An energy configuration method, device, apparatus and storage medium

Abstract

The application provides an energy configuration method and device, equipment and a storage medium, and relates to the technical field of information. The method comprises the following steps: acquiring energy supply parameters of a first device and benefit data of energy, wherein the first device is a device for generating energy. Based on a preset first condition, an energy configuration determination function is constructed by using the energy supply parameters of the first device and the benefit data of the energy, wherein the energy configuration determination function is used to output an energy configuration scheme matching the operation requirement of a second device, the second device is an energy-consuming device to be configured with energy, and the first condition comprises an energy priority and / or an energy supply constraint condition of the first device. The energy configuration scheme of the second device is determined according to the energy configuration determination function and an operation model of the first device, wherein the operation model of the first device is used to represent the physical characteristics and constraint relationship of various energy production conversion, storage and supply to the second device. The energy configuration scheme comprises energy supply configuration parameters of the operation of the first device and an energy scheme of the first device.

Description

Technical Field

[0001] This application relates to the field of information technology, and in particular to an energy configuration method, apparatus, device, and storage medium. Background Technology

[0002] With the rapid development of artificial intelligence and the digital economy, computing centers have become a key infrastructure supporting the digital transformation of society. They consume huge amounts of energy, experience significant load fluctuations, and the need for energy conservation and carbon reduction is becoming increasingly urgent.

[0003] To improve energy efficiency, integrated energy systems (IES) are introduced into computing centers to achieve multi-energy complementarity and synergistic optimization by coordinating various energy forms such as electricity, cooling, and heating.

[0004] Currently, although multi-energy coupling models have been introduced into the optimization of integrated energy system configuration, they mostly focus on economic objectives and are difficult to achieve unified optimization of economy, low carbon emissions and reliability. Summary of the Invention

[0005] This application provides an energy configuration method, apparatus, device, and storage medium for achieving unified optimization of economy, low carbon emissions, and reliability when configuring an integrated energy system.

[0006] Firstly, this application provides an energy configuration method applied to an electronic device. The method can be executed by an electronic device, a component or device applied to the electronic device (e.g., a processor, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of the electronic device, including: Obtain the energy supply parameters and energy efficiency data of the first device, which is an energy-generating device; Based on a preset first condition, an energy configuration determination function is constructed using the energy supply parameters of the first device and energy efficiency data. The energy configuration determination function is used to output an energy configuration scheme that matches the operating requirements of the second device. The second device is an energy-consuming device to be configured. The first condition includes energy priority and / or the energy supply constraints of the first device. The energy configuration scheme of the second equipment is determined based on the energy configuration determination function and the operation model of the first equipment. The operation model of the first equipment is used to characterize the physical characteristics and constraints of the production, conversion, storage and supply of various energy sources to the second equipment. The energy configuration scheme includes the power supply configuration parameters for operating the first equipment and the energy scheme for the first equipment.

[0007] In the first aspect, by acquiring the energy supply parameters and energy efficiency data of the first device, and constructing an energy configuration determination function based on preset energy priorities and device energy supply constraints, the generation process of the energy configuration scheme has a clear input basis and condition framework. Combining the operating model of the first device for various energy production, conversion, storage, and supply, it can be ensured that the output energy configuration scheme not only meets the actual operating needs of the second device but also conforms to the energy efficiency and operating constraints of the first device, thereby improving the scientific nature of the energy configuration method and supporting the realization of efficient energy coordination and optimized scheduling.

[0008] In conjunction with the first aspect, in one possible implementation, the energy priorities from highest to lowest are: renewable energy, combined cooling, heating and power (CCHP) energy, energy storage energy, and grid power, with renewable energy including wind power and / or photovoltaic power.

[0009] In this implementation, by setting a clear energy priority order, renewable energy is placed with the highest priority, followed by combined cooling, heating and power (CCHP), energy storage, and grid power. This priority strategy naturally guides the system to prioritize the consumption of clean energy sources such as wind power and solar power during the optimization process. This helps to increase the proportion of renewable energy in the energy structure, reduce dependence on traditional fossil fuels and grid power, thereby directly promoting carbon emission reduction at the operational level and supporting the green and low-carbon operation of computing centers.

[0010] In conjunction with the first aspect, in one possible implementation, the energy consumption constraint includes at least one of the following: Constraints include: power balance constraints, thermal balance constraints, cold balance constraints, equipment power constraints, energy storage operation constraints, grid interaction constraints, and carbon emission constraints.

[0011] This implementation incorporates constraints on the balance of electrical, thermal, and cooling energy, as well as various constraints related to equipment power, energy storage operation, grid interaction, and carbon emissions during the energy allocation process. This ensures that the solved energy allocation scheme maintains a real-time balance between energy supply and consumption, while also complying with the physical operating limits of various equipment and grid interaction specifications. This mechanism can prevent energy supply and demand imbalances, equipment overload, or illegal operation, thereby improving the safety and stability of the system.

[0012] In conjunction with the first aspect, in one possible implementation, energy efficiency data includes: Data on economic penalties; The economic penalty data is determined based on the operating model of the first equipment and is used to characterize the penalty cost incurred for failing to meet the energy demand of the second equipment.

[0013] In this implementation, by introducing economic penalty data determined based on the operating model, the failure to meet the energy demand of the equipment is quantified into economic costs and taken into account during the solution process. This allows the energy configuration function to not only focus on economy and energy efficiency during the solution process, but also to take power supply reliability as an intrinsic optimization element, considering reserve capacity and scheduling strategies during the configuration phase, thereby reducing the risk of power outages and improving the reliability of the second equipment operation.

[0014] In conjunction with the first aspect, in one possible implementation, the operating model of the first device includes: Wind power generation model, photovoltaic power generation model, combined cooling, heating and power (CCHP) energy model, diesel generator model, energy storage energy model and / or grid power model.

[0015] This implementation constructs a complete operational model system covering wind power generation, photovoltaic power generation, combined cooling, heating and power (CCHP), diesel generators, energy storage, and the power grid. This system can accurately characterize the physical characteristics, conversion efficiency, and output characteristics of various energy devices in actual operation. The model-based optimization process better reflects the actual behavior of the equipment, making the output energy configuration scheme more in line with engineering practice and improving the feasibility of the scheme and the accuracy of operational predictions.

[0016] In conjunction with the first aspect, in one possible implementation, the method further includes: Based on the energy configuration scheme, corresponding evaluation indicators are output, which are used to quantitatively characterize the operational performance of the energy configuration scheme. Evaluation indicators include performance indicators, environmental indicators, and / or reliability indicators.

[0017] This implementation provides multi-dimensional evaluation indicators covering performance, environment, and reliability while outputting energy configuration solutions, enabling the optimization results to have quantifiable and comparable evaluation criteria. Users can use these indicators to comprehensively judge the performance of the configuration solution in terms of energy efficiency, carbon emission reduction, and operational reliability, supporting scientific decision-making and subsequent operational optimization, and improving the overall transparency and decision-making effectiveness of the energy management system.

[0018] Secondly, this application provides an energy configuration device, comprising: The data acquisition module is used to acquire the energy supply parameters and energy efficiency data of the first device, which is an energy-generating device; The function construction module is used to construct an energy configuration determination function based on a preset first condition, using the energy supply parameters of the first device and energy efficiency data. The energy configuration determination function is used to output an energy configuration scheme that matches the operating requirements of the second device, where the second device is an energy-consuming device to be configured. The first condition includes energy priority and / or the energy supply constraints of the first device. The scheme determination module is used to determine the energy configuration scheme of the second equipment based on the energy configuration determination function and the operating model of the first equipment. The operating model of the first equipment is used to characterize the physical characteristics and constraints of various energy production, conversion, storage and supply to the second equipment. The energy configuration scheme includes the power supply configuration parameters for operating the first equipment and the energy scheme for the first equipment.

[0019] In conjunction with the second aspect, in one possible implementation, the energy priorities from highest to lowest are: renewable energy, combined cooling, heating and power (CCHP) energy, energy storage energy, and grid power, with renewable energy including wind power and / or photovoltaic power.

[0020] In conjunction with the second aspect, in one possible implementation, the energy consumption constraint includes at least one of the following: Constraints include: power balance constraints, thermal balance constraints, cold balance constraints, equipment power constraints, energy storage operation constraints, grid interaction constraints, and carbon emission constraints.

[0021] In conjunction with the second aspect, in one possible implementation, energy efficiency data includes: Data on economic penalties; The economic penalty data is determined based on the operating model of the first equipment and is used to characterize the penalty cost incurred for failing to meet the energy demand of the second equipment.

[0022] In conjunction with the second aspect, in one possible implementation, the operating model of the first device includes: Wind power generation model, photovoltaic power generation model, combined cooling, heating and power (CCHP) energy model, diesel generator model, energy storage energy model and / or grid power model.

[0023] In conjunction with the second aspect, in one possible implementation, the solution output module is also used to output corresponding evaluation indicators based on the energy configuration scheme. The evaluation indicators are used to quantitatively characterize the operating performance of the energy configuration scheme. Evaluation indicators include performance indicators, environmental indicators, and / or reliability indicators.

[0024] Thirdly, this application provides an electronic device comprising: a processor and a memory; the memory storing processor-executable instructions; when the processor is configured to execute the instructions, causing the electronic device to implement the method of the first aspect described above.

[0025] Fourthly, this application provides a computer-readable storage medium comprising: computer software instructions; which, when executed in an electronic device, cause the electronic device to implement the method described in the first aspect.

[0026] Fifthly, this application provides a computer program product comprising a computer program; when the computer program is run in an electronic device, it causes the electronic device to implement the method described in the first aspect.

[0027] The beneficial effects of the second to fifth aspects mentioned above are described in the corresponding description of the first aspect and will not be repeated here. Attached Figure Description

[0028] Figure 1 This is a schematic diagram illustrating the application environment of an energy configuration method provided in an embodiment of this application; Figure 2 A schematic diagram of an energy configuration system architecture provided in this application embodiment; Figure 3 A flowchart illustrating an energy configuration method provided in an embodiment of this application; Figure 4 This is a schematic diagram of the energy supply configuration provided in an embodiment of this application; Figure 5 This is a schematic diagram comparing carbon emissions before and after energy configuration in an embodiment of this application. Figure 6 A typical daily power supply configuration stacking area diagram provided for embodiments of this application; Figure 7 A diagram showing the stacked area of ​​the 8760-hour annual power supply provided for embodiments of this application; Figure 8 This is a schematic diagram of the composition of an energy configuration device provided in an embodiment of this application; Figure 9 This is a schematic diagram of the composition of an electronic device provided in an embodiment of this application. Detailed Implementation

[0029] The following is a detailed description of an energy configuration method, apparatus, device, and storage medium provided in this application, with reference to the accompanying drawings.

[0030] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0031] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0032] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0033] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0034] To facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish the same or similar items with essentially the same function and effect. Those skilled in the art can understand that the terms "first" and "second" are not intended to limit the quantity or execution order.

[0035] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0036] With the rapid development and widespread application of technologies such as artificial intelligence, big data, and blockchain, the demand for computing resources continues to rise, making computing centers a key infrastructure supporting the development of the digital economy. However, computing centers exhibit problems such as huge energy consumption, high operating costs, significant load fluctuations, and low energy efficiency during operation. To address these issues, the concept of integrated energy systems has been introduced into this field. This involves unified planning and coordinated optimization of various energy forms, including electricity, heat, cooling, and renewable energy, to achieve energy conservation, emission reduction, and low-carbon operation of computing centers.

[0037] Currently, the design and optimization of energy systems for computing centers typically employ static configuration or localized adjustments based on a single energy source. For example, some methods focus solely on energy consumption optimization for the power supply or cooling system, failing to adequately model the coupling and complementarity among multiple energy sources such as electricity, cooling, and heating within the system. Other methods introduce energy management algorithms, but their optimization objectives are relatively singular, and constraints are simplified, making it difficult to achieve optimal overall system performance under dynamic loads and environmental changes. Furthermore, the configuration processes of traditional optimization methods often rely on manual experience to set parameters, lacking intelligent decision-making mechanisms with multi-objective coordination and adaptive capabilities.

[0038] Therefore, there is an urgent need to develop a method that can achieve multi-energy collaborative configuration and multi-objective comprehensive optimization of computing centers, so as to maximize energy efficiency, minimize costs, and take into account the goal of low-carbon operation.

[0039] To address the aforementioned technical problems, this application provides an energy configuration method, apparatus, device, and storage medium. The approach involves acquiring the energy supply parameters and energy efficiency data of a first device, and constructing an energy configuration determination function based on preset energy priorities and device energy supply constraints. This ensures that the generation process of the energy configuration scheme has clear input criteria and a conditional framework. By combining the operating models of the first device for various energy production, conversion, storage, and supply processes, it is possible to ensure that the output energy configuration scheme not only meets the actual operating needs of the second device but also conforms to the energy efficiency and operating constraints of the first device. This enhances the scientific rigor of the energy configuration method and supports the efficient coordination and optimized scheduling of energy resources.

[0040] The embodiments provided in this application will now be described in detail with reference to the accompanying drawings.

[0041] like Figure 1 As shown, the application environment may include a computing device 100 and an application 101 that supports energy configuration.

[0042] For example, the computing device can be a terminal device or a server.

[0043] The computing device 100 includes an application 101 that supports energy configuration. The application 101, supporting energy configuration, is used to execute the energy configuration method of this embodiment, specifically including: acquiring the energy supply parameters and energy efficiency data of a first device acting as an energy supply device; constructing an energy configuration determination function based on preset first conditions (including energy priority and / or energy supply constraints of the first device); and determining the energy configuration scheme of a second device acting as an energy consumption device according to the energy configuration determination function and the operating model of the first device; wherein, the energy priority includes renewable energy as the highest priority, combined cooling, heating and power (CCHP) as the second priority, and so on. The priority order is: power (CCHP) energy first, energy storage energy second, and grid power last; energy supply constraints include at least one of the following: power balance constraints, thermal balance constraints, cold balance constraints, equipment power constraints, energy storage operation constraints, grid interaction constraints, or carbon emission constraints; energy benefit data includes economic penalty data determined based on the operation model of the first equipment, used to characterize the penalty cost incurred for failing to meet the energy demand of the second equipment; the operation model of the first equipment includes wind power generation model, photovoltaic power generation model, combined cooling, heating and power energy model, diesel generator model, energy storage energy model, or grid power model, used to characterize the physical characteristics and constraint relationships of various energy production, conversion, storage, and supply to the second equipment.

[0044] Based on the energy configuration support application 101, system modeling and optimization are completed, and the inputs, processes, and results of the energy configuration method in this embodiment are visualized. The energy configuration support application 101 can also output corresponding evaluation indicators based on the determined energy configuration scheme. The evaluation indicators are used to quantitatively characterize the operating performance of the energy configuration scheme, including performance indicators, environmental indicators, and reliability indicators.

[0045] The application 101 that supports energy configuration provides users with an interface for configuring energy systems. This interface can be a World Wide Web page accessible through a browser or a native application that needs to be downloaded and installed.

[0046] The terminal device can specifically be user equipment (UE), including but not limited to smartphones, tablets, laptops, desktop computers, and Internet of Things (IoT) terminals; the terminal accesses the network and has the capability to carry data transmission and multimedia services. The server can include a first memory and a first processor. The first memory stores an energy configuration optimization program; this energy configuration optimization program is invoked and executed by the first processor to implement the energy configuration method provided in this application. The first memory can include, but is not limited to, the following: random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), and electrically erasable programmable read-only memory (EEPROM). The first processor can consist of one or more integrated circuit chips. Optionally, the first processor can be a general-purpose processor, such as a central processing unit (CPU) or a network processor (NP). Optionally, the first processor can implement the energy configuration method provided in this application by running programs or code.

[0047] This application embodiment also provides an energy configuration system, which can be set in... Figure 1 In the application environment shown, such as Figure 2 As shown, the energy configuration system 200 may include: Energy configuration system front-end 201: It is used to provide a user interface to obtain the energy supply parameters and energy efficiency data of the first device as the energy supply equipment, and to send optimization calculation requests to the energy configuration system back-end 202; at the same time, it is used to receive and visualize the energy configuration scheme and related comprehensive evaluation indicators of the second device returned by the energy configuration system back-end 202.

[0048] Energy Configuration System Backend 202: This backend receives requests and data from the frontend, executes core energy configuration method logic, and returns calculation results to the frontend. Specifically, Energy Configuration System Backend 202 includes: The Data Management Engine 2021 receives, verifies, and stores power supply parameters from the first front-end device, energy consumption demand parameters from the second front-end device, and energy efficiency data. Energy efficiency data includes economic penalty data, representing the penalty costs incurred for failing to meet the energy demands of the second front-end device. This engine provides standardized data access interfaces for other engines.

[0049] Model Building Engine 2022: Based on preset first conditions (including energy priority and / or energy supply constraints of the first device), energy supply parameters, energy consumption demand parameters, and benefit data provided by Data Management Engine 2021, it constructs an energy configuration determination function. This function outputs an energy configuration scheme that matches the operational needs of the second device. Simultaneously, based on predefined physical relationships and rules, it generates or calls the corresponding operational model of the first device. The first device is the energy-generating equipment, and its operational model characterizes the physical characteristics and constraints of various energy production, conversion, storage, and supply to the second device. Energy supply constraints include at least one of the following: electrical balance constraints, thermal balance constraints, cold balance constraints, equipment power constraints, energy storage operation constraints, grid interaction constraints, or carbon emission constraints.

[0050] The scheduling strategy engine 2023 is used to manage and apply preset energy priority rules. The energy priorities, from high to low, are: renewable energy, combined cooling, heating and power energy, energy storage energy, and grid power. Renewable energy includes wind power and / or photovoltaic power. It provides logical constraints at the operational strategy level for optimization solutions.

[0051] The optimization engine 2024 integrates the energy configuration determination function generated by the model building engine 2022, the operating model and power supply constraints of the first device, and the energy priority rules provided by the scheduling strategy engine 2023 to form a complete computable optimization problem. This engine calls built-in intelligent optimization algorithms (such as differential evolution and genetic algorithms) to iteratively solve this problem, ultimately outputting an energy configuration scheme for the second device. The scheme includes the power supply configuration parameters for running the first device and the corresponding energy supply plan.

[0052] Output Evaluation Engine 2025: Used to simulate and calculate the performance of the energy configuration scheme output by the optimization solution engine 2024. Based on the scheme and the operating model of the first device, a series of comprehensive evaluation indicators are calculated, and the complete configuration scheme and indicator results are encapsulated and returned to the energy configuration system front-end 201. The evaluation indicators are used to quantitatively characterize the operating performance of the energy configuration scheme, including performance indicators, environmental indicators, and reliability indicators.

[0053] It should be noted that the system architecture described in the embodiments of this application is for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and does not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of system architecture, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0054] See Figure 3 This is a flowchart illustrating an energy configuration method provided in an embodiment of this application. Figure 3 As shown, the energy configuration method provided in this application can be implemented by the aforementioned computing device, specifically including the following steps S300~S302.

[0055] S300: The computing device acquires the power supply parameters and energy efficiency data of the first device.

[0056] The computing device first acquires the energy supply parameters and energy efficiency data of the primary device. The primary device is the energy-generating equipment. Examples include wind power generation equipment, photovoltaic power generation equipment, combined cooling, heating and power (CCHP) equipment, diesel generators, batteries, cold / heat storage equipment, and grid interfaces. These devices are responsible for generating or providing various forms of energy, such as electricity, cooling, and heat. The energy supply parameters include the power output of the primary device.

[0057] Energy efficiency data covers the economic costs and environmental emission factors of different energy types.

[0058] Economic costs, such as electricity prices and fuel prices, and environmental emission factors, such as carbon emission coefficients, are both used as inputs for subsequent function construction.

[0059] S301. The computing device constructs an energy configuration determination function based on the preset first conditions, using the energy supply parameters of the first device and energy efficiency data.

[0060] The energy configuration determination function outputs an energy configuration scheme that matches the operating requirements of the second device, which is the energy-consuming device to be configured. The first condition includes energy priority and / or the energy supply constraints of the first device. The second device can be an energy-consuming device in a large energy-consuming facility such as a computing center or data center, for example, a server cluster, cooling system, lighting, and auxiliary facilities.

[0061] The energy configuration determination function outputs an energy configuration scheme that matches the operational needs of the second device. The first condition includes energy priority and / or energy consumption constraints of the first device. Based on acquired energy function parameters and benefit data, the calculation device constructs the energy configuration determination function. This function is a mathematical optimization model or algorithmic decision framework that maps input data to specific energy configuration schemes. The function is constructed based on a preset first condition, which includes energy priority rules and / or energy consumption constraints. Energy priority rules define the order in which different types of energy are used during supply, while energy consumption constraints describe the physical or operational limitations that the system must adhere to in terms of energy balance, device operation, energy storage status, and environmental emissions.

[0062] During the construction process, the energy supply parameters of the first device provide the basis for load demand, the energy efficiency data quantifies the economic and environmental costs of different supply options, and the first condition sets the rules and boundaries for the form of the function and the solution range.

[0063] Specifically, the energy priority rule guiding function prioritizes energy combinations that conform to a specific order during the solution process, while energy consumption constraints ensure that the solution obtained by the function satisfies all necessary first-equipment operating limitations. By integrating the first conditions, the purpose of constructing the energy configuration determination function is to form a computable optimization problem. The solution to this problem is the energy configuration scheme in which the energy supply of the first equipment matches the operating needs of the second equipment and achieves the expected benefit target under given rules and constraints.

[0064] For example, the energy configuration determination function can be expressed in the form of:

[0065] Where C cost For total cost data, C CO2 Let be the total carbon emissions, and λ be a weighting coefficient between 0 and 1. λ is set by the user based on their preference for balancing economic efficiency and low carbon emissions; a larger λ value indicates that the economic objective is more important in the optimization, and vice versa. The solution process must satisfy the first condition.

[0066] S302. The computing device determines the energy configuration scheme of the second device based on the energy configuration determination function and the operating model of the first device.

[0067] The operating model of the first device is used to characterize the physical characteristics and constraints of the second device in the production, conversion, storage, and supply of various energy sources.

[0068] After constructing the energy configuration determination function, the computing device solves the solution by combining the operating model of the first device. The operating model of the first device characterizes the physical characteristics and constraints of various energy production, conversion, storage, and supply to the second device. This operating model is a mathematical model based on physical principles, used to accurately characterize the input-output characteristics, efficiency changes, power limits, and other physical laws governing the process of converting primary energy into usable energy (e.g., electricity, cooling, heat), storing it, or supplying it to the second device.

[0069] The energy allocation deterministic function defines the objective and framework of the optimization problem, while the operational model of the first device fills this framework with specific, computable constraints and relationships. The operational model transforms the capabilities and limitations of each first device into mathematical equations, which are then invoked in the problem-solving process defined by the energy allocation deterministic function.

[0070] During the solution process, the computing device executes optimization algorithms, such as differential evolution, genetic algorithms, or particle swarm optimization, to perform iterative calculations within the solution space. In multiple iterations, the algorithm simulates the operating state of the first device under the first condition, based on candidate device configurations and operating strategies, calculates its corresponding total cost and total carbon emissions, and evaluates its overall performance according to the objective function. By combining an abstract functional framework with a concrete device model and solving it, the aim is to find the specific device parameters and operating strategies that optimize the energy configuration deterministic function value while satisfying all physical and operational constraints, thereby transforming the optimization objective into an executable configuration and scheduling scheme.

[0071] The energy configuration scheme includes the power supply configuration parameters for operating the first equipment and the energy scheme for the first equipment.

[0072] After completing the solution, the computing device outputs the energy configuration scheme for the second device. This energy configuration scheme is the final decision result generated during the optimization calculation process, specifically including the power supply configuration parameters for operating the first device and the energy plan for the first device.

[0073] The energy supply configuration parameters specify in detail the energy supply for the first equipment during operation, such as its electricity, cooling, and heating supply. The energy scheme describes in detail the scheduling arrangements that the first equipment should take to meet the above energy consumption requirements, including the commissioning sequence of various types of first equipment (such as photovoltaic power generation equipment, wind power generation equipment, energy storage equipment, etc.).

[0074] Outputting energy configuration schemes transforms abstract models into concrete, quantifiable, and executable engineering solutions, providing direct decision-making basis for energy planning and design, equipment selection and procurement, and subsequent scheduling and operation of large-scale energy-consuming facilities.

[0075] In this embodiment, by acquiring the energy supply parameters and energy efficiency data of the first device, and constructing an energy configuration determination function based on preset energy priorities and device energy supply constraints, the generation process of the energy configuration scheme has a clear input basis and condition framework. Combined with the operating model of the first device for various energy production, conversion, storage, and supply, it can be ensured that the output energy configuration scheme not only meets the actual operating needs of the second device but also conforms to the energy efficiency and operating constraints of the first device, thereby improving the scientific nature of the energy configuration method and supporting the realization of efficient energy coordination and optimized scheduling.

[0076] In one embodiment, the energy priorities from highest to lowest are: renewable energy, combined cooling, heating and power (CCHP) energy, energy storage energy, and grid power, with renewable energy including wind power and / or photovoltaic power.

[0077] When defining the energy priority rules in the first condition, renewable energy can be given the highest priority. Here, renewable energy refers to energy derived from natural processes and that is renewable, specifically including wind power generated by wind turbines and photovoltaic power generated by converting solar radiation into electricity using photovoltaic modules. Combined cooling, heating, and power (CCHP) energy is set as the second highest priority after renewable energy. CCHP refers to energy that simultaneously generates electricity, cooling, and heating through a single system (e.g., a gas internal combustion engine or a micro gas turbine). Energy storage energy has a lower priority than CCHP energy. Energy storage energy refers to energy stored through specific devices (e.g., batteries, cold storage tanks, heat storage tanks) and released when needed. Grid electricity is set as the lowest priority; grid electricity refers to electricity purchased from the public power grid.

[0078] The purpose of setting this priority rule is to guide computing devices at the operational strategy level to maximize the use of local renewable energy while meeting load demand when constructing and solving the energy allocation deterministic function. Secondly, it is to leverage the energy efficiency advantages of combined heat and power (CHP), then use energy storage for time-shift optimization, and finally consider using grid power, which may have higher economic and environmental costs. This leads to a low-carbon and efficient energy allocation scheme.

[0079] In this embodiment, by setting a clear energy priority order, renewable energy is placed with the highest priority, followed by combined cooling, heating and power (CCHP), energy storage, and grid power. This priority strategy naturally guides the system to prioritize the consumption of clean energy sources such as wind power and photovoltaic power during the optimization process. This helps to increase the proportion of renewable energy in the energy structure, reduce dependence on traditional fossil fuels and grid power, thereby directly promoting carbon emission reduction at the operational level and supporting the green and low-carbon operation of computing centers.

[0080] In one embodiment, the energy consumption constraint includes at least one of the following: Constraints include: power balance constraints, thermal balance constraints, cold balance constraints, equipment power constraints, energy storage operation constraints, grid interaction constraints, and carbon emission constraints.

[0081] The power balance constraint requires that, within any scheduling period, the total electrical energy generated by all power sources in the second equipment (such as renewable energy, traditional generators, the power grid, and energy storage discharge) must be equal to the total electrical energy consumed and lost by the load of the first equipment (such as IT equipment, refrigeration systems, and energy storage charging). This constraint ensures the instantaneous balance of real-time power supply and demand.

[0082] For example, the power balance constraint between the first device and the second device can be expressed as:

[0083] In the formula, the left side represents the energy supply: Photovoltaics Wind power, Micro gas turbines diesel engine, Power grid purchase Energy storage and discharge.

[0084] The right side of the equation represents energy consumption: Information equipment load, UPS losses, Electric refrigeration heat pump, Auxiliary equipment Electricity sales from the power grid Energy storage and charging.

[0085] The thermal energy balance constraint and the cold energy balance constraint adopt a similar principle to the electrical energy balance, requiring real-time supply and demand balance of thermal energy and cold energy respectively.

[0086] For example, the thermal energy balance constraint between the first device and the second device can be expressed as:

[0087] In the formula, Heat load demand at time t (kW); : Heating power of the micro gas turbine at time t (kW); Heat pump heating power (kW) at time t; : Boiler power (kW) at time t; : Heat release power of the thermal storage device at time t (kW); : Charging power of the thermal storage device at time t (kW).

[0088] For example, the cold energy balance constraint between the first and second devices can be expressed as:

[0089] In the formula, Cooling load demand (kW) at time t; Absorption refrigeration power (kW) at time t; : Electric cooling power (kW) at time t; : Cooling power of the cold storage device at time t (kW); Free cooling power (kW) at time t; : Charging power of the cold storage device at time t (kW).

[0090] The equipment power constraint sets upper and lower limits for the instantaneous output power of each piece of equipment (such as a gas turbine or diesel generator) and associates it with its start-stop status, which reflects the actual operating capacity range of the equipment.

[0091] For example, the power constraint of the first device can be expressed as:

[0092] In the formula, : Output power (kW) of device i at time t; , : These represent the minimum and maximum power (kW) of device i, respectively; : The operating state of device i at time t (0 or 1).

[0093] Energy storage operation constraints specify the charging and discharging power limits, state of charge boundaries, and mutual exclusion requirements for charging and discharging actions of various energy storage devices (such as batteries and cold storage tanks), which ensures that energy storage systems operate under safe conditions.

[0094] For example, the operating constraints of the first energy storage device can be expressed as:

[0095] In the formula, , Energy storage charging and discharging power (kW) at time t; The upper and lower limits of energy storage can be expressed as:

[0096] In the formula, Energy storage upper and lower limits (kWh) Let t be the energy state of the energy storage system (kWh).

[0097] Grid interaction constraints limit the upper limit of the power that the system can purchase from the public grid and sell to the grid, which is usually determined by physical connection capacity or contractual agreements.

[0098] For example, electricity purchase constraints can be expressed as:

[0099] In the formula, Let t be the power purchased by the power grid (kW). The maximum power purchase capacity (kW) at the grid connection point.

[0100] Electricity sales constraints can be expressed as:

[0101] In the formula, Let t be the power output (kW) of the power grid at time t. The maximum power output (kW) at the grid connection point.

[0102] Carbon emission constraints set an upper limit on the total carbon emissions of a system within a specified period (such as a day or a year), or convert carbon emissions into costs and incorporate them into the objective function. This directly reflects the requirements for the environmental performance of the system.

[0103] For example, carbon emission constraints can be expressed as:

[0104] In the formula, , , Emissions from natural gas, diesel, and electricity purchased from the grid. Natural gas / diesel consumption at time t; Power purchased by the power grid at time t; The system's allowed carbon emission limit.

[0105] In this embodiment, the aforementioned constraints do not exist in isolation but rather act collectively on the energy allocation deterministic function. Energy balance constraints (electricity, heat, and cooling) constitute the core equations that the system must satisfy for operation. Equipment power constraints, energy storage operation constraints, and grid interaction constraints, based on these equations, further define the specific feasible domain for each supply or conversion stage. Carbon emission constraints impose additional environmental restrictions on the overall system trajectory. By comprehensively applying these constraints, the aim is to ensure that any energy allocation scheme generated by the energy allocation deterministic function not only mathematically optimizes the objective but is also physically achievable, operationally safe, and environmentally compliant with preset standards, thereby guaranteeing the engineering feasibility and compliance of the optimization results.

[0106] In this embodiment, the energy configuration process incorporates constraints on the balance of electrical, thermal, and cooling energy, as well as various constraints such as equipment power, energy storage operation, grid interaction, and carbon emissions. This ensures that the solved energy configuration scheme maintains a real-time balance between energy supply and consumption, while also complying with the physical operating limits of various devices and grid interaction specifications. This mechanism can prevent energy supply and demand imbalances, equipment overload, or illegal operation, thereby improving the safety and stability of the system.

[0107] In one embodiment, energy efficiency data includes: Data on economic penalties.

[0108] The economic penalty data is determined based on the operating model of the first equipment and is used to characterize the penalty cost incurred for failing to meet the energy demand of the second equipment.

[0109] Within energy efficiency data, cost data includes a specific metric: economic penalty data. The purpose of introducing economic penalty data is to incorporate the reliability of energy supply as a core cost factor into a unified evaluation while pursuing conventional efficiency goals such as economic viability and low carbon emissions.

[0110] The economic penalty data numerically represents a negative cost-benefit. Specifically, it refers to the economic penalty cost incurred when the actual supply capacity of the integrated energy system composed of the first piece of equipment cannot fully meet the energy demands of the second piece of equipment for electricity, cooling, and heating at a given moment. This data is not a fixed value but is determined based on the energy supply deficit of the first piece of equipment's operating model.

[0111] This economic penalty data establishes a direct causal quantitative relationship with the energy demand of the second device. The energy demand of the second device serves as the premise and calculation benchmark for triggering the penalty, while the economic penalty data measures the cost of the consequences when the demand is not met. When constructing the energy allocation deterministic function, the estimated supply gap is combined with the unit penalty coefficient in the economic penalty data to calculate the total reliability penalty cost, which is then added as an additive term in the optimization objective function. In this way, when solving for the optimal solution, the energy allocation deterministic function automatically balances equipment investment costs, operating costs, and potential energy shortage penalty costs, thereby guiding the optimization algorithm to find a configuration and operation scheme that, under given constraints, can control conventional costs while ensuring sufficient energy supply reliability.

[0112] For example, the energy configuration determination function is constructed as follows: At the same time, the goal of minimizing carbon emission intensity is increased to achieve a synergy between economic and environmental benefits, including: Equipment investment costs; operation and maintenance costs; fuel costs; grid transaction costs

[0113] In the formula: The total installation cost of the first equipment. The total operating and maintenance cost of the first equipment. For the fuel cost of the first piece of equipment, This refers to the cost of electricity transactions.

[0114] To punish economic costs

[0115] In the formula: ρ is the off-load penalty coefficient, which is a given value. The unsupplied quantity, i.e., the deficit, is defined linearly in the constraints:

[0116]

[0117]

[0118] In the formula: The system does not supply electrical energy at time t. The system did not supply cooling energy at time t. At time t, the system does not supply heat energy, ele represents the power supply, sup represents the supply capacity, and dem represents the load demand.

[0119] Minimize carbon emissions objective function:

[0120] In the formula: Electricity purchased from the grid at time t; : Diesel generator output at time t; Natural gas consumption at time t; , , Carbon emission factors corresponding to each energy source.

[0121] Then, the multi-objective solution is achieved through a weighted summation method:

[0122] In this embodiment, by introducing economic penalty data determined based on the operating model, the failure to meet the energy demand of the equipment is quantified into economic costs and considered during the solution process. This allows the energy configuration function to not only focus on economy and energy efficiency during the solution process, but also to take power supply reliability as an inherent optimization factor, considering reserve capacity and scheduling strategies during the configuration phase, thereby reducing the risk of power outages and improving the reliability of the second equipment operation.

[0123] In one embodiment, the operating model of the first device includes: Wind power generation model, photovoltaic power generation model, combined cooling, heating and power (CCHP) energy model, diesel generator model, energy storage energy model and / or grid power model.

[0124] A wind power generation model is a functional relationship that maps natural wind speed to electrical power output. This model calculates or determines the real-time output power of a wind turbine at a given moment, segmented by whether the wind speed reaches the cut-in wind speed, rated wind speed, or cut-out wind speed of the equipment, thus characterizing the time-varying characteristics of wind energy resources. Wind speed data can be obtained from historical meteorological observation data, typical meteorological year data, or forecast data for the local area.

[0125]

[0126] In the formula: , , These are the cut-in wind speed, cut-out wind speed, and rated wind speed of the wind turbine. This is the rated output power.

[0127] A photovoltaic (PV) power generation model is a mathematical model that describes how a photovoltaic array converts solar irradiance into direct current (DC) power. Its core is typically represented by the product of installed capacity, photoelectric conversion efficiency, and real-time solar irradiance, used to simulate the variation of PV power output with solar radiation. Solar irradiance data can be obtained from historical meteorological observations, typical meteorological year data, or forecast data for the local area.

[0128]

[0129] In the formula Photovoltaic power generation (kW) at time t. Photovoltaic installed capacity (kW). Photoelectric conversion efficiency Light intensity at time t (W / m²).

[0130] The combined cooling, heating, and power (CCHP) energy model is used to describe a system centered around a gas turbine or internal combustion engine that can simultaneously produce electricity, heat, and cooling energy. This model establishes a quantitative conversion relationship between the consumed fuel (such as natural gas) and the output electricity, heat, and cooling power through a series of simultaneous efficiency equations. It may also include models of waste heat recovery and absorption refrigeration processes to characterize its high-efficiency multi-energy production characteristics.

[0131]

[0132] In the formula: Let t be the waste heat power generated by the micro-turbine after power generation; , Let t be the power generation capacity and power generation efficiency of the micro gas turbine; The heat loss coefficient of the micro-engine; , Let t be the heating power and thermal efficiency of the waste heat boiler. , Let t be the refrigeration power and refrigeration efficiency of the absorption chiller; This represents the amount of natural gas consumed during the operation of the micro gas turbine. This refers to the micro-turbine running time; It represents the lower heating value of natural gas.

[0133] The diesel generator model is relatively simplified, focusing on defining the start-stop state variables of the diesel engine during operation, and ensuring that its output power in this state is between the minimum and maximum power specified by the equipment's technical parameters.

[0134]

[0135] In the formula : Start-stop operation status variable (0 or 1), indicating whether the diesel generator is running at time t (1 = running, 0 = shut down). : The minimum output power (kW) of a diesel generator, that is, the lower limit of the power output when the diesel generator is running; : The actual output power (kW) at time t; : The maximum output power (kW) is the upper limit of the power output when the diesel generator is running.

[0136] Energy storage models are used to abstractly represent the dynamic processes of energy storage devices such as batteries, cold storage tanks, and thermal storage devices. The core of the model is the equation describing the evolution of its internal energy state over time. It typically needs to consider the different efficiencies of the charging and discharging processes, the losses of the energy storage medium itself, and the upper and lower limits of the energy state, thereby characterizing the energy storage device's ability to transfer energy over time.

[0137]

[0138] In the formula: and The battery charge at time t and time t-1; Let t be the charging and discharging power of the battery at time t. During battery charging... ≤0; When the battery discharges, ≥0; , These represent the charging and discharging efficiencies of the battery, respectively; δ represents the battery's own discharge rate. This indicates the battery's state of charge.

[0139] The power grid model is mainly used to define the interaction interface between the integrated energy system and the external public power grid. Its focus is on quantifying the purchase or sale of electrical power and linking it to the calculation of the corresponding purchase cost or sales revenue.

[0140]

[0141] In the formula: for The cost of purchasing electricity from the main power grid during a given period; , These are the power input to the main power grid and the electricity sales price on the grid, respectively.

[0142] In this embodiment, a complete operational model system covering wind power generation, photovoltaic power generation, combined cooling, heating and power (CCHP), diesel generators, energy storage, and the power grid is constructed. This system can accurately characterize the physical characteristics, conversion efficiency, and output characteristics of various energy devices in actual operation. The optimization process based on the model better reflects the actual behavior of the equipment, making the output energy configuration scheme more in line with engineering practice and improving the feasibility of the scheme and the accuracy of operational prediction.

[0143] In one embodiment, the energy configuration method further includes: S303. The computing device outputs corresponding evaluation indicators based on the energy configuration scheme.

[0144] Evaluation metrics are used to quantitatively characterize the operational performance of energy allocation schemes. Evaluation metrics include performance metrics, environmental metrics, and / or reliability metrics.

[0145] Evaluation indicators are numerical metrics used to quantitatively assess the merits of an energy configuration scheme from multiple dimensions. Their purpose is to provide indicators of comprehensive economic cost, environmental impact, and operational stability.

[0146] Performance indicators are mainly used to measure the overall efficiency of a system in the process of energy conversion, transmission and utilization. Their purpose is to evaluate the energy economy of energy configuration schemes, such as the overall energy efficiency ratio and the charge and discharge cycle efficiency of energy storage devices.

[0147] For example, performance metrics include: Energy efficiency usage effectiveness (EEUE) is the main indicator of overall energy efficiency, defined as:

[0148] in, This represents the total annual power consumption of the data center. This represents the annual power consumption of IT equipment. A lower EEUE value indicates better energy efficiency. To reflect the year-to-year improvement, an efficiency improvement rate is introduced:

[0149] when >0 indicates improved energy efficiency.

[0150] Energy storage synergy efficiency is defined as:

[0151] in, and These represent the energy storage discharge and charging capacity, respectively. The energy round-trip efficiency of energy storage reflects the energy retention rate of the energy storage device during peak shaving and valley filling processes.

[0152] Cooling-water performance (water usage effectiveness / natural cooling utilization, WUE / NCU) is defined as follows: ,

[0153] in, This is the annual water replenishment volume. Contribute energy to natural cold sources. This represents the total energy of the cooling system. A lower WUE and a higher NCU indicate better cooling and water resource utilization performance.

[0154] Environmental indicators focus on assessing the green and low-carbon level of system operation and resource utilization. Their purpose is to quantify the environmental friendliness of the scheme, such as the proportion of renewable energy in total energy consumption, carbon emission intensity per unit of output or energy consumption, etc.

[0155] For example, environmental indicators include: Renewable energy utilization rate (RE-Rate) is defined as follows:

[0156] in, Curtailment, or renewable energy consumption, measures the degree to which renewable energy is utilized locally.

[0157] Carbon intensity per unit of energy consumption is defined as:

[0158] Among them, CO2e year This refers to annual carbon emissions, which reflects the carbon emission level per unit of energy consumption or per unit of computing power.

[0159] The Water Usage Effectiveness with Climate Adjustment (WUE-X) index is defined as follows:

[0160] in, This is a climate correction factor used to eliminate biases caused by differences in the availability of natural cold sources.

[0161] Reliability indicators are used to quantitatively evaluate the system’s ability to continuously and stably meet energy demand under various expected or unexpected operating conditions. Their purpose is to measure the level of energy supply security, such as the system’s expected energy shortage throughout the year and the availability of key equipment.

[0162] For example, reliability metrics include: Reliability evaluation index is defined as:

[0163] in, For load, For available supply, The value of energy loss per unit of unsupplied energy. Expected energy not supplied (EENS) represents the quantified result of the expected energy shortage in the system.

[0164] The multi-dimensional evaluation index (MEI) is defined as follows:

[0165] in, For the index normalized score function, The recommended weighting is 0.25:0.20:0.20:0.15:0.10:0.10. MEI is used to comprehensively evaluate the system's energy efficiency, low carbon footprint, and reliability.

[0166] Energy allocation schemes serve as the common input and computational foundation for generating all comprehensive evaluation indicators. Performance, environmental, and reliability indicators form a complementary evaluation dimension system, assessing energy efficiency, ecological impact, and supply security from three perspectives. Outputting this comprehensive evaluation index system transforms complex allocation schemes into a series of intuitive and comparable quantitative values, thereby supporting decision-makers in making horizontal comparisons of different optimization schemes or accurately identifying and adjusting the optimization direction of specific schemes.

[0167] In this embodiment, while outputting the energy configuration scheme, multi-dimensional evaluation indicators covering performance, environment, and reliability are provided, making the optimization results have quantifiable and comparable evaluation criteria. Users can use these indicators to comprehensively judge the performance of the configuration scheme in terms of energy efficiency, carbon emission reduction, and operational reliability, supporting scientific decision-making and subsequent operational optimization, and improving the overall transparency and decision-making effectiveness of the energy management system.

[0168] The energy configuration method of this application will be further described below with specific examples: The second device is the load calculation equipment of the computing center, and the first device includes wind power generation equipment, photovoltaic power generation equipment, combined cooling, heating and power equipment, energy storage energy model and power grid model.

[0169] The price of natural gas used in combined cooling, heating and power (CCHP) equipment is as follows: Non-heating season: ≈ ​​405–420 yuan / MWh (corresponding to 3.96–4.11 yuan / m³); Heating season: ≈ ​​441 yuan / MWh (corresponding to 4.31 yuan / m³). The electricity prices on the power grid are shown in Table 1: Table 1 Electricity Price

[0170] The cost of the first equipment is shown in Table 2: Table 2 Cost of the First Equipment

[0171] Based on the acquired data, an energy allocation determination function is constructed according to a preset first condition. The first condition includes energy priority rules and energy supply constraints. Priorities are set as follows: renewable energy (wind power, solar power) is highest, followed by combined cooling, heating, and power (CCHP), then energy storage, and grid power is lowest. Constraints cover real-time balance constraints for electricity, heat, and cooling, upper and lower power limits for each device, energy storage charging and discharging power and capacity constraints, grid power purchase and sale constraints, and total system carbon emission constraints. The energy allocation determination function has multiple objectives: minimizing total cost and minimizing carbon emissions. It integrates economic efficiency, low-carbon emissions, and reliability requirements through a weighted summation method. Economic penalty data is used to quantify losses caused by insufficient energy supply, ensuring that the solution meets load demand while maintaining operational reliability.

[0172] Next, the solution is obtained based on the energy configuration determination function and the operating model of the first device. The operating models include wind power generation, photovoltaic power generation, combined cooling, heating and power (CCHP) model, diesel generator model, energy storage model, and grid interaction model. These models accurately depict the physical characteristics and conversion relationships of various devices in actual operation. The solution process employs optimization algorithms (e.g., genetic algorithms) to iteratively search for optimal configuration parameters and operating strategies while satisfying the above constraints. Through this process, the system automatically prioritizes wind and photovoltaic power output, sequentially activating CCHP devices and energy storage when renewable energy is insufficient, and only considers purchasing electricity from the grid last. This ensures continuous power supply to the computing center while maximizing the proportion of clean energy and reducing carbon emissions and operating costs.

[0173] After the solution is completed, the energy configuration scheme of the second device (computing center) is output. The scheme specifically includes the commissioning sequence, power setting, energy storage charging and discharging plan and interaction strategy with the power grid for various types of first devices. Figure 4 The energy supply configuration structure obtained by this method is shown, which reflects the energy structure of multi-energy complementarity and coordinated supply; Figure 5 A comparison of the carbon emissions of the system before and after optimization using this method shows that the carbon emissions are significantly reduced after optimization, verifying the effectiveness of this method in low-carbon operation; Appendix Figure 6 The data presents the energy scenarios for a typical day in February, clearly showing the output combinations and load matching of renewable energy, combined heat and power (CHP) equipment, energy storage, and the power grid at different times. Figure 7 This demonstrates energy solutions on an annual scale, reflecting long-term configuration and scheduling strategies under seasonal variations and load fluctuations.

[0174] As can be seen, the above mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the embodiments of this application provide corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the modules and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0175] This application embodiment can divide the energy configuration device into functional modules according to the above method example. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0176] In some embodiments, this application also provides an energy configuration device. The energy configuration device may include one or more functional modules for implementing the energy configuration method of the above method embodiments.

[0177] For example, Figure 8 This is a schematic diagram illustrating the composition of an energy configuration device provided in an embodiment of this application. Figure 8 As shown, the energy configuration device 400 includes: a data acquisition module 401, a function construction module 402, and a scheme determination module 403.

[0178] The data acquisition module 401 is used to acquire the energy supply parameters and energy efficiency data of the first device, which is an energy-generating device.

[0179] The function construction module 402 is used to construct an energy configuration determination function based on a preset first condition, using the energy supply parameters of the first device and energy efficiency data. The energy configuration determination function is used to output an energy configuration scheme that matches the operating requirements of the second device. The second device is an energy-consuming device to be configured. The first condition includes energy priority and / or the energy supply constraints of the first device. The scheme determination module 403 is used to determine the energy configuration scheme of the second equipment based on the energy configuration determination function and the operating model of the first equipment. The operating model of the first equipment is used to characterize the physical characteristics and constraints of various energy production, conversion, storage and supply to the second equipment.

[0180] The energy configuration scheme includes the power supply configuration parameters for operating the first equipment and the energy scheme for the first equipment.

[0181] In one embodiment, the energy priorities from highest to lowest are: renewable energy, combined cooling, heating and power (CCHP) energy, energy storage energy, and grid power, with renewable energy including wind power and / or photovoltaic power.

[0182] In one embodiment, the energy consumption constraint includes at least one of the following: Constraints include: power balance constraints, thermal balance constraints, cold balance constraints, equipment power constraints, energy storage operation constraints, grid interaction constraints, and carbon emission constraints.

[0183] In one embodiment, energy efficiency data includes: Data on economic penalties.

[0184] The economic penalty data is determined based on the operating model of the first equipment and is used to characterize the penalty cost incurred for failing to meet the energy demand of the second equipment.

[0185] In one embodiment, the operating model of the first device includes: Wind power generation model, photovoltaic power generation model, combined cooling, heating and power (CCHP) energy model, diesel generator model, energy storage energy model and / or grid power model.

[0186] In one embodiment, the scheme determination module 403 is further configured to output corresponding evaluation indicators based on the energy configuration scheme, and the evaluation indicators are used to quantitatively characterize the operating performance of the energy configuration scheme.

[0187] Evaluation indicators include performance indicators, environmental indicators, and / or reliability indicators.

[0188] In the case of implementing the functions of the integrated modules described above in hardware, this embodiment of the invention provides a possible structural schematic diagram of the electronic device involved in the above embodiments. For example... Figure 9 As shown, the electronic device 500 includes: a processor 502, a communication interface 503, and a bus 504. Optionally, the electronic device 500 may also include a memory 501.

[0189] Processor 502 may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 502 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 502 may also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0190] Communication interface 503 is used to connect to other devices via a communication network. This communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0191] The memory 501 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0192] In one possible implementation, the memory 501 can exist independently of the processor 502. The memory 501 can be connected to the processor 502 via a bus 504 and is used to store instructions or program code. When the processor 502 calls and executes the instructions or program code stored in the memory 501, it can implement the energy configuration method provided in this embodiment of the invention.

[0193] In another possible implementation, the memory 501 can also be integrated with the processor 502.

[0194] Bus 504 can be an extended industry standard architecture (EISA) bus, etc. Bus 504 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0195] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the service calling device can be divided into different functional modules to complete all or part of the functions described above.

[0196] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by computer instructions instructing related hardware. The program can be stored in the aforementioned computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be any of the foregoing embodiments or memory. The aforementioned computer-readable storage medium can also be an external storage device of the aforementioned service invocation device, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the aforementioned service invocation device. Further, the aforementioned computer-readable storage medium can include both internal storage units of the aforementioned service invocation device and external storage devices. The aforementioned computer-readable storage medium is used to store the aforementioned computer program and other programs and data required by the aforementioned service invocation device. The aforementioned computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0197] This application also provides computer instructions. All or part of the processes in the above method embodiments can be executed by computer instructions to instruct related hardware (such as computers, processors, network devices, and terminals). The program can be stored in the aforementioned computer-readable storage medium.

[0198] This application also provides a computer program product that, when run on a computer, causes the above-described method embodiments to be executed.

[0199] This application also provides a chip system. The chip system may be composed of chips or may include chips and other discrete devices, without limitation. The chip system includes a processor and a transceiver. All or part of the processes in the above method embodiments can be completed by this chip system, such as the chip system being used to implement the functions performed by the network devices or terminals in the above method embodiments.

[0200] In one possible design, the chip system further includes a memory for storing program instructions and / or data. When the chip system is running, the processor executes the program instructions stored in the memory to enable the chip system to perform the functions performed by the network device or terminal in the above method embodiments.

[0201] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An energy allocation method, characterized in that, include: Acquire the energy supply parameters and energy efficiency data of the first device, which is an energy-generating device; Based on a preset first condition, an energy configuration determination function is constructed using the energy supply parameters of the first device and the energy efficiency data. The energy configuration determination function is used to output an energy configuration scheme that matches the operating requirements of the second device. The second device is an energy-consuming device to be configured. The first condition includes energy priority and / or the energy supply constraints of the first device. The energy configuration scheme of the second equipment is determined based on the energy configuration determination function and the operating model of the first equipment. The operating model of the first equipment is used to characterize the physical characteristics and constraints of various energy production, conversion, storage and supply to the second equipment. The energy configuration scheme includes the power supply configuration parameters for operating the first device and the energy scheme for the first device.

2. The method according to claim 1, characterized in that, The energy priorities, from highest to lowest, are: renewable energy, combined cooling, heating and power (CCHP) energy, energy storage energy, and grid power. The renewable energy includes wind power and / or photovoltaic power.

3. The method according to claim 1, characterized in that, The energy consumption constraints include at least one of the following: Constraints include: power balance constraints, thermal balance constraints, cold balance constraints, equipment power constraints, energy storage operation constraints, grid interaction constraints, and carbon emission constraints.

4. The method according to claim 1, characterized in that, The energy efficiency data includes data on economic penalties; The economic penalty data is determined based on the operating model of the first device and is used to characterize the penalty cost incurred for failing to meet the energy demand of the second device.

5. The method according to claim 1, characterized in that, The operating model of the first device includes: Wind power generation model, photovoltaic power generation model, combined cooling, heating and power (CCHP) energy model, diesel generator model, energy storage energy model and / or grid power model.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Based on the energy configuration scheme, corresponding evaluation indicators are output, which are used to quantitatively characterize the operating performance of the energy configuration scheme. The evaluation indicators include performance indicators, environmental indicators, and / or reliability indicators.

7. An energy distribution device, characterized in that, include: The data acquisition module is used to acquire the energy supply parameters and energy efficiency data of the first device, which is an energy-generating device; The function construction module is used to construct an energy configuration determination function based on a preset first condition, using the energy supply parameters of the first device and the energy efficiency data. The energy configuration determination function is used to output an energy configuration scheme that matches the operating requirements of the second device. The second device is an energy-consuming device to be configured. The first condition includes energy priority and / or the energy supply constraints of the first device. The scheme determination module is used to determine the energy configuration scheme of the second equipment based on the energy configuration determination function and the operating model of the first equipment. The operating model of the first equipment is used to characterize the physical characteristics and constraints of various energy production, conversion, storage and supply to the second equipment. The energy configuration scheme includes the power supply configuration parameters for operating the first device and the energy scheme for the first device.

8. An electronic device, characterized in that, It includes a processor and a memory, the processor being coupled to the memory; the memory is used to store computer instructions, which are loaded and executed by the processor to enable the computer device to implement the energy configuration method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes computer-executable instructions that, when executed on a computer, cause the computer to perform the energy configuration method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when run on an electronic device, causes the electronic device to perform the energy configuration method as described in any one of claims 1 to 6.

Images