A flywheel energy storage power supply-based optimal access capacity evaluation method and system

Abstract

The present application relates to the technical field of energy allocation, and particularly relates to a kind of optimal access capacity evaluation method and system based on flywheel energy storage power supply.The present application includes: by obtaining the historical load data and real-time photovoltaic output information of power grid, the peak-valley difference and climbing rate of load are quantitatively analyzed;The load fluctuation mode of different time periods is classified, the distribution interval of peak shaving demand is determined, and the preliminary capacity configuration scheme of flywheel energy storage is formulated;The preliminary capacity configuration scheme is optimized to obtain capacity interval parameters, and the flywheel energy storage output level is dynamically adjusted;The long-term utilization rate under the condition of cycle life is evaluated, and the segmented access strategy of flywheel energy storage is updated accordingly to generate the final dynamic capacity configuration result.The present application solves the problem that flywheel energy storage access capacity configuration cannot consider peak shaving demand, power grid constraints and long-term utilization efficiency, and realizes dynamic evaluation and optimal configuration of flywheel energy storage access capacity.

Description

Technical Field

[0001] This invention relates to the field of energy allocation technology, and in particular to an optimal access capacity assessment method and system based on flywheel energy storage power sources. Background Technology

[0002] With the high proportion of distributed renewable energy, especially photovoltaic (PV) power generation, being integrated into community-level power grids, traditional distribution systems dominated by centralized power sources are gradually evolving into community microgrids with multiple power sources and loads. In these microgrids, PV output exhibits significant intermittency, randomness, and volatility. Its rate of change often overlaps with user-side load behavior, leading to frequent power imbalances in the grid over short timescales. This manifests as increased peak-to-valley load differences, higher ramp rates, and increased stress on local lines and transformers. These issues not only affect power quality and reliability but also easily trigger operational risks such as equipment overload and protection malfunctions. Therefore, it is crucial to introduce peak-shaving resources with rapid response capabilities to balance microgrid power in real time.

[0003] In existing technologies, energy storage systems are widely used for peak shaving and power smoothing in microgrids. Among them, flywheel energy storage, with its advantages of high power density, fast response speed, high charge-discharge cycles, and long cycle life, is particularly suitable for dealing with rapid fluctuations in photovoltaic output and load over short timescales. However, the connected capacity of flywheel energy storage devices is not always better the larger it is. Excessive connected capacity not only increases investment costs but may also lead to idle capacity due to insufficient peak shaving demand during actual operation, reducing equipment utilization. On the other hand, insufficient connected capacity makes it difficult to provide sufficient peak shaving support under high ramp rate scenarios, affecting the stable operation of the microgrid. Therefore, how to reasonably evaluate and determine the optimal connected capacity of flywheel energy storage under complex and variable operating conditions has become a key technical problem restricting its engineering application.

[0004] Against this backdrop, there is an urgent need for a capacity assessment method that comprehensively considers load peak-to-valley differences and ramp-up rate characteristics, photovoltaic output prediction errors, grid tier constraints, and the operating characteristics of flywheel energy storage itself. This method would combine data-driven approaches and optimization algorithms to segmentally model, dynamically optimize, and real-time correct the access capacity of flywheel energy storage, thereby improving capacity utilization and the long-term economic efficiency and reliability of the system while ensuring peak-shaving performance. It is against this backdrop that the optimal access capacity assessment method based on flywheel energy storage power sources, as proposed in this invention, is proposed to address the technical problems of lack of dynamism, engineering adaptability, and long-term sustainability in existing flywheel energy storage capacity configurations. Summary of the Invention

[0005] This invention provides an optimal access capacity assessment method and system based on flywheel energy storage power sources. By systematically modeling and coordinating the load fluctuation characteristics, photovoltaic output uncertainty, power grid physical constraints, and flywheel energy storage operation characteristics in the power grid, the flywheel energy storage can automatically adjust its access capacity according to the real-time operating status, thereby achieving coordinated optimization of peak demand response, equipment utilization improvement, and stable power grid operation.

[0006] In a first aspect, the present invention provides an optimal access capacity assessment method based on flywheel energy storage power sources, the method comprising:

[0007] Step S1: Obtain historical load data and real-time photovoltaic output information of the power grid, analyze the historical load data and the real-time photovoltaic output information, and obtain the peak-valley difference characteristics and the ramp rate characteristics respectively, and use them as relevant quantitative indicators to characterize the power grid load fluctuation.

[0008] Step S2: Based on the relevant quantitative indicators, classify the load fluctuation patterns of different time periods through cluster analysis, and determine the distribution range of peak-shaving demand based on the load fluctuation patterns; formulate a preliminary capacity configuration scheme for flywheel energy storage for the distribution range.

[0009] Step S3: Optimize the preliminary capacity configuration scheme by integrating relevant grid parameters to obtain optimized capacity interval parameters; adjust the output level of flywheel energy storage according to the optimized capacity interval parameters to determine the access capacity for real-time response to peak shaving needs;

[0010] Step S4: Based on the access capacity, extract the response speed and power density characteristics of flywheel energy storage, and calculate the long-term utilization rate index of flywheel energy storage under cycle life conditions using an interpolation method; based on the long-term utilization rate index, update the segmented access strategy of flywheel energy storage, and generate the final dynamic capacity configuration result of flywheel energy storage based on the updated segmented access strategy.

[0011] As a preferred embodiment of the present invention, step S1 involves obtaining relevant quantitative indicators characterizing power grid load fluctuations, including:

[0012] The historical load data and real-time photovoltaic output information are acquired through a data acquisition device; the historical load data are processed using a time series analysis method to extract the load peak-valley difference characteristics; the load ramp-up rate characteristics are calculated based on the real-time photovoltaic output information and the historical load data; and the load peak-valley difference characteristics and the load ramp-up rate characteristics are used as quantitative indicators related to the load fluctuation and output.

[0013] As a preferred embodiment of the present invention, step S2, determining the distribution range of peak-shaving demand, includes:

[0014] The system acquires data on load peak-to-valley difference and ramp rate from relevant quantitative indicators of load fluctuation; it provides a clustering analysis method to classify the load peak-to-valley difference and ramp rate data into load fluctuation patterns, thereby obtaining multiple load fluctuation pattern categories; it analyzes the peak-shaving demand intensity corresponding to each load fluctuation pattern category; and it determines the peak-shaving demand distribution range corresponding to each type of load fluctuation pattern based on the peak-shaving demand intensity, wherein the peak-shaving demand intensity is a weighted combination of load peak-to-valley difference and ramp rate.

[0015] As a preferred embodiment of the present invention, step S2 involves formulating a preliminary capacity configuration scheme for flywheel energy storage, including:

[0016] Obtain the distribution data of the ramp rate in the distribution interval; if the ramp rate exceeds a preset threshold, divide the peak demand distribution interval into multiple capacity access sub-intervals according to the ramp rate, and set a corresponding flywheel energy storage access capacity interval for each capacity access sub-interval, generating a preliminary flywheel energy storage capacity configuration scheme that matches each peak demand distribution interval and the corresponding ramp rate.

[0017] As a preferred embodiment of the present invention, step S3, obtaining the optimized capacity interval parameters, includes:

[0018] The algorithm acquires step-growth data of transformer capacity and line current in the power grid, integrates the preliminary capacity configuration scheme of the flywheel energy storage with the step-growth data of transformer capacity and line current, and constructs an input dataset for capacity optimization. The preliminary capacity configuration scheme serves as the initial solution or search starting point for the optimization algorithm. The input dataset is then input into a preset optimization algorithm, which iteratively adjusts the flywheel energy storage capacity access interval through multiple rounds to output optimized capacity interval parameters that satisfy the power grid step constraints. The capacity interval parameters are used to limit the step size of flywheel energy storage access capacity changes in different peak-shaving demand ranges.

[0019] As a preferred embodiment of the present invention, step S3, determining the access capacity for real-time response to peak shaving requirements, includes:

[0020] The prediction error range is calculated based on historical photovoltaic power output predictions and corresponding actual measurements. Then, based on the optimized capacity interval parameters and the prediction error range, it is determined whether the optimized capacity interval parameters match the prediction error range. If they match, the flywheel energy storage output level is dynamically adjusted according to the optimized capacity interval parameters. If they do not match, the capacity interval parameters are revised. Based on the adjusted output level, the access capacity for real-time peak shaving is determined.

[0021] As a preferred embodiment of the present invention, step S4, which involves calculating the long-term utilization rate index based on the access capacity and updating the segmented access strategy, includes:

[0022] Extract response speed and power density related feature data from the access capacity; process the response speed and power density related feature data using an interpolation calculation method to calculate the long-term utilization rate under cycle lifetime; generate an adjusted capacity utilization index based on the long-term utilization rate; update the segmented access strategy using the capacity utilization index.

[0023] As a preferred embodiment of the present invention, step S4, generating the final dynamic capacity configuration result according to the segmented access strategy, includes:

[0024] Obtain the updated segmented access strategy and corresponding capacity utilization index; determine whether the capacity utilization index meets the preset conditions; if it does, integrate the segmented access strategy into the power grid control system; output the final dynamic capacity configuration result for realizing dynamic management of flywheel energy storage and peak demand response.

[0025] This invention also provides an optimal access capacity assessment system based on flywheel energy storage power sources for implementing the above-described method. The system includes:

[0026] The data acquisition unit is used to acquire historical load data and real-time photovoltaic output information of the power grid, analyze the historical load data and the real-time photovoltaic output information, and obtain the peak-valley difference characteristics and the ramp rate characteristics respectively, and use them as relevant quantitative indicators to characterize the power grid load fluctuation.

[0027] Capacity configuration unit: Based on the relevant quantitative indicators, the load fluctuation patterns of different time periods are classified through cluster analysis, and the distribution range of peak-shaving demand is determined according to the load fluctuation patterns; for the distribution range, a preliminary capacity configuration scheme for flywheel energy storage is formulated.

[0028] The capacity optimization unit is used to optimize the preliminary capacity configuration scheme by integrating relevant grid parameters to obtain optimized capacity interval parameters; and to adjust the output level of flywheel energy storage according to the optimized capacity interval parameters to determine the access capacity for real-time response to peak shaving needs.

[0029] The capacity assessment unit is used to extract the response speed and power density characteristics of flywheel energy storage based on the access capacity, and to calculate the long-term utilization rate index of flywheel energy storage under cycle life conditions through interpolation calculation method.

[0030] The capacity adjustment unit is used to update the segmented access strategy of flywheel energy storage according to the long-term utilization rate index, and generate the final dynamic capacity configuration result of flywheel energy storage based on the updated segmented access strategy.

[0031] The present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0032] The beneficial effects of this invention are as follows:

[0033] This invention quantifies key indicators such as load peak-to-valley difference and ramp rate by performing time series analysis on historical load data and real-time photovoltaic output information, providing a unified data representation basis for the intensity and rate of change of peak-shaving demand. It uses clustering algorithms to classify load fluctuation patterns, forming distribution intervals of peak-shaving demand with different intensities, enabling differentiated capacity allocation for different operating conditions. Secondly, for peak-shaving demand intervals with high ramp rates, it constructs preliminary capacity access intervals for flywheel energy storage in a segmented manner, further incorporating step-growth data of transformer capacity and line current. An optimization algorithm iteratively corrects the capacity intervals, matching the capacity change step size with the actual load-bearing characteristics of the power grid, thus establishing an effective mapping relationship between peak-shaving demand and grid constraints. Finally, it compares the optimized capacity interval parameters with the load and photovoltaic output prediction error ranges. A robust capacity configuration verification mechanism was constructed through matching and judgment, enabling flywheel energy storage to maintain stable response even under uncertainties in prediction. By extracting features such as response speed and power density from the access capacity, and combining interpolation calculations and cycle life models, long-term utilization rate was obtained and capacity utilization indicators were generated. This extends capacity assessment beyond short-term peak-shaving effects to long-term operating efficiency and equipment lifespan. The segmented access strategy that meets preset conditions was integrated into the power grid control system, outputting the final dynamic capacity configuration result. This allows flywheel energy storage to automatically adjust its access capacity according to real-time operating status, achieving synergistic optimization of peak-shaving demand response, equipment utilization improvement, and stable power grid operation. Through the cooperation of the above technical solutions, accurate assessment and dynamic management of flywheel energy storage access capacity were achieved, with overall technical performance significantly superior to existing static or experience-based capacity configuration methods. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments 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 based on these drawings without creative effort.

[0035] Figure 1This is a flowchart of an optimal access capacity evaluation method based on flywheel energy storage power in an embodiment.

[0036] Figure 2 This is a schematic diagram illustrating the method for optimizing capacity access intervals in the embodiment;

[0037] Figure 3 This is a schematic diagram illustrating the matching between the optimized capacity access interval and the grid tiered characteristics in the embodiment.

[0038] Figure 4 This is a structural diagram of an optimal access capacity assessment system based on flywheel energy storage power in one embodiment. Detailed Implementation

[0039] This invention provides a method and system for optimal access capacity assessment based on flywheel energy storage power sources. The terms "first," "second," "third," "fourth," etc. (if applicable) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0040] For ease of understanding, the specific process of the embodiments of the present invention will be described below, such as... Figure 1 As shown in the figure, an optimal access capacity assessment method based on flywheel energy storage power supply in an embodiment of the present invention includes:

[0041] Step S1: Obtain historical load data and real-time photovoltaic output information of the power grid; analyze the historical load data and the real-time photovoltaic output information to obtain the load peak-valley difference characteristics and load ramp-up rate characteristics, which are used as relevant quantitative indicators to characterize power grid load fluctuations; specifically including:

[0042] The historical load data and real-time photovoltaic output information are acquired through a data acquisition device; the historical load data are processed using a time series analysis method to extract the load peak-valley difference characteristics; the load ramp-up rate characteristics are calculated based on the real-time photovoltaic output information and the historical load data; and the load peak-valley difference characteristics and the load ramp-up rate characteristics are used as quantitative indicators related to the load fluctuation and output.

[0043] Specifically, in one implementation, to achieve optimal access capacity assessment based on flywheel energy storage power, operational data for characterizing load fluctuations is first acquired using acquisition devices installed within the power grid. These acquisition devices include smart meters installed at each power consumption node and photovoltaic inverter sensors installed on the photovoltaic power generation unit side. The smart meters continuously monitor the power load within the grid and collect and store historical load data according to a preset sampling period, for example, collecting load power data every 15 minutes to form a historical load data sequence covering a longer time scale (e.g., a one-year period), reflecting the peak and valley changes of the grid load in different time periods. Simultaneously, the photovoltaic inverter sensors monitor the output power of the photovoltaic power generation unit in real time and update the photovoltaic output information with a high time resolution, such as once per minute, to accurately reflect the dynamic changes in photovoltaic power generation under different illumination conditions. Through this acquisition method, both historical load data with a long time span and statistical significance and real-time photovoltaic output data with high time accuracy that reflects instantaneous fluctuations can be obtained simultaneously, providing a complete and reliable data foundation for subsequent load fluctuation analysis.

[0044] After obtaining the aforementioned historical load data, time series analysis is used to process the data to extract the load peak-to-valley difference characteristic, which characterizes the overall load fluctuation. Specific time series analysis methods may include the moving average method, which smooths the original load data sequence to reduce the impact of random noise on the analysis results. The maximum and minimum load values ​​within each statistical period are then identified on the smoothed load curve, and the load peak-to-valley difference within the corresponding period is calculated as the load peak-to-valley difference characteristic. Based on the extracted load peak-to-valley difference characteristic, real-time photovoltaic (PV) output information is further combined to characterize the dynamic characteristics of load changes, thereby calculating the load ramp-up rate characteristic. Specifically, the real-time PV output information and the aforementioned historical load data are timestamped according to a unified time base, ensuring that PV output changes and load changes are on the same time axis. The above corresponds to the following: Subsequently, differential calculation is performed on the load data at adjacent time points to obtain the load power difference between adjacent time points. The ratio of the load power difference between adjacent time points to the time interval is calculated to obtain the load change rate per unit time, thereby obtaining the load ramping rate characteristics. When the photovoltaic output decreases in a short period of time, causing the grid to need to compensate for power from other power sources, the corresponding load change rate will show a large positive value, thus reflecting a high degree of urgency for peak regulation. In another embodiment, in order to improve the analysis accuracy under complex weather conditions, the benchmark ramping rate of load change can be estimated first based on historical load data, and then the benchmark value can be corrected by combining real-time photovoltaic output information to obtain a load ramping rate characteristic that is more consistent with the current operating state. During the high load peak period, a weighting factor can also be introduced in the differential calculation process to highlight the impact of rapid fluctuations in photovoltaic output on the load ramping rate.

[0045] The aforementioned load peak-valley difference characteristics and load ramp-up rate characteristics are used as load fluctuation-related quantitative indicators to uniformly output load fluctuation characteristics. Specifically, the corresponding load peak-valley difference values ​​and ramp-up rate values ​​within each statistical period are encapsulated into a set of quantitative indicators and transmitted to the subsequent load fluctuation pattern classification and peak-shaving demand analysis modules. For example, if the load peak-valley difference is 10kW and the load ramp-up rate is 2kW / minute within a certain time period, the set of indicators is input as a two-dimensional feature vector into a clustering analysis algorithm to identify the load fluctuation pattern category to which that time period belongs. Through the above technical solution, data from different sources and time scales are uniformly mapped into structured quantitative indicators, thereby providing a clear and calculable input basis for the optimal access capacity assessment based on flywheel energy storage power sources, and laying the data and algorithm foundation for subsequent determination of peak-shaving demand distribution ranges and capacity configuration decisions.

[0046] Step S2: Based on the relevant quantitative indicators, classify the load fluctuation patterns of different time periods through cluster analysis, and determine the distribution range of peak-shaving demand based on the load fluctuation patterns; formulate a preliminary capacity configuration scheme for flywheel energy storage for the distribution range.

[0047] In step S2, determining the distribution range of peak-shaving demand includes:

[0048] The system acquires data on load peak-to-valley difference and ramp rate from relevant quantitative indicators of load fluctuation; it provides a clustering analysis method to classify the load peak-to-valley difference and ramp rate data into load fluctuation patterns, thereby obtaining multiple load fluctuation pattern categories; it analyzes the peak-shaving demand intensity corresponding to each load fluctuation pattern category; and it determines the peak-shaving demand distribution range corresponding to each type of load fluctuation pattern based on the peak-shaving demand intensity, wherein the peak-shaving demand intensity is a weighted combination of load peak-to-valley difference and ramp rate.

[0049] Specifically, in one implementation, based on the obtained quantitative indicators related to load fluctuations, the distribution range of peak-shaving demand is determined. Specifically, load peak-valley difference data and load ramp-up rate data are obtained from the aforementioned quantitative indicators related to load fluctuations. The load peak-valley difference and load ramp-up rate quantify the load fluctuation state of the power grid from two dimensions: the magnitude of change and the rate of change, respectively, and serve as the basic input data for subsequent pattern segmentation. Based on this, a clustering analysis method is provided to segment the aforementioned load peak-valley difference data and load ramp-up rate data to identify different types of load fluctuation patterns. Specifically, the corresponding load peak-valley difference values ​​and ramp-up rate values ​​within the same time period are combined to form a two-dimensional feature vector. Each two-dimensional feature vector corresponds to a load sample, thus forming a sample set for clustering analysis. Subsequently, the number of cluster centers K for the K-means clustering algorithm is initialized, where the value of K is based on historical... The fluctuation characteristics and coefficient of variation of historical load data are set, preferably 3 to 5, to ensure that the clustering results can cover different load operating states such as low fluctuation, medium fluctuation, and high fluctuation. In a specific embodiment, K can be selected as 4 to take into account various typical load patterns such as low fluctuation, low-medium fluctuation, medium-high fluctuation, and high fluctuation. K samples are randomly selected as initial cluster centers, and the Euclidean distance between each sample and each cluster center is calculated to assign each load sample to the nearest cluster. Subsequently, a new cluster center is calculated for the samples in each cluster as the arithmetic mean of each two-dimensional feature vector in that cluster, and the sample redistribution and cluster center update process is repeatedly executed until the change in cluster center is less than the preset convergence threshold or the maximum number of iterations is reached, thereby obtaining a stable clustering result. Finally, K load fluctuation pattern categories are formed, and each category is characterized by the typical load peak-to-valley difference and typical ramp rate corresponding to its cluster center.

[0050] After classifying load fluctuation patterns, the peak-shaving demand intensity is analyzed for each category. Specifically, for each clustered load fluctuation pattern category, the peak-shaving demand intensity is calculated based on the statistical characteristics of the samples within that category. This peak-shaving demand intensity is a weighted combination of the load peak-to-valley difference and the load ramp-up rate. The weighting coefficients of this weighted combination can be set based on historical peak-shaving event statistics. For example, a higher weight can be given to the load peak-to-valley difference to reflect its dominant influence on peak-shaving capacity demand, while a certain weight can be given to the ramp-up rate to reflect the requirements for peak-shaving response speed. In a specific embodiment, the weight of the load peak-to-valley difference can be set to 0.6, and the weight of the ramp-up rate to 0. 4. By weighting the average peak-to-valley difference and average ramp rate of each category, the peak-shaving demand intensity value for that category is obtained. Subsequently, the above peak-shaving demand intensity value is normalized with a preset benchmark intensity, and the intensity level is divided accordingly. For example, categories with normalization results below the first threshold are judged as low-intensity peak-shaving demand, categories between the first and second thresholds are judged as medium-intensity peak-shaving demand, and categories above the second threshold are judged as high-intensity peak-shaving demand. At the same time, the proportion of each load fluctuation mode category in the overall sample can also be statistically analyzed, and the overall peak-shaving risk of the power grid can be assessed in combination with its intensity level, thereby avoiding subjective judgment bias caused by relying solely on data from a single time slice.

[0051] After determining the peak-shaving demand intensity corresponding to each load fluctuation pattern category, each load fluctuation pattern is mapped to a corresponding peak-shaving demand distribution range based on the aforementioned peak-shaving demand intensity. Specifically, multiple peak-shaving demand capacity ranges are pre-defined to characterize the capacity range that flywheel energy storage needs to undertake under different peak-shaving demand levels. For example, low-intensity peak-shaving demand is mapped to a smaller peak-shaving capacity range, medium-intensity peak-shaving demand to a medium capacity range, and high-intensity peak-shaving demand to a larger capacity range. In a specific embodiment, low-intensity peak-shaving demand can be mapped to a peak-shaving demand distribution range of 0.1MW to 0.5MW, and medium-intensity peak-shaving demand can be mapped to a peak-shaving demand distribution range of 0.5MW to 1MW. The 0.2MW peak-shaving demand distribution range maps high-intensity peak-shaving demand to a peak-shaving demand distribution range of 1.2MW to 2.5MW, thus forming a one-to-one correspondence between load fluctuation mode categories, peak-shaving demand intensity, and peak-shaving demand distribution ranges. Through the above technical solution, different load fluctuation modes are clearly transformed into quantifiable and dispatchable peak-shaving demand ranges in terms of capacity, providing a clear and objective basis for the subsequent segmented configuration of flywheel energy storage capacity and the assessment of optimal access capacity. This effectively avoids the idle problem caused by excessive energy storage capacity configuration or the risk of peak-shaving failure caused by insufficient configuration, thereby improving the safety and economy of grid peak-shaving operation based on flywheel energy storage power sources.

[0052] Furthermore, in step S2, a preliminary capacity configuration scheme for flywheel energy storage is formulated, including:

[0053] Obtain the distribution data of the ramp rate in the distribution interval; if the ramp rate exceeds a preset threshold, divide the peak demand distribution interval into multiple capacity access sub-intervals according to the ramp rate, and set a corresponding flywheel energy storage access capacity interval for each capacity access sub-interval, generating a preliminary flywheel energy storage capacity configuration scheme that matches each peak demand distribution interval and the corresponding ramp rate.

[0054] Specifically, in order to enable flywheel energy storage to respond to peak-shaving demands accurately, quickly and efficiently, a matching relationship between the access capacity of flywheel energy storage and the intensity of peak-shaving demand is achieved by quantitatively modeling load ramping characteristics, dividing distribution intervals, and dynamically determining capacity access intervals. This provides reliable basic data for subsequent global optimization and real-time control.

[0055] During implementation, the distribution data of ramp rates in the peak-shaving demand distribution range are first obtained. Threshold judgment is then performed on the ramp rates in the aforementioned distribution range. That is, when the ramp rate in a certain peak-shaving demand distribution range exceeds a preset threshold, such as exceeding the maximum allowable power change rate per unit time, the peak-shaving demand corresponding to that range is considered to have strong instantaneousness and impact, requiring the introduction of flywheel energy storage for response through a refined capacity segmentation method. For this type of distribution range, the original peak-shaving demand distribution range is further segmented according to the magnitude of the ramp rate and its distribution within the range. Specifically, the capacity range is further subdivided into multiple capacity access intervals based on the magnitude of the ramp rate, so that each capacity access interval corresponds to a relatively stable load change rate range. Based on the aforementioned capacity access intervals, the upper and lower limits of the access capacity of flywheel energy storage in each interval are determined, thereby forming a segmented and tiered preliminary capacity configuration scheme.

[0056] After dividing the capacity access sub-intervals, based on the high power density, fast response speed, and suitability for high-frequency charging and discharging of flywheel energy storage devices, corresponding flywheel energy storage access capacity intervals are set for each capacity access sub-interval, forming a preliminary capacity configuration scheme. The setting of the above capacity intervals is not a simple empirical selection, but rather a combination of the statistical characteristics of the ramp rate within each sub-interval. This ensures that a larger access capacity is configured in the high ramp rate interval to meet the rapid peak shaving demand, and a relatively smaller access capacity is configured in the low ramp rate interval to reduce the risk of equipment idleness. In this way, a one-to-one correspondence is established between each peak shaving demand distribution interval and its corresponding ramp rate characteristics and the flywheel energy storage access capacity interval, generating a preliminary flywheel energy storage capacity configuration scheme that matches the dynamic characteristics of the load.

[0057] Furthermore, to improve the economy and reliability of this preliminary capacity configuration scheme in long-term operation, the system extracts key parameters related to the operating characteristics of flywheel energy storage from the above-mentioned access capacity, including characteristics such as response speed, power density, and charge / discharge frequency. Combined with the cycle life curves of flywheel energy storage under different load levels, a linear interpolation method is used to calculate the long-term utilization rate corresponding to different capacity access levels. In this way, discrete capacity points are mapped to continuous capacity utilization indicators, so that the durability and long-term operating efficiency of flywheel energy storage can be comprehensively considered in the capacity configuration scheme at the formulation stage.

[0058] Finally, the preliminary capacity configuration scheme obtained above is used as the basis for subsequent optimization and control. It is dynamically adjusted in conjunction with grid parameters. Specifically, by introducing constraints such as transformer capacity and line current carrying capacity, the segmented access strategy is modified. When the modified capacity utilization index shows that the idle risk of flywheel energy storage is further reduced and the peak-shaving response capability is improved, the updated segmented access strategy is integrated into the community microgrid control system to form the final dynamic capacity configuration result. Through the above technical solution, a systematic construction process of flywheel energy storage access capacity from load characteristic identification and distribution interval division to capacity interval determination is realized, which improves the adaptability and practical value of flywheel energy storage in participating in grid peak-shaving operation.

[0059] Step S3: Optimize the preliminary capacity configuration scheme by integrating relevant grid parameters to obtain optimized capacity interval parameters; adjust the output level of flywheel energy storage according to the optimized capacity interval parameters to determine the access capacity for real-time response to peak shaving needs;

[0060] In step S3, the optimized capacity interval parameters are obtained, including:

[0061] The algorithm acquires step-growth data of transformer capacity and line current in the power grid, integrates the preliminary capacity configuration scheme of the flywheel energy storage with the step-growth data of transformer capacity and line current, and constructs an input dataset for capacity optimization. The preliminary capacity configuration scheme serves as the initial solution or search starting point for the optimization algorithm. The input dataset is then input into a preset optimization algorithm, which iteratively adjusts the flywheel energy storage capacity access interval through multiple rounds to output optimized capacity interval parameters that satisfy the power grid step constraints. The capacity interval parameters are used to limit the step size of flywheel energy storage access capacity changes in different peak-shaving demand ranges.

[0062] Specifically, to achieve adaptive matching between the step size of flywheel energy storage access capacity changes and the grid's carrying capacity, the implementation process first acquires step-growth data of transformer capacity and line current in the grid. This is achieved by continuously collecting transformer capacity utilization and line current monitoring values ​​through real-time monitoring sensors deployed at transformers and feeders, combined with historical operation databases. The data is recorded and stored in time series form to reflect the operating status of key grid equipment during load changes. After preprocessing the collected data, the correlation between load level and transformer capacity and current values ​​is statistically analyzed to identify the segmented change characteristics of capacity and current as load increases. That is, when the load reaches or exceeds a certain threshold, the transformer capacity or line current does not increase continuously and linearly, but rather exhibits a step-like jump, thus forming a step-growth data model that reflects the physical constraints of the grid.

[0063] After obtaining the aforementioned stepped growth data, it is fused with the aforementioned preliminary flywheel energy storage capacity configuration scheme to construct an input dataset for capacity optimization. Specifically, the aforementioned preliminary capacity configuration scheme includes flywheel energy storage capacity access interval information divided for different peak-shaving demand intervals, while the stepped growth data provides the allowable step size and upper limit constraints for transformer capacity and line current variations in different load intervals. By performing unified data structuring processing on both, a mapping relationship is established between the flywheel energy storage capacity interval parameters and the corresponding grid stepped constraints. This allows the input dataset to both represent the capacity configuration intention on the peak-shaving demand side and reflect the safe operation boundary on the grid side. The aforementioned preliminary capacity configuration scheme is used as the initial solution or search starting point for the optimization algorithm to reduce the search space and improve the solution efficiency.

[0064] After constructing the input dataset, it is fed into a preset optimization algorithm to perform multiple rounds of iterative adjustments to the flywheel energy storage capacity access interval. In this embodiment, the optimization algorithm adopts the particle swarm optimization algorithm, which performs global optimization of the capacity interval parameter by simulating a swarm intelligence search mechanism. In specific implementation, each particle is defined as a flywheel energy storage capacity configuration scheme, where the particle's position vector corresponds to the capacity access interval value under each peak demand interval, and the particle's velocity vector represents the direction and magnitude of the capacity interval adjustment. In the algorithm initialization phase, the initial position of the particles is set according to the preliminary capacity configuration scheme, and the inertia weight, cognitive factor, and social factor are configured in combination with different peak demand intensities to ensure that the algorithm has good convergence performance while maintaining global search capability; during the iteration process, such as Figure 2As shown, by incorporating the step-growth data into the calculation of the fitness function, each iteration not only evaluates the effect of the capacity configuration scheme on reducing the load peak-valley difference and improving capacity utilization, but also simultaneously determines whether the scheme meets the step constraints of transformer capacity and line current under the corresponding capacity access interval. When the capacity interval parameter corresponding to a particle causes the line current or transformer load to exceed the allowable range of the current step, the fitness function will penalize it, guiding the particle to move to the capacity interval that meets the step size requirements in subsequent iterations. Through the continuous updating of particle position and velocity, the capacity access interval gradually evolves from the initial linear or empirical piecewise form to a nonlinear piecewise form that matches the step characteristics of the power grid, thereby reducing overload risk and equipment idle risk while ensuring peak-shaving effect.

[0065] After multiple iterations reach the convergence condition, such as Figure 3 As shown, the output is an optimized capacity interval parameter that satisfies the grid tiered constraint conditions. This capacity interval parameter is used to limit the step size of the access capacity change of flywheel energy storage in different peak-shaving demand intervals, enabling flywheel energy storage to respond in stages according to the grid carrying capacity in actual operation. The optimized capacity interval parameter is directly applied to the output control strategy of flywheel energy storage. By setting the corresponding output curve and adjustment logic in the grid control system, flywheel energy storage can dynamically increase or decrease its output level according to the optimization results under load changes and photovoltaic output fluctuations. The above technical solution introduces the tiered growth data of transformer capacity and line current, and combines the particle swarm optimization algorithm to automatically iteratively adjust the access interval of flywheel energy storage capacity. This forms a complete technical link from grid operation data acquisition, capacity scheme fusion modeling to optimized parameter output, avoiding the uncertainty brought by manual experience configuration. The process of determining the access capacity of flywheel energy storage satisfies grid security constraints while taking into account peak-shaving performance and long-term utilization efficiency, thereby significantly improving the engineering applicability and stability of the optimal access capacity evaluation method based on flywheel energy storage power source.

[0066] Further, in step S3, determining the access capacity for real-time response to peak-shaving demands includes:

[0067] The prediction error range is calculated based on historical photovoltaic power output predictions and corresponding actual measurements. Then, based on the optimized capacity interval parameters and the prediction error range, it is determined whether the optimized capacity interval parameters match the prediction error range. If they match, the flywheel energy storage output level is dynamically adjusted according to the optimized capacity interval parameters. If they do not match, the capacity interval parameters are revised. Based on the adjusted output level, the access capacity for real-time peak shaving is determined.

[0068] Specifically, in order to ensure that the capacity interval parameters obtained through the above optimization can accurately respond to peak-shaving needs in actual operation and have robust adaptability to prediction uncertainties, the capacity interval parameters are matched and judged with the error range of load and photovoltaic output prediction, and the output level of flywheel energy storage is dynamically adjusted on this basis, thereby realizing the real-time determination and closed-loop correction of flywheel energy storage access capacity.

[0069] During implementation, the optimized capacity interval parameters are compared and analyzed with the prediction error range to determine whether the upper and lower boundaries of the capacity interval parameters fall within the capacity demand range corresponding to the prediction error range. The prediction error range data is obtained through statistical analysis of the deviation between historical photovoltaic (PV) output prediction values ​​and corresponding actual measured values. The prediction error range, expressed as a positive or negative percentage range, characterizes the uncertainty level of PV output prediction in long-term operation. The historical PV output prediction values ​​are modeled and calculated using a PV output prediction model based on the historical sample data to obtain the historical PV output prediction values ​​for the corresponding time period. The PV output prediction model can employ one or a combination of prediction methods known in the art, such as a time-series-based prediction model. The input of the prediction model includes historical measured PV output data and corresponding meteorological parameters, and the output of the prediction model is the PV output prediction value under given time conditions. By performing replay-based prediction on historical time periods—that is, regenerating prediction results under the premise of known actual output results—it is possible to obtain the prediction results for each historical moment. A one-to-one sequence of historical photovoltaic power output prediction values ​​is generated. If the judgment result indicates that the capacity interval parameter can cover the capacity fluctuations caused by the prediction error, it is considered that the above capacity interval parameter matches the prediction error range. The output level of flywheel energy storage is dynamically adjusted according to the above optimized capacity interval parameter. Specifically, by real-time monitoring of load fluctuation status, when the load ramp rate is detected to increase and enter the corresponding peak-shaving demand range, the discharge output of flywheel energy storage is gradually increased to the upper limit of the corresponding capacity range according to the optimized capacity interval parameter. When the load fluctuation slows down, the output level is reduced accordingly. The above output adjustment is achieved through a feedback control loop, and the control cycle is updated on a minute-by-minute basis, thereby ensuring the rapid response capability of flywheel energy storage to peak-shaving demand. If the judgment result shows that the capacity interval parameter is insufficient to cope with the deviation risk caused by the prediction error, the correction mechanism of the capacity interval parameter is triggered. By re-introducing the prediction error weight or adjusting the constraints of the optimization algorithm, the capacity interval parameter is recalculated, thereby avoiding the impact of insufficient or excessive capacity configuration on the peak-shaving effect of flywheel energy storage in actual operation.

[0070] Once the optimized capacity interval parameters are confirmed to match the prediction error range, the output level of the flywheel energy storage is dynamically adjusted according to these parameters. Specifically, the control system, based on real-time monitoring of load changes and photovoltaic output fluctuations, adjusts the discharge or charging output of the flywheel energy storage step-by-step according to the capacity interval parameters corresponding to the current peak-shaving demand range. When a continuous increase in load ramp-up rate or a rapid decrease in photovoltaic output is detected, the system increases the flywheel energy storage output to the upper limit of the corresponding range according to the change step size defined by the capacity interval parameters, in order to quickly compensate for the power gap in the grid. Conversely, when load changes slow down or output fluctuations decrease, the output level is gradually adjusted back according to the same capacity interval parameters, thereby avoiding frequent and large fluctuations in output that could adversely affect equipment lifespan and system stability. This dynamic adjustment process is achieved through a closed-loop feedback control mechanism. The control cycle can be set to minutes or a finer time scale according to system requirements to ensure the real-time response capability of the flywheel energy storage to peak-shaving demands.

[0071] After dynamically adjusting the output level, the access capacity for real-time response to peak-shaving demand is determined based on the adjusted output level. Specifically, the actual output level of flywheel energy storage within a certain scheduling cycle is integrated over time to obtain the equivalent access capacity value for the corresponding cycle. This access capacity is used as the actual capacity parameter for flywheel energy storage to participate in peak shaving under the current operating conditions. This access capacity not only reflects the immediate support capability of flywheel energy storage for peak-shaving demand but also serves as the basis for subsequent calculations of capacity utilization and evaluation of the rationality of capacity configuration. By analyzing the relationship between the access capacity and the rated capacity of flywheel energy storage, operating characteristics such as response speed and power density can be further extracted and combined with linear interpolation. The value method calculates the long-term utilization rate under different cycle lifetime conditions, thus providing a quantitative basis for the continuous optimization of the segmented access strategy. By matching the optimized capacity interval parameters with the prediction error range again, and dynamically adjusting the flywheel energy storage output level under the matching conditions, the access capacity for real-time response to peak shaving demand is finally determined. The above technical solution realizes the organic connection between prediction uncertainty, capacity optimization results and real-time control behavior, so that the access capacity of flywheel energy storage can not only meet the instantaneous peak shaving demand, but also take into account long-term utilization efficiency and operational safety, thereby further improving the overall adaptability of the optimal access capacity evaluation method based on flywheel energy storage power supply in complex power grid operation scenarios.

[0072] Step S4: Based on the access capacity, extract the response speed and power density characteristics of flywheel energy storage, and calculate the long-term utilization rate index of flywheel energy storage under cycle life conditions using an interpolation method; based on the long-term utilization rate index, update the segmented access strategy of flywheel energy storage, and generate the final dynamic capacity configuration result of flywheel energy storage based on the updated segmented access strategy.

[0073] In step S4, a long-term utilization rate index is calculated based on the access capacity, and the segmented access strategy for flywheel energy storage is updated according to the long-term utilization rate index, including:

[0074] Extract response speed and power density related feature data from the access capacity; process the response speed and power density related feature data using an interpolation calculation method to calculate the long-term utilization rate under cycle lifetime; generate an adjusted capacity utilization index based on the long-term utilization rate; update the segmented access strategy using the capacity utilization index.

[0075] Specifically, to further verify and correct the rationality of flywheel energy storage capacity configuration from the perspective of long-term operation and sustainable utilization, after determining the access capacity for real-time response to peak-shaving needs, a long-term utilization rate evaluation mechanism based on cycle life is introduced. This mechanism serves as an important basis for updating the segmented access strategy for flywheel energy storage, ensuring that the capacity configuration not only meets short-term peak-shaving performance requirements but also considers equipment life constraints and system operating efficiency. During implementation, based on the determined flywheel energy storage access capacity, key characteristic data reflecting the dynamic performance and structural characteristics of the energy storage device are extracted. This characteristic data includes at least response speed data and power density data. In this context, response speed is used to characterize the time required for flywheel energy storage to reach the target output level when the load peak-valley difference changes or the ramp rate changes abruptly. Essentially, it reflects the flywheel energy storage's ability to execute peak-shaving commands. Power density, on the other hand, describes the power level that flywheel energy storage can output or absorb per unit volume. It reflects the energy release capacity of flywheel energy storage under limited space conditions. To ensure the comparability and stability of the feature data, the response speed is quantified by combining historical load data of the power grid and real-time photovoltaic output information. For example, it is uniformly expressed as the power change rate per unit time, and the power density is uniformly converted into watts per cubic meter, thus forming a structured feature dataset.

[0076] After obtaining the characteristic data related to response speed and power density, interpolation methods are further used to process the data to compensate for the discontinuous distribution of characteristic data under different operating conditions. Specifically, linear interpolation is used to establish a linear relationship between adjacent known characteristic points, estimating the response speed and power density values ​​that are not directly observed, thus maintaining the continuity of characteristic data in the capacity variation dimension. Through this interpolation process, reasonable estimates of the response speed and power density of flywheel energy storage under different access capacity levels can be obtained. The interpolated response speed and power density data are then input into a pre-established cycle life model to calculate the long-term performance of flywheel energy storage under the current capacity configuration and operating characteristics. The utilization rate, based on the performance degradation law of flywheel energy storage under different charging and discharging frequencies and power levels, is obtained by statistical fitting of historical operating data and experimental data. It is used to characterize the functional relationship between response speed, power density and the number of cycles that can be withstood. By substituting the interpolated response speed and power density into the cycle life model, the theoretical maximum number of cycles of flywheel energy storage under given operating conditions is calculated. This is then compared with the actual or predicted number of cycles to obtain the long-term utilization rate. The long-term utilization rate reflects the efficiency level of flywheel energy storage in the grid peak shaving scenario. The higher the value, the smaller the impact on equipment life while meeting peak shaving needs.

[0077] After obtaining the long-term utilization rate, an adjusted capacity utilization index is generated based on this rate. Specifically, the long-term utilization rate is weighted by a preset weighting factor, which is calibrated based on historical operating data to reflect the importance of long-term lifetime to capacity allocation decisions under different operating scenarios. This yields a capacity utilization index that comprehensively reflects peak-shaving effectiveness and lifetime constraints. This capacity utilization index serves as a quantitative basis for evaluating the rationality of the current segmented access strategy, ensuring that capacity allocation decisions no longer focus solely on instantaneous peak-shaving capacity but incorporate long-term utilization efficiency into a unified evaluation framework. Subsequently, the segmented access strategy for flywheel energy storage is assessed using this capacity utilization index. The access strategy is updated. When the capacity utilization index is higher than the set threshold, it indicates that the current capacity configuration meets peak-shaving demand while having high long-term utilization efficiency. At this time, the upper limit of the corresponding segmented access capacity interval can be appropriately relaxed to improve the peak-shaving capability of flywheel energy storage in high-demand ranges. When the capacity utilization index is lower than the set threshold, it indicates that there is a risk of capacity surplus or underutilization. The segmented access capacity interval is reduced accordingly to reduce the proportion of idle capacity and reduce the losses caused by ineffective equipment operation. Through the above strategy update, the segmented access strategy forms a differentiated configuration in different peak-shaving demand intensity ranges, thereby achieving a dynamic balance between capacity configuration and long-term utilization efficiency.

[0078] After completing the segmented access strategy update, the updated strategy parameters are integrated into the power grid control system, enabling the control system to automatically follow the new capacity intervals and access rules during subsequent scheduling and real-time power output control. This generates dynamic capacity configuration results that match the prediction error range and changes in peak-shaving demand. Through the above technical solution, a closed-loop connection is achieved from access capacity determination, feature data extraction, cycle lifetime assessment to segmented access strategy update, significantly improving the long-term reliability of the optimal access capacity assessment method based on flywheel energy storage power sources.

[0079] Further, in step S4, the final dynamic capacity configuration result of the flywheel energy storage is generated, including:

[0080] Obtain the updated segmented access strategy and corresponding capacity utilization index; determine whether the capacity utilization index meets the preset conditions; if it does, integrate the segmented access strategy into the power grid control system; output the final dynamic capacity configuration result for realizing dynamic management of flywheel energy storage and peak demand response.

[0081] Specifically, after updating the segmented access strategy and calculating the capacity utilization index, in order to enable the above evaluation and optimization results to form an executable capacity configuration scheme at the actual operation level, the effectiveness of the segmented access strategy is judged and integrated at the system level to realize the dynamic management and peak demand response of flywheel energy storage in the power grid. During the implementation process, the updated segmented access strategy and the corresponding capacity utilization index are first obtained, and it is judged whether the above capacity utilization index meets the preset conditions. The preset conditions are set according to the operating experience and safety requirements of the power grid, for example, the capacity utilization index reaches or exceeds a predetermined threshold as the judgment standard, so as to ensure that the segmented access strategy achieves a balance between peak shaving efficiency and long-term utilization. When the capacity utilization index reaches the threshold, it indicates that the current segmented access strategy can effectively reduce the risk of capacity idleness under the given load peak-valley difference and ramp rate, and the corresponding capacity configuration result is within the range allowed by the prediction error, thus possessing the feasibility for further system integration; conversely, if the capacity utilization index does not meet the preset conditions, it indicates that the current strategy may have the problem of over-configuration or under-utilization of capacity, and the system can return to the above output adjustment or strategy correction process to recalculate the relevant parameters in order to avoid invalid strategies from entering the control system.

[0082] Once the judgment results indicate that the capacity utilization index meets the preset conditions, the aforementioned segmented access strategy is integrated into the power grid control system. Specifically, this is achieved by loading the updated segmented access capacity intervals, corresponding peak-shaving demand intervals, and related constraint parameters into the scheduling and control module of the control system. This enables the control system to automatically follow the segmented access strategy when executing flywheel energy storage access and output regulation. During the integration process, the control system can simulate different load peak-valley differences and ramp-up rates through simulation or online verification to confirm that the segmented access strategy can prioritize the use of larger capacity intervals in high-intensity peak-shaving demand intervals, while automatically switching to smaller capacity intervals in low-intensity demand intervals. This ensures peak-shaving response speed while reducing idle risk and improving overall system stability.

[0083] After the integration of the segmented access strategy and control system is completed, the power grid control system outputs the final dynamic capacity configuration result to realize the dynamic management and peak demand response of flywheel energy storage. This final dynamic capacity configuration result includes at least the flywheel energy storage access capacity determined under the current operating state, the corresponding capacity interval level, and the real-time updated capacity utilization index. The control system uses this output result to schedule the charging and discharging output of the flywheel energy storage in real time. When the load ramp-up rate increases or photovoltaic output fluctuates rapidly, the access capacity of the flywheel energy storage is automatically increased according to the above configuration result to compensate for the power gap. When the load change slows down or the output stabilizes, the access capacity is reduced accordingly, ensuring that the flywheel energy storage always operates within a high utilization range. This is achieved through the output phase... The system simultaneously provides capacity utilization indicators and long-term utilization rate assessment results, enabling continuous monitoring of capacity configuration effectiveness and providing feedback data for subsequent strategy updates and parameter optimization. By acquiring updated segmented access strategies and capacity utilization indicators, judging the effectiveness of strategies, integrating effective strategies into the power grid control system, and outputting the final dynamic capacity configuration result, the above technical solution achieves a closed-loop connection between capacity assessment, strategy selection, system integration, and dynamic execution. This transforms flywheel energy storage capacity configuration from a static calculation level into a dynamic configuration result that can adaptively adjust with changes in load and renewable energy output, thereby significantly improving the operational stability and long-term economic efficiency of the optimal access capacity assessment method based on flywheel energy storage power sources.

[0084] This invention also provides an optimal access capacity assessment system based on flywheel energy storage power sources, used to implement the above-mentioned method, such as... Figure 4 As shown, the system includes:

[0085] The data acquisition unit is used to acquire historical load data and real-time photovoltaic output information of the power grid, analyze the historical load data and the real-time photovoltaic output information, and obtain the peak-valley difference characteristics and the ramp rate characteristics respectively, and use them as relevant quantitative indicators to characterize the power grid load fluctuation.

[0086] Pattern analysis: Based on the relevant quantitative indicators, the load fluctuation patterns of different time periods are classified by cluster analysis, and the distribution range of peak-shaving demand is determined according to the load fluctuation patterns; for the distribution range, a preliminary capacity configuration scheme for flywheel energy storage is formulated.

[0087] The capacity optimization unit is used to optimize the preliminary capacity configuration scheme by integrating relevant grid parameters to obtain optimized capacity interval parameters; and to adjust the output level of flywheel energy storage according to the optimized capacity interval parameters to determine the access capacity for real-time response to peak shaving needs.

[0088] The capacity assessment unit is used to extract the response speed and power density characteristics of flywheel energy storage based on the access capacity, and to calculate the long-term utilization rate index of flywheel energy storage under cycle life conditions through interpolation calculation method.

[0089] The capacity adjustment unit is used to update the segmented access strategy of flywheel energy storage according to the long-term utilization rate index, and generate the final dynamic capacity configuration result of flywheel energy storage based on the updated segmented access strategy.

[0090] The present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0091] In summary, this invention quantifies key indicators such as load peak-to-valley difference and ramp rate by performing time series analysis on historical load data and real-time photovoltaic output information, providing a unified data representation basis for the intensity and rate of change of peak-shaving demand. It uses clustering algorithms to classify load fluctuation patterns, forming distribution intervals of peak-shaving demand with different intensities, enabling differentiated capacity configuration for different operating conditions. Secondly, for peak-shaving demand intervals with high ramp rates, it constructs preliminary capacity access intervals for flywheel energy storage in a segmented manner, further incorporating step-growth data of transformer capacity and line current, and uses optimization algorithms to iteratively correct the capacity intervals, matching the capacity change step size with the actual carrying capacity characteristics of the power grid, thereby establishing an effective mapping relationship between peak-shaving demand and grid constraints. Finally, by comparing the optimized capacity interval parameters with the load and photovoltaic output prediction error range... A robust capacity configuration verification mechanism was constructed through matching and judgment, enabling flywheel energy storage to maintain stable response even under predictive uncertainties. By extracting features such as response speed and power density from the access capacity, and combining interpolation calculations and cycle life models, long-term utilization rate was obtained and capacity utilization indicators were generated. This extends capacity assessment beyond short-term peak-shaving effects to long-term operating efficiency and equipment lifespan. The segmented access strategy that meets preset conditions was integrated into the power grid control system, outputting the final dynamic capacity configuration result. This allows flywheel energy storage to automatically adjust its access capacity based on real-time operating status, achieving synergistic optimization of peak-shaving demand response, equipment utilization improvement, and stable power grid operation. Through the cooperation of the above technical solutions, accurate assessment and dynamic management of flywheel energy storage access capacity were achieved, with overall technical performance significantly superior to existing static or experience-based capacity configuration methods.

[0092] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0093] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the optimal access capacity of flywheel energy storage power sources, characterized in that, The method includes: Step S1: Obtain historical load data and real-time photovoltaic output information of the power grid, analyze the historical load data and the real-time photovoltaic output information, obtain the load peak-valley difference characteristics and load ramp-up rate characteristics respectively, and use them as relevant quantitative indicators to characterize the power grid load fluctuation; Step S2: Based on the relevant quantitative indicators, classify the load fluctuation patterns of different time periods through cluster analysis, and determine the distribution range of peak-shaving demand based on the load fluctuation patterns; formulate a preliminary capacity configuration scheme for flywheel energy storage for the distribution range. Step S3: Optimize the preliminary capacity configuration scheme by integrating relevant grid parameters to obtain optimized capacity interval parameters; adjust the output level of flywheel energy storage according to the optimized capacity interval parameters to determine the access capacity for real-time response to peak shaving needs; Step S4: Based on the access capacity, extract the response speed and power density characteristics of flywheel energy storage, and calculate the long-term utilization rate index of flywheel energy storage under cycle life conditions through interpolation calculation method; according to the long-term utilization rate index, update the segmented access strategy of flywheel energy storage, and generate the final dynamic capacity configuration result of flywheel energy storage based on the updated segmented access strategy. In step S3, the optimized capacity interval parameters are obtained, including: acquiring the step growth data of transformer capacity and line current in the power grid; fusing the preliminary capacity configuration scheme of the flywheel energy storage with the step growth data of transformer capacity and line current to construct an input dataset for capacity optimization, wherein the preliminary capacity configuration scheme serves as the initial solution or search starting point of the optimization algorithm; inputting the input dataset into a preset optimization algorithm, and through multiple rounds of iterative adjustment of the flywheel energy storage capacity access interval, outputting the optimized capacity interval parameters that satisfy the power grid step constraint conditions; wherein the capacity interval parameters are used to limit the step size of the access capacity change of flywheel energy storage in different peak-shaving demand intervals.

2. The method as described in claim 1, characterized in that, In step S1, relevant quantitative indicators characterizing power grid load fluctuations are obtained, including: The historical load data and real-time photovoltaic output information are acquired through acquisition equipment; the historical load data are processed using time series analysis methods to extract the load peak-valley difference characteristics; the load ramp-up rate characteristics are calculated based on the real-time photovoltaic output information and the historical load data; the load peak-valley difference characteristics and the load ramp-up rate characteristics are used as relevant quantitative indicators of the load fluctuation and output.

3. The method as described in claim 1, characterized in that, In step S2, the distribution range of peak-shaving demand is determined, including: The system acquires data on load peak-to-valley difference and ramp rate from relevant quantitative indicators of load fluctuation; it provides a clustering analysis method to classify the load peak-to-valley difference and ramp rate data into load fluctuation patterns, thereby obtaining multiple load fluctuation pattern categories; it analyzes the peak-shaving demand intensity corresponding to each load fluctuation pattern category; and it determines the peak-shaving demand distribution range corresponding to each type of load fluctuation pattern based on the peak-shaving demand intensity, wherein the peak-shaving demand intensity is a weighted combination of load peak-to-valley difference and ramp rate.

4. The method as described in claim 3, characterized in that, In step S2, a preliminary capacity configuration scheme for flywheel energy storage is formulated, including: Obtain the distribution data of the ramp rate in the distribution interval; if the ramp rate exceeds a preset threshold, divide the peak demand distribution interval into multiple capacity access sub-intervals according to the ramp rate, and set a corresponding flywheel energy storage access capacity interval for each capacity access sub-interval, generating a preliminary flywheel energy storage capacity configuration scheme that matches each peak demand distribution interval and the corresponding ramp rate.

5. The method as described in claim 1, characterized in that, In step S3, the access capacity for real-time response to peak shaving needs is determined, including: The prediction error range is calculated based on historical photovoltaic power output predictions and corresponding actual measurements. Then, based on the optimized capacity interval parameters and the prediction error range, it is determined whether the optimized capacity interval parameters match the prediction error range. If they match, the flywheel energy storage output level is dynamically adjusted according to the optimized capacity interval parameters. If they do not match, the capacity interval parameters are revised. Based on the adjusted output level, the access capacity for real-time peak shaving is determined.

6. The method as described in claim 1, characterized in that, In step S4, the long-term utilization rate index is calculated based on the access capacity, and the segmented access strategy is updated, including: Extract response speed and power density related feature data from the access capacity; process the response speed and power density related feature data using an interpolation calculation method to calculate the long-term utilization rate under cycle lifetime; generate an adjusted capacity utilization index based on the long-term utilization rate; update the segmented access strategy using the capacity utilization index.

7. The method as described in claim 1, characterized in that, In step S4, the final dynamic capacity configuration result is generated according to the segmented access strategy, including: Obtain the updated segmented access strategy and corresponding capacity utilization index; determine whether the capacity utilization index meets the preset conditions; if it does, integrate the segmented access strategy into the power grid control system; output the final dynamic capacity configuration result for realizing dynamic management of flywheel energy storage and peak demand response.

8. An optimal access capacity assessment system based on flywheel energy storage power supply, used to implement the method as described in any one of claims 1-7, characterized in that, The system includes: The data acquisition unit is used to acquire historical load data and real-time photovoltaic output information of the power grid, analyze the historical load data and the real-time photovoltaic output information, and obtain the load peak-valley difference characteristics and load ramp-up rate characteristics, which are used as relevant quantitative indicators to characterize the power grid load fluctuation. Capacity configuration unit: Based on the relevant quantitative indicators, the load fluctuation patterns of different time periods are classified through cluster analysis, and the distribution range of peak-shaving demand is determined according to the load fluctuation patterns; for the distribution range, a preliminary capacity configuration scheme for flywheel energy storage is formulated. The capacity optimization unit is used to optimize the preliminary capacity configuration scheme by integrating relevant grid parameters to obtain optimized capacity interval parameters; and to adjust the output level of flywheel energy storage according to the optimized capacity interval parameters to determine the access capacity for real-time response to peak shaving needs. The capacity assessment unit is used to extract the response speed and power density characteristics of flywheel energy storage based on the access capacity, and to calculate the long-term utilization rate index of flywheel energy storage under cycle life conditions through interpolation calculation method. The capacity adjustment unit is used to update the segmented access strategy of flywheel energy storage according to the long-term utilization rate index, and generate the final dynamic capacity configuration result of flywheel energy storage based on the updated segmented access strategy.

9. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the method as described in any one of claims 1-7.

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