A method and system for coordinating and regulating energy economy of a site in a time-series data fusion manner

Abstract

The present application relates to the technical field of energy storage scheduling control, in particular to a kind of time sequence data fusion's field station energy economic collaborative regulation and control method and system, comprising: obtaining the health status of battery, cumulative equivalent full cycle number, the rated charge-discharge power of battery and the electricity price of each time;According to the electricity price change in the local time range of each time, combined with the health status of battery and cumulative equivalent full cycle number at each time, the degree of loss of battery at each time is obtained, the operating mode of energy field station at each time is set, and the energy field station is further regulated and controlled in combination with the degree of loss of each battery in energy field station at each time.The present application realizes the energy field station optimization method of energy storage battery hierarchical regulation and dynamic constraint control by constructing battery state and electricity price fluctuation collaborative model.

Description

Technical Field

[0001] This invention relates to the field of energy storage dispatch and control technology, specifically to a method and system for coordinated energy economic regulation of power stations based on time-series data fusion. Background Technology

[0002] With the development of new power systems and distributed energy technologies, energy storage batteries, as the core regulation unit in energy stations, are widely used in applications such as peak shaving and valley filling, grid fluctuation smoothing, and renewable energy consumption. In actual operation, energy stations are usually composed of a large number of battery units. Each battery is affected by charging and discharging frequency, load fluctuations, and changes in environmental conditions during long-term operation, resulting in significant individual differences in their health status and aging degree. At the same time, the operating status of the external power system has obvious time-series fluctuation characteristics, especially the dynamic changes in electricity prices or energy dispatch signals within a short time scale, which makes it necessary for energy stations to respond quickly to regulation needs at different time scales.

[0003] Existing battery control methods in power plants typically focus on single-dimensional control, making it difficult for control strategies to simultaneously address system responsiveness and equipment lifespan protection requirements. This results in some high-loss batteries enduring high-frequency or deep-charge / discharge tasks for extended periods, accelerating their lifespan degradation and reducing the long-term operational stability of power plants. Summary of the Invention

[0004] This invention provides a method and system for coordinated regulation of energy economy in power plants based on time-series data fusion, in order to solve the existing problem: the existing battery regulation methods of power plants are difficult to simultaneously take into account the system response capability and equipment life protection requirements, thereby reducing the long-term operational stability of power plants.

[0005] The present invention provides a time-series data fusion-based method and system for coordinated energy economic regulation of power stations, which adopts the following technical solution: One embodiment of the present invention provides a method for coordinated energy economic regulation of power plants based on time-series data fusion, the method comprising the following steps: Obtain the battery's health status, cumulative equivalent full cycle count, rated charge / discharge power, and electricity price at each time point; Based on the electricity price changes within a local time range at each moment, the electricity price fluctuation coefficient at each moment is obtained; based on the battery health status and cumulative equivalent full cycle count at each moment, the battery wear level at each moment is obtained; combined with the electricity price fluctuation coefficient at each moment, the battery regulation intensity coefficient at each moment is obtained; based on the battery regulation intensity coefficient at each moment, the operation mode of the energy station at each moment is set. Based on the degree of wear and tear of each battery in the energy station at each time point, the overall aging degree of the energy station at each time point is obtained, and then the control batteries at each time point are selected from the energy station. Based on the degree of wear and tear of the control battery at each time point, the charging and discharging power of the control battery at each time point is obtained, and the upper limit of charging and the lower limit of discharging of the control battery at each time point are obtained to regulate the energy station.

[0006] Preferably, the method for obtaining the electricity price fluctuation coefficient at each moment based on the electricity price change within a local time range includes: For any given moment, a local time range is preset; all moments within the preceding local time range are recorded as local moments. For any local time, the absolute value of the difference in electricity price between the local time and the previous time is denoted as the electricity price change factor for the local time. The mean of the electricity price change factors at all local moments is taken as the electricity price change coefficient at that moment; the difference between the largest and smallest electricity price change factors at local moments is taken as the peak-valley price difference coefficient at that moment; the ratio of the standard deviation of the electricity price at all local moments to the mean of the electricity price at all local moments is denoted as the electricity price variation coefficient at that moment. Based on the electricity price change coefficient, peak-valley price difference coefficient, and electricity price variation coefficient at the specified time, the electricity price fluctuation coefficient at the specified time is obtained; The electricity price fluctuation coefficient at that time is positively correlated with the electricity price change coefficient at that time; The electricity price fluctuation coefficient at that time is positively correlated with the peak-valley price difference coefficient at that time; The electricity price fluctuation coefficient at that time is positively correlated with the electricity price variation coefficient at that time.

[0007] Preferably, the method for obtaining the degree of battery wear at each time point based on the battery's health status and cumulative equivalent full cycle count includes: For any given time, the degree of battery wear at that time is obtained based on the battery's health status and the cumulative equivalent full cycle count at that time; the degree of battery wear at that time is negatively correlated with the battery's health status at that time; and the degree of battery wear at that time is positively correlated with the battery's cumulative equivalent full cycle count at that time.

[0008] Preferably, the specific method for obtaining the battery regulation intensity coefficient at each time point is as follows: For any given time, the control intensity coefficient of the battery at that time is obtained based on the electricity price fluctuation coefficient at that time and the degree of battery loss at that time. The battery's regulation intensity coefficient at the specified time is positively correlated with the electricity price fluctuation coefficient at the specified time; the battery's regulation intensity coefficient at the specified time is negatively correlated with the battery's wear level at the specified time.

[0009] Preferably, the specific method for setting the operating mode of the energy station at each time point based on the battery's regulation intensity coefficient at each time point includes: Preset a high loss threshold, a low loss threshold, a high control intensity threshold, and a low control intensity threshold; For any given time, if the battery's regulation intensity coefficient is greater than the high regulation intensity threshold and the battery's loss level is less than the low loss level threshold, then the energy station's operation mode will be set to high regulation mode. If the battery's regulation intensity coefficient is greater than the low regulation intensity threshold and less than or equal to the high regulation intensity threshold at the specified time, and the battery's loss level is less than the high loss level threshold at the specified time, then the energy station's operation mode at the specified time will be set to medium-intensity regulation mode. If the operating mode of the energy station at the specified time is neither high-intensity control mode nor medium-intensity control mode, then the operating mode of the energy station at the specified time will be set to low-intensity control mode.

[0010] Preferably, the method for obtaining the overall aging level of the energy station at each time point based on the degree of wear and tear of each battery in the energy station at each time point includes: A pre-defined threshold for the degree of wear and tear is set. For any given time, the number of batteries in the energy station whose degree of wear and tear is greater than or equal to the threshold is recorded as the number of old batteries at that time. The ratio between the number of old batteries at that time and the number of batteries in the energy station is used as the aging coefficient of the energy station at that time. The overall aging level of the energy station at that time is obtained based on the degree of wear and tear of all batteries in the energy station at that time and the aging coefficient of the energy station at that time. The overall aging degree of the energy station at the specified time is positively correlated with the average degree of wear and tear of all batteries in the energy station at the specified time. The overall aging degree of the energy station at the specified time is positively correlated with the aging coefficient of the energy station at the specified time.

[0011] Preferably, the specific method for selecting the control batteries at each time point from the energy station includes: For any given time, the battery participation coefficient of the energy station is obtained based on the overall aging degree of the energy station at that time. The battery participation coefficient of the energy station at the specified time is negatively correlated with the overall aging degree of the energy station at the specified time. The product of the number of batteries in the energy station and the battery participation coefficient of the energy station at the stated time, rounded down, is taken as the number of batteries participating in regulation at the stated time. ; The energy station with the lowest loss A battery, serving as the regulating battery at the stated time.

[0012] Preferably, the method for obtaining the charging and discharging power of the controlled battery at each time point based on the degree of battery wear at each time point includes: Preset a high-intensity control coefficient, a medium-intensity control coefficient, a low-intensity control coefficient, a high-intensity aging attenuation coefficient, a medium-intensity aging attenuation coefficient, and a low-intensity aging attenuation coefficient; For any given time and any regulated battery, if the energy station's operating mode at that time is high-intensity regulation mode, then the product of the rated charge / discharge power of the regulated battery at that time and the high-intensity regulation coefficient is taken as the basic charge / discharge power of the regulated battery at that time; the product of the degree of loss of the regulated battery at that time and the high-intensity aging degradation coefficient is taken as the loss risk factor of the regulated battery at that time; the difference obtained by subtracting the loss risk factor of the regulated battery at that time from 1 is multiplied by the basic charge / discharge power of the regulated battery at that time, and this product is taken as the charge / discharge power of the regulated battery at that time. If the operating mode of the energy station at the specified time is medium-intensity control mode or low-intensity control mode, the charging and discharging power of the control battery at the specified time can be obtained similarly.

[0013] Preferably, the specific method for obtaining the upper limit of charging and the lower limit of discharging of the controlled battery at each time point is as follows: For any given time and any controlled battery, based on the degree of wear of the controlled battery at that time, the upper limit of charging and the lower limit of discharging of the controlled battery at that time are obtained; The upper limit of the charging of the control battery at the specified time is negatively correlated with the degree of loss of the control battery at the specified time. The discharge limit of the controlled battery at the specified time is positively correlated with the degree of wear of the controlled battery at the specified time.

[0014] Another embodiment of the present invention provides a power station energy economic coordinated control system based on time-series data fusion, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned time-series data fusion power station energy economic coordinated control methods.

[0015] The beneficial effects of the technical solution of this invention are as follows: This invention acquires the battery's health status, cumulative equivalent full cycle count, rated charge / discharge power, and electricity price at various times, and constructs an electricity price fluctuation coefficient based on electricity price changes within a local time range. Simultaneously, it combines the battery's health status and cycle count to construct the degree of wear, thereby quantifying the fluctuation characteristics of the battery's external operating environment and its internal lifespan degradation. Furthermore, it generates a control intensity coefficient by fusing the electricity price fluctuation coefficient and the battery's degree of wear, and accordingly sets a graded operation mode for the energy station, enabling the energy station to adaptively adjust the control intensity based on the system's dynamic fluctuations and the equipment's health status. Simultaneously, it performs aggregated analysis of the wear levels of each battery to obtain the comprehensive aging degree of the energy station, and dynamically screens batteries participating in control based on this aging degree, thereby achieving a differentiated control mechanism for batteries in different lifespan states, avoiding high-wear batteries from undertaking high-intensity control tasks for extended periods. Furthermore, it dynamically adjusts the charge / discharge power, charging upper limit, and discharging lower limit of the controlled batteries based on their wear level, causing the battery's operating boundary to adaptively shrink with the degree of aging, thereby reducing the continuous impact of extreme operating conditions on battery lifespan and improving the long-term stability of the energy station's operation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 the present invention. 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 illustrating the steps of a time-series data fusion-based method for coordinated energy and economic regulation of power stations according to the present invention. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a time-series data fusion-based power station energy economic coordinated control method and system proposed according to the present invention. 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 invention pertains.

[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of the time-series data fusion-based energy economic coordinated control method and system for power stations provided by this invention.

[0021] Please see Figure 1 The diagram illustrates a flowchart of a time-series data fusion-based method for coordinated energy economic regulation of power plants, according to an embodiment of the present invention. The method includes the following steps: Step S001: Obtain the battery's health status, cumulative equivalent full cycle count, rated charge / discharge power, and electricity price at each time point.

[0022] It should be noted that the battery's health status, cumulative equivalent full cycle count, and rated charge / discharge power reflect the battery's current lifespan, historical operating load, and actual output capacity, respectively. Electricity price, on the other hand, reflects changes in energy supply and demand at different times. Therefore, this embodiment establishes a correlation between the internal equipment operating status and external energy change status of the energy station by simultaneously acquiring equipment status data and external time-series data. Electricity price typically exhibits continuous fluctuations over time, and the regulation needs of the energy station vary significantly across different time periods. Furthermore, the battery's lifespan continuously changes during long-term operation. Therefore, it is necessary to simultaneously acquire equipment lifespan status and external time-series change data to provide fundamental data support for subsequently constructing a dynamic collaborative regulation mechanism that balances energy regulation capabilities and equipment lifespan protection.

[0023] Specifically, the battery management system of the energy station obtains the battery's state of health, cumulative equivalent full cycles, and rated charge / discharge power at each moment, and obtains the electricity price at each moment through the grid dispatch system. In this embodiment, a moment is described in 1 minute. (For the battery's state of health, the battery management system can obtain the battery's state of health for each day, and use the battery's state of health for that day as the battery's state of health at each moment within that day; for the cumulative equivalent full cycles, whenever the battery's cumulative equivalent full cycles are updated, the updated cumulative equivalent full cycles are used as the battery's cumulative equivalent full cycles at the moment after the update.)

[0024] Step S002: Based on the electricity price changes within the local time range at each moment, obtain the electricity price fluctuation coefficient at each moment; based on the battery health status and cumulative equivalent full cycle count at each moment, obtain the battery wear level at each moment; combined with the electricity price fluctuation coefficient at each moment, obtain the battery regulation intensity coefficient at each moment; based on the battery regulation intensity coefficient at each moment, set the operation mode of the energy station at each moment.

[0025] It should be noted that electricity prices in the energy market exhibit significant temporal fluctuations due to changes in grid load, renewable energy output, and energy demand. The intensity of regulation tasks undertaken by energy storage devices also varies across different fluctuation phases. Generally, the more pronounced the electricity price fluctuations within a local timeframe, the more drastic the supply and demand changes in the current energy system, requiring energy plants to possess higher dynamic regulation capabilities. However, high-frequency, high-power charging and discharging activities accelerate battery lifespan degradation. Therefore, this embodiment does not directly control the operation of energy plants solely based on electricity price changes. Instead, it further combines the current battery lifespan status with a joint analysis of external energy demand changes and internal equipment carrying capacity. An electricity price fluctuation coefficient is constructed to characterize the dynamic changes in the energy system at the current moment, and a loss level coefficient is constructed to characterize the current battery lifespan degradation state. Furthermore, a regulation intensity coefficient is constructed to synergistically integrate these two factors. This allows energy plants to prioritize the use of batteries with good lifespans for regulation when external energy fluctuations are large, while reducing the regulation intensity when equipment loss levels are high. This prevents equipment from maintaining high-intensity operation for extended periods in the later stages of its lifespan, thereby achieving a dynamic balance between energy regulation demand and equipment lifespan status.

[0026] Specifically, for any given moment, a local time range is preset. The specific value of the local time range can be set according to the actual situation. This embodiment does not make a hard requirement. In this embodiment, the local time range is 30 minutes as an example. All moments within the local time range before the given moment are recorded as local moments. For any local time, the absolute value of the difference in electricity price between the local time and the previous time is denoted as the electricity price change factor for the local time. The mean of the electricity price change factors at all local moments is taken as the electricity price change coefficient at that moment; the difference between the largest and smallest electricity price change factors at local moments is taken as the peak-valley price difference coefficient at that moment; the ratio of the standard deviation of the electricity price at all local moments to the mean of the electricity price at all local moments is denoted as the electricity price variation coefficient at that moment. Based on the electricity price change coefficient, peak-valley price difference coefficient, and electricity price variation coefficient at the specified time, the electricity price fluctuation coefficient at the specified time is obtained; The electricity price fluctuation coefficient at that time is positively correlated with the electricity price change coefficient at that time; The electricity price fluctuation coefficient at that time is positively correlated with the peak-valley price difference coefficient at that time; The electricity price fluctuation coefficient at that time is positively correlated with the electricity price variation coefficient at that time.

[0027] As an example, the specific formula for calculating the electricity price fluctuation coefficient at the stated time is as follows: ; In the formula, This represents the electricity price fluctuation coefficient at the stated time. The standard deviation of electricity prices at all local moments; This represents the average electricity price at all local moments. The coefficient representing the change in electricity price at the stated time. This represents the peak-to-valley price difference coefficient at the stated time. This represents the preset electricity price change constant. The specific value can be set according to the actual situation. This embodiment does not make a hard requirement. In this embodiment, it is used as... Taking a price of 0.05 yuan per kilowatt-hour as an example, the purpose is to eliminate the dimension of the electricity price change coefficient, thereby integrating it with the electricity price variation coefficient / peak-valley price difference coefficient under a unified scale; This represents the preset peak-valley price difference constant. The specific value can be set according to the actual situation. This embodiment does not make a hard requirement. In this embodiment, it is used as... Taking 0.3 yuan per kilowatt-hour as an example, the purpose is to eliminate the dimension of the peak-valley price difference coefficient, so as to integrate it with the electricity price variation coefficient and the electricity price change coefficient under a unified scale.

[0028] It should be noted that the electricity price change coefficient reflects the average magnitude of electricity price changes between adjacent moments within a local time range. A larger coefficient indicates more significant price changes within that local time range. The peak-valley price difference coefficient reflects the dispersion of electricity price fluctuations within a local time range. A larger coefficient indicates more pronounced high-low fluctuations within that local time range. The electricity price variation coefficient quantifies the overall dispersion of electricity prices within a local time range by using the ratio of the standard deviation to the mean, thereby reducing the impact of different absolute electricity price levels on the fluctuation analysis results. Furthermore, by fusing the electricity price change coefficient, peak-valley price difference coefficient, and electricity price variation coefficient, the resulting electricity price fluctuation coefficient not only reflects the drastic changes in electricity prices over a short period but also reflects the dynamic trends of energy supply and demand within a local time range, thus improving the accuracy and stability of subsequent energy station regulation and control analysis.

[0029] Specifically, for any given time, the degree of battery wear at that time is obtained based on the battery's health status and the cumulative equivalent full cycle count at that time; the degree of battery wear at that time is negatively correlated with the battery's health status at that time; and the degree of battery wear at that time is positively correlated with the battery's cumulative equivalent full cycle count at that time.

[0030] As an example, the specific formula for calculating the degree of battery wear at the stated time is as follows: ; In the formula, This indicates the degree of battery wear at the stated time. This indicates that the battery's health status at the stated time is negatively correlated; This indicates the cumulative equivalent full cycle count of the battery at the stated time. This represents the preset attenuation constant. The specific value can be set according to the actual situation. This embodiment does not make a hard requirement. In this embodiment, it is used as... This will be described using an example (typically, when a battery's health reaches 80%, it needs to be disposed of). The purpose is to... The calculation results are normalized. This represents the preset cumulative equivalent full-cycle constant; The specific value can be set according to the actual situation. This embodiment does not make a hard requirement. In this embodiment, it is used as... Taking 3000 times as an example, the purpose is to eliminate the dimension of the cumulative equivalent full cycle count and normalize the cumulative equivalent full cycle count; The linear normalization function can be normalized using a maximum and minimum value normalization function. The maximum and minimum values ​​can be obtained based on historical data or prior experience. Adjusting, calibrating, or optimizing the maximum and minimum values ​​does not constitute a limitation of this invention. The final normalized value range is [0, 1].

[0031] It should be noted that the lifespan of a battery typically changes continuously with repeated use during long-term operation. Battery health status reflects the battery's current remaining lifespan; a lower health status indicates more significant degradation of the internal active materials and a higher overall degree of battery aging. The cumulative equivalent full cycle count reflects the cumulative charge and discharge load intensity the battery has endured throughout its history; a higher cumulative equivalent full cycle count indicates more cycles the battery has undergone over a long period, resulting in more significant cycle losses to its internal chemical system. Therefore, by integrating the battery health status degradation and the cumulative equivalent full cycle count, the resulting loss level can reflect not only the battery's current lifespan degradation state but also the cumulative loss trend formed during long-term operation. This improves the stability and accuracy of subsequent coordinated control of batteries with different lifespan states in energy plants.

[0032] Specifically, for any given time, the control intensity coefficient of the battery at that time is obtained based on the electricity price fluctuation coefficient at that time and the degree of battery loss at that time. The battery's regulation intensity coefficient at the specified time is positively correlated with the electricity price fluctuation coefficient at the specified time; the battery's regulation intensity coefficient at the specified time is negatively correlated with the battery's wear level at the specified time.

[0033] As an example, the specific formula for calculating the battery regulation intensity coefficient at the stated time is as follows: ; In the formula, This represents the regulation intensity coefficient of the battery at the stated time. This represents the electricity price fluctuation coefficient at the stated time. This indicates the degree of battery wear at the stated time. This indicates a pre-defined minimum positive number. The specific value can be set according to the actual situation. This embodiment does not make a hard requirement. In this embodiment, it is used as... The example given is 0.01, and the purpose of this explanation is to avoid the situation where the denominator is zero when performing fraction calculations.

[0034] It should be noted that during actual operation, the control intensity of an energy power station is typically affected not only by external energy fluctuations but also by the current lifespan of the batteries. When external energy fluctuations are severe, it indicates a higher dynamic adjustment demand in the current energy system, requiring the energy power station to possess stronger charge and discharge regulation capabilities. Conversely, when battery wear is high, maintaining high-intensity operation for an extended period can further accelerate the aging of the battery's internal chemical system. The electricity price fluctuation coefficient characterizes the dynamic fluctuation level of the energy system at the current moment, while the wear level characterizes the current lifespan degradation state of the batteries. A higher wear level indicates more pronounced aging due to long-term battery operation, resulting in a correspondingly lower control intensity coefficient. Therefore, this embodiment integrates the electricity price fluctuation coefficient and the wear level to achieve a control intensity coefficient that simultaneously possesses the ability to perceive external temporal fluctuations and the constraint capability of internal lifespan status. This allows the energy power station to achieve a synergistic balance between dynamic energy demand changes and equipment lifespan protection, thereby improving the control stability and equipment lifespan maintenance capabilities during long-term operation.

[0035] Specifically, a high loss threshold, a low loss threshold, a high control intensity threshold, and a low control intensity threshold are preset. The specific values ​​of the high loss threshold, low loss threshold, high control intensity threshold, and low control intensity threshold can be set according to the actual situation. This embodiment does not make a hard requirement. In this embodiment, the high loss threshold is equal to 0.7, the low loss threshold is equal to 0.3, the high control intensity threshold is equal to 0.7, and the low control intensity threshold is equal to 0.4 as an example. For any given time, if the battery's regulation intensity coefficient is greater than the high regulation intensity threshold and the battery's loss level is less than the low loss level threshold, then the energy station's operation mode will be set to high regulation mode. If the battery's regulation intensity coefficient is greater than the low regulation intensity threshold and less than or equal to the high regulation intensity threshold at the specified time, and the battery's loss level is less than the high loss level threshold at the specified time, then the energy station's operation mode at the specified time will be set to medium-intensity regulation mode. If the operating mode of the energy station at the specified time is neither high-intensity control mode nor medium-intensity control mode, then the operating mode of the energy station at the specified time will be set to low-intensity control mode.

[0036] It should be noted that when external energy fluctuations are significant and the overall battery life is good, energy stations typically possess strong dynamic adjustment capabilities. If a low-intensity operation mode is still used in this situation, the energy adjustment capability may not be fully utilized. Conversely, when battery wear is high, maintaining high-intensity operation for an extended period can further accelerate battery aging. Therefore, this embodiment analyzes the current operating state of the energy station by jointly adjusting the intensity coefficient and the degree of wear, and dynamically classifies the operating mode of the energy station based on different threshold ranges. The high-intensity adjustment mode corresponds to operating scenarios where the current energy system experiences significant dynamic changes and the battery life is good, allowing the energy station to undertake higher-intensity dynamic adjustment tasks. The medium-intensity adjustment mode corresponds to operating scenarios where energy fluctuations and equipment life are relatively balanced. The low-intensity adjustment mode corresponds to operating scenarios where battery wear is high or external dynamic adjustment requirements are low. This allows the energy station's operating mode to dynamically switch according to changes in external energy fluctuations and internal equipment life, thereby preventing the equipment from operating at a fixed intensity for extended periods and improving the energy station's long-term adaptability and equipment lifespan maintenance capabilities.

[0037] Step S003: Based on the degree of wear and tear of each battery in the energy station at each time point, obtain the overall aging degree of the energy station at each time point, and then select the control batteries for each time point from the energy station.

[0038] It should be noted that the aging degree of different batteries in an energy power station is usually not uniform during long-term operation. Some batteries may enter a high-loss state prematurely due to long-term high-frequency operation, deep charge and discharge, or differences in operating environment. If all batteries participate equally in the overall control of the energy power station, it is easy for older batteries to continuously bear high operating loads, thereby further accelerating their lifespan decay and reducing the overall operational stability of the energy power station. Therefore, this embodiment comprehensively analyzes the loss degree of each battery in the energy power station to obtain the overall aging degree of the energy power station, and then dynamically adjusts the range of batteries participating in the control based on the overall aging degree. When the overall aging degree of the energy power station is high, by reducing the battery participation coefficient, the number of batteries participating in high-frequency dynamic adjustment at the same time can be reduced, thereby reducing the overall operational loss risk of the energy power station. At the same time, by prioritizing the participation of batteries with low loss degree in the control, batteries with better lifespan can undertake more dynamic adjustment tasks, while batteries with poor lifespan can reduce the high-intensity operation frequency. This forms a differentiated collaborative control mechanism based on the life cycle state to improve the lifespan balance and operational stability of the energy power station during long-term operation.

[0039] Specifically, a loss level threshold is preset. The specific value of the loss level threshold can be set according to the actual situation. This embodiment does not make a hard requirement. In this embodiment, the loss level threshold is 0.7 as an example. For any time, the number of batteries in the energy station with a loss level greater than or equal to the loss level threshold at that time is recorded as the number of old batteries at that time. The ratio between the number of old batteries at that time and the number of batteries in the energy station is used as the aging coefficient of the energy station at that time. The overall aging level of the energy station at that time is obtained based on the degree of wear and tear of all batteries in the energy station at that time and the aging coefficient of the energy station at that time. The overall aging degree of the energy station at the specified time is positively correlated with the average degree of wear and tear of all batteries in the energy station at the specified time. The overall aging degree of the energy station at the specified time is positively correlated with the aging coefficient of the energy station at the specified time.

[0040] As an example, the specific formula for calculating the overall aging level of the energy station at the stated time is as follows: ; In the formula, This indicates the overall aging level of the energy station at the stated time. This represents the average degree of wear and tear on all batteries in the energy station at the stated time. This indicates the aging factor of the energy station at the stated time. The linear normalization function can be normalized using a maximum and minimum value normalization function. The maximum and minimum values ​​can be obtained based on historical data or prior experience. Adjusting, calibrating, or optimizing the maximum and minimum values ​​does not constitute a limitation of this invention. The final normalized value range is [0, 1].

[0041] It should be noted that the average wear level of all batteries in the energy station is used to reflect the overall average lifespan degradation level of the energy station; the aging coefficient is used to reflect the proportion of high-wear batteries in the energy station. The larger the aging coefficient, the lower the overall lifespan stability of the energy station. By integrating the average wear level and the proportion of aging batteries, the comprehensive aging level can not only reflect the overall lifespan degradation trend of the energy station, but also the degree of aggregation of high-wear batteries in the station. This avoids the omission of cases where local aging is severe but the overall average is not obvious when analyzing based solely on the average wear level, thereby improving the stability and accuracy of subsequent energy station coordinated control analysis.

[0042] Specifically, for any given time, the battery participation coefficient of the energy station is obtained based on the overall aging degree of the energy station at that time. The battery participation coefficient of the energy station at the specified time is negatively correlated with the overall aging degree of the energy station at the specified time. As an example, the specific formula for calculating the battery participation coefficient of the energy station at the stated time is as follows: ; In the formula, This represents the battery participation coefficient of the energy station at the stated time. This indicates the overall aging level of the energy station at the stated time. This indicates the preset weight of the aging effect. The specific value can be set according to the actual situation. This embodiment does not make a hard requirement. In this embodiment, it is used as... Taking this as an example, the purpose is to map the battery participation coefficient of the energy station to a preset range in order to retain the minimum number of regulated batteries.

[0043] The product of the number of batteries in the energy station and the battery participation coefficient of the energy station at the stated time, rounded down, is taken as the number of batteries participating in regulation at the stated time. ; The energy station with the lowest loss A battery, serving as the regulating battery at the stated time.

[0044] It should be noted that the battery participation coefficient is used to characterize the proportion of batteries in the energy station that are suitable for operation at the current moment. When the overall aging degree is higher, it indicates that the energy station is aging more severely and the proportion of old batteries is larger. At this time, the battery participation coefficient is reduced accordingly, thereby reducing the number of batteries participating in regulation and avoiding excessive operating burden on aging equipment. By prioritizing the selection of batteries with the lowest degree of wear as regulation batteries, equipment with better life condition can undertake more regulation tasks, while equipment with poor life condition can obtain a relatively light-load operating environment. This ensures the regulation capacity of the energy station while slowing down the life decay rate of the overall equipment group and improving the balance and sustainability of the energy station's operation throughout its entire life cycle.

[0045] Step S004: Based on the degree of loss of the control battery at each time, obtain the charging and discharging power of the control battery at each time, and obtain the upper limit of charging and the lower limit of discharging of the control battery at each time, and regulate the energy station.

[0046] It should be noted that batteries at different lifespans typically exhibit significant differences in the charge and discharge intensities they can withstand. For batteries with high levels of wear, maintaining high charge and discharge power and a wide charge and discharge range for extended periods can easily lead to increased internal polarization, higher temperature rise, and accelerated lifespan degradation. Therefore, this embodiment, after identifying the batteries to be controlled, does not uniformly control them according to fixed operating parameters. Instead, it dynamically adjusts the charge and discharge power and charge and discharge boundaries based on the current level of battery wear. By determining the base charge and discharge power according to the operating mode and constructing a wear risk factor based on the battery's wear level, batteries with high wear levels can automatically reduce their actual charge and discharge power, thereby reducing the additional load on battery lifespan caused by high-intensity operation. Simultaneously, by dynamically lowering the upper limit of charging and raising the lower limit of discharging, the actual depth of charge and discharge of the battery can be reduced, preventing the battery from operating in a high-charge or deep-discharge state for extended periods. This reduces the aging rate of the battery's internal chemical system, thereby improving the long-term operational stability of the energy station.

[0047] Specifically, a high-intensity control coefficient, a medium-intensity control coefficient, a low-intensity control coefficient, a high-intensity aging attenuation coefficient, a medium-intensity aging attenuation coefficient, and a low-intensity aging attenuation coefficient are preset. The high-intensity control coefficient is greater than the medium-intensity control coefficient, the medium-intensity control coefficient is greater than the low-intensity control coefficient, the high-intensity aging attenuation coefficient is greater than the medium-intensity aging attenuation coefficient, and the medium-intensity aging attenuation coefficient is greater than the low-intensity aging attenuation coefficient. The specific values ​​of the high-intensity control coefficient, the medium-intensity control coefficient, the low-intensity control coefficient, the high-intensity aging attenuation coefficient, the medium-intensity aging attenuation coefficient, and the low-intensity aging attenuation coefficient can be set according to the actual situation. This embodiment does not make a hard requirement. In this embodiment, the high-intensity control coefficient is equal to 1, the medium-intensity control coefficient is equal to 0.7, the low-intensity control coefficient is equal to 0.4, the high-intensity aging attenuation coefficient is equal to 0.3, the medium-intensity aging attenuation coefficient is equal to 0.2, and the low-intensity aging attenuation coefficient is equal to 0.1 as an example. For any given time and any regulated battery, if the energy station's operating mode at that time is high-intensity regulation mode, then the product of the rated charge / discharge power of the regulated battery at that time and the high-intensity regulation coefficient is taken as the basic charge / discharge power of the regulated battery at that time; the product of the degree of loss of the regulated battery at that time and the high-intensity aging degradation coefficient is taken as the loss risk factor of the regulated battery at that time; the difference obtained by subtracting the loss risk factor of the regulated battery at that time from 1 is multiplied by the basic charge / discharge power of the regulated battery at that time, and this product is taken as the charge / discharge power of the regulated battery at that time. If the operating mode of the energy station at the specified time is medium-intensity control mode or low-intensity control mode, the charging and discharging power of the control battery at the specified time is obtained in the same way (the specific operation is to replace the high-intensity control coefficient and the high-intensity aging attenuation coefficient with the medium-intensity control coefficient or the low-intensity control coefficient, the medium-intensity aging attenuation coefficient or the low-intensity aging attenuation coefficient).

[0048] It should be noted that the high-intensity regulation mode corresponds to a higher base charge and discharge power configuration, enabling the battery to participate in more frequent and larger-amplitude charge and discharge regulation under conditions of more severe energy system fluctuations. This allows for a rapid response to the energy balance needs of the grid side. However, this also means an increase in energy throughput per unit time, resulting in a higher rate of lifespan degradation accumulation. The medium-intensity and low-intensity regulation modes gradually reduce the base charge and discharge power level and correspondingly weaken the effect of the aging degradation coefficient. This allows the battery operation to gradually shift to a regulation state focused on stability and lifespan maintenance. This enables the energy station to maintain high sensitivity to battery aging while possessing strong energy regulation capabilities. In this way, lifespan protection is strengthened under high-intensity energy regulation scenarios, and regulation restrictions are appropriately relaxed under low-intensity operation scenarios, thereby improving the long-term operational stability of the energy station.

[0049] Specifically, for any given time and any controlled battery, the upper limit of charging and the lower limit of discharging of the controlled battery at that time are obtained based on the degree of wear of the controlled battery at that time. The upper limit of the charging of the control battery at the specified time is negatively correlated with the degree of loss of the control battery at the specified time. The discharge limit of the controlled battery at the specified time is positively correlated with the degree of wear of the controlled battery at the specified time.

[0050] As an example, the specific calculation formula for obtaining the upper limit of charging and the lower limit of discharging of the controlled battery at the stated time is as follows: ; ; In the formula, This indicates the upper limit of the charging of the controlled battery at the stated time. This indicates the lower discharge limit of the controlled battery at the stated time. This indicates the degree of wear and tear on the controlled battery at the stated time. This indicates the preset initial energy storage limit; This indicates the preset upper limit of energy storage constraints; This indicates the preset initial lower limit of energy storage; This indicates the preset lower limit of the energy storage constraint; , , as well as The specific value can be set according to the actual situation. This embodiment does not make a hard requirement. In this embodiment, the value is set as follows: , , , Let's take 95%, 70%, 15%, and 40 as examples.

[0051] It should be noted that by gradually decreasing the upper limit of charging as the degree of loss increases, and gradually increasing the lower limit of discharging as the degree of loss increases, the effective charging and discharging range of the battery gradually narrows as the degree of aging increases. This ensures that the battery always operates within a relatively safe energy operating range at different stages of its lifespan. When the degree of loss is low, the battery is allowed to participate in energy regulation within a wider energy range to fully utilize its energy throughput capacity. When the degree of loss gradually increases, the probability of deep charging and deep discharging is reduced by simultaneously compressing the upper limit of charging and raising the lower limit of discharging. This reduces the continuous impact of extreme states of charge on the internal chemical system of the battery, thereby improving the long-term operational stability of the energy station.

[0052] Furthermore, after determining the charging and discharging power of the regulated battery and dynamically adjusting the upper charging limit and lower discharging limit, boundary control processing is performed on the actual operating state of the regulated battery through the constraint relationship between the charging and discharging power and the upper charging limit and lower discharging limit. This ensures that the actual charging and discharging behavior of the regulated battery at any given time is limited to its corresponding dynamic safe operating range. Specifically, when the target charging power generated based on the regulation result exceeds the upper charging limit, the actual charging power is limited to the upper charging limit. When the target discharging power is lower than the lower discharging limit, the actual discharging power is limited to the lower discharging limit, thereby preventing the battery from overcharging or over-discharging in high regulation intensity mode.

[0053] Another embodiment of the present invention provides a power station energy economic coordinated control system based on time-series data fusion, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a power station energy economic coordinated control method based on time-series data fusion in steps S001 to S004.

[0054] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for coordinated energy economic regulation of power stations based on time-series data fusion, characterized in that, The method includes the following steps: Obtain the battery's health status, cumulative equivalent full cycle count, rated charge / discharge power, and electricity price at each time point; Based on the electricity price changes within a local time range at each moment, obtain the electricity price fluctuation coefficient at each moment; based on the battery health status and cumulative equivalent full cycle count at each moment, obtain the battery wear level at each moment; combined with the electricity price fluctuation coefficient at each moment, obtain the battery regulation intensity coefficient at each moment; based on the battery regulation intensity coefficient at each moment, set the operation mode of the energy station at each moment. Based on the degree of wear and tear of each battery in the energy station at each time point, the overall aging degree of the energy station at each time point is obtained, and then the control batteries at each time point are selected from the energy station. Based on the degree of wear and tear of the control battery at each time point, the charging and discharging power of the control battery at each time point is obtained, and the upper limit of charging and the lower limit of discharging of the control battery at each time point are obtained to regulate the energy station.

2. The method for coordinated regulation and control of power station energy economy based on time-series data fusion according to claim 1, characterized in that, The method for obtaining the electricity price fluctuation coefficient at each moment based on the electricity price change within a local time range includes: For any given moment, a local time range is preset; all moments within the preceding local time range are recorded as local moments. For any local time, the absolute value of the difference in electricity price between the local time and the previous time is denoted as the electricity price change factor for the local time. The mean of the electricity price change factors at all local moments is taken as the electricity price change coefficient at that moment; the difference between the largest and smallest electricity price change factors at local moments is taken as the peak-valley price difference coefficient at that moment; the ratio of the standard deviation of the electricity price at all local moments to the mean of the electricity price at all local moments is denoted as the electricity price variation coefficient at that moment. Based on the electricity price change coefficient, peak-valley price difference coefficient, and electricity price variation coefficient at the specified time, the electricity price fluctuation coefficient at the specified time is obtained; The electricity price fluctuation coefficient at that time is positively correlated with the electricity price change coefficient at that time; The electricity price fluctuation coefficient at that time is positively correlated with the peak-valley price difference coefficient at that time; The electricity price fluctuation coefficient at that time is positively correlated with the electricity price variation coefficient at that time.

3. The method for coordinated energy economic regulation of power stations based on time-series data fusion according to claim 1, characterized in that, The method for obtaining the degree of battery wear at each time point based on the battery's health status and cumulative equivalent full cycle count includes: For any given time, the degree of battery wear at that time is obtained based on the battery's health status and the cumulative equivalent full cycle count at that time; the degree of battery wear at that time is negatively correlated with the battery's health status at that time; and the degree of battery wear at that time is positively correlated with the battery's cumulative equivalent full cycle count at that time.

4. The method for coordinated energy economic regulation of power stations based on time-series data fusion according to claim 1, characterized in that, The specific method for obtaining the battery regulation intensity coefficient at each time point is as follows: For any given time, the control intensity coefficient of the battery at that time is obtained based on the electricity price fluctuation coefficient at that time and the degree of battery loss at that time. The battery's regulation intensity coefficient at the specified time is positively correlated with the electricity price fluctuation coefficient at the specified time; the battery's regulation intensity coefficient at the specified time is negatively correlated with the battery's wear level at the specified time.

5. The method for coordinated regulation and control of power station energy economy based on time-series data fusion according to claim 1, characterized in that, The specific method for setting the operating mode of the energy station at each time point based on the battery's regulation intensity coefficient at each time point includes: Preset a high loss threshold, a low loss threshold, a high control intensity threshold, and a low control intensity threshold; For any given time, if the battery's regulation intensity coefficient is greater than the high regulation intensity threshold and the battery's loss level is less than the low loss level threshold, then the energy station's operation mode will be set to high regulation mode. If the battery's regulation intensity coefficient is greater than the low regulation intensity threshold and less than or equal to the high regulation intensity threshold at the specified time, and the battery's loss level is less than the high loss level threshold at the specified time, then the energy station's operation mode at the specified time will be set to medium-intensity regulation mode. If the operating mode of the energy station at the specified time is neither high-intensity control mode nor medium-intensity control mode, then the operating mode of the energy station at the specified time will be set to low-intensity control mode.

6. The method for coordinated regulation and control of power station energy economy based on time-series data fusion according to claim 1, characterized in that, The method for obtaining the overall aging level of the energy station at each time point based on the degree of wear and tear of each battery in the energy station at each time point includes the following specific methods: A pre-defined threshold for the degree of wear and tear is set. For any given time, the number of batteries in the energy station whose degree of wear and tear is greater than or equal to the threshold is recorded as the number of old batteries at that time. The ratio between the number of old batteries at that time and the number of batteries in the energy station is used as the aging coefficient of the energy station at that time. The overall aging level of the energy station at that time is obtained based on the degree of wear and tear of all batteries in the energy station at that time and the aging coefficient of the energy station at that time. The overall aging degree of the energy station at the specified time is positively correlated with the average degree of wear and tear of all batteries in the energy station at the specified time. The overall aging degree of the energy station at the specified time is positively correlated with the aging coefficient of the energy station at the specified time.

7. The method for coordinated energy economic regulation of power stations based on time-series data fusion according to claim 1, characterized in that, The specific method for selecting the control batteries at each time point from the energy station is as follows: For any given time, the battery participation coefficient of the energy station is obtained based on the overall aging degree of the energy station at that time. The battery participation coefficient of the energy station at the specified time is negatively correlated with the overall aging degree of the energy station at the specified time. The product of the number of batteries in the energy station and the battery participation coefficient of the energy station at the stated time, rounded down, is taken as the number of batteries participating in regulation at the stated time. ; The energy station with the lowest loss A battery, serving as the regulating battery at the stated time.

8. The method for coordinated energy economic regulation of power stations based on time-series data fusion according to claim 1, characterized in that, The specific method for obtaining the charging and discharging power of the controlled battery at each time point based on the degree of battery wear at each time point includes: Preset a high-intensity control coefficient, a medium-intensity control coefficient, a low-intensity control coefficient, a high-intensity aging attenuation coefficient, a medium-intensity aging attenuation coefficient, and a low-intensity aging attenuation coefficient; For any given time and any regulated battery, if the operating mode of the energy station at that time is high-intensity regulation mode, then the product of the rated charge / discharge power of the regulated battery at that time and the high-intensity regulation coefficient shall be used as the basic charge / discharge power of the regulated battery at that time. The product of the degree of wear and tear of the controlled battery at the specified time and the high-intensity aging degradation coefficient is used as the wear risk factor of the controlled battery at the specified time. The difference obtained by subtracting the loss risk factor of the control battery at the specified time from 1, and multiplying it by the basic charge and discharge power of the control battery at the specified time, is used as the charge and discharge power of the control battery at the specified time. If the operating mode of the energy station at the specified time is medium-intensity control mode or low-intensity control mode, the charging and discharging power of the control battery at the specified time can be obtained similarly.

9. The method for coordinated regulation and control of power station energy economy based on time-series data fusion according to claim 1, characterized in that, The specific method for obtaining the upper charging limit and lower discharging limit of the controlled battery at each time point includes: For any given time and any controlled battery, based on the degree of wear of the controlled battery at that time, the upper limit of charging and the lower limit of discharging of the controlled battery at that time are obtained; The upper limit of the charging of the control battery at the specified time is negatively correlated with the degree of loss of the control battery at the specified time. The discharge limit of the controlled battery at the specified time is positively correlated with the degree of wear of the controlled battery at the specified time.

10. A time-series data fusion-based power station energy economic coordinated control system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed by the processor, it implements the steps of the time-series data fusion-based power station energy economic coordinated control method as described in any one of claims 1-9.

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