Energy configuration methods, devices, equipment, media, and software products for computing centers

Abstract

This application relates to an energy configuration method, apparatus, equipment, medium, and program product for a computing center. The method includes: obtaining construction parameters of the computing center and determining the total annual electricity consumption of the computing center based on these parameters; determining the installed capacity of various renewable energy sources supplying power to the computing center and the installed capacity of an energy storage system based on the total annual electricity consumption and a pre-established energy configuration model; wherein the energy configuration model includes an objective function and constraints, the objective function being aimed at minimizing carbon emissions, and the constraints including constraints on the proportion of green electricity use, typical daily power generation, quarterly complementarity of renewable energy sources, installed capacity of renewable energy sources, and wind and solar energy constraints. Using this application, an energy configuration scheme that meets the requirements of stable power supply and green electricity proportion, while achieving optimal carbon emissions, can be determined.

Description

Technical Field

[0001] This application relates to the field of computing center technology, specifically to an energy configuration method, apparatus, equipment, medium, and program product for a computing center. Background Technology

[0002] Due to the rapid development of artificial intelligence (AI) technology, the demand for AI computing power has surged, leading to a continuous expansion in the number and scale of computing centers. Currently, when using multiple renewable energy sources and energy storage systems to power computing centers, economic efficiency is often the primary optimization goal. However, this power supply method may result in problems such as high carbon emissions.

[0003] Therefore, how to optimize the allocation of renewable energy, reduce carbon emissions from computing centers, and ensure a stable supply of renewable energy has become an urgent problem to be solved. Summary of the Invention

[0004] Based on the above problems, this application provides an energy configuration method, device, equipment, medium and program product for a computing center, which can determine the energy configuration scheme of wind, solar, hydro and energy storage that meets the requirements of stable power supply and green electricity ratio and achieves optimal carbon emissions based on construction parameters and regional resource data.

[0005] Firstly, this application provides an energy configuration method for a computing center, the method comprising:

[0006] Obtain the construction parameters of the computing center, and determine the total annual electricity consumption of the computing center based on the construction parameters;

[0007] Based on the annual total electricity consumption of the computing center and the pre-established energy configuration model, the installed capacity of various renewable energy sources and the installed capacity of energy storage systems to power the computing center are determined. The energy configuration model includes an objective function and constraints. The objective function aims to minimize carbon emissions, and the constraints include constraints on the proportion of green electricity use, typical daily power generation, quarterly complementarity of renewable energy, installed capacity of renewable energy, and wind and solar constraints.

[0008] Among them, the wind and solar constraints include the wind-solar-storage coefficient constraints, which include: the wind-solar-storage coefficient is greater than or equal to the difference in the state of charge coefficient.

[0009] The difference in the state of charge is the difference between the largest and smallest state of charge.

[0010] The state of charge coefficient is determined based on the charge and discharge coefficients at a preset time.

[0011] The charge / discharge coefficient for the next moment is determined based on the charge / discharge coefficient for the current moment, the energy storage system's regulation capability index, the daily output benchmark coefficient, and the energy storage system's charging and discharging efficiency.

[0012] In the technical solution of this application embodiment, the objective function of the energy allocation model aims to minimize carbon emissions. This avoids prioritizing the use of high-carbon-emission power grids to reduce costs from the outset. Furthermore, it introduces various constraints such as the proportion of green electricity usage. During the computing center planning stage, even without historical operating data, it can determine the wind, solar, hydro, and energy storage energy allocation scheme that meets the requirements of stable power supply and green electricity ratio, and achieves optimal carbon emissions, based on construction parameters and regional resource data. Moreover, the wind-solar-storage coefficient constraint limits the synergistic output of wind power, photovoltaic, and energy storage systems. Based on this constraint and the objective function, carbon emissions are calculated, enabling the energy storage system in the energy allocation scheme to regulate wind power and photovoltaic power generation within a stable output range during the day, thereby achieving a stable power supply from renewable energy sources.

[0013] In some embodiments, the objective function includes the minimum of the sum of the total carbon emissions of the computing center, the annual carbon emissions of various renewable energy sources, the annual carbon emissions of the energy storage system, and the carbon emissions from electricity purchased from the grid.

[0014] The annual carbon emissions of multiple renewable energy sources are determined based on the annual power generation and carbon emission factors of multiple renewable energy sources. The annual power generation of renewable energy sources is determined based on the installed capacity, output factor and power generation duration of renewable energy sources.

[0015] The annual carbon emissions of an energy storage system are determined based on the energy loss of the energy storage system and the carbon emission factors of various renewable energy sources.

[0016] The carbon emissions from grid-purchased electricity are determined based on the total annual electricity consumption of the computing center, the annual power generation of various renewable energy sources, the power loss of the energy storage system, and the annual abandoned power of renewable energy.

[0017] In the technical solution of this application embodiment, the objective function fully considers the annual carbon emissions of various renewable energy sources, the annual carbon emissions of energy storage systems, and the carbon emissions generated by purchasing electricity from the grid, so that the energy configuration scheme can provide stable power supply and meet carbon emission requirements.

[0018] In some embodiments, the green electricity usage ratio constraint includes: the annual green electricity usage ratio of the computing center is greater than or equal to a first ratio;

[0019] The annual green electricity usage ratio of the computing center is determined based on the annual effective power generation of various renewable energy sources and the annual total electricity consumption of the computing center;

[0020] The annual effective power generation of renewable energy is determined based on the annual power generation of renewable energy, the power loss of energy storage systems, and the annual power curtailment of renewable energy.

[0021] In the technical solution of this application embodiment, the green electricity usage ratio constraint limits the green electricity usage ratio during energy allocation. Based on this constraint and the objective function, the carbon emissions are calculated, which can enable the energy allocation scheme to meet the green electricity requirements of the computing center throughout the year.

[0022] In some embodiments, the typical daily power generation constraint includes: the typical daily green electricity usage ratio of the computing center is greater than or equal to a second ratio;

[0023] The typical daily green electricity usage ratio of a computing center is determined based on the installed capacity of various renewable energy sources, the output coefficient at a preset time within a typical day, the duration of the output coefficient, and the total annual electricity consumption of the computing center.

[0024] In the technical solution of this application embodiment, the typical daily power generation constraint limits the proportion of green electricity used on a typical day when configuring energy. Based on this constraint and the objective function, the carbon emissions are calculated, which can enable the energy configuration scheme to meet the green electricity requirements of the computing center on a typical day.

[0025] In some embodiments, the quarterly complementary constraint for renewable energy includes: the minimum monthly green electricity usage ratio of the computing center is greater than or equal to the third ratio;

[0026] The minimum monthly green electricity usage ratio of the computing center is determined based on the output coefficient of various renewable energy sources in the preset month, the number of hours in the preset month, and the total annual electricity consumption of the computing center.

[0027] In the technical solution of this application embodiment, the renewable energy quarterly constraint limits the minimum monthly green electricity usage ratio of the computing center when configuring energy. Based on this constraint and the objective function, the carbon emissions are calculated, which can enable the energy configuration scheme to meet the monthly green electricity requirements of the computing center.

[0028] In some embodiments, the renewable energy installed capacity constraints include: the installed capacity of various renewable energy sources is less than or equal to a preset maximum installed capacity.

[0029] In the technical solution of this application embodiment, the renewable energy installed capacity constraint limits the installed capacity of various renewable energy sources during energy allocation, providing constraints at the renewable energy available installed resource level. Based on this constraint and the objective function, carbon emissions are calculated, ensuring that the energy allocation scheme conforms to regional resource conditions and provides a stable power supply.

[0030] In some embodiments, the wind and solar constraints also include wind and solar power storage capacity constraints, which include: the wind power storage capacity is greater than or equal to the lower limit of wind power capacity.

[0031] The photovoltaic power generation and storage capacity is greater than or equal to the lower limit of photovoltaic capacity;

[0032] The lower limit of wind power capacity is determined based on the installed capacity of wind power generation, the typical daily power generation coefficient of wind power generation, and the wind-solar-storage ratio.

[0033] The lower limit of photovoltaic capacity is determined based on the installed capacity of photovoltaic power generation, the typical daily power generation coefficient of photovoltaic power generation, and the wind-solar-storage ratio.

[0034] In the technical solution of this application embodiment, the lower limit constraint of the energy storage system configuration capacity is constrained by the daily power generation of wind and solar power and the energy storage configuration coefficient, so as to ensure that the energy storage system has the ability to regulate.

[0035] In some embodiments, the wind and solar constraints also include wind and solar curtailment rate constraints, which include: the theoretical curtailment rate is less than or equal to the preset curtailment rate.

[0036] The theoretical curtailment rate is determined based on the annual curtailment of renewable energy and the total theoretical annual power generation.

[0037] The total theoretical annual power generation is the sum of the theoretical annual power generation of wind power and the theoretical annual power generation of photovoltaic power.

[0038] The theoretical annual power generation of wind power is determined based on the installed capacity and annual power generation of wind power.

[0039] The theoretical annual power generation of photovoltaic power generation is determined based on the installed capacity and annual power generation of photovoltaic power generation.

[0040] In the technical solution of this application embodiment, the theoretical curtailment rate is calculated by the installed capacity, monthly average output coefficient and monthly power generation duration, thereby realizing the constraint of wind and solar installed capacity at the curtailment level.

[0041] Secondly, this application also provides an energy configuration device for a computing center, the device comprising:

[0042] The electricity consumption determination module is used to obtain the construction parameters of the computing center and determine the total annual electricity consumption of the computing center based on the construction parameters.

[0043] The energy configuration module is used to determine the installed capacity of various renewable energy sources and the installed capacity of energy storage systems to power the computing center, based on the computing center's total annual electricity consumption and a pre-established energy configuration model. The energy configuration model includes an objective function and constraints. The objective function aims to minimize carbon emissions, and the constraints include constraints on the proportion of green electricity use, typical daily power generation, quarterly complementarity of renewable energy, installed capacity of renewable energy, and wind and solar constraints.

[0044] Among them, the wind and solar constraints include the wind-solar-storage coefficient constraints, which include: the wind-solar-storage coefficient is greater than or equal to the difference in the state of charge coefficient.

[0045] The difference in the state of charge is the difference between the largest and smallest state of charge.

[0046] The state of charge coefficient is determined based on the charge and discharge coefficients at a preset time.

[0047] The charge / discharge coefficient for the next moment is determined based on the charge / discharge coefficient for the current moment, the energy storage system's regulation capability index, the daily output benchmark coefficient, and the energy storage system's charging and discharging efficiency.

[0048] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method of any one of the first aspects.

[0049] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of any one of the first aspects.

[0050] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the method of any one of the first aspects. Attached Figure Description

[0051] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the alternative embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0052] Figure 1 This is a flowchart illustrating an energy configuration method for a computing center according to an embodiment of this application;

[0053] Figure 2a This is a comparison diagram of wind power output before and after energy storage distribution according to an embodiment of this application;

[0054] Figure 2b This is a comparison diagram of wind power SOC before and after adjustment according to an embodiment of this application;

[0055] Figure 2c This is a comparison diagram of the power output before and after photovoltaic power generation and energy storage in an embodiment of this application;

[0056] Figure 2d This is a comparison diagram of the photovoltaic SOC before and after adjustment according to an embodiment of this application;

[0057] Figure 3 This is a structural block diagram of an energy configuration device for a computing center according to an embodiment of this application;

[0058] Figure 4 This is an internal structural diagram of a computer device according to an embodiment of this application. Detailed Implementation

[0059] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0061] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0062] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0063] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0064] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0065] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0066] Due to the rapid development of Artificial Intelligence (AI) technology, the demand for AI computing power has surged, leading to a continuous expansion in the number and scale of computing centers. Currently, when using multiple renewable energy sources and energy storage systems to power computing centers, economic efficiency is often the primary optimization goal. However, prioritizing economic efficiency may result in prioritizing the purchase of electricity from the high-carbon-emission grid during off-peak electricity prices, leading to higher carbon emissions. Therefore, optimizing the allocation of renewable energy sources, reducing carbon emissions from computing centers, and ensuring a stable supply of renewable energy have become urgent issues to be addressed.

[0067] To address the aforementioned issues, this application provides an energy configuration method for a computing center. This method obtains the construction parameters of the computing center and determines its electricity consumption based on these parameters. Based on the computing center's electricity consumption and a pre-established energy configuration model, it determines the installed capacity of various renewable energy sources supplying power to the computing center and the installed capacity of the energy storage system. Since the objective function of the energy configuration model aims to minimize carbon emissions, it avoids prioritizing the use of high-carbon-emission grid power to reduce costs. Furthermore, it introduces constraints such as the proportion of green electricity use, typical daily power generation, quarterly renewable energy complementarity, renewable energy installed capacity, and wind and solar constraints. During the computing center planning phase, based on construction parameters and regional resource data, it can determine an energy configuration scheme that meets the requirements for stable power supply and green electricity proportion, while achieving optimal carbon emissions from wind, solar, hydro, and energy storage.

[0068] According to some embodiments of this application, refer to Figure 1 This application provides a method for configuring energy in a computing center. Taking the application of this method to a terminal as an example, it is understood that this method can also be applied to a server, and to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. Embodiments of this application may include the following steps:

[0069] Step 101: Obtain the construction parameters of the computing center and determine the total annual electricity consumption of the computing center based on the construction parameters.

[0070] Among them, computing centers are facilities with computing power, carrying capacity and storage capacity, mainly composed of infrastructure such as wind, thermal and hydropower and IT (Information Technology) software and hardware equipment, including general data centers, intelligent computing centers and supercomputing centers.

[0071] Construction parameters for computing centers include the planned rack size, rack power density, rack utilization rate, and Power Usage Effectiveness (PUE) for newly built, renovated, or expanded computing centers. Rack size refers to the total number of standard server racks that can be accommodated or are already deployed within the computing center; it is a core indicator for measuring the physical space and basic carrying capacity of the computing center. Rack power density refers to the maximum power supply that a single standard server rack can provide during design, reflecting the ability of a rack to support high-power equipment within a unit of space; the unit is usually "kilowatts per rack (kW / U)" or "kilowatts per square meter (kW / ㎡)". Rack utilization rate refers to the proportion of racks with installed IT equipment within the computing center to the total rack size; it is a key indicator for measuring the resource utilization efficiency of the computing center, usually expressed as a percentage (%). PUE is the ratio of the total power consumption of the computing center to the power consumption of its IT equipment, generally measured by the annual average PUE value.

[0072] The terminal can obtain the construction parameters of the computing center from the planning and construction documents of the newly built, renovated, or expanded computing center. Based on the construction parameters of the computing center, the total power of the IT equipment in the computing center is first calculated, and then the total energy consumption of the IT equipment is calculated based on the total power of the IT equipment and the operating time of the IT equipment. Finally, based on the total energy consumption of the IT equipment and the power utilization efficiency of the computing center, the annual total electricity consumption of the computing center is calculated.

[0073] It should be noted that the formulas provided in the embodiments of this application are all illustrative for ease of explanation, and are not limited to these examples in actual applications.

[0074] The formula for calculating the total power of IT equipment is as follows:

[0075]

[0076] Among them, P IT N represents the total power of IT equipment. rack This refers to the rack size of a computing center, typically defined as the standard rack size; P rack For rack power density, a standard rack can provide 2.5kW; η rack To determine the shelf availability rate.

[0077] The formula for calculating the total energy consumption of IT equipment is as follows:

[0078]

[0079] Among them, E IT T represents the total energy consumption of the IT equipment; T represents the equipment runtime. Considering that the IT equipment in the computing center works 24 hours a day, the monthly runtime is the number of days in the month multiplied by 24 hours, and the annual runtime is 8760 hours.

[0080] The formula for calculating the annual total electricity consumption of a computing center is as follows:

[0081]

[0082] Among them, E total The total annual electricity consumption of the computing center is represented by PUE, which is the power utilization efficiency of the computing center.

[0083] Step 102: Based on the annual total electricity consumption of the computing center and the pre-established energy configuration model, determine the installed capacity of various renewable energy sources that supply power to the computing center and the installed capacity of the energy storage system.

[0084] Among them, there are various renewable energy sources, including wind, solar, and hydropower.

[0085] The energy allocation model includes an objective function and constraints. The objective function aims to minimize carbon emissions. Constraints include green electricity usage ratio constraints, typical daily power generation constraints, quarterly renewable energy complementarity constraints, renewable energy installed capacity constraints, and wind and solar constraints. The green electricity usage ratio constraint constrains the usage ratio of renewable energy (wind, solar, and hydro). A typical day is a working day whose wind and solar power generation characteristics are most similar to most other days; the typical daily power generation constraint constrains the green electricity usage ratio on this typical day. The renewable energy seasonal complementarity constraint constrains the minimum monthly green electricity usage ratio for the computing center, considering the wet and dry seasons for hydropower to achieve seasonal complementarity between water and wind / solar power throughout the year. The renewable energy installed capacity constraint constrains the installed capacity of wind, solar, and hydropower. The wind and solar constraints constrain the charge / discharge status and curtailment rate of wind and solar power generation.

[0086] In some embodiments, the wind-solar constraint includes a wind-solar-storage coefficient constraint, which is used to constrain the charging and discharging state of the energy storage system in conjunction with wind and photovoltaic power generation. The wind-solar-storage coefficient constraint includes: the wind-solar-storage coefficient being greater than or equal to the difference in state of charge (SOC); the SOC difference being the difference between the maximum and minimum SOC; the SOC being determined based on the charging and discharging coefficients at a preset time; and the charging and discharging coefficients at the next time step being determined based on the charging and discharging coefficients at the current time step, the energy storage system's regulation capability index, the daily output benchmark coefficient, and the charging and discharging efficiency of the energy storage system.

[0087] Reference Figures 2a to 2d The horizontal axis represents time, and the vertical axis represents the output coefficient or SOC. Figure 2a The diagram shows a comparison of wind power output before and after energy storage integration. The original power output coefficient of wind power generation may exceed the lower or upper allowable limits, indicating significant fluctuations. Configuring an energy storage system for wind power generation can adjust the power output coefficient, reducing fluctuations to within 5%. Figure 2b The diagram shows a comparison of the wind power SOC before and after adjustment. The original SOC (State of Charge) of the energy storage system may be negative, but after the energy storage system is configured for wind power generation, the adjusted SOC is positive. Figure 2c The diagram shows a comparison of photovoltaic (PV) power output before and after energy storage integration. The original power output coefficient of PV power generation may exceed the lower or upper allowable limits, indicating significant fluctuations. Configuring an energy storage system for PV power generation can adjust the power output coefficient, reducing fluctuations to within 5%. Figure 2d The diagram shows a comparison of the photovoltaic SOC before and after adjustment. The original SOC of the energy storage system may be negative, but after the energy storage system is configured to generate photovoltaic power, the adjusted SOC is positive.

[0088] Understandably, considering that the computing center needs a stable power supply, but wind power and photovoltaic power generation have randomness and volatility during the day, in order to achieve a stable power supply from renewable energy, an energy storage system is set up to regulate wind power and photovoltaic power generation within a stable output range during the day. Therefore, a wind-solar-storage ratio is set.

[0089] The process of determining the wind-solar-storage allocation coefficient includes: calculating the charge-discharge coefficient at each time step; determining the state of charge coefficient at each time step based on the charge-discharge coefficient at each time step; determining the maximum and minimum state of charge coefficients from the state of charge coefficients at multiple times step; calculating the difference between the maximum and minimum state of charge coefficients, and the wind-solar-storage allocation coefficient is greater than or equal to this difference in state of charge coefficients.

[0090] The process of calculating the charge and discharge coefficients at each time step includes: optimizing the daily power output coefficients of wind and solar power to determine the daily power output benchmark coefficient μ; determining the fluctuation range based on the energy storage system's regulation capacity index δ and the daily power output benchmark coefficient μ; and calculating the charge and discharge coefficient ρ at the next time step. i,p (t+1) Based on the current charging / discharging coefficient ρ i,p (t), energy storage system regulation capacity index δ, daily output benchmark coefficient μ, energy storage system charging efficiency and discharge efficiency Sure.

[0091] The formula for calculating the charge / discharge coefficient is as follows:

[0092]

[0093] Among them, δ is the energy storage system regulation capacity index, that is, the fluctuation range of the 24-hour coordinated output of wind power, solar power and energy storage system, which can be set to 5%; μ is the daily output benchmark coefficient, which is obtained by sequential optimization based on the daily output coefficient of wind and solar power. To improve the charging efficiency of energy storage systems. The discharge efficiency of the energy storage system; ρ i,p (t) represents the charge / discharge coefficient at the current moment, ρ i,p (t+1) represents the charge / discharge coefficient at the next moment, P i (t) represents the output coefficient of renewable energy i at time t within a typical day.

[0094] The formula for calculating the state of charge coefficient is as follows:

[0095]

[0096] Where, ρ i,SOC (t) is the state-of-charge coefficient; ρ i,p (t) represents the charge / discharge coefficient at the current moment. ρ i Storage and energy allocation coefficients for wind power and photovoltaic power generation.

[0097] The formula for calculating the wind-solar-storage allocation coefficient is as follows:

[0098]

[0099] Where, ρ i ρ represents the wind-solar-storage ratio, where 'i' considers both wind and solar renewable energy sources. i,SOC (t) is the state-of-charge coefficient.

[0100] Understandably, the wind-solar-storage coefficient constraint limits the synergistic output of wind power, photovoltaic and energy storage systems. Based on this constraint and the objective function, carbon emissions can be calculated, enabling the energy storage system in the energy allocation scheme to regulate wind power and photovoltaic power generation within a stable output range during the day, thereby achieving a stable power supply from renewable energy sources.

[0101] After calculating the total annual electricity consumption of the computing center, the total annual electricity consumption is substituted into the objective function of the energy allocation model for calculation. During the calculation process, the installed capacity of various renewable energy sources and the installed capacity of energy storage systems are adjusted according to multiple constraints of the energy allocation model until the adjusted installed capacity not only meets the constraints but also minimizes the carbon emissions calculated using the objective function.

[0102] When constructing a new computing center, various power supply facilities such as wind power, photovoltaic power, and hydropower can be built based on the determined installed capacity of multiple renewable energy sources and the installed capacity of energy storage systems, and energy storage systems can be configured for the computing center. When renovating or expanding a computing center, energy configuration can be rearranged based on existing power supply facilities and energy storage systems, as well as the calculated installed capacity of multiple renewable energy sources and the installed capacity of energy storage systems.

[0103] In the above embodiments, the annual total electricity consumption of the computing center is determined based on the construction parameters of the computing center; based on the annual total electricity consumption of the computing center and the pre-established energy configuration model, the installed capacity of various renewable energy sources supplying power to the computing center and the installed capacity of the energy storage system are determined. In the technical solution of this application embodiment, the objective function of the energy configuration model aims to minimize carbon emissions, which can avoid prioritizing the use of high-carbon-emission grid electricity purchases to reduce costs from the source. Furthermore, it introduces various constraints such as the proportion of green electricity use. During the planning stage of the computing center, even without historical operating data, an energy configuration scheme of wind, solar, hydro, and energy storage that meets the requirements of stable power supply and green electricity proportion, and achieves optimal carbon emissions can be determined based on construction parameters and regional resource data.

[0104] According to some embodiments of this application, the objective function includes the minimum of the sum of the total carbon emissions of the computing center, the annual carbon emissions of various renewable energy sources, the annual carbon emissions of the energy storage system configured in the computing center, and the carbon emissions from electricity purchased from the grid; the annual carbon emissions of various renewable energy sources are determined based on the annual power generation and carbon emission factors of various renewable energy sources, and the annual power generation of renewable energy sources is determined based on the installed capacity, output coefficient, and power generation duration of renewable energy sources; the annual carbon emissions of the energy storage system are determined based on the charge-discharge cycle efficiency of the energy storage system, the wind power generation and energy storage capacity, the carbon emission factor per kilowatt-hour of wind power generation, the photovoltaic power generation and energy storage capacity, and the carbon emission factor per kilowatt-hour of photovoltaic power generation; the carbon emissions from electricity purchased from the grid are determined based on the total annual electricity consumption of the computing center, the annual power generation of various renewable energy sources, the power loss of the energy storage system, and the annual abandoned power of various renewable energy sources.

[0105] The formula for calculating the objective function is as follows:

[0106]

[0107] Among them, C e The total carbon emissions from the electricity consumed by the computing center; C i For the annual carbon emissions of various renewable energy sources, C storage C represents the annual carbon emissions of an energy storage system. grid This refers to the carbon emissions generated from purchasing electricity through the power grid.

[0108] The calculation process for the annual carbon emissions of multiple renewable energy sources includes: calculating the power generation of the i-th renewable energy source in month m based on the product of its installed capacity, power output coefficient in month m, and number of hours in month m; calculating the annual power generation of the i-th renewable energy source in month m and 12 months based on its power generation in month m; calculating the annual carbon emissions of the i-th renewable energy source based on the product of its annual power generation and carbon emission factor; and calculating the annual carbon emissions of multiple renewable energy sources based on the annual carbon emissions of the i-th renewable energy source.

[0109] The formulas for calculating the annual carbon emissions of various renewable energy sources are as follows:

[0110]

[0111] Among them, E i Let EF be the annual power generation of the i-th renewable energy source; i x is the carbon emission factor of the i-th renewable energy source; i For the installed capacity of renewable energy i; P i (m) represents the output coefficient of renewable energy i in month m; T m This refers to the number of hours in m months, i.e., the duration of power generation.

[0112] The annual carbon emissions of an energy storage system are determined based on the energy loss of the system and the carbon emission factor of renewable energy. Specifically, the energy loss of the energy storage system is equivalent to the carbon emissions generated by the additional generation of green electricity. This is calculated based on the energy storage system's charge-discharge cycle efficiency, wind power generation capacity, wind power carbon emission factor per kilowatt-hour, photovoltaic power generation capacity, and photovoltaic carbon emission factor per kilowatt-hour.

[0113] The formula for calculating the annual carbon emissions of an energy storage system is as follows:

[0114]

[0115] Among them, E loss β represents the energy loss of the energy storage system, and β represents the charge / discharge cycle efficiency of the energy storage system. For wind power generation, storage capacity, For photovoltaic power generation and storage capacity; EF wind EF is the carbon emission factor per kilowatt-hour of wind power generation. pv This refers to the carbon emission factor per kilowatt-hour of photovoltaic power generation.

[0116] The calculation process for carbon emissions from grid-purchased electricity includes: calculating the annual effective power generation of various renewable energy sources based on the sum of annual power generation of various renewable energy sources and the difference between the power loss of energy storage systems and the annual abandoned power of renewable energy sources; and calculating the carbon emissions from grid-purchased electricity based on the total annual electricity consumption of the computing center, the annual effective power generation of various renewable energy sources, and the grid carbon emission factor.

[0117] The formula for calculating carbon emissions from electricity purchased from the grid is as follows:

[0118]

[0119] Among them, E total E represents the total annual electricity consumption of the computing center. i E represents the annual electricity generation of the i-th renewable energy source. loss E represents the power loss of the energy storage system. aba For the annual amount of renewable energy curtailed, EF grid This is a carbon emission factor for the power grid.

[0120] The system obtains data from a pre-set database or data publishing center, including grid carbon emission factors, wind, solar, and hydropower carbon emission factors per kilowatt-hour, and the charge / discharge cycle efficiency of energy storage systems. After calculating the total annual electricity consumption of the computing center, these parameters are substituted into the aforementioned formula. During the calculation process, the installed capacity of wind, solar, and hydropower, as well as the installed capacity of energy storage systems, are adjusted according to multiple constraints of the energy configuration model until the carbon emissions calculated using the objective function are minimized. This yields a capacity configuration scheme for wind, solar, hydro, and energy storage that satisfies the requirements for stable power supply and green electricity ratio, while achieving optimal carbon emissions.

[0121] In the above embodiments, the objective function fully considers the annual carbon emissions of various renewable energy sources, the annual carbon emissions of energy storage systems, and the carbon emissions generated by purchasing electricity from the grid, so that the energy configuration scheme can provide stable power supply and meet carbon emission requirements.

[0122] According to some embodiments of this application, the green electricity usage ratio constraints include: the annual green electricity usage ratio of the computing center is greater than or equal to a first ratio; the annual green electricity usage ratio of the computing center is determined based on the annual effective power generation of various renewable energy sources and the annual total electricity consumption of the computing center; the annual effective power generation of renewable energy sources is determined based on the annual power generation of renewable energy sources, the power loss of the energy storage system, and the annual power curtailment of renewable energy sources.

[0123] The calculation process for the green electricity usage ratio includes: calculating the installed capacity of renewable energy i, the output coefficient of m months, and the number of operating hours to obtain the power generation of renewable energy i in m months; summing the power generation of various renewable energy sources i in m months to obtain the total power generation of various renewable energy sources in m months; calculating the annual power generation of various renewable energy sources based on the total power generation of various renewable energy sources in m months and 12 months; calculating the difference between the annual power generation of various renewable energy sources and the power loss of the energy storage system and the annual abandoned power of renewable energy to obtain the annual effective power generation of renewable energy; and calculating the ratio of the annual effective power generation of renewable energy to the total annual power of the computing center to obtain the green electricity usage ratio.

[0124] The constraints on the proportion of green electricity use are as follows:

[0125]

[0126] Wherein, α0 is the target value for the proportion of green electricity used by the computing center, i.e., the first ratio. E loss E represents the electricity loss of the energy storage system. aba For the annual amount of renewable energy curtailed, E total x represents the total annual electricity consumption of the computing center. i For the installed capacity of renewable energy i; P i (m) represents the output coefficient of renewable energy i in month m; T mis the number of hours in month m, i.e., the power generation duration.

[0127] In the above embodiments, the green power usage ratio constraint condition limits the green power usage ratio during energy allocation. Based on this constraint condition and the objective function, calculating the carbon emissions can make the energy allocation plan meet the annual green power requirements of the computing center.

[0128] According to some embodiments of the present application, the typical daily power generation constraint condition includes: the typical daily green power usage ratio of the computing center is greater than or equal to the second ratio; the typical daily green power usage ratio of the computing center is determined according to the installed capacity of various renewable energy sources, the output coefficient at a preset moment within the typical day, the duration of the output coefficient, and the annual total power consumption of the computing center.

[0129] The calculation process of the typical daily power generation includes: calculating the product of the installed capacity of renewable energy source i, the output coefficient of renewable energy source i at moment t within the typical day, and the duration of the output coefficient, to obtain the power generation of renewable energy source i at moment t within the typical day; calculating the sum of the power generations of multiple renewable energy sources at moment t within the typical day, to obtain the total power generation at moment t within the typical day; since moment t represents a moment within the typical day, summing up the total power generation at moment t within the typical day for 24 moments throughout the day, to obtain the typical daily power generation; finally, calculating the ratio between the typical daily power generation and the daily power consumption of the computing center to obtain the typical daily power generation ratio.

[0130] The typical daily power generation constraint condition is as follows:

[0131]

[0132] Among them, α1 is the target value of the typical daily green power usage ratio of the computing center, i.e., the second ratio. P i (t) is the output coefficient of renewable energy source i at moment t within the typical day, T(t) is the duration for which the output coefficient P i (t) lasts, with a value of 1h. E day is the daily power consumption of the computing center, and the daily power consumption E day of the computing center is the ratio of the annual total power consumption E total of the computing center to 365 days.

[0133] By using the normalized output coefficient to characterize the output characteristics of wind, light, and water with a typical day, it can be set that α1 = α0 to form a wind-light complementary constraint within the typical day.

[0134] In the above embodiments, the typical daily power generation constraint condition limits the green power usage ratio of the typical day during energy allocation. Based on this constraint condition and the objective function, calculating the carbon emissions can make the energy allocation plan meet the typical daily green power requirements of the computing center.

[0135] According to some embodiments of this application, the quarterly complementary constraints of renewable energy include: the minimum monthly green electricity usage ratio of the computing center is greater than or equal to a third ratio; the minimum monthly green electricity usage ratio of the computing center is determined based on the output coefficient of various renewable energy sources in a preset month, the number of hours in the preset month, and the total annual electricity consumption of the computing center.

[0136] The calculation process for the minimum monthly green electricity usage ratio of the computing center includes: obtaining the monthly power generation of renewable energy i by multiplying the installed capacity of renewable energy i, the output coefficient of m months, and the number of hours in m months; calculating the monthly power generation of various renewable energy sources based on the monthly power generation of renewable energy i; and obtaining the minimum monthly green electricity usage ratio of the computing center based on the ratio of the monthly power generation of various renewable energy sources to the monthly electricity consumption of the computing center.

[0137] The quarterly complementarity constraints for renewable energy are as follows:

[0138]

[0139] Here, α2 is the target value for the minimum monthly green electricity usage ratio of the computing center, i.e., the third ratio. i For the installed capacity of renewable energy i; P i (m) represents the output coefficient of renewable energy i in month m; T m This refers to the number of hours in month m, i.e., the duration of power generation. E month The monthly electricity consumption of the computing center is E. month E is the total annual electricity consumption of the computing center. total The ratio to 12 months.

[0140] Considering that hydropower has a high-water season and a low-water season, α2 can be slightly less than α0, forming a seasonal complementary constraint between water and wind power throughout the year.

[0141] In the above embodiments, the quarterly renewable energy constraint limits the minimum monthly green electricity usage ratio of the computing center during energy allocation. Based on this constraint and the objective function, carbon emissions are calculated, ensuring that the energy allocation scheme meets the monthly green electricity requirements of the computing center. Furthermore, by introducing complementary constraints across multiple time scales, the power supply instability caused by a single energy structure is significantly improved, enhancing the system's adaptability to seasonal fluctuations in green electricity. This ensures that a high green electricity ratio can be maintained even during periods of low hydropower or wind and solar power, effectively meeting the green electricity assessment requirements in actual operation.

[0142] According to some embodiments of this application, the renewable energy installed capacity constraints include: the installed capacity of various renewable energy sources is less than or equal to a preset maximum installed capacity.

[0143] The constraints on renewable energy installed capacity are as follows:

[0144]

[0145] in, This represents the maximum installed capacity of wind power. For the maximum installed capacity of photovoltaic power, The maximum installed capacity for hydropower is determined by considering factors such as regional resource data (e.g., the technically exploitable amount of wind, solar, and hydropower) and the growth rate of renewable energy installations.

[0146] In the above embodiments, the renewable energy installed capacity constraint limits the installed capacity of various renewable energy sources during energy allocation, providing constraints at the renewable energy available resources level. Based on this constraint and the objective function, carbon emissions can be calculated, ensuring that the energy allocation scheme conforms to regional resource conditions and provides a stable power supply.

[0147] According to some embodiments of this application, the wind-solar constraint conditions also include wind-solar-storage capacity constraint conditions, which are used to constrain the wind power generation and photovoltaic power generation capacity. The wind-solar-storage capacity constraint conditions include: the wind power generation capacity is greater than or equal to the lower limit of wind power capacity; the photovoltaic power generation capacity is greater than or equal to the lower limit of photovoltaic capacity; the lower limit of wind power capacity is determined based on the installed capacity of wind power generation, the typical daily power generation coefficient of wind power generation, and the wind-solar-storage coefficient; the lower limit of photovoltaic capacity is determined based on the installed capacity of photovoltaic power generation, the typical daily power generation coefficient of photovoltaic power generation, and the wind-solar-storage coefficient.

[0148] Calculate the installed capacity of wind power generation x wind Typical daily power coefficient T for wind power generation windt The wind-solar-storage ratio ρ corresponding to wind power generation wind The product of these factors yields the lower limit of wind power capacity. The installed capacity x for photovoltaic power generation is then calculated. solar Typical daily power coefficient T for photovoltaic power generation solart The wind-solar-storage ratio ρ corresponding to photovoltaic power generation solar The product of these two factors yields the lower limit of photovoltaic capacity.

[0149] The constraints on the capacity of wind and solar power storage are as follows:

[0150]

[0151] Where, x wind For the installed capacity of wind power generation, x solar For the installed capacity of photovoltaic power generation, T wind (t) represents the typical daily power generation coefficient for wind power generation, T solar (t) represents the typical daily power generation coefficient of photovoltaic power generation.

[0152] In the above embodiments, the lower limit of the energy storage system configuration capacity is constrained by the daily power generation of wind and solar power and the energy storage configuration coefficient to ensure that the energy storage system has the ability to regulate. Furthermore, by setting the energy storage system regulation capacity index, the lower limit of wind and solar power storage capacity is calculated in detail to ensure that the system can still operate reliably under hourly fluctuations, avoiding the impact of insufficient energy storage configuration on green electricity consumption or reliance on grid power supplementation, thereby effectively supporting the achievement of the stable power supply and carbon emission targets in the core model.

[0153] According to some embodiments of this application, the wind and solar curtailment constraints also include wind and solar curtailment rate constraints. These constraints are used to constrain the curtailment rate of wind and solar power generation. The curtailment rate refers to the percentage of renewable energy power that is abandoned during power generation due to various reasons (such as insufficient grid demand, insufficient transmission line capacity, power system dispatching problems, etc.) relative to the total generateable capacity. The wind and solar curtailment rate constraints include: the theoretical curtailment rate is less than or equal to the preset curtailment rate.

[0154] The theoretical curtailment rate is determined based on the annual curtailment of renewable energy and the total theoretical annual power generation; the total theoretical annual power generation is the sum of the theoretical annual power generation of wind power and the theoretical annual power generation of photovoltaic power; the theoretical annual power generation of wind power is determined based on the installed capacity and annual power generation of wind power; the theoretical annual power generation of photovoltaic power is determined based on the installed capacity and annual power generation of photovoltaic power.

[0155] Calculate the installed capacity of wind power generation x wind Monthly average output coefficient P wind，m Heyue power generation duration T m The product of these two factors yields the theoretical monthly power generation of wind power; based on the theoretical monthly power generation and the 12-month period, the theoretical annual power generation of wind power is obtained. Calculate the installed capacity x of photovoltaic power generation. solar Monthly average output coefficient P solar，m Heyue power generation duration T m The product of these two figures yields the theoretical monthly power generation of photovoltaic (PV) power. Based on the theoretical monthly power generation and the 12-month period, the theoretical annual power generation of PV power is calculated. The sum of the theoretical annual power generation of wind power and PV power is then used to obtain the total theoretical annual power generation E. theory Calculate the annual abandoned amount E of renewable energy. aba With total theoretical annual power generation E theory The ratio between these two values ​​yields the theoretical curtailment rate.

[0156] The constraints on wind and solar curtailment rates include:

[0157]

[0158] Where α3 is the preset curtailment rate, which can be set to 10%; E theory This represents the total theoretical annual power generation.

[0159] In the above embodiments, the theoretical curtailment rate is calculated by using installed capacity, monthly average output coefficient, and monthly power generation duration, thereby achieving the constraint of wind and solar installed capacity on curtailment.

[0160] It should be understood that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps.

[0161] Based on the same inventive concept, this application also provides an energy configuration device for a computing center to implement the energy configuration method for the computing center described above. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations in one or more embodiments of the energy configuration device for a computing center provided below can be found in the limitations of the energy configuration method for the computing center described above, and will not be repeated here.

[0162] According to some embodiments of this application, refer to Figure 3 An energy configuration device for a computing center is provided, the device comprising:

[0163] The electricity consumption determination module 201 is used to obtain the construction parameters of the computing center and determine the total annual electricity consumption of the computing center based on the construction parameters of the computing center.

[0164] The energy configuration module 202 is used to determine the installed capacity of various renewable energy sources and the installed capacity of energy storage systems that supply power to the computing center, based on the computing center's total annual electricity consumption and a pre-established energy configuration model. The energy configuration model includes an objective function and constraints. The objective function aims to minimize carbon emissions, and the constraints include constraints on the proportion of green electricity use, typical daily power generation, quarterly complementarity of renewable energy, installed capacity of renewable energy, and wind and solar constraints.

[0165] Among them, the wind and solar constraints include the wind-solar-storage coefficient constraints, which include: the wind-solar-storage coefficient is greater than or equal to the difference in the state of charge coefficient.

[0166] The difference in the state of charge is the difference between the largest and smallest state of charge.

[0167] The state of charge coefficient is determined based on the charge and discharge coefficients at a preset time.

[0168] The charge / discharge coefficient for the next moment is determined based on the charge / discharge coefficient for the current moment, the energy storage system's regulation capability index, the daily output benchmark coefficient, and the energy storage system's charging and discharging efficiency.

[0169] In some embodiments, the objective function includes the minimum of the sum of the total carbon emissions of the computing center, the annual carbon emissions of various renewable energy sources, the annual carbon emissions of the energy storage system, and the carbon emissions from electricity purchased from the grid.

[0170] The annual carbon emissions of multiple renewable energy sources are determined based on the annual power generation and carbon emission factors of multiple renewable energy sources. The annual power generation of renewable energy sources is determined based on the installed capacity, output factor and power generation duration of renewable energy sources.

[0171] The annual carbon emissions of an energy storage system are determined based on the energy loss of the energy storage system and the carbon emission factors of various renewable energy sources.

[0172] The carbon emissions from grid-purchased electricity are determined based on the total annual electricity consumption of the computing center, the annual power generation of various renewable energy sources, the power loss of the energy storage system, and the annual abandoned power of renewable energy.

[0173] In some embodiments, the green electricity usage ratio constraint includes: the annual green electricity usage ratio of the computing center is greater than or equal to a first ratio;

[0174] The annual green electricity usage ratio of the computing center is determined based on the annual effective power generation of various renewable energy sources and the annual total electricity consumption of the computing center;

[0175] The annual effective power generation of renewable energy is determined based on the annual power generation of renewable energy, the power loss of energy storage systems, and the annual power curtailment of renewable energy.

[0176] In some embodiments, the typical daily power generation constraint includes: the typical daily green electricity usage ratio of the computing center is greater than or equal to a second ratio;

[0177] The typical daily green electricity usage ratio of a computing center is determined based on the installed capacity of various renewable energy sources, the output coefficient at a preset time within a typical day, the duration of the output coefficient, and the total annual electricity consumption of the computing center.

[0178] In some embodiments, the quarterly complementary constraint for renewable energy includes: the minimum monthly green electricity usage ratio of the computing center is greater than or equal to the third ratio;

[0179] The minimum monthly green electricity usage ratio of the computing center is determined based on the output coefficient of various renewable energy sources in the preset month, the number of hours in the preset month, and the total annual electricity consumption of the computing center.

[0180] In some embodiments, the renewable energy installed capacity constraints include: the installed capacity of various renewable energy sources is less than or equal to a preset maximum installed capacity.

[0181] In some embodiments, the wind and solar constraints also include wind and solar power storage capacity constraints, which include: the wind power storage capacity is greater than or equal to the lower limit of wind power capacity.

[0182] The photovoltaic power generation and storage capacity is greater than or equal to the lower limit of photovoltaic capacity;

[0183] The lower limit of wind power capacity is determined based on the installed capacity of wind power generation, the typical daily power generation coefficient of wind power generation, and the wind-solar-storage ratio.

[0184] The lower limit of photovoltaic capacity is determined based on the installed capacity of photovoltaic power generation, the typical daily power generation coefficient of photovoltaic power generation, and the wind-solar-storage ratio.

[0185] In some embodiments, the wind and solar constraints also include wind and solar curtailment rate constraints, which include: the theoretical curtailment rate is less than or equal to the preset curtailment rate.

[0186] The theoretical curtailment rate is determined based on the annual curtailment of renewable energy and the total theoretical annual power generation.

[0187] The total theoretical annual power generation is the sum of the theoretical annual power generation of wind power and the theoretical annual power generation of photovoltaic power.

[0188] The theoretical annual power generation of wind power is determined based on the installed capacity and annual power generation of wind power.

[0189] The theoretical annual power generation of photovoltaic power generation is determined based on the installed capacity and annual power generation of photovoltaic power generation.

[0190] The various modules in the energy configuration device of the aforementioned computing center can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the electronic device in hardware form or independent of it, or stored in the memory of the electronic device in software form, so that the processor can call and execute the operations corresponding to each module.

[0191] According to some embodiments of this application, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements an energy configuration method for a computing center. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0192] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0193] According to some embodiments of this application, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory including instructions that can be executed by a processor of an electronic device to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0194] According to some embodiments of this application, a computer program product is also provided, which, when executed by a processor, can implement the above-described methods. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, some or all of the above-described methods can be implemented, wholly or partially, according to the processes or functions described in the embodiments of this application.

[0195] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0196] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0197] The embodiments described above are merely illustrative of several implementation methods of this application, intended to facilitate a detailed understanding of the technical solutions of this application, but should not be construed as limiting the scope of protection of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. It should be understood that technical solutions obtained by those skilled in the art based on the technical solutions provided in this application through logical analysis, reasoning, or limited experimentation are all within the scope of protection of the appended claims. Therefore, the scope of protection of this patent application should be determined by the content of the appended claims, and the specification and drawings can be used to interpret the content of the claims.

Claims

1. A method for energy allocation in a computing center, characterized in that, The method includes: Obtain the construction parameters of the computing center, and determine the total annual electricity consumption of the computing center based on the construction parameters of the computing center; the construction parameters of the computing center include the planned rack size, rack power density, rack utilization rate and power efficiency of the newly built, renovated or expanded computing center; Based on the annual total electricity consumption of the computing center and the pre-established energy configuration model, the installed capacity of various renewable energy sources and the installed capacity of energy storage systems that supply power to the computing center are determined. The energy configuration model includes an objective function and constraints. The objective function aims to minimize carbon emissions, and the constraints include constraints on the proportion of green electricity use, typical daily power generation, quarterly complementarity of renewable energy sources, installed capacity of renewable energy sources, and wind and solar energy constraints. The typical daily power generation constraint includes: the proportion of green electricity used by the computing center on a typical day is greater than or equal to the second ratio. The quarterly complementary constraints of renewable energy include: the minimum monthly green electricity usage ratio of the computing center is greater than or equal to the third ratio; the minimum monthly green electricity usage ratio of the computing center is determined based on the output coefficient of various renewable energy sources in a preset month, the power generation duration of the preset month, and the annual total electricity consumption of the computing center. The wind and solar constraints include wind and solar energy allocation coefficient constraints, which include: the wind and solar energy allocation coefficient is greater than or equal to the difference in the state of charge coefficient. The difference in the state of charge coefficients is the difference between the largest and the smallest state of charge coefficients. The state of charge coefficient is determined based on the charge and discharge coefficients at a preset time. The charge / discharge coefficient at the next moment is determined based on the charge / discharge coefficient at the current moment, the energy storage system regulation capability index, the daily output benchmark coefficient, and the charging and discharging efficiency of the energy storage system. The energy storage system regulation capability index is the fluctuation range of the 24-hour coordinated output of wind power, solar power, and energy storage system. The daily output benchmark coefficient is obtained by using a sequence optimization method based on the daily output coefficients of wind and solar power.

2. The method according to claim 1, characterized in that, The objective function includes the minimum of the total carbon emissions of the computing center, the annual carbon emissions of various renewable energy sources, the annual carbon emissions of the energy storage system, and the carbon emissions from electricity purchased from the grid.

3. The method according to claim 2, characterized in that, The annual carbon emissions of the various renewable energy sources are determined based on the annual power generation and carbon emission factors of the various renewable energy sources, and the annual power generation of the renewable energy sources is determined based on the installed capacity, output factor and power generation duration of the renewable energy sources; The annual carbon emissions of the energy storage system are determined based on the power loss of the energy storage system and the carbon emission factors of various renewable energy sources. The carbon emissions from the electricity purchased from the power grid are determined based on the total annual electricity consumption of the computing center, the annual power generation of various renewable energy sources, the power loss of the energy storage system, and the annual abandoned power of the renewable energy sources.

4. The method according to claim 1, characterized in that, The green electricity usage ratio constraint includes: the annual green electricity usage ratio of the computing center is greater than or equal to the first ratio; The annual green electricity usage ratio of the computing power center is determined based on the annual effective power generation of the various renewable energy sources and the annual total electricity consumption of the computing power center. The annual effective power generation of the renewable energy is determined based on the annual power generation of the renewable energy, the power loss of the energy storage system, and the annual power curtailment of the renewable energy.

5. The method according to claim 1, characterized in that, The typical daily green electricity usage ratio of the computing center is determined based on the installed capacity of various renewable energy sources, the output coefficient at a preset time within a typical day, the duration of the output coefficient, and the total annual electricity consumption of the computing center.

6. The method according to claim 1, characterized in that, The constraints on the installed capacity of renewable energy sources include: the installed capacity of each renewable energy source is less than or equal to the preset maximum installed capacity.

7. The method according to claim 1, characterized in that, The wind and solar constraints also include wind and solar power storage capacity constraints, which include: the wind power storage capacity is greater than or equal to the lower limit of wind power capacity. The photovoltaic power generation and storage capacity is greater than or equal to the lower limit of photovoltaic capacity; The lower limit of wind power capacity is determined based on the installed capacity of wind power generation, the typical daily power generation coefficient of wind power generation, and the wind-solar-storage ratio. The lower limit of photovoltaic capacity is determined based on the installed capacity of photovoltaic power generation, the typical daily power generation coefficient of photovoltaic power generation, and the wind-solar-storage ratio.

8. The method according to claim 1, characterized in that, The wind and solar constraints also include wind and solar curtailment rate constraints, which include: the theoretical curtailment rate is less than or equal to the preset curtailment rate. The theoretical curtailment rate is determined based on the annual curtailment of renewable energy and the total theoretical annual power generation. The total theoretical annual power generation is the sum of the theoretical annual power generation of wind power and the theoretical annual power generation of photovoltaic power. The theoretical annual power generation of wind power is determined based on the installed capacity and annual power generation of wind power. The theoretical annual power generation of photovoltaic power generation is determined based on the installed capacity and annual power generation of photovoltaic power generation.

9. An energy configuration device for a computing center, characterized in that, The device includes: The power consumption determination module is used to obtain the construction parameters of the computing center and determine the total annual power consumption of the computing center based on the construction parameters of the computing center; the construction parameters of the computing center include the planned rack size, rack power density, rack utilization rate and power efficiency of the newly built, renovated or expanded computing center. The energy configuration module is used to determine the installed capacity of various renewable energy sources and the installed capacity of energy storage systems that supply power to the computing center, based on the total annual electricity consumption of the computing center and a pre-established energy configuration model. The energy configuration model includes an objective function and constraints. The objective function aims to minimize carbon emissions, and the constraints include constraints on the proportion of green electricity use, typical daily power generation, quarterly complementarity of renewable energy sources, installed capacity of renewable energy sources, and wind and solar energy constraints. The typical daily power generation constraint includes: the proportion of green electricity used by the computing center on a typical day is greater than or equal to the second ratio. The quarterly complementary constraints of renewable energy include: the minimum monthly green electricity usage ratio of the computing center is greater than or equal to the third ratio; the minimum monthly green electricity usage ratio of the computing center is determined based on the output coefficient of various renewable energy sources in a preset month, the power generation duration of the preset month, and the annual total electricity consumption of the computing center. The wind and solar constraints include wind and solar energy allocation coefficient constraints, which include: the wind and solar energy allocation coefficient is greater than or equal to the difference in the state of charge coefficient. The difference in the state of charge coefficients is the difference between the largest and the smallest state of charge coefficients. The state of charge coefficient is determined based on the charge and discharge coefficients at a preset time. The charge / discharge coefficient at the next moment is determined based on the charge / discharge coefficient at the current moment, the energy storage system regulation capability index, the daily output benchmark coefficient, and the charging and discharging efficiency of the energy storage system. The energy storage system regulation capability index is the fluctuation range of the 24-hour coordinated output of wind power, solar power, and energy storage system. The daily output benchmark coefficient is obtained by using a sequence optimization method based on the daily output coefficients of wind and solar power.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 8.

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