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

Abstract

The application discloses a multi-source net-load-storage collaborative low-carbon data center energy storage system and method, and relates to the technical field of energy storage management, solves the problem that the traditional method lacks a dynamic checking mechanism and cannot quickly adjust a scheduling strategy when an actual energy supply and demand deviates from a prediction, the application classifies historical power generation data according to weather parameters (temperature, humidity, light intensity, wind strength, etc.) and generates an energy parameter interval, effectively captures the power generation law under different weather conditions, and lays a precise data foundation for subsequent prediction, secondly, the way of generating a renewable energy prediction interval based on weather forecasts breaks through the limitations of traditional single numerical prediction, adopts an interval prediction form to cover more possibilities, and combines micro-range division and assignment column locking mechanisms to make the prediction result more in line with actual power generation fluctuations and improve the forward-looking nature of renewable energy utilization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage management, in particular to a multi-source grid-load-storage collaborative low-carbon data center energy storage system and method. BACKGROUND

[0002] With the acceleration of global digitalization, as an energy-intensive infrastructure, the energy consumption and carbon emission of data centers have become increasingly prominent. Traditional data center energy storage systems generally have problems such as extensive scheduling strategy, low energy utilization rate, etc. when facing the intermittency of renewable energy (such as solar energy, wind energy) and the volatility of electricity load.

[0003] In the prior art, most energy storage methods are based on a single numerical value to predict renewable energy generation and electricity demand, which fails to fully capture the influence of weather parameters (temperature, humidity, light intensity, wind strength, etc.) on power generation characteristics, resulting in large prediction errors. At the same time, the historical electricity data is not fully mined for its time sequence characteristics, making it difficult to accurately lock the load fluctuation range, and thus causing the energy storage system charging and discharging strategy to lag. In addition, the traditional method lacks a dynamic verification mechanism, and when the actual energy supply and demand deviates from the prediction, it cannot quickly adjust the scheduling strategy, which easily leads to energy waste or supply gaps.

[0004] Under this background, how to achieve accurate interval prediction of renewable energy and electricity load through multi-source data collaborative analysis and build a dynamic energy storage regulation mechanism has become a key technical difficulty in improving the energy utilization efficiency of data centers and promoting low-carbon transformation. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a multi-source grid-load-storage collaborative low-carbon data center energy storage system and method, which solves the problem that the traditional method lacks a dynamic verification mechanism and cannot quickly adjust the scheduling strategy when the actual energy supply and demand deviates from the prediction.

[0006] To achieve the above purpose, the present application is realized by the following technical scheme: a multi-source grid-load-storage collaborative low-carbon data center energy storage method, comprising the following steps:

[0007] Step one, confirm the historical power generation data associated with the renewable energy center, identify different historical power generation data associated with different weather parameters from the confirmed historical power generation data, and generate different energy parameter intervals associated with different weather parameters, in particular:

[0008] Take the current time as the calibration time, obtain the weather parameters associated with the past historical time, and generate a numerical change curve belonging to the corresponding weather parameter according to the change of the associated weather parameters, the horizontal coordinate axis of the curve is the time line, and the vertical coordinate axis is the corresponding weather parameter value;

[0009] According to the determined numerical change curve, the maximum value and the minimum value are selected, and the numerical range of the maximum value and the minimum value is divided into ten micro ranges, and different micro ranges are assigned a value, which is K i , where i represents different weather parameters, K = 1, …, 10;

[0010] According to the confirmed micro range, the micro range to which the different weather parameters belong at the same time is confirmed, and the specific assignment value is locked, and the determined assignment value is sorted to confirm the assignment column, and the generated power generation parameters at the same time are recorded and labeled as F t , where t represents different time;

[0011] According to the confirmed different assignment columns, the same assignment column is determined from among them, and the power generation parameters F t associated with the same assignment column are confirmed t min and the maximum value F t max, generate the energy parameter interval [F t min, F t max] belonging to the corresponding assignment column;

[0012] Step two, according to the weather forecast, the weather parameters associated with the subsequent time period of the current time are confirmed, and the recorded energy parameter interval is used to generate a renewable energy prediction interval, in particular:

[0013] According to the weather forecast, the weather parameters associated with the next hour are confirmed, and the parameter change curve associated with the corresponding weather parameters within the next hour is generated;

[0014] According to the set micro range, the micro range associated with each curve point in the parameter change curve is locked, and the assignment column of the corresponding time is locked according to the associated assignment value, and the energy parameter interval recorded by the corresponding assignment column is locked, and the energy parameter intervals associated with several different times are summed up to lock the renewable energy prediction interval;

[0015] Step three, from the historical data, confirm the power consumption data associated with the power consumption area, and perform frequency spectrum verification on the associated power consumption data to lock the power consumption parameter interval, in particular:

[0016] Take the current time as the calibration time, confirm the power consumption data generated within the past 30 days of the calibration time, and confirm the power consumption data associated with each different time in a 24h time round, and generate a power consumption data change curve corresponding to 24h, and place 30 groups of power consumption data change curves in the same two-dimensional coordinate system to generate a curve atlas, the horizontal coordinate axis of the change curve is the time line, and the vertical coordinate axis is the power consumption data;

[0017] According to the determined curve map, the different power consumption data associated with the same time are locked, and based on the maximum value and the minimum value of the power consumption data, the power consumption range value YD corresponding to the time is confirmed, YD = power consumption data maximum value - power consumption data minimum value, and according to the determined power consumption range value YD, the measurement value ZD associated with the corresponding time is locked, wherein , wherein o represents different times;

[0018] Based on the measurement value ZD confirmed at the corresponding time, a group of perpendicular lines perpendicular to the corresponding time point are constructed, and the two points associated with the maximum value and the minimum value of the power consumption data are locked from the perpendicular lines, the line segment between the two points is recorded as a calibration segment, a group of measurement segments with a range length of ZD are generated, and the measurement segments are controlled to move up and down in the calibration segment, and the total number of intersection points between the corresponding measurement segments and the different power consumption data change curves in different moving processes is recorded. The moving process with the maximum intersection point total number is recorded as the optimal process, and the intersection point associated with the corresponding measurement segment in the optimal process is recorded as the characteristic intersection point, and the maximum value and the minimum value of the power consumption data between the characteristic intersection points are locked as the power consumption interval associated with the corresponding time;

[0019] Take the current time as the calibration time, confirm the time period associated with the subsequent 1h, and based on the confirmed time period, confirm the power consumption interval corresponding to different associated times in turn, and sum the confirmed several power consumption intervals to generate the power consumption parameter interval;

[0020] Step four, value comparison is performed on the confirmed renewable energy prediction interval and the power consumption parameter interval, and according to the value comparison process, it is confirmed whether the energy storage center needs to charge or discharge and execute, and the specific mode is:

[0021] The confirmed renewable energy prediction interval is calibrated as [Zymin, Zymax], and the confirmed power consumption parameter interval is calibrated as [Ydmin, Ydmax];

[0022] If Ydmax < Zymin, a charging signal is generated, and the minimum amount of electricity that the energy storage center needs to charge within the subsequent 1h is (Zymin-Ydmax);

[0023] If Ydmin > Zymax, a discharge signal is generated, and the minimum amount of electricity that the energy storage center needs to discharge within the subsequent 1h is (Ydmin-Zymax);

[0024] It also includes:

[0025] If there is an intersection range between [Zymin, Zymax] and [Ydmin, Ydmax], actual monitoring of renewable energy and power consumption parameters is carried out in the next 20 minutes, the monitored renewable energy is marked as ZZ, the monitored power consumption parameter is marked as YY, and the following is used: The to-be-verified value JY about the renewable energy prediction interval is confirmed, and the following is used again: The to-be-verified value BY about the power consumption parameter is confirmed, and the numerical difference between ZZ and the to-be-verified value JY is confirmed: if ZZ=JY, no adjustment is needed, if ZZJY, [Zymin, Zymax] is synchronously lowered by [3×(JY-ZZ)], the numerical adjustment process of the renewable energy prediction interval is completed, and if ZZ>JY, [Zymin, Zymax] is synchronously raised by [3×(ZZ-JY)];

[0026] The same processing mode as described above is used for YY and BY, and numerical adjustment is carried out on [Ydmin, Ydmax];

[0027] [Zymin, Zymax] and [Ydmin, Ydmax] after numerical adjustment are compared again to determine whether to generate a power storage signal or a discharge signal.

[0028] Preferably, a multi-source grid-load-storage collaborative low-carbon data center energy storage system comprises:

[0029] A parameter interval confirmation end confirms historical power generation data associated with a 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 intervals associated with different weather parameters;

[0030] A prediction interval confirmation end confirms weather parameters associated with a subsequent period at the current time according to a weather forecast, and generates a renewable energy prediction interval according to the recorded energy parameter interval;

[0031] A power consumption parameter confirmation end confirms power consumption data associated with a power consumption area from historical data, and locks a power consumption parameter interval through frequency spectrum verification of the associated power consumption data;

[0032] An energy storage control center carries out numerical comparison of the confirmed renewable energy prediction interval and the power consumption parameter interval, confirms whether the energy storage center needs to store power or discharge power according to the numerical comparison process, and executes.

[0033] The present application provides a multi-source grid-load-storage collaborative low-carbon data center energy storage system and method. Compared with the prior art, the following beneficial effects are achieved:

[0034] The present application effectively captures the power generation law 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, and lays a precise data foundation for subsequent prediction. Secondly, the method of generating renewable energy prediction intervals based on weather forecasts breaks through the limitations of traditional single numerical prediction, covers more possibilities in the form of interval prediction, and combines micro-range division and value assignment column locking mechanism to make the prediction results more in line with actual power fluctuations, improving the forward-looking nature of renewable energy utilization.

[0035] The power consumption data is analyzed by spectrum verification and curve spectrum analysis, and the power consumption parameter interval is determined by measuring segment dynamic optimization, which fully excavates the time sequence characteristics and fluctuation law of historical power consumption data, and accurately locks the future power consumption demand range, providing a reliable load reference for energy dispatching.

[0036] In the energy storage decision-making link, through the numerical comparison of renewable energy prediction intervals and power consumption parameter intervals, combined with real-time monitoring and dynamic adjustment mechanism, the uncertainty of energy supply and demand can be flexibly responded. When the supply and demand intervals intersect, the prediction interval is adjusted through 20-minute actual data verification to ensure the real-time and accuracy of the charging / discharging decision, and to avoid energy waste or insufficient supply.

[0037] Overall, this method realizes the precise matching of data center energy supply and demand through multi-source data collaboration, interval prediction model and dynamic regulation strategy, effectively improves the renewable energy consumption efficiency, reduces the charging and discharging loss of energy storage system, and promotes the transformation of data center to low-carbon and intelligent operation mode through fine management, which has economic and environmental benefits. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 The figure is a schematic diagram of the method of the present application;

[0039] Figure 2 The figure is a schematic diagram of the principle framework of the present application. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0041] Embodiment one

[0042] Please refer to Figure 1 The present application provides a multi-source net-load-storage collaborative low-carbon data center energy storage method, comprising the following steps:

[0043] Step one, confirm the historical power generation data associated with the renewable energy center, from the confirmed historical power generation data, identify different historical power generation data associated with different weather parameters, and generate different energy parameter intervals associated with different weather parameters. Specifically, the corresponding renewable energy center associated with the energy storage center uses renewable energy to generate electricity. In different weather conditions, there are different power generation parameters, and the weather parameters include temperature, humidity, light intensity, and wind strength, etc. The specific parameters are selected by the operator in advance;

[0044] The specific way to lock the energy parameter interval is:

[0045] Take the current time as the calibration time, get the weather parameters associated with the past historical time, and generate the value change curve belonging to the corresponding weather parameter according to the change of the associated weather parameters. The horizontal coordinate axis of the curve is the time line, and the vertical coordinate axis is the value of the corresponding weather parameter. The time line is generally in minutes;

[0046] According to the determined value change curve, select the maximum value and the minimum value, and divide the value range of the maximum value and the minimum value into ten micro ranges according to the value range, and assign different micro ranges, which is assigned to K i Where i represents different weather parameters, K = 1, …, 10, for example: assuming that the maximum wind force of the value change curve associated with the corresponding wind force parameter is 50, and the minimum wind force is 0, then ten micro ranges are confirmed from front to back: (0, 5], (5, 10], …, (45, 50];

[0047] According to the confirmed micro range, confirm the micro range to which different weather parameters belong at the same time, lock the specific assignment, and sort the determined assignment to confirm the assignment column (the sorting method is preset in advance, for example: temperature, humidity, wind, light intensity, in this order), and record the power generation parameters generated at the same time. Marked as F t Where t represents different time;

[0048] According to the confirmed different assignment columns, determine the same assignment column from them, and confirm the power generation parameters F t associated with the same assignment column, select the minimum value F t min and the maximum value F t max, generate the energy parameter interval [F t min, F tmax], that is, different assignment columns exist in different energy parameter intervals, and the determination manner of the assignment column is determined by the past weather parameters. Since the weather parameters are closely related to the power generation parameters, the energy storage and discharge of the energy storage center can be specifically judged based on the actual weather forecast in the future;

[0049] Step two, according to the weather forecast, the weather parameters associated with the subsequent period of the current time are confirmed, and the renewable energy prediction interval is generated according to the recorded energy parameter interval. Specifically, the associated renewable energy prediction interval is determined according to the actual weather forecast, which is directly obtained from the cloud;

[0050] The specific way of generating the renewable energy prediction interval is:

[0051] According to the weather forecast, the weather parameters associated with the future one hour are confirmed, and the parameter change curve corresponding to the weather parameters associated with the future one hour is generated;

[0052] According to the set micro-range, the micro-range associated with each curve point in the parameter change curve is locked, and the assignment column of the corresponding time is locked according to the associated assignment, and the energy parameter interval recorded by the corresponding assignment column is locked. The energy parameter intervals associated with several different time points are summed up to lock the renewable energy prediction interval;

[0053] Specifically, the renewable energy associated with the future one hour is predicted according to the energy regeneration of different numerical ranges. Here, the prediction belongs to the range prediction method, and the predicted value is more accurate than the single value and the range is wider.

[0054] Step three, from the historical data, confirm the power consumption data associated with the power consumption area, and perform frequency spectrum verification on the associated power consumption data to lock the power consumption parameter interval (this interval also belongs to the prediction interval, which is determined according to the past power consumption data);

[0055] The specific way of locking the power consumption parameter interval is:

[0056] Take the current time as the calibration time, confirm the power consumption data generated in the past 30 days of the calibration time, and take 24h as the time round, confirm the power consumption data associated with each different time, and generate the power consumption data change curve corresponding to 24h. 30 groups of power consumption data change curves are placed in the same two-dimensional coordinate system to generate a curve atlas. The horizontal coordinate axis of the change curve is the time line, and the vertical coordinate axis is the power consumption data. Because there are 24h different curves, they can be placed in the same coordinate system for feature verification;

[0057] According to the determined curve map, the different power consumption data associated with the same time is locked, and based on the maximum and minimum of the power consumption data, the power consumption range value YD of the corresponding time is confirmed, YD=power consumption data maximum-power consumption data minimum, and according to the determined power consumption range value YDo, the measurement value ZDo associated with the corresponding time is locked, wherein , wherein o represents different time;

[0058] Based on the measurement value ZDo confirmed at the corresponding time, a group of perpendicular lines perpendicular to the corresponding time point is constructed, and the two points associated with the maximum and minimum of the power consumption data are locked from the perpendicular lines, the line segment between the two points is recorded as the calibration segment (the length of the calibration segment is YDo), a group of measurement segments with a range length of ZDo is generated, and the measurement segments are controlled to move up and down within the calibration segment, and the total number of intersection points between the corresponding measurement segments and different power consumption data change curves in different moving processes is recorded. The moving process with the maximum number of intersection points is recorded as the optimal process, and the intersection point associated with the corresponding measurement segment in the optimal process is recorded as the characteristic intersection point. The maximum and minimum of the power consumption data between the characteristic intersection points are locked as the power consumption interval associated with the corresponding time.

[0059] Take the current time as the calibration time, confirm the time period associated with the subsequent 1h, and based on the confirmed time period, confirm the power consumption interval corresponding to different associated time in turn, and sum the confirmed power consumption intervals to generate the power consumption parameter interval (that is, the value interval associated with the power consumption data that may be generated in the future 1h. The prediction process of this part is also based on the power consumption data generated in the past 1 month. It is an interval range, which has a consideration).

[0060] Specifically, in the past period, there are different 24h round associated power consumption data, which can generate corresponding power consumption data change curves, and according to the determined several change curves, the power consumption range associated with the same time can be confirmed, so as to reduce the range again, lock the measurement range, and then confirm the intersection point through the specific way of controlling the up and down movement of the measurement range, so as to lock the corresponding power consumption interval, and predict the power consumption parameter associated with the subsequent 1h.

[0061] Step four, compare the confirmed renewable energy prediction interval and power consumption parameter interval, and confirm whether the energy storage center needs to charge or discharge according to the value comparison process and execute, and the specific way of confirmation is:

[0062] The confirmed renewable energy prediction interval is calibrated as [Zymin, Zymax], and the confirmed power consumption parameter interval is calibrated as [Ydmin, Ydmax];

[0063] If Ydmax < Zymin, a storage signal is generated, and the minimum amount of electricity that the energy storage center needs to store within the next 1h is (Zymin-Ydmax);

[0064] 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 1h is (Ydmin-Zymax);

[0065] If there is an intersection range between [Zymin, Zymax] and [Ydmin, Ydmax], actual monitoring of renewable energy and electricity consumption parameters is performed within the next 20min, the monitored renewable energy is marked as ZZ (the total value of renewable energy generated within the corresponding 20min), the monitored electricity consumption parameters are marked as YY (the total value of electricity consumption parameters generated within the corresponding 20min), and the following is used: The to-be-verified value JY about the renewable energy prediction interval is confirmed, and the following is used: The to-be-verified value BY about the electricity consumption parameters is confirmed, and the numerical difference between ZZ and the to-be-verified value JY is confirmed: if ZZ=JY, no adjustment is needed, if ZZ

[0066] The same processing mode as described above is used for YY and BY, and numerical adjustment is performed on [Ydmin, Ydmax];

[0067] The adjusted [Zymin, Zymax] and [Ydmin, Ydmax] are compared again to determine whether a storage signal or a discharge signal is generated, if the two intervals still intersect, real-time monitoring of renewable energy and electricity consumption parameters is performed, if the renewable energy at the corresponding time is greater than the electricity consumption parameters, storage is performed, if the renewable energy at the corresponding time is less than the electricity consumption parameters, discharge is performed, if the renewable energy and the electricity consumption parameters are equal, no processing is needed, this part of the processing process is the normal storage and discharge process, to complete the interval adjustment process of the corresponding energy storage center, so as to achieve a better energy adjustment processing effect.

[0068] Embodiment Two

[0069] In combination Figure 2 , a multi-source grid-load-storage collaborative low-carbon data center energy storage system, comprising:

[0070] The parameter interval 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 intervals associated with different weather parameters.

[0071] The prediction interval confirmation end confirms the weather parameters associated with the subsequent time period after the current time according to the weather forecast, and generates the renewable energy prediction interval according to the recorded energy parameter interval.

[0072] The power consumption parameter confirmation end confirms the power consumption data associated with the power consumption area from the historical data, performs frequency spectrum verification on the associated power consumption data, and locks the power consumption parameter interval.

[0073] The energy storage control center compares the confirmed renewable energy prediction interval and the power consumption parameter interval, confirms whether the energy storage center needs to store or discharge electricity according to the numerical comparison process, and executes.

[0074] Some data in the above formula are dimensionless numerical calculations, and the contents not described in detail in the specification are all prior art known to those skilled in the art.

[0075] The above embodiments are only used to illustrate the technical method of the present application and are not limited. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

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. 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 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 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 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).

5. A low-carbon data center energy storage method with multi-source grid-load-storage synergy as described in claim 4, 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 for 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.

6. A low-carbon data center energy storage system with multi-source grid-load-storage synergy, the system operating according to any one of claims 1-5, 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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