Multi-source network-load-storage collaborative low-carbon data center energy storage system and method

Abstract

The invention discloses a multi-source network-load-storage collaborative low-carbon data center energy storage system and method, relates to the technical field of energy storage management, and solves the problems that a traditional method lacks a dynamic verification mechanism, and when actual energy supply and demand deviates from prediction, a scheduling strategy cannot be quickly adjusted. According to the method, historical power generation data are classified according to weather parameters (temperature, humidity, illumination intensity, wind power intensity and the like), energy parameter intervals are generated, power generation rules under different weather conditions are effectively captured, and an accurate data foundation is laid for subsequent prediction; and secondly, a mode of generating a renewable energy prediction interval based on weather forecast breaks through the limitation of traditional single numerical prediction, adopts an interval prediction mode to cover more possibilities, and combines a micro-range division and assignment column locking mechanism to make the prediction result more fit the actual power generation fluctuation, thereby improving the perspectiveness of renewable energy utilization.

Description

Technical Field

[0001] This invention relates to the field of energy storage management technology, specifically to a low-carbon data center energy storage system and method with multi-source grid-load-storage synergy. Background Technology

[0002] With the acceleration of global digitalization, data centers, as energy-intensive infrastructure, are facing increasingly prominent issues of energy consumption and carbon emissions. Traditional data center energy storage systems generally suffer from problems such as extensive scheduling strategies and low energy utilization when facing the intermittency of renewable energy sources (such as solar and wind power) and the volatility of electricity load.

[0003] In existing technologies, most energy storage methods rely solely on single numerical values ​​to predict renewable energy generation and demand, failing to fully capture the impact of weather parameters (temperature, humidity, sunlight intensity, wind intensity, etc.) on power generation characteristics, resulting in significant prediction errors. Furthermore, insufficient mining of the temporal characteristics of historical electricity consumption data makes it difficult to accurately pinpoint load fluctuation ranges, leading to lags in the charging and discharging strategies of energy storage systems. In addition, traditional methods lack dynamic verification mechanisms; when actual energy supply and demand deviate from predictions, they cannot quickly adjust dispatch strategies, easily leading to energy waste or supply gaps.

[0004] Against this backdrop, how to achieve accurate range prediction of renewable energy and electricity load through multi-source data collaborative analysis, and how to build a dynamic energy storage regulation mechanism, have become key technical challenges in improving the energy utilization efficiency of data centers and promoting low-carbon transformation. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a low-carbon data center energy storage system and method that integrates multiple sources, grids, loads, and storage. This solves the problem that traditional methods lack a dynamic verification mechanism and cannot quickly adjust scheduling strategies when actual energy supply and demand deviate from forecasts.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a low-carbon data center energy storage method with multi-source grid-load-storage synergy, comprising the following steps: Step 1: Verify the historical power generation data associated with the renewable energy center. From the verified historical power generation data, identify the different historical power generation data associated with different weather parameters, and generate different energy parameter ranges associated with different weather parameters. The specific method is as follows: Using the current time as the calibration time, obtain the weather parameters associated with past historical times, and generate a curve showing the change of values ​​associated with the corresponding weather parameters based on the changes in the associated weather parameters. The horizontal axis of the curve is the time line, and the vertical axis is the corresponding weather parameter value. Based on the determined numerical variation curve, the maximum and minimum values ​​are selected, and the range of the maximum and minimum values ​​is divided into ten micro-ranges. A value of K is assigned to each micro-range. i , where i represents different weather parameters, and K=1, ..., 10; Based on the confirmed micro-range, the micro-ranges to which different weather parameters belong within the same time period are identified, and specific values ​​are locked. The determined values ​​are then sorted to confirm the assignment column, and the power generation parameters generated at the same time are recorded and labeled as F. t , where t represents different times; Based on the identified different assignment columns, determine the common assignment columns and associate the power generation parameters F with the common assignment columns. t Confirm and select the minimum value F from them. t min and maximum value F t max, generates the energy parameter range [F] associated with the corresponding assigned column. t min, F t max]; Step 2: Based on the weather forecast, confirm the weather parameters associated with the current time period, and generate a renewable energy prediction range based on the recorded energy parameter range. The specific method is as follows: Based on the weather forecast, the weather parameters associated with the next hour are confirmed, and parameter change curves associated with the corresponding weather parameters in the next hour are generated. Based on the set micro-range, the micro-range associated with each curve point in the parameter change curve is locked, and based on the associated assignment, the assignment column at the corresponding time is locked, and then the energy parameter interval recorded in the corresponding assignment column is locked. The energy parameter intervals associated with several different times are summed to lock the renewable energy prediction interval. Step 3: From historical data, confirm the electricity consumption data associated with the application area, and perform spectrum verification on the associated electricity consumption data to lock the electricity consumption parameter range. The specific method is as follows: Using the current time as the calibration time, the electricity consumption data generated in the past 30 days is confirmed, and the electricity consumption data associated with each different time is confirmed in 24-hour time cycles. The corresponding 24-hour electricity consumption data change curve is generated, and the 30 sets of electricity consumption data change curves are placed in the same two-dimensional coordinate system to generate a curve graph. The horizontal axis of the change curve is the time line, and the vertical axis is the electricity consumption data. Based on the determined curve, different electricity consumption data associated with the same moment are identified. Based on the maximum and minimum values ​​of the electricity consumption data, the corresponding electricity consumption range value YD is determined, where YD = maximum electricity consumption value - minimum electricity consumption value. Based on the determined electricity consumption range value YDo, the corresponding measurement value ZDo is identified. , where o represents different times; Based on the measured value ZDo confirmed at the corresponding time, a set of perpendicular lines are constructed to the corresponding time point. The points associated with the maximum and minimum values ​​of the electricity consumption data are locked from the perpendicular lines. The line segment between the two points is called the calibration segment. A set of measurement segments with a range length of ZDo is generated. The measurement segments are controlled to move up and down within the calibration segments. The total number of intersections between the corresponding measurement segments and the different electricity consumption data change curves in different movement processes is recorded. The movement process with the maximum number of intersections is called the optimal process. The intersections associated with the corresponding measurement segments in the optimal process are called characteristic intersections. The maximum and minimum values ​​of the electricity consumption data are locked from the characteristic intersections as the electricity consumption interval associated with the corresponding time. Using the current time as the calibration time, the time period associated with the next 1 hour is confirmed, and based on the confirmed time period, the power consumption intervals corresponding to different associated times are confirmed in sequence. The confirmed power consumption intervals are summed to generate a power consumption parameter interval. Step 4: Compare the confirmed renewable energy forecast range and electricity consumption parameter range. Based on the comparison process, determine whether the energy storage center needs to store or discharge electricity and execute the corresponding action. The specific method is as follows: The confirmed renewable energy forecast range is labeled as [Zymin, Zymax], and the confirmed electricity consumption parameter range is labeled as [Ydmin, Ydmax]. If Ydmax < Zymin, then an energy storage signal is generated, and the minimum amount of energy that the energy storage center needs to store in the next 1 hour is (Zymin - Ydmax). If Ydmin > Zymax, a discharge signal is generated, and the minimum amount of electricity that the energy storage center needs to discharge within the next 1 hour is (Ydmin - Zymax). Also includes: If there is an overlap between [Zymin, Zymax] and [Ydmin, Ydmax], then actual monitoring of renewable energy and electricity consumption parameters will be conducted within the next 20 minutes. The monitored renewable energy will be labeled as ZZ, and the monitored electricity consumption parameters will be labeled as YY. The following will be adopted: Confirm the value JY to be verified regarding the renewable energy forecast range, and then adopt: Confirm the value BY to be verified for the electricity consumption parameters, and confirm the numerical difference between ZZ and the value JY to be verified: if ZZ = JY, no adjustment is required; if ZZ < JY, then [Zymin, Zymax] will be adjusted down by [3 × (JY - ZZ)] to complete the numerical adjustment process for the renewable energy forecast range; if ZZ > JY, then [Zymin, Zymax] will be adjusted up by [3 × (ZZ - JY)]; For YY and BY, the same processing method described above is used to adjust the values ​​of [Ydmin, Ydmax]. The adjusted values ​​[Zymin, Zymax] and [Ydmin, Ydmax] are compared again to determine whether a storage signal or a discharge signal is generated.

[0007] Preferably, a low-carbon data center energy storage system with multi-source grid-load-storage synergy includes: The parameter range confirmation end confirms the historical power generation data associated with the renewable energy center, identifies different historical power generation data associated with different weather parameters from the confirmed historical power generation data, and generates different energy parameter ranges associated with different weather parameters. The forecast interval confirmation end confirms the weather parameters associated with the current time period based on the weather forecast, and generates a renewable energy forecast interval based on the recorded energy parameter interval. The power consumption parameter confirmation terminal confirms the power consumption data associated with the power application area from historical data, and performs spectrum verification on the associated power consumption data to lock the power consumption parameter range. The energy storage control center compares the confirmed renewable energy forecast range with the electricity consumption parameter range. Based on the comparison process, it determines whether the energy storage center needs to store or discharge electricity and then executes the corresponding action.

[0008] This invention provides a low-carbon data center energy storage system and method with multi-source grid-load-storage synergy. Compared with existing technologies, it has the following advantages: This invention effectively captures the power generation patterns under different weather conditions by classifying historical power generation data according to weather parameters (temperature, humidity, light intensity, wind intensity, etc.) and generating energy parameter intervals, thus laying an accurate data foundation for subsequent forecasts. Secondly, the method of generating renewable energy forecast intervals based on weather forecasts breaks through the limitations of traditional single numerical forecasts. By adopting an interval forecast format to cover more possibilities, and combining micro-range division and assignment column locking mechanisms, the forecast results are more in line with actual power generation fluctuations, thus improving the foresight of renewable energy utilization. Spectrum verification and curve analysis are used for electricity consumption data. By dynamically optimizing the measurement segment, the range of electricity consumption parameters is determined. The temporal characteristics and fluctuation patterns of historical electricity consumption data are fully explored to accurately pinpoint the range of future electricity demand and provide a reliable load reference for energy dispatch. In the energy storage decision-making process, by comparing the values ​​of the renewable energy forecast range with the electricity consumption parameter range, and with the real-time monitoring and dynamic adjustment mechanism, the uncertainty of energy supply and demand can be flexibly addressed. When the supply and demand ranges overlap, the forecast range is checked and adjusted by actual data over 20 minutes to ensure the real-time performance and accuracy of energy storage / discharge decisions and avoid energy waste or insufficient supply.

[0009] Overall, this method achieves precise matching of energy supply and demand in data centers through multi-source data collaboration, interval-based prediction models, and dynamic control strategies. It effectively improves the efficiency of renewable energy consumption, reduces the charging and discharging losses of energy storage systems, and promotes the transformation of data centers towards a low-carbon and intelligent operation mode through refined management, thus achieving both economic and environmental benefits. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the principle framework of the present invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] Example 1 Please see Figure 1 This application provides a low-carbon data center energy storage method with multi-source grid-load-storage synergy, including the following steps: Step 1: Confirm the historical power generation data associated with the renewable energy center. From the confirmed historical power generation data, identify the different historical power generation data associated with different weather parameters, and generate different energy parameter ranges associated with different weather parameters. Specifically, the corresponding energy storage center is associated with the corresponding renewable energy center. The corresponding renewable energy center uses renewable energy to generate electricity. Under different weather conditions, there are different power generation parameters. The weather parameters include: temperature, humidity, light intensity, and wind intensity, etc. The specific parameters are selected by the operator in advance. The specific method for locking the energy parameter range is as follows: Using the current time as the calibration time, the weather parameters associated with past historical times are obtained, and based on the changes in the associated weather parameters, a curve of numerical change associated with the corresponding weather parameter is generated. The horizontal axis of the curve is the time line, and the vertical axis is the corresponding weather parameter value. The time line is generally in minutes. Based on the determined numerical variation curve, the maximum and minimum values ​​are selected, and the range of the maximum and minimum values ​​is divided into ten micro-ranges. A value of K is assigned to each micro-range. i , where i represents different weather parameters, K=1, ..., 10. For example: in the numerical change curve associated with the corresponding wind force parameter, the maximum wind force is 50 and the minimum wind force is 0. Then, ten micro-ranges are identified from front to back: (0, 5], (5, 10], ..., (45, 50]. Based on the confirmed micro-range, identify the micro-ranges to which different weather parameters belong within the same time period, and lock in the specific values. Then, sort the determined values ​​to confirm the assignment column (the sorting method is preset in advance, for example: temperature, humidity, wind force, and solar intensity, sorted in this order). Record the power generation parameters generated at the same time period and label them as F. t , where t represents different times; Based on the identified different assignment columns, determine the common assignment columns and associate the power generation parameters F with the common assignment columns. t Confirm and select the minimum value F from them. t min and maximum value F t max, generates the energy parameter range [F] associated with the corresponding assigned column. t min, F t [max] means that different assignment columns have different energy parameter ranges, and the assignment columns are determined by past weather parameters. Since weather parameters are closely related to power generation parameters, the energy storage and discharge of the energy storage center can be specifically judged based on the actual weather forecast. Step 2: Based on the weather forecast, confirm the weather parameters associated with the current time period and generate a renewable energy prediction range based on the recorded energy parameter range. Specifically, the associated renewable energy prediction range is determined based on the actual weather forecast, which is obtained directly from the cloud. The specific method for generating the renewable energy forecast interval is as follows: Based on the weather forecast, the weather parameters associated with the next hour are confirmed, and parameter change curves associated with the corresponding weather parameters in the next hour are generated. Based on the set micro-range, the micro-range associated with each curve point in the parameter change curve is locked, and based on the associated assignment, the assignment column at the corresponding time is locked, and then the energy parameter interval recorded in the corresponding assignment column is locked. The energy parameter intervals associated with several different times are summed to lock the renewable energy prediction interval. Specifically, this means predicting the renewable energy resources associated with the next hour based on the energy regeneration situation within different numerical ranges. This prediction is a range prediction method, and the predicted values ​​are more accurate and cover a wider range than single values.

[0013] Step 3: From historical data, confirm the electricity consumption data associated with the application area, and perform spectrum verification on the associated electricity consumption data to lock the electricity consumption parameter range (this range also belongs to the prediction range and is determined based on past electricity consumption data). The specific method for locking the power consumption parameter range is as follows: Using the current time as the calibration time, the electricity consumption data generated in the past 30 days is confirmed. Using a 24-hour time cycle, the electricity consumption data associated with each different time is confirmed, and a corresponding 24-hour electricity consumption data change curve is generated. The 30 sets of electricity consumption data change curves are placed in the same two-dimensional coordinate system to generate a curve graph. The horizontal axis of the change curve is the time line, and the vertical axis is the electricity consumption data. Since there are different curves for 24 hours, they can be placed in the same coordinate system for feature verification. Based on the determined curve, different electricity consumption data associated with the same moment are identified. Based on the maximum and minimum values ​​of the electricity consumption data, the corresponding electricity consumption range value YD is determined, where YD = maximum electricity consumption value - minimum electricity consumption value. Based on the determined electricity consumption range value YDo, the corresponding measurement value ZDo is identified. , where o represents different times; Based on the measured value ZDo confirmed at the corresponding time, a set of perpendicular lines are constructed to the corresponding time point. The points associated with the maximum and minimum values ​​of the electricity consumption data are locked from the perpendicular lines. The line segment between the two points is called the calibration segment (the length of the calibration segment is YDo). A set of measurement segments with a range length of ZDo is generated. The measurement segments are controlled to move up and down within the calibration segments. The total number of intersections between the corresponding measurement segments and the different electricity consumption data change curves in different movement processes is recorded. The movement process with the maximum number of intersections is called the optimal process. The intersections associated with the corresponding measurement segments in the optimal process are called characteristic intersections. The maximum and minimum values ​​of the electricity consumption data are locked from the characteristic intersections as the electricity consumption interval associated with the corresponding time. Using the current time as the calibration time, the time period associated with the next 1 hour is confirmed. Based on the confirmed time period, the electricity consumption intervals corresponding to different associated times are confirmed sequentially. The confirmed electricity consumption intervals are summed to generate an electricity consumption parameter interval (that is, the numerical interval associated with the electricity consumption data that may be generated in the next 1 hour. This part of the prediction process is also based on the electricity consumption data generated in the corresponding area in the past month. It is an interval range and has considerations). Specifically, within past time periods, there are electricity consumption data associated with different 24-hour cycles. Corresponding electricity consumption data change curves can be generated. Based on the determined change curves, the associated electricity consumption range at the same moment can be confirmed, thereby narrowing down the range and locking the measurement range. Then, by controlling the specific method of moving the measurement range up and down, the intersection point is specifically confirmed, thereby locking the corresponding electricity consumption interval and making specific predictions on the associated electricity consumption parameters within the next hour.

[0014] Step 4: Compare the confirmed renewable energy forecast range and electricity consumption parameter range. Based on the comparison process, determine whether the energy storage center needs to store or discharge electricity and execute the corresponding action. The specific method for confirmation is as follows: The confirmed renewable energy forecast range is labeled as [Zymin, Zymax], and the confirmed electricity consumption parameter range is labeled as [Ydmin, Ydmax]. If Ydmax < Zymin, then an energy storage signal is generated, and the minimum amount of energy that the energy storage center needs to store in the next 1 hour is (Zymin - Ydmax). If Ydmin > Zymax, a discharge signal is generated, and the minimum amount of electricity that the energy storage center needs to discharge within the next 1 hour is (Ydmin - Zymax). If there is an overlap between [Zymin, Zymax] and [Ydmin, Ydmax], then actual monitoring of renewable energy and electricity consumption parameters will be conducted within the next 20 minutes. The monitored renewable energy will be denoted as ZZ (the total value of renewable energy generated within the corresponding 20 minutes), and the monitored electricity consumption parameters will be denoted as YY (the total value of electricity consumption parameters generated within the corresponding 20 minutes). The following method will be used: Confirm the value JY to be verified regarding the renewable energy forecast range, and then adopt: Confirm the value BY to be verified for the electricity consumption parameters, and confirm the numerical difference between ZZ and the value JY to be verified: if ZZ = JY, no adjustment is required; if ZZ < JY, then [Zymin, Zymax] will be adjusted down by [3 × (JY - ZZ)] to complete the numerical adjustment process for the renewable energy forecast range; if ZZ > JY, then [Zymin, Zymax] will be adjusted up by [3 × (ZZ - JY)]; For YY and BY, the same processing method described above is used to adjust the values ​​of [Ydmin, Ydmax]. The adjusted values ​​[Zymin, Zymax] and [Ydmin, Ydmax] are compared again to determine whether to generate a storage signal or a discharge signal. If the two intervals still overlap, the renewable energy and electricity consumption parameters are monitored in real time. If the renewable energy at the corresponding time is greater than the electricity consumption parameter, energy storage is performed. If the renewable energy at the corresponding time is less than the electricity consumption parameter, discharge is performed. If the renewable energy and the electricity consumption parameter are equal, no processing is required. This part of the process is the normal energy storage and discharge process, which completes the interval adjustment process of the corresponding energy storage center to achieve a better energy adjustment and processing effect.

[0015] Example 2 Combination Figure 2 A low-carbon data center energy storage system with multi-source grid-load-storage synergy, comprising: The parameter range confirmation end confirms the historical power generation data associated with the renewable energy center, identifies different historical power generation data associated with different weather parameters from the confirmed historical power generation data, and generates different energy parameter ranges associated with different weather parameters. The forecast interval confirmation end confirms the weather parameters associated with the current time period based on the weather forecast, and generates a renewable energy forecast interval based on the recorded energy parameter interval. The power consumption parameter confirmation terminal confirms the power consumption data associated with the power application area from historical data, and performs spectrum verification on the associated power consumption data to lock the power consumption parameter range. The energy storage control center compares the confirmed renewable energy forecast range with the electricity consumption parameter range. Based on the comparison process, it determines whether the energy storage center needs to store or discharge electricity and then executes the corresponding action.

[0016] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0017] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A low-carbon data center energy storage method with multi-source grid-load-storage synergy, characterized in that, Includes the following steps: Step 1: Confirm the historical power generation data associated with the renewable energy center. From the confirmed historical power generation data, identify the different historical power generation data associated with different weather parameters, and generate different energy parameter ranges associated with different weather parameters. Step 2: Based on the weather forecast, confirm the weather parameters associated with the current time period and generate a renewable energy prediction range based on the recorded energy parameter range. Step 3: From historical data, confirm the electricity consumption data associated with the application area, and perform spectrum verification on the associated electricity consumption data to lock the electricity consumption parameter range; Step 4: Compare the confirmed renewable energy forecast range and electricity consumption parameter range with numerical values. Based on the numerical comparison process, determine whether the energy storage center needs to store or discharge electricity and then execute the action.

2. The low-carbon data center energy storage method with multi-source grid-load-storage synergy as described in claim 1, characterized in that, In step one, the specific method for locking the energy parameter range is as follows: Using the current time as the calibration time, obtain the weather parameters associated with past historical times, and generate a curve showing the change of values ​​associated with the corresponding weather parameters based on the changes in the associated weather parameters. The horizontal axis of the curve is the time line, and the vertical axis is the corresponding weather parameter value. Based on the determined numerical variation curve, the maximum and minimum values ​​are selected, and the range of the maximum and minimum values ​​is divided into ten micro-ranges. A value of K is assigned to each micro-range. i , where i represents different weather parameters, and K=1, ..., 10; Based on the confirmed micro-range, the micro-ranges to which different weather parameters belong within the same time period are identified, and specific values ​​are locked. The determined values ​​are then sorted to confirm the assignment column, and the power generation parameters generated at the same time are recorded and labeled as F. t , where t represents different times; Based on the identified different assignment columns, determine the common assignment columns and associate the power generation parameters F with the common assignment columns. t Confirm and select the minimum value F from them. t min and maximum value F t max, generates the energy parameter range [F] associated with the corresponding assigned column. t min, F t max).

3. The low-carbon data center energy storage method with multi-source grid-load-storage synergy as described in claim 1, characterized in that, In step two, the specific method for generating the renewable energy forecast interval is as follows: Based on the weather forecast, the weather parameters associated with the next hour are confirmed, and parameter change curves associated with the corresponding weather parameters in the next hour are generated. Based on the set micro-range, the micro-range associated with each curve point in the parameter change curve is locked, and based on the associated assignment, the assignment column at the corresponding time is locked, and then the energy parameter interval recorded in the corresponding assignment column is locked. The energy parameter intervals associated with several different times are summed to lock the renewable energy prediction interval.

4. The low-carbon data center energy storage method with multi-source grid-load-storage synergy as described in claim 1, characterized in that, In step three, the specific method for locking the power consumption parameter range is as follows: Using the current time as the calibration time, the electricity consumption data generated in the past 30 days is confirmed, and the electricity consumption data associated with each different time is confirmed in 24-hour time cycles. The corresponding 24-hour electricity consumption data change curve is generated, and the 30 sets of electricity consumption data change curves are placed in the same two-dimensional coordinate system to generate a curve graph. The horizontal axis of the change curve is the time line, and the vertical axis is the electricity consumption data. Based on the determined curve, different electricity consumption data associated with the same moment are identified. Based on the maximum and minimum values ​​of the electricity consumption data, the corresponding electricity consumption range value YD is determined, where YD = maximum electricity consumption value - minimum electricity consumption value. Based on the determined electricity consumption range value YDo, the corresponding measurement value ZDo is identified. , where o represents different times; Based on the measured value ZDo confirmed at the corresponding time, a set of perpendicular lines are constructed to the corresponding time point. The points associated with the maximum and minimum values ​​of the electricity consumption data are locked from the perpendicular lines. The line segment between the two points is called the calibration segment. A set of measurement segments with a range length of ZDo is generated. The measurement segments are controlled to move up and down within the calibration segments. The total number of intersections between the corresponding measurement segments and the different electricity consumption data change curves in different movement processes is recorded. The movement process with the maximum number of intersections is called the optimal process. The intersections associated with the corresponding measurement segments in the optimal process are called characteristic intersections. The maximum and minimum values ​​of the electricity consumption data are locked from the characteristic intersections as the electricity consumption interval associated with the corresponding time. Using the current time as the calibration time, the time period associated with the next 1 hour is confirmed. Based on the confirmed time period, the power consumption intervals corresponding to different associated times are confirmed in sequence. The confirmed power consumption intervals are summed to generate the power consumption parameter interval.

5. A low-carbon data center energy storage method with multi-source grid-load-storage synergy as described in claim 1, characterized in that, In step four, the specific method for confirming whether the energy storage center needs to store or discharge electricity is as follows: The confirmed renewable energy forecast range is labeled as [Zymin, Zymax], and the confirmed electricity consumption parameter range is labeled as [Ydmin, Ydmax]. If Ydmax < Zymin, then an energy storage signal is generated, and the minimum amount of energy that the energy storage center needs to store in the following 1 hour is (Zymin - Ydmax). If Ydmin > Zymax, a discharge signal is generated, and the minimum amount of electricity that the energy storage center needs to discharge within the next 1 hour is (Ydmin - Zymax).

6. A low-carbon data center energy storage method with multi-source grid-load-storage synergy as described in claim 5, characterized in that, In step four, the specific methods for determining whether the energy storage center needs to store or discharge electricity also include: If there is an overlap between [Zymin, Zymax] and [Ydmin, Ydmax], then actual monitoring of renewable energy and electricity consumption parameters will be conducted within the next 20 minutes. The monitored renewable energy will be labeled as ZZ, and the monitored electricity consumption parameters will be labeled as YY. The following will be adopted: Confirm the value JY to be verified regarding the renewable energy forecast range, and then adopt: Confirm the value BY to be verified for the electricity consumption parameters, and confirm the numerical difference between ZZ and the value JY to be verified: if ZZ = JY, no adjustment is required; if ZZ < JY, then [Zymin, Zymax] will be adjusted down by [3 × (JY - ZZ)] to complete the numerical adjustment process for the renewable energy forecast range; if ZZ > JY, then [Zymin, Zymax] will be adjusted up by [3 × (ZZ - JY)]; For YY and BY, the same processing method described above is used to adjust the values ​​of [Ydmin, Ydmax]. The adjusted values ​​[Zymin, Zymax] and [Ydmin, Ydmax] are compared again to determine whether a storage signal or a discharge signal is generated.

7. A low-carbon data center energy storage system with multi-source grid-load-storage synergy, the system operating according to any one of claims 1-6, characterized in that, include: The parameter range confirmation end confirms the historical power generation data associated with the renewable energy center, identifies different historical power generation data associated with different weather parameters from the confirmed historical power generation data, and generates different energy parameter ranges associated with different weather parameters. The forecast interval confirmation end confirms the weather parameters associated with the current time period based on the weather forecast, and generates a renewable energy forecast interval based on the recorded energy parameter interval. The power consumption parameter confirmation terminal confirms the power consumption data associated with the power application area from historical data, and performs spectrum verification on the associated power consumption data to lock the power consumption parameter range. The energy storage control center compares the confirmed renewable energy forecast range with the electricity consumption parameter range. Based on the comparison process, it determines whether the energy storage center needs to store or discharge electricity and then executes the corresponding action.

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