such as 10% and 90%) to ensure the representativeness of the statistical results. The calculation of the probability distribution of output in different time periods can also be refined to the minute or second level to analyze the probability characteristics of short-term fluctuations in renewable energy, especially during critical periods of grid frequency regulation (such as load mutation periods). The integrity of the sampled data must also be considered during the calculation process, and interpolation methods (such as linear interpolation or spline interpolation) are used to fill in missing data to avoid distortion of the probability distribution.

[0051] This embodiment analyzes the probability distribution of the output of renewable energy in different time periods, thereby reflecting the output characteristics of renewable energy in different environmental conditions and at different time periods, which can be used to evaluate its potential impact on grid stability.

[0052] In this embodiment, the cumulative power distribution formula of the new energy output during all periods is expressed as follows: The cumulative power distribution of the output in each time period is calculated by using the full-time output-cumulative power distribution analysis method. The cumulative power distribution formula of time-divided output is as follows: Among them, F n represents the cumulative probability that the renewable energy power generation is within the interval [0,n] during the sampling period of historical output data; F n j represents the cumulative probability that the renewable energy power generation in the hourly period within the sampling period of historical output data is within the interval [0,n]; T represents the annual utilization hours of renewable energy; P ij [if(0≤P ij ≤n)] means that if the new energy output sampling point value at time j on day i is in the interval [0,n], the output value will be accumulated and analyzed, and the accumulated integral power value that meets the requirements will be obtained.

[0053] Specifically, the analysis of the cumulative power distribution of renewable energy output also includes an evaluation of the power generation efficiency and economy of renewable energy power generation at different output levels to guide the capacity planning and operation optimization of the energy storage system. In the calculation of the full-time output-cumulative power distribution, a visual representation of the cumulative power curve (such as the load continuity curve) can be introduced to intuitively reflect the power generation contribution of renewable energy in each output range; at the same time, the distribution pattern of curtailed power and peak-shaving demand can be analyzed in combination with the degree of matching between renewable energy output and grid demand. The calculation of the cumulative power distribution of time-divided output also needs to consider the differences in output characteristics at different times of the day (such as morning peak and evening peak). The cumulative power distribution of working days and non-working days can be statistically analyzed separately to adapt to the cyclical changes in grid load; in addition, weighting factors (such as electricity price weights) can be introduced in the calculation process to reflect the economic value of electricity in different time periods, providing a basis for energy storage charging and discharging strategies.

[0054] This embodiment analyzes the cumulative power distribution of renewable energy output at different time periods, which can be used to evaluate the power generation efficiency and economy of renewable energy power generation at different output levels. The analysis results can be used to guide the capacity planning of energy storage power stations or energy storage systems, and provide a certain basis for the charging and discharging strategies of energy storage power stations.

[0055] In this embodiment, the formula for the guaranteed rate distribution of the hourly output of new energy is expressed as follows: m = int{x%×365} A j =R D {P ij},i=1,2,…,365 in, A is the wind farm output when the guaranteed rate is x% during the j period of the year; j R is the output of renewable energy after sorting from the largest to the smallest output during the j period of the year; D {·} is a descending sorting function; m=int{·} is an integer rounding function.

[0056] Specifically, the calculation of the guaranteed rate distribution of renewable energy hourly output also includes the analysis of the reliability and stability of renewable energy output at different guaranteed rate levels (such as 90%, 95%) to evaluate its impact on the demand for grid reserve capacity. In the calculation formula, the descending order function R D {·} can be implemented using a quick sort algorithm to ensure efficient large-scale data processing. Furthermore, confidence intervals (e.g., a 95% confidence level) can be introduced to analyze the uncertainty of the guarantee rate results to enhance their credibility. Furthermore, the calculation of the guarantee rate distribution must also consider the geographical dispersion of renewable energy sites (e.g., the combined output of wind farms across multiple locations), mitigating the risk of output fluctuations in a single region through aggregate analysis. The hourly output guarantee rate distribution can be calculated separately for different seasons or weather conditions to reveal its time-varying characteristics, providing dynamic support for the efficient configuration of energy storage systems and grid scheduling.

[0057] S3: Construct a quantitative model for new energy output characteristic indicators to quantitatively calculate each indicator in the new energy output characteristic indicator system to obtain the volatility and randomness characteristic parameters of new energy output.

[0058] In this embodiment, a new energy output characteristic index quantification model is constructed to quantify and calculate each index in the new energy output characteristic index system to obtain the volatility and randomness characteristic parameters of the new energy output, including the following steps: A quantitative model for new energy output characteristic indicators was constructed to calculate the mean and variance of the average output, maximum output, time period output, and cumulative power in the secondary indicators of the new energy output characteristic indicator system. The variance was normalized to obtain the fluctuation coefficient. The skewness and kurtosis of the corresponding output and cumulative power were calculated based on the variance to obtain the offset coefficient. The fluctuation coefficient and offset coefficient were used as volatility characteristic parameters. Based on the mean and variance, normal distribution fitting is performed on the average output, maximum output, time period output and cumulative power respectively. The fitting results are compared with the probability distribution of the corresponding output and cumulative power respectively, and the random characteristic parameters are obtained based on the comparison results.

[0059] Specifically, in the quantitative analysis process, statistical methods (such as mean analysis and variance calculation) and probability models (such as normal distribution or Weibull distribution) are used to characterize volatility and randomness, while combining outlier removal and data smoothing of historical data to improve the accuracy of characteristic parameters.

[0060] This embodiment accurately calculates and evaluates each secondary indicator of the renewable energy output characteristic index system to obtain output characteristics of renewable energy in different dimensions. These output characteristics are classified and organized into two major characteristics: volatility and randomness. The volatility of renewable energy output is comprehensively evaluated by analyzing the degree to which each output deviates from the average, the percentage of the difference between the maximum and minimum outputs, and the rate of change of each output. The randomness of renewable energy output is evaluated based on the degree of deviation between the ideal and actual distribution probabilities of each output, thereby more comprehensively quantifying the renewable energy output characteristics of energy storage power stations and providing a comprehensive data foundation for evaluating the operation of energy storage power stations and energy scheduling.

[0061] S4: Adjust the grid-connected dispatching strategy of the energy storage power station based on the volatility and randomness characteristic parameters.

[0062] In this embodiment, adjusting the grid-connected dispatching strategy of the energy storage power station based on the volatility and randomness characteristic parameters includes the following steps: If the volatility characteristic parameter exceeds the volatility threshold, the frequency regulation sensitivity and charge and discharge cycle of the energy storage power station will be adjusted. If the random characteristic parameter exceeds the random threshold, the predicted configuration parameters of the predicted output of the energy storage power station and the capacity configuration of the energy storage power station will be adjusted until the volatility characteristic parameter and the random characteristic parameter are within the preset safety value range.

[0063] Specifically, when determining the charge and discharge cycles and capacity configuration of energy storage systems, it is necessary to comprehensively consider both short-term fluctuations (e.g., seconds to minutes) and long-term trends (e.g., daily or seasonal) in renewable energy output. The optimal energy storage capacity can be calculated by developing an optimization model (e.g., with the goal of minimizing curtailment or maximizing economic benefits). Furthermore, the charge and discharge cycle design must be tailored to the energy storage type (e.g., battery cycle life, flywheel response speed) to ensure the long-term operational reliability of the system. When frequency regulation is implemented for energy storage power stations, a frequency response model can be constructed based on the fluctuating characteristics of renewable energy output. Proportional-integral-derivative (PID) control or model predictive control (MPC) methods can be employed, combined with real-time grid frequency deviation data, to achieve rapid response from the energy storage system. The configuration of hybrid energy storage systems also requires optimizing the output distribution ratio of different energy storage units (e.g., supercapacitors and batteries) to balance power and energy requirements. Furthermore, grid-connected dispatch strategies can incorporate renewable energy output forecasts (e.g., short-term forecasting models based on deep learning) to enhance the predictability and accuracy of frequency regulation through feedforward control.

[0064] When configuring the capacity of energy storage power stations, the specific type should be selected based on the characteristics of renewable energy output (e.g., supercapacitors are suitable for the rapid fluctuations of wind power, while batteries are suitable for the long-term output of photovoltaic power) and the grid frequency regulation requirements (e.g., short-term high power or long-term low power). For example, electrochemical energy storage batteries can be lithium-ion batteries, sodium-sulfur batteries, or lead-carbon batteries. Lithium-ion batteries are suitable for high-energy density scenarios, while sodium-sulfur batteries are suitable for long-term energy storage in high-temperature environments. Supercapacitors are suitable for second-level frequency regulation and can be combined with batteries to form a hybrid system with complementary power and energy. Flywheel energy storage is suitable for high-frequency charging and discharging, and its mechanical inertia characteristics can provide additional virtual inertia support for the grid. Pumped hydropower storage is suitable for large-scale, long-term energy storage and can be combined with terrain conditions to achieve cross-day regulation of renewable energy generation. Furthermore, the operation of multiple energy storage combinations requires the design of intelligent control systems (e.g., collaborative control based on multi-agent systems) that dynamically adjust the charging and discharging strategies of each energy storage unit by real-time monitoring of renewable energy output and grid status to maximize overall system efficiency and lifespan.

[0065] In this embodiment, since each indicator in the renewable energy output characteristic index system can provide data guidance for a certain operational aspect of the energy storage power station, and the grid-connected scheduling of the energy storage power station mainly focuses on the charging and discharging cycle and capacity configuration of the energy storage station, other monitoring and control measures such as frequency modulation and renewable energy output forecasting serve as scheduling aids. By analyzing the volatility and randomness characteristic parameters, the fault location can be accurately located, allowing targeted adjustments to be made, so that the configuration and scheduling strategy of the energy storage system are accurately matched to the actual operating characteristics of the renewable energy, thereby improving the control efficiency and the renewable energy absorption capacity.

[0066] The design of the grid-connected dispatching strategy in this embodiment focuses not only on frequency regulation, but also on voltage stability and optimization of the wind and solar curtailment rates of renewable energy power generation. The dispatching strategy needs to be dynamically adjusted according to the real-time grid operating status and can also be combined with prediction models (such as output prediction based on machine learning) to further improve dispatching efficiency.

[0067] It can be seen from the above embodiments that at least the following substantial effects are achieved: (1) The present invention analyzes the temporal characteristics and output characteristic distribution of historical output data of renewable energy power generation in energy storage power stations, and quantitatively analyzes the volatility and randomness of renewable energy power generation from multiple time scales and statistical dimensions. Compared with the traditional single-index analysis method, it can provide more comprehensive data support for the scheduling of energy in energy storage power stations, thereby accurately characterizing the changing patterns of renewable energy output in different operating scenarios. (2) By quantifying the volatility and randomness characteristic parameters of renewable energy output, the present invention can guide the design of scientific charging and discharging cycles and capacity configuration schemes for voltage source large-capacity energy storage power stations. Compared with traditional fixed scheduling strategies, this method combines the frequency response model and the rapid response characteristics of hybrid energy storage to achieve bidirectional and precise regulation of grid frequency, effectively alleviating the impact of renewable energy intermittency on grid stability and improving frequency regulation efficiency. (3) Based on the analysis results of renewable energy output characteristics, the present invention designs a grid-connected dispatching strategy that can timely compensate for power shortages through the energy storage system when renewable energy output fluctuates greatly, thereby reducing wind and solar power curtailment. Compared with conventional dispatching methods that rely solely on traditional frequency-regulated units, this method utilizes the flexibility of energy storage to significantly improve the grid's ability to accept a high proportion of renewable energy while maintaining the stability of the system's voltage and frequency. (4) The present invention uses the energy storage system to participate in frequency regulation, which can effectively reduce the frequent start-up and shutdown and output adjustment of traditional frequency-regulating units caused by fluctuations in the output of new energy sources. Compared with the traditional method that relies entirely on rotating units to provide frequency regulation support, this method uses the fast response characteristics of energy storage to share the frequency regulation task, thereby extending the service life of the frequency-regulating units and reducing their mechanical wear and maintenance costs. (5) By quantifying the output characteristics of new energy sources in detail, the present invention can provide a specific basis for the selection of energy storage system types (such as electrochemical energy storage batteries, supercapacitors, flywheels or pumped storage) and their capacity configuration; compared with the traditional method of determining energy storage configuration based on experience or simple estimation, this method ensures the applicability and economy of energy storage systems in different application scenarios in a data-driven manner.

[0068] The specific implementation described above is a preferred implementation of a method for quantifying the output characteristics of new energy in a large-capacity energy storage power station according to the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. Any equivalent changes made in accordance with the shape and structure of the present invention are within the scope of protection of the present invention.

Claims

1. A method for quantifying the output characteristics of new energy sources in a large-capacity energy storage power station, characterized by: The following steps are involved: S1. Collect historical output data of renewable energy power generation from energy storage power stations based on data collection principles; S2. Analyze the time characteristics and output distribution characteristics of historical output data to build a new energy output characteristic index system; S3. Construct a quantitative model for new energy output characteristic indicators to quantitatively calculate each indicator in the new energy output characteristic indicator system to obtain the volatility and randomness characteristic parameters of the new energy output; S4. Adjust the grid-connected dispatching strategy of the energy storage power station based on the volatility and randomness characteristic parameters.

2. The method for quantifying the output characteristics of new energy sources in a large-capacity energy storage power station according to claim 1 is characterized by: In S1, based on the data collection principle, the historical output data of the energy storage power station's renewable energy power generation is collected, including the following steps: The historical output data of wind power, photovoltaic power generation, geothermal energy and biomass power generation within the historical time period are collected from the energy storage power station monitoring system, and the historical output data are preprocessed and normalized based on the environmental characteristics within the historical time period.

3. The method for quantifying the output characteristics of new energy sources in a large-capacity energy storage power station according to claim 1 is characterized by: In S2, the time characteristics and output distribution characteristics of historical output data are analyzed to construct a new energy output characteristic index system, including the following steps: The seasonal and daily characteristics of new energy output are obtained by analyzing the time characteristics of historical output data. The probability distribution, cumulative power distribution and guaranteed rate distribution of new energy hourly output are obtained by analyzing the output distribution characteristics of historical output data. A new energy output characteristic index system is constructed with the seasonal characteristics, daily characteristics, probability distribution, cumulative power distribution and guaranteed rate distribution of new energy hourly output as the first-level indicators and the influencing factors of the first-level indicators as the second-level indicators.

4. The method for quantifying the output characteristics of new energy sources in a large-capacity energy storage power station according to claim 3 is characterized by: The secondary indicators of the seasonal characteristics include the annual utilization hours of new energy and the monthly average output; the secondary indicators of the daily characteristics include the probability distribution of the daily maximum output of new energy and the daily average output; the secondary indicators of the probability distribution of the new energy output include the probability distribution of the output of new energy at all times and the probability distribution of the output at different time periods; the secondary indicators of the cumulative power distribution of the new energy output include the cumulative power distribution of the output of new energy at all times and the cumulative power distribution of the output at different time periods.

5. The method for quantifying the output characteristics of new energy sources in a large-capacity energy storage power station according to claim 4 is characterized by: The annual utilization hours of new energy are the ratio of the annual power generation of new energy to the corresponding power supply installed capacity, and the monthly average output of new energy is the ratio of the monthly power generation of new energy to the number of days in the corresponding month.

6. The method for quantifying the output characteristics of new energy sources in a large-capacity energy storage power station according to claim 4 is characterized by: The probability distribution of the maximum daily output of new energy is the probability that the maximum daily output is within the preset range during the sampling period of historical output data. The formula is as follows: c max,i =max{P ij ,j=1,2,…,24} c max,i ∈C max Among them, C max is the set of daily maximum outputs within the sampling period of historical output data, c max,i For set C max The elements in p max (m,n) represents the probability that the daily maximum output is within the interval (m,n); The daily average output of new energy is the ratio of the total power generation of new energy in a certain hour to the total number of power generation equipment in the corresponding hour.

7. The method for quantifying the output characteristics of new energy sources in a large-capacity energy storage power station according to claim 4 is characterized by: The probability distribution formula of the new energy output at all times is expressed as follows: Among them, if m <P ij ≤n,N ij =1, otherwise N ij =0; p(m,n) represents the probability that the output of new energy is within the interval (m,n) during the sampling period of historical output data; N ij If the value of the new energy output sampling point at time j on day i is within the interval (m,n], it will be included in the statistical analysis, where m and n are customized based on demand; The probability distribution formula of the renewable energy output in different time periods is as follows: Among them, if m <P ij ≤n,N ij =1, otherwise N ij =0, p j (m,n) represents the probability that the output of renewable energy in period j is within the interval (m,n].

8. The method for quantifying the output characteristics of new energy sources in a large-capacity energy storage power station according to claim 4 is characterized by: The cumulative power distribution formula of the new energy output during the entire period is as follows: Among them, F n represents the cumulative probability that the renewable energy power generation is within the interval [0,n] during the sampling period of historical output data; T represents the annual utilization hours of renewable energy; P ij [if(0≤P ij ≤n)] means that if the new energy output sampling point value at time j on day i is in the interval [0,n], the output value will be accumulated and analyzed, and the accumulated integral power value that meets the requirements will be obtained.

9. The method for quantifying the output characteristics of new energy sources in a large-capacity energy storage power station according to claim 4 is characterized by: In S3, a new energy output characteristic index quantification model is constructed to quantify each index in the new energy output characteristic index system to obtain the volatility and randomness characteristic parameters of the new energy output, including the following steps: A quantitative model for new energy output characteristic indicators was constructed to calculate the mean and variance of the average output, maximum output, time period output, and cumulative power in the secondary indicators of the new energy output characteristic indicator system. The variance was normalized to obtain the fluctuation coefficient. The skewness and kurtosis of the corresponding output and cumulative power were calculated based on the variance to obtain the offset coefficient. The fluctuation coefficient and offset coefficient were used as volatility characteristic parameters. Based on the mean and variance, normal distribution fitting is performed on the average output, maximum output, time period output and cumulative power respectively. The fitting results are compared with the probability distribution of the corresponding output and cumulative power respectively, and the random characteristic parameters are obtained based on the comparison results.

10. The method for quantifying the output characteristics of new energy sources in a large-capacity energy storage power station according to claim 1 is characterized by: In S4, the grid-connected dispatching strategy of the energy storage power station is adjusted based on the volatility and randomness characteristic parameters, including the following steps: if the volatility characteristic parameter exceeds the fluctuation threshold, the frequency regulation sensitivity and charge and discharge cycle of the energy storage power station are adjusted; if the randomness characteristic parameter exceeds the randomness threshold, the predicted configuration parameters of the predicted output of the energy storage power station and the capacity configuration of the energy storage power station are adjusted until the volatility characteristic parameter and the randomness characteristic parameter are within the preset safety value range.

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