Hybrid energy supply control method and system for low carbon demand scenarios

Abstract

This invention relates to the field of hybrid energy supply optimization control technology, and discloses a hybrid energy supply control method and system applied to low-carbon demand scenarios. The method involves acquiring energy demand information from multiple energy demand objects, energy supply information from multiple energy supply objects, and the energy supply cost value; the energy supply cost value is calculated based on the energy supply attribute information of each energy supply object; using the energy demand information of multiple energy demand objects as supply constraints and constructing a cost target based on the energy supply cost value of multiple energy supply objects, an energy supply allocation optimization model is established; the energy supply allocation optimization model is solved to obtain an energy supply allocation scheme; and according to the energy supply allocation scheme, a corresponding hybrid energy supply strategy is generated for each energy demand object to indicate one or more target energy supply objects to supply energy to that energy demand object. Therefore, implementing this invention can improve the flexibility of hybrid energy supply control and reduce energy supply operating costs while meeting all load demands.

Description

Technical Field

[0001] This invention relates to the field of hybrid energy supply optimization control technology, and in particular to a hybrid energy supply control method and system applied to low-carbon demand scenarios. Background Technology

[0002] In scenarios such as industrial parks and microgrids that involve multiple energy suppliers (such as photovoltaics, energy storage, and the power grid) and multiple energy loads, it is usually necessary to coordinate the various energy suppliers to provide energy to different loads in order to achieve low-carbon economic operation.

[0003] Currently, the common practice is to rely on the experience of operators or default priorities, such as "prioritizing the use of photovoltaic power generation," to roughly allocate energy supply. However, in practice, it has been found that this experience-based or default priority-based approach is difficult to adjust the allocation strategy in real time and with precision when faced with dynamic situations such as fluctuations in renewable energy output and changes in load demand, in order to reduce energy supply operating costs.

[0004] Therefore, it is particularly important to propose a technical solution that can meet all load demands while improving the flexibility of hybrid energy supply control and reducing energy supply operating costs. Summary of the Invention

[0005] This invention provides a hybrid energy supply control method and system for low-carbon demand scenarios, which can improve the flexibility of hybrid energy supply control and reduce energy supply operation costs while meeting all load demands.

[0006] To address the aforementioned technical problems, the first aspect of this invention discloses a hybrid energy supply control method applied to low-carbon demand scenarios, the method comprising: The system acquires energy demand information from multiple energy demanders, as well as energy supply information and energy supply cost from multiple energy providers; wherein the energy supply cost is calculated based on the energy supply attribute information of each energy provider. An energy allocation optimization model is established by using the energy demand information of multiple energy demand objects as supply constraints and constructing a cost target based on the energy supply value of multiple energy supply objects. Solve the energy distribution optimization model to obtain the energy distribution scheme; According to the energy supply allocation scheme, a corresponding hybrid energy supply strategy is generated for each energy demand object, and the hybrid energy supply strategy is used to indicate one or more target energy supply objects to supply energy to the energy demand object.

[0007] As an optional implementation, in the first aspect of the present invention, before obtaining the energy demand information of multiple energy demanders and the energy supply information and energy supply cost of multiple energy providers, the method further includes: For each energy provider, determine the energy supply attribute information of the energy provider. The energy supply attribute information is used to characterize the energy supply characteristics of the energy provider from multiple dimensions, including at least the energy type dimension and the low-carbon attribute dimension. Based on preset standardization rules, the energy supply attribute information of each energy supply object is converted into a unified energy supply attribute vector. The standardization rules are used to map the original attribute data of different formats and dimensions to a predetermined numerical range and data structure. The energy supply attribute vector is stored in the energy supply object feature library, which is used to provide the energy supply attribute information required to calculate the energy supply value in the step of obtaining energy demand information of multiple energy demand objects and energy supply information and energy supply value of multiple energy supply objects.

[0008] As an optional implementation, in the first aspect of the present invention, the step of calculating the energy supply cost includes: For each energy-providing object, the energy-providing attribute vector corresponding to the energy-providing object is obtained from the energy-providing object feature library; based on the energy-providing attribute vector of the energy-providing object, multiple preset cost components are calculated, wherein each cost component is used to quantify a specific cost of the energy-providing object in the energy-providing process from a corresponding dimension, and the cost component includes at least an economic cost component, an environmental cost component, and a spatiotemporal cost component; According to the preset integration rules, all the cost components of the calculated energy-providing object are integrated to generate the energy supply cost value of the energy-providing object. The integration rules are used to define the contribution weight and operation relationship of each cost component in the integrated calculation.

[0009] As an optional implementation, in the first aspect of the present invention, the step of integrating all the cost components of the calculated energy-providing object according to a preset integration rule to generate the energy supply cost of the energy-providing object includes: Obtain the demand characteristics under the current energy demand scenario, wherein the demand characteristics are used to characterize the energy consumption characteristics and priority preferences of the energy demand objects in the current energy demand scenario; Based on the demand characteristics and the preset weight mapping relationship, the dynamic weight corresponding to each cost component of the energy supply object is determined, wherein the dynamic weight is used to characterize the proportion of the cost dimension represented by the cost component in the comprehensive calculation under the current energy demand scenario; Based on the determined dynamic weights, the cost components are weighted and comprehensively calculated to generate the energy supply cost value of the energy-providing object.

[0010] As an optional implementation, in the first aspect of the present invention, the step of establishing an energy allocation optimization model by using the energy demand information of the plurality of energy demand objects as supply constraints and constructing a cost target based on the energy supply cost of the plurality of energy supply objects includes: Define a set of decision variables to characterize the energy supply and distribution relationship, wherein each decision variable in the set of decision variables is used to represent a planned energy supply from one energy provider to one energy demander; Based on the set of decision variables and the energy supply information of the multiple energy providers, a supply capacity constraint is established, wherein the supply capacity constraint is used to ensure that the planned total energy supply of each energy provider does not exceed its own energy supply capacity; Based on the set of decision variables and the energy demand information of multiple energy demand objects, a demand satisfaction constraint is established, wherein the demand satisfaction constraint is used to ensure that the total planned energy supply received by each energy demand object is not less than its own energy demand. Based on the set of decision variables and the energy supply cost of the multiple energy supply objects, a cost objective function is constructed to form an energy allocation optimization model. The cost objective function is used to calculate the total energy supply cost of all the energy supply objects under the constraints of supply capacity and demand satisfaction.

[0011] As an optional implementation, in the first aspect of the present invention, solving the energy allocation optimization model to obtain an energy allocation scheme includes: The energy allocation optimization model is subjected to normalization transformation to generate an optimization problem description in a standard solution format. The normalization transformation is used to uniformly convert the set of decision variables, the supply capacity constraints, the demand satisfaction constraints, and the cost objective function into a mathematical expression form supported by the objective solver. Based on the optimization problem description in the standard solution format, a preset optimization solver is invoked to perform the solution operation to obtain the original solution result, wherein the original solution result includes the optimal solution value of each decision variable in the set of decision variables; The original solution results are interpreted and aggregated to generate an energy allocation scheme. The interpretation and aggregation process is used to map the optimal solution value of each decision variable back to the corresponding energy allocation amount between the energy supply object and the energy demand object, and to organize all the mapped energy allocation amounts according to the energy demand object to form a complete energy allocation relationship mapping.

[0012] As an optional implementation, in the first aspect of the present invention, the step of generating a corresponding hybrid energy supply strategy for each energy-demanding object according to the energy supply allocation scheme includes: The energy supply allocation scheme is analyzed to extract all energy supply allocation records for each energy demand object, wherein each energy supply allocation record is used to represent the planned energy supply from an energy provider to the energy demand object; For each energy demand object, based on all the extracted energy allocation records, one or more target energy supply objects are identified to supply energy to it, and the energy supply contribution of each target energy supply object is calculated according to the planned energy supply corresponding to each target energy supply object. Based on the preset strategy generation rules and the energy supply contribution of each target energy supply object, a hybrid energy supply strategy is generated for each energy demand object. The strategy generation rules are used to define the generation logic of at least one strategy element among energy supply contribution, energy supply timing, energy supply switching logic, and backup energy supply arrangement.

[0013] A second aspect of this invention discloses a hybrid energy supply control system applied to low-carbon demand scenarios, the system comprising: The acquisition module is used to acquire energy demand information from multiple energy demand objects, as well as energy supply information and energy supply cost from multiple energy supply objects; wherein the energy supply cost is calculated based on the energy supply attribute information of each energy supply object; A module is established to construct a cost target based on the energy demand information of multiple energy demand objects as supply constraints and the energy supply cost of multiple energy supply objects, and to establish an energy allocation optimization model. The solver module is used to solve the energy allocation optimization model to obtain the energy allocation scheme; The generation module is used to generate a corresponding hybrid energy supply strategy for each energy demand object according to the energy supply allocation scheme. The hybrid energy supply strategy is used to indicate one or more target energy supply objects to supply energy to the energy demand object.

[0014] As an optional implementation, in a second aspect of the invention, the system further includes: The determination module is used to determine the energy supply attribute information of each energy supply object before the acquisition module acquires the energy demand information of multiple energy demand objects and the energy supply information and energy supply cost of multiple energy supply objects. The energy supply attribute information is used to characterize the energy supply characteristics of the energy supply object from multiple dimensions, and the multiple dimensions include at least the energy type dimension and the low carbon attribute dimension. The conversion module is used to convert the energy supply attribute information of each energy supply object into a unified energy supply attribute vector based on preset standardization rules. The standardization rules are used to map the original attribute data of different formats and dimensions to a predetermined numerical range and data structure. The storage module is used to store the energy supply attribute vector into the energy supply object feature library. The energy supply object feature library is used to provide the energy supply attribute information required to calculate the energy supply value in the step of obtaining energy demand information of multiple energy demand objects and energy supply information and energy supply value of multiple energy supply objects.

[0015] As an optional implementation, in a second aspect of the invention, the step of calculating the energy supply cost includes: For each energy-providing object, the energy-providing attribute vector corresponding to the energy-providing object is obtained from the energy-providing object feature library; based on the energy-providing attribute vector of the energy-providing object, multiple preset cost components are calculated, wherein each cost component is used to quantify a specific cost of the energy-providing object in the energy-providing process from a corresponding dimension, and the cost component includes at least an economic cost component, an environmental cost component, and a spatiotemporal cost component; According to the preset integration rules, all the cost components of the calculated energy-providing object are integrated to generate the energy supply cost value of the energy-providing object. The integration rules are used to define the contribution weight and operation relationship of each cost component in the integrated calculation.

[0016] As an optional implementation, in a second aspect of the invention, the step of integrating all the cost components of the calculated energy-providing object according to a preset integration rule to generate the energy supply cost of the energy-providing object includes: Obtain the demand characteristics under the current energy demand scenario, wherein the demand characteristics are used to characterize the energy consumption characteristics and priority preferences of the energy demand objects in the current energy demand scenario; Based on the demand characteristics and the preset weight mapping relationship, the dynamic weight corresponding to each cost component of the energy supply object is determined, wherein the dynamic weight is used to characterize the proportion of the cost dimension represented by the cost component in the comprehensive calculation under the current energy demand scenario; Based on the determined dynamic weights, the cost components are weighted and comprehensively calculated to generate the energy supply cost value of the energy-providing object.

[0017] As an optional implementation, in the second aspect of the present invention, the establishment module uses the energy demand information of the multiple energy demand objects as supply constraints and constructs a cost target based on the energy supply cost of the multiple energy supply objects. The specific method for establishing the energy allocation optimization model includes: Define a set of decision variables to characterize the energy supply and distribution relationship, wherein each decision variable in the set of decision variables is used to represent a planned energy supply from one energy provider to one energy demander; Based on the set of decision variables and the energy supply information of the multiple energy providers, a supply capacity constraint is established, wherein the supply capacity constraint is used to ensure that the planned total energy supply of each energy provider does not exceed its own energy supply capacity; Based on the set of decision variables and the energy demand information of multiple energy demand objects, a demand satisfaction constraint is established, wherein the demand satisfaction constraint is used to ensure that the total planned energy supply received by each energy demand object is not less than its own energy demand. Based on the set of decision variables and the energy supply cost of the multiple energy supply objects, a cost objective function is constructed to form an energy allocation optimization model. The cost objective function is used to calculate the total energy supply cost of all the energy supply objects under the constraints of supply capacity and demand satisfaction.

[0018] As an optional implementation, in the second aspect of the present invention, the specific method by which the solving module solves the energy allocation optimization model to obtain the energy allocation scheme includes: The energy allocation optimization model is subjected to normalization transformation to generate an optimization problem description in a standard solution format. The normalization transformation is used to uniformly convert the set of decision variables, the supply capacity constraints, the demand satisfaction constraints, and the cost objective function into a mathematical expression form supported by the objective solver. Based on the optimization problem description in the standard solution format, a preset optimization solver is invoked to perform the solution operation to obtain the original solution result, wherein the original solution result includes the optimal solution value of each decision variable in the set of decision variables; The original solution results are interpreted and aggregated to generate an energy allocation scheme. The interpretation and aggregation process is used to map the optimal solution value of each decision variable back to the corresponding energy allocation amount between the energy supply object and the energy demand object, and to organize all the mapped energy allocation amounts according to the energy demand object to form a complete energy allocation relationship mapping.

[0019] As an optional implementation, in a second aspect of the present invention, the specific manner in which the generation module generates a corresponding hybrid energy provision strategy for each energy demand object according to the energy supply allocation scheme includes: The energy supply allocation scheme is analyzed to extract all energy supply allocation records for each energy demand object, wherein each energy supply allocation record is used to represent the planned energy supply from an energy provider to the energy demand object; For each energy demand object, based on all the extracted energy allocation records, one or more target energy supply objects are identified to supply energy to it, and the energy supply contribution of each target energy supply object is calculated according to the planned energy supply corresponding to each target energy supply object. Based on the preset strategy generation rules and the energy supply contribution of each target energy supply object, a hybrid energy supply strategy is generated for each energy demand object. The strategy generation rules are used to define the generation logic of at least one strategy element among energy supply contribution, energy supply timing, energy supply switching logic, and backup energy supply arrangement.

[0020] A third aspect of this invention discloses another hybrid energy supply control system applied to low-carbon demand scenarios, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the hybrid energy supply control method disclosed in the first aspect of the present invention for low-carbon demand scenarios.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the hybrid energy supply control method for low-carbon demand scenarios disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: In this embodiment of the invention, energy demand information of multiple energy demand objects and energy supply information and energy supply cost of multiple energy supply objects are obtained. The energy supply cost is calculated based on the energy supply attribute information of each energy supply object. Using the energy demand information of multiple energy demand objects as supply constraints and the energy supply cost of multiple energy supply objects as the basis for constructing a cost target, an energy allocation optimization model is established. The energy allocation optimization model is solved to obtain an energy allocation scheme. Based on the energy allocation scheme, a corresponding hybrid energy supply strategy is generated for each energy demand object. The hybrid energy supply strategy is used to indicate one or more target energy supply objects to supply energy to the energy demand object. Therefore, implementing this invention can achieve systematic and quantitative optimization of the energy allocation relationship between multiple energy supply objects and multiple energy demand objects by obtaining comprehensive supply and demand information and constructing an optimization model based on "energy supply cost." This elevates traditional energy supply decisions that rely on human experience or simple rules to intelligent decisions based on mathematical models and optimization algorithms, thereby significantly improving the scientific nature, economy, and overall energy efficiency of energy allocation. While meeting all load demands, it also improves the flexibility of hybrid energy supply control and reduces energy supply operating costs. It can establish an "energy supply and distribution optimization model" using "energy demand information" as a hard constraint, ensuring that any generated energy supply and distribution scheme can strictly meet the energy needs of all energy demanders, thereby fundamentally guaranteeing the reliability and security of the energy supply system and laying a solid foundation for achieving a stable and reliable energy supply. It can construct a "cost target" based on the "energy cost-value" that integrates multiple dimensions of costs and perform optimization solutions, guiding the system to automatically find the allocation method with the lowest total energy supply cost or the best overall benefits while meeting demand. This helps reduce the overall operating cost of the system and promotes the rational substitution of high-cost, high-emission energy sources, thus directly supporting the achievement of low-carbon economic operation goals. It can automatically generate a "hybrid energy supply strategy" for each demander based on the "energy supply and distribution scheme" obtained from the optimization solution, realizing the transformation from global optimization results to individualized, executable control commands, thereby improving the pertinence and operability of the control strategy, and enabling the optimization results to directly and effectively guide the operation and scheduling of the actual system. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart of a hybrid energy supply control method for low-carbon demand scenarios disclosed in an embodiment of the present invention; Figure 2 This is a schematic flowchart of another hybrid energy supply control method for low-carbon demand scenarios disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of a hybrid energy supply control system for low-carbon demand scenarios disclosed in an embodiment of the present invention; Figure 4 This is a schematic diagram of another hybrid energy supply control system for low-carbon demand scenarios disclosed in an embodiment of the present invention; Figure 5 This is a schematic diagram of another hybrid energy supply control system for low-carbon demand scenarios disclosed in this embodiment of the invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0026] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] This invention discloses a hybrid energy supply control method and system applied to low-carbon demand scenarios. By acquiring comprehensive supply and demand information and constructing an optimization model based on the "energy supply cost," it achieves systematic and quantitative optimization of the energy supply allocation relationship between multiple energy providers and multiple energy demanders. This elevates traditional energy supply decisions, which rely on manual experience or simple rules, to intelligent decisions based on mathematical models and optimization algorithms. Consequently, it significantly improves the scientific rigor, economy, and overall energy efficiency of energy allocation, meeting all load demands while enhancing the flexibility of hybrid energy supply control and reducing energy supply operating costs. The invention establishes an "energy supply allocation optimization model" using "energy demand information" as a hard constraint, ensuring that any generated energy supply allocation scheme strictly meets the energy needs of all energy demanders. This fundamentally guarantees the reliability and security of the energy supply system, laying a solid foundation for a stable and reliable energy supply. It can construct and optimize a "cost target" based on the "energy supply value" that integrates multiple dimensions of costs. This guides the system to automatically find the allocation method with the lowest total energy supply cost or the best overall benefits while meeting demand. This helps reduce the overall operating cost of the system and promotes the rational substitution of high-cost, high-emission energy sources, thereby directly supporting the achievement of low-carbon economic operation goals. It can automatically generate a "hybrid energy supply strategy" for each demand object based on the "energy supply allocation scheme" obtained from the optimization solution. This realizes the transformation from global optimization results to individualized, executable control commands, thereby improving the pertinence and operability of the control strategy. This allows the optimization results to directly and effectively guide the operation and scheduling of the actual system. Detailed explanations follow.

[0029] Example 1 Please see Figure 1 , Figure 1 This is a schematic flowchart of a hybrid energy supply control method for low-carbon demand scenarios disclosed in an embodiment of the present invention. Figure 1 The described hybrid energy supply control method for low-carbon demand scenarios can be applied to energy devices, and also to intelligent devices associated with energy devices. These intelligent devices include, but are not limited to, one or more of the following: switching devices, cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. This invention does not limit the scope of these applications. Figure 1 As shown, this hybrid energy supply control method applied to low-carbon demand scenarios may include the following operations: 101. Obtain energy demand information from multiple energy demanders, as well as energy supply information and energy supply cost from multiple energy providers; wherein, the energy supply cost is calculated based on the energy supply attribute information of each energy provider. In this embodiment of the invention, optionally, for the acquisition of information: "multiple energy demand objects" can be different buildings, production lines, or power-consuming units within a microgrid or industrial park. "Energy demand information" includes at least the predicted or planned load demand (unit: kWh) for electricity, heat, etc., within one or more future scheduling periods (e.g., the next 24 hours, at 15-minute intervals). "Multiple energy supply objects" can include photovoltaic power generation systems, wind turbines, battery energy storage systems, gas-fired combined heat and power units, interfaces for purchasing electricity from the upper-level grid, etc. "Energy supply information" includes at least the predicted or maximum output power (unit: kW) for each energy supply object within the same future time period. "Energy supply cost" is a comprehensive unit cost indicator (e.g., yuan / kWh), calculated based on the "energy supply attribute information" of each energy supply object itself, which is pre-calculated and stored and retrieved in this step.

[0030] In this embodiment of the invention, as an optional implementation, before obtaining the energy demand information of multiple energy demanders and the energy supply information and energy supply value of multiple energy providers, the method further includes: For each energy provider, determine the energy supply attribute information of the energy provider. The energy supply attribute information is used to characterize the energy supply characteristics of the energy provider from multiple dimensions, including at least the energy type dimension and the low-carbon attribute dimension. Based on preset standardization rules, the energy supply attribute information of each energy supply object is converted into a unified energy supply attribute vector. The standardization rules are used to map the original attribute data of different formats and dimensions to a predetermined numerical range and data structure. The energy supply attribute vector is stored in the energy supply object feature library. The energy supply object feature library is used to provide the energy supply attribute information required to calculate the energy supply value in the steps of obtaining energy demand information of multiple energy demand objects and energy supply information and energy supply value of multiple energy supply objects.

[0031] In this embodiment of the invention, optionally, for determining energy supply attribute information: for each energy-providing object (such as a photovoltaic array or a gas turbine), its multi-dimensional raw data is collected. Attributes of the energy type dimension may include: energy type (such as solar energy, natural gas, grid electricity), and energy carrier form (such as alternating current, direct current, and steam). Attributes of the low-carbon attribute dimension may include: carbon dioxide equivalent emission coefficient per unit energy output (gCO2e / kWh), and whether it is a zero-carbon energy source. Furthermore, the "multiple dimensions" can also be expanded to include dispatchability dimensions (such as minimum start-up and shutdown time, ramp rate), geographical location dimensions (coordinates within the park), and lifespan loss dimensions (charge-discharge cycle loss coefficient), etc.

[0032] Those skilled in the art should understand that the above-described dimensional classifications and included attributes are merely examples. The energy type dimension aims to distinguish between primary energy sources (such as fossil fuels and renewable energy) and secondary energy forms (such as electricity, heat, and hydrogen). The low-carbon attribute dimension aims to quantify the environmental impact of the energy supply process, with carbon emission-related indicators at its core, but it can also be extended to other pollutant emission indicators. The specific selection and classification of dimensions can be flexibly added, deleted, and defined according to the assessment needs of actual application scenarios.

[0033] Further optional, for standardization transformation: due to the different formats and dimensions of the original data across different dimensions (e.g., emission coefficients are numerical, energy types are text), they cannot be directly used for calculation. Pre-defined standardization rules perform a unified transformation. For example, the text-type "energy type" is converted to a binary vector using one-hot encoding; the numerical type "emission coefficient" is scaled to the [0,1] range using max-min normalization; all transformed sub-vectors are concatenated in a fixed order to ultimately form a unique, fixed-length energy supply attribute vector for each energy-providing object. This vector is a machine-readable and computable standardized representation.

[0034] Alternatively, for building a feature library: the energy supply attribute vectors of all energy supply objects are associated with their unique identifiers (such as device IDs) and persistently stored in a database or file, forming an energy supply object feature library. This feature library serves as the system's basic data service; when it is necessary to calculate the cost of any energy supply object, its standardized attribute vectors can be retrieved in real time.

[0035] As can be seen, implementing this optional embodiment can improve the comprehensiveness and structure of the description of energy supply object characteristics by determining "energy supply attribute information" from multiple dimensions such as "energy type dimension" and "low-carbon attribute dimension," thereby providing a rich and multi-dimensional data foundation for subsequent refined cost and benefit assessments. This allows the calculation of energy value to comprehensively consider the technical characteristics and environmental impact of energy. It can convert heterogeneous raw attribute data into a unified "energy supply attribute vector" through "pre-defined standardization rules," solving the comparability and computability problems between data from different sources, in different formats, and with different dimensions. This improves the consistency and efficiency of data processing, creating the necessary conditions for subsequent automated and batch calculation of energy supply value. By constructing an "energy supply object feature library" to centrally store and manage the standardized attribute vectors of all energy supply objects, it achieves standardized management and efficient reuse of energy supply attribute information, thereby avoiding the risks of data duplication and inconsistency. This improves the reliability and maintainability of the entire system's data processing and facilitates dynamic updates of attribute information.

[0036] In this optional embodiment, as an optional implementation, the above-mentioned calculation steps for the energy supply cost value include: For each energy provider, obtain the energy supply attribute vector corresponding to the energy provider from the energy provider feature library; based on the energy supply attribute vector of the energy provider, calculate multiple preset cost components, wherein each cost component is used to quantify a specific cost of the energy provider in the energy supply process from the corresponding dimension, and the cost component includes at least an economic cost component, an environmental cost component, and a spatiotemporal cost component. According to the preset integration rules, all cost components of the calculated energy-providing object are integrated to generate the energy supply cost of the energy-providing object. The integration rules are used to define the contribution weight and operation relationship of each cost component in the integrated calculation.

[0037] In this embodiment of the invention, optionally, for obtaining the attribute vector and calculating the cost components: when it is necessary to calculate the energy supply cost of an energy-providing object (such as a gas turbine) in the current scheduling cycle, firstly, its energy supply attribute vector is queried from the energy-providing object feature library. Then, based on the values ​​of different dimensions in the vector, multiple cost components are calculated respectively: Economic cost component: Quantifying the direct monetary cost of energy supply. The calculation can be performed by combining dimensions such as energy type and fuel calorific value from the attribute vector, as well as external market parameters such as real-time fuel prices, grid connection prices, and operation and maintenance rates, to calculate the unit cost of electricity supply (yuan / kWh).

[0038] Environmental cost component: Quantifying the environmental externality cost of energy supply. The calculation primarily relies on the low-carbon attribute dimension (such as carbon emission coefficient) in the attribute vector, combined with carbon tax prices or environmental value conversion factors, to calculate the carbon emission cost per unit of electricity supplied (yuan / kWh).

[0039] Spatiotemporal cost component: quantifies the additional costs caused by the location and time characteristics of energy supply. When calculating, the geographical location dimension (distance from the demand point) and dispatchability dimension (response speed) in the attribute vector can be combined to consider network loss, transmission costs, and backup costs caused by insufficient flexibility, etc., and converted into the spatiotemporal additional cost per unit of power supply (yuan / kWh).

[0040] Optionally, for the integrated cost value: after obtaining the above multiple cost components (denoted as economic cost component C_econ, environmental cost component C_env, and spatiotemporal cost component C_spatio), they are integrated according to a preset integration rule. The integration rule defines the contribution weights (e.g., w1, w2, w3) and operational relationships (e.g., weighted summation, weighted geometric mean) of each component. For example, using weighted summation: Energy supply cost value = w1*C_econ + w2*C_env + w3*C_spatio. The weights and operational relationships can be pre-configured according to the long-term operating goals of the system, thereby generating a single, integrated energy supply cost value for fair comparison in the optimization model.

[0041] As can be seen, implementing this optional embodiment can achieve a multi-faceted and refined measurement of energy supply costs by calculating multiple "cost components," such as "economic cost component," "environmental cost component," and "spatiotemporal cost component." This expands the traditional single economic cost assessment into a comprehensive cost assessment covering multiple dimensions, including economics, environment, and spatiotemporal factors. Consequently, cost comparisons become more comprehensive and objective, better aligning with the comprehensive evaluation requirements of low-carbon development. The ability to calculate each "cost component" based on a unified "energy supply attribute vector" ensures that cost comparisons between different energy providers are based on the same data foundation and calculation logic, thereby improving the fairness and comparability of cost assessments and providing a reliable basis for fair decision-making in optimization models. The ability to integrate each "cost component" through "preset integration rules" provides a flexible and configurable cost fusion mechanism. This allows the system to adjust the weights of different cost dimensions according to different management objectives or policy orientations (such as a greater emphasis on economics or low-carbon aspects), thereby generating "energy supply cost values" with different orientations and enhancing the adaptability and scalability of the method.

[0042] In this optional embodiment, as another optional implementation, the above-mentioned process of integrating all cost components of the calculated energy-providing object according to a preset integration rule to generate the energy supply cost of the energy-providing object includes: Obtain the demand characteristics under the current energy demand scenario, where the demand characteristics are used to characterize the energy consumption characteristics and priority preferences of the energy demand objects in the current energy demand scenario; Based on demand characteristics and a preset weight mapping relationship, the dynamic weight corresponding to each cost component of the energy supply object is determined. The dynamic weight is used to characterize the proportion of the cost dimension represented by the corresponding cost component in the comprehensive calculation under the current energy demand scenario. Based on the determined dynamic weights, a weighted comprehensive calculation is performed on each cost component to generate the energy supply cost of the energy-providing object.

[0043] In this embodiment of the invention, optionally, for obtaining current demand characteristics: the demand characteristics under the current energy demand scenario are a summary of the overall energy consumption of all demand objects in the current period. This can be obtained by analyzing real-time collected load data, such as: the volatility of the total load, peak-valley characteristics, whether there are special sensitive loads that must be guaranteed power supply, and the current system operator's priority preference for economic efficiency or low-carbon practices (this can be used as a specific input parameter). These characteristics can be encoded into a feature vector or a set of key indicators.

[0044] Optionally, for determining dynamic weights: the system pre-defines a weight mapping relationship, which defines the mapping rules from "demand characteristics" to "weights of each cost component". For example, it could be a set of IF-THEN rules: IF if the current period is peak electricity consumption and carbon emission targets are urgent; THEN if the environmental cost weight w2 is increased and the economic cost weight w1 is decreased. Alternatively, it could be a small neural network model that takes a demand feature vector as input and directly outputs the dynamic weights of each component. Through this step, dynamic weights are calculated for each cost component in the current scenario.

[0045] Alternatively, for weighted comprehensive calculation: the dynamic weights obtained in the above steps are applied to each cost component calculated in the above steps, and a weighted comprehensive calculation (such as weighted summation) is performed. This allows the same energy provider (such as a gas turbine) to calculate different energy cost values ​​under different scenarios (such as when solar power is abundant at noon and during peak nighttime hours), and its value can more sensitively reflect the optimal scheduling tendency under the current scenario.

[0046] As can be seen, implementing this optional embodiment enables the calculation of cost values ​​to perceive and respond to changes in the external environment and internal demand by acquiring the "demand characteristics under the current energy demand scenario." This improves the dynamism and context-specific relevance of cost assessment, allowing optimization decisions to better adapt to differentiated demands under different time periods and load characteristics. Based on "demand characteristics," "dynamic weights" can be determined through "preset weight mapping relationships," achieving adaptive adjustment of cost component weights. This allows the cost value to dynamically reflect the most pressing cost dimension under different scenarios such as peak electricity consumption, low-carbon goals being prioritized, and large demand fluctuations, significantly improving the intelligence and accuracy of the optimization model's decision-making. The use of "dynamic weights" for "weighted comprehensive calculation" of cost components enhances the sensitivity and guidance of "energy supply cost value" as an input signal to the optimization model. This ensures that the final energy allocation scheme not only meets supply and demand balance but also better aligns with the core optimization objectives under the current scenario, thus achieving a leap from static cost optimization to dynamic scenario-adaptive optimization.

[0047] 102. Using the energy demand information of multiple energy demand objects as supply constraints, and constructing a cost target based on the energy supply value of multiple energy supply objects, an energy allocation optimization model is established. In this embodiment of the invention, the step of establishing the optimization model abstracts the actual scheduling problem into a mathematical optimization problem. The core idea is to find an energy allocation method from multiple suppliers to multiple demanders, under the premise of satisfying all demands (supply constraints), that minimizes the total cost of the allocation scheme (the cost objective constructed based on the energy supply cost). This mathematical model is the energy allocation optimization model. The specific form of the model (such as linear programming or mixed-integer programming) depends on whether the cost function and constraints are linear.

[0048] It should be noted that the specific mathematical form of the energy allocation optimization model (such as whether it is linear programming, integer programming, nonlinear programming, etc.) depends on the specific form of the cost objective function and constraints. This invention does not limit the specific form of the model. Similarly, the optimization solver can be any commercial or open-source solver capable of solving the established model type, and its specific algorithm (such as the simplex method, interior point method, heuristic algorithm, etc.) does not affect the implementation of this method.

[0049] In this embodiment of the invention, as another optional implementation, the above-mentioned energy allocation optimization model is established by using the energy demand information of multiple energy demand objects as supply constraints and constructing a cost target based on the energy supply cost of multiple energy supply objects, including: Define a set of decision variables that characterize the energy supply and distribution relationship, where each decision variable in the set represents a planned energy supply from an energy provider to an energy demander. Based on the set of decision variables and the energy supply information of multiple energy providers, a supply capacity constraint is established. The supply capacity constraint is used to ensure that the planned total energy supply of each energy provider does not exceed its own energy supply capacity. Based on the set of decision variables and the energy demand information of multiple energy demand objects, a demand satisfaction constraint is established. The demand satisfaction constraint is used to ensure that the total planned energy supply received by each energy demand object is not less than its own energy demand. Based on the set of decision variables and the energy supply cost of multiple energy providers, a cost objective function is constructed to form an energy allocation optimization model. The cost objective function is used to calculate the total energy supply cost of all energy providers under the constraints of supply capacity and demand satisfaction.

[0050] In this embodiment of the invention, optionally, for defining the set of decision variables: There can be M energy providers and N energy demanders, with T time periods planned. Then, a three-dimensional decision variable x[i, j, t] can be defined, where i=1...M, j=1...N, t=1...T. The variable x[i, j, t] is one of the decision variable sets, representing the energy (kWh) that the i-th provider plans to deliver to the j-th demander during time period t. The set of all x[i, j, t] constitutes the complete decision space.

[0051] Alternatively, a supply capacity constraint can be established: for each energy provider i and each time period t, the total energy supplied to all demanders cannot exceed its energy supply capacity S[i, t] for that time period. This can be expressed mathematically as: for all i, t, Σ_j x[i, j, t] ≤ S[i, t]. This set of inequalities constitutes the supply capacity constraint.

[0052] Optionally, a demand satisfaction constraint can be established: for each energy-demanding object j and each time period t, the total energy supplied to it by all providing objects must at least satisfy its energy demand D[j, t] for that time period. This can be expressed mathematically as: for all j and t, Σ_i x[i, j, t] ≥ D[j, t]. This set of inequalities constitutes the demand satisfaction constraint.

[0053] Optionally, for constructing the cost objective function: Let the energy supply cost of the i-th energy provider be C[i] (which can vary over time). Then, the total energy supply cost over the entire scheduling cycle is the sum of the products of all decision variables and their corresponding cost values: Total Cost = Σ_i Σ_j Σ_t (C[i, t]* x[i, j, t]). The goal of the optimization model is to find a set of values ​​for decision variables x[i, j, t] that minimizes this total cost while satisfying all the constraints in the above steps. This mathematical expression for minimizing the total cost is the cost objective function. The objective function, together with all constraints, constitutes the complete energy allocation optimization model.

[0054] As can be seen, implementing this optional embodiment can formally represent complex energy allocation relationships by defining a "set of decision variables," abstracting the actual physical allocation problem into clear mathematical variables. This lays the foundation for solving the problem using mature mathematical optimization theories and methods, thereby improving the standardization and scalability of the problem solution. It can establish "supply capacity constraints" and "demand satisfaction constraints" based on "energy supply information" and "energy demand information," respectively, ensuring that the constructed mathematical model strictly follows the two fundamental laws of the physical world: supply limits and energy demand. This guarantees that any mathematically optimal solution is physically feasible and executable, thus improving the practical application value and reliability of the optimization results. It can construct a "cost objective function" using "energy supply cost" and "decision variables," transforming complex multi-objective trade-offs (economy, low carbon, etc.) into a single-objective mathematical optimization problem through the comprehensive index of cost. This greatly reduces the complexity of the problem solution and allows direct use of efficient linear programming, mixed integer programming, and other solvers, thereby achieving the ability to quickly find globally optimal or near-optimal allocation schemes under complex constraints.

[0055] 103. Solve the energy distribution optimization model to obtain the energy distribution scheme; In this embodiment of the invention, optionally, for the solution model: a mathematical optimization solver (such as Cplex, Gurobi, or an open-source solver) can be used to solve the energy allocation optimization model established in the above steps. The solver outputs the optimal values ​​of a series of decision variables, which together constitute an energy allocation scheme. This scheme clarifies the specific energy value that each energy provider should deliver to each energy demander during each scheduling period.

[0056] In this optional embodiment, as an optional implementation method, the above-described solution to the energy allocation optimization model to obtain an energy allocation scheme includes: The energy allocation optimization model is normalized and transformed to generate an optimization problem description in a standard solution format. The normalization and transformation process is used to uniformly convert the set of decision variables, supply capacity constraints, demand satisfaction constraints and cost objective function into a mathematical expression form supported by the objective solver. The optimization problem is described based on the standard solution format. A preset optimization solver is called to perform the solution operation to obtain the original solution result, which includes the optimal solution value of each decision variable in the set of decision variables. The original solution results are interpreted and aggregated to generate an energy allocation scheme. The interpretation and aggregation process is used to map the optimal solution value of each decision variable back to the corresponding energy allocation amount between the energy supply object and the energy demand object, and to organize all the mapped energy allocation amounts according to the energy demand object to form a complete energy allocation relationship mapping.

[0057] In this embodiment of the invention, optionally, for the normalization conversion process: commercial or open-source optimization solvers (such as Cplex) typically require a problem description in a specific format (such as LP file or MPS file format). The normalization conversion process is to convert the model established in the above steps, expressed in mathematical symbols and formulas, into this standard solution format. This includes organizing and filling the decision variables, objective function coefficients, constraint matrix coefficients, and left and right constant terms of the constraints according to the syntax and data structure required by the solver, forming a complete "problem instance" that can be directly read and calculated by the solver.

[0058] Alternatively, for invoking the solver: the standardized problem description generated in the previous step is submitted to a pre-defined optimization solver. The solver internally runs its algorithm (such as the simplex method, interior point method, or branch and bound method) to perform iterative calculations and finally outputs the original solution result. This result mainly contains the optimal solution value for each decision variable in the set of decision variables; that is, it finds the specific x[i, j, t] value that minimizes the total cost and satisfies all constraints.

[0059] Further optionally, for interpretation and aggregation processing: the original solution output by the solver is a series of numerical values. The interpretation process re-associates these values ​​with their physical meaning: for example, the value 0.5 corresponds to the decision variable x[PV, Plant A, 10:00] = 0.5 kWh. Aggregation processing reorganizes the data from the perspective of the demand object: for "Plant A", it summarizes the amount of energy supplied from all time periods and all supply directions, forming a clear list: "At 10:00, 0.5 kWh is received from PV, and 2.0 kWh is received from the grid; at 10:15, 1.0 kWh is received from energy storage..." This list is the final energy allocation scheme that can be used by downstream systems, clearly depicting the complete relational mapping of energy flow.

[0060] As can be seen, implementing this optional embodiment can transform the business model into an optimization problem description in a standard solution format through "normalization transformation processing," bridging the gap between business logic and the underlying mathematical solution engine. This allows the model to be computed using mature, efficient, and stable commercial or open-source optimization solvers (such as Cplex and Gurobi), significantly improving the reliability, speed, and ability to handle large-scale problems. It automates and black-boxes the optimization calculation process by "calling a preset optimization solver to perform the solution operation," reducing the requirements for users' mathematical and programming skills and thus improving the usability and feasibility of the entire method. It can also "interpret and aggregate" the "raw solution results" output by the solver, transforming the abstract mathematical optimal solution vector into an intuitive, business-understandable "energy allocation scheme." This converts the solution results into a specific allocation plan that clearly indicates "who supplies energy to whom, when, and how much," providing direct and clear input for the subsequent generation of executable control strategies.

[0061] 104. Based on the energy supply allocation scheme, generate a corresponding hybrid energy supply strategy for each energy demand object. The hybrid energy supply strategy is used to indicate one or more target energy supply objects to supply energy to the energy demand object.

[0062] In this embodiment of the invention, optionally, the generation strategy is as follows: the energy allocation scheme is a mathematical solution for the optimization model. This is then "translated" into executable instructions for the actual control system or operators, i.e., a hybrid energy supply strategy. This strategy clearly specifies when and by which energy providers (target energy providers) will supply energy to each demand object, as well as their respective supply ratios or sequences, serving as the direct basis for generating control instructions.

[0063] As can be seen, implementing the embodiments of the present invention can achieve systematic and quantitative optimization of the energy supply and distribution relationship between multiple energy providers and multiple energy demanders by acquiring comprehensive supply and demand information and constructing an optimization model based on "energy supply cost". This elevates traditional energy supply decisions that rely on manual experience or simple rules to intelligent decisions based on mathematical models and optimization algorithms, thereby significantly improving the scientific nature, economy, and overall energy efficiency of energy distribution. While meeting the needs of all loads, it also enhances the flexibility of mixed energy supply control and reduces energy supply operating costs. The invention can establish an "energy supply and distribution optimization model" with "energy demand information" as a hard constraint, ensuring that any generated energy supply and distribution scheme strictly meets the energy needs of all energy demanders, thus fundamentally guaranteeing the reliability and security of the energy supply system and laying a solid foundation for a stable and reliable energy supply. It can construct and optimize "cost targets" based on the "energy supply value" that integrates multiple dimensions of costs. This guides the system to automatically find the allocation method with the lowest total energy supply cost or the best overall benefits while meeting demand. This helps reduce the overall operating cost of the system and promotes the rational substitution of high-cost, high-emission energy sources, thereby directly supporting the achievement of low-carbon economic operation goals. It can automatically generate "hybrid energy supply strategies" for each demand object based on the "energy supply allocation scheme" obtained from the optimization solution. This realizes the transformation from global optimization results to individualized, executable control commands, thereby improving the pertinence and operability of the control strategy. Ultimately, the optimization results can directly and effectively guide the operation and scheduling of the actual system.

[0064] Example 2 Please see Figure 2 , Figure 2 This is a schematic flowchart of another hybrid energy supply control method for low-carbon demand scenarios disclosed in an embodiment of the present invention. Figure 2 The described hybrid energy supply control method for low-carbon demand scenarios can be applied to energy devices, and also to intelligent devices associated with energy devices. These intelligent devices include, but are not limited to, one or more of the following: switching devices, cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. This invention does not limit the scope of these applications. Figure 2 As shown, this hybrid energy supply control method applied to low-carbon demand scenarios may include the following operations: 201. Obtain energy demand information from multiple energy demanders, as well as energy supply information and energy supply cost from multiple energy providers; wherein, the energy supply cost is calculated based on the energy supply attribute information of each energy provider. 202. Using the energy demand information of multiple energy demand objects as supply constraints, and constructing a cost target based on the energy supply value of multiple energy supply objects, an energy allocation optimization model is established. 203. Solve the energy distribution optimization model to obtain the energy distribution scheme; In this embodiment of the invention, for other supplementary explanations of steps 201-203, please refer to the supplementary explanations of steps 101-103 in Embodiment 1. This embodiment of the invention will not repeat them here.

[0065] 204. Analyze the energy supply allocation scheme and extract all energy supply allocation records for each energy demand object. Each energy supply allocation record is used to represent the planned energy supply from an energy provider to the energy demand object. 205. For each energy demand object, based on all extracted energy allocation records, identify one or more target energy providers to supply energy to it; 206. Calculate the energy supply contribution of each target energy provider based on the planned energy supply corresponding to each target energy provider; 207. Based on the preset strategy generation rules and the energy contribution of each target energy provider, generate a hybrid energy supply strategy for each energy demand object. The strategy generation rules are used to define the generation logic of at least one strategy element among the energy supply timing, energy supply switching logic and backup energy supply arrangement based on the energy contribution. The hybrid energy supply strategy is used to indicate one or more target energy providers that supply energy to the energy demand object.

[0066] In this embodiment of the invention, optionally, for parsing and extraction: the energy allocation scheme is read and processed row by row or record by record. For the target energy demand object "Plant A", the entire scheme is scanned to find all energy allocation records where the provider is "Plant A", forming a subset. Each record contains three key pieces of information: {Energy provider P, Time period t, Planned energy supply E}.

[0067] Further, optionally, regarding the identification of target entities and the calculation of contributions: For "Plant A", identify all energy providers with planned energy supply E > 0 from its energy supply record set; these entities are the target energy providers for powering it. Then, calculate the energy contribution of each target entity. A typical calculation method is: for target entity P, sum its energy supply to "Plant A" over all time periods to obtain the total energy supply Ep_total; then calculate the total energy supply E_all_total that "Plant A" receives from all target entities; then, the contribution of P = Ep_total / E_all_total. The contribution quantifies the relative importance of each energy provider in the scheme.

[0068] Further optional, for rule-based strategy generation: the preset strategy generation rules are a set of rules based on domain knowledge, transforming static allocation schemes into dynamic control logic. For example: Energy supply timing rule: If the contribution of photovoltaic power is high and it is during midday, the strategy is to "prioritize photovoltaic power as the main power source during midday".

[0069] The above rules are merely examples. Strategy generation rules can be a set of conditional judgment rules based on contribution thresholds, or a small rule engine or decision tree model. The core is to transform the static "allocation amount" output by the optimization model, measured in energy units, into a dynamic "control strategy" that includes execution order (timing), conditional judgments (switching logic), and redundant configuration (standby arrangements). For example, a more complex switching logic rule could be: "If the target energy provider includes both the grid and energy storage, and the energy storage's contribution exceeds threshold X, then the strategy is: during peak electricity price periods, prioritize using energy storage; when the remaining energy storage capacity is below threshold Y, automatically switch to grid power." By defining such rules, the system can transform the globally optimal allocation scheme into an executable control sequence that adapts to real-time operating conditions.

[0070] Power supply switching logic rule: If the power grid and energy storage are both target objects and their contributions are similar, the strategy is "when the load is stable, the power grid supplies power; when the load fluctuates rapidly, energy storage supplements frequency regulation".

[0071] Backup power supply arrangement rules: If an object has a very low contribution but exists, it is marked as "cold standby" in the strategy and will only be put into use when the main power supplier fails.

[0072] The system matches and applies these rules based on the calculated energy contribution to provide a strategy for generating the final hybrid energy for "Plant A". This strategy is an instruction-level output that can be directly sent to the Energy Management System (EMS) or field controllers for execution.

[0073] As can be seen, implementing this optional embodiment enables structured interpretation and information extraction of optimization result data by "analyzing the energy allocation scheme" and "extracting energy allocation records." This decomposes the macro-level allocation scheme into detailed micro-level energy supply relationships for each demand object, thus providing data preparation for generating individualized control strategies. By calculating the "energy supply contribution," the relative importance or share of each "target energy provider" in meeting the energy needs of a specific demand object can be quantified, providing crucial quantitative basis for strategy generation. This makes strategy formulation (such as primary energy source selection and backup arrangements) no longer a subjective judgment but an objective data-driven process based on optimization results. The final strategy can be generated based on "preset strategy generation rules" and combined with "energy supply contribution," achieving intelligent transformation from static allocation data to dynamic operating logic. The resulting "hybrid energy supply strategy" not only includes the energy source but may also include specific, executable instruction elements such as "energy supply timing" and "switching logic." This allows the optimization scheme to be directly implemented as an operational guide that control systems or operators can follow, completing the final link from optimization calculation to closed-loop control.

[0074] Example 3 Please see Figure 3 , Figure 3 This is a schematic diagram of a hybrid energy supply control system for low-carbon demand scenarios disclosed in an embodiment of the present invention. This hybrid energy supply control system can be applied to energy devices and also to intelligent devices associated with energy devices. These intelligent devices include, but are not limited to, one or more of the following: switching devices, cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. The present invention does not limit the scope of these devices. Figure 3 As shown, the hybrid energy supply control system applied to low-carbon demand scenarios may include: The acquisition module 301 is used to acquire energy demand information of multiple energy demand objects, as well as energy supply information and energy supply cost of multiple energy supply objects; wherein, the energy supply cost is calculated based on the energy supply attribute information of each energy supply object; Module 302 is established to use the energy demand information of multiple energy demand objects as supply constraints and the energy supply value of multiple energy supply objects as the basis to construct cost targets and establish an energy allocation optimization model. Solver module 303 is used to solve the energy distribution optimization model and obtain the energy distribution scheme; The generation module 304 is used to generate a corresponding hybrid energy supply strategy for each energy demand object according to the energy supply allocation scheme. The hybrid energy supply strategy is used to indicate one or more target energy supply objects to supply energy to the energy demand object.

[0075] As can be seen, implementing the embodiments of the present invention can achieve systematic and quantitative optimization of the energy supply and distribution relationship between multiple energy providers and multiple energy demanders by acquiring comprehensive supply and demand information and constructing an optimization model based on "energy supply cost". This elevates traditional energy supply decisions that rely on manual experience or simple rules to intelligent decisions based on mathematical models and optimization algorithms, thereby significantly improving the scientific nature, economy, and overall energy efficiency of energy distribution. While meeting the needs of all loads, it also enhances the flexibility of mixed energy supply control and reduces energy supply operating costs. The invention can establish an "energy supply and distribution optimization model" with "energy demand information" as a hard constraint, ensuring that any generated energy supply and distribution scheme strictly meets the energy needs of all energy demanders, thus fundamentally guaranteeing the reliability and security of the energy supply system and laying a solid foundation for a stable and reliable energy supply. It can construct and optimize "cost targets" based on the "energy supply value" that integrates multiple dimensions of costs. This guides the system to automatically find the allocation method with the lowest total energy supply cost or the best overall benefits while meeting demand. This helps reduce the overall operating cost of the system and promotes the rational substitution of high-cost, high-emission energy sources, thereby directly supporting the achievement of low-carbon economic operation goals. It can automatically generate "hybrid energy supply strategies" for each demand object based on the "energy supply allocation scheme" obtained from the optimization solution. This realizes the transformation from global optimization results to individualized, executable control commands, thereby improving the pertinence and operability of the control strategy. Ultimately, the optimization results can directly and effectively guide the operation and scheduling of the actual system.

[0076] In this embodiment of the invention, as an optional implementation, such as Figure 4 As shown, the system also includes: The determination module 305 is used to determine the energy supply attribute information of each energy supply object before the acquisition module 301 acquires the energy demand information of multiple energy demand objects and the energy supply information and energy supply cost of multiple energy supply objects. The energy supply attribute information is used to characterize the energy supply characteristics of the energy supply object from multiple dimensions, including at least the energy type dimension and the low carbon attribute dimension. The conversion module 306 is used to convert the energy supply attribute information of each energy supply object into a unified energy supply attribute vector based on preset standardization rules. The standardization rules are used to map the original attribute data of different formats and dimensions to a predetermined numerical range and data structure. Storage module 307 is used to store the energy supply attribute vector into the energy supply object feature library. The energy supply object feature library is used to provide the energy supply attribute information required to calculate the energy supply value in the steps of obtaining energy demand information of multiple energy demand objects and energy supply information and energy supply value of multiple energy supply objects.

[0077] As can be seen, implementing this optional embodiment can improve the comprehensiveness and structure of the description of energy supply object characteristics by determining "energy supply attribute information" from multiple dimensions such as "energy type dimension" and "low-carbon attribute dimension," thereby providing a rich and multi-dimensional data foundation for subsequent refined cost and benefit assessments. This allows the calculation of energy value to comprehensively consider the technical characteristics and environmental impact of energy. It can convert heterogeneous raw attribute data into a unified "energy supply attribute vector" through "pre-defined standardization rules," solving the comparability and computability problems between data from different sources, in different formats, and with different dimensions. This improves the consistency and efficiency of data processing, creating the necessary conditions for subsequent automated and batch calculation of energy supply value. By constructing an "energy supply object feature library" to centrally store and manage the standardized attribute vectors of all energy supply objects, it achieves standardized management and efficient reuse of energy supply attribute information, thereby avoiding the risks of data duplication and inconsistency. This improves the reliability and maintainability of the entire system's data processing and facilitates dynamic updates of attribute information.

[0078] In this optional embodiment, as an optional implementation, the above-mentioned calculation steps for the energy supply cost value include: For each energy provider, obtain the energy supply attribute vector corresponding to the energy provider from the energy provider feature library; based on the energy supply attribute vector of the energy provider, calculate multiple preset cost components, wherein each cost component is used to quantify a specific cost of the energy provider in the energy supply process from the corresponding dimension, and the cost component includes at least an economic cost component, an environmental cost component, and a spatiotemporal cost component. According to the preset integration rules, all cost components of the calculated energy-providing object are integrated to generate the energy supply cost of the energy-providing object. The integration rules are used to define the contribution weight and operation relationship of each cost component in the integrated calculation.

[0079] As can be seen, implementing this optional embodiment can achieve a multi-faceted and refined measurement of energy supply costs by calculating multiple "cost components," such as "economic cost component," "environmental cost component," and "spatiotemporal cost component." This expands the traditional single economic cost assessment into a comprehensive cost assessment covering multiple dimensions, including economics, environment, and spatiotemporal factors. Consequently, cost comparisons become more comprehensive and objective, better aligning with the comprehensive evaluation requirements of low-carbon development. The ability to calculate each "cost component" based on a unified "energy supply attribute vector" ensures that cost comparisons between different energy providers are based on the same data foundation and calculation logic, thereby improving the fairness and comparability of cost assessments and providing a reliable basis for fair decision-making in optimization models. The ability to integrate each "cost component" through "preset integration rules" provides a flexible and configurable cost fusion mechanism. This allows the system to adjust the weights of different cost dimensions according to different management objectives or policy orientations (such as a greater emphasis on economics or low-carbon aspects), thereby generating "energy supply cost values" with different orientations and enhancing the adaptability and scalability of the method.

[0080] In this optional embodiment, as another optional implementation, the above-mentioned process of integrating all cost components of the calculated energy-providing object according to a preset integration rule to generate the energy supply cost of the energy-providing object includes: Obtain the demand characteristics under the current energy demand scenario, where the demand characteristics are used to characterize the energy consumption characteristics and priority preferences of the energy demand objects in the current energy demand scenario; Based on demand characteristics and a preset weight mapping relationship, the dynamic weight corresponding to each cost component of the energy supply object is determined. The dynamic weight is used to characterize the proportion of the cost dimension represented by the corresponding cost component in the comprehensive calculation under the current energy demand scenario. Based on the determined dynamic weights, a weighted comprehensive calculation is performed on each cost component to generate the energy supply cost of the energy-providing object.

[0081] As can be seen, implementing this optional embodiment enables the calculation of cost values ​​to perceive and respond to changes in the external environment and internal demand by acquiring the "demand characteristics under the current energy demand scenario." This improves the dynamism and context-specific relevance of cost assessment, allowing optimization decisions to better adapt to differentiated demands under different time periods and load characteristics. Based on "demand characteristics," "dynamic weights" can be determined through "preset weight mapping relationships," achieving adaptive adjustment of cost component weights. This allows the cost value to dynamically reflect the most pressing cost dimension under different scenarios such as peak electricity consumption, low-carbon goals being prioritized, and large demand fluctuations, significantly improving the intelligence and accuracy of the optimization model's decision-making. The use of "dynamic weights" for "weighted comprehensive calculation" of cost components enhances the sensitivity and guidance of "energy supply cost value" as an input signal to the optimization model. This ensures that the final energy allocation scheme not only meets supply and demand balance but also better aligns with the core optimization objectives under the current scenario, thus achieving a leap from static cost optimization to dynamic scenario-adaptive optimization.

[0082] In this embodiment of the invention, as another optional implementation, the aforementioned establishing module 302 uses the energy demand information of multiple energy demand objects as supply constraints, and constructs a cost target based on the energy supply cost of multiple energy supply objects. The specific method for establishing the energy supply allocation optimization model includes: Define a set of decision variables that characterize the energy supply and distribution relationship, where each decision variable in the set represents a planned energy supply from an energy provider to an energy demander. Based on the set of decision variables and the energy supply information of multiple energy providers, a supply capacity constraint is established. The supply capacity constraint is used to ensure that the planned total energy supply of each energy provider does not exceed its own energy supply capacity. Based on the set of decision variables and the energy demand information of multiple energy demand objects, a demand satisfaction constraint is established. The demand satisfaction constraint is used to ensure that the total planned energy supply received by each energy demand object is not less than its own energy demand. Based on the set of decision variables and the energy supply cost of multiple energy providers, a cost objective function is constructed to form an energy allocation optimization model. The cost objective function is used to calculate the total energy supply cost of all energy providers under the constraints of supply capacity and demand satisfaction.

[0083] As can be seen, implementing this optional embodiment can formally represent complex energy allocation relationships by defining a "set of decision variables," abstracting the actual physical allocation problem into clear mathematical variables. This lays the foundation for solving the problem using mature mathematical optimization theories and methods, thereby improving the standardization and scalability of the problem solution. It can establish "supply capacity constraints" and "demand satisfaction constraints" based on "energy supply information" and "energy demand information," respectively, ensuring that the constructed mathematical model strictly follows the two fundamental laws of the physical world: supply limits and energy demand. This guarantees that any mathematically optimal solution is physically feasible and executable, thus improving the practical application value and reliability of the optimization results. It can construct a "cost objective function" using "energy supply cost" and "decision variables," transforming complex multi-objective trade-offs (economy, low carbon, etc.) into a single-objective mathematical optimization problem through the comprehensive index of cost. This greatly reduces the complexity of the problem solution and allows direct use of efficient linear programming, mixed integer programming, and other solvers, thereby achieving the ability to quickly find globally optimal or near-optimal allocation schemes under complex constraints.

[0084] In this optional embodiment, as an optional implementation method, the specific way in which the solving module 303 solves the energy distribution optimization model to obtain the energy distribution scheme includes: The energy allocation optimization model is normalized and transformed to generate an optimization problem description in a standard solution format. The normalization and transformation process is used to uniformly convert the set of decision variables, supply capacity constraints, demand satisfaction constraints and cost objective function into a mathematical expression form supported by the objective solver. The optimization problem is described based on the standard solution format. A preset optimization solver is called to perform the solution operation to obtain the original solution result, which includes the optimal solution value of each decision variable in the set of decision variables. The original solution results are interpreted and aggregated to generate an energy allocation scheme. The interpretation and aggregation process is used to map the optimal solution value of each decision variable back to the corresponding energy allocation amount between the energy supply object and the energy demand object, and to organize all the mapped energy allocation amounts according to the energy demand object to form a complete energy allocation relationship mapping.

[0085] As can be seen, implementing this optional embodiment can transform the business model into an optimization problem description in a standard solution format through "normalization transformation processing," bridging the gap between business logic and the underlying mathematical solution engine. This allows the model to be computed using mature, efficient, and stable commercial or open-source optimization solvers (such as Cplex and Gurobi), significantly improving the reliability, speed, and ability to handle large-scale problems. It automates and black-boxes the optimization calculation process by "calling a preset optimization solver to perform the solution operation," reducing the requirements for users' mathematical and programming skills and thus improving the usability and feasibility of the entire method. It can also "interpret and aggregate" the "raw solution results" output by the solver, transforming the abstract mathematical optimal solution vector into an intuitive, business-understandable "energy allocation scheme." This converts the solution results into a specific allocation plan that clearly indicates "who supplies energy to whom, when, and how much," providing direct and clear input for the subsequent generation of executable control strategies.

[0086] In an optional embodiment, the generation module 304 generates a corresponding hybrid energy provision strategy for each energy demand object according to the energy supply allocation scheme in the following specific ways: The energy supply allocation scheme is analyzed, and all energy supply allocation records for each energy demand object are extracted. Each energy supply allocation record represents the planned energy supply from an energy provider to the energy demand object. For each energy demand object, based on all extracted energy allocation records, one or more target energy supply objects are identified to supply energy to it, and the energy supply contribution of each target energy supply object is calculated according to the planned energy supply corresponding to each target energy supply object. Based on the preset strategy generation rules and the energy contribution of each target energy provider, a hybrid energy supply strategy is generated for each energy demander. The strategy generation rules are used to define the generation logic of at least one strategy element among the energy supply timing, energy supply switching logic, and backup energy supply arrangement based on the energy contribution.

[0087] As can be seen, implementing this optional embodiment enables structured interpretation and information extraction of optimization result data by "analyzing the energy allocation scheme" and "extracting energy allocation records." This decomposes the macro-level allocation scheme into detailed micro-level energy supply relationships for each demand object, thus providing data preparation for generating individualized control strategies. By calculating the "energy supply contribution," the relative importance or share of each "target energy provider" in meeting the energy needs of a specific demand object can be quantified, providing crucial quantitative basis for strategy generation. This makes strategy formulation (such as primary energy source selection and backup arrangements) no longer a subjective judgment but an objective data-driven process based on optimization results. The final strategy can be generated based on "preset strategy generation rules" and combined with "energy supply contribution," achieving intelligent transformation from static allocation data to dynamic operating logic. The resulting "hybrid energy supply strategy" not only includes the energy source but may also include specific, executable instruction elements such as "energy supply timing" and "switching logic." This allows the optimization scheme to be directly implemented as an operational guide that control systems or operators can follow, completing the final link from optimization calculation to closed-loop control.

[0088] Example 4 Please see Figure 5 , Figure 5 This is a schematic diagram of another hybrid energy supply control system for low-carbon demand scenarios disclosed in this invention. This hybrid energy supply control system for low-carbon demand scenarios can be applied to energy devices, and also to intelligent devices associated with energy devices. These intelligent devices include, but are not limited to, one or more of the following: switching devices, cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. This invention does not limit the scope of these devices. Figure 5 As shown, the hybrid energy supply control system applied to low-carbon demand scenarios may include: Memory 401 that stores executable program code.

[0089] Processor 402 coupled to memory 401.

[0090] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the hybrid energy supply control method for low-carbon demand scenarios described in Embodiment 1 or Embodiment 2 of the present invention.

[0091] Example 5 This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the hybrid energy supply control method for low-carbon demand scenarios described in Embodiment 1 or Embodiment 2 of this invention.

[0092] Example 6 This invention discloses a computer program product, which includes a non-transient computer storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the hybrid energy supply control method for low-carbon demand scenarios described in Embodiment 1 or Embodiment 2.

[0093] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0094] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0095] Finally, it should be noted that the hybrid energy supply control method and system for low-carbon demand scenarios disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A hybrid energy supply control method applied to a low carbon demand scenario, characterized in that, The method includes: The system acquires energy demand information from multiple energy demanders, as well as energy supply information and energy supply cost from multiple energy providers; wherein the energy supply cost is calculated based on the energy supply attribute information of each energy provider. An energy allocation optimization model is established by using the energy demand information of multiple energy demand objects as supply constraints and constructing a cost target based on the energy supply value of multiple energy supply objects. Solve the energy distribution optimization model to obtain the energy distribution scheme; According to the energy supply allocation scheme, a corresponding hybrid energy supply strategy is generated for each energy demand object, and the hybrid energy supply strategy is used to indicate one or more target energy supply objects to supply energy to the energy demand object.

2. The hybrid energy supply control method for low carbon demand scenarios according to claim 1, characterized in that, Before acquiring the energy demand information of multiple energy demanders and the energy supply information and energy cost of multiple energy providers, the method further includes: For each energy provider, determine the energy supply attribute information of the energy provider. The energy supply attribute information is used to characterize the energy supply characteristics of the energy provider from multiple dimensions, including at least the energy type dimension and the low-carbon attribute dimension. Based on preset standardization rules, the energy supply attribute information of each energy supply object is converted into a unified energy supply attribute vector. The standardization rules are used to map the original attribute data of different formats and dimensions to a predetermined numerical range and data structure. The energy supply attribute vector is stored in the energy supply object feature library, which is used to provide the energy supply attribute information required to calculate the energy supply value in the step of obtaining energy demand information of multiple energy demand objects and energy supply information and energy supply value of multiple energy supply objects.

3. The hybrid energy supply control method for low-carbon demand scenarios according to claim 2, characterized in that, The steps for calculating the energy supply cost include: For each energy-providing object, the energy-providing attribute vector corresponding to the energy-providing object is obtained from the energy-providing object feature library; based on the energy-providing attribute vector of the energy-providing object, multiple preset cost components are calculated, wherein each cost component is used to quantify a specific cost of the energy-providing object in the energy-providing process from a corresponding dimension, and the cost component includes at least an economic cost component, an environmental cost component, and a spatiotemporal cost component; According to the preset integration rules, all the cost components of the calculated energy-providing object are integrated to generate the energy supply cost value of the energy-providing object. The integration rules are used to define the contribution weight and operation relationship of each cost component in the integrated calculation.

4. The hybrid energy supply control method for low-carbon demand scenarios according to claim 3, characterized in that, The step of integrating all the cost components of the calculated energy-providing object according to preset integration rules to generate the energy supply cost of the energy-providing object includes: Obtain the demand characteristics under the current energy demand scenario, wherein the demand characteristics are used to characterize the energy consumption characteristics and priority preferences of the energy demand objects in the current energy demand scenario; Based on the demand characteristics and the preset weight mapping relationship, the dynamic weight corresponding to each cost component of the energy supply object is determined, wherein the dynamic weight is used to characterize the proportion of the cost dimension represented by the cost component in the comprehensive calculation under the current energy demand scenario; Based on the determined dynamic weights, the cost components are weighted and comprehensively calculated to generate the energy supply cost value of the energy-providing object.

5. The hybrid energy supply control method for low-carbon demand scenarios according to claim 1, characterized in that, The method of establishing an energy allocation optimization model by using the energy demand information of multiple energy demand objects as supply constraints and constructing a cost target based on the energy supply cost of multiple energy supply objects includes: Define a set of decision variables to characterize the energy supply and distribution relationship, wherein each decision variable in the set of decision variables is used to represent a planned energy supply from one energy provider to one energy demander; Based on the set of decision variables and the energy supply information of the multiple energy providers, a supply capacity constraint is established, wherein the supply capacity constraint is used to ensure that the planned total energy supply of each energy provider does not exceed its own energy supply capacity; Based on the set of decision variables and the energy demand information of multiple energy demand objects, a demand satisfaction constraint is established, wherein the demand satisfaction constraint is used to ensure that the total planned energy supply received by each energy demand object is not less than its own energy demand. Based on the set of decision variables and the energy supply cost of the multiple energy supply objects, a cost objective function is constructed to form an energy allocation optimization model. The cost objective function is used to calculate the total energy supply cost of all the energy supply objects under the constraints of supply capacity and demand satisfaction.

6. The hybrid energy supply control method for low-carbon demand scenarios according to claim 5, characterized in that, Solving the energy allocation optimization model to obtain the energy allocation scheme includes: The energy allocation optimization model is subjected to normalization transformation to generate an optimization problem description in a standard solution format. The normalization transformation is used to uniformly convert the set of decision variables, the supply capacity constraints, the demand satisfaction constraints, and the cost objective function into a mathematical expression form supported by the objective solver. Based on the optimization problem description in the standard solution format, a preset optimization solver is invoked to perform the solution operation to obtain the original solution result, wherein the original solution result includes the optimal solution value of each decision variable in the set of decision variables; The original solution results are interpreted and aggregated to generate an energy allocation scheme. The interpretation and aggregation process is used to map the optimal solution value of each decision variable back to the corresponding energy allocation amount between the energy supply object and the energy demand object, and to organize all the mapped energy allocation amounts according to the energy demand object to form a complete energy allocation relationship mapping.

7. The hybrid energy supply control method for low-carbon demand scenarios according to any one of claims 1-6, characterized in that, The strategy for generating a corresponding hybrid energy source for each energy demand object based on the energy supply allocation scheme includes: The energy supply allocation scheme is analyzed to extract all energy supply allocation records for each energy demand object, wherein each energy supply allocation record is used to represent the planned energy supply from an energy provider to the energy demand object; For each energy demand object, based on all the extracted energy allocation records, one or more target energy supply objects are identified to supply energy to it, and the energy supply contribution of each target energy supply object is calculated according to the planned energy supply corresponding to each target energy supply object. Based on the preset strategy generation rules and the energy supply contribution of each target energy supply object, a hybrid energy supply strategy is generated for each energy demand object. The strategy generation rules are used to define the generation logic of at least one strategy element among energy supply contribution, energy supply timing, energy supply switching logic, and backup energy supply arrangement.

8. A hybrid energy supply control system applied to low-carbon demand scenarios, characterized in that, The system includes: The acquisition module is used to acquire energy demand information from multiple energy demand objects, as well as energy supply information and energy supply cost from multiple energy supply objects; wherein the energy supply cost is calculated based on the energy supply attribute information of each energy supply object; A module is established to construct a cost target based on the energy demand information of multiple energy demand objects as supply constraints and the energy supply cost of multiple energy supply objects, and to establish an energy allocation optimization model. The solver module is used to solve the energy allocation optimization model to obtain the energy allocation scheme; The generation module is used to generate a corresponding hybrid energy supply strategy for each energy demand object according to the energy supply allocation scheme. The hybrid energy supply strategy is used to indicate one or more target energy supply objects to supply energy to the energy demand object.

9. A hybrid energy supply control system applied to low-carbon demand scenarios, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the hybrid energy supply control method for low-carbon demand scenarios as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the hybrid energy supply control method for low-carbon demand scenarios as described in any one of claims 1-7.

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