Power dispatching method suitable for green computing power configuration

Abstract

The invention discloses a power dispatching method suitable for green computing power configuration, and the method is characterized in that the method comprises the steps: obtaining historical load data and real-time load data; calculating a load prediction curve according to the historical load data and the real-time load data; a new energy photovoltaic power station side is provided with an optical power prediction device, an optical power historical data server and an irradiatometer. Obtaining optical power prediction, optical power historical data, irradiatometer data and meteorological data to calculate a photovoltaic power generation prediction curve; and performing power dispatching on the commercial power side according to the load prediction curve and the photovoltaic power generation prediction curve.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and in particular to a power dispatching method suitable for green computing power configuration. Background Art

[0002] Traditional data center power supply systems all use the mains as the main power source and diesel generators as the backup power source. At present, most data centers in most regions do not have two completely independent mains. In areas with developed power grid configurations, the data center power supply system introduces two mains as the main power source for each other through the ATS switching switch. After the mains power is lost, the diesel generator immediately enters the automatic start-up program after capturing the mains power loss signal, starts after a delay, and automatically switches through the ATS conversion switch between the diesel generator and the mains to replace the mains to supply power to the entire data center. In photovoltaic systems, power dispatching is a key technology. Since photovoltaic power generation is affected by weather, time and other factors, the power generation is unstable. Therefore, it is necessary to reasonably dispatch and control the photovoltaic power generation to ensure the stability and reliability of the power system.

[0003] The traditional power dispatching system mainly relies on highly controllable generators such as thermal power, nuclear power, and hydropower, and maintains the safe and stable operation of the power system through flexible coordination and rapid response. However, in the new power system, the proportion of renewable energy power generation is gradually increasing, and its intermittent, volatile and time-varying characteristics make traditional power dispatching face challenges. Renewable energy cannot follow the load changes like traditional power sources, which may lead to insufficient output of renewable energy during peak load periods or excessive output of renewable energy during low load periods. At the same time, with the large-scale grid connection of renewable energy and the access of massive interactive devices such as electric vehicles and distributed power sources, the automation capability of power dispatching also needs to be improved. Therefore, the traditional power dispatching system urgently needs to be upgraded to adapt to the efficient operation of the new power system.

[0004] Traditional data centers do not classify or categorize load types, and do not categorize computer room IT equipment, computer room power distribution equipment, computer room infrastructure equipment, etc., resulting in a large load capacity when using backup power supplies, and a large backup power supply configuration capacity. When traditional distributed photovoltaic power stations supply power to data centers, they do not consider the load fluctuations of data centers, and the fluctuations in photovoltaic power station output caused by meteorological conditions, resulting in reduced accuracy in power dispatching of the power grid. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a power dispatching method suitable for green computing power configuration to solve the problem that after the existing new energy system is connected to the distribution network, the new energy cannot follow the load changes like traditional power sources, thereby causing low power dispatching accuracy of the power grid.

[0006] In view of the above-mentioned deficiencies of the prior art, the present invention provides a power dispatching method suitable for green computing power configuration, comprising:

[0007] Obtain historical load data and real-time load data;

[0008] Calculate the load forecast curve based on historical load data and real-time load data;

[0009] Install a light power prediction device, a light power history data server and a radiation meter on the new energy photovoltaic power station side;

[0010] Obtain light power forecast, light power history data, irradiance meter data and meteorological data to calculate photovoltaic power generation forecast curve;

[0011] Electricity is dispatched on the mains side according to the load forecast curve and photovoltaic power generation forecast curve.

[0012] Preferably, the power dispatching on the mains side according to the load forecast curve and the photovoltaic power generation forecast curve comprises the following steps:

[0013] Compare the load forecast curve with the photovoltaic power generation forecast curve;

[0014] Analyze the dispatching load on the mains side according to the load forecast curve and photovoltaic power generation forecast curve;

[0015] Obtain comparison results of the load forecast curve and the photovoltaic power generation forecast curve, obtain analysis results of the load forecast curve and the photovoltaic power generation forecast curve, and obtain evaluation results.

[0016] Preferably, the acquisition of historical load data and real-time load data comprises the following steps:

[0017] The computing center dispatching load is divided into system load, flexible load, main load and general load, and the equipment side load is divided into bus load and branch load;

[0018] By identifying the load demand of the computing center, the adjustable load of the computing center is obtained from the system load, flexible load, bus load, and branch load;

[0019] The adjustable load of the computing power center includes interruptible, time-shiftable and adjustable loads.

[0020] Preferably, the calculation of the load forecast curve according to the historical load data and the real-time load data comprises the following steps:

[0021] Set forecast comparison data;

[0022] Calculate and analyze historical load data and real-time load data, and compare and analyze them with forecast comparison data;

[0023] If the comparative analysis results meet the predicted comparative data, the load forecast data is output to form a load forecast curve chart. If not, the calculation and analysis are performed again.

[0024] Preferably, the step of obtaining light power prediction, light power historical data, irradiance meter data and meteorological data to calculate the photovoltaic power generation prediction curve comprises the following steps:

[0025] Obtain the light power forecast data under the "photovoltaic" energy supply mode to calculate the photovoltaic power generation curve and photovoltaic output curve for each time period;

[0026] Energy storage is set up on the photovoltaic power station side to form an "energy storage + photovoltaic" energy supply mode;

[0027] Obtain the light power prediction data under the "energy storage + photovoltaic" energy supply mode to calculate the power generation curve of the power station;

[0028] The photovoltaic power generation prediction curve is calculated through the photovoltaic power generation curve diagram, the photovoltaic output curve for each period of time and the photovoltaic power generation curve diagram.

[0029] The present invention also provides a power dispatching system for a green computing power center, comprising:

[0030] The first computing unit is used to obtain the scheduling load of the computing power center, and obtain the adjustable load of the computing power center by identifying the load demand of the computing power center;

[0031] The second calculation unit is used to perform data calculation and analysis on the computing power load to obtain a load forecast based on the historical load data and the collected real-time load data;

[0032] The third calculation unit is used to obtain the light power prediction data, light power history data, irradiance meter data and meteorological data on the photovoltaic power station side to form a power generation power prediction;

[0033] The fourth computing unit is used to propose energy storage control strategies and form typical control methods for photovoltaic power stations under different working conditions of computing load and different meteorological conditions through load forecasting and power generation power forecasting;

[0034] The fifth calculation unit is used to provide power dispatching data to the city power side through load forecasting and power generation power forecasting.

[0035] The present invention also provides a power dispatching device for a green computing power center, the device comprising a processor and a memory; the memory is used to store program codes and transmit the program codes to the processor;

[0036] The processor is used to execute the steps of the power scheduling method for green computing power configuration described above according to the instructions in the program code.

[0037] The present invention also provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the steps of the power dispatching method for green computing power configuration described above.

[0038] The present invention provides a power dispatching method suitable for green computing power configuration. When a distributed photovoltaic power station supplies power to a data center, load forecast data is calculated by obtaining historical load data and real-time load data in the computing power center's dispatched load, photovoltaic power generation forecast data is calculated by obtaining optical power forecasts, optical power historical data, irradiance meter data and meteorological data on the photovoltaic power station side, and power dispatch is performed on the city power side according to the load forecast data and the photovoltaic power generation forecast data. Compared with the prior art, the photovoltaic power station in the present invention can combine the load fluctuations of the computing power center and the fluctuations in the photovoltaic power station output caused by meteorological conditions, so that the photovoltaic power station can follow the load changes like a traditional power supply, avoid insufficient output of new energy during peak load periods or excess output of new energy during low load periods, and improve the accuracy of power dispatching of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic flow chart of a method for obtaining charge prediction provided by an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of a flow chart of a power dispatching method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] In the description of the present invention, it should be noted that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "top", "bottom", "top surface", "bottom surface", "inside", "outside", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0043] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood to exclude the number itself, and "above", "below", "within" etc. are understood to include the number itself. If the terms "first", "second", "third" are described, they are only used for the purpose of description and distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0044] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", and "set" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The following is an explanation of the embodiments of the present invention based on its overall structure.

[0045] Example 1

[0046] See also Figure 2 , a power dispatching method suitable for green computing power configuration, comprising

[0047] Obtain historical load data and real-time load data; calculate the load forecast curve based on the historical load data and real-time load data; set up an optical power prediction device, an optical power historical data server and an irradiance meter on the new energy photovoltaic power station side; obtain optical power prediction, optical power historical data, irradiance meter data and meteorological data to calculate the photovoltaic power generation forecast curve; perform power dispatch on the city power side according to the load forecast curve and the photovoltaic power generation forecast curve.

[0048] Among them, historical load data and real-time load data in the computing power center scheduling load are obtained, and the load forecast curve is calculated based on the obtained historical load data and real-time load data.

[0049] Among them, the optical power prediction device can monitor the optical power data changes of the current photovoltaic power station in real time and accurately, and keep abreast of the dynamic situation of the power generation. The optical power historical data server stores a large amount of optical power historical data accumulated over a long period of time. These historical data record the power generation information of the power station in different periods and under different working conditions in the past, providing a basis for in-depth analysis of the periodic laws and trend changes of power generation. The irradiance meter focuses on the detailed measurement of solar irradiance. It can reflect the intensity of light irradiating to the photovoltaic panel per unit time. Because solar irradiance is one of the core driving factors of photovoltaic power generation, the accuracy of its data is directly related to the reliability of power generation prediction.

[0050] In a specific implementation scheme, an optical power prediction device, an optical power historical data server and an irradiance meter are set up on the new energy photovoltaic power station side, and the data in the above-mentioned equipment are obtained. The three sets of data obtained are combined with local meteorological data, and the combination evaluation and prediction of the algorithm model library are used to achieve high-precision short-term and ultra-short-term power generation forecasts.

[0051] The above-mentioned embodiment provides a power dispatching method suitable for green computing power configuration. When a distributed photovoltaic power station supplies power to a data center, the photovoltaic power station can combine the load fluctuations of the data center and the fluctuations in the output of the photovoltaic power station caused by meteorological conditions, so that the photovoltaic power station can follow the load changes like a traditional power supply, avoiding insufficient output of new energy during peak load periods or excess output of new energy during low load periods, thereby improving the power dispatching accuracy of the power grid.

[0052] Example 2

[0053] The method of dispatching power on the mains side according to the load forecast curve and the photovoltaic power generation forecast curve comprises the following steps:

[0054] Compare the load forecast curve with the photovoltaic power generation forecast curve.

[0055] The dispatching load on the mains side is analyzed based on the load forecast curve and photovoltaic power generation forecast curve.

[0056] Obtain comparison results of the load forecast curve and the photovoltaic power generation forecast curve, obtain analysis results of the load forecast curve and the photovoltaic power generation forecast curve, and obtain evaluation results.

[0057] Among them, based on the comparison results and analysis results, the accuracy of load forecasting and photovoltaic power generation forecasting is evaluated. The reliability and practicality of the forecasting method are evaluated, and the sources and influencing factors of the forecasting error are analyzed. For example, if there is a large deviation between the forecast result and the actual situation, it is necessary to analyze whether it is because the change in load demand exceeds expectations or photovoltaic power generation is affected by unforeseen factors.

[0058] Evaluate the effect of the load dispatching on the mains side. Consider multiple indicators such as energy efficiency, system stability, cost-effectiveness and environmental impact to evaluate the pros and cons of the dispatching strategy. For example, analyze whether optimizing the dispatching strategy can reduce the consumption of mains electricity, improve the utilization rate of photovoltaic power generation, and reduce the impact on the environment.

[0059] Example 3

[0060] See also Figure 1 , the acquisition of historical load data and real-time load data includes the following steps:

[0061] The computing center dispatching load is divided into system load, flexible load, main load and general load, and the equipment side load is divided into bus load and branch load.

[0062] By identifying the load demand of the computing power center, the adjustable load of the computing power center is obtained from the system load, flexible load, bus load, and branch load.

[0063] The adjustable load of the computing power center includes interruptible, time-shiftable and adjustable loads.

[0064] Among them, by identifying the load requirements of the computing power center, including requirements in terms of performance, energy consumption, reliability, etc., the adjustable load resources of the computing power center are mined. Based on the load dispatching of the computing power center, the interruptible, time-shiftable, and adjustable loads are mastered. Among them, when obtaining historical load data and real-time load data, it is necessary to focus on mastering the adjustable load of the computing power center. When the city power is dispatched, the power system can be optimized by obtaining the adjustable load of the computing power center. When the power supply is tight, some interruptible and time-shiftable loads can be interrupted or transferred to reduce the burden on the system. At the same time, by adjusting the power consumption of the adjustable load, the load can be peaked and valley filled, and the efficiency and stability of the power system can be improved. Therefore, in the process of obtaining the load forecast curve, it is necessary to master the interruptible, time-shiftable, and adjustable loads when obtaining historical load data and real-time load data, so as to realize high-precision short-term and ultra-short-term load forecasting of the computing power load.

[0065] Example 4

[0066] See also Figure 1 , the calculation of the load forecast curve according to the historical load data and the real-time load data comprises the following steps:

[0067] Set the forecast comparison data.

[0068] The historical load data and real-time load data are calculated and analyzed, and compared with the predicted comparison data.

[0069] If the comparative analysis results meet the predicted comparative data, the load forecast data is output to form a load forecast curve chart. If not, the calculation and analysis are performed again.

[0070] In the specific implementation plan, high-precision short-term and ultra-short-term load forecasts are carried out for computing power loads through real-time load and historical load data collection, correction and analysis. A matching historical database is established, machine learning is realized through the software, and a correction parameter system is formed through the accumulation of operating data. According to the status of uploaded data, empirical corrections can be made to improve the accuracy of load forecasting.

[0071] Example 5

[0072] The method of obtaining light power prediction, light power historical data, irradiance meter data and meteorological data to calculate the photovoltaic power generation prediction curve comprises the following steps:

[0073] Obtain the light power prediction data under the "photovoltaic" energy supply mode to calculate the photovoltaic power generation curve and the photovoltaic output curve for each time period.

[0074] Energy storage is set up on the photovoltaic power station side to form an "energy storage + photovoltaic" energy supply mode.

[0075] Obtain the light power prediction data under the "energy storage + photovoltaic" energy supply mode to calculate the power generation curve of the power station.

[0076] The photovoltaic power generation prediction curve is calculated through the photovoltaic power generation curve diagram, the photovoltaic output curve for each period of time and the photovoltaic power generation curve diagram.

[0077] Among them, in the "photovoltaic" energy supply mode, photovoltaic power generation is used as the energy source, and it is necessary to obtain the light power prediction data of photovoltaic power generation on the photovoltaic power station side, so as to calculate the photovoltaic power generation curve and the photovoltaic output curve for each time period.

[0078] The "energy storage + photovoltaic" energy supply mode refers to the photovoltaic power station side having two power supply sources: photovoltaic power generation and energy storage. By configuring energy storage on the photovoltaic power station side, the problem of imbalance between photovoltaic power supply and load power output can be solved, the abandonment rate can be reduced, and the utilization rate of clean energy can be improved. Among them, when obtaining the light power prediction data under the "energy storage + photovoltaic" energy supply mode to calculate the power generation curve of the power station, the photovoltaic + energy storage power station is configured according to the 12-hour power supply guarantee time, and the power generation curve of the power station is calculated.

[0079] Example 6

[0080] A power dispatching system for a green computing power center, comprising:

[0081] The first computing unit is used to obtain the scheduling load of the computing power center, and obtain the adjustable load of the computing power center by identifying the load demand of the computing power center.

[0082] The second calculation unit is used to perform data calculation and analysis on the computing power load to obtain load forecast based on historical load data and collected real-time load data.

[0083] The third calculation unit is used to obtain the light power prediction data, light power historical data, irradiance meter data and meteorological data on the photovoltaic power station side to form a power generation power prediction.

[0084] The fourth computing unit is used to propose energy storage control strategies and form typical control methods for photovoltaic power stations under different working conditions of computing load and different meteorological conditions through load forecasting and power generation power forecasting.

[0085] The fifth calculation unit is used to provide power dispatching data to the city power side through load forecasting and power generation power forecasting.

[0086] Among them, the power dispatching system of the green computing power center can make high-precision short-term and ultra-short-term load forecasts for computing power loads through real-time load and historical load data collection, correction and analysis. A matching historical database is established, machine learning is realized through the software, and a correction parameter system is formed through the accumulation of operating data. According to the status of uploaded data, empirical correction can be made to improve the accuracy of load prediction.

[0087] The power dispatching system of the green computing power center compares the prediction results of various prediction methods with the actual output during the operation of the new energy power generation prediction service, and automatically finds the most suitable prediction algorithm under different weather conditions to improve the accuracy of power generation prediction.

[0088] The power dispatching system of the green computing power center can combine photovoltaic power generation forecasting and load forecasting to form a typical control method for loads under different working conditions (holidays, weekdays, high computing power, low computing power) and different meteorological conditions.

[0089] For example, under high computing power conditions, the load of the computing center will increase dramatically. At this time, the backup energy storage can be activated to ensure the stability of power supply. At the same time, the operating parameters can be adjusted to reduce power consumption and improve energy efficiency.

[0090] For example, during working days, the load of the computing center is usually high. At this time, according to the load forecast results, the operation tasks can be reasonably arranged to optimize the load distribution and improve the energy utilization efficiency. At the same time, the energy storage system can be used to charge during the off-peak electricity price period and discharge during the peak electricity price period to reduce energy costs.

[0091] For example, on cloudy days, the amount of photovoltaic power generation will be reduced. At this time, the operating power can be adjusted appropriately to reduce power consumption. At the same time, the energy storage system can be used to charge during off-peak electricity price periods and discharge during peak electricity price periods to reduce energy costs.

[0092] For example, on sunny days, photovoltaic power generation is usually higher. At this time, photovoltaic power can be used first to reduce dependence on traditional energy. At the same time, according to the load forecast results, the operation tasks can be reasonably arranged to optimize load distribution and improve energy utilization efficiency.

[0093] The power dispatching system of the green computing power center can provide dispatching data to the mains side based on the prediction and analysis of computing power load and optical resources, reduce the number of grid dispatching times, and improve the accuracy of mains power dispatching.

[0094] Example 7

[0095] A power dispatching device for a green computing power center, the device comprising a processor and a memory; the memory is used to store program codes and transmit the program codes to the processor.

[0096] The processor is used to execute the steps of the power scheduling method for green computing power configuration described above according to the instructions in the program code.

[0097] Example 8

[0098] A computer-readable storage medium is used to store program codes, and the program codes are used to execute the steps of the power dispatching method for color computing power configuration described above.

[0099] In summary, the present invention provides a power dispatching method suitable for green computing power configuration, which first calculates the load forecast through the historical load data and real-time load data of the computing power center. Then, through the optical power prediction device, the optical power historical data server, the irradiance meter and the meteorological data, the combination of the algorithm model library is evaluated and predicted to achieve high-precision short-term and ultra-short-term power generation prediction. Finally, combined with the power generation prediction and load prediction, the dispatching data is provided to the city power side, the number of power grid dispatching is reduced, and the accuracy of the city power dispatching is improved.

[0100] The above is only a specific implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A power dispatching method suitable for green computing power configuration, characterized in that: include Obtain historical load data and real-time load data; Calculate the load forecast curve based on historical load data and real-time load data; Install a light power prediction device, a light power history data server and a radiation meter on the new energy photovoltaic power station side; Obtain light power forecast, light power history data, irradiance meter data and meteorological data to calculate photovoltaic power generation forecast curve; Electricity is dispatched on the mains side according to the load forecast curve and photovoltaic power generation forecast curve.

2. A power dispatching method suitable for green computing power configuration according to claim 1, characterized in that: The method of dispatching power on the mains side according to the load forecast curve and the photovoltaic power generation forecast curve comprises the following steps: Compare the load forecast curve with the photovoltaic power generation forecast curve; Analyze the dispatching load on the mains side according to the load forecast curve and photovoltaic power generation forecast curve; Obtain comparison results of the load forecast curve and the photovoltaic power generation forecast curve, obtain analysis results of the load forecast curve and the photovoltaic power generation forecast curve, and obtain evaluation results.

3. The power dispatching method applicable to green computing power configuration according to claim 1, characterized in that: The acquisition of historical load data and real-time load data comprises the following steps: The computing center dispatching load is divided into system load, flexible load, main load and general load, and the equipment side load is divided into bus load and branch load; By identifying the load demand of the computing center, the adjustable load of the computing center is obtained from the system load, flexible load, bus load, and branch load; The adjustable load of the computing power center includes interruptible, time-shiftable and adjustable loads.

4. The power dispatching method applicable to green computing power configuration according to claim 1, characterized in that: The calculation of the load forecast curve according to the historical load data and the real-time load data comprises the following steps: Set forecast comparison data; Calculate and analyze historical load data and real-time load data, and compare and analyze them with forecast comparison data; If the comparative analysis results meet the predicted comparative data, the load forecast data is output to form a load forecast curve chart. If not, the calculation and analysis are performed again.

5. The power dispatching method applicable to green computing power configuration according to claim 1, characterized in that: The method of obtaining light power prediction, light power historical data, irradiance meter data and meteorological data to calculate the photovoltaic power generation prediction curve comprises the following steps: Obtain the light power forecast data under the "photovoltaic" energy supply mode to calculate the photovoltaic power generation curve and photovoltaic output curve for each time period; Energy storage is set up on the photovoltaic power station side to form an "energy storage + photovoltaic" energy supply mode; Obtain the light power prediction data under the "energy storage + photovoltaic" energy supply mode to calculate the power generation curve of the power station; The photovoltaic power generation prediction curve is calculated through the photovoltaic power generation curve diagram, the photovoltaic output curve for each period of time and the photovoltaic power generation curve diagram.

6. A power dispatching system for a green computing power center, characterized in that: include The first computing unit is used to obtain the scheduling load of the computing power center, and obtain the adjustable load of the computing power center by identifying the load demand of the computing power center; The second calculation unit is used to perform data calculation and analysis on the computing power load to obtain a load forecast based on the historical load data and the collected real-time load data; The third calculation unit is used to obtain the light power prediction data, light power history data, irradiance meter data and meteorological data on the photovoltaic power station side to form a power generation power prediction; The fourth computing unit is used to propose energy storage control strategies and form typical control methods for photovoltaic power stations under different working conditions of computing load and different meteorological conditions through load forecasting and power generation power forecasting; The fifth calculation unit is used to provide power dispatching data to the city power side through load forecasting and power generation power forecasting.

7. A power dispatching device for a green computing power center, characterized in that: The device comprises a processor and a memory; the memory is used to store program code and transmit the program code to the processor; The processor is used to execute the steps of the power dispatching method for green computing power configuration described in claims 1-5 according to the instructions in the program code.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the steps of the power dispatching method for color computing power configuration described in claims 1-5.

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