Optical-data-storage integrated energy management optimization system and method based on computing task allocation

Abstract

The integrated energy management optimization system and method of photovoltaic, data and energy storage based on computing task allocation aims to solve the problems of high power consumption, low energy utilization efficiency and unfriendly grid access in data centers. The present invention constructs an energy management system integrating photovoltaic power generation, data centers and energy storage power stations. The intelligent scheduling module collects the task information of the data center, the output power data of the photovoltaic power generation system and the charging and discharging status data of the energy storage power station in real time, and uses the intelligent scheduling algorithm based on computing task allocation to generate the optimal power consumption strategy and collaborative operation strategy to achieve efficient energy utilization and friendly grid access. The system can dynamically adjust the power consumption strategy according to the electricity price information and the grid load situation, give priority to the use of electricity from photovoltaic power generation and energy storage power stations, reduce the burden on the grid, improve energy utilization efficiency, and enhance the reliability and stability of the data center, providing a new solution for green and intelligent data center energy management.

Description

Technical Field

[0001] The present invention relates to the cross-technical field of energy management and data center task allocation technology, and in particular to an optical-data-storage integrated energy management optimization system and method based on computing task allocation. Background Art

[0002] With the rapid development of information technology, the scale and energy consumption of data centers continue to rise, becoming an indispensable power consumer in modern society. The operation of data centers not only relies on a large amount of power supply, but also has certain characteristics in its power consumption pattern, which has a significant impact on the stability of the power grid. At the same time, in the energy field, the widespread use of renewable energy such as photovoltaic power generation and the continuous advancement of energy storage technology have provided new perspectives and solutions for energy management.

[0003] However, although photovoltaic power generation and energy storage technologies have brought new possibilities to energy management, the existing data center energy management system has failed to fully integrate the mission characteristics of the data center with photovoltaic and energy storage resources. Specifically, during the operation of traditional data centers, their mission execution often does not take into account the difference in electricity costs and grid load fluctuations, which leads to two major problems: first, the grid may be overloaded due to the high energy consumption of the data center, and second, the data center faces high operating costs due to the failure to fully utilize renewable energy and energy storage technology.

[0004] In the existing technologies of data center energy management, there is usually a lack of reasonable classification and scheduling of data center operation tasks, as well as effective use of photovoltaic and energy storage resources. For example, when executing various tasks, data centers do not intelligently classify tasks based on factors such as task latency requirements, data processing volume, and urgency, resulting in less than optimal allocation of power resources. At the same time, photovoltaic power generation systems and energy storage systems often exist as independent energy supply units, and fail to closely link with the operation needs of data centers to achieve efficient use of energy.

[0005] In addition, the existing technology lacks a comprehensive energy management system that can monitor data center mission load, photovoltaic power generation, grid power and other data in real time, and use advanced prediction algorithms to predict mission load changes, photovoltaic power generation and grid power fluctuations in the future. This lack of prediction capability makes it difficult for data centers to make reasonable energy scheduling decisions in advance during operation, further exacerbating the pressure on the grid and the operating costs of data centers.

[0006] In summary, the existing data center energy management system has obvious deficiencies in effectively integrating the task characteristics of the data center with the optical storage resources, and it is difficult to meet the needs of efficient operation of the data center and coordinated optimization of the power supply side. Therefore, it is necessary to invent an integrated optical, digital and storage energy management optimization system and method based on computing task allocation to solve the problems existing in the prior art. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide an integrated optical, digital and storage energy management optimization system and method based on computing task allocation to solve the specific problems of low energy utilization efficiency, unfriendly grid access and unreasonable task allocation existing in the data center energy management system.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is: an integrated energy management optimization system of light, data and storage based on computing task allocation, including: a data center that performs various computing tasks, a photovoltaic power generation system that generates photovoltaic power, an energy storage power station that stores and regulates electricity, and a monitoring and prediction system that monitors and predicts the task load of the data center, the photovoltaic power generation of the photovoltaic power generation system, the charging and discharging data of the energy storage power station, and the power of the power grid in real time. It also includes a data center task classification system that distinguishes between tasks with no latency requirements and tasks with latency requirements in the data center, a demand-side power response system connected to the data center task classification system, and an energy regulation system that connects the energy storage power station, the monitoring and prediction system, and the demand-side power response system, receives information from the monitoring and prediction system, and adjusts the data center's demand response to the power grid by controlling the charging and discharging power of the energy storage power station and the demand-side power response system, thereby achieving a balance between power supply and demand.

[0009] In a preferred solution, the energy storage power station includes a battery pack and an energy management unit. The battery pack adopts lithium-ion battery energy storage technology. The energy management unit is responsible for monitoring the charging and discharging status, power level, and health status of the battery, and performs charging and discharging operations according to the instructions of the energy regulation system.

[0010] In a preferred solution, the data center task classification system is integrated with an intelligent algorithm, which determines the delay requirement attributes of the task based on multi-dimensional factors such as the type of task, data processing volume, and urgency.

[0011] In the preferred scheme, the demand-side power response system formulates a power response strategy for tasks with no delay requirements determined in the data center task classification system based on the real-time status of the power grid, the power information of the energy storage power station, and the photovoltaic power generation forecast and actual data of the photovoltaic power generation system.

[0012] In a preferred solution, the demand-side power response system delays the execution of tasks that are not required based on the information provided by the energy regulation system when the power consumption of the power grid is at its peak and the photovoltaic power generation is insufficient, and utilizes the discharge of the photovoltaic power generation system and the energy storage power station to meet the power grid demand.

[0013] In a preferred solution, the demand-side power response system, based on the information provided by the energy regulation system, gives priority to executing tasks without delay requirements when the photovoltaic power generation is sufficient and the grid load is low, and incorporates the remaining power into the grid after secondary regulation by the energy storage power station.

[0014] In the preferred solution, the energy regulation system also optimizes the power interaction between the data center and the power grid based on the monitoring data, ensuring stable and safe operation of tasks with time delay requirements while reducing fluctuations in power grid operation.

[0015] In a preferred solution, the system also includes a communication link for realizing high-speed data communication between the data center, the energy storage power station, the power grid and various systems.

[0016] A method for energy management using any one of the above-mentioned optical-data-storage integrated energy management optimization systems based on computing task allocation comprises the following steps: Step 1: Classify the data center operation tasks through the data center task classification system to distinguish between tasks with no latency requirements and tasks with latency requirements; Step 2: Use the monitoring and prediction system to monitor the data center's task load, photovoltaic power generation, energy storage power, and grid power in real time and predict them; Step 3: The demand-side power response system formulates a power response strategy for tasks with no time delay requirements based on the information provided by the energy regulation system; Step 4: The energy regulation system optimizes the power interaction between the data center and the power grid based on the monitoring data, ensuring stable and safe operation of the task while reducing power grid operation fluctuations.

[0017] In the preferred scheme, the specific method of Step 2 is to delay the execution of the task when the power consumption of the power grid is at its peak and the photovoltaic power generation is insufficient, and use the photovoltaic power generation system and the energy storage power station to discharge to meet the power grid demand; when the photovoltaic power generation is sufficient and the power grid load is low, the task is executed first, and the remaining power is incorporated into the power grid after secondary regulation by the energy storage power station.

[0018] In a preferred embodiment, the method further includes synchronizing the real-time power data of the latency-required tasks to a monitoring and prediction system to further optimize the power interaction between the data center and the power grid.

[0019] In a preferred solution, the method further includes executing the remaining unexecuted tasks without latency requirements at night to reduce the operating costs of the data center.

[0020] The optical-data-storage integrated energy management optimization system and method based on computing task allocation provided by the present invention has the following beneficial effects: 1. Aiming at the specific problems existing in the data center energy management system, such as low energy utilization efficiency, unfriendly grid access, and unreasonable task allocation, the present invention proposes an integrated energy management optimization system and device for light, data, and storage based on computing task allocation. The system achieves friendly access to the grid and efficient utilization of energy by reasonably classifying and scheduling data center tasks and combining light and storage resources, thus effectively solving the above problems. 2. The task classification and scheduling implemented in the present invention realizes the reasonable classification of data center operation tasks through the data center task classification system. The system can distinguish between tasks with no delay requirements and tasks with delay requirements, and accurately judge the delay requirement attributes of tasks based on multi-dimensional factors such as task type, data processing volume, and urgency, which makes task execution more efficient and orderly, and improves the operation efficiency of the data center; 3. The present invention deeply integrates photovoltaic storage resources with data center tasks. Through the demand-side power response system, a corresponding power response strategy is formulated according to the real-time status of the power grid, the power information of the energy storage system, and the forecast and actual data of photovoltaic power generation. When the photovoltaic power generation is sufficient, the tasks with no delay requirements are prioritized for execution, and the remaining power is transmitted to the power grid after secondary regulation by the energy storage system. This integration method realizes the coordinated optimization of photovoltaic storage resources and data center tasks, and improves energy utilization efficiency. 4. The monitoring and prediction system of the present invention runs continuously, constantly updates data and provides decision-making basis for other systems. The system can monitor the task load, photovoltaic power generation, power grid power and other data of the data center in real time, and use advanced prediction algorithms to predict changes in the future. This enables the entire system to adjust the operation strategy in time to adapt to various working conditions, and realize efficient management and optimal utilization of energy; 5. The present invention effectively reduces the operating cost of the data center by reasonably scheduling data center tasks and making full use of photovoltaic power generation and energy storage resources. For tasks without latency requirements, the system can arrange execution at night when electricity rates are lower, thereby reducing electricity expenses. At the same time, by optimizing the operating strategies of photovoltaic power generation and energy storage systems, energy utilization efficiency is improved, further reducing the operating cost of the data center; 6. The present invention realizes balanced output of grid load through real-time monitoring and prediction of grid load and charge and discharge regulation of energy storage system. When grid load suddenly increases, energy storage system can discharge quickly to supplement power gap; when grid load is low, the system can reasonably arrange task execution and power transmission to grid. This regulation method effectively solves the problem of poor grid stability and improves grid stability. 7. The present invention realizes efficient management and optimal utilization of energy through the integration of photovoltaic storage resources and data center tasks and the application of technologies such as dynamic monitoring and prediction, which solves the problem of low energy utilization efficiency and improves energy utilization efficiency. At the same time, by reducing power grid operation fluctuations and reducing data center operation costs, it further promotes efficient utilization and sustainable development of energy; 8. The present invention innovatively proposes an integrated optical, digital and storage energy management optimization system and device based on computing task allocation, thereby achieving coordinated optimization of the efficient operation of the data center and the power supply side. The system not only solves the specific problems existing in the data center energy management system, but also improves energy utilization efficiency, reduces data center operating costs, and enhances the comprehensive benefits of power grid stability. This is of great significance for promoting the sustainable development of data centers and the energy field. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention will be further described below in conjunction with the accompanying drawings and implementation examples: Figure 1 It is a schematic diagram of the overall structure of the system of the present invention; Figure 2 It is a schematic diagram of the workflow of the present invention; In the figure: data center 1, photovoltaic power generation system 2, energy storage power station 3, power grid 4, data center task classification system 5, monitoring and prediction system 6, demand-side power response system 7, energy regulation system 8, computer room server 9. DETAILED DESCRIPTION

[0022] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments: Example 1 like Figure 1As shown, the integrated energy management optimization system of light, data and storage based on computing task allocation includes: a data center 1 for performing various computing tasks, a photovoltaic power generation system 2 for generating photovoltaic power generation, an energy storage power station 3 for storing and regulating electricity, and a monitoring and prediction system 6 for real-time monitoring and predicting the task load of the data center 1, the photovoltaic power generation of the photovoltaic power generation system 2, the charging and discharging data of the energy storage power station 3, and the power of the power grid 4. It also includes a data center task classification system 5 for distinguishing between tasks with no delay requirements and tasks with delay requirements in the data center 1, a demand-side power response system 7 connected to the data center task classification system 5, and an energy regulation system 8 connecting the energy storage power station 3, the monitoring and prediction system 6 and the demand-side power response system 7, receiving information from the monitoring and prediction system 6, and adjusting the demand response of the data center 1 to the power grid 4 by controlling the charging and discharging power of the energy storage power station 3 and the demand-side power response system 7, so as to achieve a balance between power supply and demand.

[0023] In this embodiment, the energy storage power station 3 includes a battery pack and an energy management unit. The battery pack adopts lithium-ion battery energy storage technology. The energy management unit is responsible for monitoring the charging and discharging status, power level, and health status of the battery, and performs charging and discharging operations according to the instructions of the energy regulation system 8.

[0024] Furthermore, the data center task classification system 5 is integrated with an intelligent algorithm, which determines the delay requirement attribute of the task according to multi-dimensional factors such as the type of task, data processing volume, and urgency.

[0025] Furthermore, the demand-side power response system 7 formulates a power response strategy for the tasks with no delay requirements determined in the data center task classification system 5 based on the real-time status of the power grid 4, the power information of the energy storage power station 3, and the photovoltaic power generation prediction and actual data of the photovoltaic power generation system 2.

[0026] Furthermore, the demand-side power response system 7 delays the execution of tasks that are not required based on the information provided by the energy regulation system 8 when the power grid 4 is at its peak and the photovoltaic power generation is insufficient, and utilizes the discharge of the photovoltaic power generation system 2 and the energy storage power station 3 to meet the needs of the power grid 4.

[0027] Furthermore, the demand-side power response system 7, based on the information provided by the energy regulation system 8, gives priority to executing tasks without delay requirements when the photovoltaic power generation is sufficient and the load on the power grid 4 is low, and incorporates the remaining power into the power grid after secondary regulation by the energy storage power station 3.

[0028] Furthermore, the energy regulation system 8 also optimizes the power interaction between the data center 1 and the power grid 4 according to the monitoring data, thereby ensuring stable and safe operation of tasks with time delay requirements while reducing operational fluctuations of the power grid 4.

[0029] Furthermore, the system also includes a communication link for realizing high-speed data communication among the data center 1, the energy storage power station 3, the power grid 4 and various systems.

[0030] Example 2 In another preferred embodiment, based on Example 1, Figure 2 As shown, the method for energy management using any one of the above-mentioned optical-data-storage integrated energy management optimization systems based on computing task allocation includes the following steps: Step 1: Classify the running tasks of data center 1 through data center task classification system 5 to distinguish tasks with no latency requirement and tasks with latency requirement; Step 2: Use the monitoring and prediction system 6 to monitor the task load, photovoltaic power generation, energy storage power and grid power of the data center 1 in real time and make predictions; Step 3: The demand-side power response system 7 formulates a power response strategy for tasks without time delay requirements based on the information provided by the energy regulation system 8; Step 4: The energy regulation system 8 optimizes the power interaction between the data center 1 and the power grid 4 according to the monitoring data, ensuring stable and safe operation of the task while reducing fluctuations in power grid operation.

[0031] In this embodiment, the specific method of Step 2 is to delay the execution of the task when the power grid 4 is at its peak and the photovoltaic power generation is insufficient, and use the photovoltaic power generation system 2 and the energy storage power station 3 to discharge to meet the power grid demand; when the photovoltaic power generation is sufficient and the power grid load is low, the task is executed first, and the remaining power is incorporated into the power grid after secondary regulation by the energy storage power station.

[0032] Furthermore, the method also includes synchronizing the real-time power data of the tasks with time delay requirements to the monitoring and prediction system 6 to further optimize the power interaction between the data center 1 and the power grid 4.

[0033] Furthermore, the method also includes executing the remaining unexecuted tasks without latency requirements at night to reduce the operating costs of the data center 1.

[0034] Example 3 In another preferred embodiment, based on embodiments 1 and 2, Figure 1 and Figure 2 As shown, the optical-data-storage integrated energy management optimization system and method based on computing task allocation will be described in detail below in conjunction with specific embodiments: The present invention has been effectively applied in a specific project of integrating a data center with a photovoltaic storage system. In this project, the data center 1 is equipped with an advanced computer room server 9 for performing various computing tasks; the photovoltaic power generation system 2 and the energy storage power station 3 are integrated with the data center 1 to form a photovoltaic, data and storage integrated system, which together provide power support for the operation of the data center 1.

[0035] 1. System composition and connection Data center 1: contains computer room servers 9, used to perform various computing tasks; Photovoltaic power generation system 2: used to convert solar energy into electrical energy, providing renewable energy for the system; Energy storage station 3: including battery packs and energy management units, used to store excess power and release it when needed; Grid 4: as an external power source, connected to energy storage power station 3 and data center 1 to achieve power complementarity; Data center task classification system 5: used to distinguish between tasks with no latency requirements and tasks with latency requirements in the data center; Monitoring and prediction system 6: real-time monitoring of data center 1’s task load, photovoltaic power generation, grid power and other data, and prediction of future changes; Demand-side power response system 7: connected to the task classification system of the data center 1, formulates a power response strategy for tasks with no delay requirements based on the real-time status of the power grid 4, the power information of the energy storage power station, and the forecast and actual data of photovoltaic power generation; The energy regulation system 8 is connected to the energy storage power station 3, receives information from the monitoring and prediction system 6, and realizes the balance of power supply and demand by controlling the charging and discharging power of the energy storage power station 3 and issuing instructions to the demand-side power response system 7 to adjust the demand response of the data center 1 to the power grid 4.

[0036] 2. Task classification and execution The tasks of the data center 1 are classified by the data center task classification system 5 to distinguish between tasks with no latency requirements (such as data backup, non-real-time data analysis, etc.) and tasks with latency requirements (such as online transaction processing, real-time data monitoring, etc.).

[0037] For tasks without time delay requirements, the demand-side power response system 7 formulates a power response strategy based on the status of the power grid 4, the power of the energy storage system and the photovoltaic power generation forecast data: for example, when the load on the power grid 4 is high and the photovoltaic power generation is insufficient, the execution of such tasks will be appropriately delayed; when the photovoltaic power generation is sufficient and the load on the power grid 4 is low, the execution of such tasks will be given priority, and the energy storage system will be used to adjust the remaining power to be incorporated into the power grid 4.

[0038] For tasks with latency requirements, on the basis of strictly controlling the total number of tasks, reasonably accept tasks with predictable computing volume and computing time; at the same time, synchronize the real-time power data of these tasks to the monitoring and prediction system 6, so as to adjust the system to optimize the power interaction between the data center 1 and the power grid 4.

[0039] 3. Real-time monitoring and dynamic adjustment The monitoring and prediction system 6 runs continuously, constantly updates data, and provides decision-making basis for other systems; When the load of the power grid 4 suddenly increases, the energy regulation system 8 instructs the energy storage system to discharge according to the monitoring data to supplement the power gap of the power grid 4, and appropriately adjusts the execution progress of tasks without delay requirements or suspends some tasks to balance the power output of the power grid; The remaining unexecuted tasks without latency requirements are scheduled to be executed at night to reduce the operating cost of data center 1.

[0040] 4. System interaction and optimization The systems exchange information through high-speed data communication links to ensure the real-time and accuracy of the data.

[0041] Based on the data from the monitoring and forecasting system 6, the energy regulation system 8 continuously optimizes the charging and discharging power of the energy storage system and the demand response of the data center 1 to the power grid 4, thereby achieving precise regulation of the load demand power curve and the actual power generation power curve.

[0042] Through continuous monitoring and prediction, the entire system can adjust its operating strategy in a timely manner to adapt to various changes in operating conditions and achieve efficient management and optimal utilization of energy.

[0043] Example 4 In another preferred embodiment, based on embodiments 1, 2, and 3, Figure 1 and Figure 2 As shown, the present invention provides an integrated energy management optimization system and method for light, data and storage based on computing task allocation. The working process of the present invention is described in detail below in combination with specific implementation scenarios to demonstrate the effect of the technical solution of the present invention in practical applications: In a typical project where a data center 1 is integrated with a photovoltaic storage system, the photovoltaic, data and energy storage integrated energy management optimization system and method based on computing task allocation described in the present invention has been fully applied. In this project, the data center 1 is equipped with advanced computer room servers 9 for performing various computing tasks. The photovoltaic power generation system 2 and the energy storage power station 3 are integrated with the data center 1 to form a photovoltaic, data and energy storage integrated system. The system is connected to the power grid 4 to achieve efficient management and utilization of energy.

[0044] First, various tasks of the data center 1 are carefully sorted out and classified through the task classification system 6; for example, data backup, non-real-time data analysis, etc. are judged as tasks with no latency requirements. Such tasks have no strict requirements on the execution time and can be arranged flexibly; while online transaction processing, real-time data monitoring, etc. are regarded as tasks with latency requirements. Such tasks have high requirements on the execution time and response speed, and need to be given priority and ensure stable operation.

[0045] When there is sufficient sunlight during the day, the monitoring and prediction system 6 monitors in real time that the photovoltaic power generation is gradually increasing, and the load of the power grid 4 is within the normal range; at this time, the demand-side power response system 7 arranges some time-delay-free tasks to start execution based on the monitoring data; at the same time, the excess photovoltaic power generation is secondary optimized by the energy storage power station 3 to ensure that the electricity is fully utilized; the energy storage power station 3 includes a battery pack and an energy management unit. The battery pack adopts advanced lithium-ion battery technology, and the energy management unit is responsible for monitoring the charging and discharging status, power level and health status of the battery to ensure the stable operation of the system.

[0046] In the case of a sudden increase in the load on the power grid 4, the energy regulation system 8 responds quickly and instructs the energy storage system to discharge according to the monitoring data to supplement the power gap of the power grid; at the same time, it appropriately adjusts the execution progress of tasks without delay requirements or suspends some tasks to balance the output of the power grid 4 and ensure the stable operation of the power grid 4; in this process, the systems exchange information through high-speed data communication links to ensure the rapid response and stable operation of the system.

[0047] After completing some tasks through demand-side response, the remaining unexecuted tasks without delay requirements are scheduled for execution at night; since electricity rates are lower at night, this arrangement can reduce the operating costs of Data Center 1; at the same time, the energy storage system can perform self-discharge maintenance or enter standby mode as needed, waiting for the next day's charge and discharge cycle.

[0048] For tasks that require time delay, the system synchronizes their real-time power data to the monitoring and prediction system 6; the regulation system optimizes the power interaction between the data center 1 and the power grid 4 based on the monitoring data, ensuring the stable and safe operation of the task while minimizing the operating fluctuations of the power grid 4; in this process, the system achieves efficient energy utilization and stable operation of the power grid 4 by accurately controlling the demand response of the data center 1 to the power grid 4.

[0049] Example 5 In another preferred embodiment, based on embodiments 1, 2, 3, and 4, in another embodiment, the optical-data-storage integrated energy management optimization system and method based on computing task allocation of the present invention is applied to a large data center project. In this project, the task types of data center 1 are more complex and diverse, including high-performance computing tasks such as big data analysis and artificial intelligence model training.

[0050] In order to meet the needs of these high-performance computing tasks, the system has optimized and upgraded the data center task classification system 5, and introduced more advanced intelligent algorithms and machine learning models; these algorithms and models can accurately judge the delay requirement attributes of tasks based on multi-dimensional factors such as task type, data processing volume, and urgency, and realize intelligent scheduling and optimization of tasks.

[0051] At the same time, the system also optimizes the configuration and management strategy of the energy storage system; adopts more efficient energy storage technology and energy management algorithms to improve the charging and discharging efficiency and energy density of the energy storage system; in addition, the system also introduces advanced energy regulation algorithms to achieve precise adjustment of the load demand power curve and the actual power generation power curve, further improving the stability and operation efficiency of the power grid.

[0052] In this embodiment, the system also fully considers the intermittent and uncertain nature of photovoltaic power generation; by real-time monitoring and prediction of photovoltaic power generation and grid 4 load conditions, the system can flexibly adjust the task execution strategy of the data center and the charging and discharging plan of the energy storage system to ensure that the system can operate stably under various working conditions and achieve efficient use of energy.

[0053] Through the description of the above embodiments, the integrated energy management optimization system and method of light, data and storage based on computing task allocation provided by the present invention has been effectively applied in the project of integrating data center 1 with light storage system; through detailed task classification, real-time monitoring and prediction, dynamic power response and energy regulation, it has achieved multiple goals such as balancing the output of power grid 4, increasing local consumption and reducing the operating costs of data center, improved the effectiveness and reliability of the energy management system, and significantly improved the overall energy efficiency of data center 1 and light storage system, providing strong technical support and practical cases for the construction of green data centers and the widespread application of renewable energy.

[0054] In the preferred solution, the energy storage power station 3 includes a battery pack and an energy management unit. The battery pack adopts lithium-ion battery energy storage technology. The energy management unit is responsible for monitoring the charging and discharging status, power level, and health status of the battery, and performs charging and discharging operations according to the instructions of the energy regulation system 8. The above settings ensure the efficient and stable operation of the energy storage power station 3, while improving the energy utilization efficiency. In addition, the close cooperation between the energy storage power station 3 and the energy regulation system 8 realizes the flexible scheduling of electricity and the balance of supply and demand, providing strong support for the stable operation of the smart grid.

[0055] In the preferred scheme, the data center task classification system 5 is integrated with an intelligent algorithm, which determines the delay requirement attributes of the task based on multi-dimensional factors such as the type of task, data processing volume, and urgency; the above settings ensure that tasks can be efficiently and accurately allocated to the corresponding processing units, thereby maximizing the use of data center resources while meeting the delay requirements, thereby improving overall operational efficiency and service quality.

[0056] In the preferred scheme, the demand-side power response system 7 formulates a power response strategy for the tasks with no delay requirements determined in the data center task classification system 5 according to the real-time status of the power grid 4, the power information of the energy storage power station 3, and the photovoltaic power generation forecast and actual data of the photovoltaic power generation system 2; the above settings effectively balance the supply and demand of the power grid 4 and improve the energy utilization efficiency; when the load of the power grid 4 is at its peak, the power consumption of the tasks with no delay requirements is automatically adjusted to reduce the pressure on the power grid 4; at the same time, the information of the energy storage power station 3 and the photovoltaic power generation system 2 is used to optimize scheduling and realize green and efficient energy management.

[0057] In the preferred scheme, the demand-side power response system 7 delays the execution of tasks without time requirements based on the information provided by the energy regulation system 8 when the power grid 4 is at its peak and the photovoltaic power generation is insufficient, and utilizes the discharge of the photovoltaic power generation system 2 and the energy storage power station 3 to meet the needs of the power grid 4; the above settings ensure the stable operation of the power grid 4. At the same time, when the photovoltaic power generation is sufficient and the power grid 4 is at its low point, the energy storage power station 3 is charged first, thereby realizing efficient utilization and optimal allocation of power resources and enhancing the flexibility and reliability of the power system.

[0058] In the preferred solution, the demand-side power response system 7, based on the information provided by the energy regulation system 8, will prioritize the execution of tasks without time delay requirements when the photovoltaic power generation is sufficient and the load of the power grid 4 is low, and the remaining power will be incorporated into the power grid after secondary regulation by the energy storage power station 3; the above settings can maximize the use of photovoltaic power generation resources and reduce energy waste while ensuring the stable operation of the power grid. When the load of the power grid gradually increases, the system will make intelligent adjustments to prioritize the power supply of key tasks and ensure the flexibility and reliability of power supply.

[0059] In the preferred scheme, the energy regulation system 8 also optimizes the power interaction between the data center 1 and the power grid 4 according to the monitoring data, ensuring the stable and safe operation of the tasks with latency requirements while reducing the operation fluctuations of the power grid 4; the above settings not only improve the energy utilization efficiency of the data center 1, but also enhance the synergy between it and the power grid 4, laying a solid foundation for building a green, intelligent and stable power supply and demand environment.

[0060] In the preferred solution, the system also includes a communication link for realizing high-speed data communication between the data center 1, the energy storage power station 3, the power grid 4 and various systems; the above settings ensure that the data center 1 can monitor the operating status of the energy storage power station in real time, dispatch the resources of the power grid 4 in time, realize efficient allocation and optimized management of energy, and at the same time improve the response speed and stability of the entire system.

[0061] In the preferred scheme, the specific method of Step 2 is to delay the execution of the task when the power grid 4 is at its peak and the photovoltaic power generation is insufficient, and use the photovoltaic power generation system 2 and the energy storage power station 3 to discharge to meet the power grid demand; when the photovoltaic power generation is sufficient and the power grid load is low, the task is executed first, and the remaining electricity is incorporated into the power grid after secondary regulation by the energy storage power station; the above settings not only effectively balance the supply and demand of the power grid 4, but also maximize the use of clean energy and reduce dependence on traditional energy. At the same time, the system can be intelligently dispatched according to real-time data to ensure the stability of the power grid and achieve efficient management and sustainable development of energy.

[0062] In the preferred scheme, the method also includes synchronizing the real-time power data of latency-required tasks to the monitoring and prediction system 6 to further optimize the power interaction between the data center 1 and the power grid 4; the above settings ensure that the data center 1 can efficiently respond to the scheduling needs of the power grid 4, while improving the execution efficiency of latency-sensitive tasks, and through precise prediction and real-time regulation, it achieves maximum energy utilization and stability of system operation.

[0063] In the preferred scheme, the method also includes executing the remaining unexecuted tasks with no delay requirements at night to reduce the operating costs of data center 1; the above settings can ensure the efficiency of task processing while making full use of the low electricity price period for calculations, thereby achieving effective savings in energy costs and further improving the overall operating efficiency of data center 1.

[0064] In summary, the integrated energy management optimization system and method of light, data and storage based on computing task allocation proposed in the present invention successfully solves the key problems existing in the energy management system of the data center 1, such as low energy utilization efficiency, unfriendly access to the power grid 4, and unreasonable task allocation; the technical solution of the present invention innovatively integrates the data center 1, photovoltaic power generation system 2 and energy storage power station 3 to form a comprehensive energy management system. This integration method is not common in previous energy management systems, especially after combining with the strategy of computing task allocation, it shows new technical characteristics; by introducing advanced data center task classification system 5 and demand-side power response system 7, the scheme can intelligently schedule the task execution of the data center 1 according to factors such as task delay requirements and power costs, thereby achieving efficient energy utilization and friendly access to the power grid; at the same time, the monitoring and prediction system 6 in the scheme can monitor and predict multi-dimensional data such as task load, photovoltaic power generation and power grid power of the data center in real time, providing strong data support for the optimized operation of the entire system; in addition, the present invention creatively adopts a dual optimization mechanism, which not only calculates The task demand-side response realizes the first optimization of photovoltaic power generation, and further improves the energy utilization efficiency through the secondary regulation of the energy storage system. This dual optimization mechanism has significant creativity in the field of energy management. In terms of algorithm application, the intelligent algorithm used in the data center task classification system 5 and the demand-side power response system 7 can accurately judge the delay requirements of the task according to multi-dimensional factors such as task type, data processing volume, and urgency, and formulate corresponding power response strategies. This intelligent algorithm application is groundbreaking in the energy management system. It is worth mentioning that the present invention not only focuses on the efficient operation of the data center 1, but also fully considers the coordinated optimization of the photovoltaic power generation system 2 and the energy storage power station 3, as well as the stable operation of the power grid 4. This idea of ​​comprehensive energy management has unique creativity in the current field of energy management. Through comprehensive system design and technological innovation, the present invention realizes the deep integration and efficient coordination of the data center 1, the photovoltaic power generation system 2 and the energy storage power station 3, and provides a new solution and practical example for building a green, intelligent and sustainable energy ecosystem, showing its outstanding contribution and broad prospects in the field of energy management.

Claims

1. An integrated energy management optimization system for light, data and storage based on computing task allocation, characterized by: The system comprises: a data center (1) for executing various computing tasks, a photovoltaic power generation system (2) for generating photovoltaic power generation, an energy storage power station (3) for storing and regulating electricity, and a monitoring and prediction system (6) for real-time monitoring and predicting the task load of the data center (1), the photovoltaic power generation of the photovoltaic power generation system (2), the charging and discharging data of the energy storage power station (3), and the power of the power grid (4). The system also comprises a data center task classification system (5) for distinguishing tasks without delay requirements and tasks with delay requirements in the data center (1), a demand-side power response system (7) connected to the data center task classification system (5), and an energy regulation system (8) connecting the energy storage power station (3), the monitoring and prediction system (6), and the demand-side power response system (7), receiving information from the monitoring and prediction system (6), and adjusting the demand response of the data center (1) to the power grid (4) by controlling the charging and discharging power of the energy storage power station (3) and the demand-side power response system (7), thereby achieving a balance between power supply and demand.

2. The optical-data-storage integrated energy management optimization system based on computing task allocation according to claim 1 is characterized in that: The energy storage power station (3) comprises a battery pack and an energy management unit. The battery pack adopts lithium-ion battery energy storage technology. The energy management unit is responsible for monitoring the charging and discharging state, power level, and health status of the battery, and performs charging and discharging operations according to the instructions of the energy regulation system (8).

3. The optical-data-storage integrated energy management optimization system based on computing task allocation according to claim 2 is characterized in that: The data center task classification system (5) is integrated with an intelligent algorithm, which determines the delay requirement attributes of the task based on multi-dimensional factors such as the task type, data processing volume, and urgency.

4. The optical-data-storage integrated energy management optimization system based on computing task allocation according to claim 3 is characterized in that: The demand-side power response system (7) formulates a power response strategy for the tasks with no delay requirements determined by the data center task classification system (5) based on the real-time status of the power grid (4), the power information of the energy storage power station (3), and the photovoltaic power generation prediction and actual data of the photovoltaic power generation system (2).

5. The optical-data-storage integrated energy management optimization system based on computing task allocation according to claim 4 is characterized in that: The demand-side power response system (7) delays the execution of tasks that are not required based on information provided by the energy regulation system (8) when the power grid (4) is at a peak power consumption and the photovoltaic power generation is insufficient, and utilizes the photovoltaic power generation system (2) and the energy storage power station (3) to discharge to meet the power grid (4) demand.

6. The optical-data-storage integrated energy management optimization system based on computing task allocation according to claim 5 is characterized in that: The demand-side power response system (7) gives priority to executing tasks without time delay requirements based on information provided by the energy regulation system (8) when the photovoltaic power generation is sufficient and the load of the power grid (4) is low, and integrates the remaining power into the power grid after secondary regulation by the energy storage power station (3).

7. The optical-data-storage integrated energy management optimization system based on computing task allocation according to claim 6 is characterized in that: The energy regulation system (8) also optimizes the power interaction between the data center (1) and the power grid (4) based on the monitoring data, thereby ensuring that tasks with time delay requirements operate stably and safely while reducing operational fluctuations of the power grid (4).

8. The optical-data-storage integrated energy management optimization system based on computing task allocation according to claim 7 is characterized in that: The system also includes a communication link for realizing high-speed data communication between the data center (1), the energy storage power station (3), the power grid (4) and various systems.

9. A method for energy management using the optical-data-storage integrated energy management optimization system based on computing task allocation as described in any one of claims 1 to 8, characterized in that: The method comprises the following steps: Step 1: Classify the running tasks of the data center (1) through the data center task classification system (5) to distinguish between tasks with no latency requirements and tasks with latency requirements; Step 2: Use the monitoring and prediction system (6) to monitor the task load, photovoltaic power generation, energy storage power and grid power of the data center (1) in real time and predict them; Step 3: The demand-side power response system (7) formulates a power response strategy for tasks without time delay requirements based on the information provided by the energy regulation system (8); Step 4: The energy regulation system (8) optimizes the power interaction between the data center (1) and the power grid (4) based on the monitoring data, ensuring stable and safe operation of the task while reducing power grid operation fluctuations.

10. The method for integrated energy management of light, data and storage based on computing task allocation according to claim 9 is characterized in that: The specific method of Step 2 is to delay the execution of the task when the power grid (4) is in peak demand and the photovoltaic power generation is insufficient, and use the photovoltaic power generation system (2) and the energy storage power station (3) to discharge to meet the grid demand; when the photovoltaic power generation is sufficient and the grid load is low, the task is executed first, and the remaining power is incorporated into the grid after secondary regulation by the energy storage power station.

11. The method for integrated energy management of light, data and storage based on computing task allocation according to claim 9 is characterized in that: The method also includes synchronizing the real-time power data of the latency-required tasks to a monitoring and prediction system (6) to further optimize the power interaction between the data center (1) and the power grid (4).

12. The method for energy management based on the optical-data-storage integrated energy management optimization system for computing task allocation according to claim 9 is characterized in that: The method also includes executing the remaining unexecuted tasks with no latency requirement at night to reduce the operating cost of the data center (1).

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