Digital twin energy scheduling system and method
By using a digital twin energy dispatching system to conduct multi-dimensional evaluation of real-time energy coordination data of regional clusters, the problem of the lack of specificity in dispatching schemes in traditional dispatching methods is solved, and efficient and stable energy dispatching and emergency response are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- STATE GRID SHANGHAI MUNICIPAL ELECTRIC POWER CO
- Filing Date
- 2025-12-11
- Publication Date
- 2026-04-17
AI Technical Summary
Traditional energy dispatching methods struggle to capture energy synergy across different regional collaboration dimensions, resulting in dispatching schemes lacking specificity, failing to fully leverage the advantages of energy complementarity, and impacting the efficient and stable operation of the energy system.
A digital twin energy dispatching system is adopted to divide the real-time energy coordination data of the target area cluster into sub-coordination datasets according to the regional cooperation dimension. These datasets are then input into the twin energy coordination model for dispatch coordination evaluation. The optimal dispatching scheme is formulated by integrating multi-dimensional evaluation values and dynamically adjusted by combining historical and real-time data.
It has improved the targeting and feasibility of energy dispatch, ensured energy response in emergency scenarios, and achieved efficient and stable dispatch of regional clusters.
Smart Images
Figure CN121886409A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of energy dispatching technology, and more specifically, to a digital twin energy dispatching system and method. Background Technology
[0002] In the field of energy dispatch, traditional dispatch methods struggle to capture energy synergy across different regional collaboration dimensions. The matching of energy production and demand and transmission efficiency between different cities cannot be fully and meticulously analyzed, resulting in a lack of targeted dispatch schemes and low energy utilization efficiency. Traditional dispatch methods fail to classify and process scenarios with different collaboration intensities, making it difficult to fully leverage the advantages of energy complementarity in high collaboration intensities and to solve the problems of energy transmission stability and response speed in low collaboration intensities. They also fail to consider factors such as the scope of regional collaboration, energy interaction density, and emergency response level, leading to incomplete evaluation results. This, in turn, affects the optimization effect of the final dispatch scheme and makes it difficult to achieve efficient and stable operation of the energy system. Summary of the Invention
[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a digital twin energy dispatching system and method.
[0004] To achieve the above objectives, the present invention provides the following technical solution: A digital twin energy dispatching method includes the following steps: The real-time energy collaboration data of the target area cluster is divided into sub-collaboration datasets according to the regional collaboration dimension. The sub-collaboration datasets are then input into the twin energy collaboration model to obtain the first energy data to be collaborated on. The first scheduling coordination evaluation value is obtained by performing a scheduling coordination evaluation based on the first energy data to be coordinated; Real-time energy coordination data is divided into a second set of energy data to be coordinated, which is in ascending order of coordination intensity, and a third set of energy data to be coordinated, which is in descending order of coordination intensity. The second set of energy data to be coordinated is processed to obtain a second scheduling coordination evaluation value, and the third set of energy data to be coordinated is processed to obtain a third scheduling coordination evaluation value. The final energy dispatch plan will be executed based on the first, second, and third dispatch coordination assessment values.
[0005] Preferably, the method further includes the following steps: Acquire historical energy coordination data for different types of regions; Extract regional energy complementarity characteristics, energy network transmission parameters, and emergency response capability indicators from historical energy synergy data; Historical energy dispatch coordination values are calculated based on the energy complementarity characteristics between root regions, energy network transmission parameters, and emergency response capability indicators. A twin energy coordination model is constructed based on the characteristics of inter-regional energy complementarity, energy network transmission parameters, emergency response capability indicators, and historical energy dispatch coordination values.
[0006] Preferably, the sub-coordinated dataset is input into the twin energy coordination model to obtain the first energy data to be coordinated, specifically including the following steps: The scheduling coordination effect of different sub-coordinated datasets was obtained through the twin energy coordination model; The optimal sub-cooperative data is obtained by filtering from the sub-cooperative dataset based on the scheduling coordination effect; The real-time energy collaboration data is divided into several data with equal collaboration intensity according to the collaboration intensity of the optimal sub-collaboration data to obtain the first energy data to be collaborated.
[0007] Preferably, the first scheduling coordination evaluation value is obtained by performing a scheduling coordination evaluation based on the first energy data to be coordinated, specifically including the following steps: Detect the network loss parameters and real-time emergency demand data corresponding to the various levels of cooperation intensity data in the first energy data to be coordinated; The basic collaborative scheduling strategy is adjusted based on the correlation between network loss parameters and real-time emergency demand data to obtain the first collaborative strategy to be executed. The first energy data to be coordinated, network loss parameters, real-time emergency demand data, and the first coordination strategy to be executed are input into the twin energy coordination model to obtain the first scheduling coordination determination value. The first coordination difference is obtained by calculating the difference between the first scheduling coordination determined value and the first standard scheduling coordination value. The first coordination compensation coefficient is obtained by the ratio of the first coordination difference to the first standard scheduling coordination value. The first scheduling coordination evaluation value is obtained by correcting the first scheduling coordination determination value based on the first coordination compensation coefficient.
[0008] Preferably, the real-time energy coordination data includes energy surplus gap data, real-time flow data of cross-regional energy allocation, and operational status data of energy network nodes.
[0009] Preferably, the basic collaborative scheduling strategy is adjusted based on the correlation between network loss parameters and real-time emergency demand data to obtain a first collaborative strategy to be executed, specifically including the following steps: The network loss parameters include power loss of transmission lines, flow loss of gas pipelines, signal loss of data transmission, and energy consumption loss of equipment operation. The real-time emergency demand data includes regional disaster early warning levels, energy security priorities, and emergency energy demand gaps. The loss threshold is obtained based on network loss parameters, and the urgency level is obtained based on real-time emergency demand data. The first collaborative strategy to be executed is obtained by adjusting the preset basic collaborative scheduling strategy based on the loss threshold and urgency level.
[0010] Preferably, the second energy data to be coordinated is processed to obtain a second scheduling coordination evaluation value, specifically including the following steps: Extract the energy interaction characteristics of each collaborative link in the second energy data to be coordinated; wherein, the energy interaction characteristics include the timeliness of energy flow between regions, the frequency of energy form conversion, and the energy supply and demand response rate; Based on the characteristics of energy interaction, several collaborative units are divided, and each collaborative unit corresponds to a set of continuous energy coordination processes; Collect data on energy storage fluctuations, energy conversion loss rates, and regional energy consumption curve trends of each collaborative unit within a preset time period. The energy buffer capacity of each cooperative unit is determined based on the fluctuation of energy storage, the energy utilization efficiency of each cooperative unit is determined based on the energy conversion loss rate, and the demand matching degree of each cooperative unit is determined based on the changing trend of the regional energy consumption curve. Based on energy buffering capacity, energy utilization efficiency, and demand matching degree, a collaborative correlation map of each collaborative unit is constructed. The collaborative correlation map is used to characterize the energy complementarity relationship and mutual influence degree between different collaborative units. The priority ranking results are obtained by sorting the energy scheduling priorities of each cooperative unit according to the collaborative association graph. The energy scheduling parameters of each cooperating unit are dynamically adjusted according to the priority ranking results. The energy scheduling parameters include the energy transmission start amount, transmission interval duration and transmission termination threshold. The coordination value of each cooperating unit is calculated in combination with the adjusted energy scheduling parameters. The second scheduling coordination evaluation value is obtained by correcting the coordination value based on the correlation strength between the cooperating unit and other cooperating units.
[0011] Preferably, the third energy data to be coordinated is processed to obtain the third scheduling coordination evaluation value, specifically including the following steps: Extract the energy interaction trajectory of each region from the third energy data to be coordinated; wherein, the energy interaction trajectory includes the energy transfer path between regions, energy handover nodes, and energy transfer duration; The energy interaction trajectory is divided into several interaction segments, and each interaction segment corresponds to a continuous inter-regional energy transfer process. Collect data on energy attenuation, node response delay, and changes in regional energy reserves during the operation of each interactive segment; The energy transmission stability of each interaction segment is determined based on the energy attenuation rate, the coordinated response speed of each interaction segment is determined based on the node response delay, and the supply and demand balance of each interaction segment is determined based on the change in regional energy reserves. The collaborative influence coefficients of each interaction segment are obtained based on energy transmission stability, collaborative response speed, and supply-demand balance. The energy dispatch adaptability of each interaction segment is evaluated based on the synergistic impact coefficient to obtain the evaluation result; The energy dispatch thresholds for each interaction segment are adaptively adjusted according to the evaluation results. The energy dispatch thresholds include the energy transfer start limit, the fluctuation range of the transfer process, and the transfer termination margin. The coordinated adaptation score of each interaction segment is obtained by combining the adjusted energy dispatch thresholds. The initial collaborative evaluation results are obtained by summing the collaborative adaptation scores of all interaction segments; The preliminary coordination evaluation results are calibrated based on the constraint strength between this interaction segment and other interaction segments to obtain the third scheduling coordination evaluation value.
[0012] Preferably, the energy dispatching scheme is executed based on the first dispatching coordination evaluation value, the second dispatching coordination evaluation value, and the third dispatching coordination evaluation value, specifically including the following steps: Identify the collaborative scenario characteristics corresponding to the first, second, and third scheduling collaborative evaluation values; wherein, the collaborative scenario characteristics include the scope of regional cooperation, energy interaction density, and emergency response level; A comprehensive coordination evaluation benchmark is obtained based on the first, second, and third scheduling coordination evaluation values and their corresponding weights. Extract the characteristic parameters that are lacking in the comprehensive collaborative evaluation benchmark for energy dispatch; wherein, the characteristic parameters include regional energy dispatch lag points and areas with weak emergency response; Develop multi-dimensional scheduling optimization directions based on characteristic parameters; among which, the optimization directions include adjusting inter-regional energy flow paths, enhancing transmission capacity, and optimizing the layout of emergency reserves; Based on the optimization direction, the optimal execution scheduling scheme is taken as the final energy scheduling scheme.
[0013] A digital twin energy dispatching system includes: The module divides the real-time energy collaboration data of the target area cluster into sub-collaboration datasets according to the regional collaboration dimension. The sub-collaboration datasets are then input into the twin energy collaboration model to obtain the first energy data to be collaborated on. Evaluation module: Based on the first energy source data to be coordinated, a scheduling coordination evaluation is performed to obtain the first scheduling coordination evaluation value; Processing module: Divides real-time energy coordination data into second energy data to be coordinated in ascending order of coordination intensity and third energy data to be coordinated in descending order; processes the second energy data to be coordinated to obtain the second scheduling coordination evaluation value, and processes the third energy data to be coordinated to obtain the third scheduling coordination evaluation value; Output module: Executes the final energy dispatching scheme based on the first, second, and third dispatching coordination evaluation values.
[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention processes real-time energy collaboration data of a target regional cluster by dividing it into sub-collaboration datasets according to regional collaboration dimensions and inputting them into a twin energy collaboration model to obtain the first set of energy data to be collaborated on. This fully explores the energy collaboration potential under different regional collaboration scenarios, providing more realistic basic data for subsequent scheduling and thus improving the targeting of energy scheduling. Based on the first set of energy data to be collaborated on, a scheduling collaboration evaluation is performed to obtain a first scheduling collaboration evaluation value, ensuring the feasibility and efficiency of the scheduling scheme. The real-time energy collaboration data is then divided into a second set of energy data to be collaborated on in ascending order of collaboration intensity and a third set of energy data to be collaborated on in descending order, and processed separately to obtain the second and third scheduling collaboration evaluation values. The final energy scheduling scheme is executed based on the first, second, and third scheduling collaboration evaluation values. By integrating multi-dimensional evaluation results, a comprehensive and optimal energy scheduling scheme is formulated. This ensures energy response in emergency scenarios, thereby achieving efficient and stable energy scheduling for the entire regional cluster. Attached Figure Description
[0015] Figure 1 This is a schematic diagram illustrating the steps of a digital twin energy dispatching method proposed in this invention; Figure 2 This invention presents a schematic diagram of a digital twin energy dispatching system. Detailed Implementation
[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0017] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.
[0018] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.
[0019] Reference Figures 1-2 As shown.
[0020] The embodiments further illustrate the digital twin energy dispatching system and method proposed in this invention.
[0021] A digital twin energy dispatching method includes the following steps: The real-time energy collaboration data of the target area cluster is divided into sub-collaboration datasets according to the regional collaboration dimension. The sub-collaboration datasets are then input into the twin energy collaboration model to obtain the first energy data to be collaborated on. The first scheduling coordination evaluation value is obtained by performing a scheduling coordination evaluation based on the first energy data to be coordinated; Real-time energy coordination data is divided into a second set of energy data to be coordinated, which is in ascending order of coordination intensity, and a third set of energy data to be coordinated, which is in descending order of coordination intensity. The second set of energy data to be coordinated is processed to obtain a second scheduling coordination evaluation value, and the third set of energy data to be coordinated is processed to obtain a third scheduling coordination evaluation value. The final energy dispatch plan will be executed based on the first, second, and third dispatch coordination assessment values.
[0022] It also includes the following steps: Acquire historical energy coordination data for different types of regions; Extract regional energy complementarity characteristics, energy network transmission parameters, and emergency response capability indicators from historical energy synergy data; Historical energy dispatch coordination values are calculated based on the energy complementarity characteristics between root regions, energy network transmission parameters, and emergency response capability indicators. A twin energy coordination model is constructed based on the characteristics of inter-regional energy complementarity, energy network transmission parameters, emergency response capability indicators, and historical energy dispatch coordination values.
[0023] First, acquire historical energy coordination data for different types of regions. For example, collect data on energy production, transmission, consumption, and coordinated scheduling in urban centers, industrial parks, agricultural areas, and remote mountainous regions.
[0024] This study extracts regional energy complementarity characteristics, energy network transmission parameters, and emergency response capability indicators from historical energy coordination data. Regional energy complementarity reflects the complementarity of energy production and demand in different regions. For example, a region may have abundant solar energy resources, generating a large surplus of solar power during the day, while an adjacent region may lack solar energy resources but have significant industrial electricity demand during the day. This temporal and spatial complementarity in energy production and demand embodies the regional energy complementarity characteristic, which is quantified by assessing the temporal and spatial distribution differences in energy supply and demand across different regions. Energy network transmission parameters include power loss in transmission lines, flow loss in gas pipelines, data transmission signal loss, and energy consumption losses during equipment operation. Taking power loss in transmission lines as an example, it is calculated by multiplying the square of the line current by the line resistance. Different transmission line materials, thicknesses, and lengths result in different loss values, reflecting the efficiency of energy transmission. Emergency response capability indicators involve regional disaster warning levels, energy security priorities, and emergency energy demand gaps. When a region issues a red alert for rainstorm disaster, the priority of energy security in that region increases significantly, and there is a shortage of emergency energy demand. By statistically analyzing the data of these indicators in historical emergency scenarios, the region's energy response capability under emergency conditions can be clearly identified.
[0025] Historical energy dispatch coordination value is calculated based on inter-regional energy complementarity characteristics, energy network transmission parameters, and emergency response capability indicators. Assuming A represents the quantified value of inter-regional energy complementarity characteristics, B represents the comprehensive quantified value of energy network transmission parameters, and C represents the quantified value of emergency response capability indicators, these three parameters are assigned corresponding weight coefficients, such as a, b, and c (and a+b+c=1). Then, the historical energy dispatch coordination value V is expressed as V=a×A+b×B+c×C.
[0026] A twin energy coordination model is constructed based on inter-regional energy complementarity characteristics, energy network transmission parameters, emergency response capability indicators, and historical energy dispatch coordination values. This data-driven model uses extracted inter-regional energy complementarity characteristics, energy network transmission parameters, and emergency response capability indicators as inputs, and historical energy dispatch coordination values as outputs. It is trained using machine learning algorithms, enabling the model to learn the mapping relationship between these input features and the output energy dispatch coordination effects. For example, after training, when given the inter-regional energy complementarity characteristics, energy network transmission parameters, and emergency response capability indicators of a specific regional cluster, the model predicts the energy dispatch coordination effect under that scenario, thus providing scientific decision support for real-time energy dispatch.
[0027] The first energy data to be coordinated is obtained by inputting the sub-coordinated dataset into the twin energy coordination model, which specifically includes the following steps: The scheduling coordination effect of different sub-coordinated datasets was obtained through the twin energy coordination model; The optimal sub-cooperative data is obtained by filtering from the sub-cooperative dataset based on the scheduling coordination effect; The real-time energy collaboration data is divided into several data with equal collaboration intensity according to the collaboration intensity of the optimal sub-collaboration data to obtain the first energy data to be collaborated.
[0028] The scheduling coordination effect of different sub-coordination datasets is obtained through a twin energy coordination model. The twin energy coordination model is trained based on historical energy data and can simulate the performance of different sub-coordination datasets in the energy scheduling process. Assuming there are multiple sub-coordination datasets corresponding to different combinations of urban commercial and industrial areas, and residential and agricultural areas, respectively, after inputting these sub-coordination datasets into the model, the model outputs the scheduling coordination effect corresponding to each dataset, such as performance data in terms of energy transmission efficiency, supply-demand matching degree, and emergency response speed.
[0029] The optimal sub-coordinated data is selected from the sub-coordinated datasets based on the scheduling coordination effect. This involves evaluating and comparing the scheduling coordination effect output by the model, and selecting the sub-coordinated data that performs best in various aspects of energy scheduling. For example, if a dataset has the highest energy transmission efficiency, the best supply-demand matching degree, and the fastest emergency response speed among multiple sub-coordinated datasets, then this dataset is selected as the optimal sub-coordinated data.
[0030] The real-time energy coordination data is divided into several data groups with equal coordination intensity based on the coordination intensity of the optimal sub-coordination data to obtain the first energy data to be coordinated. The coordination intensity of the optimal sub-coordination data is an important reference standard, reflecting the closeness of energy coordination between regions. Taking real-time energy coordination data as an example, it includes the coordination of electricity and gas energy between different regions. According to the coordination intensity standard of the optimal sub-coordination data, these real-time data are divided into several data groups with equal coordination intensity, such as high coordination intensity group, medium coordination intensity group, and low coordination intensity group. The first energy data to be coordinated is obtained through this division.
[0031] The first scheduling coordination evaluation value is obtained by performing a scheduling coordination evaluation based on the first energy data to be coordinated, specifically including the following steps: Detect the network loss parameters and real-time emergency demand data corresponding to the various levels of cooperation intensity data in the first energy data to be coordinated; The basic collaborative scheduling strategy is adjusted based on the correlation between network loss parameters and real-time emergency demand data to obtain the first collaborative strategy to be executed. The first energy data to be coordinated, network loss parameters, real-time emergency demand data, and the first coordination strategy to be executed are input into the twin energy coordination model to obtain the first scheduling coordination determination value. The first coordination difference is obtained by calculating the difference between the first scheduling coordination determined value and the first standard scheduling coordination value. The first coordination compensation coefficient is obtained by the ratio of the first coordination difference to the first standard scheduling coordination value. The first scheduling coordination evaluation value is obtained by correcting the first scheduling coordination determination value based on the first coordination compensation coefficient.
[0032] First, the network loss parameters and real-time emergency demand data corresponding to each level of cooperation intensity in the first set of energy data to be coordinated were detected. The first set of energy data to be coordinated consists of energy coordination data divided according to cooperation intensity. Network loss parameters cover power loss in transmission lines, flow loss in gas pipelines, signal loss in data transmission, and energy consumption loss during equipment operation. For example, a transmission line may experience high power loss due to aging when transmitting a certain amount of electricity. Real-time emergency demand data includes regional disaster warning levels, energy security priorities, and emergency energy demand gaps. For example, if a region experiences a strong typhoon warning, its energy security priority is raised, and a significant emergency energy demand gap exists. These parameters and data for each group were obtained by detecting data from different cooperation intensity groups in the first set of energy data to be coordinated.
[0033] The first collaborative strategy to be executed is obtained by adjusting the basic collaborative scheduling strategy based on the correlation between network loss parameters and real-time emergency demand data. The basic collaborative scheduling strategy is a pre-set conventional energy dispatching scheme. When network loss parameters and real-time emergency demand data change, the correlation between the two affects the effectiveness of the strategy. For example, if real-time emergency demand data for a certain area shows a high disaster warning level and a high priority for energy security, while the corresponding transmission line power loss is relatively large, then the energy transmission path or mode in the basic collaborative scheduling strategy will be adjusted to reduce losses and ensure emergency energy supply. After such adjustment, the first collaborative strategy to be executed is obtained.
[0034] The first set of energy data to be coordinated, network loss parameters, real-time emergency demand data, and the first coordination strategy to be executed are input into the twin energy coordination model to obtain the first scheduling coordination determinant value. The twin energy coordination model is trained on historical data and can simulate the coordination effect of the input data after scheduling execution. For example, inputting a set of first energy data with high coordination intensity, its corresponding higher transmission line power loss, real-time emergency demand data with a high disaster early warning level, and the adjusted first coordination strategy to be executed into the model, the model outputs the scheduling coordination determinant value under this condition. The scheduling coordination determinant value reflects a quantitative indicator of the coordination effect of the scheduling strategy under the current data conditions.
[0035] The first coordination difference is obtained by calculating the difference between the first determined value of scheduling coordination and the first standard scheduling coordination value; the first coordination compensation coefficient is obtained by calculating the ratio of the first coordination difference to the first standard scheduling coordination value. The first standard scheduling coordination value is a preset quantitative value of the ideal scheduling coordination effect. For example, if the first determined value of scheduling coordination is 80 and the first standard scheduling coordination value is 100, then the first coordination difference is -20, and the first coordination compensation coefficient is -20 / 100 = -0.2.
[0036] The first scheduling coordination evaluation value is obtained by correcting the first scheduling coordination determination value based on the first coordination compensation coefficient. For example, if the first scheduling coordination determination value is 80 and the first coordination compensation coefficient is -0.2, then the corrected first scheduling coordination evaluation value is 80 + 80 × (-0.2) = 64. This allows the scheduling coordination evaluation value to more accurately reflect the deviation between the actual scheduling effect and the ideal effect, as well as the actual level.
[0037] Real-time energy coordination data includes energy surplus and deficit data, real-time flow data of cross-regional energy allocation, and operational status data of energy network nodes.
[0038] Energy surplus and deficit data reflects the surplus or shortage of energy in various regions. Real-time flow data for cross-regional energy allocation reflects the real-time transmission of energy between different regions. The energy network consists of numerous nodes, such as substations and gas pressure regulating stations. The operational status of these nodes directly affects the stability of the energy network. For example, data such as the transformer operating temperature and load rate of a substation. If the transformer temperature is too high and the load rate exceeds the safety threshold, it indicates a potential safety hazard in the operation of that node, requiring timely adjustments to energy allocation or equipment maintenance to ensure the normal operation of the energy network.
[0039] The basic collaborative scheduling strategy is adjusted based on the correlation between network loss parameters and real-time emergency demand data to obtain the first collaborative strategy to be executed, which includes the following steps: Network loss parameters include power loss of transmission lines, flow loss of gas pipelines, signal loss of data transmission, and energy consumption loss of equipment operation; Real-time emergency demand data includes regional disaster early warning levels, energy security priorities, and emergency energy demand gaps; The loss threshold is obtained based on network loss parameters, and the urgency level is obtained based on real-time emergency demand data. The first collaborative strategy to be executed is obtained by adjusting the preset basic collaborative scheduling strategy based on the loss threshold and urgency level.
[0040] Network loss parameters include power loss in transmission lines, flow loss in gas pipelines, signal loss in data transmission, and energy consumption loss in equipment operation. Power loss in transmission lines refers to the energy loss during power transmission due to factors such as line resistance. For example, in a long-distance transmission line, current will generate heat due to resistance, consuming some electrical energy. Flow loss in gas pipelines is the reduction in natural gas flow during transmission due to pipeline friction and leaks. For example, in older gas pipelines, poor sealing can lead to natural gas leaks and flow loss. Signal loss in data transmission refers to the signal attenuation of energy dispatch-related data during transmission due to distance and interference. For example, energy monitoring data from remote areas may experience signal weakening when transmitted to the dispatch center due to the long distance. Energy consumption loss in equipment operation refers to the energy consumed by various devices in the energy network during operation. For example, large transformers consume a certain amount of electrical energy for cooling and operation during continuous operation.
[0041] Real-time emergency demand data includes regional disaster warning levels, energy security priorities, and emergency energy demand gaps. Regional disaster warning levels reflect the severity of disasters facing a region; for example, a red typhoon warning indicates an extremely high risk. Energy security priorities refer to the order in which energy supplies are prioritized for different regions or facilities in an emergency; for example, hospitals and fire stations, public service facilities, typically have high energy security priorities. Emergency energy demand gaps refer to the difference between a region's actual energy demand and its existing supply capacity in an emergency; for example, a disaster causing damage to local power facilities in a region can result in a significant power demand gap.
[0042] The loss threshold is obtained based on network loss parameters. This threshold is a critical value used to determine whether network loss is within an acceptable range. For example, for a transmission line, the upper limit of power loss is determined based on its material and length; exceeding this limit indicates excessive loss. The urgency level is obtained based on real-time emergency demand data. This urgency level considers factors such as disaster warning level, priority of protection, and the amount of gaps. For example, areas with high disaster warning levels, high priority of protection, and large gaps have a very high urgency level.
[0043] The first collaborative strategy to be executed is obtained by adjusting the preset basic collaborative scheduling strategy based on the loss threshold and urgency level. The basic collaborative scheduling strategy is the energy dispatch scheme under normal circumstances. When the actual loss threshold and urgency level differ from the normal situation, the strategy needs to be adjusted. For example, if the urgency level of a certain area is very high, and the power loss of the corresponding transmission line is close to the loss threshold, the energy supply of this area should be prioritized based on the basic scheduling strategy, while the power distribution of the transmission line should be adjusted to minimize losses and meet emergency needs.
[0044] The second energy data to be coordinated is processed to obtain the second scheduling coordination evaluation value, which specifically includes the following steps: Extract the energy interaction characteristics of each collaborative link in the second energy data to be coordinated; among which, the energy interaction characteristics include the timeliness of energy flow between regions, the frequency of energy form conversion, and the energy supply and demand response rate; Based on the characteristics of energy interaction, several collaborative units are divided, and each collaborative unit corresponds to a set of continuous energy coordination processes; Collect data on energy storage fluctuations, energy conversion loss rates, and regional energy consumption curve trends of each collaborative unit within a preset time period. The energy buffer capacity of each cooperative unit is determined based on the fluctuation of energy storage, the energy utilization efficiency of each cooperative unit is determined based on the energy conversion loss rate, and the demand matching degree of each cooperative unit is determined based on the changing trend of the regional energy consumption curve. Based on energy buffering capacity, energy utilization efficiency and demand matching degree, a collaborative correlation map of each collaborative unit is constructed. The collaborative correlation map is used to characterize the energy complementarity relationship and mutual influence degree between different collaborative units. The priority ranking results are obtained by sorting the energy scheduling priorities of each cooperative unit according to the collaborative association graph. The energy scheduling parameters of each cooperating unit are dynamically adjusted according to the priority ranking results. The energy scheduling parameters include the energy transmission start amount, transmission interval duration and transmission termination threshold. The coordination value of each cooperating unit is calculated in combination with the adjusted energy scheduling parameters. The second scheduling coordination evaluation value is obtained by correcting the coordination value based on the correlation strength between the cooperating unit and other cooperating units.
[0045] The energy interaction characteristics of each collaborative link in the second set of energy data to be coordinated are extracted. These characteristics include the timeliness of inter-regional energy flow, the frequency of energy form conversion, and the energy supply and demand response rate. The timeliness of inter-regional energy flow refers to the time spent transmitting energy between different regions. For example, when transmitting electricity from a western energy base to an eastern city, the length of the transmission line and the transmission technology affect the time from generation to delivery. The frequency of energy form conversion refers to the number of times energy is converted from one form to another during transmission or utilization. For example, coal is first converted into electricity, and then electricity is converted into heat for heating; this involves two form conversions. The energy supply and demand response rate refers to how quickly the energy supplier responds to changes in the energy demand of the demand side. For example, when a region experiences a sudden increase in electricity demand due to a sharp drop in temperature, can the power supply company quickly increase the power supply to meet the demand?
[0046] Based on these energy interaction characteristics, several collaborative units are defined, with each collaborative unit corresponding to a set of continuous energy coordination processes. For example, the energy coordination processes of industrial areas, residential areas, and commercial areas in a city can be divided into different collaborative units, each containing energy production, transmission, and consumption processes.
[0047] The system collects data on energy storage fluctuations, energy conversion loss rates, and regional energy consumption curve trends for each collaborative unit within a preset time period. Energy storage fluctuations refer to the magnitude of change in energy storage within a collaborative unit over a preset time period. For example, the fluctuation in the storage capacity of a gas storage tank in an industrial zone over a week due to usage and replenishment. Energy conversion loss rate refers to the proportion of energy lost during energy conversion to total input energy. For example, in coal-fired power generation, there are losses in the process of converting the chemical energy of coal into electrical energy; this loss ratio is the energy conversion loss rate. Regional energy consumption curve trends refer to the pattern of energy consumption changes within a collaborative unit over time. For example, there are significant differences in electricity consumption curves between day and night in residential areas, with less electricity used during the day and more used at night.
[0048] The energy buffering capacity of each cooperating unit is determined based on the fluctuation of energy storage. Small storage fluctuations indicate relatively stable energy storage and strong buffering capacity. For example, large energy reserves have small energy storage fluctuations and play a good buffering role when energy supply fluctuates. The energy utilization efficiency of each cooperating unit is determined based on the energy conversion loss rate. A low loss rate indicates high utilization efficiency. For example, power plants using high-efficiency power generation technology have low energy conversion loss rates and therefore high utilization efficiency. The demand matching degree of each cooperating unit is determined based on the trend of regional energy consumption curves. The closer the consumption curve matches the supply curve, the higher the demand matching degree. For example, if the energy consumption curve of a cooperating unit is stable and the supply curve of the supplier is also stable, then the demand matching degree is high.
[0049] A collaborative relationship map is constructed for each collaborative unit based on energy buffering capacity, energy utilization efficiency, and demand matching degree. This map is used to characterize the energy complementarity and mutual influence between different collaborative units. For example, one collaborative unit has strong energy buffering capacity and high utilization efficiency but average demand matching degree, while another collaborative unit has high demand matching degree but weak buffering capacity. The two are related in terms of energy complementarity and have a significant mutual influence. These relationships will be reflected in the collaborative relationship map.
[0050] The priority ranking is obtained by sorting the energy scheduling priorities of each collaborative unit based on the collaborative relationship graph. For example, collaborative units with weak energy buffer capacity, high demand matching degree and high correlation with other units will have higher scheduling priority to ensure their energy supply.
[0051] The energy scheduling parameters of each cooperating unit are dynamically adjusted according to the priority ranking results. These parameters include the initial energy transmission amount, transmission interval duration, and transmission termination threshold. For example, for a high-priority cooperating unit, the initial energy transmission amount will increase, the transmission interval duration will decrease, and the transmission termination threshold will be adjusted to ensure its energy supply. The coordination value of each cooperating unit is calculated based on these adjusted parameters. The coordination value is a quantitative representation of the energy scheduling effect of that unit.
[0052] The second scheduling coordination evaluation value is obtained by adjusting the coordination value based on the correlation strength between the cooperating unit and other cooperating units. The coordination value of a cooperating unit with a higher correlation strength is adjusted by a larger margin, which can more accurately reflect the actual scheduling effect of the unit in the entire energy coordination network.
[0053] The third energy data to be coordinated is processed to obtain the third dispatch coordination evaluation value, which specifically includes the following steps: Extract the energy interaction trajectory of each region from the third energy data to be coordinated; the energy interaction trajectory includes the energy transfer path between regions, energy handover nodes and the duration of continuous energy transfer; The energy interaction trajectory is divided into several interaction segments, and each interaction segment corresponds to a continuous inter-regional energy transfer process. Collect data on energy attenuation, node response delay, and changes in regional energy reserves during the operation of each interactive segment; The energy transmission stability of each interaction segment is determined based on the energy attenuation rate, the coordinated response speed of each interaction segment is determined based on the node response delay, and the supply and demand balance of each interaction segment is determined based on the change in regional energy reserves. The collaborative influence coefficients of each interaction segment are obtained based on energy transmission stability, collaborative response speed, and supply-demand balance. The energy dispatch adaptability of each interaction segment is evaluated based on the synergistic impact coefficient to obtain the evaluation result; Based on the evaluation results, the energy dispatch thresholds of each interaction segment are adaptively adjusted. The energy dispatch thresholds include the initial limit of energy transmission, the fluctuation range of the transmission process, and the termination margin of transmission. The coordinated adaptation score of each interaction segment is obtained by combining the adjusted energy dispatch thresholds. The initial collaborative evaluation results are obtained by summing the collaborative adaptation scores of all interaction segments; The preliminary coordination evaluation results are calibrated based on the constraint strength between this interaction segment and other interaction segments to obtain the third scheduling coordination evaluation value.
[0054] Extract the energy interaction trajectories of each region from the third set of energy data to be coordinated. These trajectories include inter-regional energy transmission paths, energy transfer nodes, and the duration of continuous energy transmission. Inter-regional energy transmission paths refer to the routes energy takes from the supply region to the demand region, such as the power transmission path from a hydropower station to a city user, passing through multiple substations and transmission lines. Energy transfer nodes are the locations where energy is transferred or converted during transmission; for example, natural gas undergoes transfer or pressure adjustment at nodes such as gas transmission stations and pressure regulating stations during its journey from the extraction site to the city user. The duration of continuous energy transmission refers to the time spent on the entire transmission process from the starting point to the destination, such as the duration of the entire transportation and unloading process of coal transported from a coal mine to a power plant by rail.
[0055] These energy interaction trajectories are divided into several interaction segments, each corresponding to a continuous inter-regional energy transfer process. For example, the coal transportation process from western coal mines to eastern power plants is divided into different interaction segments according to transportation routes and loading / unloading nodes, and the energy transfer within each interaction segment is continuous.
[0056] The system collects data on energy attenuation, node response delay, and regional energy reserve changes during the operation of each interaction segment. Energy attenuation refers to the degree of reduction in energy quantity during the transmission of energy in the interaction segment, such as the reduction in natural gas volume due to leakage or consumption during long-distance pipeline transmission. Node response delay refers to the time delay in energy processing or response at the junction node, such as the delay time for a substation to complete voltage adjustment operations after receiving a power signal. Regional energy reserve changes refer to the changes in energy reserves within the area involved in the interaction segment, such as the change in energy reserves in the terminal area of a certain interaction segment due to the energy input of that interaction segment.
[0057] The stability of energy transmission in each interaction segment is determined based on the magnitude of energy attenuation. A smaller attenuation magnitude results in higher transmission stability. For example, gas pipelines using new sealing technologies exhibit smaller energy attenuation and thus higher transmission stability. The coordinated response speed of each interaction segment is determined based on node response delay. A smaller response delay results in a faster coordinated response speed. For example, highly automated energy transfer nodes exhibit smaller node response delays and faster coordinated response speeds. The supply-demand balance of each interaction segment is determined based on changes in regional energy reserves. A high supply-demand balance occurs when changes in reserves align with changes in regional supply and demand. For example, if the energy input in a certain interaction segment ensures that the regional energy reserves just meet the changes in demand, the supply-demand balance is high.
[0058] The synergistic influence coefficient of each interaction segment is obtained based on energy transmission stability, synergistic response speed, and supply-demand balance. The synergistic influence coefficient comprehensively reflects the degree of influence of the interaction segment in energy synergy. For example, the synergistic influence coefficient of an interaction segment with high transmission stability, fast synergistic response speed, and high supply-demand balance is large.
[0059] The energy dispatch adaptability of each interaction segment is evaluated based on the synergistic impact coefficient. The interaction segment with a larger synergistic impact coefficient has a higher energy dispatch adaptability rating.
[0060] Based on the assessment results, the energy dispatch thresholds for each interaction segment are adaptively adjusted. These thresholds include the initial energy transfer limit, the fluctuation range during the transfer process, and the termination margin. For example, for interaction segments with higher assessment levels, the initial energy transfer limit will be increased, the fluctuation range during the transfer process will be reduced, and the termination margin will be adjusted to ensure energy supply. Combining these adjusted thresholds, a coordination and adaptation score is obtained for each interaction segment. This score is a quantitative representation of the scheduling adaptation of the interaction segment.
[0061] The preliminary coordination assessment result is obtained by summing the coordination adaptation scores of all interaction segments. The preliminary coordination assessment result is then calibrated according to the constraint strength between the interaction segment and other interaction segments to obtain the third scheduling coordination assessment value. For interaction segments with strong constraint strength, the calibration range of the preliminary assessment result will also be adjusted accordingly, so as to more accurately reflect the actual scheduling assessment of the interaction segment in the entire energy coordination network.
[0062] The energy dispatching scheme is executed based on the first, second, and third dispatching coordination assessment values, specifically including the following steps: Identify the collaborative scenario characteristics corresponding to the first, second, and third scheduling collaborative evaluation values; among which, the collaborative scenario characteristics include the scope of regional cooperation, energy interaction density, and emergency response level; A comprehensive coordination evaluation benchmark is obtained based on the first, second, and third scheduling coordination evaluation values and their corresponding weights. Extract the characteristic parameters that are missing in the comprehensive collaborative assessment benchmark for energy dispatch; among them, the characteristic parameters include regional energy dispatch lag points and areas with weak emergency response. Develop multi-dimensional scheduling optimization directions based on characteristic parameters; these optimization directions include adjusting inter-regional energy flow paths, enhancing transmission capacity, and optimizing the layout of emergency reserves. Based on the optimization direction, the optimal execution scheduling scheme is taken as the final energy scheduling scheme.
[0063] First, we identify the collaborative scenario characteristics corresponding to the first, second, and third collaborative evaluation values of energy dispatch. These collaborative scenario characteristics include the scope of regional cooperation, energy interaction density, and emergency response level. The scope of regional cooperation refers to the breadth of the area involved in energy dispatch, such as regional cooperation within a single city or large-scale cooperation across provinces and cities; different scopes place significantly different demands on dispatch. Energy interaction density refers to the frequency and scale of energy interaction between regions per unit time; for example, frequent and large-scale energy interaction between industrial parks results in a high energy interaction density. The emergency response level refers to the severity of the emergency situation faced by energy dispatch; for example, a low emergency response level is due to general equipment failure, while a high emergency response level is due to major natural disasters.
[0064] The comprehensive coordination evaluation benchmark is obtained by using the first, second, and third coordination evaluation values and their corresponding weights. The weights are determined based on the importance of each evaluation value in the overall energy dispatch. For example, if the scenario corresponding to the first coordination evaluation value is more common in daily dispatch, its weight may be higher. The comprehensive coordination evaluation benchmark is obtained by multiplying each of the three evaluation values by its respective weight and then summing them. This benchmark provides a comprehensive quantification of the overall energy dispatch coordination effect.
[0065] The characteristic parameters lacking in energy dispatch were extracted from the comprehensive collaborative assessment benchmark. These parameters include regional energy dispatch lag points and areas with weak emergency response. Regional energy dispatch lag points refer to areas where energy dispatch is delayed or disrupted, such as a region where power dispatch is frequently delayed due to aging transmission lines. Areas with weak emergency response refer to areas where energy dispatch response capabilities are insufficient in emergency situations, such as remote mountainous areas where energy emergency response speed is slow and guarantee capacity is weak when disasters occur.
[0066] Multi-dimensional scheduling optimization directions were formulated based on these characteristic parameters. These optimization directions include adjusting inter-regional energy flow paths, enhancing transmission capacity, and optimizing the layout of emergency reserves. Adjusting inter-regional energy flow paths refers to optimizing energy transmission routes, such as changing detours to more direct routes to improve transmission efficiency. Enhancing transmission capacity refers to improving the transmission capacity and efficiency of energy transmission facilities, such as expanding and upgrading gas pipelines to transport more natural gas. Optimizing the layout of emergency reserves refers to adjusting the location and scale of emergency energy reserves, such as adding energy reserve points in areas with weak emergency response capabilities to improve their emergency preparedness.
[0067] Based on these optimization directions, the optimal execution scheduling scheme is selected from among numerous feasible scheduling options as the final energy scheduling scheme. This optimal scheme must comprehensively address the problems of regional energy allocation lag points and weak emergency response areas, while achieving optimal configuration in terms of path, transmission capacity, and emergency reserves, thereby realizing efficient and reliable energy scheduling. For example, by adjusting the power transmission path of a certain region to strengthen its transmission capacity and deploying emergency power reserve points in its vicinity, the resulting scheduling scheme is the optimal scheme for the characteristic parameters of that region.
[0068] A digital twin energy dispatching system includes: The module divides the real-time energy collaboration data of the target area cluster into sub-collaboration datasets according to the regional collaboration dimension. The sub-collaboration datasets are then input into the twin energy collaboration model to obtain the first energy data to be collaborated on. Evaluation module: Based on the first energy source data to be coordinated, a scheduling coordination evaluation is performed to obtain the first scheduling coordination evaluation value; Processing module: Divides real-time energy coordination data into second energy data to be coordinated in ascending order of coordination intensity and third energy data to be coordinated in descending order; processes the second energy data to be coordinated to obtain the second scheduling coordination evaluation value, and processes the third energy data to be coordinated to obtain the third scheduling coordination evaluation value; Output module: Executes the final energy dispatching scheme based on the first, second, and third dispatching coordination evaluation values.
[0069] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. 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.
[0070] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment 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, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; 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; and these 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 digital twin energy scheduling method, characterized in that, Includes the following steps: The real-time energy collaboration data of the target area cluster is divided into sub-collaboration datasets according to the regional collaboration dimension. The sub-collaboration datasets are then input into the twin energy collaboration model to obtain the first energy data to be collaborated on. The first scheduling coordination evaluation value is obtained by performing a scheduling coordination evaluation based on the first energy data to be coordinated; Real-time energy coordination data is divided into a second set of energy data to be coordinated, which is in ascending order of coordination intensity, and a third set of energy data to be coordinated, which is in descending order of coordination intensity. The second set of energy data to be coordinated is processed to obtain a second scheduling coordination evaluation value, and the third set of energy data to be coordinated is processed to obtain a third scheduling coordination evaluation value. The final energy dispatch plan will be executed based on the first, second, and third dispatch coordination assessment values.
2. The digital twin energy dispatching method of claim 1, wherein, It also includes the following steps: Acquire historical energy coordination data for different types of regions; Extract regional energy complementarity characteristics, energy network transmission parameters, and emergency response capability indicators from historical energy synergy data; Historical energy dispatch coordination values are calculated based on the energy complementarity characteristics between root regions, energy network transmission parameters, and emergency response capability indicators. A twin energy coordination model is constructed based on the characteristics of inter-regional energy complementarity, energy network transmission parameters, emergency response capability indicators, and historical energy dispatch coordination values.
3. The digital twin energy dispatching method according to claim 1, characterized in that, The first energy data to be coordinated is obtained by inputting the sub-coordinated dataset into the twin energy coordination model, which specifically includes the following steps: The scheduling coordination effect of different sub-coordinated datasets was obtained through the twin energy coordination model; The optimal sub-cooperative data is obtained by filtering from the sub-cooperative dataset based on the scheduling coordination effect; The real-time energy collaboration data is divided into several data with equal collaboration intensity according to the collaboration intensity of the optimal sub-collaboration data to obtain the first energy data to be collaborated.
4. The digital twin energy dispatching method according to claim 3, characterized in that, The first scheduling coordination evaluation value is obtained by performing a scheduling coordination evaluation based on the first energy data to be coordinated, specifically including the following steps: Detect the network loss parameters and real-time emergency demand data corresponding to the various levels of cooperation intensity data in the first energy data to be coordinated; The basic collaborative scheduling strategy is adjusted based on the correlation between network loss parameters and real-time emergency demand data to obtain the first collaborative strategy to be executed. The first energy data to be coordinated, network loss parameters, real-time emergency demand data, and the first coordination strategy to be executed are input into the twin energy coordination model to obtain the first scheduling coordination determination value. The first coordination difference is obtained by calculating the difference between the first scheduling coordination determined value and the first standard scheduling coordination value. The first coordination compensation coefficient is obtained by the ratio of the first coordination difference to the first standard scheduling coordination value. The first scheduling coordination evaluation value is obtained by correcting the first scheduling coordination determination value based on the first coordination compensation coefficient.
5. The digital twin energy dispatching method according to claim 4, characterized in that, The real-time energy coordination data includes energy surplus gap data, real-time flow data of cross-regional energy allocation, and operational status data of energy network nodes.
6. The digital twin energy dispatching method according to claim 5, characterized in that, The basic collaborative scheduling strategy is adjusted based on the correlation between network loss parameters and real-time emergency demand data to obtain the first collaborative strategy to be executed, which includes the following steps: The network loss parameters include power loss of transmission lines, flow loss of gas pipelines, signal loss of data transmission, and energy consumption loss of equipment operation. The real-time emergency demand data includes regional disaster early warning levels, energy security priorities, and emergency energy demand gaps. The loss threshold is obtained based on network loss parameters, and the urgency level is obtained based on real-time emergency demand data. The first collaborative strategy to be executed is obtained by adjusting the preset basic collaborative scheduling strategy based on the loss threshold and urgency level.
7. A digital twin energy dispatching method according to claim 6, characterized in that, The second energy data to be coordinated is processed to obtain the second scheduling coordination evaluation value, specifically including the following steps: Extract the energy interaction characteristics of each collaborative link in the second energy data to be coordinated; wherein, the energy interaction characteristics include the timeliness of energy flow between regions, the frequency of energy form conversion, and the energy supply and demand response rate; Based on the characteristics of energy interaction, several collaborative units are divided, and each collaborative unit corresponds to a set of continuous energy coordination processes; Collect data on energy storage fluctuations, energy conversion loss rates, and regional energy consumption curve trends of each collaborative unit within a preset time period. The energy buffer capacity of each cooperative unit is determined based on the fluctuation of energy storage, the energy utilization efficiency of each cooperative unit is determined based on the energy conversion loss rate, and the demand matching degree of each cooperative unit is determined based on the changing trend of the regional energy consumption curve. Based on energy buffering capacity, energy utilization efficiency, and demand matching degree, a collaborative correlation map of each collaborative unit is constructed. The collaborative correlation map is used to characterize the energy complementarity relationship and mutual influence degree between different collaborative units. The priority ranking results are obtained by sorting the energy scheduling priorities of each cooperative unit according to the collaborative association graph. The energy scheduling parameters of each cooperating unit are dynamically adjusted according to the priority ranking results. The energy scheduling parameters include the energy transmission start amount, transmission interval duration and transmission termination threshold. The coordination value of each cooperating unit is calculated in combination with the adjusted energy scheduling parameters. The second scheduling coordination evaluation value is obtained by correcting the coordination value based on the correlation strength between the cooperating unit and other cooperating units.
8. The digital twin energy dispatching method according to claim 7, characterized in that, The third energy data to be coordinated is processed to obtain the third dispatch coordination evaluation value, which specifically includes the following steps: Extract the energy interaction trajectory of each region from the third energy data to be coordinated; wherein, the energy interaction trajectory includes the energy transfer path between regions, energy handover nodes, and energy transfer duration; The energy interaction trajectory is divided into several interaction segments, and each interaction segment corresponds to a continuous inter-regional energy transfer process. Collect data on energy attenuation, node response delay, and changes in regional energy reserves during the operation of each interactive segment; The energy transmission stability of each interaction segment is determined based on the energy attenuation rate, the coordinated response speed of each interaction segment is determined based on the node response delay, and the supply and demand balance of each interaction segment is determined based on the change in regional energy reserves. The collaborative influence coefficients of each interaction segment are obtained based on energy transmission stability, collaborative response speed, and supply-demand balance. The energy dispatch adaptability of each interaction segment is evaluated based on the synergistic impact coefficient to obtain the evaluation results; The energy dispatch thresholds for each interaction segment are adaptively adjusted according to the evaluation results. The energy dispatch thresholds include the energy transfer start limit, the fluctuation range of the transfer process, and the transfer termination margin. The coordinated adaptation score of each interaction segment is obtained by combining the adjusted energy dispatch thresholds. The initial collaborative evaluation results are obtained by summing the collaborative adaptation scores of all interaction segments; The preliminary coordination evaluation results are calibrated based on the constraint strength between this interaction segment and other interaction segments to obtain the third scheduling coordination evaluation value.
9. A digital twin energy dispatching method according to claim 7, characterized in that, The energy dispatching scheme is executed based on the first, second, and third dispatching coordination assessment values, specifically including the following steps: Identify the collaborative scenario characteristics corresponding to the first, second, and third scheduling collaborative evaluation values; wherein, the collaborative scenario characteristics include the regional cooperation scope, energy interaction density, and emergency response level; A comprehensive coordination evaluation benchmark is obtained based on the first, second, and third scheduling coordination evaluation values and their corresponding weights. Extract the characteristic parameters that are lacking in the comprehensive collaborative evaluation benchmark for energy dispatch; wherein, the characteristic parameters include regional energy dispatch lag points and areas with weak emergency response; Develop multi-dimensional scheduling optimization directions based on characteristic parameters; among which, the optimization directions include adjusting inter-regional energy flow paths, enhancing transmission capacity, and optimizing the layout of emergency reserves; Based on the optimization direction, the optimal execution scheduling scheme is taken as the final energy scheduling scheme.
10. A digital twin energy dispatching system, applied to the digital twin energy dispatching method according to any one of claims 1 to 9, characterized in that, include: The partitioning module divides the real-time energy collaboration data of the target area cluster into sub-collaboration datasets according to the regional collaboration dimension, and inputs the sub-collaboration datasets into the twin energy collaboration model to obtain the first energy data to be collaborated on. Evaluation module: Based on the first energy source data to be coordinated, a scheduling coordination evaluation is performed to obtain the first scheduling coordination evaluation value; Processing module: Divides real-time energy coordination data into second energy data to be coordinated in ascending order of coordination intensity and third energy data to be coordinated in descending order; processes the second energy data to be coordinated to obtain the second scheduling coordination evaluation value, and processes the third energy data to be coordinated to obtain the third scheduling coordination evaluation value; Output module: Executes the final energy dispatching scheme based on the first, second, and third dispatching coordination evaluation values.