Energy configuration determination method and device of energy system and electronic equipment

Abstract

The invention discloses an energy configuration determination method and device of an energy system and electronic equipment. The method comprises the following steps: acquiring energy data of an energy system; determining a configuration constraint corresponding to the target configuration function according to the energy data; and determining target energy configuration corresponding to the energy system according to the target configuration function and the configuration constraint. According to the invention, the technical problem of high carbon emission cost caused by inaccurate energy configuration during the energy configuration of the energy system in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of energy systems, and more specifically, to a method, apparatus, and electronic device for determining the energy configuration of an energy system. Background Technology

[0002] In related technologies, to achieve reasonable carbon emissions from energy systems and reduce their environmental impact, energy configuration is necessary. However, in these technologies, inaccurate energy configuration leads to high carbon emission costs.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, and electronic device for determining the energy configuration of an energy system, in order to at least solve the technical problem in the related art where inaccurate energy configuration leads to high carbon emission costs.

[0005] According to one aspect of the present invention, an energy configuration determination method for an energy system is provided, comprising: acquiring energy data of the energy system; determining configuration constraints corresponding to a target configuration function based on the energy data, wherein the target configuration function is a function aimed at minimizing carbon emission costs, the target configuration function includes a first sub-function, a second sub-function, and a third sub-function, the first sub-function being used to configure the carbon emissions of the energy system, the second sub-function being used to configure the carbon emission control costs of the energy system, and the third sub-function being used to configure the usage of a target energy source in the energy system, the target energy source being a power generation energy source with a carbon emission index less than a carbon emission threshold; and determining a target energy configuration corresponding to the energy system based on the target configuration function and the configuration constraints.

[0006] Optionally, determining the target energy configuration corresponding to the energy system based on the target configuration function and the configuration constraints includes: determining multiple system energy configurations corresponding to the energy system based on the target configuration function and the configuration constraints; performing multiple iterations of the multiple system energy configurations in the order of execution, determining a reference energy configuration under any iteration, wherein the reference energy configuration represents the energy configuration that minimizes the carbon emission cost of the energy system among all energy configurations obtained by iterating the multiple system energy configurations; adjusting the multiple system energy configurations according to the reference energy configuration under any iteration to obtain adjusted multiple system energy configurations, until multiple iterations are completed to obtain multiple system resource configurations adjusted according to the target iteration; and determining the target energy configuration corresponding to the energy system based on the multiple system resource configurations adjusted according to the target iteration.

[0007] Optionally, determining the configuration constraints corresponding to the target configuration function based on the energy data includes: determining system location parameters corresponding to the energy system; determining energy consumption and energy location parameters corresponding to multiple energy sources in the energy system based on the energy data, wherein the energy location parameters represent the acquisition locations of the corresponding energy sources; determining carbon emission parameters corresponding to the multiple energy sources based on the energy consumption of the multiple energy sources; determining acquisition condition parameters corresponding to the multiple energy sources based on the system location parameters, the carbon emission parameters and energy location parameters corresponding to the multiple energy sources; and determining spatial stability constraints corresponding to the target configuration function based on the acquisition condition parameters corresponding to the multiple energy sources, wherein the spatial stability constraints are used to constrain the carbon emission stability of the energy system from a spatial dimension, and the configuration constraints include the spatial stability constraints.

[0008] Optionally, determining the configuration constraints corresponding to the target configuration function based on the energy data includes: determining the energy devices corresponding to multiple energy sources in the energy system based on the energy data; determining the aging characteristics corresponding to the multiple energy devices, wherein the aging characteristics represent the changes in the aging degree of the corresponding energy devices over time; determining the carbon emission fluctuation index corresponding to the multiple energy devices based on the aging characteristics; and determining the time stability constraint corresponding to the target configuration function based on the carbon emission fluctuation index corresponding to the multiple energy devices, wherein the time stability constraint is used to constrain the carbon emission stability of the energy system from a time dimension, and the configuration constraints include the time stability constraint.

[0009] Optionally, determining the configuration constraints corresponding to the target configuration function based on the energy data includes: determining, based on the energy data, the energy utilization methods corresponding to multiple energy sources in the energy system; determining the carbon emission indices, a first trend feature, and a second trend feature corresponding to the multiple energy utilization methods, wherein the first trend feature represents the trend of the cost required to use energy using the corresponding energy utilization method over time, and the second trend feature represents the trend of the degree of adoption of the corresponding energy utilization method over time; and determining the control cost constraints corresponding to the target configuration function based on the carbon emission indices, the first trend feature, and the second trend feature corresponding to the multiple energy utilization methods, wherein the control cost constraints are used to constrain the carbon emission control costs of the energy system, and the configuration constraints include the control cost constraints.

[0010] Optionally, determining the configuration constraints corresponding to the target configuration function based on the energy data includes: determining the historical energy consumption corresponding to multiple energy sources based on the energy data, wherein the multiple energy sources include the target energy source; determining the carbon emission reduction index and the power output stability index corresponding to the target energy source; and determining the energy consumption constraints corresponding to the target configuration function based on the historical energy consumption corresponding to the multiple energy sources, the carbon emission reduction index, and the power output stability index.

[0011] Optionally, before determining the configuration constraints corresponding to the target configuration function based on the energy data, the method further includes: determining a first weight index corresponding to the first sub-function, a second weight index corresponding to the second sub-function, and a third weight index corresponding to the third sub-function; and constructing the target configuration function based on the first sub-function, the second sub-function, the third sub-function, the first weight index, the second weight index, and the third weight index.

[0012] According to one aspect of the present invention, an energy configuration determination apparatus for an energy system is provided, comprising: an acquisition module for acquiring energy data of the energy system; a first determination module for determining configuration constraints corresponding to a target configuration function based on the energy data, wherein the target configuration function includes a first sub-function, a second sub-function, and a third sub-function, the first sub-function being used to configure the carbon emissions of the energy system, the second sub-function being used to configure the carbon emission control cost of the energy system, and the third sub-function being used to configure the usage of a target energy source in the energy system, the target energy source being a power generation energy source with a carbon emission index less than a carbon emission threshold; and a second determination module for determining a target energy configuration corresponding to the energy system based on the target configuration function and the configuration constraints.

[0013] According to one aspect of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the energy configuration determination method of the energy system described in any of the preceding claims.

[0014] According to one aspect of the present invention, a computer-readable storage medium is provided, comprising: when instructions in the computer-readable storage medium are executed by a processor of an electronic device, enabling the electronic device to perform the energy configuration determination method of the energy system described in any of the preceding claims.

[0015] In this embodiment of the invention, energy data of the energy system is acquired; based on the energy data, configuration constraints corresponding to the target configuration function are determined, wherein the target configuration function is a function aimed at minimizing carbon emission costs. The target configuration function includes a first sub-function, a second sub-function, and a third sub-function. The first sub-function is used to configure the carbon emissions of the energy system, the second sub-function is used to configure the carbon emission control cost of the energy system, and the third sub-function is used to configure the usage of the target energy in the energy system. The target energy is power generation energy with a carbon emission index less than the carbon emission threshold; based on the target configuration function and the configuration constraints, the target energy configuration corresponding to the energy system is determined. Since the target configuration function encompasses a first sub-function, a second sub-function, and a third sub-function, the first sub-function focuses on configuring the carbon emissions of the energy system, the second sub-function emphasizes configuring the carbon emission control cost of the energy system, and the third sub-function is used to configure the amount of power generation energy (target energy) in the energy system whose carbon emission index is less than a threshold. Through the target configuration function, a coupled analysis of carbon emissions, carbon emission control cost, and target energy usage can be achieved. Combined with the configuration constraints determined based on the actual situation of the energy system, the target energy configuration of the energy system can be determined, realizing the unified optimization of carbon emissions, carbon emission control cost, and target energy usage. This reduces the carbon emission cost of the energy system from an overall perspective, thereby solving the technical problem in related technologies where inaccurate energy configuration leads to high carbon emission costs. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 This is a flowchart of an energy configuration determination method for an energy system according to an embodiment of the present invention;

[0018] Figure 2 This is a schematic diagram of an energy configuration determination method for an energy system in an optional embodiment of the present invention;

[0019] Figure 3 This is a schematic diagram of the energy configuration determination system structure in an optional embodiment of the present invention;

[0020] Figure 4 This is a structural block diagram of an energy configuration determination device for an energy system according to an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] Example 1

[0024] According to an embodiment of the present invention, an embodiment of an energy configuration determination method for an energy system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] Figure 1 This is a flowchart of an energy configuration determination method for an energy system according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0026] S102, Obtain energy data from the energy system.

[0027] In step S102 of this application, energy data of the energy system is obtained.

[0028] This involves energy systems, which are systems related to energy generation, transmission, distribution, and consumption. These energy systems can be equipment systems, specifically new energy equipment systems. These systems can be based on mineral resources (such as silicon, silver, lithium, cobalt, and rare earth elements) to produce new energy equipment, which is then configured with both new energy equipment (such as photovoltaic power generation equipment and wind power generation equipment) and traditional power generation equipment (such as coal-fired power generation equipment) to control carbon emissions while minimizing carbon emission costs.

[0029] This involves energy data, which is the core foundational data supporting energy system analysis and optimization, encompassing key information from multiple dimensions and sources. This energy information includes energy production data, energy consumption data, equipment operation data, and carbon emission-related data.

[0030] By acquiring multi-dimensional core basic data of the energy system, we can fully grasp the actual operating status of each link of the energy system, providing complete and accurate data support for subsequent energy allocation.

[0031] S104. Based on energy data, determine the configuration constraints corresponding to the target configuration function. The target configuration function is a function aimed at minimizing carbon emission costs. The target configuration function includes a first sub-function, a second sub-function, and a third sub-function. The first sub-function is used to configure the carbon emissions of the energy system, the second sub-function is used to configure the carbon emission control cost of the energy system, and the third sub-function is used to configure the usage of the target energy in the energy system. The target energy is power generation energy with a carbon emission index less than the carbon emission threshold.

[0032] In step S104 of this application, configuration constraints corresponding to the target configuration function are determined based on energy data.

[0033] This involves a target configuration function, which is a function constructed with the core objective of minimizing the carbon emission costs of the energy system.

[0034] This involves configuration constraints, which are limitations determined based on the actual energy data of the energy system. These constraints define the operational boundaries of the target configuration function, ensuring the feasibility and rationality of the energy configuration scheme derived from the function. These configuration constraints include spatial stability constraints, temporal stability constraints, control cost constraints, and energy consumption constraints.

[0035] This includes carbon emission costs, which are costs related to carbon emissions from the energy system.

[0036] This involves a first sub-function, which is used to quantify the carbon emissions of the energy system.

[0037] This involves a second sub-function, which is used to quantify the cost of carbon emission control.

[0038] This involves a third sub-function, which is used to quantify the amount of target energy used in the energy system.

[0039] This involves target energy sources, which are power generation energy sources with a carbon emission index lower than a preset carbon emission threshold, such as solar energy and wind energy.

[0040] This includes the carbon emission index, which represents the level of carbon emissions during the consumption of a given energy source.

[0041] This involves a carbon emission threshold, which is a pre-set threshold value for the carbon emission index used to define the target energy source. When the carbon emission index of a certain type of power generation energy is less than this threshold, it can be identified as the target energy source.

[0042] This involves energy sources for power generation, such as solar energy, wind energy, and fossil fuels.

[0043] By determining the configuration constraints corresponding to the target configuration function based on energy data, a reasonable computational boundary can be defined for the target configuration function. This ensures that the configuration scheme of the energy system determined by the target configuration function can meet the requirement of minimizing carbon emission costs while not deviating from the actual operating capacity of the configured energy system, thereby ensuring the stable and efficient operation of the energy system.

[0044] S106. Based on the target configuration function and configuration constraints, determine the target energy configuration corresponding to the energy system.

[0045] In step S106 provided in this application, the target energy configuration corresponding to the energy system is determined based on the target configuration function and configuration constraints.

[0046] This involves the target energy configuration, which is the optimal energy configuration scheme that is ultimately determined and adapted to the energy system, based on the target configuration function and the constraints of configuration.

[0047] The target configuration function enables coupled analysis of carbon emissions, carbon emission control costs, and target energy usage. Combined with configuration constraints determined based on the actual situation of the energy system, the target energy configuration of the energy system can be determined, achieving unified optimization of carbon emissions, carbon emission control costs, and target energy usage, thereby reducing the carbon emission costs of the energy system at the overall level.

[0048] Through steps S102-S106 above, energy data of the energy system is obtained; based on the energy data, configuration constraints corresponding to the target configuration function are determined, wherein the target configuration function is a function aimed at minimizing carbon emission costs. The target configuration function includes a first sub-function, a second sub-function, and a third sub-function. The first sub-function is used to configure the carbon emissions of the energy system, the second sub-function is used to configure the carbon emission control cost of the energy system, and the third sub-function is used to configure the usage of the target energy in the energy system. The target energy is power generation energy with a carbon emission index less than the carbon emission threshold; based on the target configuration function and the configuration constraints, the target energy configuration corresponding to the energy system is determined. Because the target configuration function encompasses a first sub-function, a second sub-function, and a third sub-function, the first sub-function focuses on configuring the carbon emissions of the energy system, the second sub-function emphasizes configuring the carbon emission control cost of the energy system, and the third sub-function is used to configure the amount of power generation energy (target energy) in the energy system whose carbon emission index is less than a threshold. In this way, the target configuration function can achieve coupled analysis of carbon emissions, carbon emission control cost, and target energy usage. Combined with the configuration constraints determined based on the actual situation of the energy system, the target energy configuration of the energy system can be determined, realizing unified optimization of carbon emissions, carbon emission control cost, and target energy usage. This reduces the carbon emission cost of the energy system from an overall perspective, thereby solving the technical problem in related technologies where inaccurate energy configuration leads to high carbon emission costs.

[0049] As an optional embodiment, determining the target energy configuration corresponding to the energy system based on the target configuration function and configuration constraints includes: determining multiple system energy configurations corresponding to the energy system based on the target configuration function and configuration constraints; performing multiple iterations on the multiple system energy configurations in the order of execution, determining a reference energy configuration under any iteration for each iteration, wherein the reference energy configuration represents the energy configuration that minimizes the carbon emission cost of the energy system among all energy configurations obtained through iteration; adjusting the multiple system energy configurations according to the reference energy configuration under any iteration to obtain adjusted multiple system energy configurations, until multiple iterations are completed to obtain the target iterative adjusted multiple system resource configurations; and determining the target energy configuration corresponding to the energy system based on the target iterative adjusted multiple system resource configurations.

[0050] This embodiment describes the specific steps for determining the target energy configuration corresponding to the energy system based on the target configuration function and configuration constraints.

[0051] This involves multiple system energy configurations, which are preliminary energy configuration schemes determined under the constraints of the operational logic of the target configuration function and configuration constraints.

[0052] This involves the execution order, which is the order in which the iterations take place.

[0053] This involves any iteration, which is any one iteration in a multi-iteration process. The result of this iteration will serve as the initial input for the next iteration, gradually pushing the solution closer to the optimal direction.

[0054] This involves a reference energy configuration, which is the configuration scheme that minimizes the carbon emission cost of the energy system, selected from all current system energy configurations in any iteration.

[0055] This involves adjustments, which are operations performed in any iteration to modify key parameters of the energy configurations of multiple other systems, using a reference energy configuration as a benchmark. For example, the energy configurations of other systems are made to gradually approach the reference energy configuration.

[0056] This involves target iteration, which is the last iteration in a series of iterations that reaches a preset stopping condition. The stopping condition includes the number of iterations reaching a set threshold, the difference in carbon emission costs of energy configurations of each system being less than the allowable range, and carbon emission costs no longer decreasing with iteration.

[0057] This involves multiple system resource configurations after target iteration adjustment, which are multiple performance-optimized energy configuration schemes finally obtained after target iteration adjustment.

[0058] By generating multiple initial system energy configurations based on the objective configuration function and configuration constraints, diverse basic samples are provided for subsequent optimization. Then, in each iteration, the reference energy configuration with the lowest carbon emission cost is selected according to the iteration sequence. All system energy configurations are adjusted based on this optimal reference. Continuous iterative optimization can gradually reduce the deviation between the configuration and the optimal solution and avoid local optimum traps. Finally, the target energy configuration is determined by multiple system resource configurations adjusted by the objective iteration, which ensures that the configuration scheme of the energy system achieves optimal control of carbon emission costs while meeting the constraints.

[0059] As an optional embodiment, based on energy data, the configuration constraints corresponding to the target configuration function are determined, including: determining system location parameters corresponding to the energy system; determining energy consumption and energy location parameters corresponding to multiple energy sources in the energy system based on energy data, wherein the energy location parameters represent the acquisition locations of the corresponding energy sources; determining carbon emission parameters corresponding to multiple energy sources based on the energy consumption of each energy source; determining acquisition condition parameters corresponding to multiple energy sources based on the system location parameters, as well as the carbon emission parameters and energy location parameters corresponding to multiple energy sources; and determining spatial stability constraints corresponding to the target configuration function based on the acquisition condition parameters corresponding to multiple energy sources, wherein the spatial stability constraints are used to constrain the carbon emission stability of the energy system from a spatial dimension, and the configuration constraints include spatial stability constraints.

[0060] This embodiment describes the specific steps for determining the configuration constraints corresponding to the target configuration function based on energy data.

[0061] This involves system location parameters, which are parameters used to describe the overall spatial characteristics of the energy system. These parameters include the location coordinates of the area where the energy system is located, the distribution of energy facilities within the area, and other information. They serve as the basis for analyzing the impact of energy acquisition, transmission, and carbon emissions from a spatial perspective.

[0062] This involves energy consumption, which refers to the amount of energy consumed in the energy system.

[0063] This includes energy location parameters, which are parameters used to identify the specific locations where various types of energy are obtained in an energy system, including the specific spatial location of photovoltaic power plants, the location coordinates of wind farms, and the location of coal and other supply sites.

[0064] This involves the location of energy acquisition, which is the spatial location where the energy system obtains various types of energy.

[0065] This involves carbon emission parameters, which are indicators that quantify the carbon emissions generated during the production, transmission, or consumption of different types of energy, including the carbon emission coefficient per unit of energy and the carbon emission loss rate during energy transmission.

[0066] This involves acquisition condition parameters, which are used to represent the stability of acquiring the corresponding energy, so as to reflect the reliability of acquiring the corresponding energy consumption.

[0067] This involves spatial stability constraints, which are configuration constraints that limit the stability of carbon emissions from the energy system from a spatial dimension.

[0068] This involves the spatial dimension, which is the perspective of analyzing the operation and carbon emission characteristics of energy systems from the perspective of spatial distribution, focusing on the impact of spatial elements such as energy acquisition location, system location, and energy transmission path on carbon emissions.

[0069] By determining system location parameters, the overall spatial characteristics of the energy system can be clearly defined, providing a basic positioning for subsequent analysis. Determining energy consumption and location parameters based on energy data allows for precise understanding of the scale of energy consumption and acquisition locations, providing data support for carbon emission quantification. Carbon emission parameters based on energy consumption enable the quantification of carbon emissions from different energy sources at each stage. Determining acquisition condition parameters based on system location, carbon emissions, and energy location parameters allows for the assessment of the stability and reliability of energy acquisition, avoiding carbon emission fluctuations caused by unstable acquisition. Finally, determining spatial stability constraints based on acquisition condition parameters limits the stability of carbon emissions from the energy system from a spatial perspective, thereby facilitating targeted optimization of energy acquisition and distribution strategies and effectively controlling carbon emission costs.

[0070] As an optional embodiment, determining the configuration constraints corresponding to the target configuration function based on energy data includes: determining the energy devices corresponding to multiple energy sources in the energy system based on the energy data; determining the aging characteristics corresponding to each of the multiple energy devices, wherein the aging characteristics represent the changes in the aging degree of the corresponding energy devices over time; determining the carbon emission fluctuation index corresponding to each of the multiple energy devices based on the aging characteristics; and determining the time stability constraints corresponding to the target configuration function based on the carbon emission fluctuation index corresponding to each of the multiple energy devices, wherein the time stability constraints are used to constrain the carbon emission stability of the energy system from a time dimension, and the configuration constraints include time stability constraints.

[0071] This embodiment describes the specific steps for determining the configuration constraints corresponding to the target configuration function based on energy data.

[0072] This involves energy equipment, which refers to equipment in the energy system related to energy consumption, including new energy equipment and fossil fuel power generation equipment.

[0073] This includes aging characteristics, which are features used to reflect the degree of aging of energy equipment over time. These characteristics reflect the dynamic characteristics of performance degradation during long-term operation, including information such as the rate of decline in operating efficiency and the rate of increase in energy consumption.

[0074] This includes the degree of aging, which refers to the extent to which the performance of energy equipment deviates from its initial state due to long-term use, environmental erosion, component wear and tear, and other factors.

[0075] This includes the carbon emission volatility index, which quantifies the change in carbon emission levels of energy equipment over time at different aging stages, reflecting the stability of the equipment's carbon emissions.

[0076] This involves time stability constraints, which are configuration constraints that limit the stability of carbon emissions from the energy system from a time perspective.

[0077] This involves the time dimension, which is a perspective that analyzes the characteristics of energy system operation and carbon emissions from the perspective of time evolution, focusing on the impact of time elements such as different time periods and the entire life cycle of equipment on carbon emissions.

[0078] By identifying equipment corresponding to multiple energy sources within an energy system based on energy data and clarifying the aging characteristics of this equipment, the impact of equipment aging on carbon emissions can be quantified. Calculating a carbon emission volatility index based on aging characteristics allows for accurate assessment of the carbon emission stability of equipment at different aging stages. Furthermore, setting time stability constraints based on the carbon emission volatility index limits carbon emission fluctuations over time, ensuring that the energy system avoids increased carbon emissions due to equipment aging during long-term operation. This provides an analytical basis for minimizing carbon emission costs in the future.

[0079] As an optional embodiment, determining the configuration constraints corresponding to the target configuration function based on energy data includes: determining the energy utilization methods corresponding to multiple energy sources in the energy system based on energy data; determining the carbon emission index, first trend feature, and second trend feature corresponding to the multiple energy utilization methods, wherein the first trend feature represents the trend of the cost required to use energy using the corresponding energy utilization method over time, and the second trend feature represents the trend of the degree of adoption of the corresponding energy utilization method over time; and determining the control cost constraint corresponding to the target configuration function based on the carbon emission index, first trend feature, and second trend feature corresponding to the multiple energy utilization methods, wherein the control cost constraint is used to constrain the carbon emission control cost of the energy system, and the configuration constraints include the control cost constraint.

[0080] This embodiment describes the specific steps for determining the configuration constraints corresponding to the target configuration function based on energy data.

[0081] This involves energy utilization methods, which are the specific ways in which different types of energy are converted or consumed in an energy system, such as direct coal combustion power generation and solar photovoltaic power generation.

[0082] This includes a first trend feature, which describes the pattern of how the cost required to adopt a certain energy utilization method changes over time.

[0083] Among them, a second trend feature is involved, which is a regular feature used to describe the evolution of the scale of a certain energy use method in the energy system over time. For example, the use of wind power grid connection is gradually expanding, while the use of oil-fired power generation is declining year by year due to its high carbon emissions.

[0084] This includes control cost constraints, which are configuration constraints used to limit carbon emissions from the energy system from the perspective of control costs.

[0085] Identifying the energy utilization methods corresponding to multiple energy sources within an energy system based on energy data clarifies the conversion and consumption patterns of different energy sources within the system. By analyzing the carbon emission indices, primary trend characteristics, and secondary trend characteristics of these utilization methods, control cost constraints can be determined. This ensures that while optimizing carbon emissions, the increased carbon emission control costs caused by adopting high-cost or high-carbon-emission utilization methods are avoided, thereby optimizing the carbon emission costs of the energy system.

[0086] Furthermore, for the target configuration function, mineral availability constraints can be set, where the mineral data includes multiple mineral types. This can be determined as follows: Based on the mineral data, determine the predetermined reserve data corresponding to each of the multiple mineral types, and the system scenario corresponding to the new energy equipment system; based on the system scenario corresponding to the new energy equipment system, determine the mineral demand parameters corresponding to the new energy equipment system, where energy data includes mineral data, and the mineral demand parameters represent the degree of demand of the new energy equipment system for each mineral type; based on the mineral demand parameters corresponding to the new energy equipment system, and the predetermined reserve data corresponding to each of the multiple mineral types, determine the mineral availability constraints corresponding to the new energy equipment system. These mineral availability constraints are used to constrain the availability corresponding to each of the multiple mineral types to constrain carbon emission control costs, and the mineral demand parameters include the mineral demand coefficients corresponding to each of the multiple new energy devices.

[0087] As an optional embodiment, determining the configuration constraints corresponding to the target configuration function based on energy data includes: determining the historical energy consumption corresponding to multiple energy sources based on energy data, wherein the multiple energy sources include the target energy source; determining the carbon emission reduction index and power output stability index corresponding to the target energy source; and determining the energy consumption constraints corresponding to the target configuration function based on the historical energy consumption, carbon emission reduction index, and power output stability index corresponding to the multiple energy sources.

[0088] This embodiment describes the specific steps for determining the configuration constraints corresponding to the target configuration function based on energy data.

[0089] This includes historical energy consumption, which is a record of the actual consumption or production of multiple energy sources by the energy system within a specific time period in the past.

[0090] This involves multiple energy sources, which are various types of energy in the energy system, including renewable energy and traditional fossil energy. Traditional fossil energy includes coal and natural gas, while renewable energy includes photovoltaic and wind power.

[0091] This includes target energy sources, which are energy sources with carbon emission indices lower than a preset carbon emission threshold, such as renewable energy sources like solar, wind, and hydropower.

[0092] This includes the carbon emission reduction index, which is an indicator that quantifies the carbon emission reduction effect that the target energy source can achieve compared to traditional high-carbon emission energy sources.

[0093] This includes the output stability index, which is an indicator used to assess the stability of the supply of a target energy source over time during its production or supply process.

[0094] By determining the historical energy consumption of multiple energy sources based on energy data, we can understand the past consumption or production patterns of different types of energy in the energy system, providing historical references for judging the reasonable range of future energy consumption. Determining the carbon emission reduction index and power output stability index corresponding to the target energy source clarifies the dual characteristics of the target energy source in terms of carbon emission reduction and supply security, avoiding the problem of focusing only on emission reduction while ignoring supply fluctuations. Determining energy consumption constraints based on historical energy consumption, carbon emission reduction index, and power output stability index enables the setting of scientific consumption limit rules for different energy sources. This ensures that when allocating energy consumption, the energy system can both improve carbon emission reduction capabilities through the target energy source and maintain energy supply and demand balance based on historical patterns and supply stability. Ultimately, this provides reasonable consumption dimension constraints for the target allocation function, ensuring that the allocation of the target energy source meets low-carbon requirements while not deviating from the actual energy consumption capacity and supply stability requirements of the system.

[0095] As an optional embodiment, before determining the configuration constraints corresponding to the target configuration function based on energy data, the method further includes: determining a first weight index corresponding to the first sub-function, a second weight index corresponding to the second sub-function, and a third weight index corresponding to the third sub-function; and constructing the target configuration function based on the first sub-function, the second sub-function, the third sub-function, the first weight index, the second weight index, and the third weight index.

[0096] This embodiment describes the specific steps before determining the configuration constraints corresponding to the target configuration function based on energy data.

[0097] This involves a first weight index, which is used to quantify the importance of the first sub-function in the target configuration function.

[0098] This involves a second weighting index, which is used to quantify the importance of the second sub-function in the target configuration function.

[0099] This involves a third weighting index, which is used to quantify the importance of the third sub-function in the target configuration function.

[0100] By determining the first weight index corresponding to the first sub-function, the second weight index corresponding to the second sub-function, and the third weight index corresponding to the third sub-function, the importance of the three sub-functions in the target configuration function can be quantified. Thus, the target configuration function can be constructed based on these sub-functions and their weight indices, which can comprehensively consider the role of each sub-function and ensure that the target configuration function fully and accurately reflects the multifaceted characteristics and needs of the energy system.

[0101] Based on the above embodiments and optional embodiments, an optional implementation method is provided, which is described in detail below.

[0102] In related technologies, to achieve reasonable carbon emissions from energy systems and reduce their environmental impact, energy configuration is necessary. However, in these technologies, inaccurate energy configuration leads to high carbon emission costs.

[0103] There is currently no effective solution to the above problems.

[0104] In view of this, an optional embodiment of the present invention provides an energy configuration determination method and system for an energy system, which can effectively solve the above-mentioned technical problems.

[0105] (a) Methods for determining the energy allocation of an energy system:

[0106] Figure 2 This is a schematic diagram of an energy configuration determination method for an energy system in an optional embodiment of the present invention, such as... Figure 2 As shown below, a detailed description will be provided.

[0107] S1, acquire energy data from the energy system;

[0108] For example, energy consumption data, equipment operation status data, and carbon emission detection data are collected from new energy equipment systems to construct a multi-source data fusion model. Then, data cleaning algorithms are used to denoise, complete, and detect anomalies in the collected data to form an energy and carbon emission dataset (i.e., energy data). This enables a comprehensive understanding of the operating characteristics of the energy system and provides a reliable data foundation for subsequent operations.

[0109] S2, Based on energy data, determine the configuration constraints corresponding to the target configuration function. The target configuration function is a function aimed at minimizing carbon emission costs. The target configuration function includes a first sub-function, a second sub-function, and a third sub-function. The first sub-function is used to configure the carbon emissions of the energy system, the second sub-function is used to configure the carbon emission control cost of the energy system, and the third sub-function is used to configure the usage of the target energy in the energy system. The target energy is power generation energy with a carbon emission index less than the carbon emission threshold.

[0110] Furthermore, prior to S2, it also includes:

[0111] Determine the first weight index corresponding to the first sub-function, the second weight index corresponding to the second sub-function, and the third weight index corresponding to the third sub-function; construct the target configuration function based on the first sub-function, the second sub-function, the third sub-function, the first weight index, the second weight index, and the third weight index.

[0112] For example, a multi-objective optimization model can be established, and the energy configuration of an energy system can be generated through a multi-objective evolutionary algorithm or a robust optimization algorithm. The objective function can be expressed as:

[0113]

[0114] in, For energy allocation, Indicates the total carbon emissions of the system. This represents operating costs (i.e., carbon emission control costs). This indicates the penetration rate of renewable energy (i.e., the amount of target energy used). , , These represent the weighting coefficients, corresponding to the first weighting index, the second weighting index, and the third weighting index, respectively.

[0115] In addition, the constraints of the objective function may include: the balance constraints of energy supply and consumption, the constraints of key mineral resources, and the constraints of equipment operation.

[0116] Step Six: Visualize the optimization results, including energy structure evolution, carbon emission trends, changes in carbon assets, and emission reduction effects. Users can adjust energy dispatch strategies and carbon emission analysis based on system feedback to achieve improved energy efficiency and a continuous reduction in carbon emissions.

[0117] Furthermore, S2 may also include:

[0118] Determine the system location parameters corresponding to the energy system; based on energy data, determine the energy consumption and energy location parameters corresponding to multiple energy sources in the energy system, where the energy location parameters represent the corresponding energy acquisition locations; based on the energy consumption corresponding to each of the multiple energy sources, determine the carbon emission parameters corresponding to each of the multiple energy sources; based on the system location parameters, as well as the carbon emission parameters and energy location parameters corresponding to each of the multiple energy sources, determine the acquisition condition parameters corresponding to each of the multiple energy sources; based on the acquisition condition parameters corresponding to each of the multiple energy sources, determine the spatial stability constraints corresponding to the target configuration function, where the spatial stability constraints are used to constrain the carbon emission stability of the energy system from a spatial dimension, and the configuration constraints include spatial stability constraints.

[0119] For example, based on cleaned historical operating data, a time-series prediction model for energy and carbon emissions is constructed, and machine learning and time series analysis methods are used to predict the energy demand, power generation output and carbon emission trends of the energy system in different future periods, so as to obtain carbon emission parameters corresponding to multiple energy sources.

[0120] Furthermore, a carbon emission control cost accounting model can be established based on carbon emission monitoring results and carbon emission allocation data, as follows:

[0121] Total carbon emissions from the energy system It can be determined using the following formula:

[0122]

[0123] in, For equipment Carbon emissions from energy equipment.

[0124] Then combine carbon allocation data and carbon emission adjustment costs Assess the cost of carbon emission control The formula is:

[0125]

[0126] Furthermore, S2 may also include:

[0127] Based on energy data, identify the energy devices corresponding to multiple energy sources in the energy system; determine the aging characteristics corresponding to each of the multiple energy devices, whereby the aging characteristics represent the changes in the aging degree of the corresponding energy devices over time; based on the aging characteristics corresponding to each of the multiple energy devices, determine the carbon emission fluctuation index corresponding to each of the multiple energy devices; based on the carbon emission fluctuation index corresponding to each of the multiple energy devices, determine the time stability constraint corresponding to the target configuration function, whereby the time stability constraint is used to constrain the carbon emission stability of the energy system from the time dimension, and the configuration constraint includes the time stability constraint.

[0128] Furthermore, for the target configuration function, mineral availability constraints (i.e., key mineral resource constraints) can be set, where the mineral data includes multiple mineral types. This can be determined in the following ways:

[0129] Based on mineral data, the predetermined reserve data corresponding to multiple mineral types and the system scenarios corresponding to the new energy equipment system are determined. Based on the system scenarios corresponding to the new energy equipment system, the mineral demand parameters corresponding to the new energy equipment system are determined. Here, energy data includes mineral data, and mineral demand parameters represent the degree of demand of the new energy equipment system for each mineral type. Based on the mineral demand parameters corresponding to the new energy equipment system and the predetermined reserve data corresponding to multiple mineral types, the mineral availability constraints corresponding to the new energy equipment system are determined. Here, the mineral availability constraints are used to constrain the availability corresponding to multiple mineral types to constrain the carbon emission control costs. The mineral demand parameters include the mineral demand coefficients corresponding to multiple new energy devices.

[0130] For example, new energy equipment such as photovoltaic panels, wind turbines, and energy storage batteries rely on specific key minerals for their production and operation. These key minerals are those with an importance index greater than a importance threshold. The importance index indicates the degree of importance of the corresponding mineral to the production of new energy equipment, and multiple mineral types correspond one-to-one with multiple key minerals. Therefore, the availability of minerals limits the maximum deployment scale of these new energy equipment. Different types of new energy equipment have different impacts on the cost of carbon emission control. For example, solar and wind power have lower operating costs but higher initial investment costs, while the cost of energy storage systems is closely related to their energy density and cycle life. The availability of mineral resources directly determines which equipment can be deployed on a large scale and which equipment will have limited deployment, thus affecting the cost of carbon emission control. To address this, considering the resource constraints (i.e., mineral availability constraints) of key minerals in new energy installations and energy storage systems within the energy system, a mineral resource constraint model is established to ensure that the planning and scheduling of the energy system are carried out within the supply capacity of key minerals. In other words, the mineral availability constraint can be expressed as:

[0131]

[0132] in, Indicates the first The availability of key minerals, This is the mineral demand coefficient per unit of equipment (i.e., new energy equipment). This refers to the installed capacity of the corresponding device i (i.e., new energy equipment).

[0133] Furthermore, S2 may also include:

[0134] Based on energy data, determine the energy utilization methods corresponding to multiple energy sources in the energy system; determine the carbon emission indices, first trend features, and second trend features corresponding to the multiple energy utilization methods. The first trend feature represents the trend of the cost required to use energy using the corresponding energy utilization method over time, and the second trend feature represents the trend of the degree of adoption of the corresponding energy utilization method over time. Based on the carbon emission indices, first trend features, and second trend features corresponding to the multiple energy utilization methods, determine the control cost constraints corresponding to the target configuration function. The control cost constraints are used to constrain the carbon emission control costs of the energy system, and the configuration constraints include control cost constraints.

[0135] Furthermore, S2 may also include:

[0136] Based on energy data, determine the historical energy consumption corresponding to multiple energy sources, including the target energy source; determine the carbon emission reduction index and power output stability index corresponding to the target energy source; based on the historical energy consumption, carbon emission reduction index, and power output stability index corresponding to the multiple energy sources, determine the energy consumption constraints corresponding to the target configuration function.

[0137] S3, based on the target configuration function and configuration constraints, determines the target energy configuration corresponding to the energy system.

[0138] Furthermore, S3 may also include:

[0139] Based on the target configuration function and configuration constraints, multiple system energy configurations corresponding to the energy system are determined. Following the execution order of multiple iterations of the multiple system energy configurations, a reference energy configuration is determined for each iteration, where the reference energy configuration represents the energy configuration that minimizes the carbon emission cost of the energy system among all energy configurations obtained through iteration. Based on the reference energy configuration at each iteration, the multiple system energy configurations are adjusted to obtain adjusted multiple system energy configurations until multiple iterations are completed, resulting in the target iterative adjusted multiple system resource configurations. Based on the target iterative adjusted multiple system resource configurations, the target energy configuration corresponding to the energy system is determined.

[0140] (ii) Energy allocation determination system of the energy system:

[0141] Figure 3 This is a schematic diagram of the energy configuration determination system structure in an optional embodiment of the present invention, such as... Figure 3 As shown, the energy configuration determination system for an energy system is used to implement the energy configuration determination method for an energy system, including:

[0142] The Energy Integration and Analysis Module is used to acquire energy data from the energy system, such as the collection, cleaning, and fusion of energy consumption, equipment operation, and carbon emission data within the system, enabling real-time monitoring and analysis of the energy system. Specifically, the module utilizes data cleaning and preprocessing algorithms, carbon emission accounting algorithms, and time series forecasting and visualization technologies to uniformly collect and integrate energy consumption, power generation, and carbon emission data from various devices within the new energy equipment system. This allows for comprehensive, accurate, and real-time monitoring and analysis of the new energy equipment system, providing data support for energy allocation decisions.

[0143] The carbon emission control cost accounting module is used to perform total carbon emission accounting analysis and carbon emission control cost assessment based on carbon emission data and carbon allocation data. Specifically, based on energy emission data, carbon emission data, and system carbon emission control cost data collected from various devices, the module constructs a carbon emission allocation and reduction calculation model. This enables dynamic analysis of the carbon emission control cost of the new energy equipment system and can connect to and statistically analyze relevant data such as system carbon emissions and energy consumption, automatically completing the total carbon emission control cost calculation to provide an analytical basis for optimizing energy allocation in the energy system.

[0144] Carbon emission configuration module: This module is used to determine the energy configuration of an energy system based on a multi-objective optimization model, such as energy structure adjustment and carbon emission reduction methods. When performing objective optimization, the multi-objective optimization model can consider constraints from key mineral resources. Specifically, the carbon emission configuration module includes equipment carbon emission optimization algorithms. Based on real-time monitoring of system carbon emissions and energy, it identifies and optimizes the energy efficiency and carbon emissions of new energy equipment systems according to their actual needs.

[0145] Furthermore, the energy fusion analysis module is responsible for collecting and integrating energy consumption, power generation, and carbon emission data from various devices within the new energy equipment system. Combined with data cleaning algorithms and time series prediction models, it forms a unified data support platform. For example, users install data acquisition and data detection modules on the devices or systems requiring data collection and testing. This module can automatically collect real-time energy consumption and power generation data from key equipment during mineral development from the connected devices or systems, and automatically calculate the carbon emission data of the corresponding equipment using relevant carbon emission accounting algorithms, while also detecting outliers in the data in real time. On the system's overall overview page for the new energy equipment system, from left to right and top to bottom, the system displays overall carbon emission information, carbon emission intensity ranking information for each part of the system, overall system location information, information for each part of the system (such as workshops), overall system energy consumption curve information, and system energy structure information. The energy fusion analysis module for the new energy equipment system enables comprehensive, accurate, and real-time detection and analysis of the equipment within the new energy equipment system, providing data support for optimizing the energy configuration of the energy system.

[0146] Furthermore, the carbon emission control cost accounting module, based on the carbon emission accounting model and carbon emission control cost algorithm, calculates the total carbon emissions, carbon allocation, and emission reduction of each piece of equipment, process, and the entire system, achieving dynamic carbon emission control cost assessment. The carbon emission control cost accounting module can establish a carbon emission database for each part of the system (such as each workshop), recording data on a 1-hour time scale, including carbon emissions, electricity consumption, year-on-year and month-on-month changes in carbon emissions and electricity consumption, providing a basis for energy system energy allocation optimization analysis.

[0147] Furthermore, the carbon emission configuration module, combining key mineral resource constraints, equipment operation constraints, and carbon emission costs, establishes a multi-objective optimization model to achieve joint optimization of energy structure, emission reduction pathways, and energy storage configuration. The carbon emission configuration module primarily displays the key energy consumption operation status of each workshop process within the system, carbon emission contribution rate, carbon emission volume of key equipment, overall carbon emission reduction information, and carbon emission information for each workshop process, enabling precise monitoring of the carbon emission status of the new energy equipment system. The overall carbon emission reduction information includes hourly, daily, and monthly carbon emission reduction data, and the new energy output information can also include hourly, daily, and monthly new energy output.

[0148] The above optional implementation methods can achieve at least the following beneficial effects:

[0149] (1) Compared with related technologies, the present invention constructs a target configuration function. Since the target configuration function includes a first sub-function, a second sub-function, and a third sub-function, the first sub-function focuses on configuring the carbon emissions of the energy system, the second sub-function focuses on configuring the carbon emission control cost of the energy system, and the third sub-function is used to configure the usage of power generation energy (target energy) with a carbon emission index less than a threshold in the energy system. Through the target configuration function, the coupled analysis of carbon emissions, carbon emission control cost, and target energy usage can be achieved. Combined with the configuration constraints determined according to the actual situation of the energy system, the target energy configuration of the energy system can be determined, realizing the unified optimization of carbon emissions, carbon emission control cost, and target energy usage. This reduces the carbon emission cost of the energy system from the overall level, thereby solving the technical problem in related technologies where the energy configuration of the energy system is inaccurate, resulting in high carbon emission costs.

[0150] (2) Compared with related technologies, this invention can clarify the overall spatial characteristics of the energy system by determining the system location parameters, providing a basic positioning for subsequent analysis; by determining the energy consumption and energy location parameters based on energy data, the scale of energy consumption and acquisition location can be accurately grasped, providing data support for carbon emission quantification; by determining the carbon emission parameters based on energy consumption, the carbon emission of different energy sources at each stage can be quantified; by determining the acquisition condition parameters based on the system location, carbon emission and energy location parameters, the stability and reliability of energy acquisition can be assessed, avoiding carbon emission fluctuations due to unstable acquisition; finally, by determining the spatial stability constraints based on the acquisition condition parameters, the stability of carbon emissions of the energy system can be limited from the spatial dimension, which helps to optimize the energy acquisition and distribution strategy in a targeted manner, and thus effectively control carbon emission costs.

[0151] (3) Compared with related technologies, this invention identifies the equipment corresponding to multiple energy sources in the energy system based on energy data, and after clarifying the aging characteristics of the equipment, it can quantify the impact of equipment aging on carbon emissions. Calculating the carbon emission fluctuation index based on aging characteristics allows for accurate assessment of the carbon emission stability of equipment at different aging stages. Furthermore, setting time stability constraints based on the carbon emission fluctuation index limits carbon emission fluctuations from a time perspective, ensuring that the energy system avoids increased carbon emissions due to equipment aging during long-term operation, thus providing an analytical basis for minimizing carbon emission costs in the future.

[0152] (4) Compared with related technologies, this invention determines the energy utilization methods corresponding to multiple energy sources in the energy system based on energy data, and can clarify the utilization forms such as conversion and consumption of different energy sources in the system. By analyzing the carbon emission index, first trend characteristics and second trend characteristics of these utilization methods, control cost constraints are determined, which can ensure that while optimizing carbon emissions, the increase in carbon emission control costs caused by adopting high-cost or high-carbon-emission utilization methods is avoided, thereby achieving the optimization of carbon emission costs of the energy system.

[0153] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0154] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0155] Example 2

[0156] According to an embodiment of the present invention, an apparatus for implementing the energy configuration determination method of the above-described energy system is also provided. Figure 4 This is a structural block diagram of an energy configuration determination device for an energy system according to an embodiment of the present invention, such as... Figure 4 As shown, the device includes: an acquisition module 402, a first determination module 404, and a second determination module 406. The device will be described in detail below.

[0157] The acquisition module 402 is used to acquire energy data of the energy system; the first determination module 404, connected to the acquisition module 402, is used to determine the configuration constraints corresponding to the target configuration function based on the energy data, wherein the target configuration function includes a first sub-function, a second sub-function, and a third sub-function, the first sub-function is used to configure the carbon emissions of the energy system, the second sub-function is used to configure the carbon emission control cost of the energy system, and the third sub-function is used to configure the usage of the target energy in the energy system, the target energy being power generation energy with a carbon emission index less than the carbon emission threshold; the second determination module 406, connected to the first determination module 404, is used to determine the target energy configuration corresponding to the energy system based on the target configuration function and the configuration constraints.

[0158] It should be noted that the above-mentioned acquisition module 402, the first determination module 404 and the second determination module 406 correspond to steps S102 to S106 in the energy configuration determination method for implementing an energy system. The multiple modules and the corresponding steps are the same in terms of implementation instances and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0159] Example 3

[0160] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to execute instructions to implement the energy configuration determination method of the energy system of any of the above embodiments.

[0161] Example 4

[0162] According to another aspect of the present invention, a computer-readable storage medium is also provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the energy configuration determination method of the energy system described above.

[0163] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0164] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0165] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0166] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0167] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0168] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0169] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining the energy configuration of an energy system, characterized in that, include: Acquire energy data from the energy system; Based on the energy data, configuration constraints corresponding to the target configuration function are determined. The target configuration function is a function aimed at minimizing carbon emission costs. The target configuration function includes a first sub-function, a second sub-function, and a third sub-function. The first sub-function is used to configure the carbon emissions of the energy system, the second sub-function is used to configure the carbon emission control cost of the energy system, and the third sub-function is used to configure the usage of the target energy in the energy system. The target energy is power generation energy with a carbon emission index less than the carbon emission threshold. Based on the target configuration function and the configuration constraints, the target energy configuration corresponding to the energy system is determined.

2. The method according to claim 1, characterized in that, Determining the target energy configuration corresponding to the energy system based on the target configuration function and the configuration constraints includes: Based on the target configuration function and the configuration constraints, determine multiple system energy configurations corresponding to the energy system. According to the execution order of multiple iterations of the multiple system energy configurations, for any one of the multiple iterations of the multiple system energy configurations, a reference energy configuration is determined under any one iteration, wherein the reference energy configuration is the energy configuration that minimizes the carbon emission cost of the energy system among all energy configurations obtained by the multiple system energy configurations through iteration; Based on the reference energy configuration under any iteration, the energy configurations of the multiple systems are adjusted to obtain the adjusted energy configurations of the multiple systems. This process is repeated until multiple iterations are completed to obtain the adjusted resource configurations of the multiple systems after the target iteration. Based on the target iteratively adjusted multiple system resource configurations, the target energy configuration corresponding to the energy system is determined.

3. The method according to claim 1, characterized in that, The step of determining the configuration constraints corresponding to the target configuration function based on the energy data includes: Determine the system location parameters corresponding to the energy system; Based on the energy data, the energy consumption and energy location parameters corresponding to multiple energy sources in the energy system are determined, wherein the energy location parameters represent the corresponding energy acquisition location; Based on the energy consumption corresponding to each of the multiple energy sources, determine the carbon emission parameters corresponding to each of the multiple energy sources; Based on the system location parameters, and the carbon emission parameters and energy location parameters corresponding to the multiple energy sources, the acquisition condition parameters corresponding to the multiple energy sources are determined respectively; Based on the acquisition condition parameters corresponding to the multiple energy sources, spatial stability constraints corresponding to the target configuration function are determined. The spatial stability constraints are used to constrain the carbon emission stability of the energy system from a spatial dimension, and the configuration constraints include the spatial stability constraints.

4. The method according to claim 1, characterized in that, The step of determining the configuration constraints corresponding to the target configuration function based on the energy data includes: Based on the energy data, determine the energy devices corresponding to each of the multiple energy sources in the energy system; Aging characteristics are determined for each of multiple energy devices, wherein the aging characteristics are used to represent the changes in the aging degree of the corresponding energy devices over time; Based on the aging characteristics of the various energy devices, carbon emission fluctuation indices are determined for each of the various energy devices. Based on the carbon emission fluctuation index corresponding to the plurality of energy devices, a time stability constraint corresponding to the target configuration function is determined, wherein the time stability constraint is used to constrain the carbon emission stability of the energy system from the time dimension, and the configuration constraint includes the time stability constraint.

5. The method according to claim 1, characterized in that, The step of determining the configuration constraints corresponding to the target configuration function based on the energy data includes: Based on the energy data, determine the energy utilization methods corresponding to the multiple energy sources in the energy system; Determine carbon emission indices corresponding to multiple energy utilization methods, a first trend feature and a second trend feature. The first trend feature is used to represent the trend of the cost required to use energy when adopting the corresponding energy utilization method over time, and the second trend feature is used to represent the trend of the degree of adoption of the corresponding energy utilization method over time. Based on the carbon emission indices, first trend characteristics, and second trend characteristics corresponding to the various energy utilization methods, control cost constraints corresponding to the target configuration function are determined, wherein the control cost constraints are used to constrain the carbon emission control costs of the energy system, and the configuration constraints include the control cost constraints.

6. The method according to claim 1, characterized in that, The step of determining the configuration constraints corresponding to the target configuration function based on the energy data includes: Based on the energy data, determine the historical energy consumption corresponding to multiple energy sources, wherein the multiple energy sources include the target energy source; Determine the carbon emission reduction index and power output stability index corresponding to the target energy source; Based on the historical energy consumption corresponding to the multiple energy sources, the carbon emission reduction index, and the power output stability index, the energy consumption constraint corresponding to the target configuration function is determined.

7. The method according to any one of claims 1 to 6, characterized in that, Before determining the configuration constraints corresponding to the target configuration function based on the energy data, the method further includes: Determine the first weight index corresponding to the first sub-function, the second weight index corresponding to the second sub-function, and the third weight index corresponding to the third sub-function; Based on the first sub-function, the second sub-function, the third sub-function, the first weight index, the second weight index, and the third weight index, a target configuration function is constructed.

8. An energy configuration determination device for an energy system, characterized in that, include: The acquisition module is used to acquire energy data from the energy system. The first determining module is used to determine the configuration constraints corresponding to the target configuration function based on the energy data. The target configuration function includes a first sub-function, a second sub-function, and a third sub-function. The first sub-function is used to configure the carbon emissions of the energy system, the second sub-function is used to configure the carbon emission control cost of the energy system, and the third sub-function is used to configure the usage of the target energy in the energy system. The target energy is a power generation energy with a carbon emission index less than the carbon emission threshold. The second determining module is used to determine the target energy configuration corresponding to the energy system based on the target configuration function and the configuration constraints.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the energy configuration determination method for the energy system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the energy configuration determination method of the energy system as described in any one of claims 1 to 7.

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