Computing-accelerated composite energy storage system rapid simulation method and system

Abstract

The invention relates to a composite energy storage system rapid simulation method and system capable of accelerating calculation. The method comprises the steps of obtaining output prediction data according to historical meteorological data and historical output data, and obtaining load prediction data according to historical load data; according to the SOC difference value and the energy transmission value of the historical same-state moment and the next moment, the charging and discharging efficiency is determined; according to the temperature difference value and the charging and discharging efficiency between each historical same-state moment and the current moment, the charging and discharging efficiency at the current moment is determined; calculating a scheduling demand of the energy storage unit according to the output prediction data and the load prediction data; according to the charging and discharging efficiency, the frequency component of the scheduling requirement and the SOC value of each energy storage unit at the current moment, determining the SOC value of each energy storage unit at the next moment; and scheduling planning is carried out based on the SOC value of each energy storage unit at the next moment, and simulation is carried out through a simulation platform. The operation stability of the composite energy storage system can be improved.

Description

Technical Field

[0001] This application relates to the field of energy storage system simulation technology, and in particular to a rapid simulation method and system for composite energy storage systems that accelerates computation. Background Technology

[0002] Composite energy storage systems refer to a type of energy storage that combines multiple energy storage technologies to provide flexible adjustment under different load and operating conditions. These systems can demonstrate superior performance in dealing with peak loads, load balancing, and energy feedback. For example, composite energy storage systems that combine lithium battery energy storage units and flywheel energy storage units. Due to the differences in characteristics of different types of energy storage units and the complexity of multiple devices working together, how to efficiently and accurately simulate and predict the operating characteristics and performance of composite energy storage systems has become a major challenge in technical research.

[0003] In existing simulations of composite energy storage systems, the charging and discharging principles of different energy storage units are often modeled separately. Since composite energy storage units contain multiple energy storage units with different structures and charging and discharging principles, multiple models are often required, which increases the burden of preliminary simulation preparation and slows down the simulation efficiency. Furthermore, existing simulations of energy storage systems only call data in real time, resulting in unsmooth switching between energy storage units and making it impossible to adapt to complex composite energy storage units. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a rapid simulation method and system for composite energy storage systems that accelerates computation. The specific technical solution adopted is as follows: Firstly, a rapid simulation method for a composite energy storage system is provided to accelerate computation, the method comprising: Based on historical meteorological data and historical power output data, power output forecast data is obtained, and based on historical load data, load forecast data is obtained. Based on the SOC difference between the historical state time and the next time of the lithium battery energy storage unit, and the energy transfer value between the historical state time and the next time, the charging and discharging efficiency at the historical state time is determined; the historical state time indicates the time when the difference between the current SOC value of the lithium battery energy storage unit and the current time is less than a preset threshold, and the corresponding lithium battery energy storage unit model is the same as the current lithium battery energy storage unit model. The SOC value of the lithium battery energy storage unit at each time is obtained according to the preset SOC model of the lithium battery energy storage unit. Based on the temperature difference between each historical moment in the same state and the current moment, and the charging and discharging efficiency of that historical moment in the same state, the charging and discharging efficiency of the lithium battery energy storage unit at the current moment is determined. Based on output forecast data and load forecast data, the scheduling requirements of energy storage units are calculated; energy storage units include lithium battery energy storage units and flywheel energy storage units. Based on the charging and discharging efficiency of each energy storage unit, the frequency component in the scheduling requirements, and the SOC value of each energy storage unit at the current moment, the SOC value of each energy storage unit at the next moment is determined respectively; the charging and discharging efficiency of the flywheel energy storage unit is obtained according to the preset efficiency curve, and the SOC value of the flywheel energy storage unit at each moment is obtained according to the preset SOC model of the flywheel energy storage unit. Scheduling planning is performed based on the SOC value of each energy storage unit at the next time step, and simulation is conducted through a simulation platform.

[0005] Optionally, the step of predicting power output forecast data based on meteorological data and historical power output data includes: Based on historical meteorological data, the light intensity is predicted to obtain a light intensity prediction sequence within the first preset time period; For each light intensity value in the light intensity prediction sequence, multiple historical light intensity values ​​with the smallest difference from the light intensity value are retrieved from the historical light intensity data, and the photovoltaic output value at the time corresponding to the multiple historical light intensity values ​​is obtained. The average value of the multiple photovoltaic output values ​​is calculated to obtain the photovoltaic independent output prediction value corresponding to the light intensity value, so as to obtain the photovoltaic independent output prediction sequence corresponding to the light intensity prediction sequence. The predicted light intensity sequence is subjected to sliding matching in the historical light intensity sequence. The mean of the absolute difference between the predicted light intensity sequence and the historical light intensity subsequence in each window is calculated, and the window with the smallest mean is selected as the first target window. The historical power output sequence corresponding to the first target window is determined as the overall photovoltaic power output prediction sequence. The historical light intensity data includes the historical light intensity sequence. Calculate the absolute difference between the predicted independent photovoltaic output values ​​at each adjacent time point in the photovoltaic independent output prediction sequence to obtain the photovoltaic change sequence; The photovoltaic output prediction value at that moment is determined based on the maximum value in the photovoltaic change sequence, the photovoltaic independent output prediction value at each moment in the photovoltaic independent output prediction sequence, and the photovoltaic overall output prediction value at that moment in the photovoltaic overall output prediction sequence. Based on historical meteorological data, wind speed and wind direction are predicted to obtain wind speed prediction sequences and wind direction prediction sequences within the first preset time period. For each wind speed value in the wind speed prediction sequence, multiple historical wind speed values ​​with the smallest difference from the given wind speed value are retrieved from historical wind speed data. The wind power output value at the corresponding time for each of these multiple historical wind speed values ​​is obtained, and the average of these multiple wind power output values ​​is calculated to obtain the wind speed independent power output prediction value corresponding to the given wind speed value, thus obtaining the wind speed independent power output prediction sequence corresponding to the wind speed prediction sequence. For each wind direction value in the wind direction prediction sequence, multiple historical wind direction values ​​with the smallest difference from the given wind direction value are retrieved from historical wind direction data. The wind direction output value at the corresponding time for each of these multiple historical wind direction values ​​is obtained, and the average of these multiple wind direction output values ​​is calculated to obtain the wind direction independent power output prediction value corresponding to the given wind direction value, thus obtaining the wind direction independent power output prediction sequence corresponding to the wind direction prediction sequence. The wind speed prediction sequence is subjected to sliding matching within the historical wind speed sequence. The mean of the absolute difference between the wind speed prediction sequence and the historical wind speed subsequence within each window is calculated, and the window with the smallest mean is selected as the second target window. The historical power output sequence corresponding to the second target window is determined as the overall wind speed power output prediction sequence. Historical wind speed data includes historical wind speed sequences. The wind direction prediction sequence is subjected to sliding matching within the historical wind direction sequence. The mean of the absolute difference between the wind direction prediction sequence and the historical wind direction subsequence within each window is calculated, and the window with the smallest mean is selected as the third target window. The historical power output sequence corresponding to the third target window is determined as the overall wind direction power output prediction sequence. Historical wind speed data includes historical wind direction sequences. Calculate the absolute difference between the wind speed independent output prediction values ​​at each adjacent time in the wind speed independent output prediction sequence to obtain the wind speed change sequence; calculate the absolute difference between the wind direction independent output prediction values ​​at each adjacent time in the wind direction independent output prediction sequence to obtain the wind direction change sequence. The wind speed output prediction value for that moment is determined based on the maximum value in the wind speed change sequence, the wind speed independent output prediction value at each moment in the wind speed independent output prediction sequence, and the wind speed overall output prediction value at that moment in the wind speed overall output prediction sequence; the wind direction output prediction value for that moment is determined based on the maximum value in the wind direction change sequence, the wind direction independent output prediction value at each moment in the wind direction independent output prediction sequence, and the wind direction overall output prediction value at that moment in the wind direction overall output prediction sequence. The average of the wind speed forecast and the wind direction power output forecast for each moment is calculated to obtain the wind power output forecast for that moment; the power output forecast data includes the photovoltaic power output forecast and the wind power output forecast.

[0006] Optionally, the step of obtaining load prediction data based on historical load data includes: Obtain historical load data within a second preset time period prior to the current moment; the second preset time period is greater than the first preset time period; For any point in the current forecast period, search for historical points in the historical load data that are several points apart from that point, and determine the load value of that historical point as the load forecast value for that point, so as to obtain the load forecast data for the current forecast period.

[0007] Optionally, determining the charge / discharge efficiency at the historical same-state moment based on the SOC difference between the historical same-state moment and the next moment, and the energy transfer value between the historical same-state moment and the next moment, includes: Based on the current SOC value of the lithium battery energy storage unit, multiple historical moments in the same state are determined from the historical operating data of lithium battery energy storage units of the same model. For each historical state moment, the charge / discharge efficiency at that historical state moment is determined by the ratio of the energy transfer value between that historical state moment and the next moment to the absolute difference between the SOC value at that historical state moment and the SOC value at the next moment. Specifically, if the difference between the SOC value at that historical state moment and the next moment is greater than zero, the charge / discharge efficiency at that historical state moment is the charging efficiency; if the difference between the SOC value at that historical state moment and the next moment is less than zero, the charge / discharge efficiency at that historical state moment is the charging efficiency; if the difference between the SOC value at that historical state moment and the next moment is equal to zero, the charge / discharge efficiency determination operation is not performed.

[0008] Optionally, determining the charge / discharge efficiency of the lithium-ion battery storage unit at the current moment based on the temperature difference between each historical time in the same state and the current moment, and the charge / discharge efficiency at that historical time in the same state, includes: Calculate the reciprocal of the absolute difference in temperature between each historical moment in the same state and the current moment to obtain the reciprocal of the temperature at that historical moment in the same state; Calculate the product of the reciprocal of the temperature at each historical moment in the same state and the charge / discharge efficiency at that historical moment in the same state, and sum the reciprocals of the temperature at multiple historical moments in the same state to determine the charge / discharge efficiency of the lithium battery energy storage unit at the current moment.

[0009] Optionally, calculating the scheduling requirements of the energy storage unit based on the output forecast data and load forecast data includes: The sum of the predicted photovoltaic power output and the predicted wind power output is determined as the power output prediction data; The difference between the output forecast data and the load forecast data at each time moment is calculated to obtain the scheduling demand of the energy storage unit at each time moment; if the scheduling demand is greater than zero, the energy storage unit is charged; if the scheduling demand is less than zero, the energy storage unit is discharged; if the scheduling demand is equal to zero, the energy storage unit does not work.

[0010] Optionally, determining the SOC value of each energy storage unit at the next time step based on the charging and discharging efficiency of each energy storage unit, the frequency component in the scheduling demand, and the SOC value of each energy storage unit at the current time includes: The scheduling requirements at the current moment are filtered based on Kalman filtering. Components that are greater than the separation threshold are identified as high-frequency instantaneous components, and components that are not greater than the separation threshold are identified as low-frequency steady-state components. If only high-frequency transient components are present, the flywheel energy storage unit is activated; if only low-frequency steady-state components are present, the lithium battery energy storage unit is activated; if both high-frequency transient components and low-frequency steady-state components are present, both the flywheel energy storage unit and the lithium battery energy storage unit are activated. The energy required for the flywheel energy storage unit to exchange at the current moment is calculated based on its charging and discharging efficiency and the proportion of high-frequency instantaneous components in the dispatch demand. Similarly, the energy required for the lithium-ion battery energy storage unit to exchange at the current moment is calculated based on its charging and discharging efficiency and the proportion of low-frequency steady-state components in the dispatch demand. A positive sign in response to the dispatch demand indicates that the energy storage unit is discharging, while a negative sign indicates that the energy storage unit is charging. The energy storage units include both flywheel energy storage units and lithium-ion battery energy storage units. Based on the energy required for exchange by the flywheel energy storage unit at the current moment, the SOC value of the flywheel energy storage unit at the current moment, and the rated capacity of the flywheel energy storage unit, predict the SOC value of the flywheel energy storage unit at the next moment; based on the energy required for exchange by the lithium battery energy storage unit at the current moment, the SOC value of the lithium battery energy storage unit at the current moment, and the rated capacity of the lithium battery energy storage unit, predict the SOC value of the lithium battery energy storage unit at the next moment.

[0011] Secondly, a rapid simulation system for a composite energy storage system is provided to accelerate computation, the system comprising: The forecasting module is used to predict power output data based on historical meteorological data and historical power output data, and to predict load data based on historical load data. The first determining module is used to determine the charging and discharging efficiency of the historical same state moment based on the SOC difference between the historical same state moment and the next moment, and the energy transfer value between the historical same state moment and the next moment. The historical same state moment indicates the moment when the difference between the current SOC value of the lithium battery energy storage unit and the current moment is less than a preset threshold, and the corresponding lithium battery energy storage unit model is the same as the model of the lithium battery energy storage unit. The SOC value of the lithium battery energy storage unit at each moment is obtained according to the preset SOC model of the lithium battery energy storage unit. The second determining module is used to determine the charging and discharging efficiency of the lithium battery energy storage unit at the current moment based on the temperature difference between each historical time in the same state and the current moment, and the charging and discharging efficiency at the historical time in the same state. The calculation module is used to calculate the scheduling requirements of the energy storage units based on the output forecast data and load forecast data; the energy storage units include lithium battery energy storage units and flywheel energy storage units. The third determining module is used to determine the SOC value of each energy storage unit at the next moment based on the charging and discharging efficiency of each energy storage unit, the frequency component in the scheduling requirements, and the SOC value of each energy storage unit at the current moment; the charging and discharging efficiency of the flywheel energy storage unit is obtained according to the preset efficiency curve, and the SOC value of the flywheel energy storage unit at each moment is obtained according to the preset SOC model of the flywheel energy storage unit. The simulation module is used to perform scheduling planning based on the SOC value of each energy storage unit at the next time step, and to perform simulation through the simulation platform.

[0012] Optionally, the prediction module is further configured to: Based on historical meteorological data, the light intensity is predicted to obtain a light intensity prediction sequence within the first preset time period; For each light intensity value in the light intensity prediction sequence, multiple historical light intensity values ​​with the smallest difference from the light intensity value are retrieved from the historical light intensity data, and the photovoltaic output value at the time corresponding to the multiple historical light intensity values ​​is obtained. The average value of the multiple photovoltaic output values ​​is calculated to obtain the photovoltaic independent output prediction value corresponding to the light intensity value, so as to obtain the photovoltaic independent output prediction sequence corresponding to the light intensity prediction sequence. The predicted light intensity sequence is subjected to sliding matching in the historical light intensity sequence. The mean of the absolute difference between the predicted light intensity sequence and the historical light intensity subsequence in each window is calculated, and the window with the smallest mean is selected as the first target window. The historical power output sequence corresponding to the first target window is determined as the overall photovoltaic power output prediction sequence. The historical light intensity data includes the historical light intensity sequence. Calculate the absolute difference between the predicted independent photovoltaic output values ​​at each adjacent time point in the photovoltaic independent output prediction sequence to obtain the photovoltaic change sequence; The photovoltaic output prediction value at that moment is determined based on the maximum value in the photovoltaic change sequence, the photovoltaic independent output prediction value at each moment in the photovoltaic independent output prediction sequence, and the photovoltaic overall output prediction value at that moment in the photovoltaic overall output prediction sequence. Based on historical meteorological data, wind speed and wind direction are predicted to obtain wind speed prediction sequences and wind direction prediction sequences within the first preset time period. For each wind speed value in the wind speed prediction sequence, multiple historical wind speed values ​​with the smallest difference from the given wind speed value are retrieved from historical wind speed data. The wind power output value at the corresponding time for each of these multiple historical wind speed values ​​is obtained, and the average of these multiple wind power output values ​​is calculated to obtain the wind speed independent power output prediction value corresponding to the given wind speed value, thus obtaining the wind speed independent power output prediction sequence corresponding to the wind speed prediction sequence. For each wind direction value in the wind direction prediction sequence, multiple historical wind direction values ​​with the smallest difference from the given wind direction value are retrieved from historical wind direction data. The wind direction output value at the corresponding time for each of these multiple historical wind direction values ​​is obtained, and the average of these multiple wind direction output values ​​is calculated to obtain the wind direction independent power output prediction value corresponding to the given wind direction value, thus obtaining the wind direction independent power output prediction sequence corresponding to the wind direction prediction sequence. The wind speed prediction sequence is subjected to sliding matching within the historical wind speed sequence. The mean of the absolute difference between the wind speed prediction sequence and the historical wind speed subsequence within each window is calculated, and the window with the smallest mean is selected as the second target window. The historical power output sequence corresponding to the second target window is determined as the overall wind speed power output prediction sequence. Historical wind speed data includes historical wind speed sequences. The wind direction prediction sequence is subjected to sliding matching within the historical wind direction sequence. The mean of the absolute difference between the wind direction prediction sequence and the historical wind direction subsequence within each window is calculated, and the window with the smallest mean is selected as the third target window. The historical power output sequence corresponding to the third target window is determined as the overall wind direction power output prediction sequence. Historical wind speed data includes historical wind direction sequences. Calculate the absolute difference between the wind speed independent output prediction values ​​at each adjacent time in the wind speed independent output prediction sequence to obtain the wind speed change sequence; calculate the absolute difference between the wind direction independent output prediction values ​​at each adjacent time in the wind direction independent output prediction sequence to obtain the wind direction change sequence. The wind speed output prediction value for that moment is determined based on the maximum value in the wind speed change sequence, the wind speed independent output prediction value at each moment in the wind speed independent output prediction sequence, and the wind speed overall output prediction value at that moment in the wind speed overall output prediction sequence; the wind direction output prediction value for that moment is determined based on the maximum value in the wind direction change sequence, the wind direction independent output prediction value at each moment in the wind direction independent output prediction sequence, and the wind direction overall output prediction value at that moment in the wind direction overall output prediction sequence. The average of the wind speed forecast and the wind direction power output forecast for each moment is calculated to obtain the wind power output forecast for that moment; the power output forecast data includes the photovoltaic power output forecast and the wind power output forecast.

[0013] Optionally, the prediction module is further configured to: Obtain historical load data within a second preset time period prior to the current moment; the second preset time period is greater than the first preset time period; For any point in the current forecast period, search for historical points in the historical load data that are several points apart from that point, and determine the load value of that historical point as the load forecast value for that point, so as to obtain the load forecast data for the current forecast period.

[0014] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of this application.

[0015] This application offers the following advantages: It calculates the scheduling requirements of energy storage units based on historical meteorological data, historical power output data, and historical load data. Combining this with a frequency component decomposition strategy for scheduling requirements, it identifies the distribution characteristics of high-frequency instantaneous components and low-frequency steady-state components. Utilizing the charging and discharging efficiency of each energy storage unit and its current SOC value, it predicts the state-of-charge evolution trend for the next moment, thereby completing the coordinated deployment planning of flywheel and lithium-ion battery energy storage units before scheduling execution. This effectively avoids decision delays and power surges during real-time switching, significantly improving the response smoothness and operational stability of the composite energy storage system when dealing with net load fluctuations. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a rapid simulation method for a composite energy storage system to accelerate computation, as described in one embodiment. Figure 2 This is a schematic diagram of a rapid simulation system for a composite energy storage system that accelerates computation, as shown in one embodiment. Figure 3 This is a schematic diagram of the structure of an electronic device in one embodiment. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a rapid simulation method and system for a composite energy storage system based on this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] The following, with reference to the accompanying drawings, details a specific scheme for a rapid simulation method for a composite energy storage system provided in this application, which accelerates computation. For example... Figure 1 As shown, the method includes: S11. Based on historical meteorological data and historical power output data, predict the power output data and predict the load data based on historical load data.

[0021] Output indication: The active power output of photovoltaic or wind power to the grid or load at a certain moment, usually in kW or MW.

[0022] Simulations of composite energy storage units primarily involve the responses of various energy storage units within the energy storage system. Therefore, prior to simulation, it is necessary to collect a large number of parameters for the energy storage units. Furthermore, considering the energy plant and load fluctuations within the composite energy storage system, relevant data on the energy plant and load must be collected. The specific data collection process is as follows: 1. Meteorological data collection For wind power stations, their output is directly affected by wind speed and wind direction, so it is necessary to collect wind direction and wind speed data first: (1) Install wind speed and direction sensors, with the sensors 10m above the ground and unobstructed; (2) Configure the DAQ acquisition frequency (15 minutes / time) using LabVIEW and transmit it to the local computer in real time; (3) Use a Python script to automatically read DAQ data and format and store it as CSV.

[0023] For photovoltaic power plants, their output is directly affected by the intensity of sunlight, so it is necessary to collect sunlight intensity data. (1) Install a light intensity sensor, with the sensor 1.5m above the ground and unobstructed; (2) Configure the DAQ acquisition frequency (15 minutes / time) using LabVIEW and transmit it to the local computer in real time; (3) Use a Python script to automatically read DAQ data and format and store it as CSV.

[0024] Finally, weather forecast data, including wind speed, wind direction, and light intensity, were obtained through third-party commercial meteorological APs (prioritizing data sources within 1km of the station) and publicly available data from meteorological centers.

[0025] 2. Output data of photovoltaic and wind power stations (1) Connecting to the SCADA system: Connect to the SCADA server using the opcua library in Python via the OPC UA protocol (Open Platform Communications Unified Architecture); (2) Data extraction: Set the collection frequency to 5 minutes / time, and extract the fields of "total active power output of photovoltaic power station" and "total active power output of wind power station"; (3) Format conversion: Convert the JSON data exported by SCADA into CSV format and add time tags to facilitate subsequent simulation condition classification.

[0026] 3. Connect load size data (1) Install a power sensor (connected in series to the main input terminal of the load), connect the DAQ data acquisition unit, and set the acquisition frequency to 15 minutes / time; (2) Read the "total active power" data through the RS485 interface and parse the data frame using a Python script; (3) Compare the power sensor and smart meter data daily. If the error exceeds 1%, recalibrate the sensor.

[0027] 4. Energy storage unit data reading For the flywheel energy storage unit, directly read the flywheel speed, flywheel inertia and SOC (State of Charge) of the energy storage unit recorded by the flywheel controller. For lithium battery energy storage units, the temperature of the lithium battery and the SOC of the energy storage unit are directly read from the lithium battery BMS.

[0028] For hybrid energy storage systems, the primary function is to store electricity. These systems need to accept different types of electricity, especially clean energy sources like wind and solar power. Typically, electricity from different power plants is directly stored in the storage system, and then distributed according to actual power demand. During the energy storage process, to ensure the system can respond quickly, it's generally necessary to predict the electricity levels at different power plants and plan the rapid response of the storage units in advance.

[0029] In weather forecast data, key parameters affecting wind and solar power output are read: wind speed, wind direction, and solar irradiance. Wind speed and wind direction affect wind power output, while solar irradiance affects solar power output. Based on these parameters, wind and solar power output is predicted.

[0030] In one embodiment, based on meteorological data and historical power output data, power output forecast data is obtained, including: Based on historical meteorological data, the light intensity is predicted to obtain a light intensity prediction sequence within the first preset time period; For each light intensity value in the light intensity prediction sequence, multiple historical light intensity values ​​with the smallest difference from the light intensity value are retrieved from the historical light intensity data, and the photovoltaic output value at the time corresponding to the multiple historical light intensity values ​​is obtained. The average value of the multiple photovoltaic output values ​​is calculated to obtain the photovoltaic independent output prediction value corresponding to the light intensity value, so as to obtain the photovoltaic independent output prediction sequence corresponding to the light intensity prediction sequence. The predicted light intensity sequence is subjected to sliding matching in the historical light intensity sequence. The mean of the absolute difference between the predicted light intensity sequence and the historical light intensity subsequence in each window is calculated, and the window with the smallest mean is selected as the first target window. The historical power output sequence corresponding to the first target window is determined as the overall photovoltaic power output prediction sequence. The historical light intensity data includes the historical light intensity sequence. Calculate the absolute difference between the predicted independent photovoltaic output values ​​at each adjacent time point in the photovoltaic independent output prediction sequence to obtain the photovoltaic change sequence; The photovoltaic output prediction value at that moment is determined based on the maximum value in the photovoltaic change sequence, the photovoltaic independent output prediction value at each moment in the photovoltaic independent output prediction sequence, and the photovoltaic overall output prediction value at that moment in the photovoltaic overall output prediction sequence. Based on historical meteorological data, wind speed and wind direction are predicted to obtain wind speed prediction sequences and wind direction prediction sequences within the first preset time period. For each wind speed value in the wind speed prediction sequence, multiple historical wind speed values ​​with the smallest difference from the given wind speed value are retrieved from historical wind speed data. The wind power output value at the corresponding time for each of these multiple historical wind speed values ​​is obtained, and the average of these multiple wind power output values ​​is calculated to obtain the wind speed independent power output prediction value corresponding to the given wind speed value, thus obtaining the wind speed independent power output prediction sequence corresponding to the wind speed prediction sequence. For each wind direction value in the wind direction prediction sequence, multiple historical wind direction values ​​with the smallest difference from the given wind direction value are retrieved from historical wind direction data. The wind direction output value at the corresponding time for each of these multiple historical wind direction values ​​is obtained, and the average of these multiple wind direction output values ​​is calculated to obtain the wind direction independent power output prediction value corresponding to the given wind direction value, thus obtaining the wind direction independent power output prediction sequence corresponding to the wind direction prediction sequence. The wind speed prediction sequence is subjected to sliding matching within the historical wind speed sequence. The mean of the absolute difference between the wind speed prediction sequence and the historical wind speed subsequence within each window is calculated, and the window with the smallest mean is selected as the second target window. The historical power output sequence corresponding to the second target window is determined as the overall wind speed power output prediction sequence. Historical wind speed data includes historical wind speed sequences. The wind direction prediction sequence is subjected to sliding matching within the historical wind direction sequence. The mean of the absolute difference between the wind direction prediction sequence and the historical wind direction subsequence within each window is calculated, and the window with the smallest mean is selected as the third target window. The historical power output sequence corresponding to the third target window is determined as the overall wind direction power output prediction sequence. Historical wind speed data includes historical wind direction sequences. Calculate the absolute difference between the wind speed independent output prediction values ​​at each adjacent time in the wind speed independent output prediction sequence to obtain the wind speed change sequence; calculate the absolute difference between the wind direction independent output prediction values ​​at each adjacent time in the wind direction independent output prediction sequence to obtain the wind direction change sequence. The wind speed output prediction value for that moment is determined based on the maximum value in the wind speed change sequence, the wind speed independent output prediction value at each moment in the wind speed independent output prediction sequence, and the wind speed overall output prediction value at that moment in the wind speed overall output prediction sequence; the wind direction output prediction value for that moment is determined based on the maximum value in the wind direction change sequence, the wind direction independent output prediction value at each moment in the wind direction independent output prediction sequence, and the wind direction overall output prediction value at that moment in the wind direction overall output prediction sequence. The average of the wind speed forecast and the wind direction power output forecast for each moment is calculated to obtain the wind power output forecast for that moment; the power output forecast data includes the photovoltaic power output forecast and the wind power output forecast.

[0031] The first prediction duration can be set according to the actual situation, for example, 3 hours.

[0032] The wind direction value is expressed in the form of an angle.

[0033] Before making a prediction, it is necessary to first determine a time scale, that is, the time range corresponding to a data point. In this embodiment, the prediction is performed on a 15-minute time scale, that is, a key parameter is read every 15 minutes, and the accuracy of the obtained prediction data is also 15 minutes. Then, the prediction range is determined, that is, a prediction range of length T0 is determined with the current time as the starting time, that is, the power output within T0 is predicted. In this embodiment, T0 is set to 3 hours.

[0034] The prediction process for photovoltaic power generation is as follows: By combining historical meteorological data with real-time weather forecasts, future illumination intensity prediction data covering the forecast area is generated. The temporal resolution of the future illumination intensity prediction data is typically 1 minute, 5 minutes, or 10 minutes. The future illumination intensity prediction data is segmented into 15-minute time scales, and the arithmetic mean of the illumination intensity values ​​within each segment is calculated to obtain the average illumination intensity for the corresponding 15-minute time period. All the average illumination intensity values ​​within each 15-minute period are arranged in chronological order to form an illumination intensity prediction sequence, where each element represents the average illumination intensity within that 15-minute time period.

[0035] In the historical light intensity data, identify the N0 light intensities that are most similar to each light intensity value in the current light intensity prediction sequence, and calculate the photovoltaic output value at the time corresponding to the N0 light intensity values, which are then used as the independent output prediction value for each light intensity value in the current light intensity prediction sequence.

[0036] The current light intensity prediction sequence is sliding-matched with the historical light intensity sequence. The mean of the absolute difference between the light intensity prediction sequence and the historical light intensity subsequence within each window is calculated. When the mean of the absolute difference is the smallest, the best match of the current light intensity sequence is obtained. That is, the window with the smallest mean is selected as the first target window. The historical output sequence corresponding to the first target window of the historical light intensity sequence corresponding to the best match is used as the overall output prediction sequence of the light intensity prediction sequence.

[0037] The absolute difference between the photovoltaic independent output prediction values ​​at each adjacent time in the light intensity prediction sequence is calculated to obtain the photovoltaic change sequence. The mean of the change is calculated. The larger the mean of the change, the greater the fluctuation of the independent output prediction value of the light intensity prediction sequence, the more unstable the prediction, and the less accurate the independent output prediction value.

[0038] At this point, calculate the ratio K of the mean change in the independent output prediction value to the maximum value in the change sequence, and use 1-K as the weight of the independent output prediction value.

[0039] In the solar irradiance prediction sequence, the predicted independent power output at time i is FA, and the predicted overall power output at time i is FB. Therefore, the predicted photovoltaic power output at time i is... for: ; Based on the above method, the photovoltaic power output prediction value at the corresponding time of the current prediction range is obtained.

[0040] Similarly, for wind power generation, the predicted wind power output based on wind speed and the predicted wind power output based on wind direction are obtained for the corresponding time within the current prediction range. Then, the average of the predicted wind power output based on wind speed and the predicted wind power output based on wind direction at the same time is determined as the predicted wind power output for that time.

[0041] In practical operation, energy storage systems need to dynamically allocate energy according to the actual load. Therefore, the simulation process of energy storage systems needs to consider the dynamic response to changes in the actual load. In order to respond quickly, load prediction is also required.

[0042] Therefore, in one embodiment, the load prediction data is obtained based on historical load data, including: Obtain historical load data within a second preset time period prior to the current moment; the second preset time period is greater than the first preset time period; For any point in the current forecast period, search for historical points in the historical load data that are several points apart from that point, and determine the load value of that historical point as the load forecast value for that point, so as to obtain the load forecast data for the current forecast period.

[0043] The second preset duration can be set according to the actual situation, for example, 4 weeks. The prediction period can be set according to the actual situation, for example, 1 day.

[0044] Because power load has obvious temporal regularity, such as high electricity consumption in industrial parks during the day and low electricity consumption at night, and low electricity consumption in residential areas during the day and high electricity consumption at night, and the same regularity on different days within a week, we first determine a time period for the load regularity. In this embodiment, we directly set the load period to 1 week and the prediction time scale (time granularity) to 15 minutes, that is, a prediction moment contains a 15-minute time range. Then we determine the prediction period, that is, taking the current moment as the starting moment, we determine a prediction period of length T1, that is, predict the load within T1. In this embodiment, we set T1 to 1 day.

[0045] In the historical data, retrieve the historical load data for the four weeks prior to the current moment as the reference load. Each data point represents the average load over the corresponding 15-minute period.

[0046] Given that the known load pattern has a time period of one week, or 672 (calculated based on 7×24×4) prediction times, for a prediction time within the current prediction range, the historical load data is searched for a time interval of [missing value]. The time (z is an integer multiple parameter, with z taking values ​​of 1, 2, 3, 4) is used as the load forecast value for the corresponding forecast time in the current forecast period, so as to obtain the load forecast value for each time in the current forecast period.

[0047] S12. Determine the charging and discharging efficiency at the historical state time based on the SOC difference between the historical state time and the next time of the lithium battery energy storage unit and the energy transfer value between the historical state time and the next time.

[0048] Among them, the historical state time indicates the time when the difference between the current SOC value of the lithium battery energy storage unit and the current time is less than a preset threshold, and the corresponding lithium battery energy storage unit model is the same as that of the current lithium battery energy storage unit. The SOC value of the lithium battery energy storage unit at each time is obtained according to the preset SOC model of the lithium battery energy storage unit.

[0049] The energy transfer value indicates the actual energy that a lithium-ion battery storage unit charges or releases between two points in time.

[0050] The preset SOC models for lithium-ion battery energy storage units and flywheel energy storage units are obtained according to the following steps: For composite energy storage systems, energy management, which takes into account the charging and discharging control and distribution of storage units, is the core of the energy storage system. In energy storage system simulation, the model construction of energy storage units is the focus of the simulation.

[0051] The energy storage units involved in this application embodiment include lithium battery energy storage units and flywheel energy storage units. Different energy storage units have their own models. At this time, the establishment of a large number of different models and simulation calculations during the simulation process limit the simulation speed of the energy storage system. According to its overall charging and discharging logic, the role of all energy storage units is to charge and discharge. Therefore, its overall model can be represented by the amount of electrical energy of the energy storage units. Generally, the state of charge (SOC) model can be directly used.

[0052] For lithium-ion battery energy storage units, the State of Charge (SOC) model is commonly used. The construction method can employ the ampere-hour integration method, which calculates the change in charge capacity by integrating the charging and discharging current. The specific SOC model for a lithium-ion battery energy storage unit is as follows: ; in It is the initial stage of lithium battery energy storage units. , It is the battery's rated capacity. It represents the real-time current, where t is the t-th time.

[0053] For flywheel energy storage units, the State of Charge (SOC) can also be used to reflect the proportion of the flywheel's currently stored energy to its maximum available energy. The calculation is not based on the electrical energy of chemical substances, but on the mechanical kinetic energy. The specific process is as follows: Given that the current rotational speed of the flywheel is n and its moment of inertia is H, then the current stored energy of the flywheel is... for: ; At time t, the maximum speed of the flywheel is... Then the flywheel's maximum stored energy for: ; The SOC model of the flywheel energy storage unit is obtained as follows: ; Thus, the SOC models corresponding to the lithium battery energy storage unit and the flywheel energy storage unit are obtained.

[0054] The known SOC of lithium-ion battery energy storage units and flywheel energy storage units reflects the current charge state of the energy storage units. However, in actual energy storage unit scheduling, the energy conversion efficiency of the energy storage units needs to be considered in order to allow the energy storage system to play its maximum scheduling role.

[0055] For energy storage units with different states of charge (SOC), the corresponding energy conversion efficiency is different, so it is necessary to estimate the charge and discharge efficiency first.

[0056] In one embodiment, determining the charge / discharge efficiency at a historical state time based on the SOC difference between the lithium-ion battery energy storage unit at a historical state time and the next time, and the energy transfer value at that historical state time and the next time, includes: Based on the current SOC value of the lithium battery energy storage unit, multiple historical moments in the same state are determined from the historical operating data of lithium battery energy storage units of the same model. For each historical state moment, the charge / discharge efficiency at that historical state moment is determined by the ratio of the energy transfer value between that historical state moment and the next moment to the absolute difference between the SOC value at that historical state moment and the SOC value at the next moment. Specifically, if the difference between the SOC value at that historical state moment and the next moment is greater than zero, the charge / discharge efficiency at that historical state moment is the charging efficiency; if the difference between the SOC value at that historical state moment and the next moment is less than zero, the charge / discharge efficiency at that historical state moment is the charging efficiency; if the difference between the SOC value at that historical state moment and the next moment is equal to zero, the charge / discharge efficiency determination operation is not performed.

[0057] The preset threshold can be set according to the actual situation, for example, 5.

[0058] At the current moment, the known SOC of the lithium-ion battery energy storage unit is... Then, from the historical operating data of lithium battery energy storage units of the same model as the current lithium battery, the historical moment when the difference between the corresponding SOC value and the SOC value of the current lithium battery energy storage unit is less than a preset threshold is called the historical same state moment. In this embodiment, the preset threshold is set to 5, and other embodiments can be set by themselves.

[0059] For each historical state-constrained moment, calculate the SOC value of the lithium-ion battery energy storage unit at that historical state-constrained moment and its value at the next moment. The absolute difference of the values ​​is denoted as It also reads the actual discharge or charge amount of the corresponding energy storage unit from the same historical state moment to the next moment, i.e., the energy transfer value. At a given point in time, determine the charge / discharge efficiency at that historical point in time. Charge and discharge efficiency The calculation formula is: When the difference between the SOC value at the same historical moment and the next moment is greater than zero, the charge / discharge efficiency at that same historical moment is the charging efficiency, i.e. hour, The charging efficiency is indicated by the difference between the SOC value at the same historical state moment and the next moment, which is less than zero. In other words, the charging efficiency at that historical state moment is the charging efficiency. hour, This indicates the discharge efficiency; if the difference between the SOC value at the same historical state moment and the next moment is zero, the operation to determine the charge and discharge efficiency is not performed.

[0060] S13. Based on the temperature difference between each historical time in the same state and the current time, and the charging and discharging efficiency of that historical time in the same state, determine the charging and discharging efficiency of the lithium battery energy storage unit at the current time.

[0061] In one embodiment, determining the charge / discharge efficiency of the lithium-ion battery energy storage unit at the current moment based on the temperature difference between each historical time point in the same state and the current moment, and the charge / discharge efficiency at that historical time point in the same state, includes: Calculate the reciprocal of the absolute difference in temperature between each historical moment in the same state and the current moment to obtain the reciprocal of the temperature at that historical moment in the same state; Calculate the product of the reciprocal of the temperature at each historical moment in the same state and the charge / discharge efficiency at that historical moment in the same state, and sum the reciprocals of the temperature at multiple historical moments in the same state to determine the charge / discharge efficiency of the lithium battery energy storage unit at the current moment.

[0062] Given that multiple historical states at the same time point are known, the charging and discharging efficiency at the current SOC can be obtained at each historical state point. The charging and discharging efficiencies at multiple current SOCs together reflect the charging and discharging efficiency of the energy storage unit at the current SOC. The charging and discharging efficiency of the lithium battery energy storage unit is actually affected by the electrochemical reaction, in which temperature directly affects the electrochemical reaction rate. The energy storage unit will continuously generate heat during operation, and the temperature of the energy storage unit affects the charging and discharging efficiency of the energy storage unit.

[0063] Therefore, the more consistent the temperature of the energy storage unit corresponding to the historical same-state moment is with the temperature of the current energy storage power supply at the current moment, the more the charge-discharge efficiency at the current SOC obtained at the corresponding historical same-state moment can represent the charge-discharge efficiency of the current energy storage unit at the current SOC; at this time, the charge-discharge efficiency of the lithium-ion energy storage unit at the current moment is obtained. It is: ; Among them, is the charge-discharge efficiency of the lithium-ion energy storage unit at the current moment, x is the xth historical same-state moment, m is the number of multiple historical same-state moments, is the temperature consistency of the energy storage unit corresponding to the xth historical same-state moment and the current moment, that is, the reciprocal of the temperature at this historical same-state moment, The calculation method of is the reciprocal of the absolute difference between the temperature at the xth historical same-state moment and the current moment, is the sum of the reciprocals of the temperatures of multiple historical same-state moments corresponding to the current moment, is the charge-discharge efficiency at the xth historical same-state moment. Determine the charge-discharge efficiency of the current lithium-ion energy storage unit at each moment, so as to obtain the charge efficiency and discharge efficiency corresponding to each SOC (0.2 < SOC < 0.8) of the lithium-ion energy storage unit.

[0064] S14. Calculate the scheduling demand of the energy storage unit according to the output prediction data and load prediction data.

[0065] Among them, the energy storage unit includes a lithium-ion energy storage unit and a flywheel energy storage unit.

[0066] In one embodiment, calculating the scheduling demand of the energy storage unit according to the output prediction data and load prediction data includes: Determine the sum of the photovoltaic output prediction value and the wind power output prediction value as the output prediction data; Calculate the difference between the output prediction data and the load prediction data at each moment to obtain the scheduling demand of the energy storage unit at each moment; in response to the scheduling demand being greater than zero, the energy storage unit charges; in response to the scheduling demand being less than zero, the energy storage unit discharges; in response to the scheduling demand being equal to zero, the energy storage unit does not work.

[0067] During the operation of the energy storage system, the main purpose is to balance the energy field and load fluctuations, so as to ensure the stability of the bus voltage. Among them, the stability of the bus voltage needs to ensure the consistent relationship between the electric energy on the load side and the energy side. Generally, when the electric energy in the energy field is greater than the load electric energy demand, the energy storage unit charges; when the electric energy in the energy field is less than the load side electric energy demand, the energy storage unit discharges; when the electric energy in the energy field is equal to the load side electric energy demand, the energy storage unit does not work.

[0068] In the above process, the output and load of the energy station are predicted. Therefore, the response strategy of the energy storage unit can be planned in advance by using the relationship between the predicted output and the predicted load of the energy station.

[0069] Based on the differences and changing relationships between the predicted output and load values ​​of energy power plants, the balancing needs of the busbars are reflected, and balancing is performed using energy storage units. The first step involves determining the charging and discharging control of the energy storage power source. The general process is as follows: the time scale of the energy power plant output prediction (including wind power and photovoltaic power prediction) is adjusted to 5 minutes. After increasing the time scale, linear interpolation is used to supplement the time periods without predicted values. Similarly, the time scale of the load prediction is adjusted to 5 minutes, and linear interpolation is used to supplement the time periods without predicted values.

[0070] Starting from the same moment, sum the predicted wind power output and photovoltaic power output at the same moment to obtain the predicted power output data of the energy station at the corresponding moment. Calculate the predicted power output data of energy stations at the same time. With load forecast data The difference yields the scheduling requirement at that moment. The calculation formula is: .in This indicates that the predicted output of the energy station is greater than the predicted load, and the energy storage unit needs to be charged. This indicates that the predicted output data of the energy station is less than the predicted load data, and the energy storage unit needs to discharge. This indicates that the energy station's output forecast data equals the load forecast data, and the energy storage unit is not working.

[0071] S15. Based on the charging and discharging efficiency of each energy storage unit, the frequency component in the scheduling requirements, and the SOC value of each energy storage unit at the current moment, determine the SOC value of each energy storage unit at the next moment.

[0072] The charging and discharging efficiency of the flywheel energy storage unit is obtained based on a preset efficiency curve. This efficiency curve can be calibrated experimentally, establishing the correlation between rotational speed and charging / discharging efficiency. In actual operation, the system reads the current rotational speed of the flywheel in real time and obtains the charging and discharging efficiency under the current operating conditions by looking up a table or interpolating the efficiency curve.

[0073] The SOC value of the flywheel energy storage unit at each moment is obtained according to the preset SOC model of the flywheel energy storage unit.

[0074] After determining the charging and discharging of the energy storage unit, the specific energy storage unit needs to be selected. The current scheduling of energy storage units is primarily based on the real-time SOC of the energy storage unit and the frequency of fluctuations in the energy station and load. It is known that flywheels have a fast charging and discharging rate but a smaller capacity, while lithium batteries have a slower charging and discharging rate but a larger capacity. Therefore, in combined lithium battery and flywheel energy storage, the scheduling logic generally follows that flywheels handle high-frequency fluctuations, while lithium batteries handle steady-state regulation.

[0075] In one embodiment, the SOC value of each energy storage unit at the next time moment is determined based on the charging and discharging efficiency of each energy storage unit, the frequency component in the scheduling demand, and the SOC value of each energy storage unit at the current time moment, including: The scheduling requirements at the current moment are filtered based on Kalman filtering. Components that are greater than the separation threshold are identified as high-frequency instantaneous components, and components that are not greater than the separation threshold are identified as low-frequency steady-state components. If only high-frequency transient components are present, the flywheel energy storage unit is activated; if only low-frequency steady-state components are present, the lithium battery energy storage unit is activated; if both high-frequency transient components and low-frequency steady-state components are present, both the flywheel energy storage unit and the lithium battery energy storage unit are activated. The energy required for the flywheel energy storage unit to exchange at the current moment is calculated based on its charging and discharging efficiency and the proportion of high-frequency instantaneous components in the dispatch demand. Similarly, the energy required for the lithium-ion battery energy storage unit to exchange at the current moment is calculated based on its charging and discharging efficiency and the proportion of low-frequency steady-state components in the dispatch demand. A positive sign in response to the dispatch demand indicates that the energy storage unit is discharging, while a negative sign indicates that the energy storage unit is charging. The energy storage units include both flywheel energy storage units and lithium-ion battery energy storage units. Based on the energy required for exchange by the flywheel energy storage unit at the current moment, the SOC value of the flywheel energy storage unit at the current moment, and the rated capacity of the flywheel energy storage unit, predict the SOC value of the flywheel energy storage unit at the next moment; based on the energy required for exchange by the lithium battery energy storage unit at the current moment, the SOC value of the lithium battery energy storage unit at the current moment, and the rated capacity of the lithium battery energy storage unit, predict the SOC value of the lithium battery energy storage unit at the next moment.

[0076] The separation threshold can be set according to the actual situation, for example, 0.1Hz.

[0077] For the energy storage system call at the current moment, the regular call logic applies, and the specific process is as follows: Define the scheduling requirements at the current moment. , which is the difference between the power output forecast data and the load forecast data at the current moment, where t0 is the current moment.

[0078] Based on Kalman filtering, for the current time... The filter process separates the high-frequency instantaneous components and the low-frequency steady-state components. The separation threshold is 0.1Hz, meaning that components greater than 0.1Hz are high-frequency instantaneous components, and the remaining components are low-frequency steady-state components. Kalman filtering is a well-known existing technique and will not be described in detail here.

[0079] If only high-frequency transient components exist, then the flywheel energy storage unit is activated; If only low-frequency steady-state components exist, then the lithium battery energy storage unit is activated; If there are high-frequency instantaneous components and low-frequency steady-state components, then both the flywheel energy storage unit and the lithium battery energy storage unit will be activated simultaneously.

[0080] Determine the SOC value of different energy storage units at the current moment and analyze the SOC feasibility, where when When the SOC of the energy storage unit is less than 80%, it indicates that the energy storage unit is charging. If the required SOC is less than 80%, the energy storage unit will be used directly; otherwise, it will not be used and another energy storage unit will be used instead. When the SOC of the energy storage unit is greater than 80%, it indicates that the energy storage unit is discharging. At this time, the SOC of the energy storage unit needs to be greater than 80%. If the energy storage unit to be called meets this requirement, it will be called directly. If it does not meet this requirement, it will not be called and another energy storage unit will be used instead.

[0081] Based on the above-mentioned calls, subsequent call strategies (pre-scheduling) can be planned in advance by predicting the power output and load fluctuations of the power station, so as to improve the response speed. The pre-scheduling time range is 30 minutes, and the pre-scheduling time scale is 5 minutes.

[0082] The pre-scheduled invocation process is as follows: (1) Based on the above method, obtain the current time. scheduling requirements ,in This represents the difference between the load forecast data and the output forecast data. (2) Obtain the current time of each lithium battery energy storage unit. State of charge At the current moment, with the flywheel energy storage unit of Simultaneously, obtain their respective charge and discharge efficiencies: the charge and discharge efficiency of the flywheel energy storage unit. The efficiency curve is obtained by querying the preset efficiency curve; the lithium battery energy storage unit at the current moment... charge and discharge efficiency Based on the above calculations The steps to obtain; (3) Scheduling requirements Based on frequency component decomposition, the proportion of high-frequency instantaneous components in scheduling requirements is: The proportion of low-frequency steady-state components in scheduling demand is Calculate the internal energy that the flywheel energy storage unit and the lithium battery energy storage unit need to exchange during the current scheduling period: the energy that the flywheel energy storage unit needs to exchange at the current moment. The calculation formula is: The energy that a lithium-ion battery energy storage unit needs to exchange at the current moment. The calculation formula is: =[ ×( )×Δt] / Where Δt is the scheduling time step; (4) According to The sign indicates the direction of charging and discharging: when When the value is greater than 0, the system is short of power, the energy storage unit discharges, and the internal energy decreases; when... When the energy level is less than 0, the system has a surplus, the energy storage unit is charged, and the internal energy increases. (5) Update the next time step respectively SOC prediction value: The lithium battery energy storage unit at the next moment State of charge for: ,in, The rated capacity (or equivalent energy capacity) of the lithium-ion battery energy storage unit, the flywheel energy storage unit at the next moment State of charge for: ,in, The rated capacity (or equivalent energy capacity) of the flywheel energy storage unit. (6) Based on the difference between the output forecast data and the load forecast data at the next time step, the scheduling demand at the next time step is predicted. Based on the predicted scheduling demand at the next moment and the predicted SOC values ​​of the lithium battery energy storage unit and the flywheel energy storage unit, and First, determine whether each energy storage unit has the capability to perform charging and discharging operations at any given time: if the lithium battery energy storage unit... If the energy level approaches the lower limit (e.g., below 10%), further discharge is limited; if it approaches the upper limit (e.g., above 90%), charging is limited. The same principle applies to flywheel energy storage units; their speed is determined based on their equivalent state of charge (SOC) to ensure they are within a safe operating range. Based on this, combined with… The size and frequency characteristics are dynamically adjusted to adjust the distribution ratio of high-frequency and low-frequency components—for example, when lithium batteries are unavailable, some low-frequency demand is temporarily transferred to the flywheel, or vice versa.

[0083] (7) Repeat steps (2) to (6) to generate a complete pre-scheduling sequence for energy storage units within the preset scheduling time range. Through the embodiments of this application, the system can plan the switching of the operating status of energy storage units in advance, reduce real-time response delay, and improve the switching rate and system stability.

[0084] S16. Based on the SOC value of each energy storage unit at the next moment, a scheduling plan is made and simulated through a simulation platform.

[0085] Based on the above process, the power dispatch of the current composite energy storage system is determined, with the aim of stabilizing the bus voltage. At this time, simulation is used to analyze the working status of the composite energy storage system under different energy fluctuations and different load fluctuations.

[0086] A co-simulation scheme using Simulink and PSCAD is adopted. Simulink is responsible for control strategy development, i.e., embedding the calling logic of the energy storage unit, and for modeling the energy storage unit, i.e., embedding the unified model of the energy storage unit constructed in the above steps. PSCAD is responsible for electromagnetic transient simulation of the power system, including grid faults, power output fluctuations of energy plants, and load fluctuations.

[0087] (2) Set the time step, where the dynamic time step range of the power electronic device is 1μs-10μs; the time step range of the energy storage unit's charge and discharge response is 100μs-1ms; and the time step range of the energy dispatch strategy execution is 1min-15min.

[0088] (3) Perform real-time monitoring, including using the Simulink Dashboard module to create a GUI (Graphical User Interface) to display parameters such as temperature and SOC in real time; configure Prometheus to monitor performance indicators and set threshold alarms, such as sending a warning email if memory usage is >80%.

[0089] (4) Simulation verification: Real-time tracking of bus voltage. The stability of the current composite energy storage system is reflected by the fluctuation of bus voltage. The fluctuation of bus voltage is the absolute value of the difference between bus voltages at adjacent times. Then, high and low thresholds are set. When the bus voltage fluctuation is greater than the high threshold, the current composite energy storage system is judged to be unstable. When the bus voltage fluctuation is greater than or equal to the low threshold and less than or equal to the high threshold, the current composite energy storage system is judged to be generally stable. When the bus voltage fluctuation is less than the low threshold, the current composite energy storage system is judged to be stable.

[0090] This application calculates the scheduling requirements of energy storage units based on historical meteorological data, historical power output data, and historical load data. Combining this with a frequency component decomposition strategy for scheduling requirements, it identifies the distribution characteristics of high-frequency instantaneous components and low-frequency steady-state components. Utilizing the charging and discharging efficiency of each energy storage unit and its current SOC value, it predicts the state of charge evolution trend for the next moment, thereby completing the coordinated deployment planning of flywheel and lithium-ion battery energy storage units before scheduling execution. This effectively avoids decision delays and power surges during real-time switching, significantly improving the response smoothness and operational stability of the hybrid energy storage system when dealing with net load fluctuations.

[0091] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0092] This application also provides a rapid simulation system for composite energy storage systems to accelerate computation, such as... Figure 2 As shown, the system includes: The prediction module 21 is used to predict power output prediction data based on historical meteorological data and historical power output data, and to predict load prediction data based on historical load data. The first determining module 22 is used to determine the charging and discharging efficiency of the historical same state moment based on the SOC difference between the historical same state moment and the next moment of the lithium battery energy storage unit and the energy transfer value between the historical same state moment and the next moment. The historical same state moment indicates the moment when the difference between the SOC value of the lithium battery energy storage unit and the current moment is less than a preset threshold, and the corresponding lithium battery energy storage unit model is the same as the model of the lithium battery energy storage unit. The SOC value of the lithium battery energy storage unit at each moment is obtained according to the preset SOC model of the lithium battery energy storage unit. The second determining module 23 is used to determine the charging and discharging efficiency of the lithium battery energy storage unit at the current moment based on the temperature difference between each historical time in the same state and the current moment, and the charging and discharging efficiency at the historical time in the same state. The calculation module 24 is used to calculate the scheduling requirements of the energy storage unit based on the output prediction data and load prediction data; the energy storage unit includes a lithium battery energy storage unit and a flywheel energy storage unit. The third determining module 25 is used to determine the SOC value of each energy storage unit at the next moment based on the charging and discharging efficiency of each energy storage unit, the frequency component in the scheduling requirements, and the SOC value of each energy storage unit at the current moment; the charging and discharging efficiency of the flywheel energy storage unit is obtained according to the preset efficiency curve, and the SOC value of the flywheel energy storage unit at each moment is obtained according to the preset SOC model of the flywheel energy storage unit. Simulation module 26 is used to perform scheduling planning based on the SOC value of each energy storage unit at the next moment, and to perform simulation through the simulation platform.

[0093] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs.

[0094] Figure 3 This is a schematic diagram of the structure of an electronic device according to an example embodiment of this application. The electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor. When the processor executes the computer program, it implements the method described in any of the above embodiments. Figure 3 The electronic device 30 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0095] like Figure 3 As shown, the electronic device 30 can be manifested as a general-purpose computing device, such as a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including memory 32 and processor 31).

[0096] Bus 33 includes a data bus, an address bus, and a control bus.

[0097] The memory 32 may include volatile memory, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.

[0098] The memory 32 may also include a program tool 325 (or utility) having a set (at least one) program module 324, such program module 324 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0099] The processor 31 executes various functional applications and data processing, such as the methods provided in any of the above embodiments, by running computer programs stored in the memory 32.

[0100] Electronic device 30 can also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 35. Furthermore, electronic device 30 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public network, such as the Internet) via network adapter 36. As shown, network adapter 36 communicates with other modules of electronic device 30 via bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with electronic device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0101] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0102] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in any of the above embodiments.

[0103] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0104] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0105] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method described in any of the above embodiments.

[0106] The program code for executing the computer program product of this application can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0107] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

[0109] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A rapid simulation method for a composite energy storage system with accelerated computation, characterized in that, The method includes: Based on historical meteorological data and historical power output data, power output forecast data is obtained, and based on historical load data, load forecast data is obtained. Based on the SOC difference between the historical state time and the next time of the lithium battery energy storage unit, and the energy transfer value between the historical state time and the next time, the charging and discharging efficiency at the historical state time is determined; the historical state time indicates the time when the difference between the current SOC value of the lithium battery energy storage unit and the current time is less than a preset threshold, and the corresponding lithium battery energy storage unit model is the same as the current lithium battery energy storage unit model. The SOC value of the lithium battery energy storage unit at each time is obtained according to the preset SOC model of the lithium battery energy storage unit. Based on the temperature difference between each historical moment in the same state and the current moment, and the charging and discharging efficiency of that historical moment in the same state, the charging and discharging efficiency of the lithium battery energy storage unit at the current moment is determined. Based on output forecast data and load forecast data, the scheduling requirements of energy storage units are calculated; energy storage units include lithium battery energy storage units and flywheel energy storage units. Based on the charging and discharging efficiency of each energy storage unit, the frequency component in the scheduling requirements, and the SOC value of each energy storage unit at the current moment, the SOC value of each energy storage unit at the next moment is determined respectively; the charging and discharging efficiency of the flywheel energy storage unit is obtained according to the preset efficiency curve, and the SOC value of the flywheel energy storage unit at each moment is obtained according to the preset SOC model of the flywheel energy storage unit. Scheduling planning is performed based on the SOC value of each energy storage unit at the next time step, and simulation is conducted through a simulation platform.

2. The rapid simulation method for a composite energy storage system with accelerated computation as described in claim 1, characterized in that, The process of predicting power output based on meteorological data and historical power output data includes: Based on historical meteorological data, the light intensity is predicted to obtain a light intensity prediction sequence within the first preset time period; For each light intensity value in the light intensity prediction sequence, multiple historical light intensity values ​​with the smallest difference from the light intensity value are retrieved from the historical light intensity data, and the photovoltaic output value at the time corresponding to the multiple historical light intensity values ​​is obtained. The average value of the multiple photovoltaic output values ​​is calculated to obtain the photovoltaic independent output prediction value corresponding to the light intensity value, so as to obtain the photovoltaic independent output prediction sequence corresponding to the light intensity prediction sequence. The predicted light intensity sequence is subjected to sliding matching in the historical light intensity sequence. The mean of the absolute difference between the predicted light intensity sequence and the historical light intensity subsequence in each window is calculated, and the window with the smallest mean is selected as the first target window. The historical power output sequence corresponding to the first target window is determined as the overall photovoltaic power output prediction sequence. The historical light intensity data includes the historical light intensity sequence. Calculate the absolute difference between the predicted independent photovoltaic output values ​​at each adjacent time point in the photovoltaic independent output prediction sequence to obtain the photovoltaic change sequence; The photovoltaic output prediction value at that moment is determined based on the maximum value in the photovoltaic change sequence, the photovoltaic independent output prediction value at each moment in the photovoltaic independent output prediction sequence, and the photovoltaic overall output prediction value at that moment in the photovoltaic overall output prediction sequence. Based on historical meteorological data, wind speed and wind direction are predicted to obtain wind speed prediction sequences and wind direction prediction sequences within the first preset time period. For each wind speed value in the wind speed prediction sequence, multiple historical wind speed values ​​with the smallest difference from the given wind speed value are retrieved from historical wind speed data. The wind power output value at the corresponding time for each of these multiple historical wind speed values ​​is obtained, and the average of these multiple wind power output values ​​is calculated to obtain the wind speed independent power output prediction value corresponding to the given wind speed value, thus obtaining the wind speed independent power output prediction sequence corresponding to the wind speed prediction sequence. For each wind direction value in the wind direction prediction sequence, multiple historical wind direction values ​​with the smallest difference from the given wind direction value are retrieved from historical wind direction data. The wind direction output value at the corresponding time for each of these multiple historical wind direction values ​​is obtained, and the average of these multiple wind direction output values ​​is calculated to obtain the wind direction independent power output prediction value corresponding to the given wind direction value, thus obtaining the wind direction independent power output prediction sequence corresponding to the wind direction prediction sequence. The wind speed prediction sequence is subjected to sliding matching within the historical wind speed sequence. The mean of the absolute difference between the wind speed prediction sequence and the historical wind speed subsequence within each window is calculated, and the window with the smallest mean is selected as the second target window. The historical power output sequence corresponding to the second target window is determined as the overall wind speed power output prediction sequence. Historical wind speed data includes historical wind speed sequences. The wind direction prediction sequence is subjected to sliding matching within the historical wind direction sequence. The mean of the absolute difference between the wind direction prediction sequence and the historical wind direction subsequence within each window is calculated, and the window with the smallest mean is selected as the third target window. The historical power output sequence corresponding to the third target window is determined as the overall wind direction power output prediction sequence. Historical wind speed data includes historical wind direction sequences. Calculate the absolute difference between the wind speed independent output prediction values ​​at each adjacent time in the wind speed independent output prediction sequence to obtain the wind speed change sequence; calculate the absolute difference between the wind direction independent output prediction values ​​at each adjacent time in the wind direction independent output prediction sequence to obtain the wind direction change sequence. The wind speed output prediction value for that moment is determined based on the maximum value in the wind speed change sequence, the wind speed independent output prediction value at each moment in the wind speed independent output prediction sequence, and the wind speed overall output prediction value at that moment in the wind speed overall output prediction sequence; the wind direction output prediction value for that moment is determined based on the maximum value in the wind direction change sequence, the wind direction independent output prediction value at each moment in the wind direction independent output prediction sequence, and the wind direction overall output prediction value at that moment in the wind direction overall output prediction sequence. The average of the wind speed forecast and the wind direction power output forecast for each moment is calculated to obtain the wind power output forecast for that moment; the power output forecast data includes the photovoltaic power output forecast and the wind power output forecast.

3. The rapid simulation method for a composite energy storage system with accelerated computation as described in claim 1, characterized in that, The process of obtaining load prediction data based on historical load data includes: Obtain historical load data within a second preset time period prior to the current moment; the second preset time period is greater than the first preset time period; For any point in the current forecast period, search for historical points in the historical load data that are several points apart from that point, and determine the load value of that historical point as the load forecast value for that point, so as to obtain the load forecast data for the current forecast period.

4. The rapid simulation method for a composite energy storage system with accelerated computation as described in claim 1, characterized in that, The step of determining the charge / discharge efficiency at a historical state time based on the SOC difference between the lithium battery energy storage unit at a historical state time and the next time, and the energy transfer value at that historical state time and the next time, includes: Based on the current SOC value of the lithium battery energy storage unit, multiple historical moments in the same state are determined from the historical operating data of lithium battery energy storage units of the same model. For each historical state moment, the charge / discharge efficiency at that historical state moment is determined by the ratio of the energy transfer value between that historical state moment and the next moment to the absolute difference between the SOC value at that historical state moment and the SOC value at the next moment. Specifically, if the difference between the SOC value at that historical state moment and the next moment is greater than zero, the charge / discharge efficiency at that historical state moment is the charging efficiency; if the difference between the SOC value at that historical state moment and the next moment is less than zero, the charge / discharge efficiency at that historical state moment is the charging efficiency; if the difference between the SOC value at that historical state moment and the next moment is equal to zero, the charge / discharge efficiency determination operation is not performed.

5. The rapid simulation method for a composite energy storage system with accelerated computation as described in claim 4, characterized in that, The step of determining the charging and discharging efficiency of the lithium battery energy storage unit at the current moment based on the temperature difference between each historical time point and the current moment, and the charging and discharging efficiency at each historical time point, includes: Calculate the reciprocal of the absolute difference in temperature between each historical moment in the same state and the current moment to obtain the reciprocal of the temperature at that historical moment in the same state; Calculate the product of the reciprocal of the temperature at each historical moment in the same state and the charge / discharge efficiency at that historical moment in the same state, and sum the reciprocals of the temperature at multiple historical moments in the same state to determine the charge / discharge efficiency of the lithium battery energy storage unit at the current moment.

6. The rapid simulation method for a composite energy storage system with accelerated computation as described in claim 2, characterized in that, The step of calculating the scheduling requirements of the energy storage unit based on the output forecast data and load forecast data includes: The sum of the predicted photovoltaic power output and the predicted wind power output is determined as the power output prediction data; The difference between the output forecast data and the load forecast data at each time moment is calculated to obtain the scheduling demand of the energy storage unit at each time moment; if the scheduling demand is greater than zero, the energy storage unit is charged; if the scheduling demand is less than zero, the energy storage unit is discharged; if the scheduling demand is equal to zero, the energy storage unit does not work.

7. The rapid simulation method for a composite energy storage system with accelerated computation as described in claim 6, characterized in that, The step of determining the SOC value of each energy storage unit at the next time moment based on the charging and discharging efficiency of each energy storage unit, the frequency component in the scheduling demand, and the SOC value of each energy storage unit at the current time includes: The scheduling requirements at the current moment are filtered based on Kalman filtering. Components that are greater than the separation threshold are identified as high-frequency instantaneous components, and components that are not greater than the separation threshold are identified as low-frequency steady-state components. If only high-frequency transient components are present, the flywheel energy storage unit is activated; if only low-frequency steady-state components are present, the lithium battery energy storage unit is activated; if both high-frequency transient components and low-frequency steady-state components are present, both the flywheel energy storage unit and the lithium battery energy storage unit are activated. The energy required for the flywheel energy storage unit to exchange at the current moment is calculated based on its charging and discharging efficiency and the proportion of high-frequency instantaneous components in the dispatch demand. Similarly, the energy required for the lithium-ion battery energy storage unit to exchange at the current moment is calculated based on its charging and discharging efficiency and the proportion of low-frequency steady-state components in the dispatch demand. A positive sign in response to the dispatch demand indicates that the energy storage unit is discharging, while a negative sign indicates that the energy storage unit is charging. The energy storage units include both flywheel energy storage units and lithium-ion battery energy storage units. Based on the energy required for exchange by the flywheel energy storage unit at the current moment, the SOC value of the flywheel energy storage unit at the current moment, and the rated capacity of the flywheel energy storage unit, predict the SOC value of the flywheel energy storage unit at the next moment; based on the energy required for exchange by the lithium battery energy storage unit at the current moment, the SOC value of the lithium battery energy storage unit at the current moment, and the rated capacity of the lithium battery energy storage unit, predict the SOC value of the lithium battery energy storage unit at the next moment.

8. A rapid simulation system for a composite energy storage system to accelerate computation, characterized in that, The system includes: The forecasting module is used to predict power output data based on historical meteorological data and historical power output data, and to predict load data based on historical load data. The first determining module is used to determine the charging and discharging efficiency of the historical same state moment based on the SOC difference between the historical same state moment and the next moment, and the energy transfer value between the historical same state moment and the next moment. The historical same state moment indicates the moment when the difference between the current SOC value of the lithium battery energy storage unit and the current moment is less than a preset threshold, and the corresponding lithium battery energy storage unit model is the same as the model of the lithium battery energy storage unit. The SOC value of the lithium battery energy storage unit at each moment is obtained according to the preset SOC model of the lithium battery energy storage unit. The second determining module is used to determine the charging and discharging efficiency of the lithium battery energy storage unit at the current moment based on the temperature difference between each historical time in the same state and the current moment, and the charging and discharging efficiency at the historical time in the same state. The calculation module is used to calculate the scheduling requirements of the energy storage units based on the output forecast data and load forecast data; the energy storage units include lithium battery energy storage units and flywheel energy storage units. The third determining module is used to determine the SOC value of each energy storage unit at the next moment based on the charging and discharging efficiency of each energy storage unit, the frequency component in the scheduling requirements, and the SOC value of each energy storage unit at the current moment; the charging and discharging efficiency of the flywheel energy storage unit is obtained according to the preset efficiency curve, and the SOC value of the flywheel energy storage unit at each moment is obtained according to the preset SOC model of the flywheel energy storage unit. The simulation module is used to perform scheduling planning based on the SOC value of each energy storage unit at the next time step, and to perform simulation through the simulation platform.

9. The rapid simulation system for a composite energy storage system with accelerated computation as described in claim 8, characterized in that, The prediction module is also used for: Based on historical meteorological data, the light intensity is predicted to obtain a light intensity prediction sequence within the first preset time period; For each light intensity value in the light intensity prediction sequence, multiple historical light intensity values ​​with the smallest difference from the light intensity value are retrieved from the historical light intensity data, and the photovoltaic output value at the time corresponding to the multiple historical light intensity values ​​is obtained. The average value of the multiple photovoltaic output values ​​is calculated to obtain the photovoltaic independent output prediction value corresponding to the light intensity value, so as to obtain the photovoltaic independent output prediction sequence corresponding to the light intensity prediction sequence. The predicted light intensity sequence is subjected to sliding matching in the historical light intensity sequence. The mean of the absolute difference between the predicted light intensity sequence and the historical light intensity subsequence in each window is calculated, and the window with the smallest mean is selected as the first target window. The historical power output sequence corresponding to the first target window is determined as the overall photovoltaic power output prediction sequence. The historical light intensity data includes the historical light intensity sequence. Calculate the absolute difference between the predicted independent photovoltaic output values ​​at each adjacent time point in the photovoltaic independent output prediction sequence to obtain the photovoltaic change sequence; The photovoltaic output prediction value at that moment is determined based on the maximum value in the photovoltaic change sequence, the photovoltaic independent output prediction value at each moment in the photovoltaic independent output prediction sequence, and the photovoltaic overall output prediction value at that moment in the photovoltaic overall output prediction sequence. Based on historical meteorological data, wind speed and wind direction are predicted to obtain wind speed prediction sequences and wind direction prediction sequences within the first preset time period. For each wind speed value in the wind speed prediction sequence, multiple historical wind speed values ​​with the smallest difference from the given wind speed value are retrieved from historical wind speed data. The wind power output value at the corresponding time for each of these multiple historical wind speed values ​​is obtained, and the average of these multiple wind power output values ​​is calculated to obtain the wind speed independent power output prediction value corresponding to the given wind speed value, thus obtaining the wind speed independent power output prediction sequence corresponding to the wind speed prediction sequence. For each wind direction value in the wind direction prediction sequence, multiple historical wind direction values ​​with the smallest difference from the given wind direction value are retrieved from historical wind direction data. The wind direction output value at the corresponding time for each of these multiple historical wind direction values ​​is obtained, and the average of these multiple wind direction output values ​​is calculated to obtain the wind direction independent power output prediction value corresponding to the given wind direction value, thus obtaining the wind direction independent power output prediction sequence corresponding to the wind direction prediction sequence. The wind speed prediction sequence is subjected to sliding matching within the historical wind speed sequence. The mean of the absolute difference between the wind speed prediction sequence and the historical wind speed subsequence within each window is calculated, and the window with the smallest mean is selected as the second target window. The historical power output sequence corresponding to the second target window is determined as the overall wind speed power output prediction sequence. Historical wind speed data includes historical wind speed sequences. The wind direction prediction sequence is subjected to sliding matching within the historical wind direction sequence. The mean of the absolute difference between the wind direction prediction sequence and the historical wind direction subsequence within each window is calculated, and the window with the smallest mean is selected as the third target window. The historical power output sequence corresponding to the third target window is determined as the overall wind direction power output prediction sequence. Historical wind speed data includes historical wind direction sequences. Calculate the absolute difference between the wind speed independent output prediction values ​​at each adjacent time in the wind speed independent output prediction sequence to obtain the wind speed change sequence; calculate the absolute difference between the wind direction independent output prediction values ​​at each adjacent time in the wind direction independent output prediction sequence to obtain the wind direction change sequence. The wind speed output prediction value for that moment is determined based on the maximum value in the wind speed change sequence, the wind speed independent output prediction value at each moment in the wind speed independent output prediction sequence, and the wind speed overall output prediction value at that moment in the wind speed overall output prediction sequence; the wind direction output prediction value for that moment is determined based on the maximum value in the wind direction change sequence, the wind direction independent output prediction value at each moment in the wind direction independent output prediction sequence, and the wind direction overall output prediction value at that moment in the wind direction overall output prediction sequence. The average of the wind speed forecast and the wind direction power output forecast for each moment is calculated to obtain the wind power output forecast for that moment; the power output forecast data includes the photovoltaic power output forecast and the wind power output forecast.

10. The rapid simulation system for a composite energy storage system with accelerated computation as described in claim 8, characterized in that, The prediction module is also used for: Obtain historical load data within a second preset time period prior to the current moment; the second preset time period is greater than the first preset time period; For any point in the current forecast period, search for historical points in the historical load data that are several points apart from that point, and determine the load value of that historical point as the load forecast value for that point, so as to obtain the load forecast data for the current forecast period.

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