Carbon management method and system based on digital twinning

Abstract

The application provides an energy-carbon management method and system based on digital twinning, and belongs to the technical field of energy-carbon management. The method comprises the following steps: predicting energy-carbon data of a target region in a preset period based on a target digital twinning model to obtain predicted energy data and predicted carbon emission data; marking data in the predicted energy data and the predicted carbon emission data that does not meet a corresponding condition as to-be-adjusted data, and determining a to-be-adjusted data amount of the to-be-adjusted data; determining an adjustable parameter of each energy-using equipment in the preset period for each energy-using equipment; determining a priority score corresponding to each energy-using equipment based on the to-be-adjusted data, the adjustable parameter of each energy-using equipment in the preset period, and a preset unit adjustment cost; and performing energy-carbon management on each energy-using equipment based on the priority score corresponding to each energy-using equipment and the to-be-adjusted data amount. The application can reduce blindness and randomness in the energy-carbon management process.

Description

Technical Field

[0001] This application belongs to the field of energy and carbon management technology, and more specifically, relates to an energy and carbon management method and system based on digital twins. Background Technology

[0002] Energy carbon management, through dynamic monitoring, prediction, and optimized regulation of energy consumption and carbon emissions, can help achieve carbon reduction targets and reduce energy waste.

[0003] However, existing technologies for energy and carbon management of actual equipment on-site are somewhat blind, generally managing only a few random or pre-set devices, often resulting in frequent adjustments and excessive resource waste due to excessive device adjustments. Summary of the Invention

[0004] The purpose of this application is to provide a digital twin-based energy and carbon management method and system to reduce the blindness and randomness in the energy and carbon management process.

[0005] A first aspect of this application provides an energy and carbon management method based on digital twins, comprising: Based on the target digital twin model, the energy and carbon data of the target area are predicted during a preset period to obtain predicted energy data and predicted carbon emission data; the target digital twin model is constructed based on the actual layout of energy-consuming equipment in the target area; If the predicted energy data and / or predicted carbon emission data do not meet the corresponding conditions, the data in the predicted energy data and predicted carbon emission data that do not meet the corresponding conditions will be marked as data to be adjusted, and the amount of data to be adjusted will be determined. For each energy-consuming device in the target area, adjustable parameters for that device are determined based on its energy consumption characteristics and energy-carbon characteristics within a preset time period. The energy-carbon characteristics are used to characterize the energy-carbon conversion efficiency of the energy-consuming device. The adjustable parameters include adjustable energy quantity and adjustable carbon emissions. Based on the data to be adjusted, the adjustable parameters of each energy-consuming device in the preset time period, and the preset unit adjustment cost, the priority score corresponding to each energy-consuming device is determined. Energy and carbon management is performed on each energy-consuming device based on its priority score and the amount of data to be adjusted.

[0006] A second aspect of this application provides an energy and carbon management system based on digital twins, comprising: The energy and carbon data prediction module is used to predict the energy and carbon data of a target area within a preset time period based on the target digital twin model, and obtain predicted energy data and predicted carbon emission data; the target digital twin model is constructed based on the actual layout of energy-consuming equipment in the target area; The module for determining data to be adjusted is used to mark data in the predicted energy data and / or predicted carbon emission data that do not meet the corresponding conditions as data to be adjusted, and to determine the amount of data to be adjusted in the data to be adjusted if the predicted energy data and / or predicted carbon emission data do not meet the corresponding conditions. The adjustable parameter determination module is used to determine the adjustable parameters of each energy-consuming device in the target area based on the energy consumption characteristics and energy-carbon characteristics of the energy-consuming device in the preset time period; wherein, the energy-carbon characteristics are used to characterize the energy-carbon conversion efficiency of the energy-consuming device; the adjustable parameters include: adjustable energy amount and adjustable carbon emissions; The priority score determination module is used to determine the priority score of each energy-consuming device based on the data to be adjusted, the adjustable parameters of each energy-consuming device in a preset time period, and the preset unit adjustment cost. The energy and carbon management module is used to manage the energy and carbon of each energy-consuming device based on its priority score and the amount of data to be adjusted.

[0007] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described digital twin-based energy and carbon management method.

[0008] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described energy and carbon management method based on digital twins.

[0009] The beneficial effects of the energy and carbon management method and system based on digital twins provided in this application are as follows: This embodiment uses a digital twin model to predict energy and carbon data for a preset time period, facilitating advance energy and carbon management in the target area. It further identifies the data to be adjusted and the amount of data to be adjusted, providing adjustment direction and data support for subsequent energy and carbon management. Secondly, by determining the adjustable energy and adjustable carbon emissions of each energy-consuming device in the target area, this embodiment quantifies the adjustability of each device, providing data support for the rational allocation of adjustment tasks. Finally, by determining the priority score for each energy-consuming device, this embodiment can screen out devices with higher cost-effectiveness in energy and carbon adjustments, prioritizing the management of key devices and reducing the randomness and blindness in selecting adjustment devices during energy and carbon management, further improving management efficiency and resource utilization efficiency. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic flowchart of an energy and carbon management method based on digital twins provided in an embodiment of this application; Figure 2 A structural block diagram of a digital twin-based energy and carbon management system provided in an embodiment of this application; Figure 3 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0013] It is understood that in the embodiments of this application, data related to user information is involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with relevant laws, regulations and standards.

[0014] It should be noted that the terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein.

[0015] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0016] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a digital twin-based energy and carbon management method according to an embodiment of this application. The digital twin-based energy and carbon management method can be executed by an electronic device and may include steps S101-S105.

[0017] S101: Based on the target digital twin model, predict the energy and carbon data of the target area in a preset time period to obtain predicted energy data and predicted carbon emission data.

[0018] In this embodiment, the target area is the region to be subject to energy and carbon management, which can be an industrial park or a smart city area, etc. The target digital twin model is constructed based on the actual layout of energy-consuming devices in the target area. The target digital twin model contains multiple virtual energy-consuming devices, which correspond one-to-one with the energy-consuming devices in the target area. The target digital twin model has built-in calculation logic for energy consumption and carbon emissions, which can reproduce the energy and carbon flow process of each energy-consuming device in the real-world scenario. The construction process of the target digital twin model will not be described in detail in this embodiment.

[0019] In this embodiment, the predicted energy data is the energy consumption forecast of the target area within a preset time period, output by the target digital twin model through simulation calculation. Similarly, the predicted carbon emission data is the carbon emission forecast of the target area within a preset time period, output by the target digital twin model through simulation calculation. In this embodiment, the preset time period can be a future period, specifically a future day or month.

[0020] S102: If the predicted energy data and / or predicted carbon emission data do not meet the corresponding conditions, then the data in the predicted energy data and predicted carbon emission data that do not meet the corresponding conditions are marked as data to be adjusted, and the amount of data to be adjusted is determined.

[0021] In this embodiment, if at least one of the predicted energy data and predicted carbon emission data does not meet the corresponding conditions, the above-mentioned marking operation is triggered.

[0022] In this embodiment, if both the predicted energy data and the predicted carbon emission data meet the corresponding conditions, it means that no additional energy and carbon management is currently required.

[0023] In this embodiment, the condition corresponding to the predicted energy data can be a preset energy consumption threshold, and the condition corresponding to the predicted carbon emission data can be a preset carbon emission quota value. If the predicted energy data exceeds the preset energy consumption threshold, the predicted energy data does not meet the corresponding condition. If the predicted carbon emission data exceeds the preset carbon emission quota value, the predicted carbon emission data does not meet the corresponding condition. In this embodiment, both the preset energy consumption threshold and the preset carbon emission quota value can be set based on historical data and experience. For example, the preset energy consumption threshold can be 1,146,000 kWh, and the preset carbon emission quota value can be 66.23 tCO2.

[0024] In this embodiment, the amount of data to be adjusted may include the amount of energy to be adjusted and / or the amount of carbon emissions to be adjusted. The amount of energy to be adjusted is equal to the difference between the predicted energy data value and the preset energy consumption threshold, and the amount of carbon emissions to be adjusted is equal to the difference between the predicted carbon emission data value and the preset carbon emission quota value.

[0025] S103: For each energy-consuming device in the target area, determine the adjustable parameters of the energy-consuming device during the preset time period based on the energy consumption characteristics and energy-carbon characteristics of the device during the preset time period.

[0026] In this embodiment, the energy-carbon characteristics are used to characterize the energy-carbon conversion efficiency of energy-consuming equipment; the adjustable parameters include: adjustable energy quantity and adjustable carbon emissions.

[0027] In this embodiment, the energy-carbon characteristic can specifically be the carbon emission factor of the energy-consuming equipment. The energy consumption characteristic can include: the adjustable upper limit of energy consumption per unit time and the adjustable duration upper limit. The adjustable upper limit of energy consumption per unit time refers to the amount of energy consumption that the energy-consuming equipment can reduce within a unit time. Reducing the energy consumption of a certain energy-consuming equipment can be achieved by reducing the load of that equipment. Since different energy-consuming equipment may correspond to different energy consumption characteristics at different times in an industrial park, this embodiment needs to obtain the energy consumption characteristics within a preset time period. In this embodiment, relevant personnel can set it according to the actual production cycle. For example, if a production cycle is one day, the production equipment cannot reduce its load during working hours, but can appropriately reduce its load after working hours. The specific reduction percentage and duration can be set by the user. For example, public lighting can be reduced by 10% for 8 hours, and central air conditioning can be reduced by 8% for 6 hours.

[0028] In one embodiment, determining adjustable parameters of the energy-consuming device during a preset time period based on its energy consumption characteristics and energy-carbon characteristics includes: The adjustable energy amount of the energy-consuming equipment during a preset time period is determined based on the energy consumption characteristics of the equipment. The adjustable carbon emissions of the energy-consuming equipment during the preset period are determined based on its energy-carbon characteristics and the adjustable energy quantity of the equipment during the preset period.

[0029] In this embodiment, the adjustable energy amount of the energy-consuming device during a preset period is equal to the product of the adjustable upper limit of energy consumption per unit time and the upper limit of adjustable duration, and the adjustable carbon emission of the energy-consuming device during a preset period is equal to the product of the adjustable energy amount of the energy-consuming device during the preset period and the carbon emission factor.

[0030] S104: Based on the data to be adjusted, the adjustable parameters of each energy-consuming device in the preset time period, and the preset unit adjustment cost, determine the priority score corresponding to each energy-consuming device.

[0031] In this embodiment, the data to be adjusted may include three possibilities: containing only predicted energy data, containing only predicted carbon emission data, and containing both predicted energy data and predicted carbon emission data. When the data to be adjusted contains only predicted energy data, or only predicted carbon emission data, the priority score is calculated in the same way. However, the calculation method for the priority score when the data to be adjusted contains both predicted energy data and predicted carbon emission data differs from the calculation method when the data to be adjusted contains only predicted energy data and only predicted carbon emission data.

[0032] In this embodiment, taking the example that the data to be adjusted only contains predicted energy data, the priority score is calculated based on the energy adjustment contribution capacity and economic efficiency of the energy-consuming equipment. Specifically, for each energy-consuming equipment, the adjustable energy amount of the equipment in a preset time period and a preset unit adjustment cost are normalized, and the normalized data is weighted and calculated. The weights can be set by the user to obtain the priority score of the energy-consuming equipment. In this embodiment, the larger the adjustable energy amount of the energy-consuming equipment in the preset time period, the larger the normalized value; the higher the preset unit adjustment cost of the energy-consuming equipment in the preset time period, the smaller the normalized value (this can be achieved by normalizing using the reciprocal of the preset unit adjustment cost). Calculating the priority score based on the above two dimensions minimizes the number of energy-consuming equipment to be adjusted and reduces costs, thereby reducing the blindness and randomness in the energy and carbon management process. The higher the priority score, the more preferentially it will be adjusted in subsequent energy and carbon management processes. When the data to be adjusted contains only predicted carbon emission data, the calculation method for the priority score will not be repeated. When the data to be adjusted contains both predicted energy data and predicted carbon emission data, the calculation method for the priority score will be detailed in subsequent embodiments.

[0033] S105: Perform energy and carbon management on each energy-consuming device based on its priority score and the amount of data to be adjusted.

[0034] In this embodiment, the amount of data to be adjusted can include the following three possibilities: containing only the amount of energy to be adjusted (corresponding to the data to be adjusted containing only predicted energy data), containing only the amount of carbon emissions to be adjusted (corresponding to the data to be adjusted containing only predicted carbon emission data), and containing both the amount of energy to be adjusted and the amount of carbon emissions to be adjusted (corresponding to the data to be adjusted containing both predicted energy data and predicted carbon emission data). When the amount of data to be adjusted contains only the amount of energy to be adjusted, or only the amount of carbon emissions to be adjusted, the energy and carbon management methods are the same. However, the energy and carbon management methods when the amount of data to be adjusted contains both the amount of energy to be adjusted and the amount of carbon emissions to be adjusted are different from the energy and carbon management methods when the amount of data to be adjusted contains only the amount of energy to be adjusted and only the amount of carbon emissions to be adjusted.

[0035] In this embodiment, taking the case where the amount of data to be adjusted only contains the amount of energy to be adjusted as an example, an energy-consuming device is selected as the energy-consuming device to be adjusted in order of priority score from high to low; Perform the following energy management operations to allocate regulating energy to the energy-consuming equipment to be adjusted: If the first difference is greater than or equal to the adjustable energy amount of the energy-consuming equipment to be adjusted, then the adjustable energy amount is allocated to the energy-consuming equipment to be adjusted based on the adjustable energy amount of the equipment to be adjusted; the first difference is the difference between the energy amount to be adjusted and the currently allocated adjustable energy amount. If the first difference is less than the adjustable energy amount of the energy-consuming equipment to be adjusted, then the adjustable energy amount is allocated to the energy-consuming equipment to be adjusted based on the first difference. The process is repeated cyclically, selecting an energy-consuming device as the energy-consuming device to be adjusted in descending order of priority score, and performing the energy management operations described above, until the allocated amount of regulated energy is not less than the amount of energy to be adjusted.

[0036] In this embodiment, the energy and carbon management method will not be described in detail when the amount of data to be adjusted only contains the amount of carbon emissions to be adjusted. The energy and carbon management method when the amount of data to be adjusted contains both the amount of energy to be adjusted and the amount of carbon emissions to be adjusted will be described in detail in subsequent embodiments.

[0037] As can be seen from the above, this embodiment uses a digital twin model to predict energy and carbon data for a preset time period, facilitating advance energy and carbon management of the target area. It further identifies the data to be adjusted and the amount of data to be adjusted, providing adjustment direction and data support for the subsequent energy and carbon management process. Secondly, this embodiment quantifies the adjustability of each energy-consuming device by determining its adjustable energy and adjustable carbon emissions, providing data support for the subsequent rational allocation of adjustment tasks. Finally, by determining the priority score corresponding to each energy-consuming device, this embodiment can screen out devices with more cost-effectiveness in energy and carbon adjustment, prioritizing the management of key devices, reducing the blindness and randomness in selecting adjustment devices during energy and carbon management, and further improving management efficiency and resource utilization efficiency.

[0038] In one embodiment of this application, the process of constructing the target digital twin model involves a deviation correction process for the target digital twin model, which can be implemented in the following manner: The physical operation data and real-time energy data of each energy-consuming device in the target area are collected by the sensing and monitoring devices deployed on each energy-consuming device in the target area, and the real-time carbon emission data corresponding to each energy-consuming device is determined based on the real-time energy data of each energy-consuming device. Based on the physical operation data of each energy-consuming device, obtain the virtual energy data and virtual carbon emission data output by the target digital twin model; The virtual-to-real deviation value is determined based on real-time energy data, virtual energy data, real-time carbon emission data, and virtual carbon emission data; The deviation of the target digital twin model is corrected based on the virtual-real deviation value.

[0039] In this embodiment, the sensing and monitoring device can be an electricity meter, gas meter, temperature sensor, equipment load sensor, or operating status monitor, etc. Physical operating data refers to parameters collected by the sensing and monitoring device that characterize the real-time operating status of the energy-consuming equipment, such as the equipment's real-time load rate, operating speed, start / stop status, and the ambient temperature and humidity of the target area. Real-time energy data refers to the actual amount of energy consumed by the energy-consuming equipment at the current moment, directly collected by the sensing and monitoring device; it represents the true energy consumption result of the physical system. Real-time carbon emission data is the actual carbon emission calculated based on the real-time energy data and the corresponding carbon emission factor. In this embodiment, the carbon emission is equal to the product of the energy data and the carbon emission factor.

[0040] In this embodiment, virtual energy data and virtual carbon emission data are the energy consumption and carbon emissions of the virtual mirror side simulated and calculated by the target digital twin model after the collected physical operation data is input into it. The virtual-real deviation value is used to measure the matching degree between the digital twin model and the physical system; the larger the virtual-real deviation value, the smaller the matching degree between the digital twin model and the physical system. In this embodiment, the virtual-real deviation value can specifically include an energy deviation value and a carbon emission deviation value. The energy deviation value is equal to the difference between real-time energy data and virtual energy data, and the carbon emission deviation value is equal to the difference between real-time carbon emission data and virtual carbon emission data.

[0041] In this embodiment, the virtual energy data and virtual carbon emission data output by the target digital twin model are essentially the sum of data from each virtual energy-consuming device contained therein. Therefore, when correcting the deviation of the target digital twin model, for each virtual energy-consuming device, the degree of matching between the sub-virtual energy data and sub-virtual carbon emission data corresponding to that virtual energy-consuming device and the real-time energy data and real-time carbon emission data of the corresponding physical energy-consuming device can be determined. Specifically, this can include the following steps: The global virtual-to-real deviation value is broken down by the dimension of virtual energy-consuming device, and the sub-virtual-to-real deviation value corresponding to each virtual energy-consuming device is calculated, including sub-energy deviation value and sub-carbon emission deviation value; wherein, sub-energy deviation value = real-time energy data of the corresponding physical energy-consuming device - sub-virtual energy data of the virtual energy-consuming device, and sub-carbon emission deviation value = real-time carbon emission data of the corresponding physical energy-consuming device - sub-virtual carbon emission data of the virtual energy-consuming device.

[0042] For each virtual energy-consuming device, it is determined whether its corresponding sub-energy deviation value and sub-carbon emission deviation value exceed a preset sub-deviation threshold. If they do, the virtual energy-consuming device is marked as a deviation contributing device, i.e., the main source of the global deviation. The preset sub-deviation threshold can be set by the user. In this embodiment, there can be two preset sub-deviation thresholds, i.e., one preset sub-deviation threshold corresponds to the sub-energy deviation value, and another preset sub-deviation threshold corresponds to the sub-carbon emission deviation value.

[0043] For each marked deviation contributing device, analyze the cause of its deviation individually. For example, if the sub-energy deviation value exceeds the preset sub-deviation threshold, the cause may be an error in the load and energy consumption calculation formula of the virtual energy-consuming device when constructing the target digital twin model. This formula can be adjusted based on a preset correction method, such as adjusting the parameters or coefficients in the formula according to a preset step size. If only the sub-carbon emission deviation exceeds the preset sub-deviation threshold, the cause may be an error in the carbon emission factor configuration of the virtual energy-consuming device. This carbon emission factor of the virtual energy-consuming device can be adjusted based on another preset step size. Repeat the above deviation correction steps until the sub-energy deviation value and sub-carbon emission deviation value of each virtual energy-consuming device do not exceed the preset sub-deviation threshold.

[0044] As can be seen from the above, this embodiment breaks down the global virtual-to-real deviation value according to the dimension of virtual energy-consuming devices, calculates the sub-energy deviation value and sub-carbon emission deviation value corresponding to each virtual energy-consuming device, and determines whether they exceed the preset sub-device deviation threshold. This allows for the identification of specific problematic devices, providing a basis for subsequent individual analysis and correction of problematic devices. It avoids ignoring individual device problems due to overall correction, thus improving the accuracy and efficiency of deviation correction. This embodiment analyzes the causes of deviation for the marked deviation contributing devices separately, which can fundamentally solve the problems existing in the target digital twin model and improve the accuracy and reliability of the target digital twin model.

[0045] In one embodiment of this application, if the data to be adjusted is predicted energy data and predicted carbon emission data, then for each energy-consuming device, a priority score is determined based on the data to be adjusted, the adjustable energy amount of the energy-consuming device in a preset time period, the adjustable carbon emission amount, and a preset unit adjustment cost, including: The energy-carbon synergy contribution coefficient of the energy-consuming equipment is determined based on the adjustable energy amount, adjustable carbon emissions, energy amount to be adjusted, and carbon emissions to be adjusted during a preset period. The energy-carbon synergy contribution coefficient is used to characterize the synergistic optimization capability of the energy-consuming equipment when adjusting energy and carbon emissions simultaneously. The energy cost efficiency and carbon emission cost efficiency of the energy-consuming equipment are determined based on the preset unit adjustment cost; the energy cost efficiency represents the adjustable energy quantity under the unit adjustment cost; the carbon emission cost efficiency represents the adjustable carbon emission quantity under the unit adjustment cost. The priority score for the energy-consuming equipment is determined based on its energy-carbon synergy contribution coefficient, energy cost efficiency, and carbon emission cost efficiency.

[0046] In this embodiment, the energy-carbon synergistic contribution coefficient can be calculated based on the following formula: ,in, Indicates the energy-carbon synergistic contribution coefficient. These are weighting coefficients, which can all be 0.5 or set by the user. This refers to the adjustable energy output of the energy-consuming equipment. Energy quantity to be adjusted This refers to the adjustable carbon emissions of the energy-consuming equipment. Carbon emissions to be adjusted.

[0047] In this embodiment, the preset unit adjustment cost includes the unit adjustable cost of energy and the unit adjustable cost of carbon emissions. The unit adjustable cost of energy refers to the control cost corresponding to achieving a 1-unit reduction in energy consumption, and the unit adjustable cost of carbon emissions refers to the control cost corresponding to achieving a 1-unit reduction in carbon emissions.

[0048] Energy cost efficiency and carbon emission cost efficiency are calculated based on the following formula: , ,in Indicates energy cost efficiency. Indicates carbon emission cost efficiency. This represents the unit adjustable cost of energy. The unit adjustable cost representing carbon emissions.

[0049] In this embodiment, the energy-carbon synergy contribution coefficient, energy cost efficiency, and carbon emission cost efficiency of the energy-consuming equipment can be normalized to eliminate the unit dimension problem, and a weighted calculation can be performed to obtain a priority score. The weights in the weighted calculation can be evenly distributed or set according to requirements.

[0050] As can be seen from the above, when the data to be adjusted is predicted energy data and predicted carbon emission data, the embodiments of this application introduce an energy-carbon synergy contribution coefficient, so that the selected energy-consuming equipment has a certain adjustment capability for both energy and carbon emissions, minimizing the number of energy-consuming equipment to be adjusted. At the same time, the adjustment cost is considered when calculating the priority score, and the synergistic optimization capability of the equipment, the economics of energy adjustment, and the economics of carbon emission adjustment are comprehensively considered, thereby optimizing the energy-carbon management strategy, improving the scientificity and rationality of decision-making, and reducing the blindness and randomness in the energy-carbon management process.

[0051] In one embodiment of this application, if the data to be adjusted is predicted energy data and predicted carbon emission data, then energy and carbon management is performed on each energy-consuming device based on its priority score and the amount of data to be adjusted, including: Select one energy-consuming device as the energy-consuming device to be adjusted according to the priority score from high to low; Perform the following energy management operations to allocate regulating energy to the energy-consuming equipment to be adjusted: If the first difference is greater than or equal to the adjustable energy amount of the energy-consuming equipment to be adjusted, then the adjustable energy amount is allocated to the energy-consuming equipment to be adjusted based on the adjustable energy amount of the equipment to be adjusted; the first difference is the difference between the energy amount to be adjusted and the currently allocated adjustable energy amount. If the first difference is less than the adjustable energy amount of the energy-consuming equipment to be adjusted, then the adjustable energy amount is allocated to the energy-consuming equipment to be adjusted based on the first difference. The process is repeated in a loop, selecting an energy-consuming device from the unselected energy-consuming devices as the energy-consuming device to be adjusted and performing energy management operations in order of priority score from high to low, until the allocated amount of regulated energy is not less than the amount of energy to be adjusted. The allocated regulated carbon emissions are determined based on the regulated energy amount and energy-carbon characteristics of each energy-consuming device to be regulated when performing energy management operations. Energy and carbon management is carried out on each energy-consuming device with unallocated regulated energy based on the relationship between the allocated regulated carbon emissions and the carbon emissions to be adjusted.

[0052] In this embodiment, the selected energy-consuming device to be adjusted is allocated regulating energy according to the following rules until the allocated regulating energy is greater than or equal to the total energy to be adjusted: Case 1: If the first difference is greater than or equal to the adjustable energy of the device, then allocate all the adjustable energy of the device, then update the first difference (subtract the adjustable energy of the device), and then select the next device with a higher score. Scenario 2: If the first difference is less than the adjustable energy amount of the equipment, then only the amount equal to the first difference is allocated. At this time, the allocated energy amount just meets the total demand, and the energy adjustment task ends.

[0053] For example, if the energy to be adjusted is 100,000 kWh, and device A can adjust 80,000 kWh, with an initial first difference of 100,000 kWh (≥80,000), then 80,000 kWh will be allocated, and the first difference will be 20,000 kWh; then device B can be selected, with an adjustment of 50,000 kWh, with a first difference of 20,000 kWh (<50,000), then 20,000 kWh will be allocated, and the energy management operation will end.

[0054] In this embodiment, the actual regulated energy amount of each participating device can be multiplied by its respective energy-carbon characteristics, and the sum can be obtained to obtain the allocated regulated carbon emissions. By comparing the allocated regulated carbon emissions with the carbon emissions to be adjusted, if the allocated regulated carbon emissions are greater than or equal to the carbon emissions to be adjusted, the carbon emission target has been achieved and no further action is required; if the allocated regulated carbon emissions are less than the carbon emissions to be adjusted, then the carbon emission adjustment needs to be performed again from the unselected devices, sorted by priority.

[0055] In one embodiment, energy carbon management is performed on each energy-consuming device with unallocated regulated energy based on the relationship between allocated regulated carbon emissions and carbon emissions to be adjusted, including: In response to the allocated regulated carbon emissions being greater than or equal to the carbon emissions to be adjusted, no operation is performed on any energy-consuming equipment for any unallocated regulated energy amount; In response to the fact that the allocated regulated carbon emissions are less than the carbon emissions to be adjusted, the energy-consuming equipment to be adjusted is re-determined from the energy-consuming equipment with unallocated regulated energy in descending order of priority score; The following carbon emission management operations are performed cyclically to allocate regulated carbon emissions to the newly identified energy-consuming equipment to be adjusted: If the second difference is greater than or equal to the adjustable carbon emissions of the newly determined energy-consuming equipment to be adjusted, then the adjustable carbon emissions of the newly determined energy-consuming equipment to be adjusted are allocated based on the adjustable carbon emissions of the newly determined energy-consuming equipment to be adjusted; the second difference is the difference between the carbon emissions to be adjusted and the currently allocated adjustable carbon emissions. If the second difference is less than the adjustable carbon emissions of the re-determined energy-consuming equipment to be adjusted, then the adjusted carbon emissions will be allocated based on the second difference. The process is repeated in descending order of priority score, re-identifying the energy-consuming equipment to be adjusted and the carbon emission management operations from the energy-consuming equipment that has not been allocated energy for adjustment, until the allocated carbon emission adjustment amount is not less than the carbon emission to be adjusted.

[0056] In this embodiment, if the allocated adjusted carbon emissions are greater than or equal to the carbon emissions to be adjusted, no operation is performed on the remaining unallocated equipment, and the carbon management process ends.

[0057] If the allocated adjusted carbon emissions are less than the carbon emissions to be adjusted, the carbon emission target has not been met, and the supplementary adjustment (carbon emission management operation) process will be initiated, with the following rules: The following operation is repeated until the allocated adjusted carbon emissions are no less than the carbon emissions to be adjusted: From the equipment that has not been allocated any energy adjustment capacity, sort them by priority score from highest to lowest, and select one piece of equipment for supplementary adjustment; compare the second difference with the adjustable carbon emissions of that equipment to determine the allocation quota: If the second difference is greater than or equal to the adjustable carbon emissions of the equipment, then allocate all the adjustable carbon emissions of the equipment, update the second difference (subtract the amount), and then select the next high-priority equipment. If the second difference is less than the adjustable carbon emission of the equipment, then only the carbon emission equal to the second difference is allocated. At this point, the carbon emission target is just achieved, and the adjustment process ends.

[0058] In this embodiment, when performing carbon emission management operations, the priority score can be recalculated, and the carbon emission management operations can be performed based on the recalculated priority score. The calculation method for recalculating the priority score is the same as the calculation method for the priority score in the previous embodiment when the data to be adjusted only contains predicted carbon emission data. To save computing resources, the priority score is not recalculated in this embodiment; the carbon emission management operation is still performed using the already calculated priority score.

[0059] As can be seen from the above, this embodiment selects energy-consuming equipment for adjustment based on priority scores from high to low. The priority score integrates the energy-carbon synergy contribution coefficient, energy cost efficiency, and carbon emission cost efficiency, ensuring that equipment performing best in both energy and carbon dual-objective optimization and cost control is prioritized for adjustment. This scientific ranking based on multi-dimensional indicators avoids the problem of blindly selecting equipment, concentrating adjustment resources on the most critical equipment and significantly improving management efficiency. When the carbon emission target is not achieved, this embodiment re-selects equipment from the remaining equipment according to priority for supplementary adjustment, ensuring that the additional adjustment to the carbon emission target is completed at the lowest cost.

[0060] In one embodiment of this application, an alarm is triggered in response to the fact that the adjustable energy amount of each energy-consuming device in the target area is less than the energy amount to be adjusted during a preset period, and / or the adjustable carbon emission amount of each energy-consuming device in the target area is less than the carbon emission amount to be adjusted during a preset period.

[0061] In this embodiment, if the adjustable capacity of each energy-consuming device in the target area cannot meet the required energy amount and / or carbon emission amount to be adjusted, an alarm will be issued to remind relevant personnel that the preset requirements cannot be met by adjusting the internal devices alone, so that relevant personnel can take further action.

[0062] Corresponding to the energy and carbon management method based on digital twins in the above embodiments, Figure 2 This is a structural block diagram of a digital twin-based energy and carbon management system provided in one embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 2 The digital twin-based energy and carbon management system 20 includes: an energy and carbon data prediction module 21, a data to be adjusted determination module 22, an equipment adjustable parameter determination module 23, a priority score determination module 24, and an energy and carbon management module 25.

[0063] Among them, the energy and carbon data prediction module 21 is used to predict the energy and carbon data of the target area in a preset period based on the target digital twin model, so as to obtain the predicted energy data and predicted carbon emission data; the target digital twin model is constructed based on the actual layout of energy-consuming equipment in the target area; The data to be adjusted determination module 22 is used to mark the data in the predicted energy data and / or predicted carbon emission data that do not meet the corresponding conditions as data to be adjusted, and to determine the amount of data to be adjusted in the data to be adjusted if the predicted energy data and / or predicted carbon emission data do not meet the corresponding conditions. The adjustable parameter determination module 23 is used to determine the adjustable parameters of each energy-consuming device in the target area based on the energy consumption characteristics and energy-carbon characteristics of the energy-consuming device in the preset time period; wherein, the energy-carbon characteristics are used to characterize the energy-carbon conversion efficiency of the energy-consuming device; the adjustable parameters include: adjustable energy amount and adjustable carbon emissions; The priority score determination module 24 is used to determine the priority score corresponding to each energy-consuming device based on the data to be adjusted, the adjustable parameters of each energy-consuming device in a preset time period, and the preset unit adjustment cost. The energy and carbon management module 25 is used to manage the energy and carbon of each energy-consuming device based on the priority score of each device and the amount of data to be adjusted.

[0064] In one embodiment of this application, the digital twin-based energy and carbon management system 20 further includes: Deviation correction module, used for: The physical operation data and real-time energy data of each energy-consuming device in the target area are collected by the sensing and monitoring devices deployed on each energy-consuming device in the target area, and the real-time carbon emission data corresponding to each energy-consuming device is determined based on the real-time energy data of each energy-consuming device. Based on the physical operation data of each energy-consuming device, obtain the virtual energy data and virtual carbon emission data output by the target digital twin model; The virtual-to-real deviation value is determined based on real-time energy data, virtual energy data, real-time carbon emission data, and virtual carbon emission data; The deviation of the target digital twin model is corrected based on the virtual-real deviation value.

[0065] In one embodiment of this application, the amount of data to be adjusted includes the amount of energy to be adjusted and the amount of carbon emissions to be adjusted; If the data to be adjusted is predicted energy data and predicted carbon emission data, then for each energy-consuming device, the priority score determination module 24 is used to determine the energy-carbon synergy contribution coefficient of the energy-consuming device based on the adjustable energy amount, adjustable carbon emission amount, energy amount to be adjusted and carbon emission amount to be adjusted of the energy-consuming device in a preset period; the energy-carbon synergy contribution coefficient is used to characterize the synergistic optimization capability of the energy-consuming device when adjusting energy and carbon emissions at the same time. The energy cost efficiency and carbon emission cost efficiency of the energy-consuming equipment are determined based on the preset unit adjustment cost; the energy cost efficiency represents the adjustable energy quantity under the unit adjustment cost; the carbon emission cost efficiency represents the adjustable carbon emission quantity under the unit adjustment cost. The priority score for the energy-consuming equipment is determined based on its energy-carbon synergy contribution coefficient, energy cost efficiency, and carbon emission cost efficiency.

[0066] In one embodiment of this application, the amount of data to be adjusted includes the amount of energy to be adjusted and the amount of carbon emissions to be adjusted; if the data to be adjusted is predicted energy data and predicted carbon emission data, then the energy and carbon management module 25 is specifically used to select an energy-consuming device as the energy-consuming device to be adjusted in order of priority score from high to low. Perform the following energy management operations to allocate regulating energy to the energy-consuming equipment to be adjusted: If the first difference is greater than or equal to the adjustable energy amount of the energy-consuming equipment to be adjusted, then the adjustable energy amount is allocated to the energy-consuming equipment to be adjusted based on the adjustable energy amount of the equipment to be adjusted; the first difference is the difference between the energy amount to be adjusted and the currently allocated adjustable energy amount. If the first difference is less than the adjustable energy amount of the energy-consuming equipment to be adjusted, then the adjustable energy amount is allocated to the energy-consuming equipment to be adjusted based on the first difference. The process is repeated in a loop, selecting an energy-consuming device from the unselected energy-consuming devices as the energy-consuming device to be adjusted and performing energy management operations in order of priority score from high to low, until the allocated amount of regulated energy is not less than the amount of energy to be adjusted. The allocated regulated carbon emissions are determined based on the regulated energy amount and energy-carbon characteristics of each energy-consuming device to be regulated when performing energy management operations. Energy and carbon management is carried out on each energy-consuming device with unallocated regulated energy based on the relationship between the allocated regulated carbon emissions and the carbon emissions to be adjusted.

[0067] In one embodiment of this application, the carbon management module 25 is further configured to not perform operations on each energy-consuming device with each unallocated amount of regulated energy in response to the allocated regulated carbon emissions being greater than or equal to the carbon emissions to be adjusted. In response to the fact that the allocated regulated carbon emissions are less than the carbon emissions to be adjusted, the energy-consuming equipment to be adjusted is re-determined from the energy-consuming equipment with unallocated regulated energy in descending order of priority score; The following carbon emission management operations are performed cyclically to allocate regulated carbon emissions to the newly identified energy-consuming equipment to be adjusted: If the second difference is greater than or equal to the adjustable carbon emissions of the newly determined energy-consuming equipment to be adjusted, then the adjustable carbon emissions of the newly determined energy-consuming equipment to be adjusted are allocated based on the adjustable carbon emissions of the newly determined energy-consuming equipment to be adjusted; the second difference is the difference between the carbon emissions to be adjusted and the currently allocated adjustable carbon emissions. If the second difference is less than the adjustable carbon emissions of the re-determined energy-consuming equipment to be adjusted, then the adjusted carbon emissions will be allocated based on the second difference. The process is repeated in descending order of priority score, re-identifying the energy-consuming equipment to be adjusted and the carbon emission management operations from the energy-consuming equipment that has not been allocated energy for adjustment, until the allocated carbon emission adjustment amount is not less than the carbon emission to be adjusted.

[0068] In one embodiment of this application, the adjustable parameter determination module 23 is specifically used to determine the adjustable energy amount of the energy-consuming device during a preset time period based on the energy consumption characteristics of the energy-consuming device during the preset time period. The adjustable carbon emissions of the energy-consuming equipment during the preset period are determined based on its energy-carbon characteristics and the adjustable energy quantity of the equipment during the preset period.

[0069] In one embodiment of this application, the amount of data to be adjusted includes the amount of energy to be adjusted and / or the amount of carbon emissions to be adjusted; The energy and carbon management system 20 based on digital twins also includes: an alarm module, used to issue an alarm in response to the fact that the adjustable energy amount of each energy-consuming device in the target area is less than the energy amount to be adjusted during a preset period, and / or that the adjustable carbon emissions of each energy-consuming device in the target area are less than the carbon emissions to be adjusted during a preset period.

[0070] See Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of the modules in the aforementioned system embodiments, for example... Figure 2 The functions of the carbon data prediction module 21, the data to be adjusted determination module 22, the equipment adjustable parameter determination module 23, the priority score determination module 24, and the carbon management module 25 are shown.

[0071] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0072] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0073] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.

[0074] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation methods described in the digital twin-based energy and carbon management method provided in the embodiments of this application, or they can execute the implementation methods of the electronic devices described in the embodiments of this application, which will not be repeated here.

[0075] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0076] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., provided on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0077] Those skilled in the art will recognize that the modules / units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0078] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules, units, or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or modules / units, or it may be an electrical, mechanical, or other form of connection.

[0080] The modules / units described as separate components may or may not be physically separate. Similarly, the components shown as modules / units may or may not be physical modules / units; they may be located in one place or distributed across multiple network modules / units. Some or all of the modules / units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0081] Furthermore, the functional modules / units in the various embodiments of this application can be integrated into one processing module / unit, or each module / unit can exist physically separately, or two or more modules / units can be integrated into one module / unit. The integrated modules / units described above can be implemented in hardware or in the form of software functional modules / units.

[0082] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A digital twin-based energy and carbon management method, characterized in that, include: Based on the target digital twin model, the energy and carbon data of the target area are predicted in a preset time period to obtain predicted energy data and predicted carbon emission data. The target digital twin model is constructed based on the actual layout of energy-consuming devices in the target area. The deviation correction process of the target digital twin model includes: collecting physical operation data and real-time energy data of each energy-consuming device in the target area through sensing and monitoring devices deployed on each energy-consuming device; determining the real-time carbon emission data corresponding to each energy-consuming device based on the real-time energy data; obtaining virtual energy data and virtual carbon emission data output by the target digital twin model based on the physical operation data of each energy-consuming device; determining the virtual-real deviation value based on the real-time energy data, the virtual energy data, the real-time carbon emission data, and the virtual carbon emission data; and correcting the deviation of the target digital twin model based on the virtual-real deviation value. If the predicted energy data and / or the predicted carbon emission data do not meet the corresponding conditions, then the data in the predicted energy data and the predicted carbon emission data that do not meet the corresponding conditions are marked as data to be adjusted, and the amount of data to be adjusted for the data to be adjusted is determined. For each energy-consuming device in the target area, adjustable parameters for that device are determined based on its energy consumption characteristics and energy-carbon characteristics over a preset time period. The energy-carbon characteristics characterize the energy-carbon conversion efficiency of the energy-consuming device. The adjustable parameters include adjustable energy consumption and adjustable carbon emissions. Based on the data to be adjusted, the adjustable parameters of each energy-consuming device during the preset time period, and the preset unit adjustment cost, the priority score corresponding to each energy-consuming device is determined. Energy and carbon management is performed on each energy-consuming device based on its priority score and the amount of data to be adjusted. The amount of data to be adjusted includes the amount of energy to be adjusted and the amount of carbon emissions to be adjusted. If the data to be adjusted is the predicted energy data and the predicted carbon emission data, then the energy and carbon management of each energy-consuming device based on the priority score corresponding to each energy-consuming device and the amount of data to be adjusted includes: Select one energy-consuming device as the energy-consuming device to be adjusted according to the priority score from high to low; Perform the following energy management operations to allocate regulated energy to the energy-consuming equipment to be regulated: If the first difference is greater than or equal to the adjustable energy amount of the energy-consuming device to be adjusted, then an adjustment energy amount is allocated to the energy-consuming device to be adjusted based on the adjustable energy amount of the energy-consuming device to be adjusted; the first difference is the difference between the energy amount to be adjusted and the currently allocated adjustment energy amount; If the first difference is less than the adjustable energy amount of the energy-consuming device to be adjusted, then the adjustable energy amount is allocated to the energy-consuming device to be adjusted based on the first difference. The process is repeated in a loop, selecting an energy-consuming device from the unselected energy-consuming devices as the energy-consuming device to be adjusted, in descending order of priority score, and performing the energy management operation, until the allocated amount of regulated energy is not less than the amount of energy to be adjusted; The allocated regulated carbon emissions are determined based on the regulated energy amount and energy-carbon characteristics of each energy-consuming device to be regulated when performing energy management operations. In response to the allocated regulated carbon emissions being greater than or equal to the carbon emissions to be adjusted, no operation is performed on any energy-consuming equipment with any unallocated regulated energy. In response to the fact that the allocated regulated carbon emissions are less than the carbon emissions to be adjusted, the energy-consuming equipment to be adjusted is re-determined from the energy-consuming equipment with unallocated regulated energy in descending order of priority score; The following carbon emission management operations are performed cyclically to allocate regulated carbon emissions to the newly identified energy-consuming equipment to be adjusted: If the second difference is greater than or equal to the redefined adjustable carbon emissions of the energy-consuming equipment to be adjusted, then an adjustable carbon emission is allocated to the redefined energy-consuming equipment to be adjusted based on the redefined adjustable carbon emissions; the second difference is the difference between the carbon emissions to be adjusted and the currently allocated adjustable carbon emissions. If the second difference is less than the adjustable carbon emissions of the re-determined energy-consuming equipment to be adjusted, then the adjusted carbon emissions are allocated based on the second difference for the re-determined energy-consuming equipment to be adjusted. The process is repeated in descending order of priority score, re-determining the energy-consuming devices to be adjusted and the carbon emission management operation from the energy-consuming devices that have not been allocated energy for adjustment, until the allocated carbon emission adjustment amount is not less than the carbon emission to be adjusted.

2. The energy and carbon management method based on digital twins as described in claim 1, characterized in that, The amount of data to be adjusted includes the amount of energy to be adjusted and the amount of carbon emissions to be adjusted. If the data to be adjusted consists of the predicted energy data and the predicted carbon emission data, then for each energy-consuming device, a priority score is determined based on the data to be adjusted, the adjustable energy amount and adjustable carbon emission amount of the energy-consuming device in a preset time period, and a preset unit adjustment cost, including: The energy-carbon synergy contribution coefficient of the energy-consuming equipment is determined based on the adjustable energy amount, adjustable carbon emissions, the energy amount to be adjusted, and the carbon emissions to be adjusted during a preset period. The energy-carbon synergy contribution coefficient is used to characterize the synergistic optimization capability of the energy-consuming equipment when simultaneously adjusting energy and carbon emissions. The energy cost efficiency and carbon emission cost efficiency of the energy-consuming equipment are determined based on the preset unit adjustment cost; the energy cost efficiency represents the adjustable energy amount under the unit adjustment cost; the carbon emission cost efficiency represents the adjustable carbon emission amount under the unit adjustment cost. The priority score for the energy-consuming equipment is determined based on its energy-carbon synergy contribution coefficient, energy cost efficiency, and carbon emission cost efficiency.

3. The energy and carbon management method based on digital twins as described in claim 1, characterized in that, The determination of adjustable parameters of the energy-consuming equipment within a preset time period based on its energy consumption characteristics and energy-carbon characteristics includes: The adjustable energy amount of the energy-consuming equipment during the preset time period is determined based on the energy consumption characteristics of the equipment during the preset time period. The adjustable carbon emissions of the energy-consuming equipment during the preset period are determined based on the energy-carbon characteristics and the adjustable energy amount of the energy-consuming equipment during the preset period.

4. The energy and carbon management method based on digital twins as described in claim 1, characterized in that, The amount of data to be adjusted includes the amount of energy to be adjusted and / or the amount of carbon emissions to be adjusted; The method further includes: An alarm is triggered if the adjustable energy amount of each energy-consuming device in the target area is less than the energy amount to be adjusted during a preset period, and / or if the adjustable carbon emissions of each energy-consuming device in the target area are less than the carbon emissions to be adjusted during a preset period.

5. A digital twin-based energy and carbon management system for implementing the energy and carbon management method as described in any one of claims 1-4, characterized in that, include: The energy and carbon data prediction module is used to predict the energy and carbon data of a target area within a preset time period based on a target digital twin model, thereby obtaining predicted energy data and predicted carbon emission data; the target digital twin model is constructed based on the actual layout of energy-consuming equipment in the target area; The data to be adjusted determination module is used to mark the data in the predicted energy data and / or the predicted carbon emission data that do not meet the corresponding conditions as data to be adjusted if the predicted energy data and / or the predicted carbon emission data do not meet the corresponding conditions, and to determine the amount of data to be adjusted in the data to be adjusted. The adjustable parameter determination module is used to determine the adjustable parameters of each energy-consuming device in a target area based on its energy consumption characteristics and energy-carbon characteristics within a preset time period; wherein, the energy-carbon characteristics are used to characterize the energy-carbon conversion efficiency of the energy-consuming device; the adjustable parameters include: adjustable energy quantity and adjustable carbon emissions; The priority score determination module is used to determine the priority score corresponding to each energy-consuming device based on the data to be adjusted, the adjustable parameters of each energy-consuming device in a preset time period, and the preset unit adjustment cost. The energy and carbon management module is used to manage the energy and carbon of each energy-consuming device based on the priority score corresponding to each device and the amount of data to be adjusted.

6. The energy and carbon management system based on digital twins as described in claim 5, characterized in that, Also includes: Deviation correction module, used for: The physical operation data and real-time energy data of each energy-consuming device in the target area are collected by the sensing and monitoring devices deployed on each energy-consuming device in the target area, and the real-time carbon emission data corresponding to each energy-consuming device is determined based on the real-time energy data of each energy-consuming device. Based on the physical operation data of each energy-consuming device, obtain the virtual energy data and virtual carbon emission data output by the target digital twin model; The virtual-to-real deviation value is determined based on the real-time energy data, the virtual energy data, the real-time carbon emission data, and the virtual carbon emission data. The target digital twin model is corrected for deviations based on the virtual-to-real deviation value.

7. The energy and carbon management system based on digital twins as described in claim 5, characterized in that, The amount of data to be adjusted includes the amount of energy to be adjusted and the amount of carbon emissions to be adjusted. If the data to be adjusted is predicted energy data and predicted carbon emission data, then for each energy-consuming device, the priority score determination module is specifically used to determine the energy-carbon synergistic contribution coefficient of the energy-consuming device based on the adjustable energy amount, adjustable carbon emission amount, the energy amount to be adjusted, and the carbon emission amount to be adjusted of the energy-consuming device in a preset period; the energy-carbon synergistic contribution coefficient is used to characterize the synergistic optimization capability of the energy-consuming device when adjusting energy and carbon emissions simultaneously. The energy cost efficiency and carbon emission cost efficiency of the energy-consuming equipment are determined based on the preset unit adjustment cost. The energy cost efficiency represents the adjustable energy quantity per unit of adjustment cost; the carbon emission cost efficiency represents the adjustable carbon emission quantity per unit of adjustment cost. The priority score for the energy-consuming equipment is determined based on its energy-carbon synergy contribution coefficient, energy cost efficiency, and carbon emission cost efficiency.

Images