Distributed Energy Storage Power System Optimal Scheduling Method and System

Abstract

The present application relates to the field of energy storage scheduling technology, and discloses a method and system for optimizing the scheduling of a distributed energy storage power system. The method includes: collecting system operation data and performing normalization processing and anomaly detection, and combining the standardized data to predict 24-hour load demand and renewable energy output; performing cost calculation and constraint analysis based on the predicted data to generate a day-ahead scheduling plan; updating the operating status and adjusting the output data in a 15-minute cycle; performing multi-time scale layered processing on power fluctuations; based on this, optimizing the configuration of energy storage capacity and evaluating life characteristics; and optimizing and updating parameters through operating indicator monitoring and analysis. The present application achieves efficient utilization and life extension of the energy storage system through multi-dimensional performance evaluation and parameter optimization.

Description

Technical Field

[0001] The present application relates to the field of energy storage scheduling, and in particular to a method and system for optimizing scheduling of a distributed energy storage power system. Background Art

[0002] With the continuous expansion of renewable energy generation and the increasing complexity of power system structures, energy storage systems are being widely used as a key regulatory tool in grid operations. Energy storage systems, with their fast response speed and wide regulation range, play a vital role in renewable energy consumption, load regulation, and frequency control. Currently, most energy storage systems are deployed in a centralized manner, participating in grid operations through unified dispatch and control strategies. The capacity configuration and lifespan management of energy storage systems are also receiving increasing attention, and the industry has conducted extensive research on optimal configuration and lifespan assessment of energy storage systems.

[0003] However, existing energy storage system scheduling methods have obvious shortcomings: First, the configuration of energy storage capacity lacks in-depth analysis of regional power grid characteristics, and often uses empirical formulas for simple calculations, resulting in a mismatch between the configuration of the energy storage system and actual demand; second, the scheduling and control strategy of the energy storage system is too extensive, and does not fully consider the impact of the charging and discharging process on battery life, which can easily cause excessive loss of energy storage equipment; third, the performance evaluation method of the energy storage system is single, lacking a systematic analysis of key parameters such as charge and discharge depth and number of cycles, making it difficult to accurately predict the life characteristics of energy storage equipment; finally, the optimization of the operating parameters of the energy storage system lacks an effective feedback mechanism, and the control strategy cannot be dynamically adjusted according to the actual performance status of the equipment. Summary of the Invention

[0004] The present application provides a distributed energy storage power system optimization scheduling method and system for achieving efficient utilization and life extension of the energy storage system through multi-dimensional performance evaluation and parameter optimization.

[0005] In the first aspect, the present application provides a method for optimizing and dispatching a distributed energy storage power system, which comprises: collecting real-time operating data of the system through a data acquisition network, normalizing and detecting outliers on the collected operating data to obtain standardized data, and performing a 24-hour negative system forecast based on the standardized data to generate a system forecast data set; performing cost calculation and data statistics on the fuel consumption of thermal power units, new energy power generation, and charging and discharging of energy storage systems based on the system forecast data set, and outputting day-ahead dispatch plan data through constraint screening and power balance analysis; based on the day-ahead dispatch plan data, the system is dispatched by time segment processing in a 15-minute cycle. The operating status data is dynamically updated, and the output data of the power generation unit is adjusted according to the power deviation calculation results to form real-time scheduling data. Based on the real-time scheduling data, the system power fluctuation is layered at the hourly, minutely and secondly levels, and power compensation calculations are performed according to the charge and discharge characteristics of the energy storage system to obtain multi-time scale control data. The multi-time scale control data is used to calculate the regional allocation of the energy storage system capacity, and the life span is evaluated through statistical analysis of the charge and discharge depth and the number of cycles to generate energy storage optimization data. Based on the energy storage optimization data, the system operation indicators are monitored in real time and data is accumulated. The scheduling parameters are corrected and updated through indicator comparison analysis to obtain system optimization data.

[0006] In a second aspect, the present application provides a distributed energy storage power system optimization and scheduling system, the distributed energy storage power system optimization and scheduling system comprising:

[0007] The detection module is used to collect real-time operation data of the system through the data acquisition network, normalize the collected operation data and detect outliers to obtain standardized data, perform system prediction on a 24-hour cycle based on the standardized data, and generate a system prediction data set;

[0008] A statistics module is used to perform cost calculation and data statistics on the fuel consumption of thermal power units, the power generation of new energy sources, and the charge and discharge of energy storage systems based on the system prediction data set, and output day-ahead scheduling plan data through constraint screening and power balance analysis;

[0009] An updating module is configured to dynamically update the system operation status data based on the day-ahead scheduling plan data through time-segment processing in a 15-minute cycle, and adjust the power generation unit output data according to the power deviation calculation result to form real-time scheduling data;

[0010] A hierarchical module is used to perform hierarchical processing of system power fluctuations at the hourly, minutely, and secondly levels based on the real-time scheduling data, perform power compensation calculations based on the charge and discharge characteristics of the energy storage system, and obtain multi-timescale control data;

[0011] an allocation module for calculating the regional allocation of the energy storage system capacity using the multi-timescale control data, performing lifespan assessment through statistical analysis of charge and discharge depth and cycle number, and generating energy storage optimization data;

[0012] The correction module is used to monitor the system operation indicators in real time and accumulate data based on the energy storage optimization data, and to correct and update the scheduling parameters through indicator comparison and analysis to obtain system optimization data.

[0013] In the technical solution provided by the present application, by dividing the multi-time scale control data into regions, accurately calculating the power fluctuation characteristics of each region, and combining the regional power characteristic data to determine the reasonable allocation of energy storage capacity, the problem of unreasonable capacity configuration of the energy storage system is effectively solved; the deep statistical data and cycle statistical data are used to systematically analyze the charging and discharging process, establish a complete energy storage unit performance evaluation system, and realize accurate prediction of the life characteristics of energy storage equipment; based on the performance evaluation data, the operating parameters of the energy storage system are dynamically optimized to form a closed-loop regulation and control mechanism, which significantly improves the utilization efficiency of the energy storage system; through the hierarchical and partitioned data processing method, the accurate matching and allocation of energy storage capacity are achieved, avoiding the waste of energy storage resources; life assessment is performed based on the charge and discharge depth and the number of cycles, and a health management mechanism for the energy storage system is established to extend the service life of the equipment; using real-time monitoring and data accumulation methods, the system operation indicators are continuously tracked and optimized and adjusted to ensure the long-term stable operation of the energy storage system. The technical solution of the present invention not only improves the regulation performance of the energy storage system through multi-dimensional data analysis and refined parameter control, but also realizes the effective management of equipment life. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 A schematic diagram of an embodiment of a method for optimizing and scheduling a distributed energy storage power system in an embodiment of the present application;

[0016] Figure 2 To normalize the collected operating data and detect outliers to obtain a time series diagram of standardized data;

[0017] Figure 3 This is a flow chart of cost calculation and data statistics in the embodiment of the present application;

[0018] Figure 4This is a schematic diagram of an embodiment of a distributed energy storage power system optimization and scheduling system in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The embodiments of the present application provide a method and system for optimizing the scheduling of a distributed energy storage power system. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0020] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of the distributed energy storage power system optimization scheduling method in the embodiment of the present application includes:

[0021] Step S101: collect real-time operating data of the system through a data acquisition network, perform normalization processing and outlier detection on the collected operating data to obtain standardized data, perform system prediction for a 24-hour period based on the standardized data, and generate a system prediction data set;

[0022] Step S102: Cost calculation and data statistics are performed on the fuel consumption of the thermal power units, the power generation of new energy sources, and the charge and discharge of the energy storage system based on the system prediction data set. After constraint screening and power balance analysis, the day-ahead scheduling plan data is output;

[0023] Step S103: Based on the day-ahead dispatch plan data, the system operation status data is dynamically updated through 15-minute time-segment processing, and the power generation unit output data is adjusted according to the power deviation calculation result to form real-time dispatch data;

[0024] Step S104: Based on the real-time dispatch data, the system power fluctuation is processed at the hourly, minutely, and secondly levels, and power compensation calculations are performed based on the charge and discharge characteristics of the energy storage system to obtain multi-timescale control data.

[0025] Step S105: Utilize multi-timescale control data to calculate regional allocation of energy storage system capacity, perform lifespan assessment through statistical analysis of charge and discharge depth and cycle number, and generate energy storage optimization data;

[0026] Step S106: Based on the energy storage optimization data, the system operation indicators are monitored in real time and data is accumulated. The scheduling parameters are corrected and updated through indicator comparison and analysis to obtain system optimization data.

[0027] It is understandable that the execution subject of this application can be a distributed energy storage power system optimization and dispatching system, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0028] Specifically, system operating data is collected through a data acquisition network. This data includes wind farm output data (wind turbine speed, rotor torque, and output power), photovoltaic power station power generation data (light intensity, module temperature, and output power), thermal power unit operating parameter data (boiler pressure, turbine speed, and generator power), energy storage system status parameter data (state of charge (SOC), charge and discharge power, and operating temperature), and load demand data (industrial, commercial, and residential loads). This raw data is normalized to bring data of varying dimensions into the [0, 1] range for ease of subsequent processing. Outlier detection utilizes the 3σ principle, calculating the mean and standard deviation of the data. Data outside the range of ±3 times the standard deviation of the mean is marked as outliers and removed, generating standardized data. A 24-hour sliding window analysis is performed on the standardized data, with a window length of 24 hours and a step size of 1 hour. Data features within each window are extracted. Load demand forecasting uses the time series characteristics of historical load data, analyzes load variations on weekdays and weekends, and combines environmental factors such as temperature and humidity to predict load trends over the next 24 hours. Renewable energy output forecasting uses wind speed data from wind farms and sunlight intensity data from photovoltaic power plants to analyze output characteristics, predict power generation over the next 24 hours, and generate a system forecast dataset.

[0029] After obtaining the system forecast data set, the fuel consumption costs of the thermal power units are calculated, including startup and shutdown costs and operating costs. Renewable energy generation is calculated based on the forecasted wind and photovoltaic power output data, while the energy storage system's charge and discharge capacity is calculated based on their charge and discharge efficiency and capacity constraints. The data statistics process includes calculating the output distribution, fluctuation characteristics, and correlation analysis of various power generation equipment. Constraint screening considers system power balance constraints (total generator output equals total load demand) and equipment operating constraints (such as thermal power unit ramp rate constraints and output limits). A linear programming approach is used to find the optimal solution and output the day-ahead scheduling plan data. Based on the day-ahead scheduling plan data, the 24-hour period is divided into 96 15-minute time periods, and the system operating status is dynamically updated before the start of each period. Real-time operating data is collected to calculate the power deviation from the planned value. The power output of the power units is adjusted based on the magnitude and trend of the deviation. The adjustment strategy prioritizes the rapid response of the energy storage system, followed by output regulation of renewable energy generation and output adjustment of thermal power units, generating real-time scheduling data.

[0030] Real-time dispatch data is processed at multiple time scales. The hourly level primarily handles the economic dispatch of thermal power units, the minute level handles the smoothing of renewable energy output fluctuations, and the second level handles the rapid response of system frequency. Based on these parameters, power compensation values at different time scales are calculated, and then the dynamic compensation values at different time scales are calculated. Power control at each level is coordinated to obtain multi-time scale control data. The regional allocation of energy storage system capacity is based on multi-time scale control data, analyzing the power fluctuation characteristics of each region and calculating the required energy storage capacity. The depth of charge and discharge during the operation of the energy storage system is statistically analyzed, recording the depth value and duration of each charge and discharge cycle. The cycle count statistics include the accumulation of complete charge and discharge cycles and partial charge and discharge cycles. This data is used to evaluate the life characteristics of energy storage units and generate energy storage optimization data.

[0031] Real-time monitoring of system operating indicators, including power balance accuracy (the deviation between planned and actual power), system frequency quality (the root mean square value of frequency deviation), and renewable energy absorption rate (the ratio of actual renewable energy power generation to theoretical power generation). By establishing an indicator database and conducting comparative analysis of historical data, we can identify optimization opportunities for system operation, dynamically calibrate and update scheduling parameters, and obtain system optimization data.

[0032] For example, during data processing, wind farm power data is normalized by dividing the raw power value by the rated power value. For a 100MW wind farm, if the actual power at a given moment is 60MW, the normalized value is 0.6. Outlier detection is performed using the 3σ principle, calculating the mean (e.g., 0.5) and standard deviation (e.g., 0.1) of the 24-hour data. Data outside the range [0.2, 0.8] is marked as outliers. For output data from multiple wind turbines, a correlation coefficient matrix is first calculated to identify turbine groups with similar output characteristics for data fusion. During the charge and discharge process of the energy storage system, the SOC curve is recorded, and the charge and discharge depth distribution is statistically analyzed, such as the percentage of cycles with a depth of 80% and the percentage of cycles with a depth of 50%. These data are directly related to the lifespan assessment of the energy storage unit. When monitoring system operating indicators, power balance accuracy is determined by calculating the deviation between planned and actual power. Frequency quality is determined by sampling and statistically analyzing system frequency fluctuations. For the new energy consumption rate, theoretical power generation is calculated in conjunction with meteorological data and then compared with actual power generation.

[0033] In the embodiment of the present application, by dividing the multi-time scale control data into regions, accurately calculating the power fluctuation characteristics of each region, and combining the regional power characteristic data to determine the reasonable allocation of energy storage capacity, the problem of unreasonable capacity configuration of the energy storage system is effectively solved; the deep statistical data and cycle statistical data are used to systematically analyze the charging and discharging process, establish a complete energy storage unit performance evaluation system, and achieve accurate prediction of the life characteristics of energy storage equipment; based on the performance evaluation data, the operating parameters of the energy storage system are dynamically optimized to form a closed-loop regulation and control mechanism, which significantly improves the utilization efficiency of the energy storage system; through the hierarchical and partitioned data processing method, the accurate matching and allocation of energy storage capacity is achieved, avoiding the waste of energy storage resources; life assessment is performed based on the charge and discharge depth and the number of cycles, and a health management mechanism for the energy storage system is established to extend the service life of the equipment; using real-time monitoring and data accumulation methods, the system operation indicators are continuously tracked and optimized and adjusted to ensure the long-term stable operation of the energy storage system. The technical solution of the present invention not only improves the regulation performance of the energy storage system through multi-dimensional data analysis and refined parameter control, but also achieves effective management of the equipment life.

[0034] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0035] (1) Collect wind farm output data, photovoltaic power station power generation data, thermal power unit operating parameter data, energy storage system status parameter data and load demand data through the data acquisition network to generate real-time system operation data;

[0036] (2) Perform normalization calculations on the maximum and minimum values of the system's real-time operating data, detect and eliminate outliers using the 3σ criterion, and generate standardized data;

[0037] (3) Divide the standardized data into sliding windows according to a 24-hour period, perform time series analysis on the data in each time window, and generate time series feature data;

[0038] (4) Extract the load variation law and renewable energy output fluctuation characteristics based on the time series characteristic data, perform data dimensionality reduction processing, and generate a feature data set;

[0039] (5) Perform correlation analysis on the feature data set, fuse the highly correlated data, and generate a fused data set;

[0040] (6) The fused data set is used for forecast analysis according to a 24-hour cycle, and the load demand and renewable energy output are predicted and calculated to generate a system forecast data set.

[0041] Specifically, if Figure 2 The figure shows a time series diagram of standardized data obtained by normalizing and detecting outliers in collected operating data. This diagram illustrates the complete process of data processing and prediction in a distributed energy storage power system. First, operating data from various power equipment is collected through a data acquisition network, including wind farm output data, photovoltaic power station power generation data, thermal power unit operating parameter data, energy storage system status parameter data, and load demand data. The collected raw data undergoes maximum and minimum value normalization, mapping data of varying dimensions and orders of magnitude to the interval [0, 1]. Outlier detection is then performed using the 3σ criterion. The mean μ and standard deviation σ of the dataset are calculated, and outliers outside the range [μ - 3σ, μ + 3σ] are removed to generate a standardized dataset. The standardized data is then partitioned into sliding windows with a fixed window length of 24 hours and a sliding step of 1 hour. Time series analysis is performed on the data within each time window to extract statistical features (mean, variance, peak, and valley values), time domain features (trend, periodicity, and randomness), and frequency domain features (primary frequency components) to generate time series feature data. Based on time-series feature data, we analyze load variations and renewable energy output fluctuations. Principal component analysis (PCA) is used for dimensionality reduction to generate a feature dataset. Correlation analysis is performed on the feature dataset, and the Pearson correlation coefficient is calculated. Highly correlated features with absolute correlation coefficients greater than 0.8 are fused, and a weighted average method is used to generate a fused dataset. Finally, a 24-hour forecast analysis is performed based on the fused dataset. Combining historical data characteristics, environmental factors, and operating status, a system forecast dataset is generated using a predictive calculation formula.

[0042] A data collection network is established to collect operational data from various types of power equipment. Wind farm output data includes parameters such as wind turbine speed, rotor torque, and real-time power output. Photovoltaic power station power generation data includes parameters such as light intensity, component temperature, and actual power generation. Thermal power unit operating parameter data includes parameters such as boiler steam pressure, turbine speed, and real-time generator power. Energy storage system status parameter data includes parameters such as state of charge (SOC), charge and discharge power, and system temperature. Load demand data includes data on industrial, commercial, and residential power loads. These data are collected according to a unified data format and sampling period to form the original data set.

[0043] The collected raw data is normalized to its maximum and minimum values, and data of different dimensions and magnitudes are uniformly mapped to the interval [0,1]. The normalization formula is:

[0044]

[0045] in, represents the normalized data value, X represents the original data value, represents the minimum value in the data set, Represents the maximum value in the dataset. For each type of data (such as wind farm output, photovoltaic power generation, and load demand), the maximum possible value in the historical data is selected as the maximum value. For example, wind farm rated capacity and photovoltaic power station installed capacity. Based on historical data, the minimum value is selected as the minimum value for each type of data. For power generation data, the minimum value is typically 0. Outlier detection is performed on the normalized data using the 3σ criterion. This involves calculating the mean μ and standard deviation σ of the dataset. Data points outside the range [μ - 3σ, μ + 3σ] are marked as outliers and removed to generate a standardized dataset.

[0046] The standardized data is divided into sliding windows with a fixed window length of 24 hours and a sliding step of 1 hour, forming a series of continuous data windows. Time series analysis is performed on the data within each window to extract statistical features (mean, variance, peak, and valley values), time domain features (trend, periodicity, and randomness), and frequency domain features (main frequency components), generating time series feature data for each window. Based on this time series feature data, load variation patterns are analyzed, including intraday load curve characteristics, load differences between weekdays and weekends, and seasonal load variations. Fluctuations in renewable energy output are also analyzed, including wind power fluctuation cycles and diurnal variations in photovoltaic power generation. Dimensionality reduction is performed on these feature data, using principal component analysis to extract the main eigenvectors and remove redundant information, generating a reduced feature dataset. Correlation analysis is performed on various indicators in the feature dataset, calculating the Pearson correlation coefficient to identify highly correlated features. Features with an absolute correlation coefficient greater than 0.8 are fused using a weighted average method to combine highly correlated features. The weight coefficient is determined based on the information content of each feature to generate the fused dataset.

[0047] Based on the 24-hour forecast analysis of the fused data set, the forecast calculation formula for load demand and renewable energy output is:

[0048]

[0049] in, represents the predicted value at time t, Indicates the characteristics of historical data for the same period. Indicates the characteristics of environmental factors, Indicates the operating status characteristics, 、 、 is the comprehensive weight coefficient of various features, 、 、 is the weight coefficient of each feature, n, m, Represents the number of each type of features.

[0050] For example, after normalization, the power data of a wind farm was calculated within a 24-hour window to have a mean of 0.45 and a standard deviation of 0.15. Using the 3σ criterion, a power value of 0.95 was identified as an outlier. Time series analysis of the 96 normalized sampling points extracted the main frequency components for the 4-hour and 12-hour periods. After dimensionality reduction, principal component features with an explained variance contribution exceeding 85% were retained. Correlation analysis revealed a correlation coefficient of 0.92 between wind speed and output power, assigning weights of 0.6 and 0.4, respectively, for data fusion. Based on the prediction formula, combined with historical data, weather forecasts, and equipment status, the output forecast for the next 24 hours was calculated to generate a system prediction dataset.

[0051] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0052] (1) Extract the operating condition data of the thermal power units based on the system prediction data set, calculate the hourly fuel consumption and count the daily cumulative value, divide and accumulate the renewable energy power generation data according to the time series, and perform segmented statistics on the charging and discharging data of the energy storage system;

[0053] (2) Using the fuel consumption of thermal power units, the power generation of new energy sources, and the charge and discharge capacity of energy storage systems, calculate the unit power generation cost of each type of unit and summarize the power generation cost data;

[0054] (3) Optimize and screen the power generation cost data of various units based on the system power balance constraints, and eliminate data combinations that do not meet the power balance requirements;

[0055] (4) Combined with the unit climbing rate constraint, output upper and lower limit constraint, and power factor constraint, the filtered data is screened again to eliminate the data combination that does not meet the equipment operation constraints;

[0056] (5) Sort the data that meet the dual constraints according to the principle of minimum cost and extract the output data of the optimal power generation combination scheme;

[0057] (6) The optimal power generation combination plan is split into 24-hour data and time series is reorganized to generate day-ahead scheduling plan data.

[0058] Specifically, if Figure 3The figure below is a schematic diagram of the cost calculation and data statistics process for an embodiment of the present application, illustrating the process for generating a day-ahead scheduling plan. First, thermal power unit operating condition data, renewable energy power generation data, and energy storage system charge and discharge data are extracted from the system forecast data set for processing. For the thermal power unit data, fuel consumption is calculated by considering boiler efficiency, turbine efficiency, and generator efficiency. For the renewable energy data, the cumulative power generation of wind farms and photovoltaic power stations is calculated. For the energy storage system, charge and discharge cycle data is recorded. Next, power generation costs are calculated. For thermal power units, fuel costs, start-up and shutdown costs, and maintenance costs are considered; for renewable energy, equipment depreciation and operation and maintenance costs are considered; and for the energy storage system, charge and discharge losses and equipment usage costs are considered. Cost data for all units is summarized. Next, a dual constraint check is performed: first, a power balance constraint is applied to ensure that the total output on the generator side equals the load demand; second, unit operating constraints, including ramp rate, output upper and lower limits, and power factor constraints, are applied. Data combinations that do not meet the constraints are eliminated. Finally, the solutions that meet the constraints are ranked according to the principle of minimum cost. The optimal power generation combination solution is extracted, split and reorganized according to a 24-hour cycle, and the final day-ahead scheduling plan data is generated. The arrows in the figure indicate the data flow, and the sub-figures indicate the specific steps of each processing link.

[0059] The operating data of the thermal power units is extracted from the system prediction dataset. This operating data includes key parameters such as boiler steam pressure, turbine speed, and generator power output. Hourly fuel consumption is calculated based on these parameters. The calculation of fuel consumption takes into account the unit's thermal characteristic curve and efficiency curve, primarily involving losses in boiler, turbine, and generator efficiency. Hourly fuel consumption is recorded in a time series data table, and the cumulative consumption value for the 24-hour period is calculated. Renewable energy power generation data is processed as a time series, with hourly cumulative statistics for wind farm and photovoltaic power station output data. Wind farm output data must account for the conversion characteristics of wind speed and power, while photovoltaic power station output data must incorporate variations in light intensity. Energy storage system charge and discharge data is segmented according to operating cycles, recording information such as the start and end times, charge and discharge power, and state of charge changes for each charge and discharge cycle. The unit power generation cost of each unit is calculated based on fuel consumption, renewable energy power generation, and energy storage charge and discharge data. The cost of generating electricity from a thermal power unit primarily includes fuel costs, startup and shutdown costs, and equipment maintenance costs. The cost of renewable energy generation primarily considers equipment depreciation and operation and maintenance costs. Energy storage system costs include charging and discharging losses and equipment operating costs. These cost data are aggregated by power generation type and time series to form a cost data table.

[0060] System power balance constraints require that the total output on the generation side must equal the total demand on the load side. Therefore, it is necessary to optimize and screen the power generation cost data for each type of unit. The total power generation and total load demand for each time period are calculated, and the power balance deviation is calculated. Data combinations with deviations exceeding the allowable range are eliminated. The power balance calculation must account for line losses and transformer losses. Unit operating constraints are crucial for ensuring safe equipment operation. Ramp rate constraints limit the rate of change of thermal power unit output, output upper and lower limits define the unit's operating range, and power factor constraints affect power quality. Data that meets power balance constraints undergoes a secondary screening to eliminate operating scenarios that exceed the unit's physical constraints.

[0061] After filtering through the dual constraints, the data is sorted based on the principle of minimum cost. The sorting process comprehensively considers the power generation costs of each unit, the system's operating costs, and the degree of constraint satisfaction. From the sorting results, the power generation combination with the lowest cost and that meets all constraints is extracted to obtain the optimal output data for each unit. The optimal power generation combination is processed based on a 24-hour cycle. The overall plan is divided into 24-hour segments, each containing the output data of each unit, the energy storage system's charge and discharge schedule, and system operating parameters. This data is then reorganized according to temporal logic to form the day-ahead scheduling plan data.

[0062] For example, for a thermal power unit with a boiler steam pressure of 13 MPa, a turbine speed of 3000 rpm, and a generator power output of 300 MW, the hourly fuel consumption is calculated to be 110 tons of standard coal based on the thermodynamic characteristic curve. Daily consumption is calculated over 24 hours. The output of the wind farm's six wind turbines at different wind speeds, the power generation of the photovoltaic power plant's 500 photovoltaic panels at different light intensities, and the power and energy changes of the energy storage system over a complete charge and discharge cycle are also recorded. Based on the unit price of fuel, equipment depreciation, and maintenance costs, the thermal power unit's generation cost is calculated to be 0.4 yuan / kWh, the renewable energy generation cost is 0.2 yuan / kWh, and the energy storage system cost is 0.1 yuan / kWh. Power balance calculations reveal that the total power generation during a certain period deviates from the load demand by more than 5%, so the data combination for that period is eliminated. Given that the thermal power unit's ramp rate is limited to 2 MW per minute, some plans with excessive output variations are eliminated. The generation combination with the lowest total cost is selected and broken down into a 24-hour scheduling plan.

[0063] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0064] (1) Divide the day-ahead dispatch plan data into 15-minute time intervals to generate benchmark output data for 96 time periods;

[0065] (2) Sampling the system operating status in each time period, collecting the real-time output of the wind farm, the power generation power of the photovoltaic power station, the output of the thermal power unit, and the charge and discharge power of the energy storage system, and generating real-time operating data;

[0066] (3) Compare and calculate the real-time operating data with the benchmark output data to obtain the power deviation value of each power generation unit and generate power deviation data;

[0067] (4) Judge the positive and negative values of the power deviation data, determine the output adjustment direction of the power generation unit, and calculate the adjusted power value of each unit;

[0068] (5) Generate output instructions for thermal power units and charge and discharge instructions for energy storage systems based on the adjusted power value, and make real-time corrections to the output data of power generation units;

[0069] (6) Combine the corrected power generation unit output data in time series, update the system operation status according to a 15-minute cycle, and generate real-time scheduling data.

[0070] Specifically, the day-ahead dispatch data is divided into 15-minute time units, resulting in a total of 96 time periods within a 24-hour period. Each time period contains the planned output values for each generating unit, including the rated output of thermal power units, the predicted output of wind farms, the predicted power generation of photovoltaic power plants, and the planned charge and discharge power values of the energy storage system. This data serves as the baseline output data for real-time scheduling and is used for subsequent deviation calculation and control adjustments. Real-time status sampling is performed for each of the 96 time periods at a frequency of once per minute. The collected data includes the wind farm's current actual power output (wind turbine speed, rotor torque, actual generated power), the PV plant's actual power generation data (light intensity, module temperature, real-time power), thermal power unit operating parameters (boiler pressure, turbine speed, generator power), and the energy storage system's operating status (state of charge (SOC), real-time charge and discharge power, and system temperature). After data preprocessing and formatting, these sampled data are converted into real-time operating data for the current time period.

[0071] Compare and analyze the real-time operating data with the benchmark output data to calculate the power deviation of each power generation unit. The calculation formula for power deviation is:

[0072]

[0073] in, Indicates the power deviation value of the power generation unit, and Represent the actual output and benchmark output of the i-th thermal power unit, and Represent the actual output and benchmark output of the j-th new energy unit, and Represent the actual power and reference power of the kth energy storage unit respectively, 、 、 is the weight coefficient of each type of equipment, and n, m, and l represent the number of each type of equipment respectively.

[0074] The calculated power deviation data is judged as positive or negative. A positive value indicates that the actual output is greater than the planned value and the output needs to be reduced. A negative value indicates that the actual output is less than the planned value and the output needs to be increased. The power value is adjusted according to the direction and size of the deviation. The calculation formula is:

[0075]

[0076] in, Indicates the adjusted power value, is the overall adjustment coefficient, is the adjustment weight of each unit, is the power deviation of each unit, is the upper limit of the adjustment capability of each unit, is the time attenuation coefficient, To adjust the duration. Based on the calculated adjusted power value, output adjustment commands for the thermal power units and charge and discharge commands for the energy storage system are generated. The command signals include three elements: adjustment direction, adjustment amplitude, and execution time. The output data of each power generation unit is corrected in real time. The correction value must meet the physical constraints of the equipment (such as ramp rate limit, output upper and lower limits, etc.). The corrected output data of each unit is reassembled in chronological order, and the system operating status is updated on a rolling basis every 15 minutes to generate real-time scheduling data.

[0077] For example, a thermal power unit has a baseline output of 300 MW, but its actual sampled output is 280 MW. The calculated power deviation is -20 MW, indicating a need for increased output. Given the unit's ramp rate limit of 2 MW / minute, the maximum output increase within 15 minutes is 30 MW. At the same time, the wind farm's actual output is 15 MW lower than planned due to reduced wind speeds, forcing the energy storage system to switch to discharge mode to compensate for the power shortfall. After adjustment and calculation, the thermal power unit increases its output by 20 MW, and the energy storage system discharges 15 MW to balance the power deviation. These corrections are recorded in the next 15-minute scheduling cycle, and rolling optimization continues.

[0078] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0079] (1) Divide the real-time scheduling data into hourly data, minutely data, and secondly data according to the time scale to generate hierarchical processing data;

[0080] (2) Analyze the power fluctuation trend in hourly data, calculate the output adjustment of thermal power units, and generate hourly power control data;

[0081] (3) Perform fluctuation amplitude statistics on minute-level data, calculate the output changes of wind farms and photovoltaic power stations, and generate minute-level power control data;

[0082] (4) Perform frequency fluctuation analysis on second-level data, extract the instantaneous power fluctuation value of the system, and generate second-level power control data;

[0083] (5) Based on the charging efficiency and discharging efficiency of the energy storage system, calculate the power compensation value corresponding to each time scale and generate energy storage compensation data;

[0084] (6) Time series fusion of hourly power control data, minute-level power control data, second-level power control data and energy storage compensation data to generate multi-time scale control data.

[0085] Specifically, real-time dispatch data is divided into three levels based on temporal resolution: hourly data, sampled over a 60-minute period, is primarily used for economic dispatch and load tracking; minute-level data, sampled over a 1-minute period, is primarily used for power balancing and fluctuation smoothing; and second-level data, sampled over a 1-second period, is primarily used for frequency regulation and transient response. Trend analysis of hourly data is performed by fitting the power curve using linear regression, and calculating the slope to represent the trend. A positive slope indicates an upward trend in power, while a negative slope indicates a downward trend. Based on the trend analysis results, the output adjustment of the thermal power unit is calculated, taking into account the unit's ramp rate constraints and operating cost characteristics. The adjusted output data of the thermal power unit is recorded in the hourly power control data table.

[0086] The fluctuation amplitude statistics of minute-level data are mainly for new energy power generation units. The calculation formula for the output change of wind farms and photovoltaic power stations is:

[0087]

[0088] in, Indicates the total output change of new energy, and Represent the power output of wind farm and photovoltaic power station respectively, and is the weight coefficient of each unit, is the time decay factor, is the response time, q and r represent the number of wind farms and photovoltaic power plants, respectively.

[0089] Frequency fluctuation analysis is performed on second-level data, using Fast Fourier Transform to extract the frequency characteristics of power fluctuations, focusing on fluctuation components within the 0.1-1 Hz range. Power fluctuation values are statistically analyzed in segments, with the amplitude, frequency, and duration of each statistical interval recorded. This data is used for subsequent energy storage compensation control.

[0090] The calculation formula for the power compensation value of the energy storage system at different time scales is:

[0091]

[0092] in, Indicates the total compensation power value, 、 、 Represents the power deviation at the hour, minute and second levels respectively, 、 、 is the compensation coefficient of each time scale, 、 、 is the charge and discharge efficiency of the energy storage system at each time scale, and a, b, and c represent the number of compensation points at each time scale, respectively.

[0093] Control data from each time scale is sequentially integrated according to a unified data format, and control instructions are synthesized using a weighted superposition method. The weighting coefficients need to consider the response characteristics and priority of the control at each time scale. High-frequency fluctuations are preferentially addressed by the energy storage system, while low-frequency fluctuations are mainly regulated by conventional units.

[0094] For example, within a dispatch cycle of a distributed energy storage power system, hourly data shows an upward trend in thermal power generation output. Linear regression calculations yield a positive slope, indicating increased load demand. Minute-level data reveals power fluctuations at the wind farm due to gusts, necessitating calculation of the minute-by-minute output change. Spectral analysis of second-level data reveals a significant power fluctuation component around 0.5 Hz. The energy storage system calculates compensation values based on the fluctuation characteristics at these three time scales: providing capacity support for hourly trend changes, smoothing minute-level fluctuations in renewable energy, and providing a rapid response to second-level frequency disturbances. These control data are combined in a time series to generate hierarchically coordinated control instructions.

[0095] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0096] (1) Divide the multi-time scale control data according to the regional power grid structure, calculate the power fluctuation characteristics of each region, and generate regional power characteristic data;

[0097] (2) Statistically analyze regional power characteristic data, calculate the required energy storage system capacity for each region, and generate capacity allocation data;

[0098] (3) Record the operating status of the energy storage system based on the capacity allocation data, calculate the depth of charge and depth of discharge, and generate depth statistics;

[0099] (4) Accumulate the depth statistical data in time series, calculate the number of charge and discharge cycles of the energy storage system, and generate cycle statistical data;

[0100] (5) Calculate the performance attenuation value of the energy storage unit based on the depth statistics and cycle statistics to generate life assessment data;

[0101] (6) Correlate and analyze the life assessment data with the capacity allocation data, optimize the operating parameters of the energy storage system, and generate energy storage optimization data.

[0102] Specifically, based on the regional power grid's topology, control data is allocated to different regional units. Each regional unit contains wind farm power data, photovoltaic power station power data, thermal power unit operating data, and energy storage system status data. The power fluctuation characteristics of each region are analyzed, primarily including the amplitude, frequency, and duration of power fluctuations. By calculating the power fluctuation characteristics of various power generation units within the region, regional power characteristic data is generated. This data records the power fluctuation patterns, regulation requirements, and energy storage application scenarios for each region. Based on this regional power characteristic data, energy storage capacity is optimized, and statistical analysis is performed on the fluctuation patterns of renewable energy generation, load variation characteristics, and grid regulation requirements for each region. The required energy storage capacity for each region is determined by integrating hourly trend changes, minute-level power fluctuations, and second-level frequency disturbances. Capacity calculations take into account power response requirements and energy support requirements. The resulting capacity allocation data includes the rated power and rated capacity of each regional energy storage system.

[0103] After deploying the energy storage system according to the capacity allocation data, the operating status parameters of the energy storage units need to be recorded. The main focus is on two key indicators: depth of charge (the ratio of the state of charge to the rated capacity) and depth of discharge (the ratio of the discharge amount to the rated capacity). This depth data is statistically analyzed to record the frequency, duration, and transition patterns of different depth intervals to generate depth statistics, which reflect the usage intensity and operating mode of the energy storage units. The depth statistics are accumulated in chronological order to calculate the number of charge and discharge cycles of the energy storage system. Cycle statistics are divided into complete cycles (charge and discharge depths both reach the set threshold) and partial cycles (charge and discharge depths do not reach the threshold). A weighted accumulation is performed for each type of cycle, recording the depth distribution, time interval, and temperature conditions during the cycle process to generate cycle statistics.

[0104] The performance degradation of energy storage units is evaluated based on depth statistics and cycle statistics. Performance degradation is primarily manifested in capacity degradation and increased internal resistance, requiring a comprehensive analysis based on factors such as charge and discharge depth, number of cycles, and operating temperature. By establishing a degradation model, the remaining capacity and equivalent life of the energy storage unit are calculated to generate life assessment data. Finally, the life assessment data is correlated with the capacity allocation data to optimize the operating strategy of the energy storage system. Based on the performance status of the energy storage unit, operating parameters such as charge and discharge power limits, depth thresholds, and response times are adjusted. During the optimization process, it is necessary to balance the relationship between energy storage life and regulation performance to generate the final energy storage optimization data.

[0105] For example, a regional power grid includes a 300MW wind farm, a 200MW photovoltaic power plant, and a 600MW thermal power unit. Analysis of regional power fluctuations revealed hourly load variations of approximately 50MW, minute-by-minute fluctuations in renewable energy sources of approximately 30MW, and second-by-second frequency disturbances requiring a 10MW rapid response capability. Based on these characteristics, an energy storage system with a rated power of 50MW and a rated capacity of 100MWh was configured. In actual operation, the energy storage system's depth of charge (DOC) was observed to be concentrated between 20% and 80%, while its depth of discharge (DOC) was primarily distributed between 30% and 70%. Over a month of operation, approximately 90 full cycles and 270 partial cycles were completed. Performance evaluation revealed a capacity retention rate of 99% for the energy storage unit. Based on this information, the DOC thresholds were optimized, and the operating range was appropriately narrowed to mitigate performance degradation. These optimized parameters were updated to the energy storage system's controller and serve as the control basis for the next round of operation.

[0106] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0107] (1) Extract the system power balance accuracy, frequency quality and new energy consumption rate based on the energy storage optimization data, sample and record them in a 15-minute cycle, and generate operating indicator data;

[0108] (2) Accumulate the operating indicator data in time series, calculate the statistical characteristic values and change trends of each indicator, and generate indicator statistical data;

[0109] (3) Compare and analyze the indicator statistical data with the historical operation data, calculate the deviation value of each indicator, and generate indicator deviation data;

[0110] (4) Correct the output parameters of the thermal power unit and the charging and discharging parameters of the energy storage system based on the index deviation data to generate parameter correction data;

[0111] (5) Verify the rationality of parameter correction data, screen parameter combinations that meet system constraints, and generate parameter update data;

[0112] (6) Import the parameter update data into the system dispatch control unit, update the operating parameters of the generator set and energy storage system, and generate system optimization data.

[0113] Specifically, three key operational indicators are extracted from energy storage optimization data: system power balance accuracy (the absolute value of the difference between planned and actual power), frequency quality (the root mean square value of system frequency deviation), and renewable energy consumption rate (the ratio of actual renewable energy power generation to theoretical power generation). These indicators are recorded using a 15-minute sampling process. The sampled data includes a timestamp, measurement value, and data quality indicator. Power balance accuracy is determined by comparing the dispatch plan value with the measured value. Frequency quality is calculated using frequency sampling. The renewable energy consumption rate is determined by calculating the theoretical power generation based on meteorological data and comparing it with the actual power generation. The collected operational indicator data is processed through time series accumulation, and statistical characteristic values including mean, standard deviation, skewness, and kurtosis are calculated. The mean reflects the overall level of the indicator, the standard deviation indicates the degree of fluctuation, the skewness indicates the asymmetry of the data distribution, and the kurtosis describes the degree of peaks in the data distribution. Time series analysis methods are used to extract trend information, including long-term trends, cyclical changes, and random fluctuations. These statistical characteristics and trend information are compiled into indicator statistics.

[0114] The generated indicator statistics are compared and analyzed with historical operating data, including data from the same period last year and the same period last month, and typical operating data (optimal operating conditions and standard operating conditions). The deviation between the current and historical indicators is calculated to determine the degree of deviation for each indicator. Deviation analysis takes into account factors such as seasonality, load characteristics, and equipment status, generating a dataset containing deviation values for each indicator. Operating parameters are corrected based on indicator deviation data. These corrections include parameters such as the thermal power unit's set power, ramp rate limit, and output upper and lower limits, as well as parameters such as the energy storage system's charge and discharge power limits, state of charge threshold, and response time. Parameter correction follows the principle of "larger adjustments for larger deviations, smaller adjustments for smaller deviations," with the adjustment amount proportional to the deviation value. Corrected parameter values are recorded as parameter correction data.

[0115] Parameter correction data is validated for rationality, including whether parameter values are within the physical constraints of the equipment, whether the parameter combination meets the system power balance requirements, and whether parameter changes conform to the dynamic characteristics of the equipment. By setting multiple verification conditions, parameter combinations that meet all constraints are selected to generate parameter update data. The selection of parameter combinations requires consideration of the priorities and mutual influences of the various constraints. Verified parameter update data is imported into the dispatch control unit to update the operating parameters of the thermal power units and the control parameters of the energy storage system. Parameter updates utilize a smooth transition to avoid system fluctuations caused by sudden parameter changes. The updated operating parameter set constitutes the system optimization data, serving as the basis for the next round of optimized dispatch.

[0116] For example, during the operation of a distributed energy storage system, 15-minute sampling records revealed deviations in the power balance accuracy indicator. Statistical analysis of the operating data for the past four hours, calculating the mean and standard deviation, revealed a large standard deviation, indicating significant power fluctuations. Comparing these statistical values with historical data for the same period revealed that the current power fluctuations were outside the normal range. Analysis revealed that the ramp rate settings for the thermal power units were improper, resulting in an inadequate response to power changes. To address this issue, the ramp rate parameters for the thermal power units were adjusted, and the participation of the energy storage system was increased. After verification of the constraints, the new parameter combination was confirmed to meet the operational requirements of the equipment and system. The adjusted parameters were updated in the control system, improving power balance control.

[0117] The above describes the distributed energy storage power system optimization scheduling method in the embodiment of the present application. The following describes the distributed energy storage power system optimization scheduling system in the embodiment of the present application. Figure 4 In the embodiments of the present application, an embodiment of the distributed energy storage power system optimization and dispatching system includes:

[0118] The detection module 201 is used to collect real-time operating data of the system through the data collection network, perform normalization processing and outlier detection on the collected operating data to obtain standardized data, perform system prediction for a 24-hour period based on the standardized data, and generate a system prediction data set;

[0119] Statistics module 202 is used to calculate the cost and statistics of the fuel consumption of thermal power units, the power generation of renewable energy, and the charge and discharge of energy storage systems based on the system prediction data set, and output the day-ahead dispatch plan data through constraint screening and power balance analysis;

[0120] Update module 203 is used to dynamically update the system operation status data based on the day-ahead scheduling plan data through 15-minute period processing, and adjust the power generation unit output data according to the power deviation calculation result to form real-time scheduling data;

[0121] A stratification module 204 is configured to perform stratified processing of system power fluctuations at the hourly, minutely, and secondly levels based on the real-time scheduling data, perform power compensation calculations based on the charge and discharge characteristics of the energy storage system, and obtain multi-timescale control data;

[0122] Allocation module 205, configured to utilize the multi-timescale control data to perform regional allocation calculations on the energy storage system capacity, perform lifespan assessment through statistical analysis of charge and discharge depth and cycle count, and generate energy storage optimization data;

[0123] The correction module 206 is used to monitor the system operation indicators in real time and accumulate data based on the energy storage optimization data, and to correct and update the scheduling parameters through indicator comparison and analysis to obtain system optimization data.

[0124] Through the collaborative cooperation of the above-mentioned components, by dividing the multi-time scale control data into regions, accurately calculating the power fluctuation characteristics of each region, and combining the regional power characteristic data to determine the reasonable allocation of energy storage capacity, the problem of unreasonable capacity configuration of the energy storage system is effectively solved; the deep statistical data and cycle statistical data are used to systematically analyze the charging and discharging process, establish a complete energy storage unit performance evaluation system, and realize accurate prediction of the life characteristics of energy storage equipment; based on the performance evaluation data, the operating parameters of the energy storage system are dynamically optimized to form a closed-loop regulation and control mechanism, which significantly improves the utilization efficiency of the energy storage system; through the hierarchical and partitioned data processing method, the accurate matching and allocation of energy storage capacity are achieved, avoiding the waste of energy storage resources; life assessment is carried out according to the depth of charge and discharge and the number of cycles, and a health management mechanism of the energy storage system is established to extend the service life of the equipment; using real-time monitoring and data accumulation methods, the system operation indicators are continuously tracked and optimized and adjusted to ensure the long-term stable operation of the energy storage system. The technical solution of the present invention not only improves the regulation performance of the energy storage system through multi-dimensional data analysis and refined parameter control, but also realizes the effective management of equipment life.

[0125] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A distributed energy storage power system optimization scheduling method, characterized in that: The distributed energy storage power system optimization scheduling method includes: The system collects real-time operating data through the data acquisition network, normalizes the collected operating data and detects outliers to obtain standardized data. Based on the standardized data, the load demand forecast and new energy output forecast for a 24-hour cycle are performed to generate a system prediction data set; Based on the system prediction data set, cost calculation and data statistics are performed on the fuel consumption of thermal power units, the power generation of new energy sources, and the charge and discharge of energy storage systems. After constraint screening and power balance analysis, the day-ahead dispatch plan data is output; Based on the day-ahead dispatch plan data, the system operation status data is dynamically updated through time-segment processing in a 15-minute cycle, and the power generation unit output data is adjusted according to the power deviation calculation results to form real-time dispatch data; Based on the real-time dispatch data, the system power fluctuation is processed at the hourly, minutely, and secondly levels, and power compensation calculations are performed based on the charge and discharge characteristics of the energy storage system to obtain multi-timescale control data; Utilizing the multi-timescale control data, regional allocation calculations are performed on the energy storage system capacity, lifespan assessment is performed through statistical analysis of charge and discharge depth and cycle number, and energy storage optimization data is generated; Based on the energy storage optimization data, the system operation indicators are monitored in real time and data is accumulated. The scheduling parameters are corrected and updated through indicator comparison and analysis to obtain system optimization data.

2. The distributed energy storage power system optimization scheduling method according to claim 1 is characterized in that: The system collects real-time operating data through the data acquisition network, performs normalization processing and outlier detection on the collected operating data to obtain standardized data, and performs 24-hour load demand forecasting and new energy output forecasting based on the standardized data to generate a system prediction data set, including: According to the data acquisition network, wind farm output data, photovoltaic power station power generation data, thermal power unit operating parameter data, energy storage system status parameter data and load demand data are collected respectively to generate real-time system operation data; Performing maximum and minimum normalization calculations on the real-time operating data of the system, detecting and eliminating outliers using the 3σ criterion, and generating standardized data; Divide the standardized data into sliding windows according to a 24-hour period, perform time series analysis on the data in each time window, and generate time series feature data; Extract the load variation law and the new energy output fluctuation characteristics according to the time series characteristic data, perform data dimensionality reduction processing, and generate a characteristic data set; Performing correlation analysis on the feature data set, fusing highly correlated data to generate a fused data set; The fused data set is subjected to forecast analysis according to a 24-hour cycle, and forecast calculations are performed on load demand and new energy output to generate the system forecast data set.

3. The distributed energy storage power system optimization scheduling method according to claim 1, characterized in that: The system performs cost calculation and data statistics on the fuel consumption of thermal power units, the power generation of new energy sources, and the charge and discharge of the energy storage system based on the system prediction data set, and outputs the day-ahead scheduling plan data through constraint screening and power balance analysis, including: Based on pre-extracted operating data of thermal power units, hourly fuel consumption is calculated and daily cumulative values are tallied. Renewable energy generation data is divided and accumulated according to time series, and charging and discharging data of the energy storage system is statistically segmented. Using the fuel consumption of thermal power units, the power generation of new energy sources, and the charge and discharge capacity of energy storage systems, the unit power generation cost of each type of unit is calculated and the power generation cost data is summarized; Based on the load demand forecast and renewable energy output forecast in the system forecast data set, the power generation cost data of various units are optimized and screened according to the system power balance constraint, and data combinations that do not meet the power balance requirements are eliminated; Combined with the unit climbing rate constraint, output upper and lower limit constraint, and power factor constraint, the filtered data is screened again to eliminate data combinations that do not meet the equipment operation constraints; Sort the data that meet the dual constraints according to the principle of minimum cost and extract the output data of the optimal power generation combination scheme; The optimal power generation combination plan is subjected to data splitting and time sequence reorganization in a 24-hour period to generate the day-ahead scheduling plan data.

4. The distributed energy storage power system optimization scheduling method according to claim 1, characterized in that: Based on the day-ahead dispatch plan data, the system operation status data is dynamically updated through 15-minute period processing, and the power generation unit output data is adjusted according to the power deviation calculation result to form real-time dispatch data, including: Divide the day-ahead dispatch plan data into periods of 15 minutes to generate benchmark output data for 96 time periods; Sampling the system operating status in each time period, collecting real-time wind farm output, photovoltaic power station power generation, thermal power unit output, and energy storage system charge and discharge power, to generate real-time operating data; Comparing and calculating the real-time operating data with the benchmark output data, obtaining a power deviation value of each power generation unit, and generating power deviation data; Performing a positive or negative value judgment on the power deviation data, determining the output adjustment direction of the power generation unit, and calculating the adjusted power value of each unit; Generate output instructions for thermal power units and charge and discharge instructions for energy storage systems based on the adjusted power value, and make real-time corrections to the output data of power generation units; The corrected power generation unit output data is combined in time series, and the system operation status is updated according to a 15-minute cycle to generate the real-time scheduling data.

5. The distributed energy storage power system optimization scheduling method according to claim 1, characterized in that: According to the real-time scheduling data, the system power fluctuation is hierarchically processed at the hourly, minutely, and secondly levels, and power compensation calculation is performed according to the charge and discharge characteristics of the energy storage system to obtain multi-time scale control data, including: Dividing the real-time scheduling data into hourly data, minutely data, and secondly data according to the time scale to generate hierarchical processing data; Performing trend analysis on the power fluctuations in the hourly data, calculating the output adjustment of the thermal power unit, and generating hourly power control data; Performing fluctuation amplitude statistics on the minute-level data, calculating output changes of the wind farm and photovoltaic power station, and generating minute-level power control data; Performing frequency fluctuation analysis on the second-level data, extracting instantaneous power fluctuation values of the system, and generating second-level power control data; Based on the charging and discharging efficiency of the energy storage system, the power compensation value corresponding to each time scale is calculated to generate energy storage compensation data; The hourly power control data, minutely power control data, secondly power control data and energy storage compensation data are time-series fused to generate the multi-time-scale control data.

6. The distributed energy storage power system optimization scheduling method according to claim 1, characterized in that: The method utilizes the multi-time-scale control data to calculate the regional allocation of the energy storage system capacity, performs lifespan assessment through statistical analysis of charge and discharge depth and cycle number, and generates energy storage optimization data, including: Dividing the multi-timescale control data according to the regional power grid structure, calculating the power fluctuation characteristics of each region, and generating regional power characteristic data; Performing statistical analysis on the regional power characteristic data, calculating the energy storage system capacity required for each region, and generating capacity allocation data; Recording the operating status of the energy storage system according to the capacity allocation data, calculating the depth of charge and depth of discharge, and generating depth statistics; Performing time series accumulation on the depth statistical data, calculating the number of charge and discharge cycles of the energy storage system, and generating cycle statistical data; Calculate the performance attenuation value of the energy storage unit based on the depth statistical data and the cycle statistical data to generate life assessment data; The life assessment data and the capacity allocation data are correlated and analyzed, and the operating parameters of the energy storage system are optimized and calculated to generate the energy storage optimization data.

7. The distributed energy storage power system optimization scheduling method according to claim 1, characterized in that: Based on the energy storage optimization data, the system operation indicators are monitored in real time and data is accumulated. The scheduling parameters are corrected and updated through indicator comparison and analysis to obtain system optimization data, including: Extracting the system's power balance accuracy, frequency quality, and new energy consumption rate based on the energy storage optimization data, sampling and recording the data in a 15-minute cycle to generate operating indicator data, where the power balance accuracy is the absolute value of the difference between the planned power value and the actual power value; Accumulate the operating indicator data in time series, calculate the statistical characteristic value and change trend of each indicator, and generate indicator statistical data; Compare and analyze the indicator statistical data with the historical operation data, calculate the deviation value of each indicator, and generate indicator deviation data; Correcting the output parameters of the thermal power unit and the charging and discharging parameters of the energy storage system according to the indicator deviation data to generate parameter correction data; Performing rationality verification on the parameter correction data, screening parameter combinations that meet system constraints, and generating parameter update data; The parameter update data is imported into the system dispatch control unit to update the operating parameters of the generator set and the energy storage system to generate the system optimization data.

8. A distributed energy storage power system optimization and dispatching system, used to implement the distributed energy storage power system optimization and dispatching method according to any one of claims 1 to 7, characterized in that: The distributed energy storage power system optimization and dispatching system includes: The detection module is used to collect real-time operating data of the system through the data acquisition network, normalize the collected operating data and detect outliers to obtain standardized data, and perform 24-hour load demand forecasting and new energy output forecasting based on the standardized data to generate a system prediction data set; A statistics module is used to perform cost calculation and data statistics on the fuel consumption of thermal power units, the power generation of new energy sources, and the charge and discharge of energy storage systems based on the system prediction data set, and output day-ahead scheduling plan data through constraint screening and power balance analysis; An updating module is configured to dynamically update the system operation status data based on the day-ahead scheduling plan data through time-segment processing in a 15-minute cycle, and adjust the power generation unit output data according to the power deviation calculation result to form real-time scheduling data; A hierarchical module is used to perform hierarchical processing of system power fluctuations at the hourly, minutely, and secondly levels based on the real-time scheduling data, perform power compensation calculations based on the charge and discharge characteristics of the energy storage system, and obtain multi-timescale control data; an allocation module for calculating the regional allocation of the energy storage system capacity using the multi-timescale control data, performing lifespan assessment through statistical analysis of charge and discharge depth and cycle number, and generating energy storage optimization data; The correction module is used to monitor the system operation indicators in real time and accumulate data based on the energy storage optimization data, and to correct and update the scheduling parameters through indicator comparison and analysis to obtain system optimization data.

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